Updated on 2025/03/25

写真a

 
YAMADA ISAO
 
Organization
School of Engineering Professor
Title
Professor
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News & Topics

Degree

  • Doctor of Engineering ( Tokyo Institute of Technology )

Research Interests

  • Communication Theory

  • 最適化工学

  • 信号処理工学

  • Signal Processing

  • 情報通信工学

  • Optimization Theory

Research Areas

  • Manufacturing Technology (Mechanical Engineering, Electrical and Electronic Engineering, Chemical Engineering) / Communication and network engineering

Education

  • Tokyo Institute of Technology   Graduate School, Division of Science and Engineering

    - 1990

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  • 東京工業大学大学院   理工学研究科   電気電子工学

    - 1990

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    Country： Japan

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  • University of Tsukuba

    - 1985

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    Country： Japan

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Research History

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Professional Memberships

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Committee Memberships

  • IEEE   Associate Editor (IEEE Transactions on Circuits and Systems 1)、Associate Editor (IEEE Transactions on Signal Processing)、Senior Member  

    2006 - 2007   

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    Committee type：Academic society

    IEEE

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  • IEEE   編集委員 (IEEE Transactions on Circuits and Systems 1)、編集委員(IEEE Transactions on Signal Processing)、シニアメンバー、CAS Japan chap. secretary  

    2006 - 2007   

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    Committee type：Academic society

    IEEE

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  • SITA   2004-  

    2004 - 2005   

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    Committee type：Academic society

    SITA

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  • 情報理論とその応用学会   企画理事 、評議員、企画理事、無任所幹事  

    2004 - 2005   

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    Committee type：Academic society

    情報理論とその応用学会

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  • 日本応用数理学会   正員  

    2004   

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    Committee type：Academic society

    日本応用数理学会

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  • 電気学会   正員(調査委員会委員)  

    2001   

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    Committee type：Academic society

    電気学会

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  • American Mathematical Society   member  

    2000   

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    Committee type：Academic society

    American Mathematical Society

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  • AMS   member  

    2000   

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    Committee type：Academic society

    AMS

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  • 電子情報通信学会   英文論文誌編集委員 、信号処理研究専門委員会幹事 、信号処理研究専門委員会委員 、基礎境界ソサイエティ庶務幹事  

    1999 - 2000   

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    Committee type：Academic society

    電子情報通信学会

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  • SIAM   member  

    1998   

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    Committee type：Academic society

    SIAM

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  • SIAM   member  

    1998   

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    Committee type：Academic society

    SIAM

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  • Multidimensional Systems Theory and Signal Processing   Associate editor  

    1997   

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    Committee type：Academic society

    Multidimensional Systems Theory and Signal Processing

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  • Multidimensional Systems Theory and Signal Processing   Associate editor  

    1997   

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    Committee type：Academic society

    Multidimensional Systems Theory and Signal Processing

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Books

  • 適応フィルタの学習アルゴリ ズム窶買Aルゴリズムの基本原理と性能解析

    朝倉書店  2010 

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  • 工学のための関数解析

    数理工学社(サイエンス社)  2009  （ ISBN:9784901683623 ）

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  • 数学が切り拓く情報通信の世界

    アーク出版  2007  （ ISBN:9784860590567 ）

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  • A Numerically Robust Hybrid Steepest Descent Method for the Convexly Constrained Generalized Inverse Problems

    in Inverse Problems, Image Analysis, and Medical Imaging, (Z. Nashed and O. Scherzer, Eds.) Contemporary Mathematics Book Series, 313 (Americam Mathematical Society)  2002 

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  • A Numerically Robust Hybrid Steepest Descent Method for the Convexly Constrained Generalized Inverse Problems

    in Inverse Problems, Image Analysis, and Medical Imaging, (Z. Nashed and O. Scherzer, Eds.) Contemporary Mathematics Book Series, 313 (Americam Mathematical Society)  2002 

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  • The Hybrid Steepest Descent Method for the Variational Inequality Problem over the Intersection of Fixed Point Sets of Nonexpansive Mappings, in Inherently Parallel Algorithms in Feasibility and Optimization and their Applications, (D. Butnariu, Y. Cen・・・

    Elsevier  2001 

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    The Hybrid Steepest Descent Method for the Variational Inequality Problem over the Intersection of Fixed Point Sets of Nonexpansive Mappings, in Inherently Parallel Algorithms in Feasibility and Optimization and their Applications, (D. Butnariu, Y. Censor, S. Reich)

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  • The Hybrid Steepest Descent Method for the Variational Inequality Problem over the Intersection of Fixed Point Sets of Nonexpansive Mappings, in Inherently Parallel Algorithms in Feasibility and Optimization and their Applications, (D. Butnariu, Y. Cen・・・

    Elsevier  2001 

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    The Hybrid Steepest Descent Method for the Variational Inequality Problem over the Intersection of Fixed Point Sets of Nonexpansive Mappings, in Inherently Parallel Algorithms in Feasibility and Optimization and their Applications, (D. Butnariu, Y. Censor, S. Reich)

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  • 電子情報通信ハンドブック（分担:信号理論）

    電子情報通信学会編  1998 

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MISC

  • Minimizing the Moreau Envelope of Nonsmooth Convex Functions over the Fixed Point Set of Certain Quasi-Nonexpansive Mappings

    Isao Yamada, Masahiro Yukawa, Masao Yamagishi

    FIXED-POINT ALGORITHMS FOR INVERSE PROBLEMS IN SCIENCE AND ENGINEERING   49   345 - +   2011

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    Language：English   Publisher：SPRINGER  

    The first aim of this paper is to present a useful toolbox of quasi-nonexpansive mappings for convex optimization from the viewpoint of using their fixed point sets as constraints. Many convex optimization problems have been solved through elegant translations into fixed point problems. The underlying principle is to operate a certain quasi-nonexpansive mapping T iteratively and generate a convergent sequence to its fixed point. However, such a mapping often has infinitely many fixed points, meaning that a selection from the fixed point set Fix(T) should be of great importance. Nevertheless, most fixed point methods can only return an "unspecified" point from the fixed point set, which requires many iterations. Therefore, based on common sense, it seems unrealistic to wish for an "optimal" one from the fixed point set. Fortunately, considering the collection of quasi-nonexpansive mappings as a toolbox, we can accomplish this challenging mission simply by the hybrid steepest descent method, provided that the cost function is smooth and its derivative is Lipschitz continuous. A question arises: how can we deal with "nonsmooth" cost functions?
    The second aim is to propose a nontrivial integration of the ideas of the hybrid steepest descent method and the Moreau-Yosida regularization, yielding a useful approach to the challenging problem of nonsmooth convex optimization over Fix(T). The key is the use of smoothing of the original nonsmooth cost function by its Moreau-Yosida regularization whose derivative is always Lipschitz continuous. The field of application of hybrid steepest descent method can be extended to the minimization of the ideal smooth approximation over Fix(T). We present the mathematical ideas of the proposed approach together with its application to a combinatorial optimization problem: the minimal antenna-subset selection problem under a highly nonlinear capacity-constraint for efficient multiple input multiple output (MIMO) communication systems.

    DOI： 10.1007/978-1-4419-9569-8_17

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  • Diffusion Least-Mean Squares With Adaptive Combiners: Formulation and Performance Analysis

    Noriyuki Takahashi, Isao Yamada, Ali H. Sayed

    IEEE TRANSACTIONS ON SIGNAL PROCESSING   58 ( 9 )   4795 - 4810   2010.9

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    This paper presents an efficient adaptive combination strategy for the distributed estimation problem over diffusion networks in order to improve robustness against the spatial variation of signal and noise statistics over the network. The concept of minimum variance unbiased estimation is used to derive the proposed adaptive combiner in a systematic way. The mean, mean-square, and steady-state performance analyses of the diffusion least-mean squares (LMS) algorithms with adaptive combiners are included and the stability of convex combination rules is proved. Simulation results show i) that the diffusion LMS algorithm with the proposed adaptive combiners outperforms those with existing static combiners and the incremental LMS algorithm, and ii) that the theoretical analysis provides a good approximation of practical performance.

    DOI： 10.1109/TSP.2010.2051429

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  • Multi-Domain Adaptive Learning Based on Feasibility Splitting and Adaptive Projected Subgradient Method

    Masahiro Yukawa, Konstantinos Slavakis, Isao Yamada

    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES   E93A ( 2 )   456 - 466   2010.2

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    Language：English   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

    We propose the multi-domain adaptive learning that enables us to find a point meeting possibly time-varying specifications simultaneously in multiple domains. e.g. space, time, frequency, etc. The novel concept is based on the idea of feasibility splitting - dealing with feasibility in each individual domain. We show that the adaptive projected subgradient method (Yamada, 2003) realizes the multi-domain adaptive learning by employing (i) a projected gradient operator with respect to a 'fixed' proximity function reflecting the time-invariant specifications and (ii) a subgradient projection with respect to 'time-varying' objective functions reflecting the time-varying specifications. The resulting algorithm is Suitable for real-time implementation, because it requires no more than metric projections onto closed convex sets each of which accommodates the specification in each domain. A convergence analysis and numerical examples are presented.

    DOI： 10.1587/transfun.E93.A.456

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  • Nirmal Kumar Bose 先生を偲んで

    山田功

    IEICE Fundamentals Review   4 ( 1 )   2010

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  • Adaptive Learning in A World of Projections: A Unifying Framework for Linear and Nonlinear Classification and Regression Tasks

    Sergios Theodoridis, Konstantinos Slavakis, Isao Yamada

    IEEE Signal Processing Magazine   (accepted)   2010

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  • Optimization treasure trove: a strategic paradigm in advanced signal processing

    Isao YAMADA

    Tokyo Institute of Technology Bulletin   ( 15 )   2010

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  • Optimization treasure trove: a strategic paradigm in advanced signal processing

    Isao YAMADA

    Tokyo Institute of Technology Bulletin   ( 15 )   2010

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  • In Memory of Professor Nirmal Kumar Bose

    Isao YAMADA

    IEICE Fundamentals Review   4 ( 1 )   2010

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  • Adaptive Learning in A World of Projections: A Unifying Framework for Linear and Nonlinear Classification and Regression Tasks

    Sergios Theodoridis, Konstantinos Slavakis, Isao Yamada

    IEEE Signal Processing Magazine   (accepted)   2010

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  • Robust Reduced-Rank Adaptive Algorithm Based on Parallel Subgradient Projection and Krylov Subspace

    Masahiro Yukawa, Rodrigo C. de Lamare, Isao Yamada

    IEEE TRANSACTIONS ON SIGNAL PROCESSING   57 ( 12 )   4660 - 4674   2009.12

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    In this paper, we propose a novel reduced-rank adaptive filtering algorithm exploiting the Krylov subspace associated with estimates of certain statistics of input and output signals. We point out that, when the estimated statistics are erroneous (e. g., due to sudden changes of environments), the existing Krylov-subspace-based reduced-rank methods compute the point that minimizes a "wrong" mean-square error (MSE) in the subspace. The proposed algorithm exploits the set-theoretic adaptive filtering framework for tracking efficiently the optimal point in the sense of minimizing the "true" MSE in the subspace. Therefore, compared with the existing methods, the proposed algorithm is more suited to adaptive filtering applications. A convergence analysis of the algorithm is performed by extending the adaptive projected subgradient method (APSM). Numerical examples demonstrate that the proposed algorithm enjoys better tracking performance than the existing methods for system identification problems.

    DOI： 10.1109/TSP.2009.2027397

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  • Adaptive Constrained Learning in Reproducing Kernel Hilbert Spaces: The Robust Beamforming Case

    Konstantinos Slavakis, Sergios Theodoridis, Isao Yamada

    IEEE TRANSACTIONS ON SIGNAL PROCESSING   57 ( 12 )   4744 - 4764   2009.12

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    This paper establishes a new paradigm for convexly constrained adaptive learning in reproducing kernel Hilbert spaces (RKHS). Although the technique is of a general nature, we present it in the context of the beamforming problem. A priori knowledge, like beampattern specifications and constraints concerning robustness against steering vector errors, takes the form of multiple closed convex sets in a high ( possibly infinite) dimensional RKHS. Every robustness constraint is shown to be equivalent to a min-max optimization task formed by means of the robust statistics epsilon-insensitive loss function. Such a multiplicity of specifications turns out to obtain a simple expression by using the rich frame of fixed-point sets of certain mappings defined in a Hilbert space. Moreover, the cost function, that the final solution has to optimize, is expressed as an infinite sequence of convex, non-differentiable loss functions, springing from the sequence of the incoming training data. A novel adaptive beamforming design, of linear complexity with respect to the number of unknown parameters, to such a constrained nonlinear learning problem is derived by employing a very recently developed version of the adaptive projected subgradient method (APSM). The method produces a sequence that, under mild conditions, exhibits properties like the strong convergence to a beamformer that satisfies all of the imposed constraints, and in the meantime asymptotically minimizes the sequence of the loss functions imposed by the training data. The numerical examples demonstrate that the proposed method displays increased resolution in cases where the classical linear beamforming solutions collapse. Moreover, it leads to solutions, which are in agreement with the imposed a priori knowledge, as opposed to unconstrained online kernel regression techniques.

    DOI： 10.1109/TSP.2009.2027771

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  • Steady-State Mean-Square Performance Analysis of a Relaxed Set-Membership NLMS Algorithm by the Energy Conservation Argument

    Noriyuki Takahashi, Isao Yamada

    IEEE TRANSACTIONS ON SIGNAL PROCESSING   57 ( 9 )   3361 - 3372   2009.9

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    This paper presents an analysis of the steady-state mean-square error (MSE) of the set-membership normalized least-mean square (SM-NLMS) algorithm with relaxation and regularization parameters. These parameters are introduced for the purpose of deriving in a unified way the steady-state MSE performances of the epsilon-normalized least mean square (epsilon-NLMS) algorithm and a special case of the adaptive parallel subgradient projection (PSP) algorithm. The approach of the paper is to employ the energy conservation relation as a starting point of our analysis. This relation enables us to avoid the transient analysis of the SM-NLMS algorithm, which is in general hard due to the nonlinearity of the SM-NLMS algorithm. As a result, a few nonlinear equations whose solutions are theoretical steady-state MSEs are derived, where two types of reasonable assumptions are introduced to overcome the nonlinearity of the SM-NLMS algorithm. Our results are generalizations of well-known results of the steady-state MSE of the epsilon-NLMS. Extensive simulations show the close agreement between our theories and experiments.

    DOI： 10.1109/TSP.2009.2020747

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  • An Adaptive Projected Subgradient Approach to Learning in Diffusion Networks

    Renato L. G. Cavalcante, Isao Yamada, Bernard Mulgrew

    IEEE TRANSACTIONS ON SIGNAL PROCESSING   57 ( 7 )   2762 - 2774   2009.7

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    We present an algorithm that minimizes asymptotically a sequence of nonnegative convex functions over diffusion networks. In the proposed algorithm, at each iteration the nodes in the network have only partial information of the cost function, but they are able to achieve consensus on a possible minimizer asymptotically. To account for possible node failures, position changes, and/or reachability problems (because of moving obstacles, jammers, etc.), the algorithm can cope with changing network topologies and cost functions, a desirable feature in online algorithms where information arrives sequentially. Many projection-based algorithms can be straightforwardly extended to (probabilistic) diffusion networks with the proposed scheme. The system identification problem in distributed networks is given as one example of a possible application.

    DOI： 10.1109/TSP.2009.2018648

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  • A Flexible Peak-To-Average Power Ration Reduction Scheme for OFDM Systems by the Adaptive Projected Subgradient Method

    Renato L. G. Cavalcante, Isao Yamada

    IEEE TRANSACTIONS ON SIGNAL PROCESSING   57 ( 4 )   1456 - 1468   2009.4

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    One of the main issues of the orthogonal frequency-division multiplexing (OFDM) modulation is the high peak-to-average power ratio (PAPR) of the transmitted signal, which adversely affects the complexity of power amplifiers. In this paper, we consider transmitters that reduce the PAPR by slightly disturbing the symbols in carriers used to transmit information and by sending dummy symbols-i.e., symbols not conveying information-in unused carriers. The optimal choice of the data and dummy symbols is determined by the solution of a convex optimization problem. To reduce the PAPR with low complexity, we apply a modified version of the adaptive projected subgradient method to a sequence of convex cost functions closely related to the original optimization problem. The resulting algorithm achieves near-optimal PAPR in practical scenarios, generalizes existing algorithms based on Polyak's method, and can easily handle multiple constraints.

    DOI： 10.1109/TSP.2008.2011821

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  • Stochastic MV-PURE Estimator-Robust Reduced-Rank Estimator for Stochastic Linear Model

    Tomasz Piotrowski, Renato L. G. Cavalcante, Isao Yamada

    IEEE TRANSACTIONS ON SIGNAL PROCESSING   57 ( 4 )   1293 - 1303   2009.4

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    This paper proposes a novel linear estimator named stochastic MV-PURE estimator, developed for the stochastic linear model, and designed to provide improved performance over the linear minimum mean square error (MMSE) Wiener estimator in cases prevailing in practical, real-world settings, where at least some of the second-order statistics of the random vectors under consideration are only imperfectly known. The proposed estimator shares its main mathematical idea and terminology with the recently introduced minimum-variance pseudo-unbiased reduced-rank estimator (MV-PURE), developed for the linear regression model. The proposed stochastic MV-PURE estimator minimizes the mean square error (MSE) of its estimates subject to rank constraint and inducing minimium distortion to the target random vector. Therefore, the stochastic MV-PURE combines the techniques of the reduced rank Wiener filter (named in this paper RR-MMSE) and the distortionless-constrained estimator (named in this paper C-MMSE), in order to achieve greater robustness against noise or model errors than RR-MMSE and C-MMSE. Furthermore, to ensure that the stochastic MV-PURE estimator combines the reduced-rank and minimum-distortion approaches in the MSE-optimal way, we propose a rank selection criterion which minimizes the MSE of the estimates obtained by the stochastic MV-PURE. As a numerical example, we employ the stochastic MV-PURE, RR-MMSE, C-MMSE, and MMSE estimators as linear receivers in a MIMO wireless communication system. This example is chosen as a typical signal processing scenario, where the statistical information on the data, on which the estimates are built, is only imperfectly known. We verify that the stochastic MV-PURE achieves the lowest MSE and symbol error rate (SER) in such settings by employing the proposed rank selection criterion.

    DOI： 10.1109/TSP.2009.2011839

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  • An ergodic algorithm for the power-control games for CDMA data networks

    Hideaki Iiduka, Isao Yamada

    Journal of Mathematical Modelling and Algorithms   8 ( 1 )   1 - 18   2009.3

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    In this paper, we consider power control for the uplink of a direct-sequence code-division multiple-access data network. In the uplink, the purpose of power control is for each user to transmit enough power so that it can achieve the required quality of service without causing unnecessary interference to other users in the system. One method that has been very successful in solving this purpose for power control is the game-theoretic approach. The problem for power control is modified as a Nash equilibrium problem in which each user can choose its transmit power in order to maximize its own utility, and a Nash equilibrium is an ideal solution of the power-control game. We present a noncooperative power-control game in which each user can choose the transmit power in a way that it gets the sufficient signal-to-interference-plus-noise ratio and maximizes its own utility. To ensure the existence of a solution, we also propose the variational inequality problem which is connected with the proposed game. On a linear receiver, we deal with the matched filter receiver. Next we present a new ergodic algorithm for the proposed power control because the existing iterative algorithms can not be applied effectively to the proposed power control. We also present convergence analysis for the proposed algorithm. In addition, applying the proposed algorithm to the proposed power control, we provide numerical examples for the transmit power, the signal-to-interference-plus-noise ratio and so on. Numerical results for the proposed algorithm shall show that as compared with the existing power-control game and its method, all users in the network can enjoy the sufficient signal-to-interference-plus-noise ratio and achieve the required quality of service. © 2009 Springer Science+Business Media B.V.

    DOI： 10.1007/s10852-008-9099-4

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  • An ergodic algorithm for the power-control games for CDMA data networks

    Hideaki IIDUKA, Isao YAMADA

    Journal of Mathematical Modelling and Algorithms   8 ( 1 )   1 - 18   2009

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  • 妄想力・造形力・創造力- ある理工系研究者の雑感

    山田功

    季刊誌 造形ジャーナル   54 ( 1 )   6 - 9   2009

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  • A Unified View of Adaptive Variable-Metric Projection Algorithms

    Masahiro Yukawa, Isao Yamada

    EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING   2009 ( Article ID 589260 )   1 - 13   2009

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    Language：English   Publisher：HINDAWI PUBLISHING CORPORATION  

    We present a unified analytic tool named variable-metric adaptive projected subgradient method (V-APSM) that encompasses the important family of adaptive variable-metric projection algorithms. The family includes the transform-domain adaptive filter, the Newton-method-based adaptive filters such as quasi-Newton, the proportionate adaptive filter, and the Krylov-proportionate adaptive filter. We provide a rigorous analysis of V-APSM regarding several invaluable properties including monotone approximation, which indicates stable tracking capability, and convergence to an asymptotically optimal point. Small metric-fluctuations are the key assumption for the analysis. Numerical examples show (i) the robustness of V-APSM against violation of the assumption and (ii) the remarkable advantages over its constant-metric counterpart for colored and nonstationary inputs under noisy situations. Copyright (C) 2009 M. Yukawa and I. Yamada. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

    DOI： 10.1155/2009/589260

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  • 最適化と信号処理（後編）～低階数最小分散擬似不偏推定法～(招待解説論文)

    山田功

    映像情報メディア学会誌   63 ( 9 )   1207 - 1212   2009

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  • A USE OF CONJUGATE GRADIENT DIRECTION FOR THE CONVEX OPTIMIZATION PROBLEM OVER THE FIXED POINT SET OF A NONEXPANSIVE MAPPING

    Hideaki Iiduka, Isao Yamada

    SIAM JOURNAL ON OPTIMIZATION   19 ( 4 )   1881 - 1893   2009

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    Language：English   Publisher：SIAM PUBLICATIONS  

    In this paper, we discuss the convex optimization problem over the fixed point set of a nonexpansive mapping. The main objective of the paper is to accelerate the hybrid steepest descent method for the problem. To this goal, we present a new iterative scheme that utilizes the conjugate gradient direction. Its convergence to the solution is guaranteed under certain assumptions. In order to demonstrate the effectiveness, performance, and convergence of our proposed algorithm, we present numerical comparisons of the algorithm with the existing algorithm.

    DOI： 10.1137/070702497

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  • 妄想力・造形力・創造力-理工系大学院の教育現場からの提言

    山田功

    栄町小中学校教育振興会全体研修会 (July 31, 2009)   2009

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  • 最適化と信号処理（前編）～射影勾配法の二つの一般化～(招待解説論文)

    山田功

    映像情報メディア学会誌   63 ( 8 )   1088 - 1093   2009

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  • Optimization and signal processing, Part II, Minimum-variance pseudounbiased reduced-rank estimator

    Isao Yamada

    Kyokai Joho Imeji Zasshi/Journal of the Institute of Image Information and Television Engineers   63 ( 9 )   1207 - 1212   2009

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    Language：Japanese   Publishing type：Book review, literature introduction, etc.   Publisher：Inst. of Image Information and Television Engineers  

    DOI： 10.3169/itej.63.1207

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  • MV-PURE estimator: Minimum-variance pseudo-unbiased reduced-rank estimator for linearly constrained ill-conditioned inverse problems

    Tomasz Piotrowski, Isao Yamada

    IEEE TRANSACTIONS ON SIGNAL PROCESSING   56 ( 8 )   3408 - 3423   2008.8

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    Language：English   Publisher：IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC  

    This paper proposes a novel estimator named minimum-variance pseudo-unbiased reduced-rank estimator (MV-PURE) for the linear regression model, designed specially for the case where the model matrix is ill-conditioned and the unknown deterministic parameter vector to be estimated is subjected to known linear constraints. As a natural generalization of the Gauss-Markov (BLUE) estimator, the MV-PURE estimator is a solution of the following hierarchical nonconvex constrained optimization problem directly related to the mean square error expression. In the first-stage optimization, under a rank constraint, we minimize simultaneously all unitarily invariant norms of an operator applied to the unknown parameter vector in view of suppressing bias of the proposed estimator. Then, in the second-stage optimization, among all pseudo-unbiased reduced-rank estimators defined as the solutions of the first-stage optimization, we find the one achieving minimum variance. We derive a closed algebraic form of the MV-PURE estimator and show that well-known estimators-the Gauss-Markov (BLUE) estimator, the generalized Marquardt's reduced-rank estimator, and the minimum-variance conditionally unbiased affine estimator subject to linear restrictions-are all special cases of the W-PURE estimator. We demonstrate the effectiveness of the proposed estimator in a numerical example, where we employ the MV-PURE estimator to the ill-conditioned problem of reconstructing a 2-D image subjected to linear constraints from blurred, noisy observation. This example demonstrates that the MV-PURE estimator outperforms all aforementioned estimators, as it achieves smaller mean square error for all values of signal-to-noise ratio.

    DOI： 10.1109/TSP.2008.921716

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  • Parallel algorithms for variational inequalities over the Cartesian product of the intersections of the fixed point sets of nonexpansive mappings

    Noriyuki Takahashi, Isao Yamada

    JOURNAL OF APPROXIMATION THEORY   153 ( 2 )   139 - 160   2008.8

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    Language：English   Publisher：ACADEMIC PRESS INC ELSEVIER SCIENCE  

    This paper presents a framework of iterative algorithms for the variational inequality problem over the Cartesian product of the intersections of the fixed point sets of nonexpansive mappings in real Hilbert spaces. Strong convergence theorems are established under a certain contraction assumption with respect to the weighted maximun norm. The proposed framework produces as a simplest example the hybrid steepest descent method, which has been developed for solving the monotone variational inequality problem over the intersection of the fixed point sets of nonexpansive mappings. An application to a generalized power control problem and numerical examples are demonstrated. (C) 2008 Elsevier Inc. All rights reserved.

    DOI： 10.1016/j.jat.2008.03.001

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  • A deep monotone approximation operator based on the best quadratic lower bound of convex functions

    Masao Yamagishi, Isao Yamada

    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES   E91A ( 8 )   1858 - 1866   2008.8

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    Language：English   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

    This paper presents a closed form solution to a problem of constructing the best lower bound of a convex function under certain conditions. The function is assumed (I) bounded below by -rho, and (II) differentiable and its derivative is Lipschitz continuous with Lipschitz constant L. To construct the lower bound, it is also assumed that we can use the values rho and L together with the values of the function and its derivative at one specified point. By using the proposed lower bound, we derive a computationally efficient deep monotone approximation operator to the level set of the function. This operator realizes better approximation than subgradient projection which has been utilized, as a monotone approximation operator to level sets of differentiable convex functions as well as nonsmooth convex functions. Therefore, by using the proposed operator, we can improve many signal processing algorithms essentially based on the subgradient projection.

    DOI： 10.1093/ietfec/e91-a.8.1858

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  • An efficient adaptive minor subspace extraction using exact nested orthogonal complement structure

    Masaki Misono, Isao Yamada

    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES   E91A ( 8 )   1867 - 1874   2008.8

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    This paper presents a new adaptive minor subspace extraction algorithm based on an idea of Peng and Yi ('07) for approximating the single minor eigenvector of a covariance matrix. By utilizing the idea inductively in the nested orthogonal complement subspaces, the proposed algorithm succeeds to relax the numerical sensitivity which has been annoying conventional adaptive minor subspace extraction algorithms for example, Oja algorithm ('82) and its stabilized version: O-Oja algorithm ('02). Simulation results demonstrate that the proposed algorithm realizes more stable convergence than O-Oja algorithm.

    DOI： 10.1093/ietfec/e91-a.8.1867

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  • Online kernel-based classification using adaptive projection algorithms

    Konstantinos Slavakis, Sergios Theodoridis, Isao Yamada

    IEEE TRANSACTIONS ON SIGNAL PROCESSING   56 ( 7 )   2781 - 2796   2008.7

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    The goal of this paper is to derive a novel online algorithm for classification in reproducing kernel hilbert spaces (RKHS) by exploiting projection-based adaptive filtering tools. The paper brings powerful convex analytic and set theoretic estimation arguments in machine learning by revisiting the standard kernel-based classification as the problem of finding: a point which belongs to a closed halfspace (a special closed convex set) in an RKHS. In this way, classification in an online setting, where data arrive sequentially, is viewed as the problem of finding a point (classifier) in the nonempty intersection of an infinite sequence of closed halfspaces in the RKHS. Convex analysis is also used to introduce sparsification arguments in the design by imposing an additional simple convex constraint on the norm of the classifier. An algorithmic solution to the resulting optimization problem, where new convex constraints are added every time instant, is given by the recently introduced adaptive projected subgradient method (APSM), which generalizes a number of well-known projection-based adaptive filtering algorithms such as the classical normalized least mean squares (NLMS) and the affine projection algorithm (APA). Under mild conditions, the generated sequence of estimates enjoys monotone approximation, strong convergence, asymptotic optimality, and a characterization of the limit point. Further, we show that the additional convex constraint on the norm of the classifier naturally leads to an online sparsification of the resulting kernel series expansion. We validate the proposed design by considering the adaptive equalization problem of a nonlinear channel, and by comparing it with classical as well as with recently developed stochastic gradient descent techniques.

    DOI： 10.1109/TSP.2008.917376

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  • An Edge-Preserving Super-Precision for Simultaneous Enhancement of Spacial and Grayscale Resolutions

    Hiroshi Hasegawa, Ohtsuka Toshinori, ISAO YAMADA, KOHICHI SAKANIWA

    IEICE Transactions on Fundamentals   E91-A ( 2 )   673 - 681   2008

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  • 国際会議報告：2008 IEEE International Conference on Acoustics, Speech, and Signal Processing

    山田功

    電子情報通信学会誌   91 ( 8 )   753   2008

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  • A use of conjugate gradient direction for the convex optimization problem over the fixed point set of a nonexpansive mapping

    Hideaki Iiduka, Isao Yamada

    SIAM Journal on Optimization   19 ( 4 )   1881 - 1893   2008

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    In this paper, we discuss the convex optimization problem over the fixed point set of a nonexpansive mapping. The main objective of the paper is to accelerate the hybrid steepest descent method for the problem. To this goal, we present a new iterative scheme that utilizes the conjugate gradient direction. Its convergence to the solution is guaranteed under certain assumptions. In order to demonstrate the effectiveness, performance, and convergence of our proposed algorithm, we present numerical comparisons of the algorithm with the existing algorithm. © 2009 Society for Industrial and Applied Mathematics.

    DOI： 10.1137/070702497

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  • An Edge-Preserving Super-Precision for Simultaneous Enhancement of Spacial and Grayscale Resolutions

    Hiroshi Hasegawa, Ohtsuka Toshinori, ISAO YAMADA, KOHICHI SAKANIWA

    IEICE Transactions on Fundamentals   E91-A ( 2 )   673 - 681   2008

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  • Adaptive parallel quadratic-metric projection algorithms

    Masahiro Yukawa, Konstantinos Slavakis, Isao Yamada

    IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING   15 ( 5 )   1665 - 1680   2007.7

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    This paper indicates that an appropriate design of metric leads to significant improvements in the, adaptive projected subgradient method (APSM), which unifies a wide range of projection-based algorithms [including normalized least mean square (NLMS) and affine projection algorithm (APA)]. The key is to incorporate a priori (or a posteriori) information on characteristics of an estimandum, a system to be estimated, into the metric design. We propose a family of efficient adaptive filtering algorithms based on a parallel use of quadratic-metric projection, which assigns every point to the nearest point in a closed convex set in a quadratic-metric sense. We present two versions: 1) constant-metric and 2) variable-metric, i.e., the metric function employed is 1) constant and 2) variable among iterations. As a constant-metric version, adaptive parallel quadratic-metric projection (APQP) and adaptive parallel min-max quadratic-metric projection (APMQP) algorithms are naturally derived by APSM, being endowed with desirable properties such as convergence to a point optimal in asymptotic sense. As a variable-metric version, adaptive parallel variable-metric projection (APVP) algorithm is derived by a generalized APSM, enjoying an extended monotone property at each iteration: By employing a simple quadratic-metric, the computational complexity of the proposed algorithms is kept linear with respect to the filter length. Numerical examples demonstrate the remarkable advantages of the proposed algorithms in an application to acoustic echo cancellation.

    DOI： 10.1109/TASL.2007.896655

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  • Ideal adaptive filtering is tied closely to convex projection

    ISAO YAMADA

    The Journal of the Acoustical Society of Japan   63 ( 10 )   600 - 605   2007

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  • やさしい解説: 凸射影と適応フィルタリングの親密な関係 - はらぺこJAWSから学ぶ理想的な信号処理

    山田功

    日本音響学会誌   63 ( 10 )   600 - 605   2007

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  • The adaptive projected subgradient method over the fixed point set of strongly attracting nonexpansive mappings

    Konstantinos Slavakis, Isao Yamada, Nobuhiko Ogura

    NUMERICAL FUNCTIONAL ANALYSIS AND OPTIMIZATION   27 ( 7-8 )   905 - 930   2006.12

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    Language：English   Publisher：TAYLOR & FRANCIS INC  

    This paper presents an algorithmic solution, the adaptive projected subgradient method, to the problem of asymptotically minimizing a certain sequence of non-negative continuous convex functions over the fixed point set of a strongly attracting nonexpansive mapping in a real Hilbert space. The method generalizes Polyak's subgradient algorithm for the convexly constrained minimization of a fixed nonsmooth function. By generating a strongly convergent and asymptotically optimal point sequence, the proposed method not only offers unifying principles for many projection-based adaptive filtering algorithms but also enhances the adaptive filtering methods with the set theoretic estimation's armory by allowing a variety of a priori information on the estimandum in the form, for example, of multiple intersecting closed convex sets.

    DOI： 10.1080/01630560600884661

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  • Pairwise optimal weight realization - Acceleration technique for set-theoretic adaptive parallel subgradient projection algorithm

    Masahiro Yukawa, Isao Yamada

    IEEE TRANSACTIONS ON SIGNAL PROCESSING   54 ( 12 )   4557 - 4571   2006.12

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    The adaptive parallel subgradient projection (PSP) algorithm was proposed in 2002 as a set-theoretic adaptive filtering algorithm providing fast and stable convergence, robustness against noise, and low computational complexity by using weighted parallel projections onto multiple time-varying closed half-spaces. In this paper, we present a novel weighting technique named pairwise optimal weight realization (POWER) for further acceleration of the adaptive PSP algorithm. A simple closed-form formula is derived to compute the projection onto the intersection of two closed half-spaces defined by a triplet of vectors. Using the formula inductively, the proposed weighting technique realizes a good direction of update. The resulting weights turn out to be pairwise optimal in a certain sense. The proposed algorithm has the inherently parallel structure composed of q primitive functions, hence its total computational complexity O(qrN) is reduced to O(rN) with q concurrent processors (r: a constant positive integer). Numerical examples demonstrate that the proposed technique for r = 1 yields significantly faster convergence than not only adaptive PSP with uniform weights, affine projection algorithm, and fast Newton transversal filters but also the regularized recursive least squares algorithm.

    DOI： 10.1109/TSP.2006.881225

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  • An efficient distributed power control for infeasible downlink scenarios - Global-local fixed-point-approximation technique

    Noriyuki Takahashi, Masahiro Yukawa, Isao Yamada

    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES   E89A ( 8 )   2107 - 2118   2006.8

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    In this paper, we present an efficient downlink power control scheme, for wireless networks, based on two key ideas: (i) global-local fixed-point-approximation technique (GLOFPAT) and (ii) bottleneck removal criterion (BRC). The proposed scheme copes with all scenarios including infeasible case where no power allocation can provide all multiple accessing users with target quality of service (QoS). For feasible case, the GLOFPAT efficiently computes a desired power allocation which corresponds to the allocation achieved by conventional algorithms. For infeasible case, the GLOFPAT offers valuable information to detect bottleneck users, to be removed based on the BRC, which deteriorate overall QoS. The GLOFPAT is a mathematically-sound distributed algorithm approximating desired power allocation as a unique fixed-point of an isotone mapping. The unique fixed-point of the global mapping is iteratively computed by fixed-point-approximations of multiple distributed local mappings, which can be computed in parallel by base stations respectively. For proper detection of bottleneck users, complete analysis of the GLOFPAT is presented with aid of the Tarski's fixed-point theorem. Extensive simulations demonstrate that the proposed scheme converges faster than the conventional algorithm and successfully increases the number of happy users receiving target QoS.

    DOI： 10.1093/ietfec/e89-a.8.2107

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  • Asymptotic regularity of linear power bounded operators

    HK Xu, Yamada, I

    TAIWANESE JOURNAL OF MATHEMATICS   10 ( 2 )   417 - 429   2006.2

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    Language：English   Publisher：MATHEMATICAL SOC REP CHINA  

    Let T be a linear power bounded operator on a Banach space X and let S-lambda = (1 - lambda)I + lambda T be the averaged map of T, where lambda is an element of (0, 1). It is shown that S-lambda is asymptotically regular on X; that is, lim(n ->infinity) parallel to S(lambda)(n)x - S(lambda)(n+1)x parallel to = 0 for every x is an element of X. Hence the sequence {S(lambda)(n)x} converges strongly provided it has a weak cluster point.

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  • Efficient fast stereo acoustic echo cancellation based on pairwise optimal weight realization technique

    Masahiro Yukawa, Noriaki Murakoshi, Isao Yamada

    EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING   2006   Article ID 84797, 15 pages   2006

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    Language：English   Publisher：HINDAWI PUBLISHING CORPORATION  

    In stereophonic acoustic echo cancellation (SAEC) problem, fast and accurate tracking of echo path is strongly required for stable echo cancellation. In this paper, we propose a class of efficient fast SAEC schemes with linear computational complexity (with respect to filter length). The proposed schemes are based on pairwise optimal weight realization (POWER) technique, thus realizing a "best" strategy (in the sense of pairwise and worst-case optimization) to use multiple-state information obtained by preprocessing. Numerical examples demonstrate that the proposed schemes significantly improve the convergence behavior compared with conventional methods in terms of system mismatch as well as echo return loss enhancement (ERLE).

    DOI： 10.1155/ASP/2006/84797

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  • Efficient blind MAI suppression in DS/CDMA systems by embedded constraint parallel projection techniques

    M Yukawa, RLG Cavalcante, Yamada, I

    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES   E88A ( 8 )   2062 - 2071   2005.8

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    This paper presents two novel blind set-theoretic adaptive filtering algorithms for suppressing "Multiple Access Interference (MAI)," which is one of the central burdens in DS/CDMA systems. We naturally formulate the problem of MAI suppression as an asymptotic minimization of a sequence of cost functions under some linear constraint defined by the desired user's signature. The proposed algorithms embed the constraint into the direction of update, and thus the adaptive filter moves toward the optimal filter without stepping away from the constraint set. In addition, using parallel processors, the proposed algorithms attain excellent performance with linear computational complexity. Geometric interpretation clarifies an advantage of the proposed methods over existing methods. Simulation results demonstrate that the proposed algorithms achieve (i) much higher speed of convergence with rather better bit error rate performance than other blind methods and (ii) much higher speed of convergence than the non-blind NLMS algorithm (indeed, the speed of convergence of the proposed algorithms is comparable to the non-blind RLS algorithm).

    DOI： 10.1093/ietfec/e88-a.8.2062

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  • An iterative MPEG super-resolution with an outer approximation of framewise quantization constraint

    Hiroshi Hasegawa, Toshiyuki Ono Isao, Yamada Kohichi Sakaniwa

    IEICE Trans. Fundamentals   E88-A ( 9 )   2427 - 2435   2005

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  • A DEEP OUTER APPROXIMATING HALF SPACE OF THE LEVEL SET OF CERTAIN QUADRATIC FUNCTIONS

    Nobuhiko Ogura, Isao Yamada

    JOURNAL OF NONLINEAR AND CONVEX ANALYSIS   6 ( 1 )   187 - 201   2005

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    Language：English   Publisher：YOKOHAMA PUBL  

    Construction of a tighter and simpler outer approximation, covering a given closed convex set but excluding its specified exterior point, is a key for successful realization of certain iterative algorithms for convex feasibility problems or convex optimization problems. In particular, need for efficient outer approximations of the level set of convex quadratic function is increasing in set-theoretic formulations of signal and image processings. In this paper, we present a closed form expression of a tight outer approximation, of the level set of convex quadratic function, by translating its standard subgradient outer approximation. The proposed outer approximation is a simple half space being always a proper subset of the standard subgradient outer approximation.

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  • A deep outer approximating half space of the level set of certain quadratic functions

    Nobuhiko Ogura, Isao Yamada

    Journal of nonlinear and convex analysis   6 ( 1 )   187 - 201   2005

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  • An iterative MPEG super-resolution with an outer approximation of framewise quantization constraint

    Hiroshi Hasegawa, Toshiyuki Ono Isao, Yamada Kohichi Sakaniwa

    IEICE Trans. Fundamentals   E88-A ( 9 )   2427 - 2435   2005

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  • A fast blind multiple access interference reduction in DS/CDMA systems based on adaptive projected subgradient method

    RLG Cavalcante, Yamada, I, K Sakaniwa

    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES   E87A ( 8 )   1973 - 1980   2004.8

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    This paper presents a novel blind multiple access interference (MAI) suppression filter in DS/CDMA systems. The filter is adaptively updated by parallel projections onto a series of convex sets. These sets are defined based on the received signal as well as a priori knowledge about the desired user's signature. In order to achieve fast convergence and good performance at steady state, the adaptive projected subgradient method (Yamada et al., 2003) is applied. The proposed scheme also jointly estimates the desired signal amplitude and the filter coefficients based on an approximation of an EM type algorithm, following the original idea proposed by Park and Doherty, 1997. Simulation results highlight the fast convergence behavior and good performance at steady state of the proposed scheme.

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  • Efficient adaptive stereo echo canceling schemes based on simultaneous use of multiple state data

    M Yukawa, Yamada, I

    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES   E87A ( 8 )   1949 - 1957   2004.8

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    Language：English   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

    In this paper, we propose two adaptive filtering schemes for Stereophonic Acoustic Echo Cancellation (SAEC), which are based on the adaptive projected subgradient method (Yamada et al., 2003). To overcome the so-called non-uniqueness problem, the schemes utilize a certain preprocessing technique which generates two different states of input signals. The first one simultaneously uses, for fast convergence, data from two states of inputs, meanwhile the other selects, for stability, data based on a simple min-max criteria. In addition to the above difference, the proposed schemes commonly enjoy (i) robustness against noise by introducing the stochastic property sets, and (ii) only linear computational complexity, since it is free from solving systems of linear equations. Numerical examples demonstrate that the proposed schemes achieve, even in noisy situations, compared with the conventional technique, (i) much faster and more stable convergence in the learning process as well as (ii) lower level misidentification of echo paths and higher level Echo Return Loss Enhancement (ERLE) around the steady state.

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  • Convex feasibility problem with prioritized hard constraints - Double layered projected gradient method

    N Ogura, Yamada, I

    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES   E87A ( 4 )   872 - 878   2004.4

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    Language：English   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

    In this paper, we introduce the following m-layered hard constrained convex feasibility problem HCF(m): Find a point a E 17, where Gamma(0) := H (a real Hilbert space), Gamma(i) := arg min g(i)(Fi-1) and gi(u) := Sigma(j=1)(Mi) w(i,j)d(2)(u,C-i,C-j) are defined for (i) nonempty closed convex sets C-i,C-j subset of H and (ii) weights w(i,j) > 0 satisfying Sigma(j=1)(Mi) w(i,j) = 1 (i is an element of {1,(...),m), j is an element of {1,(...),M-i}). This problem is regarded as a natural extension of the standard convex feasibility problem: find a point u is an element of boolean AND(i=1)(M) C-i not equivalent to 0, where Ci subset of H (i is an element of {1,(...), M}) are closed convex sets. Unlike the standard problem, HCF(m) can handle the inconsistent case; i.e., boolean AND(i,j) C-i,C-j = 0, which unfortunately arises in many signal processing, estimation and design problems. As an application of the hybrid steepest descent method for the asymptotically shrinking nonexpansive mapping, we present an algorithm, based on the use of the metric projections onto C-i,C-j, which generates a sequence (u(n)) satisfying lim(n-->infinity)(u(n), Gamma(3)) = 0 (for M-1 = 1) when at least one of C-1,C-1 or C-2,C-j's is bounded and H is finite dimensional. An application of the proposed algorithm to the pulse shaping problem is given to demonstrate the great flexibility of the method.

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  • Algebraic unitary root counting for finding minima of generalized power spectrum

    Isao YAMADA Kazuhiro OGUCHI

    Proceedings of 19th IEICE Signal Processing Symposium (in CD-ROM)   2004

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  • Discrete time-frequency projection filtering based on an alias-free discrete time-frequency analysis

    Hiroshi HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    IEICE Trans. Fundamentals   E87-A ( 6 )   1537 - 1545   2004

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  • ハイブリッド最急降下法 -- 階層構造を持つ凸最適化問題の解法

    山田功

    応用数理 (日本応用数理学会誌)   14 ( 3 )   248 - 258   2004

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  • An iterative MPEG super-resolution with an outer approximation of frame-wise quantization constraint

    Hiroshi HASEGAWA Toshiyuki, ONO Isao, YAMADA Kohichi, SAKANIWA

    Proceedngs of 2004 IEEE International Workshop on Multimedia Signal Processing (MMSP-2004, Siena, Sept.) (in CD-ROM)   2004

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  • Adaptive Projected Subgradient Method and Set Theoretic Adaptive Filtering with Multiple Convex Constraints

    Konstantinos SLAVAKIS Isao YAMADA Nobuhiko OGURA

    The 38th Asilomar Conference on Signals, Systems and Computers, California, Nov. 2004.   2004

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  • Acceleration of adaptive parallel projection algorithms by pairwise optimal weight realization

    Masahiro YUKAWA Isao YAMADA

    Proceedings of 12th European Signal Processing Conference (EUSIPCO-2004, Vienna, Sept.) (in CD-ROM)   2004

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  • A fast blind multiple access interference reduction in DS/CDMA systems by adaptive projected subgradient method

    Renato CAVALCANTE Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 12th European Signal Processing Conference (EUSIPCO-2004, Vienna, Sept.) (in CD-ROM)   2004

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  • Hybrid steepest descent method for the variational inequality problem over the fixed point sets of certain quasi-nonexpansive mappings (INVITED)

    Isao YAMADA

    Victoria International Conference 2004, Feb. Wellington (International meeting in cooperation with the Israel Mathematical Union, the New Zealand Mathematical Society and the New Zealand Institute of Mathematics and its Applications)   2004

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  • Adaptive projected subgradient method and its acceleration techniques (INVITED)

    Isao YAMADA Nobuhiko, OGURA Masahiro YUKAWA

    IFAC Workshop on Adaptation and Learning in Control and Signal Processing (ALCOSP 2004, Yokohama, Aug.), (in CD-ROM)   2004

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  • Acceleration technique for adaptive parallel subgradient projection algorithm and its properties

    Masahiro YUKAWA Isao YAMADA

    Proceedings of 19th IEICE Signal Processing Symposium (in CD-ROM)   2004

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  • Adaptive projected subgradient method for asymptotic minimization of sequence of nonnegative convex functions

    Yamada, I, N Ogura

    NUMERICAL FUNCTIONAL ANALYSIS AND OPTIMIZATION   25 ( 7-8 )   593 - 617   2004

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    Language：English   Publisher：MARCEL DEKKER INC  

    This paper presents an algorithm, named adaptive projected subgradient method that can minimize asymptotically a certain sequence of nonnegative convex functions over a closed convex set in a real Hilbert space. The proposed algorithm is a natural extension of the Polyak's subgradient algorithm, for nonsmooth convex optimization problem with a fixed target value, to the case where the convex objective itself keeps changing in the whole process. The main theorem, showing the strong convergence of the algorithm as well as the asymptotic optimality of the sequence generated by the algorithm, can serve as a unified guiding principle of a wide range of set theoretic adaptive filtering schemes for nonstationary random processes. These include not only the existing adaptive filtering techniques; e.g., NLMS, Projected NLMS, Constrained NLMS, APA, and Adaptive parallel outer projection algorithm etc., but also new techniques; e.g., Adaptive parallel min-max projection algorithm, and their embedded constraint versions. Numerical examples show that the proposed techniques are well-suited for robust adaptive signal processing problems.

    DOI： 10.1081/NFA-200045806

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  • Adaptive projected subgradient method and its applications to set theoretic adaptive filtering

    Isao YAMADA Nobuhiko OGURA

    Proceedings of the 37th Asilomar Conference on Signals, Systems and Computers, (California, November, 2003)   2004

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  • Minimizing quadratic functions over reduced rank matrices

    Jamal ELBADRAOUI, Isao YAMADA

    Proceedings of 19th IEICE Signal Processing Symposium (in CD-ROM)   2004

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  • Efficient blind DS/CDMA revceivers by embedded constraint adaptive parallel projection techniques

    Masahiro YUKAWA Renato, L, G. CAVALCANTE, Isao YAMADA

    Proceedings of 19th IEICE Signal Processing Symposium (in CD-ROM)   2004

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  • An edge-preserving inverse halftoning by successive outer approximation techniques

    Takuya OKADA Hiroshi, HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 19th IEICE Signal Processing Symposium (in CD-ROM)   2004

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  • A multidimensional associative memory neural network to recall nearest pattern from input

    Heming SUN, Hiroshi HASEGAWA Isao YAMADA

    Proceedings of 19th IEICE Signal Processing Symposium (in CD-ROM)   2004

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  • An adaptive super-resolution for noisy and blurred image sequences

    Toshiyuki ONO, Hiroshi HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 19th IEICE Signal Processing Symposium (in CD-ROM).   2004

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  • Resolution enhancement of color images subject to bounded total variations

    Ryota SASAHARA Hiroshi, HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 19th IEICE Signal Processing Symposium (in CD-ROM)   2004

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  • An efficient fast stereophonic acoustic echo canceler by pairwise optimal weight realization technique

    Noriaki MURAKOSHI Masahiro, YUKAWA Isao YAMADA

    Proceedings of 19th IEICE Signal Processing Symposium (in CD-ROM)   2004

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  • An efficient speed-up weighting technique for adaptive parallel projection algorithm

    Masahiro YUKAWA Isao YAMADA

    Proceedings of 2004 IEICE General Conference (in CD-ROM)   2004

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  • An Optimal Outer Approximation for the Adaptive Parallel Outer Projection Algorithm

    Nobuhiko OGURA, Isao YAMADA

    Proceedings of 2004 IEICE General Conference (in CD-ROM)   2004

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  • A super-resolution of movies based on adaptive parallel subgradient projection technique

    Toshiyuki ONO Hiroshi, HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 2004 IEICE General Conference (in CD-ROM)   2004

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  • A successive least-squares super-resolution of MPEG video sequences

    Hiroshi, HASEGAWA Toshiyuki, ONO Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 2004 IEICE General Conference (in CD-ROM)   2004

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  • Hybrid steepest descent method for variational inequality problem over the fixed point set of certain quasi-nonexpansive mappings

    Yamada, I, N Ogura

    NUMERICAL FUNCTIONAL ANALYSIS AND OPTIMIZATION   25 ( 7-8 )   619 - 655   2004

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    Language：English   Publisher：MARCEL DEKKER INC  

    The hybrid steepest descent method is an algorithmic solution to the variational inequality problem over the fixed point set of nonlinear mapping and applicable to broad range of convexly constrained nonlinear inverse problems in real Hilbert space. In this paper, we show that the strong convergence theorem [Yamada, I. (2001). The hybrid steepest descent method for the variational inequality problem over the intersection of fixed point sets of nonexpansive mappings. In: Butnariu, D., Censor, Y., Reich, S., eds. Inherently Parallel Algorithm for Feasibility and Optimization and Their Applications. Elsevier, pp. 473-504] of the method for nonexpansive mapping can be extended to a strong convergence theorem of the method for the variational inequality problem over the fixed point set of certain quasi-nonexpansive mappings called quasi-shrinking mapping. We also present a convergence theorem of the method for paramonotone variational inequality problem over the bounded fixed point set of quasi-shrinking mapping. By these generalizations, we can approximate successively to the solution of the convex optimization problem over the fixed point set of wide range of subgradient projection operators in real Hilbert space.

    DOI： 10.1081/NFA-200045815

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  • Efficient blind DS/CDMA revceivers by embedded constraint adaptive parallel projection techniques

    Masahiro YUKAWA Renato, L, G. CAVALCANTE, Isao YAMADA

    Proceedings of 19th IEICE Signal Processing Symposium (in CD-ROM)   2004

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  • Discrete time-frequency projection filtering based on an alias-free discrete time-frequency analysis

    Hiroshi HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    IEICE Trans. Fundamentals   E87-A ( 6 )   1537 - 1545   2004

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  • A fast blind multiple access interference reduction in DS/CDMA systems based on adaptive projected subgradient method

    Renato L, G. CAVALCANTE, Isao, YAMADA Kohichi, SAKANIWA

    IEICE Transactions Fundamentals   E87-A ( 8 )   1973 - 1980   2004

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  • Adaptive Projected Subgradient Method and Set Theoretic Adaptive Filtering with Multiple Convex Constraints

    Konstantinos SLAVAKIS Isao YAMADA Nobuhiko OGURA

    The 38th Asilomar Conference on Signals, Systems and Computers, California, Nov. 2004.   2004

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  • Convex feasibility problem with prioritized hard Constraints--double layered projected gradient method

    Nobuhiko OGURA, Isao YAMADA

    IEICE Transactions Fundamentals   E87-A ( 4 )   872 - 878   2004

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  • A fast blind multiple access interference reduction in DS/CDMA systems by adaptive projected subgradient method

    Renato CAVALCANTE Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 12th European Signal Processing Conference (EUSIPCO-2004, Vienna, Sept.) (in CD-ROM)   2004

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  • An iterative MPEG super-resolution with an outer approximation of frame-wise quantization constraint

    Hiroshi HASEGAWA Toshiyuki, ONO Isao, YAMADA Kohichi, SAKANIWA

    Proceedngs of 2004 IEEE International Workshop on Multimedia Signal Processing (MMSP-2004, Siena, Sept.) (in CD-ROM)   2004

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  • Acceleration of adaptive parallel projection algorithms by pairwise optimal weight realization

    Masahiro YUKAWA Isao YAMADA

    Proceedings of 12th European Signal Processing Conference (EUSIPCO-2004, Vienna, Sept.) (in CD-ROM)   2004

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  • Hybrid steepest descent method for the variational inequality problem over the fixed point sets of certain quasi-nonexpansive mappings (INVITED)

    Isao YAMADA

    Victoria International Conference 2004, Feb. Wellington (International meeting in cooperation with the Israel Mathematical Union, the New Zealand Mathematical Society and the New Zealand Institute of Mathematics and its Applications)   2004

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  • Adaptive projected subgradient method and its acceleration techniques (INVITED)

    Isao YAMADA Nobuhiko, OGURA Masahiro YUKAWA

    IFAC Workshop on Adaptation and Learning in Control and Signal Processing (ALCOSP 2004, Yokohama, Aug.), (in CD-ROM)   2004

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  • Acceleration technique for adaptive parallel subgradient projection algorithm and its properties

    Masahiro YUKAWA Isao YAMADA

    Proceedings of 19th IEICE Signal Processing Symposium (in CD-ROM)   2004

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  • Algebraic unitary root counting for finding minima of generalized power spectrum

    Isao YAMADA Kazuhiro OGUCHI

    Proceedings of 19th IEICE Signal Processing Symposium (in CD-ROM)   2004

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  • An edge-preserving inverse halftoning by successive outer approximation techniques

    Takuya OKADA Hiroshi, HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 19th IEICE Signal Processing Symposium (in CD-ROM)   2004

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  • A multidimensional associative memory neural network to recall nearest pattern from input

    Heming SUN, Hiroshi HASEGAWA Isao YAMADA

    Proceedings of 19th IEICE Signal Processing Symposium (in CD-ROM)   2004

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  • An adaptive super-resolution for noisy and blurred image sequences

    Toshiyuki ONO, Hiroshi HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 19th IEICE Signal Processing Symposium (in CD-ROM).   2004

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  • Resolution enhancement of color images subject to bounded total variations

    Ryota SASAHARA Hiroshi, HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 19th IEICE Signal Processing Symposium (in CD-ROM)   2004

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  • An Optimal Outer Approximation for the Adaptive Parallel Outer Projection Algorithm

    Nobuhiko OGURA, Isao YAMADA

    Proceedings of 2004 IEICE General Conference (in CD-ROM)   2004

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  • An efficient fast stereophonic acoustic echo canceler by pairwise optimal weight realization technique

    Noriaki MURAKOSHI Masahiro, YUKAWA Isao YAMADA

    Proceedings of 19th IEICE Signal Processing Symposium (in CD-ROM)   2004

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  • A successive least-squares super-resolution of MPEG video sequences

    Hiroshi, HASEGAWA Toshiyuki, ONO Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 2004 IEICE General Conference (in CD-ROM)   2004

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  • An efficient speed-up weighting technique for adaptive parallel projection algorithm

    Masahiro YUKAWA Isao YAMADA

    Proceedings of 2004 IEICE General Conference (in CD-ROM)   2004

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  • A super-resolution of movies based on adaptive parallel subgradient projection technique

    Toshiyuki ONO Hiroshi, HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 2004 IEICE General Conference (in CD-ROM)   2004

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  • Hybrid steepest descent method for variational inequality problem over the fixed point set of certain quasi-nonexpansive mappings

    Isao Yamada, Nobuhiko Ogura

    Numerical Functional Analysis and Optimization   25 ( 7-8 )   619 - 655   2004

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    The hybrid steepest descent method is an algorithmic solution to the variational inequality problem over the fixed point set of nonlinear mapping and applicable to broad range of convexly constrained nonlinear inverse problems in real Hilbert space. In this paper, we show that the strong convergence theorem [Yamada, I. (2001). The hybrid steepest descent method for the variational inequality problem over the intersection of fixed point sets of nonexpansive mappings. In: Butnariu, D., Censor, Y., Reich, S., eds. Inherently Parallel Algorithm for Feasibility and Optimization and Their Applications. Elsevier, pp. 473-504] of the method for nonexpansive mapping can be extended to a strong convergence theorem of the method for the variational inequality problem over the fixed point set of certain quasi-nonexpansive mappings called quasi-shrinking mapping. We also present a convergence theorem of the method for paramonotone variational inequality problem over the bounded fixed point set of quasi-shrinking mapping. By these generalizations, we can approximate successively to the solution of the convex optimization problem over the fixed point set of wide range of subgradient projection operators in real Hilbert space.

    DOI： 10.1081/NFA-200045815

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  • Adaptive projected subgradient method and its applications to set theoretic adaptive filtering

    Isao YAMADA Nobuhiko OGURA

    Proceedings of the 37th Asilomar Conference on Signals, Systems and Computers, (California, November, 2003)   2004

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  • Minimizing quadratic functions over reduced rank matrices

    Jamal ELBADRAOUI, Isao YAMADA

    Proceedings of 19th IEICE Signal Processing Symposium (in CD-ROM)   2004

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  • A note on robust adaptive Volterra filtering based on parallel subgradient projection techniques

    Yamada, I, T Okada, K Sakaniwa

    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES   E86A ( 8 )   2065 - 2068   2003.8

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    A robust adaptive filtering algorithm was established recently (I. Yamada, K. Slavakis, K. Yamada 2002) based on the interactive use of statistical noise information and the ideas developed originally for efficient algorithmic solutions to the convex feasibility problems. The algorithm is computationally efficient and robust to noise because it requires only an iterative parallel projection onto a series of closed half spaces highly expected to contain the unknown system to be identified and is free from the computational load of solving a system of linear equations. In this letter, we show the potential applicability of the adaptive algorithm to the identification problem for the second order Volterra systems. The numerical examples demonstrate that a straightforward application of the algorithm to the problem soundly realizes fast and stable convergence for highly colored excited speech like input signals in possibly noisy environments.

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  • Computation of symmetric positive definite Toeplitz matrices by the hybrid steepest descent method

    K Slavakis, Yamada, I, K Sakaniwa

    SIGNAL PROCESSING   83 ( 5 )   1135 - 1140   2003.5

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    Language：English   Publisher：ELSEVIER SCIENCE BV  

    This paper studies the problem of finding the nearest symmetric positive definite Toeplitz matrix to a given symmetric one. Additional design constraints, which are also formed as closed convex sets in the real Hilbert space of all symmetric matrices, are imposed on the desired matrix. An algorithmic solution to the problem given by the hybrid steepest descent method is established also in the case of inconsistent design constraints, (C) 2003 Elsevier Science B.V. All rights reserved.

    DOI： 10.1016/S0165-1684(03)00002-1

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  • A parametrization of solution to generalized Eckart-Young problem

    Jamal ELBADRAOUI, Isao YAMADA

    Proceedings of 18th Digital Signal Processing Symposium (Ise-Shima, November)   2003

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  • 適応射影劣勾配法 --- 適応信号処理アルゴリズムの新しい視点 (招待講演)

    山田功

    電子情報通信学会技術研究報告[ディジタル信号処理研究会/電気音響研究会](姫路, 5月16日, 2003)   DSP2003 ( 25 )   31--38   2003

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  • Adaptive parallel outer projection algorithm based on supporting hyperplane approximation

    Nobuhiko OGURA, Isao YAMADA

    Technical Report of IEICE DSP2003   DSP2003 ( 22 )   13--18   2003

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  • Adaptive Projected Subgradient Method and Its Applications to Signal Processing Problems (Plenary Talk by Isao Yamada)

    Isao YAMADA Nobuhiko OGURA

    The 3rd International Conference on Nonlinear Analysis and Convex Analysis   2003

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  • ハイブリッド最急降下法と階層型最適化問題

    山田功

    産業総合研究所: 脳神経情報研究部門(筑波, 5月23日, 2003)   2003

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  • An orthogonal matrix optimization by Dual Cayley Parametrization Technique

    Isao YAMADA, Takato EZAKI

    Proceedings of 4th International Symposium on Independent Component Analysis and Blind Signal Separation --- ICA2003 (Nara, April)   2003

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  • 逆問題の強い味方～ハイブリッド最急降下法～

    山田功

    電子情報通信学会東京支部 地域イベント (茨城大, 10月27日, 2003)   2003

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  • Efficient fast adaptive filtering schemes based on simultaneous use of multiple state data

    Masahiro YUKAWA Isao YAMADA

    Proceedings of 18th Digital Signal Processing Symposium (Ise-Shima, November)   2003

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  • Adaptive Projected Subgradient Method and Its Applications to Signal Processing Problems (Plenary Talk by Isao Yamada)

    Isao YAMADA Nobuhiko OGURA

    The 3rd International Conference on Nonlinear Analysis and Convex Analysis   2003

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  • Locally reduced-rank optimal filtering and its approximation by successive alternating minmization

    Isao YAMADA Jamal ELBADRAUI

    Proceedings of IEEE ICASSP2003 (Hong Kong, April)   2003

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  • An orthogonal matrix optimization by Dual Cayley Parametrization Technique

    Isao YAMADA, Takato EZAKI

    Proceedings of 4th International Symposium on Independent Component Analysis and Blind Signal Separation --- ICA2003 (Nara, April)   2003

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  • A truncated polynomial interpolation and its application to polynomially WLS design of IIR filters

    Hiroshi HASEGAWA Masashi, NAKAGAWA Isao, YAMADA Kohichi, SAKANIWA

    IEICE Trans. on Fundamentals   E86-A ( 7 )   1742--1748   2003

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  • Adaptive parallel subgradient projection techniques and input sliding technique for stereophonic accoustic echo cancellation

    Masahiro YUKAWA Isao YAMADA

    Proceedings of the 8th International Workshop on Accoustic Echo and Noise Control   55 - 58   2003

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  • A note on robust adaptive Volterra filtering based on parallel Subgradient projection techniques

    Isao YAMADA Takuya, OKADA Kohichi, SAKANIWA

    IEICE Transactions Fundamentals   E86-A ( 8 )   2065 - 2068   2003

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  • Locally reduced-rank optimal filtering and its approximation by successive alternating minmization

    Isao YAMADA Jamal ELBADRAUI

    Proceedings of IEEE ICASSP2003 (Hong Kong, April)   2003

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  • A truncated polynomial interpolation and its application to polynomially WLS design of IIR filters

    Hiroshi HASEGAWA Masashi, NAKAGAWA Isao, YAMADA Kohichi, SAKANIWA

    IEICE Trans. on Fundamentals   E86-A ( 7 )   1742--1748   2003

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  • Adaptive parallel subgradient projection techniques and input sliding technique for stereophonic accoustic echo cancellation

    Masahiro YUKAWA Isao YAMADA

    Proceedings of the 8th International Workshop on Accoustic Echo and Noise Control   55 - 58   2003

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  • Hybrid steepest descent method over the fixed point set of certain quasi-nonexpansive mapping

    Isao YAMADA Nobuhiko OGURA

    Proceedings of 18th Digital Signal Processing Symposium (Ise-Shima, November)   2003

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  • 射影型適応アルゴリズムの新展開 --- 射影劣こう配法による統一的視点とその応用

    山田功

    電子情報通信学会誌   86 ( 8 )   654--658   2003

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  • A blind multiple access interfererence reduction in DS/CDMA systems based on adaptive projected subgradient method

    Renato CAVALCANTE Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 18th Digital Signal Processing Symposium (Ise-Shima, November)   2003

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  • A reconstruction of high-resolution images from MPEG compressed video sequences

    Hiroshi HASEGAWA Toshiyuki, ONO Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 18th Digital Signal Processing Symposium (Ise-Shima, November)   2003

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  • Efficient fast adaptive filtering schemes based on simultaneous use of multiple state data

    Masahiro YUKAWA Isao YAMADA

    Proceedings of 18th Digital Signal Processing Symposium (Ise-Shima, November)   2003

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  • A parametrization of solution to generalized Eckart-Young problem

    Jamal ELBADRAOUI, Isao YAMADA

    Proceedings of 18th Digital Signal Processing Symposium (Ise-Shima, November)   2003

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  • Adaptive projected subgradient method --- A unified view of projection based adaptive filtering

    DSP2003 ( 25 )   31--38   2003

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  • Adaptive parallel outer projection algorithm based on supporting hyperplane approximation

    Nobuhiko OGURA, Isao YAMADA

    Technical Report of IEICE DSP2003   DSP2003 ( 22 )   13--18   2003

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  • A reconstruction of high-resolution images from MPEG compressed video sequences

    Hiroshi HASEGAWA Toshiyuki, ONO Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 18th Digital Signal Processing Symposium (Ise-Shima, November)   2003

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  • Hybrid steepest descent method over the fixed point set of certain quasi-nonexpansive mapping

    Isao YAMADA Nobuhiko OGURA

    Proceedings of 18th Digital Signal Processing Symposium (Ise-Shima, November)   2003

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  • A blind multiple access interfererence reduction in DS/CDMA systems based on adaptive projected subgradient method

    Renato CAVALCANTE Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 18th Digital Signal Processing Symposium (Ise-Shima, November)   2003

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  • A higher order generalization of an alias-free discrete time-frequency analysis

    H Hasegawa, Y Miki, Yamada, I, K Sakaniwa

    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES   E85A ( 8 )   1774 - 1780   2002.8

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    Language：English   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

    In this paper, we propose a novel higher order time-frequency distribution (GDH) for a discrete time signal. This distribution is defined over the original discrete time-frequency grids through a delicate discretization of an equivalent expression of a higher order distribution, for a continuous time signal, in [4]. We also present a constructive design method, for the kernel of the GDH, by which the distribution satisfies (i) the alias free condition as well as (ii) the marginal conditions. Numerical examples show that the proposed distributions reasonably suppress the artifacts which are observed severely in the Wigner distribution and its simple higher order generalization.

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  • An efficient robust adaptive filtering algorithm based on parallel subgradient projection techniques

    Isao Yamada, Konstantinos Slavakis, Kenyu Yamada

    IEEE Transactions on Signal Processing   50 ( 5 )   1091 - 1101   2002.5

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    This paper presents a novel robust adaptive filtering scheme based on the interactive use of statistical noise information and the ideas developed originally for efficient algorithmic solutions to the convex feasibility problems. The statistical noise information is quantitatively formulated as stochastic property closed convex sets by the simple design formulae developed in this paper. A simple set-theoretic inspection also leads to an important statistical reason for the sensitivity to noise of the affine projection algorithm (APA). The proposed adaptive algorithm is computationally efficient and robust to noise because it requires only an iterative parallel projection onto a series of closed half spaces that are highly expected to contain the unknown system to be identified and is free from the computational load of solving a system of linear equations. The numerical examples show that the proposed adaptive filtering scheme realizes dramatically fast and stable convergence for highly colored excited speech like input signals in severe noise situations.

    DOI： 10.1109/78.995065

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  • An efficient robust adaptive filtering algorithm based on parallel subgradient projection techniques

    Yamada, I, K Slavakis, K Yamada

    IEEE TRANSACTIONS ON SIGNAL PROCESSING   50 ( 5 )   1091 - 1101   2002.5

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    Language：English   Publisher：IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC  

    This paper presents a novel robust adaptive filtering scheme based on the interactive use of statistical noise information and the ideas developed originally for efficient algorithmic solutions to the convex feasibility problems. The statistical noise information is quantitatively formulated as stochastic property closed convex sets by the simple design formulae developed in this paper. A simple set-theoretic inspection also leads to an important statistical reason for the sensitivity to noise of the affine projection algorithm (APA).
    The proposed adaptive algorithm is computationally efficient and robust to noise because it requires only an iterative parallel projection onto a series of closed half spaces that are highly expected to contain the unknown system to be identified and is free from the computational load of solving a system of linear equations. The numerical examples show that the proposed adaptive filtering scheme realizes dramatically fast and stable convergence for highly colored excited speech like input signals in severe noise situations.

    DOI： 10.1109/78.995065

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  • Algebraic phase unwrapping and zero distribution of polynomial for continuous-time systems

    Yamada, I, NK Bose

    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-FUNDAMENTAL THEORY AND APPLICATIONS   49 ( 3 )   298 - 304   2002.3

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    Language：English   Publisher：IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC  

    An analytic solution is provided to the symbolic algebra-based computational problem for the unwrapped phase (that can be uniquely expressed as an integral involving itself and its derivative) of a continuous-time linear time-invariant system whose characteristic polynomial has coefficients belonging to the algebraically closed field of complex numbers. This solution is based on the use of the classical Cauchy indices. Application and adaptation of this analytic solution to an arbitrary univariate polynomial, yields its zero distribution with respect to the unbounded imaginary axis in the complex plane. Importantly, the algorithm that yields this zero distribution is designed to enforce the nonoccurrence of singular cases and can be implemented to any desired accuracy by rational operations.

    DOI： 10.1109/81.989163

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  • An efficient robust stereophonic acoustic echo canceler based on parallel subgradient projection techniques

    Isao YAMADA Masahiro YUKAWA

    Proceedings of ICSP2002(Beijing, August)   2002

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  • An optimization of the relaxed signal adapted wavelet by semidefinite programming techniques

    Koji AKITA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of ICSP2002 (Beijing, August)   2002

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  • A truncated polynomial interpolation theorem and its application to the WLS design of IIR filters

    Hiroshi HASEGAWA Masashi, NAKAGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of IEEE ISCAS2002 (Arizona, May)   2002

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  • A Non-strictly Convex Minimization over the Fixed Point Set of the Asymptotically Shrinking Nonexpansive Mapping

    Nobuhiko OGURA, Isao YAMADA

    Numerical Functional Analysis and Optimization   23 ( 1&2 )   113 - 137   2002

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  • A higher order generalization of an alias-free discrete time-frequency analysis

    Hiroshi HASEGAWA Yasuhiro MIKI Isao, YAMADA Kohichi, SAKANIWA

    IEICE Trans. on Fundamentals   E79-A ( 8 )   1774 - 1780   2002

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  • Spectrum estimation of real vector wide sense stationary processes by the hybrid steepest desent method

    Konstantinos SLAVAKIS Isao, YAMADA Kohichi, SAKANIWA

    IEEE Proceedings of ICASSP2002   2002

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  • A higher order generalization of an alias-free discrete time-frequency analysis

    Hiroshi HASEGAWA Yasuhiro MIKI Isao, YAMADA Kohichi, SAKANIWA

    IEEE Proceedings of ICASSP2002   2002

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  • An estimation of multicomponent polynomial phase signals

    Yasuhiro MIKI, Hiroshi, HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 17th Digital Signal Processing Symposium (Hakodate, November)   2002

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  • Time-frequency projection filtering based on alias-free discrete time-frequency analysis

    Hiroshi HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of the 25th Symposium on Information Theory and Its Applications   71 - 74   2002

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  • Approximation of locally reduced-rank optimal filtering

    Isao YAMADA Jamal ELBADRAOUI

    Proceedings of the 25th Symposium on Information Theory and Its Applications   171 - 174   2002

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  • An adaptive Volterra filtering based on parallel subgradient projection techniques

    Takuya OKADA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of the 25th Symposium on Information Theory and Its Applications   391 - 394   2002

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  • Ideal descent direction technique over the Stiefel manifold ---Resolution of nonconvex constraints by Dual Cayley parametrization

    Takato EZAKI Isao YAMADA

    Proceedings of the 25th Symposium on Information Theory and Its Applications   167 - 170   2002

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  • An efficient robust adaptive Volterra filtering based on parallel subgradient projection techniques

    Takuya OKADA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 17th Digital Signal Processing Symposium(Hakodate, November)   2002

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  • An estimation of multicomponent polynomial phase signals

    Yasuhiro MIKI, Hiroshi, HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 17th Digital Signal Processing Symposium (Hakodate, November)   2002

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  • A matrix optimization technique over Stiefel manifold based on alternating Cayley parametrization

    Takato EZAKI Isao YAMADA

    Proceedings of 17th Digital Signal Processing Symposium (Hakodate, November)   2002

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  • An efficient robust adaptive multi-channel acoustic echo canceler based on 2-stage parallel subgradient projection techniques

    Masahiro YUKAWA Isao YAMADA

    Proceedings of 17th Digital Signal Processing Symposium (Hakodate, November)   2002

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  • Hybrid steepest descent method -Fixed point theory meets signal processing (Keynote speech)

    Isao YAMADA

    Proceedings of IEEE APCCAS2002 (Singapore, December)   2002

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  • A multi-stage convex optimization technique for convexly constrained inverse problems

    Isao YAMADA

    Proceedings of 17th Digital Signal Processing Symposium (Hakodate, November)   2002

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  • A matrix optimization technique over Stiefel manifold based on alternating Cayley parametrization

    Takato EZAKI Isao YAMADA

    Proceedings of 17th Digital Signal Processing Symposium (Hakodate, November)   2002

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  • An efficient robust adaptive multi-channel acoustic echo canceler based on 2-stage parallel subgradient projection techniques

    Masahiro YUKAWA Isao YAMADA

    Proceedings of 17th Digital Signal Processing Symposium (Hakodate, November)   2002

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  • Hybrid steepest descent method -Fixed point theory meets signal processing (Keynote speech)

    Isao YAMADA

    Proceedings of IEEE APCCAS2002 (Singapore, December)   2002

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  • A multi-stage convex optimization technique for convexly constrained inverse problems

    Isao YAMADA

    Proceedings of 17th Digital Signal Processing Symposium (Hakodate, November)   2002

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  • An optimization of the relaxed signal adapted wavelet by semidefinite programming techniques

    Koji AKITA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of ICSP2002 (Beijing, August)   2002

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  • A truncated polynomial interpolation theorem and its application to the WLS design of IIR filters

    Hiroshi HASEGAWA Masashi, NAKAGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of IEEE ISCAS2002 (Arizona, May)   2002

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  • An efficient robust stereophonic acoustic echo canceler based on parallel subgradient projection techniques

    Isao YAMADA Masahiro YUKAWA

    Proceedings of ICSP2002(Beijing, August)   2002

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  • A higher order generalization of an alias-free discrete time-frequency analysis

    Hiroshi HASEGAWA Yasuhiro MIKI Isao, YAMADA Kohichi, SAKANIWA

    IEEE Proceedings of ICASSP2002   2002

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  • A Non-strictly Convex Minimization over the Fixed Point Set of the Asymptotically Shrinking Nonexpansive Mapping

    Nobuhiko OGURA, Isao YAMADA

    Numerical Functional Analysis and Optimization   23 ( 1&2 )   113 - 137   2002

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  • Analysis of multicomponent polynomial phase signals using unwrapped phase and geometry on triangle

    Yasuhiro MIKI, Hiroshi, HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of the 25th Symposium on Information Theory and Its Applications   75 - 78   2002

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  • Spectrum estimation of real vector wide sense stationary processes by the hybrid steepest desent method

    Konstantinos SLAVAKIS Isao, YAMADA Kohichi, SAKANIWA

    IEEE Proceedings of ICASSP2002   2002

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  • Analysis of multicomponent polynomial phase signals using unwrapped phase and geometry on triangle

    Yasuhiro MIKI, Hiroshi, HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of the 25th Symposium on Information Theory and Its Applications   75 - 78   2002

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  • Robust 2-stage adaptive parallel subgradient projection techniques for multi-channel acoustic echo cancellation

    Masahiro YUKAWA Isao YAMADA

    Proceedings of the 25th Symposium on Information Theory and Its Applications   271 - 274   2002

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  • Time-frequency projection filtering based on alias-free discrete time-frequency analysis

    Hiroshi HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of the 25th Symposium on Information Theory and Its Applications   71 - 74   2002

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  • An adaptive Volterra filtering based on parallel subgradient projection techniques

    Takuya OKADA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of the 25th Symposium on Information Theory and Its Applications   391 - 394   2002

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  • Ideal descent direction technique over the Stiefel manifold ---Resolution of nonconvex constraints by Dual Cayley parametrization

    Takato EZAKI Isao YAMADA

    Proceedings of the 25th Symposium on Information Theory and Its Applications   167 - 170   2002

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  • Approximation of locally reduced-rank optimal filtering

    Isao YAMADA Jamal ELBADRAOUI

    Proceedings of the 25th Symposium on Information Theory and Its Applications   171 - 174   2002

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  • An efficient robust adaptive Volterra filtering based on parallel subgradient projection techniques

    Takuya OKADA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 17th Digital Signal Processing Symposium(Hakodate, November)   2002

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  • Robust 2-stage adaptive parallel subgradient projection techniques for multi-channel acoustic echo cancellation

    Masahiro YUKAWA Isao YAMADA

    Proceedings of the 25th Symposium on Information Theory and Its Applications   271 - 274   2002

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  • Biorthogonal unconditional bases of compactly supported matrix valued wavelets

    K. Slavakis, I. Yamada

    Numerical Functional Analysis and Optimization   22 ( 1-2 )   223 - 253   2001.2

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    Language：English  

    In this paper, we introduce the concept of biorthogonal matrix valued wavelets. We elaborate on perfect reconstruction matrix filter banks which are assembled by matrix FIR filters and we deduce that the resulting matrix valued wavelet functions have compact support. Moreover, we form biorthogonal unconditional bases for the space of matrix valued signals. To validate the theory, a class of biorthogonal and orthonormaI matrix valued wavelets are given. The connection of the present scheme with the theory of multiwavelets are also explored.

    DOI： 10.1081/NFA-100103795

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  • An optimal wavelet design by the hybrid steepest descent method

    Koji AKITA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 16th Digital Signal Processing Symposium   2001

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  • An Algorithm for Projection onto Certain Polyhedron and Its Application to Inversion of Imaging Spectrometry Data

    Nobuhiko OGURA, Isao YAMADA

    Proceedings of 16th Digital Signal Processing Symposium   2001

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  • Compactly Supported Matrix Valued Wavelets---Biorthogonal Unconditional Bases (invited)

    Konstantinos SLAVAKIS Isao YAMADA

    Proceedings of IEEE ISCAS2001   CD-ROM   2001

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  • A generalization of the Walsh's theorem for weighted least-squares rational approximation

    Hiroshi HASEGAWA Masashi, NAKAGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of the 16th Digital Signal Processing Symposium   2001

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  • The multi-layered hard-constrained convex feasibility problem

    Nobuhiko OGURA, Isao YAMADA

    2nd International Conference on Nonlinear Analysis and Convex Analysis   2001

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  • An Efficient Robust Adaptive Filtering Scheme Based on Pallel Subgradient Projection Techniques

    Isao YAMADA Konstantinos SLAVAKIS Kenyu YAMADA

    Proceedings of IEEE ICASSP2001   CD-ROM   2001

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  • Signal estimation by non-strictly convex minimization over the fixed point set of nonexpansive mapping (invited)

    Isao YAMADA

    Seminaire Methodes Mathematiques du Traitement d'Images C.N.R.S & UNIVERSITE PARIS 6 (PIERRE ET MARIE CURIE) Laboratoire d'Analyse Numerique,   2001

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  • Hybrid Steepest Descend Method and its Application to Connvexly Constrained Inverse Problems (invited)

    Isao YAMADA

    The 106th annual meeting of the American Mathematical Society, New Orleans   149   2001

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  • 凸射影アルゴリズムとハイブリッド最急降下法の新展開(招待講演)

    山田功

    電子情報通信学会基礎境界ソサイエティ大会講演論文集   265 - 266   2001

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  • The Hybrid Steepest Descent Method and Its Applications to Constrained Inverse Problems (invited)

    Isao YAMADA Nobuhiko OGURA

    The 10th International Colloquium on Numerical Analysis and Computer Sciences with Applications, Plovdiv Bulgaria,   2001

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  • An optimal design of signal adapted orthonormal wavelet by the hybrid steepest descent method

    Koji AKITA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 24th Symposium on Information Theory and Its Applications (SITA2001)   2001

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  • An optimal design of signal adapted orthonormal wavelet by the hybrid steepest descent method

    Koji AKITA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 24th Symposium on Information Theory and Its Applications (SITA2001)   2001

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  • A higher order generalization of an alias-free discrete time-frequency analysis

    Hiroshi HASEGAWA Yasuhiro MIKI Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 24th Symposium on Information Theory and Its Applications (SITA2001)   2001

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  • Spectrum estimation as a problem of finding symmetric positive definite block toeplitz matrices by the Hybrid Steepest Descent Method

    Konstantinos SLAVAKIS Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 24th Symposium on Information Theory and Its Applications (SITA2001)   2001

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  • Inconsistent Convex Feasibility Problem with Multiple and Prioritized Hard Constraints

    Nobuhiko OGURA, Isao YAMADA

    Proceedings of 24th Symposium on Information Theory and Its Applications (SITA2001)   2001

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  • A truncated polynomial interpolation theorem and its application to the WLS design of IIR filters

    Hiroshi HASEGAWA Masashi, NAKAGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 24th Symposium on Information Theory and Its Applications (SITA2001)   2001

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  • Spectrum estimation as a problem of finding symmetric positive definite Toeplitz Matrices by the Hybrid Steepest Descent Method

    Konstantinos SLAVAKIS Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 16th Digital Signal Processing Symposium   2001

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  • A Numerically robust hybrid steepest descent method

    Nobuyasu SHIRAKAWA Nobuhiko, OGURA Isao YAMADA

    Proceedings of 24th Symposium on Information Theory and Its Applications (SITA2001)   2001

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  • On the Numerical Robustness of the Hybrid Steepest Descent Method

    Nobuyasu SHIRAKAWA Nobuhiko, OGURA Isao YAMADA

    Proceedings of 16th Digital Signal Processing Symposium   2001

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  • The multi-layered hard-constrained convex feasibility problem

    Nobuhiko OGURA, Isao YAMADA

    2nd International Conference on Nonlinear Analysis and Convex Analysis   2001

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  • The Hybrid Steepest Descent Method and Its Applications to Constrained Inverse Problems (invited)

    Isao YAMADA Nobuhiko OGURA

    The 10th International Colloquium on Numerical Analysis and Computer Sciences with Applications, Plovdiv Bulgaria,   2001

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  • Signal estimation by non-strictly convex minimization over the fixed point set of nonexpansive mapping (invited)

    Isao YAMADA

    Seminaire Methodes Mathematiques du Traitement d'Images C.N.R.S & UNIVERSITE PARIS 6 (PIERRE ET MARIE CURIE) Laboratoire d'Analyse Numerique,   2001

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  • A Numerically robust hybrid steepest descent method

    Nobuyasu SHIRAKAWA Nobuhiko, OGURA Isao YAMADA

    Proceedings of 24th Symposium on Information Theory and Its Applications (SITA2001)   2001

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  • Inconsistent Convex Feasibility Problem with Multiple and Prioritized Hard Constraints

    Nobuhiko OGURA, Isao YAMADA

    Proceedings of 24th Symposium on Information Theory and Its Applications (SITA2001)   2001

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  • An optimal wavelet design by the hybrid steepest descent method

    Koji AKITA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 16th Digital Signal Processing Symposium   2001

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  • Spectrum estimation as a problem of finding symmetric positive definite Toeplitz Matrices by the Hybrid Steepest Descent Method

    Konstantinos SLAVAKIS Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 16th Digital Signal Processing Symposium   2001

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  • An Algorithm for Projection onto Certain Polyhedron and Its Application to Inversion of Imaging Spectrometry Data

    Nobuhiko OGURA, Isao YAMADA

    Proceedings of 16th Digital Signal Processing Symposium   2001

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  • On the Numerical Robustness of the Hybrid Steepest Descent Method

    Nobuyasu SHIRAKAWA Nobuhiko, OGURA Isao YAMADA

    Proceedings of 16th Digital Signal Processing Symposium   2001

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  • A generalization of the Walsh's theorem for weighted least-squares rational approximation

    Hiroshi HASEGAWA Masashi, NAKAGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of the 16th Digital Signal Processing Symposium   2001

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  • An Efficient Robust Adaptive Filtering Scheme Based on Pallel Subgradient Projection Techniques

    Isao YAMADA Konstantinos SLAVAKIS Kenyu YAMADA

    Proceedings of IEEE ICASSP2001   CD-ROM   2001

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  • Compactly Supported Matrix Valued Wavelets---Biorthogonal Unconditional Bases (invited)

    Konstantinos SLAVAKIS Isao YAMADA

    Proceedings of IEEE ISCAS2001   CD-ROM   2001

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  • Hybrid Steepest Descend Method and its Application to Connvexly Constrained Inverse Problems (invited)

    Isao YAMADA

    The 106th annual meeting of the American Mathematical Society, New Orleans   149   2001

    　More details

  • A higher order generalization of an alias-free discrete time-frequency analysis

    Hiroshi HASEGAWA Yasuhiro MIKI Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 24th Symposium on Information Theory and Its Applications (SITA2001)   2001

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  • Spectrum estimation as a problem of finding symmetric positive definite block toeplitz matrices by the Hybrid Steepest Descent Method

    Konstantinos SLAVAKIS Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 24th Symposium on Information Theory and Its Applications (SITA2001)   2001

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  • A truncated polynomial interpolation theorem and its application to the WLS design of IIR filters

    Hiroshi HASEGAWA Masashi, NAKAGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 24th Symposium on Information Theory and Its Applications (SITA2001)   2001

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  • A simple least-squares design of M-D IIR filters with fixed separable denominator based on multivariate division algorithm

    H Hasegawa, Yamada, I, K Sakaniwa

    MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING   11 ( 4 )   339 - 358   2000.10

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    Language：English   Publisher：KLUWER ACADEMIC PUBL  

    In this paper, we propose a simple algorithmic solution to the best approximation problem of finding the nearest multivariate rational function, with a fixed separable denominator polynomial, from a given multivariate polynomial, where the numerator polynomial is desired to minimize the integral of the squared error over the distinguished boundary of the unit polydisc. The proposed algorithm does not require any numerical integration or numerical root finding technique because this is realized based on the standard multivariate division algorithm.
    A simple observation of the proposed algorithm leads to an ideal membership problem characterizing the solution to the problem. A relation of this characterization and a multivariate generalization of the Walsh's Theorem is also discussed with another ideal membership problem derived by applying a corollary of the Hilbert Nullstellensatz to the Walsh's Theorem. Although the discussion to derive the latter ideal membership problem seems to be roundabout, such a characterization would be useful for further generalization, for example to some weighted least-squares approximation.
    Numerical examples demonstrate the practical applicability of the proposed method to design problems of multidimensional IIR filters.

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  • A simple nonlinear pre-filtering for a set-theoretic linear blind deconvolution scheme

    M Kato, Yamada, I, K Sakaniwa

    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES   E83A ( 8 )   1651 - 1653   2000.8

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    Language：English   Publishing type：Rapid communication, short report, research note, etc. (scientific journal)   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

    In this letter, we remark a well-known nonlinear filtering technique realize immediate effect to suppress the influence of the additive measurement noise in the input to a set theoretic linear blind deconvolution scheme. Numerical examples show E-separating nonlinear pre-filtering techniques work suitably to this noisy blind deconvolution problem.

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  • Robust Identificatin of Nonlinear System by Reduced Rank Volterra Filter

    Tomohiro SEKIGUCHI Isao, YAMADA Kohichi, SAKANIWA

    Technical Report of IEICE   DSP99   19 - 24   2000

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  • An Efficient Robust Adaptive Algorithm Based on Parallel Subgradient Projection Techniques

    Isao YAMADA Kenyu, YAMADA Konstantinos SLAVAKIS

    Technical Report of IEICE   DSP2000   41 - 46   2000

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  • A relaxation of the hybrid steepest descent method for wider class of inverse problems

    Nobuhiko OGURA, Isao YAMADA

    Technical Report of IEICE   DSP2000   9 - 12   2000

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  • Kernel design of generalized class of discrete-time discrete-frequency distribution based on Hybrid steepest descent method

    Hiroshi HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    Technical Report of IEICE   DSP2000   75 - 80   2000

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  • Reduced Rank Volterra Filter for Robust Identification of Nonlinear Systems

    Isao YAMADA Tomohiro, SEKIGUCHI Kohichi SAKANIWA

    Proceedings of the 2nd International Workshop on Multidimensional (ND) Systems -NDS2000   171 - 176   2000

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  • Algebraic Phase Unwrapping and Zero Distribution of Complex Polynomial for Continuous-Time Systems

    Isao YAMADA Nirmal, K. BOSE

    Technical Report of IEICE   DSP2000   33 - 39   2000

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  • A Design of Near Perfect Reconstruction QMF Banks Based on Hybrid Steepest Descent Method

    Hiroshi HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    IEICE Transactions Fundamentals   E83-A ( 8 )   1523 - 1530   2000

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  • Algebraic Phase Unwrapping and Zero Distribution of Polynomial for Continuous-Time Systems

    Isao YAMADA Nirmal, K. BOSE

    Proceedings of the 2nd International Workshop on Multidimensional (ND) Systems - NDS2000   177 - 181   2000

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  • The Hybrid Steepest Descend Method for the Variational Inequality Problem over the Fixed Point Set of Nonexpansive Mapping

    Isao YAMADA

    Inherently parallel algorithms in feasibility and optimization and their application   30 - 31   2000

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  • 凸射影アルゴリズムの考え方とハイブリッド最急降下法

    山田功

    電子情報通信学会誌   83 ( 8 )   616 - 623   2000

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  • An Efficient Robust Adaptive Algorithm Based on Parallel Subgradient Projection Techniques

    Isao YAMADA Kenyu, YAMADA Konstantinos SLAVAKIS

    Technical Report of IEICE   DSP2000   41 - 46   2000

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  • A relaxation of the hybrid steepest descent method for wider class of inverse problems

    Nobuhiko OGURA, Isao YAMADA

    Technical Report of IEICE   DSP2000   9 - 12   2000

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  • Kernel design of generalized class of discrete-time discrete-frequency distribution based on Hybrid steepest descent method

    Hiroshi HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    Technical Report of IEICE   DSP2000   75 - 80   2000

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  • Reduced Rank Volterra Filter for Robust Identification of Nonlinear Systems

    Isao YAMADA Tomohiro, SEKIGUCHI Kohichi SAKANIWA

    Proceedings of the 2nd International Workshop on Multidimensional (ND) Systems -NDS2000   171 - 176   2000

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  • Algebraic Phase Unwrapping and Zero Distribution of Complex Polynomial for Continuous-Time Systems

    Isao YAMADA Nirmal, K. BOSE

    Technical Report of IEICE   DSP2000   33 - 39   2000

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  • A Design of Near Perfect Reconstruction QMF Banks Based on Hybrid Steepest Descent Method

    Hiroshi HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    IEICE Transactions Fundamentals   E83-A ( 8 )   1523 - 1530   2000

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  • Algebraic Phase Unwrapping and Zero Distribution of Polynomial for Continuous-Time Systems

    Isao YAMADA Nirmal, K. BOSE

    Proceedings of the 2nd International Workshop on Multidimensional (ND) Systems - NDS2000   177 - 181   2000

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  • The Hybrid Steepest Descend Method for the Variational Inequality Problem over the Fixed Point Set of Nonexpansive Mapping

    Isao YAMADA

    Inherently parallel algorithms in feasibility and optimization and their application   30 - 31   2000

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  • Robust Identificatin of Nonlinear System by Reduced Rank Volterra Filter

    Tomohiro SEKIGUCHI Isao, YAMADA Kohichi, SAKANIWA

    Technical Report of IEICE   DSP99   19 - 24   2000

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  • Hybrid steepest descent method for asymptotically shrinking mapping

    Nobuhiko OGURA, Isao YAMADA

    Proceedings of the 23rd symposium on Information Theory and Its Applications (SITA2000)   SITA2000   2000

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  • Kernel synthesis for generalized time-frequency distributions based on Hybrid steepest descent method

    Hiroshi HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proc. of 15th Digital Signal Processing Symposium   103 - 108   2000

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  • Kernel synthesis for generalized time-frequency distributions based on Hybrid steepest descent method

    Hiroshi HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proc. of 15th Digital Signal Processing Symposium   103 - 108   2000

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  • Hybrid steepest descent method for asymptotically shrinking mapping

    Nobuhiko OGURA, Isao YAMADA

    Proceedings of the 23rd symposium on Information Theory and Its Applications (SITA2000)   SITA2000   2000

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  • An associative memory neural network to recall nearest pattern from input

    Yamada, I, S Iino, K Sakaniwa

    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES   E82A ( 12 )   2811 - 2817   1999.12

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    Language：English   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

    This paper proposes an associative memory neural network whose limiting state is the nearest point in a polyhedron from a given input. Two implementations of the proposed associative memory network are presented based on Dykstra's algorithm and a fixed point theorem for nonexpansive mappings. By these implementations, the set of all correctable errors by the network is characterized as a dual cone of the polyhedron at each pattern to be memorized, which leads to a simple amplifying technique to improve the error correction capability. It is shown by numerical examples that the proposed associative memory realizes much better error correction performance than the conventional one based on POCS at the expense of the increase of necessary number of iterations in the recalling stage.

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  • A set-theoretic blind image deconvolution based on hybrid steepest descent method

    M Kato, Yamada, I, K Sakaniwa

    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES   E82A ( 8 )   1443 - 1449   1999.8

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    Language：English   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

    Recently, Kundur and Hatzinakos showed that a linear restoration filter designed by using the almost obvious a priori knowledge on the original image, such as (i) nonnegativity of the true image and (ii) the smallest rectangle encompassing the original object, can realize a remarkable performance for a blind image deconvolution problem. In this paper, we propose a new set-theoretic blind image deconvolution scheme based oil a recently developed convex projection technique called Hybrid Steepest Descent Method (HSDM), where some partial information can be utilized set-theoretically by parallel projections onto convex sets while the others are incorporated in a cost function to be minimized by a steepest descent method. Numerical comparisons with the standard set-theoretic scheme based on POCS illustrate the effectiveness of the proposed scheme.

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  • On convex projection algorithms from POCS to Hybrid steepest descent method

    Isao Yamada

    Technical Report of IEICE   DSP99   67 - 74   1999

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  • Biorthogonal Bases of Compactly Supported Matrix Valued Wavelets

    Konsantinos SLAVAKIS Isao YAMADA

    Proceedings of 1999 International Symposium on Signal Processing and Its Applications   22 - 25   1999

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  • A note on multivariate generalization of Lanczos principle and its application to signal processing problems

    Hiroshi HASEGAWA Toshio, NAKAO Isao, YAMADA Kohichi, SAKANIWA

    Technical Report of IEICE, DSP-98   ( 188 )   1 - 7   1999

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  • Approximation of Convexly Constrained Pseudoinverse by Hybrid Steepest Descent Method

    ISAO YAMADA

    Proceedings of 1999 IEEE International Symposium on Circuits and Systems   V   37?40   1999

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  • On convex projection algorithms from POCS to Hybrid steepest descent method

    Isao Yamada

    Technical Report of IEICE   DSP99   67 - 74   1999

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  • An Optimal Set-theoretic Blind Deconvolution Scheme based on Hybrid Steepest Descent Method

    Isao YAMADA Masanori, KATO Kohichi, SAKANIWA

    Proceedings of 1999 IEEE International Conference on Acoustics, Speech and Signal Processing   VI   3261?3264   1999

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  • Convex projection approach to design of two-channel linear phase FIR QMF banks in magnitude product space

    Hiroshi HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    Technical Report of IEICE, DSP-99   99 ( 68 )   7 - 12   1999

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    Language：English   Publisher：The Institute of Electronics, Information and Communication Engineers  

    Recently, Haddad et al. characterized required specifications for two-channel QMF banks by multiple constraint sets and they obtained a QMF bank with good performance by finding a point in the intersection of these constraint sets by using POCS (projection onto convex sets). Unfortunately, it is shown in this paper, the convergence of their method isn't guaranteed because one of their constraint sets is not convex. In this paper, we first introduce a notion of Magnitude Product Space in which a pair of the magnitude responses in analysis bank is simply expressed as a point. In this space, the constraint sets are formulated more briefly and it is shown that the power complementary condition is approximated by an appropriate closed convex set in multiple ring-shaped region. Furthermore, it becomes clear that a constraint set, which Haddad et al. defined to approximate the condition, is not convex. We propose a new design method that is adaptively modifying these convex sets and iteratively solve an optimization problem over the intersection of them by applying Hybrid Steepest Descent Method. The design specification is often too tight to be realized, hence these constraint sets may have no intersection, but our method has a merit of the guarantee of convergence to a solution which approximate the specification over a set of all realizable filters even if the intersection is empty. Finally, we also show our method realizes a QMF bank with better performance than that of Haddad et al.'s by a numerical example.

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  • A note on design of two channel linear phase FIR QMF banks based on Hybrid Steepest Descent Method

    Hiroshi HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 14th Digital Signal Processing Symposium   237 - 242   1999

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  • A note on design of two-channel linear phase FIR QMF banks based on Hybrid Steepest Descent Method

    HASEGAWA H.

    Proc. 14th Digital Signal Processing Symposium   237 - 242   1999

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  • A simple M-dimensional model-based phase unwrapping based on integration by parts

    Yamada, I, K Nakajima

    ISCAS '99: PROCEEDINGS OF THE 1999 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOL 5   V   33 - 36   1999

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    Language：English   Publisher：IEEE  

    In this paper, we propose a simple method for model based multidimensional phase unwrapping to estimate the multivariate polynomial phase from an observed complex signal. The main idea of the proposed method is a simple reduction of the original problem of multidimensional phase estimation to a system of linear equations based on the well known integration by parts. By this reduction, exhaustive numerical maximization techniques become unnecessary, unlike the conventional methods, and any linear inverse technique can be applied with additional, if necessary, a priori knowledge to suppress the effect of any perturbation due to the measurement noises or the numerical errors caused mainly by numerical integrations.

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  • Least-squares design of M-D IIR filters with fixed separable denominator based on multivariate division algorithm

    Hiroshi HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 1999 European Conference on Circuits Theory and Design   1139 - 1142   1999

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  • A Nonlinear Pre-filtering Technique for Set-Theoretic Linear Blind Deconvolution Scheme

    Isao YAMADA Masanori, KATO Kohichi, SAKANIWA

    Proceedings of 1999 International Conference on Image Processing   401 - 405   1999

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  • Biorthogonal Bases of Compactly Supported Matrix Valued Wavelets

    Konsantinos SLAVAKIS Isao YAMADA

    Proceedings of 1999 International Symposium on Signal Processing and Its Applications   22 - 25   1999

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  • A note on multivariate generalization of Lanczos principle and its application to signal processing problems

    Hiroshi HASEGAWA Toshio, NAKAO Isao, YAMADA Kohichi, SAKANIWA

    Technical Report of IEICE, DSP-98   ( 188 )   1 - 7   1999

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  • Approximation of Convexly Constrained Pseudoinverse by Hybrid Steepest Descent Method

    ISAO YAMADA

    Proceedings of 1999 IEEE International Symposium on Circuits and Systems   V   37?40   1999

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  • An Optimal Set-theoretic Blind Deconvolution Scheme based on Hybrid Steepest Descent Method

    Isao YAMADA Masanori, KATO Kohichi, SAKANIWA

    Proceedings of 1999 IEEE International Conference on Acoustics, Speech and Signal Processing   VI   3261?3264   1999

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  • A Nonlinear Pre-filtering Technique for Set-Theoretic Linear Blind Deconvolution Scheme

    Isao YAMADA Masanori, KATO Kohichi, SAKANIWA

    Proceedings of 1999 International Conference on Image Processing   401 - 405   1999

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  • Convex Projection Approach to Design of Two-Channel Linear Phase FIR QMF Banks in Magnitude Product Space

    HASEGAWA Hiroshi, YAMADA Isao, SAKANIWA Kohichi

    Technical report of IEICE. DSP   99 ( 68 )   7 - 12   1999

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    Language：English   Publisher：The Institute of Electronics, Information and Communication Engineers  

    Recently, Haddad et al. characterized required specifications for two-channel QMF banks by multiple constraint sets and they obtained a QMF bank with good performance by finding a point in the intersection of these constraint sets by using POCS (projection onto convex sets). Unfortunately, it is shown in this paper, the convergence of their method isn't guaranteed because one of their constraint sets is not convex. In this paper, we first introduce a notion of Magnitude Product Space in which a pair of the magnitude responses in analysis bank is simply expressed as a point. In this space, the constraint sets are formulated more briefly and it is shown that the power complementary condition is approximated by an appropriate closed convex set in multiple ring-shaped region. Furthermore, it becomes clear that a constraint set, which Haddad et al. defined to approximate the condition, is not convex. We propose a new design method that is adaptively modifying these convex sets and iteratively solve an optimization problem over the intersection of them by applying Hybrid Steepest Descent Method. The design specification is often too tight to be realized, hence these constraint sets may have no intersection, but our method has a merit of the guarantee of convergence to a solution which approximate the specification over a set of all realizable filters even if the intersection is empty. Finally, we also show our method realizes a QMF bank with better performance than that of Haddad et al.'s by a numerical example.

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  • Least-squares design of M-D IIR filters with fixed separable denominator based on multivariate division algorithm

    Hiroshi HASEGAWA Isao, YAMADA Kohichi, SAKANIWA

    Proceedings of 1999 European Conference on Circuits Theory and Design   1139 - 1142   1999

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  • Algebraic Multidimensional Phase Unwrapping and Zero Distribution of Complex Polynomials---Characterization of Multivariate Stable Polynomials

    Isao Yamada, Kaoru Kurosawa, Hiroshi Hasegawa, Kohichi Sakaniwa

    IEEE Transactions on Signal Processing   46 ( 6 )   1639 - 1667   1998

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  • Algebraic Multidimensional Phase Unwrapping and Zero Distribution of Complex Polynomials---Characterization of Multivariate Stable Polynomials

    Isao Yamada, Kaoru Kurosawa, Hiroshi Hasegawa, Kohichi Sakaniwa

    IEEE Transactions on Signal Processing   46 ( 6 )   1639 - 1667   1998

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  • A Note on Constrained Least Sqnares Design of M-D FIR Filter Based on Convex Projection Technique

    Isao Yamada, Hiroshi Hasegawa, Kohichi Sakaniwa

    IEICE Trans. Fundamentals   E81-A ( 8 )   1586 - 1591   1998

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  • Quadratic optimization of fixed points of nonexpansive mappings in Hilbert space

    Yamada, I, N Ogura, Y Yamashita, K Sakaniwa

    NUMERICAL FUNCTIONAL ANALYSIS AND OPTIMIZATION   19 ( 1-2 )   165 - 190   1998

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    Language：English   Publisher：TAYLOR & FRANCIS INC  

    Finding an optimal point in the intersection of the fixed point sets of a family of nonexpansive mappings is a frequent problem in various areas of mathematical science and engineering. Let T(i) (i = 1, 2,..., N) be nonexpansive mappings on a Hilbert space H, and let Theta : H --> R be a quadratic function defined by Theta(x) := 1/2(Ax, x)-(b, x) for all x is an element of H, where A : H --> H is a strongly positive bounded self-adjoint linear operator. Then, for each sequence of scalar parameters (lambda(n)) satisfying certain conditions, we propose an algorithm that generates a sequence converging strongly to a unique minimizer u* of Theta over the intersection of the fixed point sets of all the T(i)'s. This generalizes some results of Halpern (1967), Lions (1977), Wittmann (1992), and Bauschke (1996). In particular, the minimization of Theta over the intersection (1) boolean AND(N) C(i) of closed convex sets C(i) can be handled by taking T(i) to the metric projection P(Ci) onto C(i) without introducing any special inner product that depends on A. We also propose an algorithm that generates a sequence converging to a unique minimizer of Theta over K(phi) := {u is an element of K / phi(u) = inf phi(K)} not equal 0, where K is a given closed convex set and phi(x) := Sigma(i=1)(N), w(i)d(x, C(i)) for positive weights w(i) (i = 1,...,N). This is applicable to the inconsistent case (1) boolean AND(N) C(i) = 0 as well.

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  • A Note on Constrained Least Sqnares Design of M-D FIR Filter Based on Convex Projection Technique

    Isao Yamada, Hiroshi Hasegawa, Kohichi Sakaniwa

    IEICE Trans. Fundamentals   E81-A ( 8 )   1586 - 1591   1998

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  • Minimizing certain convex functions over the intersection of the fixed point sets of nonexpansive mappings

    F Deutsch, Yamada, I

    NUMERICAL FUNCTIONAL ANALYSIS AND OPTIMIZATION   19 ( 1-2 )   33 - 56   1998

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    Language：English   Publisher：TAYLOR & FRANCIS INC  

    Let T(i) (i = 1,2,...,N) be nonexpansive mappings on a Hilbert space H, and let Theta : H --> RU{infinity} be a function which has a uniformly strongly positive and uniformly bounded second (Frechet) derivative over the convex huh of T(i)(H) for some i. We first prove that Theta has a unique minimum over the intersection of the fixed point sets of all the T(i)'s at some point u*. Then a cyclic hybrid steepest descent algorithm is proposed and we prove that it converges to u*. This generalizes some recent results of Wittmann (1992), Combettes (1995), Bauschke (1996), and Yamada, Ogura, Yamashita, and Sakaniwa (1997).
    In particular, the minimization of Theta over the intersection boolean AND(1)(N)C(i) of closed convex sets C(i) can be handled by taking T(i) to be the metric projection P(Ci) onto C(i). We also propose a modification of our algorithm to handle the inconsistent case (i.e., when boolean AND(1)(N)C(i) is empty) as well.

    Web of Science

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  • Quadratic Optimization of Fixed Points of Nonexpansive Mappings in Hilbert Space

    Isao Yamada Nobuhiko Ogura Yukihiko Yamashita, Kohichi Sakaniwa

    Numerical Functional Analysis and Optimization   19 ( 1-2 )   165 - 190   1998

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  • Simple derivation of stability radius of multivariate systems

    Hiroshi Hasegawa Isao, Yamada, Kohichi Sakaniwa

    Electronics Letters   33 ( 11 )   937   1997

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  • Simple derivation of stability radius of multivariate systems

    Hiroshi Hasegawa Isao, Yamada, Kohichi Sakaniwa

    Electronics Letters   33 ( 11 )   937   1997

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  • A fast neural network learning with guaranteed convergence to zero system error

    T Ajimura, Yamada, I, K Sakaniwa

    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES   E79A ( 9 )   1433 - 1439   1996.9

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    Language：English   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

    It is thought that we have generally succeeded in establishing learning algorithms for neural networks, such as the back-propagation algorithm. However two major issues remain to be solved. First, there are possibilities of being trapped at a local minimum in learning. Second, the convergence rate is too slow. Chang and Ghaffar proposed to add a new hidden node, whenever stopping at a local minimum, and restart to train the new net until the error converges to zero. Their method designs newly generated weights so that the new net after introducing a new hidden node has less error than that at the original local minimum. In this paper, we propose a new method that improves their convergence rate. Our proposed method is expected to give a Lower system error and a larger error gradient magnitude than their method at a starling point of the new net, which leads to a faster convergence rate. Actually it is shown through numerical examples that the proposed method gives a much better performance than the conventional Chang and Ghaffar's method.

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  • Constrained Parallel Projection methods for optimal signal estimation and design

    Isao Yamada, Nobuhiko Ogura, Kohichi Sakaniwa

    Proc. of International Conference on Image Processing '96   3   67 - 72   1996

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  • An optimal fixed point theorem for nonexpansive operator and its application to set theoretic signal estimation -optimization with inconsistent convex constraints-

    Isao Yamada Nobuhiko Ogura Yukihiko Yamashita, Kohichi Sakaniwa

    Proc. of International Symposium on Information Theory and Its application   736 - 742   1996

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  • An optimal fixed point theorem for nonexpansive operator and its application to set theoretic signal estimation -optimization with inconsistent convex constraints-

    Isao Yamada Nobuhiko Ogura Yukihiko Yamashita, Kohichi Sakaniwa

    Proc. of International Symposium on Information Theory and Its application   736 - 742   1996

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  • Constrained Parallel Projection methods for optimal signal estimation and design

    Isao Yamada, Nobuhiko Ogura, Kohichi Sakaniwa

    Proc. of International Conference on Image Processing '96   3   67 - 72   1996

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  • An Algebraic Phase Unwrapping Algorithm and Zero Distribution of Complex Polynomials

    Isao Yamada, Kaoru Kurosawa, Hiroshi Hasegwa, Kohichi Sakaniwa

    IEICE technical report. Image engineering   95-1 ( 1 )   1 - 8   1995

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    Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

    In this paper, we propose an algorithm that computes the exact unwrapped phase for multidimensional signals of finite support and show that the proposed algorithm can be used to compute the zero distribution of any univariate complex polynomial. For any multidimensional sequence, its unwrapped phase is expressed as a finite sum of Lebesgue integrals of some univariate real functions. We first show that any sequence of finite support has a bounded unwrapped phase and the phase unwrapping problem for multidimensional signals can be reduced to that for one dimensional complex signals. We then show that the unwrapped phase for any one dimensional finite complex signal can be decomposed into sum of unwrapped phases for a complex symmetric signal and a signal of no zero on the unit circle. It is proved that the unwrapped phase for a complex symmetric signal is given by a linear phase, of which coefficient is the center degree of the signal. It is also shown that for a signal of no zero on the unit circle its unwrapped phase can be determined by using a sturm sequence starting from two real functions of the same signs, in (0,2π), as those of the real and the imaginary parts of some shifted version of the signal. Moreover, by using the proposed phase unwrapping algorithm, we show that for a given multidimensional signal of no zero in the multidimensional frequency domain its multidimensional linear phase component can be explicitly extracted and for any univariate complex polynomial its zero distribution can be computed exactly.

    CiNii Books

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  • 多次元連続位相導出アルゴリズムと多項式の零点分布決定への応用

    電子情報通信学会 技術研究所報告   95-1   1 - 8   1995

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  • A MULTIDIMENSIONAL ISOMORPHIC OPERATOR AND ITS PROPERTIES - A PROPOSAL OF FINITE-EXTENT MULTIDIMENSIONAL CEPSTRUM

    YAMADA, I, K SAKANIWA, S TSUJII

    IEEE TRANSACTIONS ON SIGNAL PROCESSING   42 ( 7 )   1766 - 1785   1994.7

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    Language：English   Publisher：IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC  

    In this paper, we propose a new multidimensional homomorphic operator that replaces the conventional complex cepstrum transformation.
    We treat multidimensional signals of finite support since any. signal we can actually observe and deal with is of finite support. We first show that for any sequence of finite support there exists a coordinate transformation that transforms the support of a given sequence into the first quadrant in the multidimensional signal space. We then propose a new multidimensional homomorphic operator PSI which transforms a sequence of finite support into another sequence of finite support in the first quadrant. It is proved that the operator PSI is an isomorphism between two multidimensional signal spaces of finite support where finite convolution and usual addition, respectively, are defined as binomial operations. It is also shown that unlike the conventional complex cepstrum, the proposed operator PSI is quite simple to compute and requires no complicated procedure like phase unwrapping, while it maintains the special features of the conventional complex cepstrum transformation that are useful in homomorphic signal processing. Moreover we clarify some algebraic structure of the multidimensional signal space with the finite convolution as a binomial operation.
    Finally it is shown by a numerical example that the deconvolution system using the proposed operator PSI gives a much better result than the conventional complex cepstrum method.

    DOI： 10.1109/78.298283

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  • MA System Identification from dispectrum

    Isao Yamada, Kohichi Sakaniwa

    Electronics Letters   30 ( 23 )   1919 - 1920   1994

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  • Global Optimization Algorithm based on Excluding Hyperspheres

    Isao Yamada, Kohichi Sakaniwa

    29 - 32   1994

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  • MA System Identification from dispectrum

    Isao Yamada, Kohichi Sakaniwa

    Electronics Letters   30 ( 23 )   1919 - 1920   1994

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  • Excluding Hyperspheres for Global Optimization

    Isao Yamada, Kohichi Sakaniwa

    Proceedings of International Symposium on Information Theory and Its Applications   757 - 762   1994

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  • Global Optimization Algorithm based on Excluding Hyperspheres

    第17回情報理論とその応用シンポジウム予稿集   29 - 32   1994

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  • 探索領域限定学習法と非探索領域の改良

    山田功, 宮村剛志, 坂庭好一

    電子情報通信学会 論文誌   J77-DII ( 9 )   1859 - 1881   1994

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  • Excluding Hyperspheres for Global Optimization

    Isao Yamada, Kohichi Sakaniwa

    Proceedings of International Symposium on Information Theory and Its Applications   757 - 762   1994

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  • Maximally decimated直線位相完全QMFシステムの一設計法

    黒沢馨, 山本和人, 山田功

    電子情報通信学会論文誌(A)   J76-A ( 3 )   1993

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  • An Optimal Design for a Homomorphic Deconvolution System

    Isao Yamada, Kohichi Sakaniwa

    IEEE Trans. on Signal Processing   40   9   1992

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  • An Optimal Design for a Homomorphic Deconvolution System

    Isao Yamada, Kohichi Sakaniwa

    IEEE Trans. on Signal Processing   40   9   1992

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  • New Multi-dimensional Homomorphic Operator and Its Properties

    Isao YAMADA Kohichi, SAKANIWA

    IEEE 7'th Work shop on Multi-dinensional signal Processing   1991

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  • 探索領域限定学習法とそのニューラルネットワークへの応用

    宮村剛志, 山田功, 坂庭好一

    電子情報通信学会技術研究報告   NC91   31 - 37   1991

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  • Restricted Learning and Its Application to Nearal Network Training

    Tsuyoshi MIYAMURA Isao, YAMADA Kohichi, SAKANIWA

    1991 IEEE Workshop on Neural Networks for Signal Processing   1991

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  • Restricted Learning and Its Application to Nearal Network Training

    Tsuyoshi MIYAMURA Isao, YAMADA Kohichi, SAKANIWA

    1991 IEEE Workshop on Neural Networks for Signal Processing   1991

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  • New Multi-dimensional Homomorphic Operator and Its Properties

    Isao YAMADA Kohichi, SAKANIWA

    IEEE 7'th Work shop on Multi-dinensional signal Processing   1991

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  • A New Homorphic Deconvolution System and Its Optimal Design

    Trans. IEICE of Japan(A)   73   4   1990

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  • 新しい準同型変換を用いた信号分離システムの最適設計

    山田功, 坂庭好一

    電子情報通信学会論文賞(A)   73   4   1990

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  • 新しい準同型変換とその逆畳込み問題への応用

    山田功, 坂庭好一

    電子情報通信学会論文賞(A)   72   10   1989

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  • A New Homomorphic Operator and Its Application to Deconvolution Problems

    Trans. IEICE of Japan(A)   72   10   1989

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Presentations

  • Incremental adaptive filtering over distributed networks using parallel projection onto hyperslabs

    2008 

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  • Reduced-rank extension of BLUE and deep lipschitzian gradient projector for inverse problems

    IUCr2008(International Union of Crystallography - Micro Symposium : Real space direct method)  2008 

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  • An Invitation to Signal Processing Algorithms Based on Computational Fixed Point Theory of Quasi-nonexpansive Mapping

    Proceedings of Sophia Symposium 2007 - Modern Mathematics and Its Application to Modern Technology  2008 

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  • 低階数最良近似行列を用いた非負行列因子逐次分解法に関する一考察

    2008 

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  • Directions for Use and Efficient Computation of the Stochastic MV-PURE Estimator

    Proceedings of IEICE SIP Symposium 2008  2008 

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  • Set-Theoretic DS/CDMA Receivers for Fading Channels by Adaptive Projected Subgradient Method

    IEEE GLOBECOM 2005  2005 

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  • An adaptive super-resolution of videos with noise information on camera systems

    IEEE ICASSP 2005  2005 

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  • High-resolution DOA estimation by algebraic phase unwrapping algorithm

    IEEE ISCAS2005 -- Special Session on Multidimensional Systems and Signal Processing  2005 

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  • A Color Super-Resolution with Multiple Nonsmooth Constraints by Hybrid Steepest Descent Method

    IEEE ICIP 2005  2005 

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    Presentation type：Poster presentation  

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  • Efficient Adaptive Blind MAI Suppression in DS/CDMA by Embedded Constraint Parallel Projection Techniques

    IEEE ICASSP 2005  2005 

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    Presentation type：Poster presentation  

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  • Stereo Echo Canceler by Adaptive Projected Subgradient Method with Multiple Room-Acoustics Information

    2005 IWAENC (International Workshop on Acoustic Echo and Noise Control)  2005 

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    Presentation type：Poster presentation  

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  • An Efficient Fast Stereo Echo Canceler by Pairwise Optimal Weight Realization Technique

    2005 EUSIPCO (European Signal Processing Conference)  2005 

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    Presentation type：Poster presentation  

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  • Counting the number of DOAs in specified ranges by algebraic phase unwrapping algorithms

    The 28th Symposium on Information Theory and Its Applications  2005 

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  • Symbolic Computation of DOA Distribution by Algebraic Phase Unwrapping Algorithm

    7th Asian Symposium on Computer Mathematics (ASCM 2005)  2005 

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  • Blind and Nonblind DS/CDMA Receivers by Adaptive Projected Subgradient Method

    The 28th Symposium on Information Theory and Its Applications  2005 

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  • 指数重み付複素ケプストラム法による信号分離システムの最適設計

    電子情報通信学会論文誌(A)  1988 

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  • Nonstrictly convex minimization over the bounded fixed point set of the nonexpansive mapping

    Numerical Functional Analysis and Optimization  2003 

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  • An Optimal Design of Homomorphic Deconvolution System

    Trans. IEICE of Japan(A)  1988 

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  • Hybrid Steepest Descent Method and Adaptive Projected Subgradient Method --- Their Unified View and Signal Processing Applications (INVITED)

    2005 

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  • Nonstrictly convex minimization over the bounded fixed point set of the nonexpansive mapping

    Numerical Functional Analysis and Optimization  2003 

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  • Hierarchical Constrained Optimization Problems and Their Signal Processing Applications

    Sophia Symposium 2007 - Modern Mathematics and Its Applications to Modern Technology  2007 

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  • Set-Theoretic Reduced-Rank Adaptive Filtering by Adaptive Projected Subgradient Method

    The 41st Asilomar Conference on Signals, Systems and Computers  2007 

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  • Optimization over possibly nonconvex fixed point set of certain mappings and its signal processing applications

    Second Mathematical Programming Society International Conference on Continuous Optimization ICCOPT II & MOPTA-07  2007 

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  • Hybrid Jacobi method and Hybrid Gauss-Seidel method for signal processing over distributed network

    Proceedings of the 2007 International Workshop on Spectral Methods and Multirate Signal Processing (SMMSP 2007)  2007 

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  • Minimum-Variance Pseudo-Unbiased Reduced-Rank Estimator (MV-PURE) and its applications to ill-conditioned inverse problems

    Proceedings of the 2007 International Workshop on Spectral Methods and Multirate Signal Processing (SMMSP 2007)  2007 

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  • Tutorial Lecture : Machine Learning and Signal Processing Applications

    IEEE ICASSP 2007  2007 

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  • Online sparse kernel-based classification by projection

    2007 IEEE International Workshop on Machine Learning for Signal Processing (MLSP2007)  2007 

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  • Multiaccess Interference Reduction in OSTBC-MIMO Systems by Adaptive Projected Subgradient Method

    IEEE ICASSP2007  2007 

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  • Adaptive Parallel Variable-Metric Projection Algorithm --- An Application to Acoustic Echo Cancelletion

    IEEE ICASSP2007  2007 

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  • 最適化と計算代数の融合が拓く信号処理 - 過去・現在・未来への旅-(特別講演)

    2008 

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  • Online Kernel-Based Classification by Projections

    IEEE ICASSP2007  2007 

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  • Adaptive Processing in a World of Projection (Plenary Lecture)

    2008 

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  • Multi-Domain Adaptive Learning

    2009 IEICE Society Conference  2009 

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  • A Symbolic Algorithm for DOA Distribution by Algebraic Phase Unwrapping Techniques

    IEICE SIP Symposium 2005  2005 

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  • Why the Stochastic MV-PURE Estimator Excels in Highly Noisy Situations ?

    2009 

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    Presentation type：Poster presentation  

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  • An edge-preserving super-precision from multiple quantized images

    IEICE SIP symposium 2005  2005 

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  • Signal Processing Applications of a Pair of Simple Fixed Point Algorithms (Invited)

    2009 

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  • Efficient Robust Capon Beamforming as a Time-Varying Convex Feasibility Problem

    IEICE SIP Symposium 2005  2005 

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  • Steady-State Analysis of Constrained Normalized-Type Adaptive Filters for CDMA Systems

    IEICE SIP Symoosium 2005  2005 

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  • An Improvement of Subgradient Projection Operator by Composing Monotone Functions

    2009 

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  • Learning in diffusion networks with an adaptive projected subgradient method

    2009 

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  • An Embedded-Constraint Adaptive Beamformer in the Presence of Spatially-Correlated Interferences --- Convergence Speed of Constrained and Projected NLMS Algorithms

    IEICE SIP Symposium 2005  2005 

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  • Diffusion Least-Mean Squares with Adaptive Combiners

    Proceedings of IEEE ICASSP 2009  2009 

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  • An Efficient Heuristic Adaptive Power Control for Downlink Transmission in Infeasible Scenarios

    IEICE SIP Symposium 2005  2005 

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  • Acceleration of the Adaboost with Exact Plane Search

    Proceedings of the 2009 IEICE General Conference  2009 

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  • Efficient robust adaptive beamforming by the Adaptive Projected Subgradient Method ---A set-theoretic time-varying approach over multiple a priori constraints

    IEICE Technical Workshop on Signal Processing  2005 

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  • Signal Processing in Dual Domain By Adaptive Projected Subgradient Method

    DSP 2009  2009 

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  • Stereo Echo Canceler by Adaptive Projected Subgradient Method with Multiple a Priori Information

    2005 IEICE Engineering Sciences Society Conference  2005 

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  • Learning in diffusion networks with an adaptive projected subgradient method

    2009 

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  • A Note on Inflation Parameter for Adaptive Parallel Subgradient Projection Algorithms -- An Optimal Design in Case of Single Projection

    IEICE Technical Workshop on Signal Processing  2005 

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  • Diffusion Least-Mean Squares with Adaptive Combiners

    Proceedings of IEEE ICASSP 2009  2009 

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  • Theory and Applications of Set Theoretic Adaptive Filtering with Multiple A-Priori Convex Constraints

    IEICE Technical Workshop on Signal Processing  2005 

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  • Acceleration of the Adaboost with Exact Plane Search

    Proceedings of the 2009 IEICE General Conference  2009 

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  • Set-Theoretic DS/CDMA Receivers for Fading Channels by Adaptive Projected Subgradient Method

    IEEE GLOBECOM 2005  2005 

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  • Signal Processing in Dual Domain By Adaptive Projected Subgradient Method

    DSP 2009  2009 

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  • Hybrid Steepest Descent Method and Adaptive Projected Subgradient Method --- Their Unified View and Signal Processing Applications (INVITED)

    2005 

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  • A New Problem Formulation of MIMO Antenna Selection: Iterative Minimization of the Moreau Envelope of l_1-norm under Nonlinear Constraints

    Proceedings of IEICE SIP Symposium 2009  2009 

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  • An adaptive super-resolution of videos with noise information on camera systems

    IEEE ICASSP 2005  2005 

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    Presentation type：Poster presentation  

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  • Minimum-Variance Pseudo-Unbiased Reduced-Rank Estimator and Its Applications

    2009 

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  • High-resolution DOA estimation by algebraic phase unwrapping algorithm

    IEEE ISCAS2005 -- Special Session on Multidimensional Systems and Signal Processing  2005 

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  • A Color Super-Resolution with Multiple Nonsmooth Constraints by Hybrid Steepest Descent Method

    IEEE ICIP 2005  2005 

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    Presentation type：Poster presentation  

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  • An Analysis for a Certain Family of Adaptive Filtering Algorithms Based on Variable-metric Adaptive Projected Subgradient Method

    Proceedings of IEICE SIP Symposium 2009  2009 

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  • Efficient Adaptive Blind MAI Suppression in DS/CDMA by Embedded Constraint Parallel Projection Techniques

    IEEE ICASSP 2005  2005 

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    Presentation type：Poster presentation  

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  • Probability Optimization for Probabilistic Diffusion LMS Algorithms

    Proceedings of IEICE SIP Symposium 2009  2009 

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  • An Efficient Fast Stereo Echo Canceler by Pairwise Optimal Weight Realization Technique

    2005 EUSIPCO (European Signal Processing Conference)  2005 

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    Presentation type：Poster presentation  

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  • Alternating minimization techniques for the recovery of low-rank matrices in the presence of sparse errors

    Proceedings of IEICE SIP Symposium 2009  2009 

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  • Using Time-Varying Soft-Thresholding Techniques for Adaptive Identification of Sparse Systems

    Proceedings of IEICE SIP Symposium 2009  2009 

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  • Symbolic Computation of DOA Distribution by Algebraic Phase Unwrapping Algorithm

    7th Asian Symposium on Computer Mathematics (ASCM 2005)  2005 

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  • ISTA/FISTA-type algorithms in the presence of an additional convex constraint

    Proceedings of IEICE SIP Symposium 2009  2009 

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  • Stereo Echo Canceler by Adaptive Projected Subgradient Method with Multiple Room-Acoustics Information

    2005 IWAENC (International Workshop on Acoustic Echo and Noise Control)  2005 

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  • ISTA/FISTA-type algorithms in the presence of an additional convex constraint

    Proceedings of IEICE SIP Symposium 2009  2009 

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  • Efficient Robust Capon Beamforming as a Time-Varying Convex Feasibility Problem

    IEICE SIP Symposium 2005  2005 

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  • Probability Optimization for Probabilistic Diffusion LMS Algorithms

    Proceedings of IEICE SIP Symposium 2009  2009 

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  • An improvement of convergence speed and image quality of an adaptive super-resolution

    IEICE SIP Symposium 2005  2005 

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  • Minimizing the Moreau envelope of nonsmooth convex function over the fixed point set of certain quasi-nonexpansive mappings

    Interdisciplinary Workshop on Fixed-Point Algorithms in Science and Engineering  2009 

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  • Steady-State Analysis of Constrained Normalized-Type Adaptive Filters for CDMA Systems

    IEICE SIP Symoosium 2005  2005 

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  • Using Time-Varying Soft-Thresholding Techniques for Adaptive Identification of Sparse Systems

    Proceedings of IEICE SIP Symposium 2009  2009 

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  • An edge-preserving super-precision from multiple quantized images

    IEICE SIP symposium 2005  2005 

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  • Multi-Domain Adaptive Learning

    2009 IEICE Society Conference  2009 

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  • An Embedded-Constraint Adaptive Beamformer in the Presence of Spatially-Correlated Interferences --- Convergence Speed of Constrained and Projected NLMS Algorithms

    IEICE SIP Symposium 2005  2005 

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  • A Robust Function Estimation in Reproducing Kernel Hilbert Space Based on Finite Dimensional Reformulations

    APSIPA ASC 2009  2009 

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  • An Efficient Heuristic Adaptive Power Control for Downlink Transmission in Infeasible Scenarios

    IEICE SIP Symposium 2005  2005 

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  • 低階数最小分散擬似不偏推定法の考え方 (依頼講演)

    シンポジウム(ABS-1：ブロードバンド通信のための信号処理技術)  2009 

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  • Efficient robust adaptive beamforming by the Adaptive Projected Subgradient Method ---A set-theoretic time-varying approach over multiple a priori constraints

    IEICE Technical Workshop on Signal Processing  2005 

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  • 階層構造を持つ最適化問題と信号処理(招待講演)

    第21回RAMPシンポジウム (日本オペレーションズリサーチ学会)  2009 

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  • Stereo Echo Canceler by Adaptive Projected Subgradient Method with Multiple a Priori Information

    2005 IEICE Engineering Sciences Society Conference  2005 

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  • Theory and applications of set theoretic adaptive filtering with multiple a-priori convex constraints -- Part II: Proof of convergence theorem

    IEICE Technical Workshop on Signal Processing  2005 

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  • Signal Processing Applications of a Pair of Simple Fixed Point Algorithms (Invited)

    2009 

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  • An Improvement of Subgradient Projection Operator by Composing Monotone Functions

    2009 

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  • Minimum-Variance Pseudo-Unbiased Low-Rank Estimation -- A Generalization of Marquardt's Estimator for Ill-Conditioned Inverse Problems

    IEICE Technical Workshop on Signal Processing  2005 

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  • Why the Stochastic MV-PURE Estimator Excels in Highly Noisy Situations ?

    2009 

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  • On optimality of POWER Weighting Technique for Adaptive Filtering

    IEICE Technical Workshop on Signal Processing  2005 

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  • Tutorial Lecture : Machine Learning and Signal Processing Applications

    IEEE ICASSP 2007  2007 

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  • Online sparse kernel-based classification by projection

    2007 IEEE International Workshop on Machine Learning for Signal Processing (MLSP2007)  2007 

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  • Adaptive Parallel Variable-Metric Projection Algorithm --- An Application to Acoustic Echo Cancelletion

    IEEE ICASSP2007  2007 

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    Presentation type：Poster presentation  

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  • Online Kernel-Based Classification by Projections

    IEEE ICASSP2007  2007 

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    Presentation type：Poster presentation  

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  • Multiaccess Interference Reduction in OSTBC-MIMO Systems by Adaptive Projected Subgradient Method

    IEEE ICASSP2007  2007 

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  • Efficient monotone approximation operator to convex level sets by one dimensional quadratic best lower bound

    IEICE SIP Symposium 2007  2007 

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  • Tutorial Lecture: Signal Processing Applications of Fixed Point Theory of Quasi-nonexpansive Mapping

    6th International Conference on Information, Communications and Signal Processing (ICICS 2007)  2007 

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  • Steady-state analysis of adaptive filtering based on projection onto hyperslab

    IEICE SIP Symposium 2007  2007 

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  • Adaptive minor subspace extraction based on modified Oja-Xu MCA learning

    IEICE SIP Symposium 2007  2007 

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  • OFDM Peak-to-Average Power Ratio Reduction by Adaptive Projected Subgradient Method

    IEICE SIP Symposium 2007  2007 

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  • An Application of MV-PURE Estimator to Robust Detection Problem in Multiaccess MIMO Wireless Systems

    IEICE SIP Symposium 2007  2007 

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  • Efficient monotone approximation operator to convex level sets by one dimensional quadratic best lower bound

    IEICE SIP Symposium 2007  2007 

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  • Tutorial Lecture: Signal Processing Applications of Fixed Point Theory of Quasi-nonexpansive Mapping

    6th International Conference on Information, Communications and Signal Processing (ICICS 2007)  2007 

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  • Adaptive minor subspace extraction based on modified Oja-Xu MCA learning

    IEICE SIP Symposium 2007  2007 

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  • An Application of MV-PURE Estimator to Robust Detection Problem in Multiaccess MIMO Wireless Systems

    IEICE SIP Symposium 2007  2007 

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  • Steady-state analysis of adaptive filtering based on projection onto hyperslab

    IEICE SIP Symposium 2007  2007 

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  • Hierarchical Constrained Optimization Problems and Their Signal Processing Applications

    Sophia Symposium 2007 - Modern Mathematics and Its Applications to Modern Technology  2007 

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  • OFDM Peak-to-Average Power Ratio Reduction by Adaptive Projected Subgradient Method

    IEICE SIP Symposium 2007  2007 

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  • Set-Theoretic Reduced-Rank Adaptive Filtering by Adaptive Projected Subgradient Method

    The 41st Asilomar Conference on Signals, Systems and Computers  2007 

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  • Optimization over possibly nonconvex fixed point set of certain mappings and its signal processing applications

    Second Mathematical Programming Society International Conference on Continuous Optimization ICCOPT II & MOPTA-07  2007 

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  • Hybrid Jacobi method and Hybrid Gauss-Seidel method for signal processing over distributed network

    Proceedings of the 2007 International Workshop on Spectral Methods and Multirate Signal Processing (SMMSP 2007)  2007 

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  • Minimum-Variance Pseudo-Unbiased Reduced-Rank Estimator (MV-PURE) and its applications to ill-conditioned inverse problems

    Proceedings of the 2007 International Workshop on Spectral Methods and Multirate Signal Processing (SMMSP 2007)  2007 

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  • Multi-Domain Adaptive Filtering by Feasibility Splitting

    IEEE ICASSP 2010  2010 

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    Presentation type：Poster presentation  

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  • Algebraic Structure of Minimum-Variance Pseudo-Unbiased Low-Rank Estimators

    IEICE SIP Symposium 2006  2006 

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  • A Deterministic Analysis of Variable-Metric Adaptive Filtering Algorithms under Small Metric-Fluctuations

    IEEE ICASSP 2010  2010 

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    Presentation type：Poster presentation  

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  • MAI mitigation by Adaptive Projected Subgradient method in OSTBC-MIMO Systems

    IEICE SIP Symposium 2006  2006 

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  • Efficient Dual Cayley Parametrization Technique for ICA with Orthogonality Constraints

    2006 ICA Research Network Workshop  2006 

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  • A Sparse Adaptive Filtering Using Time-Varying Soft-Thresholding Techniques

    2010 

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    Presentation type：Poster presentation  

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  • Adaptive quadratic-metric parallel subgradient projection algorithm and its application to acoustic echo cancellation

    EUSIPCO 2006  2006 

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  • Fixed-Point Approximations of Certain Quasi-Nonexpansive Mappings and Their Signal Processing Applications

    Special Workshop on Optimization Theory and Related Topics, Technion 窶骭€ Israel Institute of Technology & the University of Haifa, Israel (11-14, Jan., 2010), Invited  2010 

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  • An Efficient Heuristic Approach to Infeasible Downlink Power Control Probelm

    IEEE ICASSP2006  2006 

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  • Link Probability Control for Probabilistic Diffusion Least-Mean Squares over Resource-Constrained Networks

    IEEE ICASSP 2010  2010 

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  • Steady-state Performance of Constrained Normalized Adaptive Filters for CDMA Systems

    IEEE ICASSP2006  2006 

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    Presentation type：Poster presentation  

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  • Adaptive Beamforming by Constrained Parallel Projection in the Presence of Spatially-Correlated Interferences

    IEEE ICASSP2006  2006 

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    Presentation type：Poster presentation  

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  • Minimum-Variance Pseudo-Unbiased Low-Rank Estimator for Ill-Conditioned Inverse Problems

    IEEE ICASSP2006  2006 

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    Presentation type：Poster presentation  

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  • Robust Capon Beamforming by the Adaptive Projected Subgradient Method

    IEEE ICASSP2006  2006 

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    Presentation type：Poster presentation  

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  • An effective acoustic echo canceling algorithm by the adaptive projected subgradient method with a special metric

    IEICE Technical Workshop on Signal Processing  2006 

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  • Adaptive projected subgradient method and its robust signal processing applications

    (Special Session --- Set Membership Filtering and Its Applications : organized by Yih-Fang Huang) 2006 IEEE International Symposium on Circuits and Systems  2006 

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  • Link Probability Control for Probabilistic Diffusion Least-Mean Squares over Resource-Constrained Networks

    IEEE ICASSP 2010  2010 

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  • Efficient Dual Cayley Parametrization Technique for ICA with Orthogonality Constraints

    2006 ICA Research Network Workshop  2006 

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  • A Sparse Adaptive Filtering Using Time-Varying Soft-Thresholding Techniques

    2010 

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    Presentation type：Poster presentation  

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  • Adaptive quadratic-metric parallel subgradient projection algorithm and its application to acoustic echo cancellation

    EUSIPCO 2006  2006 

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  • Sparse system identification by exponentially weighted adaptive parallel projection and generalized soft-thresholding

    Proceedings of APSIPA ASC 2010  2010 

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  • Adaptive Beamforming by Constrained Parallel Projection in the Presence of Spatially-Correlated Interferences

    IEEE ICASSP2006  2006 

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    Presentation type：Poster presentation  

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  • Fixed-Point Approximations of Certain Quasi-Nonexpansive Mappings and Their Signal Processing Applications

    Special Workshop on Optimization Theory and Related Topics, Technion 窶骭€ Israel Institute of Technology & the University of Haifa, Israel (11-14, Jan., 2010), Invited  2010 

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  • An Efficient Heuristic Approach to Infeasible Downlink Power Control Probelm

    IEEE ICASSP2006  2006 

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    Presentation type：Poster presentation  

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  • Steady-state Performance of Constrained Normalized Adaptive Filters for CDMA Systems

    IEEE ICASSP2006  2006 

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    Presentation type：Poster presentation  

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  • Two Product-Space Formulations for Unifying Multiple Metrics in Set-Theoretic Adaptive Filtering

    The 44th Asilomar Conference on Signals, Systems and Computers  2010 

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  • Optimization and Signal Processing Based on Fixed-Point Characterizations of Closed Convex Sets (Tutorial Lecture )

    2010 

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  • Minimum-Variance Pseudo-Unbiased Low-Rank Estimator for Ill-Conditioned Inverse Problems

    IEEE ICASSP2006  2006 

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    Presentation type：Poster presentation  

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  • Low Complexity Projection-based Adaptive Algorithm for Sparse System Identification and Signal Reconstruction

    The 44th Asilomar Conference on Signals, Systems and Computers  2010 

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  • Robust Capon Beamforming by the Adaptive Projected Subgradient Method

    IEEE ICASSP2006  2006 

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    Presentation type：Poster presentation  

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  • Numerically stable algorithms for adaptive generalized minor subspace extraction

    IEICE Technical Workshop on Signal Processing  2010 

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  • An effective acoustic echo canceling algorithm by the adaptive projected subgradient method with a special metric

    IEICE Technical Workshop on Signal Processing  2006 

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  • Distributed multiagent learning with a broadcast adaptive subgradient method

    The 9th International Conference on Autonomous Agents and Multiagent Systems  2010 

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  • Adaptive projected subgradient method and its robust signal processing applications

    (Special Session --- Set Membership Filtering and Its Applications : organized by Yih-Fang Huang) 2006 IEEE International Symposium on Circuits and Systems  2006 

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  • Minimal Antenna-Subset Selection under Capacity Constraint for Power-Efficiency MIMO Systems: A Relaxed L1 Minimization Approach

    IEEE ICASSP 2010  2010 

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  • On learning directions for orthogonal matrix optimization in Cayley parameter space

    IEICE SIP Symposium 2006  2006 

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  • Alternating MinimizationTechniques for the Efficient Recovery of a Sparsely Corrupted Low-Rank Matrix

    IEEE ICASSP 2010  2010 

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  • Hybrid Jacobi Method and Its Applications

    IEICE SIP Symposium 2006  2006 

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  • Hierarchical Nonconvex Constrained Optimization and Its Signal Processing Applications

    2008 

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  • An Improvement of Subgradient Projection Operator and Its Machine Learning Application

    Proceedings of IEICE SIP Symposium 2008  2008 

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  • Robust Adaptive Nonlinear Beamforming by Kernels and Projection Mappings

    EUSIPCO 2008  2008 

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  • Adaptive Processing in a World of Projection (Plenary Lecture)

    2008 

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  • Incremental adaptive filtering over distributed networks using parallel projection onto hyperslabs

    Technical Report of IEICE  2008 

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  • Reduced-rank extension of BLUE and deep lipschitzian gradient projector for inverse problems

    IUCr2008(International Union of Crystallography - Micro Symposium : Real space direct method)  2008 

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  • An Invitation to Signal Processing Algorithms Based on Computational Fixed Point Theory of Quasi-nonexpansive Mapping

    Proceedings of Sophia Symposium 2007 - Modern Mathematics and Its Application to Modern Technology  2008 

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  • A Nonnagative Matrix Factorization Based on Best Low-Rank Matrix Approximation

    2008 

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  • A New Problem Formulation of MIMO Antenna Selection: Iterative Minimization of the Moreau Envelope of l_1-norm under Nonlinear Constraints

    Proceedings of IEICE SIP Symposium 2009  2009 

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  • Alternating minimization techniques for the recovery of low-rank matrices in the presence of sparse errors

    Proceedings of IEICE SIP Symposium 2009  2009 

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  • A Symbolic Algorithm for DOA Distribution by Algebraic Phase Unwrapping Techniques

    IEICE SIP Symposium 2005  2005 

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  • An Analysis for a Certain Family of Adaptive Filtering Algorithms Based on Variable-metric Adaptive Projected Subgradient Method

    Proceedings of IEICE SIP Symposium 2009  2009 

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  • Directions for Use and Efficient Computation of the Stochastic MV-PURE Estimator

    Proceedings of IEICE SIP Symposium 2008  2008 

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  • 最小分散低階数擬似不偏推定法(MV-PURE)とロバスト信号処理 [チュートリアル講演]

    2008 

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  • Hierarchical Nonconvex Constrained Optimization and Its Signal Processing Applications

    2008 

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  • An Improvement of Subgradient Projection Operator and Its Machine Learning Application

    Proceedings of IEICE SIP Symposium 2008  2008 

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  • Robust Adaptive Nonlinear Beamforming by Kernels and Projection Mappings

    EUSIPCO 2008  2008 

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  • An improvement of convergence speed and image quality of an adaptive super-resolution

    IEICE SIP Symposium 2005  2005 

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  • Sparse system identification by exponentially weighted adaptive parallel projection and generalized soft-thresholding

    Proceedings of APSIPA ASC 2010  2010 

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  • On optimality of POWER Weighting Technique for Adaptive Filtering

    IEICE Technical Workshop on Signal Processing  2005 

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  • Minimum-Variance Pseudo-Unbiased Reduced-Rank Estimator and Its Applications

    2009 

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  • Theory and applications of set theoretic adaptive filtering with multiple a-priori convex constraints -- Part II: Proof of convergence theorem

    IEICE Technical Workshop on Signal Processing  2005 

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  • Low Complexity Projection-based Adaptive Algorithm for Sparse System Identification and Signal Reconstruction

    The 44th Asilomar Conference on Signals, Systems and Computers  2010 

    　More details

  • Theory and Applications of Set Theoretic Adaptive Filtering with Multiple A-Priori Convex Constraints

    IEICE Technical Workshop on Signal Processing  2005 

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  • Two Product-Space Formulations for Unifying Multiple Metrics in Set-Theoretic Adaptive Filtering

    The 44th Asilomar Conference on Signals, Systems and Computers  2010 

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  • Minimum-Variance Pseudo-Unbiased Low-Rank Estimation -- A Generalization of Marquardt's Estimator for Ill-Conditioned Inverse Problems

    IEICE Technical Workshop on Signal Processing  2005 

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  • 信号処理の新しい道具箱 --- 非拡大写像の不動点理論とアルゴリズム

    [ナイトセッション・チュートリアル]2006年度画像符号化シンポジウム/2006年度映像メディア処理シンポジウム  2006 

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  • 閉凸集合の不動点表現を用いた最適化アルゴリズムと信号処理 (チュートリアル講演)

    2010 

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  • A Note on Inflation Parameter for Adaptive Parallel Subgradient Projection Algorithms -- An Optimal Design in Case of Single Projection

    IEICE Technical Workshop on Signal Processing  2005 

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  • Numerically stable algorithms for adaptive generalized minor subspace extraction

    IEICE Technical Workshop on Signal Processing  2010 

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  • Hybrid Jacobi Method and Its Applications

    IEICE SIP Symposium 2006  2006 

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  • Distributed multiagent learning with a broadcast adaptive subgradient method

    The 9th International Conference on Autonomous Agents and Multiagent Systems  2010 

    　More details

  • Minimal Antenna-Subset Selection under Capacity Constraint for Power-Efficiency MIMO Systems: A Relaxed L1 Minimization Approach

    IEEE ICASSP 2010  2010 

    　More details

  • MAI mitigation by Adaptive Projected Subgradient method in OSTBC-MIMO Systems

    IEICE SIP Symposium 2006  2006 

    　More details

  • Alternating MinimizationTechniques for the Efficient Recovery of a Sparsely Corrupted Low-Rank Matrix

    IEEE ICASSP 2010  2010 

    　More details

  • On learning directions for orthogonal matrix optimization in Cayley parameter space

    IEICE SIP Symposium 2006  2006 

    　More details

  • Multi-Domain Adaptive Filtering by Feasibility Splitting

    IEEE ICASSP 2010  2010 

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    Presentation type：Poster presentation  

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  • 信号処理と不動点最適化アルゴリズム

    [チュートリアル企画：次世代信号処理を切り拓く新しい計算技法] 電子情報通信学会 ソサイエティ大会  2006 

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  • A Deterministic Analysis of Variable-Metric Adaptive Filtering Algorithms under Small Metric-Fluctuations

    IEEE ICASSP 2010  2010 

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    Presentation type：Poster presentation  

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  • Algebraic Structure of Minimum-Variance Pseudo-Unbiased Low-Rank Estimators

    IEICE SIP Symposium 2006  2006 

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  • A Robust Function Estimation in Reproducing Kernel Hilbert Space Based on Finite Dimensional Reformulations

    APSIPA ASC 2009  2009 

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    Presentation type：Poster presentation  

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  • Blind and Nonblind DS/CDMA Receivers by Adaptive Projected Subgradient Method

    The 28th Symposium on Information Theory and Its Applications  2005 

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  • Minimizing the Moreau envelope of nonsmooth convex function over the fixed point set of certain quasi-nonexpansive mappings

    Interdisciplinary Workshop on Fixed-Point Algorithms in Science and Engineering  2009 

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  • Counting the number of DOAs in specified ranges by algebraic phase unwrapping algorithms

    The 28th Symposium on Information Theory and Its Applications  2005 

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  • An Introduction to Minimum-Variance Pseudo-Unbiased Reduced-Rank Estimator (Invited)

    2009 

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Works

  • 凸射影法に基づく適応信号処理アルゴリズムの開発 ---雑音に強く計算量の小さい適応信号処理アルゴリズムの新方式

    2001 - 2002

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    Work type：Artistic work  

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Awards

  • IEICE Excellent Paper Award

    2009  

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  • IEICE Achievement Award

    2009  

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  • 電子情報通信学会業績賞

    2009  

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    Country：Japan

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  • 電子情報通信学会論文賞

    2009  

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    Country：Japan

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  • IEEE Circuits and Systems Society Certificate of Appreciation (For contribution as a 2006-2007 Associate Editor of the IEEE Transactions on Circuits and Systems, Part 1)

    2008  

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  • 藤野研究賞

    2008  

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    Country：Japan

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  • IEEE Circuits and Systems Society 功労賞 (IEEE Transactions on Circuits and Systems, Part 1の Asscociate Editor 2006-2007としての貢献)

    2008  

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  • Fujino Prize

    2008  

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  • 電子情報通信学会論文賞

    2006  

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    Country：Japan

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  • IEICE Excellent Paper Award

    2006  

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  • ドコモ・モバイル・サイエンス賞（基礎科学部門）

    2005  

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    Country：Japan

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  • The DoCoMo Mobile Science Award (2005)

    2005  

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  • 国際コミュニケーション基金優秀研究賞

    2004  

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    Country：Japan

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  • International Communication Foundation Research Award

    2004  

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  • 電子情報通信学会論文賞

    1994  

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    Country：Japan

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  • IEICE Excellent Paper Award

    1994  

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  • IEICE Young Researcher Award

    1992  

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  • 電子情報通信学会学術奨励賞

    1992  

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    Country：Japan

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  • IEICE Excellent Paper Award

    1990  

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  • 電子情報通信学会論文賞

    1990  

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    Country：Japan

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Research Projects

  • 最適化理論

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    Grant type：Competitive

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  • 次世代信号処理方式の理論と応用

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    Grant type：Competitive

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  • 多次元システム理論

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    Grant type：Competitive

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  • 雑音の影響にロバストな適応信号処理方式とその通信システムへの応用に関する研究

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    Grant type：Competitive

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  • 次世代非線形射影法に基づく高精彩信号・画像推定法とその応用に関する研究

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    Grant type：Competitive

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  • Optimization Theory

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    Grant type：Competitive

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  • Mathematical/Multidimensional/Adaptive/Statistical Signal Processing

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    Grant type：Competitive

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  • Signal recovery based on novel nonlinear projection techniques

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    Grant type：Competitive

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  • Multidimensional System Theory

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    Grant type：Competitive

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  • An efficient robust adaptive algorithm and its applications to communication systems

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    Grant type：Competitive

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