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49th Annual Conference on Information Sciences and Systems (CISS)

作者： Zhu, Jingyi Spall, James C. Johns Hopkins Univ Dept Appl Math & Stat Baltimore MD 21218 USA Johns Hopkins Univ Appl Phys Lab Baltimore MD 21218 USA

ISBN: (纸本)9781479984282

Time-varying formulation is pervasive in dynamic control systems, where tracking parameters characterizing dynamic properties is central task. General conditions for exponential stability of such systems have been fully discussed in prior work of others. In this paper we build practical (computable) error bound analysis of the stochastic gradient algorithm when the loss function is time-dependent and quadratic in the parameters, as arising from standard linear regression model. The long term goal is to address this problem in general nonlinear models. This paper is the first step towards this aim.

关键词： least-mean-squares algorithm LMS Adaptive Control Constant Gain

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Speech Enhancement Using Convex Combination of Fractional least-mean-squares algorithm

Speech Enhancement Using Convex Combination of Fractional Le...

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International Symposium on Telecommunications

作者： Sina Ghalami Osgouei Masoud Geravanchizadeh Department of Electrical & Computer Engineering University of Tabriz

ISBN: (纸本)9781424481835

In this paper, we propose a novel method, called convex combination of fractional LMS (CC-FLMS) for dual-channel speech enhancement based on a modified version of LMS algorithm. Here, we use a modified least-mean-squares algorithm, known as FLMS, by incorporating the fractional derivative term in the weight adaptation equation of the standard least-mean-squares (LMS) algorithm. Combinational approaches provide an interesting way to improve adaptive filtering performance. We use the convex combination of two transversal filters for speech enhancement. The individual filters, designed by the FLMS algorithm, are independently adapted using their own error signals, while the combination is adapted by means of a stochastic gradient algorithm to minimize the error of the overall structure. Experimental results show that the proposed method has a better performance in dual-channel speech enhancement in a sense of mean-squares error (MSE) and speech quality improvement than the LMS, FLMS, and CC-LMS algorithms.

关键词： Speech Enhancement Combinational adaptive filter least-mean-squares algorithm Adaptive filtering Fractional least-mean-squares

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Achieving Robustness in Blind Modulo Analog-to-Digital Conversion

Achieving Robustness in Blind Modulo Analog-to-Digital Conve...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Weiss, Amir Faculty of Engineering Bar-Ilan University Israel

ISBN: (纸本)9798350368741

The need to digitize signals with intricate spectral characteristics often challenges traditional analog-to-digital converters (ADCs). The recently proposed modulo-ADC architecture offers a promising alternative by leveraging inherent features of the input signals. This approach can dramatically reduce the number of bits required for the conversion while maintaining the desired fidelity. However, the core algorithm of this architecture, which utilizes a prediction filter, functions properly only when the respective prediction error is bounded. In practice, this assumption may not always hold, leading to considerable instability and performance degradation. To address this limitation, we propose an enhanced modulo-unfolding solution without this assumption. We develop a reliable detector to successfully unfold the signals, yielding a robust solution. Consequently, the reinforced system maintains proper operation in scenarios where the original approach fails, while also reducing the quantization noise. We present simulation results that demonstrate the superior performance of our approach in a representative setting. © 2025 IEEE.

关键词： adaptive filtering blind signal processing data conversion least-mean-squares algorithm MAP detection

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Real-time streaming video with adaptive bandwidth control and DCT-based error concealment

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-ANALOG AND DIGITAL SIGNAL PROCESSING 1999年第7期46卷 951-956页

作者： Chung, YJ Kim, JW Kuo, CCJ Univ So Calif Integrated Media Syst Ctr Los Angeles CA 90089 USA Univ So Calif Dept Elect Engn Syst Los Angeles CA 90089 USA Kongju Natl Univ Dept Elect Engn Kongju 314701 South Korea

This work deals with dropped streaming video packets from the viewpoint of minimizing future dropped packets, as well as restoring data lost in packets already dropped, specifically geared toward real-time transmission of H.263+ bitstream. Minimization of future dropped packets is achieved by an adaptive least mean squares bandwidth (BW) controller predicting the available bandwidth supported by the Internet with relatively little loss. Restoration of lost video data is performed by a combination of error concealment (EC) techniques, including interpolation of border pixels in DCT domain. All algorithms presented are computationally inexpensive and thus suited for real-time video applications.

关键词： available bandwidth feedback channel least-mean-squares algorithm real-time rate control video streaming

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Robustness conditions of the LMS algorithm with time-variant matrix step-size

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SIGNAL PROCESSING 2000年第9期80卷 1787-1794页

作者： Rupp, M Cezanne, J Bell Labs Lucent Technol Wireless Res Lab Holmdel NJ 07733 USA Bell Labs Lucent Technol Digital Tech Dept Holmdel NJ 07733 USA

Gradient-type algorithms commonly employ a scalar step-size, i.e., each entry of the regression vector is multiplied by the same value before updating the coefficients. More flexibility, however, is obtained when this step-size is of matrix size. It allows not only to individually scaling the entries of the regression vector but rotations and decorrelations are possible as well due to the choice of the matrix. A well-known example for the use of a fixed step-size matrix is the Newton-LMS algorithm. For such a fixed step-size matrix, conditions are well known under which a gradient-type algorithm converges. This article, however, presents robustness and convergence conditions for a least-mean-square (LMS) algorithm with time-variant matrix step-size. On the example of a channel estimator used in a cellular hand-phone, it is shown that the choice of a particular step-size matrix leads to considerable improvement over the fixed step-size case. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： time-variant step-size matrix least-mean-squares algorithm channel estimation cellular phone

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Adaptive noise cancelling based on independent component analysis

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ELECTRONICS LETTERS 2002年第15期38卷 832-833页

作者： Park, HM Oh, SH Lee, SY Korea Adv Inst Sci & Technol Brain Sci Res Ctr Taejon 305701 South Korea Korea Adv Inst Sci & Technol Dept Elect Engn & Comp Sci Taejon 305701 South Korea

A method for adaptive noise cancelling based on independent component analysis (ICA) is presented. Although the conventional least-mean-squares (LMS) algorithm removes noise components based on second-order correlation, the proposed algorithm can utilise higher-order statistics. Experimental results show that the proposed algorithm provides considerable performance improvement.

关键词： higher order statistics noise components noise learning rule ICA correlation methods second-order correlation least-mean-squares algorithm LMS algorithm adaptive noise cancellation Interpolation and function approximation (numerical analysis) Signal processing and detection Signal processing theory Other topics in statistics adaptive signal processing independent component analysis least mean squares methods

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Noise removal from duplicate paleoceanographic time-series: The use of adaptive filtering techniques

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MATHEMATICAL GEOLOGY 1998年第5期30卷 557-574页

作者： Trauth, MH Univ Kiel Inst Geol Palaontol D-24098 Kiel Germany

This study demonstrates that adaptive filters can be used successfully to remove noise from duplicate paleoceanographic time-series. Conventional methods for noise canceling such as fixed filters cannot be applied to paleoceanographic time-series if optimal filtering is to be achieved, because the signal-to-noise ratio is unknown and varies with time. In contrast, an adaptive filter automatically extracts information without any prior initialization of the filter parameters. Two basic adaptive filtering methods, the gradient-based stochastic least-mean-squares (LMS) algorithm and the recursive least-squares (RLS) algorithm have been modified for paleoceanographic applications. The RLS algorithm can he used for noise removal from duplicate records corrupted by stationary noise, for example, carbonate measurements, species counts, or density data. The RLS filter performance is characterized by high accuracy and fast rate of convergence. The modified LMS algorithm out-performs the RLS procedure in a nonstationary environment (e.g., stable isotope records) but at the price of a slower rate of convergence and a reduced accuracy in the final estimate. The application of both algorithms is demonstrated by means of carbonate and stable isotope data.

关键词： least-mean-squares algorithm recursive least-squares algorithm signal-to-noise ratio inverse modeling

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BLIND MODULO ANALOG-TO-DIGITAL CONVERSION OF VECTOR PROCESSES 47

BLIND MODULO ANALOG-TO-DIGITAL CONVERSION OF VECTOR PROCESSE...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Weiss, Amir Huang, Everest Ordentlich, Or Wornell, Gregory W. MIT Dept EECS Cambridge MA 02139 USA MIT Lincoln Lab Cambridge MA 02139 USA Hebrew Univ Jerusalem Sch CSE Jerusalem Israel

ISBN: (纸本)9781665405409

In a growing number of applications, there is a need to digitize a (possibly high) number of correlated signals whose spectral characteristics are challenging for traditional analog-to-digital converters (ADCs). Examples, among others, include multiple-input multiple-output systems where the ADCs must acquire at once several signals at a very wide but sparsely and dynamically occupied bandwidth supporting diverse services. In such scenarios, the resolution requirements can be prohibitively high. As an alternative, the recently proposed modulo-ADC architecture can in principle require dramatically fewer bits in the conversion to obtain the target fidelity, but requires that spatiotemporal information be known and explicitly taken into account by the analog and digital processing in the converter, which is frequently impractical. Building on our recent work, we address this limitation and develop a blind version of the architecture that requires no such knowledge in the converter. In particular, it features an automatic modulo-level adjustment and a fully adaptive modulo-decoding mechanism, allowing it to asymptotically match the characteristics of the unknown input signal. Simulation results demonstrate the successful operation of the proposed algorithm.

关键词： data conversion blind signal processing adaptive filtering least-mean-squares algorithm

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Acceleration tracking control combining adaptive control and off-line compensators for six-degree-of-freedom electro-hydraulic shaking tables

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ISA Transactions 2017年 70卷 322-337页

作者： Shen, Gang Li, Xiang Zhu, Zhencai Tang, Yu Zhu, Weidong Liu, Shanzeng School of Mechatronic Engineering China University of Mining and Technology Xuzhou 221116 China Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment China University of Mining and Technology Xuzhou 221116 China Department of Mechanical Engineering University of Maryland Baltimore County Baltimore 21250 MD United States

An electro-hydraulic shaking table (EHST) is an essential experimental facility to simulate in real-time actual vibration situations. An adaptive controller combined with off-line compensators is proposed to improve the acceleration frequency bandwidth and tracking accuracy of a six-degree-of-freedom (6-DOF) EHST. A servo controller has been employed to implement acceleration closed-loop and coordinate control of the 6-DOF EHST. A recursive extended least-squares algorithm is employed to identify acceleration closed-loop transfer functions and a zero-phase-error tracking controller is used to design off-line inverse model compensators using the identified transfer functions. However, the off-line compensators cannot compensate in real-time varying dynamics of the 6-DOF EHST;so an online adaptive controller with a least-mean-squares (LMS) algorithm based on a delay compensator is employed. The proposed controller combines advantages of the off-line compensators and the online adaptive controller, which guarantees both a fast rate of convergence of the LMS algorithm and high-fidelity acceleration tracking accuracy of the 6-DOF EHST. Some experimental studies have been conducted on a 6-DOF EHST and experimental results show that acceleration tracking control performances, including the rate of convergence of the LMS algorithm and acceleration tracking accuracy, have been improved compared to a conventional three-variable controller and adaptive controllers. © 2017 ISA

关键词： Adaptive control Electro-hydraulic shaking table least-mean-squares algorithm Off-line inverse model compensator Recursive extended least-squares algorithm System identification

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