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Projection-Free Stochastic Bi-Level Optimization

IEEE TRANSACTIONS ON signal processing 2022年 70卷 6332-6347页

作者： Akhtar, Zeeshan Bedi, Amrit Singh Thomdapu, Srujan Teja Rajawat, Ketan Indian Inst Technol Kanpur Dept Elect Engn Kanpur 208016 India Univ Maryland Inst Syst Res College Pk MD 20742 USA

Bi-level optimization, where the objective function depends on the solution of an inner optimization problem, provides a flexible framework for solving a rich class of problems such as hyper-parameter optimization and model-agnostic meta-learning. This work puts forth the first Stochastic Bi-level Frank-Wolfe (SBFW) algorithm to solve the stochastic bi-level optimization problems in a projection-free manner. We utilize a momentum based gradient tracker that results in a sample complexity of O(epsilon(-3)) for convex outer objectives and O(epsilon(-4)) for non-convex outer objectives with strongly convex inner objectives. The stochastic compositional optimization problems (a special case of bi-level optimization problems entailing the minimization of a composition of two expected-value functions) are also considered within the same rubric. The proposed Stochastic Compositional Frank-Wolfe (SCFW) algorithm is shown to achieve a sample complexity of O (epsilon(-2)) for convex objectives and O(epsilon(-3)) for non-convex objectives, at par with the state-of-the-art rates of projection-free algorithms for single-level problems. The usefulness and flexibility of SBFW and SCFW algorithms are demonstrated via extensive numerical tests. We show that SBFW outperforms the state-of-the-art methods for the problem of matrix completion with denoising, and achieve improvements of up to 82% in terms of the wall-clock time required to achieve a particular level of accuracy. Furthermore, we demonstrate the improved performance of SCFW over the competing projection-free variants on the policy evaluation problem in reinforcement learning.

关键词： Optimization signal processing algorithms Stochastic processes Noise reduction Complexity theory Noise measurement Data models Bi-level stochastic optimization frank-wolfe algorithm projection free algorithm

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Optimization and Learning With Information Streams: Time-varying algorithms and applications

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IEEE signal processing MAGAZINE 2020年第3期37卷 71-83页

作者： Dall'Anese, Emiliano Simonetto, Andrea Becker, Stephen Madden, Liam Univ Colorado Boulder Dept Elect Comp & Energy Engn Boulder CO 80309 USA Univ Minnesota Minneapolis MN 55455 USA Natl Renewable Energy Lab Golden CO 80401 USA IBM Res Ireland Optimizat & Control Grp Dublin Ireland Delft Univ Technol Delft Netherlands Catholic Univ Louvain Louvain La Neuve Belgium IBM Res Yorktown Hts NY USA Univ Colorado Boulder Appl Math Dept Boulder CO USA

There is a growing cross-disciplinary effort in the broad domain of optimization and learning with streams of data, applied to settings where traditional batch optimization techniques cannot produce solutions at time scales that match the interarrival times of the data points due to computational and/or communication bottlenecks. Special types of online algorithms can handle this situation, and this article focuses on such time-varying optimization algorithms, with emphasis on machine leaning (ML) and signal processing (SP) as well as data-driven control (DDC). Approaches for the design of time-varying or online first-order optimization methods are discussed, with emphasis on algorithms that can handle errors in the gradient, as may arise when the gradient is estimated. Insights into performance metrics and accompanying claims are provided, along with evidence of cases where algorithms that are provably convergent in batch optimization may perform poorly in an online regime. The role of distributed computation is discussed. Illustrative numerical examples for a number of applications of broad interest are provided to convey key ideas.

关键词： Optimization signal processing algorithms Measurement Prediction algorithms Electronic mail Heuristic algorithms Clustering algorithms

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A Bayesian Approach to Recurrence in Neural Networks

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2021年第8期43卷 2527-2537页

作者： Garner, Philip N. Tong, Sibo Idiap Res Inst CH-1920 Martigny Switzerland

We begin by reiterating that common neural network activation functions have simple Bayesian origins. In this spirit, we go on to show that Bayes's theorem also implies a simple recurrence relation;this leads to a Bayesian recurrent unit with a prescribed feedback formulation. We show that introduction of a context indicator leads to a variable feedback that is similar to the forget mechanism in conventional recurrent units. A similar approach leads to a probabilistic input gate. The Bayesian formulation leads naturally to the two pass algorithm of the Kalman smoother or forward-backward algorithm, meaning that inference naturally depends upon future inputs as well as past ones. Experiments on speech recognition confirm that the resulting architecture can perform as well as a bidirectional recurrent network with the same number of parameters as a unidirectional one. Further, when configured explicitly bidirectionally, the architecture can exceed the performance of a conventional bidirectional recurrence.

关键词： Logic gates Probabilistic logic Bayes methods signal processing algorithms Hidden Markov models Training Computer architecture Neural networks bayesian statistics recurrence forward-backward algorithm speech recognition bidirectional LSTM

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Blind carrier phase tracking with guaranteed global convergence

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IEEE TRANSACTIONS ON signal processing 1997年第7期45卷 1889-1894页

作者： Belouchrani, A Ren, W UNIV CALIF BERKELEY DEPT ELECT ENGN & COMP SCIBERKELEYCA 94720

Conventional blind carrier recovery algorithms have been observed to show unstable behaviors for large constellation modulation schemes such as 64-QAM or 256-QAM, In this paper, we propose a new carrier tracking approach that has guaranteed global convergence, First, we show that the carrier tracking problem is equivalent to a blind source separation problem, which involves the separation of a linear unitary mixture of two independent components that are the real and imaginary parts of the emitted signal, Then, by using a blind source separation procedure, we derive a new and robust carrier tracking algorithm with guaranteed global convergence. Some numerical simulations are provided to illustrate the effectiveness of the proposed method.

关键词： Convergence signal processing algorithms Blind equalizers Adaptive equalizers Intersymbol interference Blind source separation Quadrature amplitude modulation Phase noise Algorithm design and analysis Acoustic transducers

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DOA Estimation Based on Coherent Integration-Sparse Bayesian Learning With Time-Variant Gain-Phase Errors

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第6期59卷 7951-7962页

作者： Lu, Chaojie Zheng, Jibin Yang, Tianyuan Chang, Rui Liu, Hongwei Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Chinese Acad Sci Natl Key Lab Microwave Imaging Technol Aerosp Informat Res Inst Beijing 100045 Peoples R China

Direction of arrival (DOA) estimation is an important topic of array signal processing and channels without gain-phase errors or with time-invariant gain-phase errors are assumed in most of the DOA estimation algorithms. However, with the measured data of a reconnaissance receiver system, we verify that the gain-phase error between channels is time-variation. Based on this fact, a more practical signal model with time-variant gain-phase errors is constructed. To obtain accurate DOA estimation, an algorithm termed as coherent integration-sparse Bayesian learning with time-variant gain-phase errors is (CI-SBLVGPE) devised which has two superiorities, i.e., 1) the CI operation integrates the signal energy into a single snapshot, which decreases the gain-phase errors, makes the compensation possible, and guarantees a low computational complexity;2) the priori information of the gain-phase errors, source DOA, and noise is used to reduce the computational complexity and improve the robustness. With mathematical analyses, numerical simulations, and real data processing, we validate the constructed model and superiorities of the proposed method.

关键词： Direction-of-arrival estimation Estimation signal processing algorithms Bayes methods Phased arrays Data models Measurement uncertainty Coherent integration DOA estimation gain-phase errors sparse Bayesian learning

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Multipulse Technique Exploiting the Intermodulation of Ultrasound Waves in a Nonlinear Medium

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IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL 2009年第3期56卷 520-535页

作者： Biagi, Elena Breschi, Luca Vannacci, Enrico Masotti, Leonardo Univ Florence Florence Italy

In recent years, the nonlinear properties of materials have attracted much interest in nondestructive testing and in ultrasound diagnostic applications. Acoustic nonlinear parameters represent an opportunity to improve the information that can be extracted from a medium such as structural organization and pathologic status of tissue. In this paper, a method called pulse subtraction intermodulation (PSI), based on a multipulse technique., is presented and investigated both theoretically and experimentally. This method allows separation of the intermodulation products, which arise when 2 separate frequencies are transmitted in a nonlinear medium, from fundamental and second harmonic components, making them available for improved imaging techniques or signal processing algorithms devoted to tissue characterization. The theory of intermodulation product generation was developed according the Khokhlov-Zabolotskaya-Kuznetsov (KZK) nonlinear propagation equation, which is consistent with experimental results. The description of the proposed method, characterization of the intermodulation spectral contents, and quantitative results coming from in vitro experimentation are reported and discussed in this paper.

关键词： intermodulation products signal processing algorithms second harmonic component Imaging multiple-pulse tissue characterization Nondestructive Testing Material properties Ultrasonic Waves intermodulation nonlinearity

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Convolutive Blind Source Separation for Communication signals Based on the Sliding Z-Transform

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IEEE ACCESS 2020年 8卷 41213-41219页

作者： Jia, Yinjie Xu, Pengfei Hohai Univ Coll Comp & Informat Nanjing 210098 Peoples R China Huaiyin Inst Technol Fac Elect Informat Engn Huaian 223003 Peoples R China

Convolutive blind source separation (CBSS) is one of the main branches in the field of intelligent signal processing. Inspired by the thought of sliding discrete Fourier transform (DFT), an idea of the sliding Z-transform is introduced in the present study. Based on the sliding Z-transform, a method called the sliding Z-transform for CBSS (ZCBSS) is applied to CBSS for communication signals with the same or different frequency. It is found that the deduced algorithm can accomplish the separation task without setting any preset parameter and directly recover time-domain sources from the convolutive mixtures with the help of robust instantaneous blind source separation algorithms. Simulations are carried out accordingly, and it is concluded that the proposed approach is effective in terms of both separation performance and signal de-noising.

关键词： Blind source separation Mathematical model signal processing algorithms Finite impulse response filters Discrete Fourier transforms Time-domain analysis Frequency-domain analysis Blind source separation convolutive mixture sliding Z-transform signal de-noising

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signal processing of automotive collision warning radar

Signal processing of automotive collision warning radar

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International Conference on signal processing Proceedings (ICSP)

作者： Zhang Jian-Hui Liu Guo-Sui Gu Hong Su Wei-Min RCEET Nanjing University of Science and Technology of PLA Nanjing Jiangsu China

A stepped-FMCW radar waveform applied to automotive collision warning systems (forward looking automotive collision warning radar (FLAR) or autonomous intelligent cruise control systems (AICC)) is proposed, which is easy to generate digitally and its signal processing reduced the requirement of the computational speed compared to the conventional high resolution radar. We also suggested a error approach algorithm for multiple vehicle target detection and confirmed its usefulness in eliminating the false target by computer simulation.

关键词： Radar signal processing Automotive engineering signal processing algorithms Alarm systems Intelligent systems Computational intelligence Intelligent control Intelligent vehicles Control systems signal generators

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Impact of architecture extensions for media signal processing on data-path organization

Impact of architecture extensions for media signal processin...

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Asilomar Conference on signals, Systems & Computers

作者： A.A. Faroqui V.G. Oklobdzija Synopsys Inc. Mountain View CA USA ACSEL Electrical and Computer Engineering Department University of California Davis CA USA

ISBN: (纸本)0780365143

Media signal processing requires high computing power and the algorithms exhibit a great deal of parallelism on low precision data. The basic components of multi-media objects are usually simple integers with 8, 12, or 16 bits of precision. In order to support efficient processing of media signals, instructions set architecture (ISA) of the traditional processors requires modifications. In this paper, we present the quantitative analysis and the computational complexity required to perform media processing. Main classes of instructions that are needed for the required level of performance of the Media Processor are identified. Their efficient implementation and effect on the processor datapath is discussed. The main operations required in media processing are Addition (with or without saturation), Multiplication (with or without rounding), Sum of Products, and Average of two numbers.

关键词： signal processing Computer architecture signal processing algorithms Microprocessors Instruction sets Concurrent computing Parallel processing Video compression Fast Fourier transforms Very large scale integration

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Quasioptimal Spatiotemporal signal processing Algorithm for Radar Imaging

Quasioptimal Spatiotemporal Signal Processing Algorithm for ...

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International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science

作者： V. V. Pavlikov M. V. Nechyporuk Nguyen Van Huu A. D. Sobkolov Aerospace Radio-Electronic Systems Department National Aerospace University “Kharkiv Aviation Institure” Kharkiv Ukraine National Aerospace University “Kharkiv Aviation Institure” Kharkiv Ukraine

ISBN: (数字)9781728155661

ISBN: (纸本)9781728155678

The problem of algorithm synthesis for processing of the signals at the output of a spatially distributed antenna system for the formation of radio images from aerospace carriers has been solved. In order to synthesize the optimal algorithm, the maximum likelihood method was used. As a result of analyzing the physical nature of the signal processing operations, a transition to the quasi-optimal processing, which simplifies the technical implementation of the algorithm, is proposed. The main feature of this algorithm lies in the fact is that it allows to receive a radio image in the direction of the nadir within the range of angles that is currently not visible by the radars from aerospace carriers. In this range of angles, an image is usually formed by optical means. A scheme of the radar complex has been developed and modeling of radio image formation has been carried out in accordance with the algorithm.

关键词： geophysical image processing maximum likelihood estimation optimisation radar imaging Radar imaging maximum likelihood estimation signal processing algorithms radar signal processing algorithms Image formation images Angle Radio maximum likelihood NADIR

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