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Stochastic Adaptive Optimization With Dithers

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第1期67卷 189-202页

作者： Xie, Siyu Liang, Shu Wang, Le Yi Yin, George Chen, Wen Wayne State Univ Dept Elect & Comp Engn Detroit MI 48202 USA Tongji Univ Dept Control Sci & Engn Shanghai 201804 Peoples R China Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai 201210 Peoples R China Univ Connecticut Dept Math Storrs CT 06269 USA Wayne State Univ Div Engn Technol Detroit MI 48202 USA

Optimization methods are essential and have been used extensively in a broad spectrum of applications. Most existing literature on optimization algorithms does not consider systems that involve unknown system parameters. This article studies a class of stochastic adaptive optimization problems in which identification of unknown parameters and search for the optimal solutions must be performed simultaneously. Due to a fundamental conflict between parameter identifiability and optimality in such problems, we introduce a method of adding stochastic dither signals into the system, which provide sufficient excitation for estimating the unknown parameters, leading to convergent adaptive optimization algorithms. Joint identification and optimization algorithms are developed and their simultaneous convergence properties of parameter estimation and optimization variable updates are proved. Under both noise-free and noisy observations, the corresponding convergence rates are established. The main results of this article reveal certain fundamental relationships and tradeoff among updating step sizes, dither magnitudes, parameter estimation errors, optimization accuracy, and convergence rates. Simulation case studies are used to illustrate the adaptive optimization algorithms and their main properties.

关键词： Optimization Convergence signal processing algorithms Stochastic processes Parameter estimation Noise measurement Estimation Adaptive optimization algorithm convexity identification optimization stochastic dither

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Novel Recovery algorithms for Block Sparse signals With Known and Unknown Borders

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 3726-3742页

作者： Haghighatpanah, Neda Gohary, Ramy H. Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada

This paper presents two novel Bayesian learning recovery algorithms for block sparse signals corresponding to two distinct cases. In the first case, the signals within each block are correlated and the block borders are known, whereas in the second case, the block borders are unknown and the signal elements are uncorrelated. For the first case, the proposed recovery algorithm differs from existing ones in two aspects. First, in each iteration, the optimal block covariances are obtained based on the data estimated in previous iterations. Hence, it does not rely on prior assumptions nor does it restrict the covariance matrices to have a particular structure. Second, the decision to declare a block as zero or non-zero is based on hypothesis testing, which ensures that the probability of erroneous detection is minimized. For the second case, we introduce a new prior model which is characterized by elastic dependencies among neighbouring signal elements. Using this model, we develop a novel Bayesian learning algorithm which iterates between estimating the dependencies among the signal elements and updating the Gaussian prior model. Numerical simulations illustrate the effectiveness of the proposed algorithms.

关键词： signal processing algorithms Bayes methods Brain modeling Image reconstruction Numerical models Hidden Markov models Data models Bayesian learning block sparse recovery compressed sensing expectation maximization Markov model nonuniform sampling

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IoT Based Weather Forecasting for Rainfall Prediction Using Intensified Long Short-Term Memory Artificial Neural Network

IoT Based Weather Forecasting for Rainfall Prediction Using ...

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2024 International Conference on signal processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Vigneshwaran, V. Avinash, B. Titus Casimir, A. Vijay, D. Jayakumar, D. IFET College of Engineering Department of Computer Science Engineering Tamil Nadu Villupuram India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540685

The purpose of this study was to evaluate the possible uses of artificial neural networks (ANNs) and intensified long short-term memory (LSTM) for rainfall forecasting. The effectiveness of these weather forecasting techniques was evaluated and assigned a score. Materials and Techniques: For the sample test analysis, intensified LSTM and ANN with pre-test power (G-power) of 80% and alpha error rate (Err) of 0.05 were used. The results of the MATLAB simulations show that the ANN has an accuracy of 95.68% and the Intensified LSTM has an accuracy of 87.99%. We discover that the achieved accuracy ratio is 0.034 (p 0.05) using the statistical application SPSS. According to this study, when it comes to advanced weather forecasting, the ANN algorithm outperforms the Intensified LSTM technique in terms of rainfall prediction. © 2024 IEEE.

关键词： Accuracy Rain Error analysis Weather forecasting signal processing algorithms signal processing Prediction algorithms

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"Self-Wiener" Filtering: Data-Driven Deconvolution of Deterministic signals

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 468-481页

作者： Weiss, Amir Nadler, Boaz MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA Weizmann Inst Sci Dept Comp Sci & Appl Math Fac Math & Comp Sci IL-7610001 Rehovot Israel

We consider the problem of robust deconvolution, and particularly the recovery of an unknown deterministic signal convolved with a known filter and corrupted by additive noise. We present a novel, non-iterative data-driven approach. Specifically, our algorithm works in the frequency-domain, where it tries to mimic the optimal unrealizable non-linear Wiener-like filter as if the unknown deterministic signal were known. This leads to a threshold-type regularized estimator, where the threshold at each frequency is determined in a data-driven manner. We perform a theoretical analysis of our proposed estimator, and derive approximate formulas for its Mean Squared Error (MSE) at both low and high signal-to-Noise Ratio (SNR) regimes. We show that in the low SNR regime our method provides enhanced noise suppression, and in the high SNR regime it approaches the optimal unrealizable solution. Further, as we demonstrate in simulations, our solution is highly suitable for (approximately) bandlimited or frequency-domain sparse signals, and provides a significant gain of several dBs relative to other methods in the resulting MSE.

关键词： Deconvolution signal to noise ratio Optimized production technology signal processing algorithms Convolution Frequency estimation Noise measurement Deconvolution thresholding Wiener filter

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Fast and Accurate Linear Fitting for an Incompletely Sampled Gaussian Function With a Long Tail

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IEEE signal processing MAGAZINE 2022年第6期39卷 76-84页

作者： Wu, Kai Zhang, J. Andrew Guo, Y. Jay UTS Global Big Data Technol Ctr Sydney NSW 2007 Australia Univ Technol Sydney Sch Elect & Data Engn Sydney NSW 2007 Australia CSIRO Data61 Sydney NSW Australia Natl ICT Australia Networked Syst Sydney NSW Australia ZTE Corp Nanjing Peoples R China New South Wales Connect Innovat Network Sydney NSW Australia

Fitting experiment data onto a curve is a common signal processing technique to extract data features and establish the relationship between variables. Often, we expect the curve to comply with some analytical function and then turn data fitting into estimating the unknown parameters of a function. Among analytical functions for data fitting, the Gaussian function is the most widely used one due to its extensive applications in numerous science and engineering fields. To name just a few, the Gaussian function is highly popular in statistical signal processing and analysis, thanks to the central limit theorem [1] , and the Gaussian function frequently appears in the quantum harmonic oscillator, quantum field theory, optics, lasers, and many other theories and models in physics [2] ; moreover, the Gaussian function is widely applied in chemistry for depicting molecular orbitals, in computer science for imaging processing, and in artificial intelligence for defining neural networks.

关键词： Quantum computing Simulation Feature extraction Quantum mechanics signal processing algorithms Gaussian processes signal processing Neural networks Laser theory

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Novel Multilayer Extreme Learning Machine as a Massive MIMO Receiver for Millimeter Wave Communications

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IEEE ACCESS 2022年 10卷 58965-58981页

作者： Carrera, Diego Fernando Vargas-Rosales, Cesar Zabala-Blanco, David Yungaicela-Naula, Noe M. Azurdia-Meza, Cesar A. Marey, Mohamed Firoozabadi, Ali Dehghan Univ Tecnolog Empresarial Guayaquil Sch Technol Guayaquil 090511 Ecuador Tecnol Monterrey Sch Sci & Engn Monterrey 64849 Mexico Univ Catolic Maule Dept Comp Sci & Ind Talca 3466706 Chile Univ Chile Dept Elect Engn Santiago 8370451 Chile Prince Sultan Univ Coll Engn Smart Syst Engn Lab Riyadh 11586 Saudi Arabia Univ Tecnol Metropolitana Dept Elect Santiago 7800002 Chile

Wireless communication systems working in millimeter-wave (mmWave) frequency bands offer higher bandwidths than traditional radio frequency schemes. This technology allows multibeam steering and data multiplexing with the help of massive multiple-input multiple-output (MIMO) systems. However, supporting large bandwidths at mmWave frequencies is challenging due to the use of large antenna arrays with beamforming, sampling signals with large bandwidths, and baseband signal processing operations at gigabit data rates. Due to the wider bandwidth and higher signal processing requirements of mmWave systems, low-complexity receiver algorithms become important. Previously reported investigations assumed the use of hybrid beamforming structures that reduce power consumption and signal processing tasks. Therefore, the use of artificial neural networks (ANNs) becomes relevant for the processing of mmWave signals as reported in earlier works. In this article, to carry out MIMO combining processing for mmWave communications, we propose a fully complex multilayer extreme learning machine (M-ELM) neural network. We investigate the tuning of the number of neurons in each hidden layer for the proposed method to maximize the system performance and decrease the complexity of the receiver. We compare the results of the introduced M-ELM algorithm with a fully complex extreme learning machine (ELM), fully real ELM, and M-ELM defined in the real plane in terms of spectral efficiency, bit error rate, computational complexity, and processing time. Furthermore, we compare the novel M-ELM strategy with traditional linear MIMO receivers, such as Maximum Ratio and Minimum Mean Square Error, as well as to a multilayer perceptron (MLP) neural network trained offline. The numerical results show that with a good balance between the overall performance and computational cost of the ANN, the fully complex M-ELM MIMO receiver outperforms the other evaluated schemes.

关键词： Receivers Channel estimation Millimeter wave communication signal processing algorithms OFDM Nonhomogeneous media Massive MIMO 5G NR beamforming machine learning massive MIMO millimeter wave multilayer ELM

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Optimized Population Monte Carlo

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 2489-2501页

作者： Elvira, Victor Chouzenoux, Emilie Univ Edinburgh Sch Math Edinburgh EH8 9YL Midlothian Scotland Alan Turing Inst London England Univ Paris Saclay INRIA F-91190 Gif Sur Yvette France

Adaptive importance sampling (AIS) methods are increasingly used for the approximation of distributions and related intractable integrals in the context of Bayesian inference. Population Monte Carlo (PMC) algorithms are a subclass of AIS methods, widely used due to their ease in the adaptation. In this paper, we propose a novel algorithm that exploits the benefits of the PMC framework and includes more efficient adaptive mechanisms, exploiting geometric information of the target distribution. In particular, the novel algorithm adapts the location and scale parameters of a set of importance densities (proposals). At each iteration, the location parameters are adapted by combining a versatile resampling strategy (i.e., using the information of previous weighted samples) with an advanced optimization-based scheme. Local second-order information of the target distribution is incorporated through a preconditioning matrix acting as a scaling metric onto a gradient direction. A damped Newton approach is adopted to ensure robustness of the scheme. The resulting metric is also used to update the scale parameters of the proposals. We discuss several key theoretical foundations for the proposed approach. Finally, we show the successful performance of the proposed method in three numerical examples, involving challenging distributions.

关键词： Proposals Monte Carlo methods Artificial intelligence Bayes methods signal processing algorithms Inference algorithms Standards Importance sampling Monte Carlo methods population Monte Carlo newton algorithm covariance adaptation stochastic optimization langevin dynamics

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A Variational Bayesian Inference-Inspired Unrolled Deep Network for MIMO Detection

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 423-437页

作者： Wan, Qian Fang, Jun Huang, Yinsen Duan, Huiping Li, Hongbin Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA

The great success of deep learning (DL) has inspired researchers to develop more accurate and efficient symbol detectors for multi-input multi-output (MIMO) systems. Existing DL-based MIMO detectors, however, suffer several drawbacks. To address these issues, in this paper, we develop a model-driven DL detector based on variational Bayesian inference. Specifically, the proposed unrolled DL architecture is inspired by an inverse-free variational Bayesian learning framework which circumvents matrix inversion via maximizing a relaxed evidence lower bound. Two networks are respectively developed for independent and identically distributed (i.i.d.) Gaussian channels and arbitrarily correlated channels. The proposed networks, referred to as VBINet, have only a few learnable parameters and thus can be efficiently trained with a moderate amount of training samples. The proposed VBINet-based detectors can work in both offline and online training modes. An important advantage of our proposed networks over state-of-the-art MIMO detection networks such as OAMPNet and MMNet is that the VBINet can automatically learn the noise variance from data, thus yielding a significant performance improvement over the OAMPNet and MMNet in the presence of noise variance uncertainty. Simulation results show that the proposed VBINet-based detectors achieve competitive performance for both i.i.d. Gaussian and realistic 3GPP MIMO channels.

关键词： Detectors MIMO communication Training Bayes methods Neural networks Deep learning signal processing algorithms MIMO detection variational Bayesian inference unrolled deep networks

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A Computationally Efficient Connectivity Index for Weighted Directed Graphs With Application to Underwater Sensor Networks

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IEEE ACCESS 2022年 10卷 54822-54832页

作者： Mahboubi, Hamid Asadi, M. Mehdi Blouin, Stephane Aghdam, Amir G. Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada Polytech Montreal Dept Elect Engn Montreal PQ H3T 1J4 Canada Def Res & Dev Canada DRDC Atlantic Res Ctr Dartmouth NS B2Y 3Z7 Canada

This paper investigates the global connectivity of complex networks with random links. An expected communication graph with weighted edges is used to model the network. The notion of weighted vertex connectivity (WVC) introduced in the literature as a generalization of the notion of vertex connectivity, is known to be effective in measuring the connectivity of this type of network. However, given the computational complexity of the WVC, a numerically efficient approximate measure for that is more desirable. In this paper, a polynomial-time approximation to the WVC is derived, which is less conservative than the previously introduced approximate measure. It is shown that under some conditions the proposed approximation is identical to the WVC. Simulation results demonstrate the usefulness of the proposed measure.

关键词： Sea measurements Underwater acoustics Wireless sensor networks Weight measurement signal processing algorithms Directed graphs Computational efficiency Graph connectivity directed weighted graphs sensor networks

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License Plate Localization in Complex Environments Based on Improved GrabCut Algorithm

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IEEE ACCESS 2022年 10卷 88495-88503页

作者： Shi, Hengliang Zhao, Dongnan Luoyang Polytech Automobile & Rail Transit Sch Yibin 471023 Luoyang Peoples R China Henan Univ Sci & Technol Informat Engn Sch Luoyang 471000 Luolong Peoples R China

Aiming at the problem that the existed license plate detection method lacking of accuracy and speed, an improved lightweight detection algorithm for license plate detection in natural scenarios was proposed. First, the traditional GrabCut algorithm needs to interactively provide a candidate frame in order to perform the target detection work. We replace the candidate frame by introducing the Aspect ratio of the license plate as the foreground extraction feature to automate the detection of the license plate by GrabCut algorithm. Then, in order to improve the detection precision of traditional target detection algorithms, we introduced the Wiener filter, which is widely used in the field of digital signal processing, and Combine with Bernsen algorithm to complete image noise reduction. Finally, the algorithm was tested with the CCPD dataset, which contains many vehicle images from different complex natural scenes, especially low-resolution images. The experimental results shows that improved GrabCut algorithm achieves an average accuracy of 99.34% for license plate localization and a detection speed of 0.29s/frame, which has better accuracy and real-time performance compared with traditional GrabCut and other license plate localization algorithms.

关键词： License plate recognition Location awareness Feature extraction Image segmentation signal processing algorithms Mathematical models Image color analysis Wiener filters Object detection Aspect ratio GrabCut license plate location object detection Wiener filter

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