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PENDANTSS: PEnalized Norm-Ratios Disentangling Additive Noise, Trend and Sparse Spikes

IEEE signal processing LETTERS 2023年 30卷 215-219页

作者： Zheng, Paul Chouzenoux, Emilie Duval, Laurent Univ Paris Saclay Cent Supelec Inria Ctr Vis Numer F-91190 Gif Sur Yvette France Rhein Westfal TH Aachen Chair Informat Theory & Data Analyt D-52074 Aachen Germany IFP Energies Nouvelles F-92852 Rueil Malmaison France

detrending, deconvolution: usual restoration tasks, traditionally decoupled. Coupled formulations entail complex ill-posed inverse problems. We propose PENDANTSS for joint trend removal and blind deconvolution of sparse peak-like signals. It blends a parsimonious prior with the hypothesis that smooth trend and noise can somewhat be separated by low-pass filtering. We combine the generalized quasi-norm ratio Smoothed One-Over-Two/Smoothed p-Over -q (SOOT/SPOQ) sparse penalties l(p)/l(q) with the Baseline Estimation And Denoising with Sparsity (BEADS) ternary-assisted source separation algorithm. This results in a both convergent and efficient tool, with a novel Trust-Region block alternating variable metric forward-backward approach. It outperforms comparable methods, when applied to typically peaked analytical chemistry signals. Reproducible code is provided.

关键词： Market research Kernel Deconvolution signal processing algorithms Noise reduction Optimization Image restoration Blind deconvolution sparse signal trend estimation non-convex optimization forward-backward splitting alternating minimization source separation

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Infrared and Visible Image Fusion via Multiscale Receptive Field Amplification Fusion Network

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IEEE signal processing LETTERS 2023年 30卷 493-497页

作者： Ji, Chuanming Zhou, Wujie Lei, Jingsheng Ye, Lv Zhejiang Univ Sci & Technol Sch Informat & Elect Engn Hangzhou 310023 Peoples R China

Infrared and visible image fusion, which highlights radiometric and detailed texture information and completely and accurately describes objects, is a long-standing and well-studied task in computer vision. Existing convolutional neural network-based approaches that leverage end-to-end networks to fuse infrared and visible images have made significant progress. However, most approaches typically extract the features in the encoder segment and use a coarse fusion strategy. Unlike these algorithms, this study proposes a multiscale receptive field amplification fusion network (MRANet) to effectively extract the local and global features from images. Particularly, we extract long-range information in the encoder segment using a convolutional residual structure as the main backbone and a simplified uniformer as an auxiliary backbone, both of which are ResNet-inspired. Additionally, we propose an effective multiscale fusion strategy based on an attention mechanism to integrate the two modalities. Extensive experiments demonstrate that MRANet performs efficiently on image fusion datasets.

关键词： Feature extraction Image fusion Convolution Transformers signal processing algorithms Computer architecture Image edge detection transformer CNN deep learning

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Robust Constrained Affine-Projection-Like Adaptive Filtering algorithms Using the Modified Huber Function

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第3期70卷 1214-1218页

作者： Xu, Wenjing Zhao, Haiquan Lv, Shaohui Southwest Jiaotong Univ Key Lab Magnet Suspens Technol & Maglev Vehicle Minist Educ Chengdu 610031 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

In this brief, two robust constrained affine-projection-like M-estimate (CAPLM) adaptive filtering algorithms are proposed, which solve the problem that the traditional constrained affine projection (CAP) algorithm is not robust to impulsive interference, and realize unbiased output in some applications where the desired signal is unavailable or unnecessary. Specifically, a modified Huber function (MHF) based robust AP-like (APL) minimization problem with constraints is defined and can be transformed into two different unconstrained optimization problems by using Lipschitz continuity and two different methods, and then CAPLM-I and CAPLM-II algorithms are obtained respectively. Both CAPLM algorithms can avoid a certain amount of computational complexity caused by the inversion of the input signal matrix in classical AP algorithm, and realize the robustness to impulsive interference. In addition, the mean square stabilities of them are analyzed, and the corresponding stable step size ranges are also given. Simulation results show that the proposed CAPLM-I and CAPLM-II algorithms perform well in system identification and beamforming applications in impulsive noise environment, and provide lower steady-state error and faster convergence speed than other compared algorithms.

关键词： signal processing algorithms Filtering algorithms Array signal processing Matrices Computational complexity Steady-state Interference Constrained adaptive filtering affine-projection-like M-estimate impulsive noise beamforming

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Gossiped and Quantized Online Multi-Kernel Learning

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IEEE signal processing LETTERS 2023年 30卷 468-472页

作者： Ortega, Tomas Jafarkhani, Hamid Univ Calif Irvine Ctr Pervas Commun & Comp Irvine CA 92697 USA Univ Calif Irvine EECS Dept Irvine CA 92697 USA

In instances of online kernel learning where little prior information is available and centralized learning is unfeasible, past research has shown that distributed and online multi-kernel learning provides sub-linear regret as long as every pair of nodes in the network can communicate (i.e., the communications network is a complete graph). In addition, to manage the communication load, which is often a performance bottleneck, communications between nodes can be quantized. This letter expands on these results to non-fully connected graphs, which is often the case in wireless sensor networks. To address this challenge, we propose a gossip algorithm and provide a proof that it achieves sub-linear regret. Experiments with real datasets confirm our findings.

关键词： Kernel signal processing algorithms Radio frequency Quantization (signal) Mathematical models Task analysis Hilbert space Federated learning gossip algorithms kernel-based learning quantization sensor networks

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Efficient signal processing: Harnessing Distributed Arithmetic for High-Speed FFT Operations

Efficient Signal Processing: Harnessing Distributed Arithmet...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： M. Bharathi Krithikaa Mohanarangam Yasha Jyothi M Shirur ECE Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati Andhra Pradesh India Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Electronics and Communication Engineering BNM Institute of Technology Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

The present article introduces a novel approach to significantly enhance the computational efficiency of modern Digital signal processing (DSP) processors. The algorithm enhances the efficiency of Sum of Products (SOP) operations, which are essential for real-time signal processing applications, by utilizing Distributed Arithmetic (DA) techniques for the Fast Fourier Transform (FFT) and its inverse (Inverse FFT) (IDFT) computations. The present study undertakes an exhaustive quantitative analysis to assess the computational delays of the proposed 2, 4, and 8-point Fast Fourier Transform (FFT) and Inverse Discrete Fourier Transform (IDFT) across various bit representations (4, 8, and 16-bit). For example, when using 4-bit representations, the computational delays for a 2-point Fast Fourier Transform (FFT) are measured to be 13.318ns, 30.629ns for a 4-point FFT, and 27.779ns for an 8-point FFT. Similarly, the delays for the 2-point Fast Fourier Transform (FFT) in an 8-bit representation are 13.727ns, the 4-point FFT in 22.276ns, and the 8-point FFT in 30.592ns. The study further includes the addition of appropriate delays for IDFT operations, which enhances the understanding of the effectiveness of the approach suggested in various scenarios. Furthermore, the research presents and evaluates three distinct categories of transforms, namely 2-point, 4-point, and 8-point FFT and IDFT structures. These structures utilize different quantities of Look-Up Tables (LUTs) for computational purposes. The research showcases the exceptional computing efficiency of the suggested design through detailed quantitative analysis, emphasizing its potential advantages in improving DSP implementations in practical situations.

关键词： Fast Fourier transforms Statistical analysis signal processing algorithms Digital signal processing Real-time systems Delays Table lookup Computational efficiency Velocity measurement Arithmetic

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Virtualization of Guitar Pickups Through Circuit Inversion

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IEEE signal processing LETTERS 2023年 30卷 458-462页

作者： Giampiccolo, Riccardo Bernardini, Alberto Sarti, Augusto Politecn Milan Dipartimento Elettron Informaz & Bioingn DEIB Milan Italy

A method for circuit system inversion has been recently employed to develop digital algorithms for loudspeaker virtualization. In this brief, building on such promising results, we propose an algorithm that flips the paradigm by virtualizing sensors rather than actuators. In particular, we derive direct and inverse nonlinear circuital models of guitar pickup systems by assuming string vertical excitations, and we then present a virtualization algorithm based on circuit inversion. The proposed circuital models are then implemented in the discrete-time domain in a fully explicit fashion (i.e., with no use of iterative solvers) by employing Wave Digital Filter principles. Finally, we validate and test the designed algorithm on the physical output voltage of a guitar pickup system, making it sound as if acquired by two other magnetic pickups characterized by a different nonlinear behavior.

关键词： Integrated circuit modeling Magnetic domains Magnetic circuits Freeports signal processing algorithms Virtualization Magnetic tunneling Guitar pickups nonlinear model sensor virtualization wave digital filters

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Beampattern Synthesis With Virtual Covariance Matrix and Response Control Vector Design

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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2023年第12期22卷 2876-2880页

作者： Xiao, Ye Gan, Lu Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

This letter presents the minimized ripple array pattern control (MRAPC) algorithm to realize beampattern synthesis. Inspired by adaptive array theory, the virtual normalized interference-plus-noise covariance matrix is constructed to realize sidelobe suppression, and the response control vector is designed to shape the mainlobe. The minimum array response variation principle is introduced to solve response control problems, and the designed weights are obtained in closed-form. Considering the mainlobe ripple term, an efficient scheme is presented to realize beampattern synthesis with less response deviation. Simulation results validate the effectiveness and efficiency of the MRAPC algorithm for precise array response control and beampattern synthesis.

关键词： Adaptive arrays signal processing algorithms Phased arrays Array signal processing Shape Sensor arrays Covariance matrices Adaptive array theory antenna array array response control array signal processing beampattern synthesis

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Research on the Application of Deep Learning-Based Image Recognition Algorithm in signal processing

Research on the Application of Deep Learning-Based Image Rec...

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Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Jing Lv Hunan Chemical Vocational and Technical College Zhuzhou China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

A GAN-based image recognition algorithm is presented to solve these problems. Firstly, the GAN frame is composed of a generator and a discriminator. The generator can produce real images or remove noise by learning the signal image. Then, the image is compared with the real image. In the pre-processing phase, the standard, denotation and enhancement of the signal image are carried out to guarantee the data quality and the validity of the characteristic. Experiments show that the precision of GAN-based image recognition is up to 96.3%, which is more than 15% than traditional methods (e.g., SVM). In the signal denoising task, the model significantly improves the signal-to-noise ratio (SNR) by 20% and effectively removes image noise. In addition, GAN also performs extremely well in pattern recognition tasks, with a correct recognition rate of 94.7%. Compared with traditional deep learning algorithms, the GAN model shows stronger robustness when processing signals with more noise or incomplete data.

关键词： Deep learning Training Image recognition signal processing algorithms Generative adversarial networks Generators Robustness Pattern recognition Image denoising signal to noise ratio

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Application of LMS Adaptive Filtering Algorithm Based on Fractional Order Iterative Order in Marine Acoustic signal processing

Application of LMS Adaptive Filtering Algorithm Based on Fra...

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Smart Grid and Artificial Intelligence (SGAI), International Conference on

作者： Yonghao Zhou Zhengang Wan Jiangsu University of Science and Technology Zhenjiang China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

In this paper, an improved fractional order LMS filtering algorithm is proposed. By dynamically adjusting the step size and fractional order, the parameters are optimized in real time according to the errors, and the convergence speed and steady-state accuracy are improved. The forgetting factor is introduced to enhance the tracking ability of time-varying signal. The simulation results show that the improved algorithm performs well in the processing of complex Marine acoustic signals containing noise, and has a wide application prospect in the fields of Marine communication and detection.

关键词： Heuristic algorithms Simulation signal processing algorithms Filtering algorithms Acoustics Real-time systems Steady-state Smart grids Noise measurement Convergence

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SLN-RED: Regularization by Simultaneous Local and Nonlocal Denoising for Image Restoration

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IEEE signal processing LETTERS 2023年 30卷 578-582页

作者： He, Liangtian Zhang, Qinghua Yang, Xuesong Wang, Yilun Wang, Chao Anhui Univ Sch Math Sci Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China Machinify Inc Palo Alto CA 94301 USA Zhejiang Ocean Univ Coll Math Phys & Informat Key Lab Oceanog Big Data Min & Applicat Zhejiang P Zhoushan 316022 Peoples R China

Regularization by denoising (RED) framework has shown impressive performance for many imaging inverse problems, by leveraging the denoising method in defining an explicit regularization. In this letter, we propose a novel SLN-RED scheme for image restoration by exploiting the local and nonlocal denoisers simultaneously. Theoretically, we proves that for bounded denoisers, the SLN-RED under ADMM scheme with a continuation strategy converges to a fixed-point. Numerical experiments on deblurring and super-resolution tasks demonstrate promising performance of the proposed algorithm.

关键词： Convergence Noise reduction Image restoration signal processing algorithms Task analysis Superresolution Optimization regularization by denoising (RED) bounded denoiser ADMM fixed-point convergence

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