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FastPSA: A Fast Version of the Principal Skewness Analysis

IEEE signal processing LETTERS 2025年 32卷 1226-1230页

作者： Gao, Jingyu Geng, Xiurui Ji, Luyan Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Chinese Acad Sci Key Lab Technol Geospatial Informat Proc & Applica Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

Recently, principal skewness analysis (PSA) has been introduced into the domain of feature extraction. It is equivalent to the skewness version of Fast independent component analysis (FastICA). Unlike FastICA, PSA does not require all sample points when searching for the projection directions, making it faster. However, for the data of dimension L, PSA needs to calculate the eigenvectors of L tensors, each of size LxLxL. When L is large, PSA still requires significant computational time to find all projection directions. In this letter, we find that the (m+1)th projection direction in PSA can be obtained by calculating the eigenvector of a tensor of size (L-m) x (L-m) x (L-m). Furthermore, we propose a fast version of PSA (FastPSA) that is mathematically equivalent to PSA. The experimental results demonstrate that FastPSA has lower computational complexity than PSA.

关键词： Tensors Principal component analysis Feature extraction Computational complexity Vectors signal processing algorithms Training Hyperspectral imaging Data mining Computational efficiency FastICA principal skewness analysis hyperspectral image

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A Robust Adaptive Beamforming Algorithm for Mismatch and Impulsive Noise Circumstance

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Zhang, Xuejun Feng, Dazheng Wang, Zhonggen Gan, Fubao Anhui Univ Sci & Technol Sch Elect & Informat Huainan 232001 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

This letter provides a solution to a robust adaptive beamformer used in remote sensing systems under the effect of two factors: 1) the impulsive noise satisfying Gaussian mixture distribution and 2) unknown model mismatches, such as look direction errors and sensor location perturbations. First, a beamformer output-based cost function is designed to effectively suppress the influence of impulsive noise. Second, the weight vector of the beamformer is regularized through norm constraints to reduce the impact of model mismatches. Furthermore, an efficient iterative scheme is established to quickly minimize the designed cost function based on their properties, and a robust adaptive beamformer can be obtained. Finally, computer simulations are presented to demonstrate the robust performance and fast convergence of the novel algorithm in the presence of impulsive noise and model mismatches.

关键词： Noise Cost function Array signal processing Adaptation models Vectors Loading Interference signal to noise ratio Costs signal processing algorithms impulsive noise model mismatch regularization robust adaptive beamformer

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Channel Estimation and Beamforming Design for MF-RIS-Aided Communication Systems

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IEEE signal processing LETTERS 2025年 32卷 916-920页

作者： Pan, Zaihao Wang, Wen Nie, Gaofeng Zheng, Ailing Ni, Wanli Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Pervas Commun Ctr Purple Mt Labs Nanjing 211111 Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

In this letter, we study the beamforming design for channel estimation of multi-functional reconfigurable intelligent surface (MF-RIS)-aided multi-user communications that supports simultaneous signal reflection, refraction, and amplification. A least square (LS) based channel estimator is proposed for MF-RIS by considering both the coupled MF-RIS beams and the introduced thermal noise. With the discrete fourier transform (DFT)-matrix, the MF-RIS beamforming design problem is simplified under the proposed LS channel estimator. The optimal MF-RIS beamforming design that achieves the Cram & eacute;r-Rao lower bound (CRLB) of channel estimator is obtained with the proposed alternating optimization algorithm. Simulation results demonstrate the effectiveness of the proposed beamforming design in reducing the impact of thermal noise.

关键词： Channel estimation Array signal processing Reconfigurable intelligent surfaces Noise Thermal noise signal processing algorithms Reflection Estimation error Optimization Lower bound Beamforming design channel estimation least square estimator multi-functional RIS

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A Two-Level Weighted Low-Complexity Adaptive Beamforming Method

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IEEE signal processing LETTERS 2025年 32卷 1246-1250页

作者： Du, Yuxi Cui, Weijia Ba, Bin Chen, Yangyang Zhang, Long PLA Strateg Support Informat Engn Univ Zhengzhou 450001 Peoples R China

Aiming at the problem of high computational complexity of adaptive beamforming techniques in array radar systems, a two-level weighted low-complexity adaptive beamforming method is proposed in this letter. First, the uniform linear array is divided into subarrays and each subarray has the same elements. The desired signal received by each array element in the subarray is then superimposed by compensating for the delay using a first level weighting. Finally, the interference signals and noise are suppressed using a second level weighting to obtain the ideal output signal to interference plus noise ratio. Simulation results verify the effectiveness and reliability of the proposed method.

关键词： Interference Antenna arrays signal to noise ratio Array signal processing Noise Vectors Computational complexity signal processing algorithms Covariance matrices Receiving antennas Two-level weighted adaptive beamforming signal to interference plus noise ratio

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Edge Learning via Message Passing: Distributed Estimation Framework Based on Gaussian Mixture Model

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IEEE INTERNET OF THINGS JOURNAL 2024年第21期11卷 34409-34419页

作者： Li, Xiang Yuan, Weijie Zhang, Kecheng Wu, Nan Southern Univ Sci & Technol Sch Syst Design & Intelligent Mfg Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Shenzhen Key Lab Robot & Comp Vis Shenzhen 518055 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China

To leverage distributed data communication and learning in sensor networks effectively, edge learning (EL) methods have garnered significant attention. In the realm of distributed sensor networks, achieving consensus estimation of interested variables stands as a pivotal challenge. To address this challenge using EL methods, several approaches have been proposed combining message passing (MP) algorithms. In this article, we first describe the distributed consensus algorithm based on MP and summarize the sampling-based and parameter-based representation of the beliefs exchanged in the distributed MP algorithm. To improve the accuracy of estimation while retaining the low-complexity advantage of the parametric representation method, we propose a distributed consensus framework based on the Gaussian mixture model (GMM) MP. We approximate and keep the form beliefs as GMM in the iterations. Two different simulation scenarios are performed to shed light on the proposed distributed consensus estimation framework, i.e., static target localization and dynamic target tracking. Finally, simulation results show the performance advantages of the algorithm proposed.

关键词： Estimation Robot sensing systems Location awareness Accuracy signal processing algorithms Message passing Internet of Things Consensus algorithm distributed estimation edge learning (EL) factor graph Gaussian mixture model (GMM) message passing (MP)

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An Improved Kramers-Kronig Receiver With Upsampling-Free and Low Carrier-to-signal Power Ratio

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2024年第19期42卷 6731-6738页

作者： Luo, Shiwei Li, Jianping Yu, Xinkuo Qin, Yuwen Lin, Ning Xu, Ou Xiang, Meng Fu, Songnian Guangdong Univ Technol Inst Adv Photon Technol Sch Informat Engn Guangzhou 510006 Peoples R China Guangdong Prov Key Lab Photon Informat Technol Guangzhou 510006 Peoples R China Guangdong Commun Serv Co Ltd Guangzhou 510630 Peoples R China

The Kramers-Kronig (KK) receiver breaks through the limitation that a single photodetector (PD) can only process the intensity information, and has become a research hotspot in optical communication system. However, in the conventional KK algorithm, the required nonlinear operations (such as logarithm (log) and exponential and square-root (sqrt) functions) significantly broadens the signal spectrum, it is necessary to employ the digital upsampling at the beginning of the digital signal processing (DSP). The high sampling rate will bring multiple hardware resources and power consumption, which becomes a major obstacle to implement KK receiver. In this paper, we propose a novel improved algorithm for the KK receiver, which does not need the digital upsampling and lowers the required carrier-to-signal power ratio (CSPR) with a comparison of the typical upsampling-free KK receiver. In the proposed improved algorithm, we adopt the mathematical approximations of Taylor's expansion and Newton's tangent method to avoid the use of log and sqrt functions. Then we removed the exponential function by expressing complex signal in Cartesian form (i.e., real plus imaginary). Whereas the real part of it is induced by employing imaginary part-induced signal-signal beat interference (SSBI) removal and sqrt operation to recover. Meanwhile, we use the simplified hybrid KK-SSBI cancellation (SHKK-SSBIC) technology to reduce the required CSPR by the system. We validate our proposed algorithm by transmitting 160 Gb/s single-sideband (SSB) signal. The experimental results show that compared with the upsampling-free KK receiver, the proposed scheme can realize the reduction of the required CSPR by 1 dB and 0.8 dB respectively under the Nyquist sampling rate at (back-to-back) BTB and 80 km transmission. Moreover, our proposed method achieves a 2 dB system sensitivity improvement in the BTB scenario. We also discuss the hardware implementation of the improved KK algorithm and its computationa

关键词： Amplitude modulation Sensitivity Optical receivers Optical imaging signal processing algorithms Costs Approximation algorithms Kramers-Kronig receiver Newton's tangent method upsampling-free

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Improved Free-of-CPP ADMM-Based Iterative Decoding Algorithm of Binary LDPC Codes

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IEEE signal processing LETTERS 2025年 32卷 211-215页

作者： Bai, Jing An, Zedong Chi, Yuhao Song, Guanghui Yuen, Chau Shijiazhuang Tiedao Univ Sch Informat Sci & Technol Shijiazhuang 050043 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 487372 Singapore

Iterative decoding algorithms based on the alternating direction method of multipliers (ADMM) decoding of low density parity check (LDPC) codes has emerged as an alternating decoding method and bringed a boom of research on drawing upon mathematical optimization to LDPC decoding. Improving error-correcting performance is a key issue to enhance the superiority of ADMM decoding. In this letter, we investigate an efficient ADMM-based iterative decoder for binary LDPC codes. First, we build an mathematical programming equivalence of the maximum likelihood (ML) decoding problem by transforming parity-check constraints to multiple equivalent linear constraints and eliminating check-polytope projection (CPP). Then, an iterative algorithm based on ADMM technique is developed to solve this free-of-CPP (FCPP) equivalence and each ADMM update can be computed efficiently. Moreover, the proposed ADMM-FCPP decoding algorithm is analyzed to display a linear complexity to the length of the LDPC code at each iteration. Finally, simulation results demonstrate the superiority of the proposed decoder in error-correcting performance compared with the state-of-the-art ADMM-based decoders.

关键词： Maximum likelihood decoding Iterative decoding Convex functions Vectors signal processing algorithms Optimization Complexity theory Simulation Programming profession Low-density parity-check (LDPC) codes alternating direction method of multipliers (ADMM) check- polytope projection (CPP) mathematical programming mathematical programming

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Edge-Preserving Consensus by Exploring the Feedback-Based Masking Method

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IEEE signal processing LETTERS 2025年 32卷 936-940页

作者： He, Wei Jiang, Jinyang Liu, Tianyue Aikoun, Yves Fidele Mao, Yi Fudan Univ Acad Engn & Technol Shanghai Engn Res Ctr AI & Robot Shanghai 200433 Peoples R China Nanjing Univ Posts & Telecommun Sch Automat & Artificial Intelligence Nanjing 210023 Peoples R China Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China Hohai Univ Key Lab Maritime Intelligent Network Informat Tec Minist Educ Changzhou 213200 Peoples R China

This brief deals with the edge-preserving consensus in networked agent groups, which seeks to keep the information of the state trajectories of agents private by utilizing an edge-based decentralized protocol design. A novel type of decentralized feedback-based masking approach is adopted for designing local channel filters such that the agents reach consensus under the heterogenous channel filters, where the information of the filter associated with a specific communication link can only be known by the agents who share this communication link. Our design guarantees asymptotical convergence to the consensus value for both the cases of the non-recursive and recursive nominal filters. Moreover, the algorithms for designing the masking elements are provided, by using the parallel realization of several stable fundamental elements and the bisection method for searching the gain.

关键词： Privacy Protocols Transfer functions Eigenvalues and eigenfunctions signal processing algorithms Multi-agent systems Design methodology Artificial intelligence Wireless sensor networks Vehicle dynamics Edge-preserving consensus security distributed networks channel filter

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Validation of a Wearable System for Respiratory Rate Monitoring in Dogs

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IEEE ACCESS 2024年 12卷 80308-80316页

作者： Angelucci, Alessandra Birettoni, Francesco Bufalari, Antonello Aliverti, Andrea Politecn Milan Dipartimento Elettron Informaz & Bioingn I-20133 Milan Italy Univ Perugia Dept Vet Med I-06126 Perugia Italy

One of the most relevant physiological parameters in dogs is respiratory rate (RR). The aim of this paper is to present a novel wearable system that allows to accurately estimate RR in dogs, and to compare it to a gold standard in static conditions. Data from 12 dogs were acquired while the animals were anesthetized and attached to a vital signs monitor. The experimental setup consisted of three Inertial Measurement Units (IMUs) applied on the dog, and a video camera filming the RR value shown on the monitor. The range of RR values analyzed in the study is 0 to 29 breaths per minute, read by the vital signs monitor. The mean RMSE for the data acquisitions is 1.68 breaths per minute. The values of the filtering parameters that allow to obtain the best performance depend on the specific acquisition. This result demonstrates that adaptive filtering is a viable method for the application. Future developments include tests on a larger dataset, and trials on dogs in unconstrained environments and during movement.

关键词： Dogs Biomedical monitoring Monitoring Quaternions Wearable devices signal processing algorithms Optical fiber sensors respiratory monitoring veterinary medicine wearables

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Noisy One-Bit Compressed Sensing With Side-Information

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 3792-3804页

作者： Kafle, Swatantra Wimalajeewa, Thakshila Varshney, Pramod K. Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA Johns Hopkins Univ Appl Phys Lab Laurel MD 20723 USA

We consider the problem of sparse signal reconstruction from noisy one-bit compressed measurements when the receiver has access to side-information (SI). We assume that compressed measurements are corrupted by additive white Gaussian noise before quantization and sign-flip error after quantization. A generalized approximate message passing-based method for signal reconstruction from noisy one-bit compressed measurements is proposed which is then extended for the case where the receiver has access to a signal that aids signal reconstruction, i.e., side-information. Two different scenarios of side-information are considered-a) side-information consisting of support information only, and b) side information consisting of support and amplitude information. SI is either a noisy version of the signal or a noisy estimate of the support of the signal. We develop reconstruction algorithms from one-bit measurements using noisy SI available at the receiver. Laplacian distribution and Bernoulli distribution are used to model the noise which when applied to the signal yields the SI for the above two cases. The Expectation-Maximization algorithm is used to estimate the noise parameter using noisy one-bit compressed measurements and the SI. We show that one-bit compressed measurement-based signal reconstruction is quite sensitive to noise, and the reconstruction performance can be significantly improved by exploiting side-information at the receiver when available.

关键词： Noise measurement Receivers Quantization (signal) signal processing algorithms Compressed sensing signal reconstruction Approximation algorithms Sparse signal reconstruction one-bit compressed measurements generalized approximate message passing side-information

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