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Improved Phase Generated Carrier Demodulation for Time-Division Multiplexed FBG Accelerometers

JOURNAL OF LIGHTWAVE TECHNOLOGY 2025年第2期43卷 942-947页

作者： Li, Tingfeng Chen, Fengyi Wang, Ruohui Qiao, Xueguang Univ Shaanxi Prov Engn Res Ctr Opt Fiber Well Logging Technol Oil & Xian 710127 Peoples R China Northwest Univ Sch Phys Xian 710127 Peoples R China

A phase generated carrier (PGC) demodulation system for fiber Bragg grating (FBG) time-domain multiplexed accelerometers array is proposed. To eliminate environmental noise effect on the Michelson interferometer(MI), a reference FBG is used. Additionally, a PGC-improved demodulation algorithm is proposed to facilitate precise demodulation of the acceleration signal. The distortion of the demodulated signal due to modulation depth fluctuations is eliminated by constructing a quadrature signal containing only the test signal. The numerical simulation results show that the output signal of the PGC-Improved demodulation algorithm is independent of the operating point of the interferometer, the carrier delay of the modulation signal, and the modulation depth. The vibration test results of the FBG accelerometer based on the flexible hinge show that the PGC-Improved algorithm achieves a gain of 15.01 dB and 57.2 dB relative to PGC-DCM and PGC-Arctan at frequencies and accelerations of 60 Hz and 1 g. The minimum total harmonic distortion value of the PGC-Improved algorithm is 0.008 at modulation depths from 1.5 rd to 3.5 rd. The noise level of the system is approximately 4 x 10(-5) rad provided that the MI is subjected to 60 Hz perturbation.

关键词： Demodulation Delays Modulation signal processing algorithms Fiber gratings Phase modulation Accelerometers Fiber-optic sensors phase generated carrier reference FBG

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Robust Direction-of-Arrival Estimation With Outliers and Partly Calibrated Uniform Linear Array

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 4825-4834页

作者： Xiao, Wudang Li, Yingsong Yu, Canping Zhao, Luyu Pan, Mengguan de Lamare, Rodrigo C. Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China Pontif Catholic Univ Rio De Janeiro PUC Rio Ctr Telecommun Res CETUC BR-22451900 Gavea Brazil Univ York Dept Elect Engn York YO10 5DD England

This article presents a three-step direction-of-arrival estimation method aimed at addressing impulsive noise and array gain-phase errors. Initially, a robustness function combining the logarithmic function with least squares is introduced to address impulsive noise. This robust function is utilized to formulate a low-rank matrix approximation problem for acquiring noise-free received data, which is solved using the alternating-direction method of multipliers. Subsequently, exploiting the obtained noise-free received data, the array gain-phase errors are estimated using the root-rank reduction estimator. Finally, based on the acquired noise-free received data and gain-phase errors, a minimization problem incorporating the logarithmic function is formulated. To efficiently solve this minimization problem, it is transformed into a weighted l(2,1)-norm minimization problem, and the fast-iterative-shrinkage-thresholding algorithm is employed. Compared to existing methods, the proposed approach demonstrates superior estimation accuracy and precision while achieving an attractive tradeoff between performance and computational complexity.

关键词： Direction-of-arrival estimation Estimation Vectors signal processing algorithms Sensor arrays Multiple signal classification Minimization Manifolds Gaussian noise Fitting Direction-of-arrival (DOA) estimation gain-phase error log-sum minimization outliers partly calibrated array sparse signal recovery

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FPGA Implementation of Staged Projection Refining Multiple Orthogonal Matching Pursuit Algorithm for Compressed Sensing

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2025年第5期33卷 1334-1347页

作者： Liu, Sujuan Liang, Yichen Zhang, Zixing Wan, Peiyuan Beijing Univ Technol Coll Microelect Beijing 100124 Peoples R China

Reconstruction algorithms are an integral part of compressed sensing (CS) theory, which can reliably reconstruct the original signal from the low-dimensional compressed signal. The orthogonal matching pursuit (OMP) algorithm has been widely studied and extensively selected in hardware implementations. However, the low reconstruction success rate of the OMP algorithm under high sparsity conditions has led to the proposal and application of more reconstruction algorithms in hardware implementations. In this article, a staged projection refining multiple OMP (SPR-MOMP) algorithm is proposed based on the OMP algorithm. This algorithm improves the reconstruction accuracy by refining the support set using a staged backtracking strategy. It also employs a multiple-atom selection strategy for parallel expansion of the support set, ensuring reconstruction efficiency. The reconstruction simulation demonstrates that the SPR-MOMP algorithm achieves a higher reconstruction success rate than the OMP algorithm, with fewer iterations. A hardware architecture applying the SPR-MOMP algorithm is designed and implemented on a Virtex UltraScale+ field-programmable gate array (FPGA) with N = 1024, M = 256, and K = 36. The proposed architecture achieves a reconstruction signal-to-noise ratio (RSNR) of 44.27 dB, with 20-bit data width and 15-bit fractional width. The maximum clock frequency of the architecture is 200 MHz, enabling reconstruction within 276.6 mu s. The proposed architecture achieves a lower dynamic power consumption of 1929 mW.

关键词： Computer architecture Matching pursuit algorithms Hardware Field programmable gate arrays Image reconstruction Heuristic algorithms Backtracking Reconstruction algorithms signal processing algorithms Sensors Compressed sensing (CS) field-programmable gate array (FPGA) hardware reconstruction orthogonal matching pursuit (OMP) QR decomposition (QRD)

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Generalization Error Matters in Decentralized Learning Under Byzantine Attacks

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 843-857页

作者： Ye, Haoxiang Ling, Qing Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China

Recently, decentralized learning has emerged as a popular peer-to-peer signal and information processing paradigm that enables model training across geographically distributed agents in a scalable manner, without the presence of any central server. When some of the agents are malicious (also termed as Byzantine), resilient decentralized learning algorithms are able to limit the impact of these Byzantine agents without knowing their number and identities, and have guaranteed optimization errors. However, analysis of the generalization errors, which are critical to implementations of the trained models, is still lacking. In this paper, we provide the first analysis of the generalization errors for a class of popular Byzantine-resilient decentralized stochastic gradient descent (DSGD) algorithms. Our theoretical results reveal that the presence of Byzantine agents introduces additional error terms in the generalization error bounds, which are independent on the number of training samples. Numerical experiments are conducted to confirm our theoretical results.

关键词： Training Stochastic processes Optimization signal processing algorithms Federated learning Distance learning Computer aided instruction Aggregates Uncertainty Topology Decentralized learning Byzantine-resilience generalization error

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A Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed Gradient Descent

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 827-842页

作者： Wang, Shuche Tan, Vincent Y. F. Natl Univ Singapore Inst Operat Res & Analyt Singapore 117602 Singapore Natl Univ Singapore Dept Math Dept Elect & Comp Engn Singapore 117602 Singapore Natl Univ Singapore Inst Operat Res & Analyt Singapore 117602 Singapore

Distributed gradient descent algorithms have come to the fore in modern machine learning, especially in parallelizing the handling of large datasets that are distributed across several workers. However, scant attention has been paid to analyzing the behavior of distributed gradient descent algorithms in the presence of adversarial corruptions instead of random noise. In this paper, we formulate a novel problem in which adversarial corruptions are present in a distributed learning system. We show how to use ideas from (lazy) mirror descent to design a corruption-tolerant distributed optimization algorithm. Extensive convergence analysis for (strongly) convex loss functions is provided for different choices of the stepsize. We carefully optimize the stepsize schedule to accelerate the convergence of the algorithm, while at the same time amortizing the effect of the corruption over time. Experiments based on linear regression, support vector classification, and softmax classification on the MNIST dataset corroborate our theoretical findings.

关键词： Vectors Servers Noise measurement signal processing algorithms Machine learning algorithms Convergence Uplink Mirrors Downlink Machine learning Distributed gradient descent mirror descent adversarial corruptions noisy channels convergence rates

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Time Scale Network: An Efficient Shallow Neural Network for Time Series Data in Biomedical Applications

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2025年第1期19卷 129-139页

作者： Meyer, Trevor Shultz, Camden Dehak, Najim Moro-Velazquez, Laureano Irazoqui, Pedro Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA

Time series data is often composed of information at multiple time scales, particularly in biomedical data. While numerous deep learning strategies exist to capture this information, many make networks larger, require more data, are more demanding to compute, and are difficult to interpret. This limits their usefulness in real-world settings facing even modest computational or data constraints and can further complicate their translation into real-time processing or edge device applicaitons. We present a minimal, computationally efficient Time Scale Network combining the translation and dilation sequence used in discrete wavelet transforms with traditional convolutional neural networks and back-propagation. The network simultaneously learns features at many time scales for sequence classification with significantly reduced parameters and operations. We demonstrate advantages in Atrial Dysfunction detection including: superior accuracy-per-parameter and accuracy-per-operation, fast training and inference speeds, and visualization and interpretation of learned patterns in atrial dysfunction detection on ECG signals. We also demonstrate impressive performance in seizure prediction using EEG signals, where our network isolated a few time scales that could be strategically selected to achieve 90.9% accuracy using only 1,133 active parameters and consistently converged on pulsatile waveform shapes. This method does not rest on any constraints or assumptions regarding signal content and could be leveraged in any area of time series analysis dealing with signals containing features at many time scales.

关键词： Transforms signal processing algorithms Time-frequency analysis Feature extraction Accuracy signal processing Computer architecture Efficiency wavelet shallow NN biomedical

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Synchronous Acquisition Control System and Coordinate Correction Algorithm for Subway Tunnel Comprehensive Detection Equipment

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IEEE SENSORS JOURNAL 2025年第1期25卷 1440-1451页

作者： Sun, Minghao Xu, Xianlei Zhou, Donglin Li, Jinglin He, Xinyao China Univ Min & Technol Sch Earth Sci & Surveying & Mapping Beijing 100083 Peoples R China State Key Lab Fine Explorat & Intelligent Dev Coal Beijing 100083 Peoples R China

Crack, cavity, and leakage defects in subway tunnels significantly threaten tunnel operational safety. Due to the limited detection time window, the trend in the tunnel detection has shifted toward using multiple types of devices for comprehensive and rapid detection. However, the varying working methods and data structures of these devices present a significant challenge in achieving synchronous acquisition control and obtaining high-precision positioning data. The author's team proposed a comprehensive detection vehicle equipped with multiple devices. A synchronous acquisition control system was designed and developed based on the devices mounted on the detection vehicle. This system includes pulse signal processing, acquisition circuits, and corresponding software, enabling synchronous acquisition control and data association storage for different types of devices. To address the challenge of high-precision positioning of multisource heterogeneous data, a coordinate correction algorithm based on tunnel ring seam information was introduced. This algorithm significantly improves upon conventional tunnel positioning methods by positioning across different detection data, enhancing accuracy to the millimeter level. Through simulation experiments and field experiments in tunnels, the effectiveness of the synchronous acquisition control system and coordinate correction algorithm was verified. This method is particularly effective in long-distance detection, as it reduces cumulative mileage error and improves positioning accuracy. This study can provide effective technical support for the precise positioning of subway tunnel detection results and enhances the overall operational safety of subway tunnels.

关键词： Sensors Accuracy Public transportation Laser radar signal processing algorithms Cameras Point cloud compression Synchronization Sensor systems Control systems Comprehensive detection coordinate correction subway tunnel synchronous acquisition control

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Deterministic Maximum Likelihood Algorithm for Sky-Ground Waves Recognition in ELoran Systems

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第4期74卷 6685-6690页

作者： Li, Yuzhou Xia, Xing Liu, Di Dong, Yan Sheng, Min Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China Key Lab Smart Earth Beijing 100029 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

It is crucial to recognize the sky and ground waves, i.e., to determine the delays of the waves, in enhanced long-range navigation (eLoran) systems. However, the performance of most existing algorithms sharply decreases when the delay differences Delta tau between the sky and ground waves are small or the signal-to-noise ratios (SNRs) are low. In this paper, after transforming the originally time-domain sky-ground waves recognition problem to the frequency domain, we interestingly find that it can be effectively addressed by the deterministic maximum likelihood (DML). Thus, we design a DML-based sky-ground waves recognition algorithm. The verification results based on both the simulated and the realistic data show that the DML outperforms the four representative algorithms, including the IFFT, the FOC-MUSIC, the WRELAX, and the LM, in terms of the success rate in recognizing the groundwave, particularly in situations with small Delta tau or low SNRs. Numerically, compared to the IFFT, the LM, and the FOC-MUSIC, the success rate achieved by the DML is improved by 67.11%, 66.67%, and 64.19% respectively, and the WRELAX fails to work, in a challenging situation of SNR =0 dB and Delta tau=37 mu s.

关键词： Delays Time-domain analysis signal processing algorithms Direction-of-arrival estimation Multiple signal classification Global navigation satellite system Discrete Fourier transforms Accuracy Vectors Time-frequency analysis Sky-ground waves recognition DML eLoran

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Robust Multichannel Decorrelation via Tensor Einstein Product

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 275-291页

作者： Chang, Shih-Yu Wu, Hsiao-Chun Liu, Guannan San Jose State Univ Dept Appl Data Sci San Jose CA 95192 USA Louisiana State Univ Sch Elect Engn & Comp Sci Baton Rouge LA 70803 USA Yuan Ze Univ Innovat Ctr AI Applicat Chungli 32003 Taiwan

Decorrelation of multichannel signals has played a crucial preprocessing role (in prewhitening and orthogonalization) for many signal processing applications. Classical decorrelation techniques can only be applied for signal vectors. Nonetheless, many emerging big-data and sensor-network applications involve signal tensors (signal samples required to be arranged in a tensor form of arbitrary orders). Meanwhile, the existing tensor-decorrelation methods have serious limitations. First, the correlation-tensors have to be of certain particular orders. Second, the unrealistic assumption of the specific signal-tensor form, namely the canonical polyadic (CP) form, is made. Third, the correlation-tensor has to be full-rank or an extra preprocessor based on principal component analysis is required for any non-full-rank correlation tensor. To remove the aforementioned impractical limitations, we propose a novel robust approach for high-dimensional multichannel decorrelation, which can accommodate signal tensors of arbitrary orders, forms, and ranks without any need of extra preprocessor. In this work, we introduce two new tensor-decorrelation algorithms. Our first new algorithm is designed to tackle full-rank correlation-tensors and our second new algorithm is designed to tackle non-full-rank correlation-tensors. Meanwhile, we also propose a new parallel-computing paradigm to accelerate our proposed new tensor-decorrelation algorithms. To demonstrate the applicability of our proposed new scheme, we also apply our proposed new tensor-decorrelation approach to pre-whiten the tensor signals and analyze the corresponding convergence-speed and misadjustment performances of the tensor least-mean-squares (TLMS) filter. Finally, we assess the computational- and memory-complexities of our proposed new algorithms by simulations over both artificial and real data. Simulation results show that our proposed new multichannel-decorrelation algorithms outperform the existing tensor-decorrel

关键词： Tensors Accuracy Computational modeling Simulation signal processing algorithms Estimation Filtering algorithms Vectors Decorrelation Convergence Tensor signal processing Einstein product multichannel decorrelation correlation tensor whitening eigenspread

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Practical Compact Deep Compressed Sensing

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2025年第3期47卷 1610-1626页

作者： Chen, Bin Zhang, Jian Peking Univ Sch Elect & Comp Engn Shenzhen 518055 Peoples R China

Recent years have witnessed the success of deep networks in compressed sensing (CS), which allows for a significant reduction in sampling cost and has gained growing attention since its inception. In this paper, we propose a new practical and compact network dubbed PCNet for general image CS. Specifically, in PCNet, a novel collaborative sampling operator is designed, which consists of a deep conditional filtering step and a dual-branch fast sampling step. The former learns an implicit representation of a linear transformation matrix into a few convolutions and first performs adaptive local filtering on the input image, while the latter then uses a discrete cosine transform and a scrambled block- diagonal Gaussian matrix to generate under-sampled measurements. Our PCNet is equipped with an enhanced proximal gradient descent algorithm-unrolled network for reconstruction. It offers flexibility, interpretability, and strong recovery performance for arbitrary sampling rates once trained. Additionally, we provide a deployment-oriented extraction scheme for single-pixel CS imaging systems, which allows for the convenient conversion of any linear sampling operator to its matrix form to be loaded onto hardware like digital micro-mirror devices. Extensive experiments on natural image CS, quantized CS, and self-supervised CS demonstrate the superior reconstruction accuracy and generalization ability of PCNet compared to existing state-of-the-art methods, particularly for high-resolution images.

关键词： Artificial neural networks Image reconstruction Imaging Matrix converters Hardware Filtering Training signal processing algorithms Optimization Micromirrors Compressed sensing sampling matrix single-pixel imaging algorithm unrolling and structural reparameterization

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