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Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe Conference on

作者： Zhuo Sun Xin Yan Wuhan Business University Wuhan China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

This study proposes an all-weather monitoring system for horse walking rooms based on signal processing technology. The fast Fourier transform (FFT) algorithm is used to optimize the monitoring accuracy and achieve efficient and accurate real-time monitoring. The system consists of a sensor array, a signal processing module, and a data analysis module, which can monitor key parameters such as temperature, humidity, and motion status. First, the system collects the original signals of multiple sensors, suppresses noise through a filtering algorithm, and extracts effective information; then, the FFT algorithm is used to perform frequency domain analysis on the signal, which improves the efficiency and accuracy of extracting key signal features. The simulation test results show that the monitoring error of the optimized system is reduced to 0.85% under typical conditions, which is significantly better than the 1.8% of the traditional method. In high-noise and complex environments, the system signal interference reduction rate is 45%, and the robustness is improved by about 30%. The experiment verifies the monitoring accuracy and stability of the system under all-weather conditions, providing strong support for the intelligent management of horse walking rooms.

关键词： Legged locomotion Temperature sensors Accuracy Fast Fourier transforms signal processing algorithms signal processing Feature extraction Real-time systems Robustness Monitoring

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Enhancing Underwater DOA Estimation Accuracy With Limited Datasets Using Task-Restructured Deep Mutual Learning

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IEEE signal processing LETTERS 2025年 32卷 966-970页

作者： Zhang, Qinzheng Wang, Haiyan Shen, Xiaohong Yan, Yongsheng Zhao, Zhongda Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Northwestern Polytech Univ Key Lab Ocean Acoust & Sensing Minist Ind & Informat Technol Xian 710072 Peoples R China Shaanxi Univ Sci & Technol Sch Elect Informat & Artificial Intelligence Xian 710021 Peoples R China

This paper aims to address the issue of low accuracy in underwater Direction of Arrival (DOA) estimation using Deep Learning (DL) methods, which arises due to the scarcity of underwater data caused by the difficulties in conducting underwater experiments. For multi-snapshot sampled signals, we segment the snapshots and reconstruct the task into a problem of processing few-snapshot data within an expanded dataset. By utilizing the new task, we employ a deep mutual learning (DML) model to enhance the accuracy of the original task's DOA estimates. Experimental results demonstrate that under conditions of small and limited datasets, our approach effectively improves the accuracy of DL-based DOA estimation methods.

关键词： Direction-of-arrival estimation Estimation Accuracy Training signal processing algorithms Hands Fitting Deep learning Underwater acoustics Feature extraction Enhancing underwater DOA estimation DML limited datasets task restructuring

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Source Number Estimation in 2D Uniform Circular and L-Shaped Arrays With Unknown Nonuniform Noise

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2025年第1期72卷 348-352页

作者： He, Mengxia Chan, S. C. Univ Hong Kong Dept Elect & Elect Engn Hong Kong SAR Peoples R China

Classical source number estimators are usually derived under the assumption of uniform white noise, which may degrade substantially with unknown nonuniform sensor noise. Advanced estimators designed for nonuniform noise are however computationally expensive with limited performance in unfavorable conditions, such as low signal-to-noise ratio, small number of snapshots, close angular separations and sources with different transmitter powers. This brief proposes a new likelihood ratio statistics-based method for source number estimation under uncorrelated nonuniform noise. Using the asymptotic theory, it is shown that the likelihood ratio follows a chi-square distribution. Hence, the number of sources can be estimated via a sequence of hypothesis tests using maximum likelihood estimators (MLEs) of the covariance matrix with different assumed source numbers. The low complexity subspace algorithm is proposed to obtain the ML estimates. Theoretical analysis demonstrates that the proposed estimator is consistent in the general asymptotic regime. Simulation results on 2D arrays such as uniform circular and L-shaped arrays show that the proposed estimator achieves a higher correct detection probability in unfavorable conditions and is more robust against nonuniformity of noise than state-of-the-art estimators.

关键词： Noise Covariance matrices signal processing algorithms Maximum likelihood estimation Sensor arrays Vectors Optimization Transmitters signal to noise ratio Light rail systems Array signal processing source number estimation nonuniform noise likelihood ratio test

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Diffusion Generalized Minimum Total Error Entropy Algorithm

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IEEE signal processing LETTERS 2025年 32卷 751-755页

作者： Cai, Peng Lin, Dongyuan Qian, Junhui Zheng, Yunfei Wang, Shiyuan Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Chongqing Univ Chongqing Key Lab Biopercept & Intelligent Informa Chongqing 400044 Peoples R China

Both the minimum error entropy (MEE) and mixture MEE (MMEE) are extensively employed in distributed adaptive filters, exhibiting their robustness against non-Gaussian noise by capturing high-order statistical information from network data. However, the fixed shape of the Gaussian kernel function existing in MEE and MMEE restricts their flexibility, leading to reduced robustness and deteriorated performance. To address this issue, a novel diffusion generalized minimum total error entropy (DGMTE) algorithm is first proposed in this letter, using a generalized MEE criterion to significantly improve the performance of error-in-variables models-based algorithms under non-Gaussian noise. Moreover, as a special case of DGMTE, a generalized minimum total error entropy (GMTE) algorithm is also proposed, and the local convergence analysis of DGMTE is given. Finally, simulations show the superiorities of DGMTE in comparison with other representative algorithms.

关键词： Noise signal processing algorithms Entropy Shape Adaptation models Kernel Estimation Distributed algorithms Cost function Convergence Adaptive filter distributed estimation errors-in-variables model generalized minimum error entropy total least squares

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RIS-NOMA Assisted Covert Transmission for Integrated Sensing and Communication With Statistical CSI

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第1期14卷 13-17页

作者： Yu, Chao Feng, Chenye Xie, Wenwu Zhu, Peng Peng, Xin Yang, Liang Hunan Inst Sci & Technol Sch Informat Sci & Engn Yueyang 414006 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China

This letter proposes a covert beamforming framework for a RIS-NOMA assisted integrated sensing and communication system under statistical CSI. Specifically, the dual functional base station transmits both public and covert signal to users in NOMA protocol and performs sensing function simultaneously while avoiding the detection of covert signal by an eavesdropper. The RIS is deployed to help both covert transmission and sensing performance. By jointly designing the transmit beamforming vectors and the reflection coefficients of the RIS, we aim to maximize the covert signal transmission rate. We propose an alternative algorithm to decouple the variables and split the problem into two subproblems. The penalty-based method is utilized to optimize the transmit beamforming and the majorization-minimization method is adopted to transform the quartic radar SNR constraint into a quadratic constraint. Simulation results show the effectiveness of our proposed covert transmission design.

关键词： Reconfigurable intelligent surfaces Array signal processing Integrated sensing and communication signal to noise ratio NOMA Wireless communication Radar Wireless sensor networks Vectors signal processing algorithms Reconfigurable intelligent surface covert communication integrated sensing and communications statistical CSI

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Non-Decreasing Concave Regularized Minimization for Principal Component Analysis

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IEEE signal processing LETTERS 2025年 32卷 486-490页

作者： Zheng, Qinghai Zhuang, Yixin Fuzhou Univ Coll Comp & Data Sci Fuzhou 350108 Peoples R China

As a widely used method in signal processing, Principal Component Analysis (PCA) performs both the compression and the recovery of high dimensional data by leveraging the linear transformations. Considering the robustness of PCA, how to discriminate correct samples and outliers in PCA is a crucial and challenging issue. In this paper, we present a general model, which conducts PCA via a non-decreasing concave regularized minimization and is termed PCA-NCRM for short. Different from most existing PCA methods, which learn the linear transformations by minimizing the recovery errors between the recovered data and the original data in the least squared sense, our model adopts the monotonically non-decreasing concave function to enhance the ability of model in distinguishing correct samples and outliers. To be specific, PCA-NCRM enlarges the attention to samples with smaller recovery errors and diminishes the attention to samples with larger recovery errors at the same time. The proposed minimization problem can be efficiently addressed by employing an iterative re-weighting optimization. Experimental results on several datasets show the effectiveness of our model.

关键词： Principal component analysis Minimization Optimization signal processing algorithms Robustness High dimensional data Dimensionality reduction Lagrangian functions Iterative algorithms Adaptation models Iterative re-weighting optimization principal component analysis (PCA) unsupervised dimensionality reduction

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Frequency Domain Prediction of Tonal signals With Time-Varying Pitches

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IEEE signal processing LETTERS 2025年 32卷 31-35页

作者： Guo, Ning Edler, Bernd Joint Inst Friedrich Alexander Univ Erlangen Nurnb Int Audio Labs Erlangen IIS D-91058 Erlangen Germany

In this letter, we propose an Extended Frequency Domain Joint Harmonics Prediction (EFDJHP) algorithm, which does long term prediction directly in the transform domain on tonal signals with time-varying pitches for transform speech and audio coding. EFDJHP is an algorithm extension of a previously proposed Frequency Domain Joint Harmonics Prediction (FDJHP) algorithm where a constant pitch between neighboring frames was assumed (Guo and Edler, 2021). A linearly changing pitch between adjacent frames is assumed in EFDJHP, where the linearity can be assumed to be local and updated across frames. EFDJHP works in a backward prediction fashion without additional algorithmic delay, and needs very few side information for the forward adaption of the predictor coefficients. Bitrate saving analysis and a listening test show that EFDJHP can improve the coding efficiency on tonal signals with frequent pitch variations such as singing voices and speech.

关键词： Harmonic analysis Transforms Time-frequency analysis Prediction algorithms Audio coding Speech coding signal processing algorithms Time-domain analysis Mathematical models Chirp Long term prediction time varying pitch audio coding FDJHP

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signal-Domain Fully Coherent Accurate Measurement and Tracking of High-Speed Target

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IEEE signal processing LETTERS 2025年 32卷 986-990页

作者： Zhou, Qinquan Wei, Shaopeng Zhang, Lei Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen 518107 Peoples R China China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China

The high speed and maneuverability of the spacecraft make the traditional wideband tracking algorithms fail to achieve echo coherent recovery under low SNR, resulting in reduced tracking accuracy. This paper proposes a fully coherent accurate measurement and tracking method based on the multi-channel hypothesis to realize the fully coherent recovery of dechirp echo and Doppler phase disambiguation. The algorithm performs initialization estimation and designs the multi-channel hypothesis velocity ambiguity interval. Then, it traverses the interval to perform the fully coherent accurate measurement and tracking to obtain the motion state update, employed to echo coherent recovery. Finally, the N/M criterion is introduced to evaluate the envelope alignment of the recovered echoes and filter out the unambiguous velocity. Experiments demonstrate its remarkable performance in low SNR conditions.

关键词： Radar tracking Target tracking Accuracy signal to noise ratio Doppler effect Sea measurements signal processing algorithms Phase measurement Wideband Frequency-domain analysis Wideband radar high-speed target tracking Doppler phase disambiguation fully coherent recovery

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Contextual Direct Position Determination for Path Loss Informed Localization

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IEEE signal processing LETTERS 2025年 32卷 1241-1245页

作者： Zhao, Licheng Pu, Wenqiang Zhou, Rui You, Ming-Yi Shi, Qingjiang Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China Natl Key Lab Electromagnet Space Secur Jiaxing 314033 Peoples R China Tongji Univ Sch Software Engn Shanghai 201804 Peoples R China

In this letter, we look into the emitter localization task within the Direct Position Determination (DPD) paradigm. This paradigm is by essence a largest eigenvalue problem which treats the channel attenuation variables as free parameters. We consider the channel fading physical rule on electromagnetic signal propagation and reformulate the traditional DPD problem with a channel contextual prior. Thereafter, we develop iterative optimization algorithms based on the majorization-minimization (MM) framework. Numerical results show that the proposed algorithms outperform the traditional DPD estimators with better localization performance.

关键词： Receivers Location awareness Optimization Minimization Eigenvalues and eigenfunctions signal processing algorithms Electromagnetics Discrete Fourier transforms Indexes Fading channels Emitter localization direct position determina- tion channel contextual prior majorization-minimization

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Pseudo-Incremental Normalized Quantity-Based Phase Selection Method for Systems With Conventional and Inverter-Based Resources

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IEEE TRANSACTIONS ON POWER DELIVERY 2025年第2期40卷 1227-1230页

作者： Lopes, Felipe V. Davi, Moises J. B. B. Leite Jr, E. J. S. Reis, Raphael L. A. Silva, Kleber M. Zat, Guilherme Oleskovicz, Mario Univ Fed Paraiba Dept Elect Engn BR-58051900 Joao Pessoa Brazil Univ Fed Paraiba Dept Renewable Energy Engn BR-58051900 Joao Pessoa Brazil Univ Sao Paulo Dept Elect & Comp Engn BR-05508220 Sao Paulo Brazil Itaipu Parquetec BR-85867900 Foz Do Iguacu Brazil Univ Brasilia Dept Elect Engn BR-70910900 Brasilia Brazil

This letter proposes a settings-free Pseudo-Incremental Normalized (PIN) quantity-based phase selection method, which is suitable for systems with conventional and inverter-based resources (IBRs). The proposed solution process voltage and current PIN components in the same way, being flexible for applications in weak and strong terminals. The proposed method is initially evaluated by means of PSCAD simulations with the aim to compare it against five other competing solutions. Then, real-world records are used to validate the proposed method application in actual fault scenarios. In both evaluation stages, faults on lines that interconnect IBRs to synchronous generator-dominated grids are considered, allowing to assess the proposed solution under the influence of conventional generations and IBRs. The obtained results attest that the proposed phase selection method is robust, flexible and reliable, being promising for systems with conventional generations and/or IBRs.

关键词： Pins Voltage PSCAD Monitoring Sensitivity Protection Fault location signal processing algorithms Power grids Electrical engineering Fault diagnosis incremental component inverter-based resources phase selection power systems

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