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DSS-o-SAGE: Direction-Scan Sounding-Oriented SAGE Algorithm for Channel Parameter Estimation in mmWave and THz Bands

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2025年第4期73卷 1969-1983页

作者： Li, Yuanbo Han, Chong Chen, Yi Yu, Ziming Yin, Xuefeng Shanghai Jiao Tong Univ Terahertz Wireless Commun TWC Lab Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Terahertz Wireless Commun TWC Lab Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr CMIC Shanghai 200240 Peoples R China Huawei Technol Co Ltd Chengdu 611731 Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China

Investigation of millimeter-wave (mmWave) and Terahertz (THz) channels relies on channel measurements and estimation of multipath component (MPC) parameters. As a common measurement technique in the mmWave and THz bands, direction-scan sounding (DSS) resolves angular information and increases the measurable distance. Through mechanical rotation, the DSS creates a virtual multiantenna sounding system, which however incurs signal phase instability and large data sizes, which are not fully considered in existing estimation algorithms and thus make them ineffective. To tackle this research gap, in this article, a DSS-oriented space-alternating generalized expectation-maximization (DSS-o-SAGE) algorithm is proposed for channel parameter estimation in mmWave and THz bands. To appropriately capture the measured data in mmWave and THz DSS, the phase instability is modeled by the scanning-direction-dependent signal phases. Furthermore, based on the signal model, the DSS-o-SAGE algorithm is developed, which not only addresses the problems brought by phase instability, but also achieves ultralow computational complexity by exploiting the narrow antenna beam property of DSS. Simulations in synthetic channels are conducted to demonstrate the efficacy of the proposed algorithm and explore the applicable region of the far-field approximation (FFA) in DSS-o-SAGE. Last but not least, the proposed DSS-o-SAGE algorithm is applied in real measurements in an indoor corridor scenario at 300 GHz. Compared with results using the baseline noise elimination method, the channel is characterized more correctly and reasonably based on the DSS-o-SAGE.

关键词： Millimeter wave communication Channel estimation Terahertz communications Antenna measurements Parameter estimation Computational modeling signal processing algorithms Channel measurement channel parameter estimation direction-scan sounding (DSS) sixth-generation (6G) and beyond space-alternating generalized expectation-maximization (SAGE) Terahertz (THz) communications

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The Evolution of System on Chip Integrated Circuits for Hearing-Aid signal processing

The Evolution of System on Chip Integrated Circuits for Hear...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： James Ryan Ivo Coenen Robert Brennan Onsemi Waterloo Canada Onsemi Switzerland

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Integrated circuits used in modern, digital hearing aids are mixed-signal devices that combine analog and digital circuits on the same die. Due to the extreme size and power constraints encountered in hearing aids, the assignment of die area for such systems-on-chip (SOCs) must be carefully considered. The relative allocation between analog circuits, digital gates and on-chip memory has been relatively constant for traditional DSP-based systems. The precision and computational requirements for artificial intelligence (AI) based algorithms, however, are very different from traditional DSP. This is driving changes in die area allocation, starting with some recently launched devices. Despite these changes, projections based on recent growth trends show that future SOCs will only be able to support the smallest neural networks currently proposed for hearing aids. Minimizing data precision and pruning small-valued weights are shown to be essential to deploy deep-learning algorithms on hearing aid SOCs.

关键词： Integrated circuits signal processing algorithms Random access memory signal processing Hearing aids System-on-chip Resource management Artificial intelligence Speech processing System analysis and design

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Detection of Small Targets on the Sea Surface Based on High-Dimensional ConvH Classifier

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Wu, Xijie Liu, Tianpeng Liu, Yongxiang Liu, Li Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China

This article proposes a viable and easy-to-operate one-class classifier (OCC) of high-dimensional ConvH (high-D ConvH), under the background of feature detection for small targets on the sea surface. Our classifier breaks the bottleneck of current ConvH classifier, i.e., being confined only to the detection within 3-D feature space, by addressing the following two issues: 1) the current ConvH decision criterion is limited to 3-D and 2) the unacceptable learning time of ConvH under high-D space. For the first issue, derived from the half-space description of ConvH, we propose a fast ConvH decision criterion, which can be extended to any dimensional space and owns lower algorithmic complexity. Accordingly, a corresponding fast ConvH decision algorithm is designed. For the second issue, we first give an approach to accelerate ConvH learning, based on which we propose two fast ConvH learning algorithms of different mechanisms. The rigorous mathematical derivations prove that the proposed algorithms are correct and feasible. Combining the above algorithms, the high-D ConvH classifier is obtained, and then a novel feature detection method for small target on the sea surface is presented by integrating the high-D ConvH classifier into existing feature detection process. The experiments on the CSIR datasets reveal that: 1) the high-D ConvH classifier is successfully applied to the 7-D feature space, and it owns shorter learning and decision time than existing methods and 2) the proposed detection method owns superior detection performance compared with its competitors in high-D space.

关键词： Feature extraction Classification algorithms Clutter Vectors Feature detection Three-dimensional displays Sea surface Training signal processing algorithms Convex hulls Convex hull (ConvH) CSIR dataset feature detection high-dimensional sea clutter

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Optimizing Artificial Neural Networks Using Mountain Gazelle Optimizer

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IEEE ACCESS 2025年 13卷 50464-50479页

作者： Karabiyik, Muhammed Abdulhamid Turkoglu, Bahaeddin Asuroglu, Tunc Nigde Omer Halisdemir Univ Dept Comp Technol TR-51240 Nigde Turkiye Ankara Univ Dept Artificial Intelligence & Data Engn TR-06830 Ankara Turkiye Tampere Univ Fac Med & Hlth Technol Tampere 33720 Finland VTT Tech Res Ctr Finland Tampere 33101 Finland

The performance of artificial neural networks heavily depends on the optimization of network parameters, specifically weights and biases, during the training process. Effectively adjusting these parameters is essential to minimize the error between predicted and actual outputs. While traditional training algorithms, such as gradient-based methods, have been widely used, they often face challenges like premature convergence and stagnation in local optima. Training an ANN can, therefore, be viewed as an optimization problem, where the goal is to fine-tune parameters to achieve accurate and efficient performance. In this study, we introduce a novel approach to optimizing neural network parameters using the Mountain Gazelle Optimizer (MGO), a nature-inspired metaheuristic algorithm that mimics the social hierarchy and behavioral patterns of wild mountain gazelles. The MGO algorithm leverages its unique features, including hierarchical social structure and adaptive movement strategies, to effectively navigate the complex parameter space of neural networks. The algorithm's search mechanism integrates four key behavioral strategies: Territorial Solitary Males (TSM) for refining optimal solutions, Maternity Herds (MH) for balancing exploration and exploitation, Bachelor Male Herds (BMH) for global exploration, and Migration to Search for Food (MSF) for introducing randomness to prevent stagnation in local optima. These mechanisms work collaboratively, ensuring a dynamic and balanced optimization process throughout the training phase. To evaluate the effectiveness of the proposed approach, we conducted comprehensive experiments using various classification datasets from the UCI repository. The performance of the MGO-based neural network optimizer was compared with traditional backpropagation and several state-of-the-art optimization algorithms. Experimental results demonstrate that the proposed method exhibits superior performance in terms of convergence speed and avoiding l

关键词： Artificial neural networks Training Convergence Metaheuristics Heuristic algorithms Refining Accuracy signal processing algorithms Vectors Prediction algorithms Artificial neural network mountain gazelle optimizer optimization

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Ground-Moving Target Imaging Based on High-Order Motion Parameter Estimation for SAR With Maneuvering Trajectory

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Wang, Yifan Tang, Shiyang Zhang, Linrang Jiang, Chenghao He, Zixuan Han, Jiahao Chen, Zhanye Chen, Hongmeng Xu, Daobao Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Southeast Univ State Key Lab Millimeter Waves Nanjing 210096 Peoples R China Southeast Univ Inst Electromagnet Space Nanjing 210096 Peoples R China Beijing Inst Radio Measurement Beijing 100854 Peoples R China Nanjing Res Inst Elect Technol Nanjing 210039 Peoples R China

Maneuver provides flexibility for highly squinted synthetic aperture radar (SAR) and also means complicated signal characteristics in the echo, especially for ground moving target imaging (GMTIm). This article analyzes the interaction of parameters between the maneuvering platform and the moving target. The analysis suggests that three key factors should be taken into account: azimuth spectrum aliasing, Doppler centroid ambiguity, and high-order errors. To deal with these challenges, a novel GMTIm methodology for maneuvering platform is presented. The proposed approach employs an advanced parameter estimation method based on the extended generalized high-order ambiguity function (EGHAF), which enables simultaneous estimation of high-order phase coefficients across all orders in low signal-to-noise ratio (SNR) conditions through 2-D extension. Due to the estimation and compensation for higher order phases, which are usually ignored in conventional methods, the proposed method is more suitable for moving target imaging with maneuvering platforms. The superiority of the proposed approach is verified by simulation and real data results.

关键词： Trajectory Doppler effect Imaging Azimuth Synthetic aperture radar Geometric modeling Parameter estimation Vectors Radar imaging signal processing algorithms Ground moving target imaging (GMTIm) maneuvering platform parameter estimation synthetic aperture radar (SAR)

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Gauss Hermite Fourier Features Based on Maximum Correntropy Criterion for Adaptive Filtering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年第3期72卷 1438-1451页

作者： Zhou, Tian Wang, Shiyuan Qian, Junhui Zheng, Yunfei Zhang, Qiangqiang Xiao, Yingying Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Chongqing Key Lab Biopercept & Intelligent Informa Chongqing 400044 Peoples R China

Kernel adaptive filters (KAFs) are a class of nonlinear adaptive filters developed in the reproducing kernel Hilbert space, and are particularly suitable for addressing signal processing issues involving data streams and unknown nonlinearities. However, KAFs endure the issue of network structure growth as the number of training samples increases. To this end, a novel structural sparsification method for KAFs, i.e., Gauss Hermite Fourier features (GHFF) method, is first proposed by combining Gauss Hermite quadrature integration rule and Fourier transform. Subsequently, the GHFF method is integrated with the maximum correntropy criterion in filter design, leading to the development of two new adaptive filtering algorithms, i.e., GHFF maximum correntropy (GHFFMC) algorithm and stochastic batch GHFFMC (SB-GHFFMC) algorithm. The proposed GHFFMC and SB-GHFFMC algorithms are expected to exhibit excellent capabilities in characterizing the unknown nonlinear relationships within the data, along with robustness to outliers. Meanwhile, SB-GHFFMC is anticipated to exhibit superior filtering performance in comparison with GHFFMC, as it leverages a general and flexible batch gradient descent method for model optimization. Simulations on nonlinear system identification and time-series prediction of Chua's circuits confirm the performance superiorities of the proposed algorithms compared to other robust KAFs and RFF-based filters.

关键词： Kernel Approximation algorithms signal processing algorithms Polynomials Robustness Integrated circuit modeling Fourier transforms Filters Eigenvalues and eigenfunctions Adaptation models Gauss quadrature random Fourier features Gauss Hermite quadrature maximum correntropy criterion stochastic batch

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Beamforming Optimization for Robust Sensing and Communication in Dynamic mmWave MIMO Networks

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2025年第4期43卷 1354-1370页

作者： Li, Lei Zhang, Jiawei Chang, Tsung-Hui Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China

Acquiring accurate channel state information (CSI) at low overhead is crucial for millimeter wave MIMO communications but is challenging in dynamic environments. In this work, we exploit the emerging integrated sensing and communication (ISAC) beamforming technique for concurrent CSI sensing and data transmission. Despite its low overhead, the corresponding ISAC transmit beamforming design faces a complex trade-off between CSI sensing accuracy and communication interference management. To address this, we formulate the beamforming design as an optimization problem minimizing the maximum Cram & eacute;r-Rao bound (CRB) of CSI sensing errors subject to the users' worst-case communication rates under CSI errors. To efficiently solve the problem, we step-by-step propose three algorithms. The first algorithm is based on the semidefinite relaxation and successive convex optimization techniques, which can serve as a benchmark algorithm but suffers high computational complexity. To efficiently handle the worst-case objective and rate constraints, we propose a complexity-reduced algorithm based on the primal-dual optimization method and first-order min-max algorithm. Furthermore, we dismiss SDR and employ the block coordinate descent method combined with cheap gradient descent steps to achieve a low-complexity algorithm. Extensive simulations show the proposed ISAC beamforming design and low-complexity algorithms can provide robust communication performance and significantly outperform existing schemes.

关键词： Sensors Array signal processing Millimeter wave communication Optimization signal processing algorithms Minimax techniques Integrated sensing and communication Vectors Prediction algorithms Approximation algorithms CSI acquisition ISAC robust beamforming low-complexity design

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Submillimeter Acoustic Vibration Measurement and Monitoring Using a Single Smartphone

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Yang, Liu Li, Xiaofei Wang, Wenwu Wang, Xinheng Li, Peisong Liu, Guangyao Wang, Zhi Zhejiang Univ Coll Control Sci & Engn Hangzhou 310000 Peoples R China Westlake Univ Sch Engn Hangzhou 310030 Peoples R China Westlake Inst Adv Study Inst Adv Technol Hangzhou 310024 Peoples R China Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 7XH England Xian Jiaotong Liverpool Univ Sch Adv Technol Suzhou 215123 Peoples R China Zhejiang Univ Huzhou Inst Huzhou 313098 Peoples R China

The accurate vibration measurement is crucial for monitoring and diagnosing industrial equipment. Existing solutions require either installing contact sensors on the equipment or using noncontact sensors such as laser. Both approaches involve complex deployment, stringent environmental conditions, and high cost. As a better alternative, we propose a submillimeter acoustic vibration measurement system using a single smartphone, called Mobile-Vib. First, we develop a novel acoustic ranging method that builds on traditional acoustic ranging techniques, incorporating the reflection principle of acoustic signals from vibrating objects. This approach addresses the challenge of acoustic signal refresh rate in vibration measurement by employing advanced signal design and processing techniques. Second, we design a noise removal algorithm utilizing the dual-channel technology of smartphones to minimize multipath signals and noise interference, enabling accurate phase estimation. To mitigate the impact of unrelated human motions in real-world measurements, we implement an optimization-based method to correct distortions and reduce errors. Finally, by clarifying the relationship between phase changes and actual displacement, we enable tracking of vibration displacement in industrial environments. We have implemented Mobile-Vib, and the extensive experimental results demonstrate an average error of approximately 0.629 mm in displacement estimation and 5.6 Hz in frequency estimation at a 1-m distance from the vibrating object in real industrial vibration monitoring scenarios.

关键词： Vibrations Vibration measurement Acoustics Monitoring Sensors Microphones Interference signal processing algorithms Reflection Millimeter wave radar Acoustic echo ranging smartphone vibration monitoring

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Real-Time signal processing for Degree of Hand Grasping and Rotation Based on Electrical Impedance Measurements

Real-Time Signal Processing for Degree of Hand Grasping and ...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Mariya Y. Yakim Alfiya R. Yusupova Anastasia A. Yagodkina Alexander V. Kobelev Sergey I. Shchukin Biomedical Department Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

Bioimpedance measurements provide more valuable information on contractions compared to EMG. In a typical hand EMG system, the dual-channel electrode system is placed between the lateral and medial side of the limb. Detailed studies on dual channel bioimpedance systems signal processing for gesture classification and muscle contraction degree still do not exist. In this study we propose a signal processing algorithm for bioimpedance measurements to determine the degree of muscle contraction during grasping and rotation. The developed algorithm includes preliminary filtering, transition definition, classification of two types of movement - grasping and rotation, and determination of the degree of movement performed. This method can be applied in prosthetics as well as in various muscle-machine and muscle-computer interfaces.

关键词： Hands signal processing algorithms Bioimpedance Grasping signal processing Muscles Real-time systems Electromyography Rotation measurement Prosthetics

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Differential Privacy With Higher Utility by Exploiting Coordinate-Wise Disparity: Laplace Mechanism Can Beat Gaussian in High Dimensions

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 2836-2851页

作者： Muthukrishnan, Gokularam Kalyani, Sheetal Indian Inst Technol Madras Dept Elect Engn Chennai 600036 India

Conventionally, in a differentially private additive noise mechanism, independent and identically distributed (i.i.d.) noise samples are added to each coordinate of the response. In this work, we formally present the addition of noise that is independent but not identically distributed (i.n.i.d.) across the coordinates to achieve tighter privacy-accuracy trade-off by exploiting coordinate-wise disparity in privacy leakage. In particular, we study the i.n.i.d. Gaussian and Laplace mechanisms and obtain the conditions under which these mechanisms guarantee privacy. The optimal choice of parameters that ensure these conditions are derived considering (weighted) mean squared and l(p)(p)-errors as measures of accuracy. Theoretical analyses and numerical simulations demonstrate that the i.n.i.d. mechanisms achieve higher utility for the given privacy requirements compared to their i.i.d. counterparts. One of the interesting observations is that the Laplace mechanism outperforms Gaussian even in high dimensions, as opposed to the popular belief, if the irregularity in coordinate-wise sensitivities is exploited. We also demonstrate how the i.n.i.d. noise can improve the performance in private (a) coordinate descent, (b) principal component analysis, and (c) deep learning with group clipping.

关键词： Noise Privacy Sensitivity Accuracy Gaussian noise Differential privacy Gaussian distribution Additive noise signal processing algorithms Perturbation methods non-identical noise sensitivity profile Gaussian mechanism Laplace mechanism

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