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14th Annual Summit and Conference of the Asia-Pacific-signal-and-Information-processing-Association (APSIPA ASC)

作者： Mawalim, Candy Olivia Okada, Shogo Unoki, Masashi Japan Adv Inst Sci & Technol 1-1 Asahidai Nomi Ishikawa 9231292 Japan

ISBN: (数字)9786165904773

ISBN: (纸本)9786165904773

Speaker anonymization has been developed to protect personally identifiable information while retaining other encapsulated information in speech. Datasets, metrics, and protocols for evaluating speaker anonymization have been defined in the Voice Privacy Challenge (VPC). However, existing privacy metrics focus on evaluating general speaker individuality anonymization, which is represented by an x-vector. This study aims to investigate the effect of anonymization on the perception of gender. Understanding how anonymization caused gender transformation is essential for various applications of speaker anonymization. We proposed speaker anonymization methods across genders based on phase-vocoder time-scale modification (PV-TSM). Subsequently, in addition to the VPC evaluation, we developed a gender classifier to evaluate a speaker's gender anonymization. The objective evaluation results showed that our proposed method can successfully anonymize gender. In addition, our proposed methods outperformed the signal processing-based baseline methods in anonymizing speaker individuality represented by the x-vector in ASVeval while maintaining speech intelligibility.

关键词： Measurement Data privacy Privacy Protocols signal processing algorithms Information processing Information filtering

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A Sequential Experience-driven Contextual Bandit Policy for MIMO TWAF Online Relay Selection 23

A Sequential Experience-driven Contextual Bandit Policy for ...

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23rd International Workshop on signal processing Advances in Wireless Communication (SPAWC)

作者： Gupta, Ankit Sellathurai, Mathini Ratnarajah, Tharmalingam Heriot Watt Univ Sch Engn & Phys Sci EPS Edinburgh Midlothian Scotland Univ Edinburgh Inst Digital Commun Edinburgh Midlothian Scotland

ISBN: (数字)9781665494557

ISBN: (纸本)9781665494557

In this work, we derive a sequential experience-driven contextual bandit (CB)-based policies for online relay selection in multiple-input multiple-output (MIMO) two-way amplify-and-forward (TWAF) relay networks, where the relays are provided with quantized imperfect channel gain information. The proposed CB-based policy acquires information about the optimal relay node by resolving the exploration-versus-exploitation dilemma. In particular, we propose a linear upper confidence bound (LinUCB)-based CB policy, and an adaptive active greedy (AAG)-based CB policy that utilizes active learning heuristics. With simulation results, we show that the proposed CB-based policies can reduce the feedback overhead by a factor of eight and time-cost by 70% while outperforming the best conventional Gram-Schmidt (GS) algorithm.

关键词： Wireless communication Simulation Conferences signal processing algorithms Channel estimation Relay networks (telecommunication) signal processing

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High-Accuracy DOA Estimation of Coherent signals Based on the Time-Space Mutual Correlation Smoothing

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2024年 21卷 1页

作者： Lan, Xiaoyu Wang, Xiaoshuang Liang, Mingshen Ma, Shuang Tian, Ye Shenyang Aerosp Univ Coll Elect & Informat Engn Shenyang 110136 Peoples R China

To solve the problem of low estimation accuracy of spatial smoothing algorithm in scenarios of low signal-to-noise ratio (SNR) and small number of snapshots, a high-accuracy direction of arrival (DOA) estimation algorithm of coherent signals based on the time-space mutual correlation smoothing (TSMCS) is proposed in this letter. First, according to the strong correlation of the signal and the weak correlation of the noise in time and space domains, the time-space mutual correlation matrix of different sub-arrays is constructed to suppress the noise. Secondly, a set of high-order covariance matrices is obtained by multiplying all time-space mutual correlation matrices by their respective conjugate transpose, followed by spatial smoothing. Subsequently, TSMCS covariance matrices are meticulously decomposed and reconstructed, resulting in a more comprehensive matrix. Then, the particle swarm optimization (PSO) algorithm is exploited to optimize the signal subspace by adjusting the delay value. Finally, the traditional TLS-estimation signal parameters via the rotational invariance technique (ESPRIT) algorithm are employed to estimate the DOA of coherent signals. Simulation results demonstrate that the proposed algorithm has higher estimation accuracy and resolution.

关键词： Covariance matrices Estimation Direction-of-arrival estimation Correlation Smoothing methods signal processing algorithms Delays Coherent signal direction of arrival (DOA) particle swarm optimization (PSO) time-space mutual correlation smoothing (TSMCS)

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Multi-Frequency Radar signal processing for Moving Target Detection

Multi-Frequency Radar Signal Processing for Moving Target De...

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International Radar Symposium (IRS)

作者： Huaiyang Gong Nikita Petrov Oleg Krasnov Alexander Yarovoy Microwave Sensing Signals and Systems (MS3) Delft University of Technology Delft the Netherlands

ISBN: (数字)9788395602054

ISBN: (纸本)9781665466301

This paper presents algorithms for joint signal processing of data from two radars located on the rooftop of TU Delft: PARSAX, operating in S-band, and MESEWI, operating in X-band [1]. In particular, the problem of data alignment in space (2D map) and time is addressed by observing moving targets of opportunity in the high-resolution mode. After the data alignment procedure, the detection algorithms for optimal fusing of dual-polarization and multi-frequency data are proposed. The detection results are considered the input for moving target (an auto) tracking and its signature extraction. The developed techniques were tested on the data records in experimental scenarios.

关键词： Target tracking signal processing algorithms Radar detection Radar Object detection Radar tracking Radar signal processing

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COMPETITIVE MULTI-AGENT REINFORCEMENT LEARNING WITH SELF-SUPERVISED REPRESENTATION 47

COMPETITIVE MULTI-AGENT REINFORCEMENT LEARNING WITH SELF-SUP...

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47th IEEE International Conference on Acoustics, Speech and signal processing (ICASSP)

作者： Su, DiJia Lee, Jason D. Mulvey, John M. Poor, H. Vincent Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781665405409

We present MASRL: Competitive Multi-Agent Self-supervised representations for Reinforcement Learning in the multi-agent competitive environment. MASRL introduces a simple but effective self-supervised task: predicting a learning agent's opponent's future move. In doing this, the agent learns a stronger representation from this additional signal, focusing not only on itself but also on its opponent. By understanding and anticipating the opponent's future moves, MASRL allows the learning agent to develop effective strategies for opponent exploitation. Our method stabilizes training, improves sample efficiency, and allows the agent to generalize and adapt its playing strategy to other unseen expert opponents. On the Multi-Agent Atari benchmark, MASRL achieves remarkable performance, outperforming other strong baselines. Examples of demo videos can be found at: https://***/view/compmarl

关键词： Training Conferences signal processing algorithms Focusing Finance Reinforcement learning signal processing

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CoMeFa: Compute-in-Memory Blocks for FPGAs 30

CoMeFa: Compute-in-Memory Blocks for FPGAs

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IEEE 30th International Symposium on Field-Programmable Custom Computing Machines (FCCM)

作者： Arora, Aman Anand, Tanmay Borda, Aatman Sehgal, Rishabh Hanindhito, Bagus Kulkarni, Jaydeep John, Lizy K. Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781665483322

Block RAMs (BRAMs) are the storage houses of FPGAs, providing extensive on-chip memory bandwidth to the compute units implemented using Logic Blocks (LBs) and Digital signal processing (DSP) slices. We propose modifying BRAMs to convert them to CoMeFa (Compute-In-Memory Blocks for FPGAs) RAMs. These RAMs provide highly-parallel compute-in-memory by combining computation and storage capabilities in one block. CoMeFa RAMs utilize the true dual port nature of FPGA BRAMs and contain multiple programmable single-bit bit-serial processing elements. CoMeFa RAMs can be used to compute in any precision, which is extremely important for evolving applications like Deep Learning. Adding CoMeFa RAMs to FPGAs significantly increases their compute density. We explore and propose two architectures of these RAMs: CoMeFa-D (optimized for delay) and CoMeFa-A (optimized for area). Compared to existing proposals, CoMeFa RAMs do not require changing the underlying SRAM technology like simultaneously activating multiple rows on the same port, and are practical to implement. CoMeFa RAMs are versatile blocks that find applications in numerous diverse parallel applications like Deep Learning, signal processing, databases, etc. By augmenting an Intel Arria-10-like FPGA with CoMeFa-D (CoMeFa-A) RAMs at the cost of 3.8% (1.2%) area, and with algorithmic improvements and efficient mapping, we observe a geomean speedup of 2.55x (1.85x), across several representative benchmarks. Replacing all or some BRAMs with CoMeFa RAMs in FPGAs can make them better accelerators of modern compute-intensive workloads.

关键词： Deep learning Power demand Random access memory signal processing algorithms Digital signal processing Parallel processing System-on-chip

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Complex Surface Electromyography signal Gesture Recognition Based on Multipathway Featured Scale Convolutional Neural Network

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷

作者： Liu, Tie Bai, Dianchun Ma, Le Du, Qiang Yokoi, Hiroshi Shenyang Univ Technol Sch Elect Engn Shenyang 110870 Peoples R China Northeast Elect Power Univ Sch Automa Engn Jilin 132012 Peoples R China Univ Electrocommun Dept Mech & Intelligent Syst Engn Chofu 1828585 Japan

Surface electromyography-based gesture recognition and prosthetic hand control using deep learning (DL) have become increasingly significant in the field of human-computer interaction. This study aims to enhance the control of prosthetic hands driven by complex gestures, addressing the challenge of low-resolution gesture differentiation caused by the coupling and superposition of surface electromyography signals in DL models. We propose a DL-based framework for the recognition of complex surface electromyography signals, utilizing a multipathway approach to acquire raw surface electromyography signals, process them in the time-frequency domain, and extract features using multiscale convolutional networks. The processed surface electromyography features are then analyzed in parallel to enhance accuracy. This method effectively processes multiple signals concurrently and extracts diverse feature sets. By collecting data from six channels, it achieves an 88.56% recognition rate for 16 complex hand gestures, enabling control of ten distinct prosthetic hand movements. By leveraging multidimensional continuous surface electromyography images, we have developed a feature model that resolves the issues of signal coupling and superposition in multichannel surface electromyography data, allowing for precise control of prosthetic hand movements.

关键词： Convolutional neural networks (CNNs) electromyography feature extraction hand gesture recognition muscle activity signal processing algorithms Convolutional neural networks (CNNs) electromyography feature extraction hand gesture recognition muscle activity signal processing algorithms

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An Optimization-Based User Scheduling Framework for mmWave Massive MU-MIMO Systems 23

An Optimization-Based User Scheduling Framework for mmWave M...

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23rd International Workshop on signal processing Advances in Wireless Communication (SPAWC)

作者： Palhares, Victoria Studer, Christoph Swiss Fed Inst Technol Dept Informat Technol & Elect Engn Zurich Switzerland

ISBN: (数字)9781665494557

ISBN: (纸本)9781665494557

We propose a novel user equipment (UE) scheduling framework for millimeter-wave (mmWave) massive multiuser (MU) multiple-input multiple-output (MIMO) wireless systems. Our framework determines (sub)sets of UEs that should transmit simultaneously in a given time slot by approximately solving a nonconvex optimization problem using forward-backward splitting. Our UE scheduling framework is flexible in the sense that it (i) supports a variety of cost functions, including postequalization mean square error and sum rate, and (ii) enables precise control over the minimum and maximum number of resources the UEs should occupy. We demonstrate the efficacy of our framework using realistic mmWave channel vectors generated with a commercial ray-tracer. We show that our UE scheduler outperforms a range of existing scheduling methods and closely approaches the performance of an exhaustive search.

关键词： Wireless communication Schedules Millimeter wave technology signal processing algorithms Quality of service signal processing Cost function

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Joint Optimization of Edge Caching and Collective Recommendation for Offloading Maximization 16

Joint Optimization of Edge Caching and Collective Recommenda...

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16th IEEE International Conference on signal processing (ICSP)

作者： Han, Shengqian Wei, Dasen Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China

ISBN: (数字)9781665460569

ISBN: (纸本)9781665460569

This paper studies the optimization of the recommendation-enhanced wireless edge caching. Different from existing works that mainly considered the personalized recommendation, we study the combination of collective recommendation and edge caching, and jointly optimize the probabilistic recommendation and probabilistic caching policies, aimed at maximizing the average offloading probability. We propose two algorithms to solve the joint optimization problem, and conduct a data-based analysis for the impact of collective recommendation on request probability of users. Simulation results show the increased traffic offloading brought by the proposed method.

关键词： Wireless communication Law enforcement Simulation signal processing algorithms signal processing Probabilistic logic Optimization

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Ridgelet Transform Based on Optimal Basic Wavelet and Its Application in Seismic Discontinuity Detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Zhao, Liang Gao, Jinghuai Li, Zhen Tian, Yajun Zhao, Haoqi Yang, Tao Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Peoples R China

High-dimensional time-frequency (TF) transforms are essential tools in seismic data processing. However, commonly used transforms such as Ridgelet, Curvelet, and Contourlet exhibit limitations in time-shifting invariance and basis function selection, which impacts on their effectiveness in seismic data analysis. To address these limitations, this study introduces optimal basic wavelet (OBW)-Ridgelet, a novel approach integrating the OBW with the Ridgelet transform. By combining OBW with Ridgelet, this method aims to enhance the TF localization for seismic structural analysis and time-shifting invariance property. We also present a workflow for seismic discontinuity detection, employing the C3 algorithm to the decomposed seismic data to get multiscale coherence and introduce the similarity coefficient for scale selection of the multiscale coherence. Synthetic and field data examples demonstrate the effectiveness and robustness of the proposed method, yielding promising results for seismic signal interpretation. The integration of OBW-Ridgelet enriches the toolkit for seismic signal analysis and holds the potential for refining seismic feature detection and interpretation in practical applications.

关键词： Transforms Continuous wavelet transforms Coherence Wavelet analysis Location awareness Sparse approximation signal processing algorithms Optimal basic wavelet (OBW) Ridgelet transform three-parameter wavelet (TPW)

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