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

作者： Han Wang Eduardo Pérez Iris A. M. Huijben Hans van Gorp Ruud Van Sloun Florian Römer Applied AI Signal Processing and Data Analysis Fraunhofer Institute for Nondestructive Testing IZFP Germany Electronic Measurements and Signal Processing Ilmenau University of Technology Germany Department of Electrical Engineering Eindhoven University of Technology The Netherlands

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multidimensional data acquisition often requires extensive time and poses significant challenges for hardware and software regarding data storage and processing. Rather than designing a single compression matrix as in conventional compressed sensing, structured compressed sensing yields dimension-specific compression matrices, reducing the number of optimizable parameters. Recent advances in machine learning (ML) have enabled task-based supervised learning of subsampling matrices, albeit at the expense of complex downstream models. Additionally, the sampling resource allocation across dimensions is often determined in advance through heuristics. To address these challenges, we introduce Structured COmpressed Sensing with Automatic Resource Allocation (SCOSARA) with an information theory-based unsupervised learning strategy. SCOSARA adaptively distributes samples across sampling dimensions while maximizing Fisher information content. Using ultrasound localization as a case study, we compare SCOSARA to state-of-the-art ML-based and greedy search algorithms. Simulation results demonstrate that SCOSARA can produce high-quality subsampling matrices that achieve lower Cramér Rao Bound values than the baselines. In addition, SCOSARA outperforms other ML-based algorithms in terms of the number of trainable parameters, computational complexity, and memory requirements while automatically choosing the number of samples per axis.

关键词： Location awareness Ultrasonic imaging Supervised learning signal processing algorithms Software Resource management Sparse matrices Speech processing Compressed sensing Unsupervised learning

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Stability and Performance Analysis of Interfered Digital Systems with Generalized Overflow Nonlinearities

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IFAC-PapersOnLine 2024年 57卷 339-344页

作者： Srinivasulu Jogi Mounika Pulikonda Priyanka Kokil Department of Electronics and Communication Engineering National Institute of Technology Tiruchirappalli Tiruchirappalli Tamil Nadu 620015 India Advanced Signal and Image Processing (ASIP) Lab Department of Electronics and Communication Engineering Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai 600127 India

This paper investigates the problem of local stability for fixed-point interfered digital filters with generalized overflow nonlinearities. First, a familiar form of overflow nonlinearity function, covering the nonlinearities like zeroing, saturation, two's complement, and triangular is established. Second, the asymptotic stability condition for the digital filters is formulated in local context without external interference. Third, with external interference the work is extended to investigate the local stability and to attain H ∞ performance of the digital filter using generalized nonlinearity function. Further, the conventional global stability results can be established as special cases of presented local approach. Finally, sufficient numerical examples are presented to highlight the merit of proposed approach.

关键词： Asymptotic stability External interference Generalized overflow nonlinearity Limit cycle oscillations Local stability

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Analysis of Waves and Human Interaction Using Beach Webcams

Analysis of Waves and Human Interaction Using Beach Webcams

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OCEANS

作者： Harel Mendelman Tomer Masssas Yair Moshe Signal and Image Processing Laboratory (SIPL) Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering Technion – Israel Institute of Technology Haifa Israel

Automatic monitoring of beach video streams is important for improving safety, environmental monitoring, research, and education related to beach activities. This paper introduces a novel approach for monitoring water bodies by analyzing beach video streams. In contrast to earlier works, in the proposed approach we analyze not only the behavior of water bodies, or of humans on beach videos, but also the interactions between them. By integrating human activity and water behavior analysis, the approach provides new insights that are unattainable by analyzing each separately. To accomplish this objective, deep neural networks are utilized to analyze video streams from existing beach webcams. The analysis includes the detection and tracking of people and waves, as well as higher level analysis. We use the task of characterizing surfing conditions as a case study and demonstrate our ability to estimate the values of key parameters that can help determine the quality of a surf spot at a given time.

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Joint Precoding and Fronthaul Compression for Cell-Free MIMO With Hybrid Topology

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IEEE Internet of Things Journal 2025年第11期12卷 18125-18136页

作者： Chen, Yijie Xia, Wenchao Zhang, Jun Hou, Xiaoyun Wong, Kai-Kit Zhu, Hongbo Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Wireless Communications Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing210003 China Nanjing University of Posts and Telecommunications Institute of Signal Processing and Transmission Nanjing210003 China University College London Department of Electronic and Electrical Engineering LondonWC1E 7JE United Kingdom Yonsei University Seoul03722 Korea Republic of

Cell-free multiple-input-multiple-output generally uses a star topology for superior communication but faces high costs due to long cables. An economical alternative, the stripe topology, is suitable for specific deployments but cannot meet user demands in densely populated areas due to limited fronthaul capacity. To address these limitations, we propose a hybrid network structure combining stripe and star topologies, ensuring system performance while reducing deployment costs. In such a network, joint precoding and fronthaul compression is considered to maximize system sum-rate and an alternating optimization (AO) algorithm is proposed. However, the AO algorithm involves an iterative process and complex matrix calculations, making it unsuitable for practical applications. To deal with this issue, we propose a low-complexity iterative gradient descent (IGD) algorithm with simple matrix operations. To further reduce online computational complexity, we propose a novel deep unfolding neural network (DUNN) scheme, which is interpretable and scalable, based on the IGD algorithm. Simulation results show that the hybrid topology significantly improves system capacity compared to the stripe-only topology. Additionally, the DUNN achieves a tradeoff between the achievable sum-rate performance and the corresponding computational complexity. © 2014 IEEE.

关键词： Iterative methods

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FastNet for Symbol Detection in Massive MIMO Systems

FastNet for Symbol Detection in Massive MIMO Systems

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IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Swati Bhattacharya K.V.S. Hari Department of Electrical Communication Engineering Statistical Signal Processing Lab Indian Institute of Science Bangalore India

In massive multiple-input-multiple-output (MIMO) systems, a major limiting factor for symbol detection is the amount of computational complexity required. Symbol detection in unquantized massive MIMO systems have been studied in the context of both traditional and machine learning methods. In this paper, we propose a hybrid framework that replaces some neural network layers with simple gradient descent layers to reduce complexity. Simulations showed that a judicious choice of the number of such layers can lead to significant reduction in the range of 35-50%, in computational complexity, with marginal change in performance.

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InfaSafe: A Comprehensive, Non-invasive Infant Monitoring System

InfaSafe: A Comprehensive, Non-invasive Infant Monitoring Sy...

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IEEE International Conference on Electro-Information Technology

作者： Lukas Klicker Alex Maliwat Joanna Findura Xinrui Yu Mikhail Gromov Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL USA

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

InfaSafe emerges as a novel approach to infant health monitoring, uniquely positioned at the convergence of advanced artificial intelligence and edge computing. This system is designed not as a definitive solution but as an advanced platform for comprehensive data archiving, offering valuable insights into the complex and elusive nature of Sudden Unexpected Infant Death (SUID). InfaSafe utilizes AI algorithms for real-time pose estimation, breathing surveillance, and cry analysis, all within an edge computing framework that facilitates prompt and efficient data handling. This paper explores the development and capabilities of InfaSafe, underscoring its role in providing crucial, real-time insights and alerts for caregivers and its potential to contribute significantly to our understanding of neonatal health and SUID. The focus is on leveraging technological advancements to gather comprehensive data, which can be instrumental in shaping future research and interventions in neonatal care.

关键词： Temperature sensors Temperature measurement Pediatrics Temperature distribution Prototypes Real-time systems Thermal analysis

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Video Compression Artifacts Removal With Spatial-Temporal Attention-Guided Enhancement

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IEEE Transactions on Multimedia 2024年 26卷 5657-5669页

作者： Jiang, Nanfeng Chen, Weiling Lin, Jielian Zhao, Tiesong Lin, Chia-Wen Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou350116 China Department of Electrical Engineering National Tsing Hua University Hsinchu30013 Taiwan Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan

Recently, many compression algorithms are applied to decrease the cost of video storage and transmission. This will introduce undesirable artifacts, which severely degrade visual quality. Therefore, Video Compression Artifacts Removal (VCAR) aims at reconstructing a high-quality video from its corrupted version of compression. Generally, this task is considered as a vision-related instead of media-related problem. In vision-related research, the visual quality has been significantly improved while the computational complexity and bitrate issues are less considered. In this work, we review the performance constraints of video coding and transfer to evaluate the VCAR outputs. Based on the analyses, we propose a Spatial-Temporal Attention-Guided Enhancement Network (STAGE-Net). First, we employ dynamic filter processing, instead of conventional optical flow method, to reduce the computational cost of VCAR. Second, we introduce self-attention mechanism to design Sequential Residual Attention Blocks (SRABs) to improve visual quality of enhanced video frames with bitrate constraints. Both quantitative and qualitative experimental results have demonstrated the superiority of our proposed method, which achieves high visual qualities and low computational costs. © 2023 IEEE.

关键词： Computational complexity

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Speech Stuttering Detection and Removal Using Deep Neural Networks 11th

Speech Stuttering Detection and Removal Using Deep Neural Ne...

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11th International Conference on Computer engineering and Networks, CENet2021

作者： Rajput, Shaswat Nersisson, Ruban Raj, Alex Noel Joseph Mary Mekala, A. Frolova, Olga Lyakso, Elena School of Electrical Engineering Vellore Institute of Technology Vellore India Key Laboratory of Digital Signal and Image Processing of Guangdong Province Department of Electronic Engineering College of Engineering Shantou University Shantou China School of Information Technology and Engineering Vellore Institute of Technology Vellore India St. Petersburg State University Saint Petersburg Russia

ISBN: (纸本)9789811665530

There are more than 70 million people worldwide who suffer from stuttering problems. This will affect the confidence of public speaking in people who suffer from this issue. To solve this problem many people take therapy sessions but the therapy sessions are a temporary solution, as soon as they leave therapy sessions this problem might arise again. This work aims to use state of the art machine learning algorithms that have improved over the past few years to solve this problem. We have used the dataset from UCLASS archives which provide the data for stuttered speech *** format with time-aligned transcriptions. We have tried different algorithms and optimized our model by hyper parameter tuning to maximize the model’s accuracy. The algorithm is tested on random speech data with low to heavy stuttering from the same dataset, and it is observed that there is significant reduction in the Word Error Rate (WER) for most of the test cases. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning algorithms

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Digital Twin-Based Transfer Learning for Collaborative Robot Systems: A Proof of Concept

Digital Twin-Based Transfer Learning for Collaborative Robot...

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International Conference on Information Networking

作者： Supat Roongpraiwan Zongdian Li Mohsen Pourghasemian Haris Gacanin Kei Sakaguchi Department of Electrical and Electronic Engineering Tokyo Institute of Technology Tokyo Japan Chair for Distributed Signal Processing RWTH Aachen University Aachen Germany

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

Nowadays, the increasing trend toward digitalization has driven the extensive adoption of collaborative robotic automation across industries, yet a significant limitation is the robots' adaptability to unexpected and dynamic environments. This research introduces a Digital Twin (DT)-based Transfer Learning (TL) approach that combines DTs and Machine Learning (ML) to enhance adaptability in collaborative robot systems. The proposed system uses DT cyberspace for pre-training ML algorithms and leverages TL to apply this knowledge to real-world applications. This innovative approach efficiently trains state-of-the-art ML models, delivering exceptional performance while reducing the required time and data resources. The proof-of-concept experiments, employing the proposed DT-based TL to control soccer robots, demonstrate a remarkable 96% reduction in training time while maintaining a high level of adaptability, achieving a 70% goal accuracy rate in dynamic scenarios.

关键词： Training Automation Accuracy Machine learning algorithms Service robots Transfer learning Cyberspace

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Recursive Subproduct Codes with Reed-Muller-like Structure

Recursive Subproduct Codes with Reed-Muller-like Structure

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IEEE International Symposium on Information Theory

作者： Aditya Siddheshwar Lakshmi Prasad Natarajan Prasad Krishnan Signal Processing and Communications Research Center International Institute of Information Technology Hyderabad India Department of Electrical Engineering Indian Institute of Technology Hyderabad Sangareddy India

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

We study a family of subcodes of the $m-\mathbf{dimensional}$ product code $\mathcal{C}^{\otimes m}$ (‘subproduct codes’) that have a recursive Plotkin-like structure, and which include Reed-Muller (RM) codes and Dual Berman codes as special cases. We denote the codes in this family as $\mathcal{C}^{\otimes[r,m]}$ , where $r\in\{0,1,\ \ldots,\ m\}$ is the ‘order’ of the code. These codes allow a ‘projection’ operation that can be exploited in iterative decoding, viz., the sum of two carefully chosen subvectors of any codeword in $\mathcal{C}^{\otimes[r,m]}$ belongs to $\mathcal{C}^{\otimes[r-1,m-1]}$ . Recursive subproduct codes provide a wider range of rates and block lengths compared to RM codes while possessing several of their structural properties, such as the Plotkin-like design, the projection property, and fast ML decoding of first-order codes. Our simulation results for first-order and second-order codes, that are based on a belief propagation decoder and a local graph search algorithm, show instances of subproduct codes that perform either better than or within 0.5 dB of comparable RM codes and CRC-aided Polar codes.

关键词： Codes Product codes Simulation Decoding Polar codes Iterative decoding Belief propagation

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