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检索条件"任意字段=Conference on Advanced Signal Processing Algorithms"

共 3487 条记录，以下是91-100 订阅

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BrahMap: A scalable and modular map-making framework for the CMB experiments

BrahMap: A scalable and modular map-making framework for the...

引用

Euromicro conference on Parallel, Distributed and Network-Based processing

作者： Avinash Anand Giuseppe Puglisi Dipartimento di Fisica Università di Roma Tor Vergata Via della Ricerca Scientifica 1 Roma Italy INFN Sezione di Roma2 Università di Roma Tor Vergata Via della Ricerca Scientifica 1 Roma Italy Dipartimento di Fisica e Astronomia Università degli Studi di Catania Via S. Sofia 64 Catania Italy INAF Osservatorio Astrofisico di Catania Via S.Sofia 78 Catania Italy INFN Sezione di Catania Via S.Sofia 64 Catania Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The cosmic microwave background (CMB) experiments have reached an era of unprecedented precision and complexity. Aiming to detect the primordial B-mode polarization signal, these experiments will soon be equipped with $10^{4}$ to $10^{5}$ detectors. Consequently, future CMB missions will face the substantial challenge of efficiently processing vast amounts of raw data to produce the initial scientific outputs - the sky maps - within a reasonable time frame and with available computational resources. To address this, we introduce BrahMap, a new map-making framework that will be scalable across both CPU and GPU platforms. Implemented in C++ with a user-friendly Python interface for handling sparse linear systems, BrahMap employs advanced numerical analysis and high-performance computing techniques to maximize the use of super-computing infrastructure. This work features an overview of the BrahMap’s capabilities and preliminary performance scaling results, with application to a generic CMB polarization experiment.

关键词： Numerical analysis Scalability Object oriented modeling Pipelines Graphics processing units Numerical models Parallel algorithms Object oriented programming Physics Python

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Researches on Resource Allocation With Reinforcement Learning in Cell-Free Networks

Researches on Resource Allocation With Reinforcement Learnin...

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International conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Yunseong Lee Donghyun Lee Junsuk Oh Chihyun Song Wonjong Noh Sungrae Cho School of Computer Science and Engineering Chung-Ang University Seoul Republic of Korea School of Software Hallym University Chuncheon Republic of Korea

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

This paper explores reinforcement learning (RL) based on resource allocation in cell-free networks, a promising alternative to traditional cellular architectures. Cell-free networks eliminate cell boundaries by using distributed access points (APs) to collaboratively serve users, improving spectral efficiency and service uniformity. However, managing power allocation, beamforming, and AP clustering in such decentralized environments presents new challenges. We present several RL-based algorithms aimed at optimizing key network functions. Specifically, the study explores dynamic power control, advanced beamforming techniques, and multi-agent RL frameworks for clustering access points. The proposed methods leverage the adaptability of RL to optimize network performance in real-time under varying conditions.

关键词： Wireless communication Array signal processing Spectral efficiency Microprocessors Refining Power control Reinforcement learning Computer architecture Real-time systems Resource management

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Proceedings. 2003 IEEE International conference on Robotics, Intelligent Systems and signal processing (IEEE Cat. No.03EX707)

Proceedings. 2003 IEEE International Conference on Robotics,...

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IEEE International conference on Robotics, Intelligent Systems and signal processing

The following topics are dealt with: advanced robot control; multi-modal human-robot communication; humanoid robots; matching algorithms; robot motion planning; wavelet and its application; grasping and manipulation; intelligent motion control; advanced measurement technology; robot vision; virtual reality; advanced technology for intelligent systems; collective intelligence; bio-robotics; stereo vision; multi-robot systems; spectrum analysis; manufacturing technologies; mobile robots; microrobots and micromanipulators; tele-robotics; smart materials and structures; radar image processing; advanced signal processing; skill and learning; spectrum analysis; networked robotics; image processing; communication systems; nanotechnologies; image understanding; computational intelligence; information theory; advanced automation technology; and intelligent systems.

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FineQ: Software-Hardware Co-Design for Low-Bit Fine-Grained Mixed-Precision Quantization of LLMs

FineQ: Software-Hardware Co-Design for Low-Bit Fine-Grained ...

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Design, Automation and Test in Europe conference and Exhibition

作者： Xilong Xie Liang Wang Limin Xiao Meng Han Lin Sun Shuai Zheng Xiangrong Xu School of Computer Science and Engineering Beihang University Beijing China Jiangsu Shuguang Optoelectric Co. Ltd

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Large language models (LLMs) have significantly advanced the natural language processing paradigm but impose substantial demands on memory and computational resources. Quantization is one of the most effective ways to reduce memory consumption of LLMs. However, advanced single-precision quantization methods experience significant accuracy degradation when quantizing to ultra-low bits. Existing mixed-precision quantization methods are quantized by groups with coarse granularity. Employing high precision for group data leads to substantial memory overhead, whereas low precision severely impacts model accuracy. To address this issue, we propose FineQ, software-hardware co-design for low-bit fine-grained mixed-precision quantization of LLMs. First, FineQ partitions the weights into finer-grained clusters and considers the distribution of outliers within these clusters, thus achieving a balance between model accuracy and memory overhead. Then, we propose an outlier protection mechanism within clusters that uses 3 bits to represent outliers and introduce an encoding scheme for index and data concatenation to enable aligned memory access. Finally, we introduce an accelerator utilizing temporal coding that effectively supports the quantization algorithm while simplifying the multipliers in the systolic array. FineQ achieves higher model accuracy compared to the SOTA mixed-precision quantization algorithm at a close average bit-width. Meanwhile, the accelerator achieves up to 1.79x energy efficiency and reduces the area of the systolic array by 61.2%.

关键词： Quantization (signal) Accuracy Large language models Memory management Clustering algorithms Systolic arrays Encoding Energy efficiency Natural language processing Protection

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Proceedings of SPIE: advanced signal processing algorithms, Architectures, and Implementations XII

Proceedings of SPIE: Advanced Signal Processing Algorithms, ...

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advanced signal processing algorithms, Architectures, and Implementations XII

This Volume 4791 of the conference proceedings contains 47 papers. Topics discussed include advanced signal processing algorithms, architectures, time-scale analysis, time-frequency analysis, real-time implementations, structured matrices, regularization techniques, image processing, wireless communication, array processing, and high-performance arithmetic for real-time applications.

关键词： algorithms

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Integrating novel methodologies, tools, and IT resources for graduate level courses in high performance computing and advanced signal processing algorithms

Integrating novel methodologies, tools, and IT resources for...

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ITHET 2005: 6th International conference on Information Technology Based Higher Education and Training, 2005

作者： Rodriguez, Domingo A. Santiago, Nayda G. Automated Information Processing Group University of Puerto Rico Mayagüez Parallel and Distributed Processing Group University of Puerto Rico Mayagüez

ISBN: (纸本)0780391411

This work presents an approach at integrating novel methodologies for teaching graduate level courses in the areas of High Performance Computing (HPC) and advanced signal processing algorithms (ASPA) for Computer Engineering and Computer Science and Engineering Curricula. The novel teaching methodology presented here in High Performance Computing centers on the use of innovative empirical methods, i.e., exploratory data analysis, experiment design, etc., for studying computer performance, whereas an operator signal algebra approach is considered a novel methodology for the studying of advanced signal processing algorithms. The work also discusses an on going concerted effort at utilizing common tools and IT resources in both courses to provide students a holistic learning experience. © 2005 IEEE.

关键词： Engineering education

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advanced Comparative Analysis of Time-Frequency signal processing Techniques and Machine Learning Models for Optimized Biomedical Classification

Advanced Comparative Analysis of Time-Frequency Signal Proce...

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Intelligent Control, Computing and Communications (IC3), International conference on

作者： Uma Reddy Sorabh Lakhanpal Jay Singh Talari Meena Sarah Abbas Shakir G. Sakthi Lohitha Mallireddy Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Dept. of EEE GL Bajaj Institute of Technology and Management Gr. Noida U.P. India Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Department of Electrical Electronics & Communication Engineering Galgotias university Gerater Noida Uttar Pradesh India Department of CSE GRIET Hyderabad Telangana India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

This research aims at comparing the performance of TF analysis in classifying biomedical signals, with special reference to ECG and EEG signals using PhysioNet database as the major source of data. Fourier Transform and Wavelet Transform, are used to extract the feature from signals for the time-frequency analysis. These features are then used for classification using the algorithms like Random Forest and Neural Networks. All computations are performed with SciPy, NumPy, and Scikit-learn libraries. The study assesses the methods considering the classification performance, computational cost, and interpretability toward determining which approaches offer the best solution for the biomedical signal classification. It needs to be mentioned that the presented results are aimed at helping researchers and practitioners in choosing proper methods for biomedical applications of ECG and EEG signals, focused on the PhysioNet database.

关键词： Wavelet transforms Time-frequency analysis Accuracy Biological system modeling Electrocardiography Feature extraction Wavelet analysis Brain modeling Electroencephalography Random forests

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advanced signal processing algorithms for sound and vibration beyond the FFT

Advanced signal processing algorithms for sound and vibratio...

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SAE 2009 Noise and Vibration conference and Exhibition, NVC 2009

作者： Veggeberg, Kurt Zhang, Nanxiong National Instruments China China

Several advanced signal processing algorithms beyond the FFT such as time-frequency analysis, quefrency, cestrum, wavelet analysis, and AR modeling uses are outlined. These advanced algorithms can solve some sound and vibration challenges that FFT-based algorithms cannot solve. Looking at signal characteristics of a unit under test in the time-frequency plane, it is possible to get a better understanding of signal characteristics. This is an overview of these algorithms and some application examples, such as speaker testing, bearing fault detection, dashboard motor testing, and engine knock detection where they can be applied to NVH applications. Copyright © 2009 SAE International.

关键词： Fast Fourier transforms

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Proceedings of SPIE - advanced signal processing: algorithms, architectures, and implementations VII

Proceedings of SPIE - Advanced signal processing: Algorithms...

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advanced signal processing: algorithms, Architectures and Implementations VII

The proceeding contains 53 papers from the conference of SPIE advanced VIII signal processing: algorithms, Architectures, and Implementations. The topics:include pattern recognition under translation and scale changes, instanteous frequency of a propagating pulse;data-driven time-frequency and time-scale detectors;time-varying frequencies of a signal;circulant preconditioners from B-splines;extensions to total variation denoising and Jacobi method for signal subspace computation.

关键词： signal processing

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Parkinson’s Disease Detection Using advanced Wave signal processing on Spiral Drawing Tests 3rd

Parkinson’s Disease Detection Using Advanced Wave Signal Pr...

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3rd International conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： SasiRekha, S. Shankar, R. Duraisamy, S. Department of Computer Science Chikkanna Government Arts College Tirupur India

ISBN: (纸本)9783031862892

The main motor skills are impacted by Parkinson’s disease (PD), a neurodegenerative ailment that is both chronic and progressive. The signs of this condition include tremors, stiffness, bradykinesia (slow movement), and postural instability. The degeneration of brain cells that produce dopamine is the root cause of these symptoms. This study introduces an innovative algorithmic framework for predicting Parkinson’s disease (PD) using spiral drawing tests analyzed through advanced wave signal processing techniques. By collecting spiral drawings from patients via a digital tablet and converting these into wave signals, the framework employs three key algorithms: Wavelet Transform-Based Feature Extraction (WTFE), which decomposes signals into frequency bands to extract subtle features indicative of PD;a Gated Recurrent Unit with Long Short-Term Memory (GRU-LSTM) hybrid neural network that captures both short-term and long-term dependencies in the signal data to detect tremor patterns;and Hybrid Ensemble Classification (HEC), which integrates multiple classifiers—Support Vector Machines, Random Forests, and Gradient Boosting Machines—aggregated through a weighted voting scheme. Evaluated on a dataset of drawings from PD patients and healthy controls, the framework demonstrated high accuracy in distinguishing PD, suggesting it as a promising tool for early and objective diagnosis, thereby facilitating timely and effective intervention. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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