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SPECIFICATION AND ANALYSIS OF parallel DISTRIBUTED SOFTWARE AND SYSTEMS BY PETRI NETS WITH TRANSITION ENABLING FUNCTIONS

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IEEE TRANSACTIONS ON SOFTWARE engineering 1992年第3期18卷 252-261页

作者： PAPELIS, YE CASAVANT, TL Parallel Processing Laboratory Department of Electrical and Computer Engineering University of Iowa Iowa IA USA

This paper investigates a new approach for visually specifying parallel/distributed software using Petri Nets (PN's) extended with Transition Enabling Functions (TEF's). The approach is demonstrated to be useful in the specification of decision-making activities that control distributed computing systems. PN's are employed because of their highly visual nature that can give insight into the nature of the controller of such a system and because of their well-known analytical properties. In order to increase the expressive power of PN's, the extension of TEF's is used. The main focus of this paper is the specification and analysis of parallel/distributed software and systems. A key element of this approach is a set of rules derived to automatically transform such an extended net into a basic PN. Once the rules have been applied to transform the specification, well-known analytical methods can be used to investigate characteristic properties of the system and validate correct operation.

关键词： DISTRIBUTED SOFTWARE parallel SOFTWARE SOFTWARE SPECIFICATION parallel PETRI NETS TRANSITION ENABLING FUNCTIONS CONCURRENCY

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MAPPING computer-VISION-RELATED TASKS ONTO RECONFIGURABLE parallel-processing SYSTEMS

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computer 1992年第2期25卷 54-63页

作者： SIEGEL, HJ ARMSTRONG, JB WATSON, DW Parallel Processing Laboratory School of Electrical Engineering Purdue University West Lafayette IN USA

A tutorial overview of how selected computer-vision-related algorithms can be mapped onto reconfigurable parallel-processing systems is presented. The reconfigurable parallel-processing system assumed for the discussions is a multiprocessor system capable of mixed-mode parallelism; that is, it can operate in either the SIMD or MIMD modes of parallelism and can dynamically switch between modes at instruction-level granularity with generally negligible overhead. In addition, it can be partitioned into independent or communicating submachines, each having the same characteristics as the original machine. Furthermore, this reconfigurable system model uses a flexible multistage cube interconnection network, which allows the connection patterns among the processors to be varied. It is demonstrated how reconfigurability can be used by reviewing and examining five computer-vision-related algorithms, each one emphasizing a different aspect of reconfigurability.

关键词： Concurrent computing parallel processing Partitioning algorithms Very large scale integration Hardware Switches Algorithm design and analysis Prototypes Discrete Fourier transforms computer vision

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AUGMENTING ADA FOR SIMD parallel processing

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IEEE TRANSACTIONS ON SOFTWARE engineering 1985年第9期11卷 970-977页

作者： CLINE, CL SIEGEL, HJ PASM Parallel Processing Laboratory School of Electrical Engineering Purdue University

In order to program SIMD (single instruction stream-multiple data stream) parallel machines used for tasks such as speech and image processing, a language with explicit parallel constructs is often desirable. The language Ada, developed by the Department of Defense, is used here as a basis for such a language. Extensions of Ada, which allow the user to specify such operations as interprocessor communications and activation of processors, are proposed. These features are demonstrated by showing their use in a common speech processing algorithm, the parallel FFT.

关键词： SIMD Machines Ada FFT parallel processing Programming Languages

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An Enhancing Stability Control Strategy of VSG-Based Power Source Under PWM Rectifier Load

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Gaoxiang Liu, Bowen Song, Wenhao Jining, Chen Liu, Xiao Guangxi University Guangxi Key Laboratory of Power System Optimization and Energy Technology School of Electrical Engineering Nanning530004 China Guangxi Zhuang Autonomous Region Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning530200 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing School of Computer Electronics and Information Nanning530004 China

Under PWM rectifier load, the power source based on virtual synchronous generator (VSG) is prone to cause mid-frequency oscillation. Firstly, the system oscillation mechanism and characteristic of the VSG-based power source are analyzed based on impedance method. Due to the negative resistance and capacitive characteristic in the middle frequency range, the PWM rectifier load is prone to interact with the VSG-based power source, which can lead to impedance resonance. Secondly, to improve the stability of the VSG-based power source under the PWM rectifier load, a voltage differentiation feedforward control is proposed. In addition, since the mid-frequency impedance magnitude decrease obviously, the improved VSG-based power source is closer to the ideal voltage source, which can automatically suppress the middle harmonics induced by uncontrollable rectifier load. Finally, the proposed control and the corresponding theoretical analysis are verified by simulations and experiments. © 1975-2011 IEEE.

关键词： Synchronous generators

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Coverage analysis and handover strategy in RF/VLC heterogeneous networks

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Applied Optics 2025年第15期64卷 4160-4173页

作者： Renhai Feng Yatong Guo Muhammad Zain Yousaf Baseem Khan Liping Luo School of Electrical and Information Engineering Tianjin University Tianjin 300072 China School of Physics and Electronic Engineering Xinjiang Normal University Ürümqi 830054 China Center for Renewable Energy and Microgrids Huanjiang Laboratory Zhejiang University Zhuji Zhejiang 311816 China School of Electrical and Information Engineering Hubei University of Automotive Technology Shiyan China School of Aeronautics and Astronautics Zhejiang University Hangzhou 310027 China zain.yousaf@*** Department of Electrical and Computer Engineering Hawassa University Hawassa Ethiopia baseemkh@hu.edu.et School of Electronics and Information Guangxi Minzu University Nanning 530006 China Guangxi Colleges and Universities Engineering Research Center for Multi-modal Information Intelligent Sensing Processing and Application Nanning 530006 China

Coverage analysis in heterogeneous networks (HetNets) remains insufficient, especially under user mobility and diverse association strategies. To address this, this paper proposes a contact probability and coverage approximation based on association strategy (CPCA-AS) to analyze the coverage probability (CPr) in visible light communication (VLC) and radio frequency (RF) HetNets. Based on the proposed stochastic geometry framework, CPCA-AS evaluates coverage performance under various configurations, including RF-only, VLC-only, and hybrid RF/VLC scenarios. The model incorporates a contact probability mechanism and considers two strategies: opportunistic VLC and maximum received signal power (MRSP). For VLC networks, CPCA-AS utilizes the Gil-Pelaez inversion theorem, and Laplace transforms to efficiently approximate CPr. For RF networks, Markov and Gamma inequalities form conservative accurate approximations and lower bounds. CPCA-AS effectively captures the influence of the photodetector’s field-of-view (FoV) on interference and association behavior. Monte Carlo simulations validate the accuracy of the CPCA-AS. Results demonstrate that CPCA-AS improves CPr. The opportunistic VLC strategy performs better in high-interference and large FoV scenarios. In contrast, the MRSP strategy is more effective in low-interference and low-rate environments.

关键词： Fourier transforms Light sources Neural networks Numerical simulation Systems design Visible light communications

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High-order diversity feature learning for pedestrian attribute recognition

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Neural Networks 2025年 188卷 107463页

作者： Wu, Junyi Huang, Yan Gao, Min Niu, Yuzhen Chen, Yuzhong Wu, Qiang Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fujian Fuzhou China Australian Artificial Intelligence Institute University of Technology Sydney NSW Australia Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fujian Fuzhou China School of Electrical and Data Engineering University of Technology Sydney NSW Australia

Pedestrian attribute recognition (PAR) involves accurately identifying multiple attributes present in pedestrian images. There are two main approaches for PAR: part-based method and attention-based method. The former relies on existing segmentation or region detection methods to localize body parts and learn corresponding attribute-specific feature from the corresponding regions, where the performance heavily depends on the accuracy of body region localization. The latter adopts the embedded attention modules or transformer attention to exploit detailed feature. However, it can focus on certain body regions but often provide coarse attention, failing to capture fine-grained details, the learned feature may also be interfered with by irrelevant information. Meanwhile, these methods overlook the global contextual information. This work argues for replacing coarse attention with detailed attention and integrating it with global contextual feature from ViT to jointly represent attribute-specific regions. To tackle this issue, we propose a High-order Diversity Feature Learning (HDFL) method for PAR based on ViT. We utilize a polynomial predictor to design an Attribute-specific Detailed Feature Exploration (ADFE) module, which can construct the high-order statistics and gain more fine-grained feature. Our ADFE module is a parameter-friendly method that provides flexibility in deciding its utilization during the inference phase. A Soft-redundancy Perception Loss (SPLoss) is proposed to adaptively measure the redundancy between feature of different orders, which can promote diverse characterization of features. Experiments on several PAR datasets show that our method achieves a new state-of-the-art (SOTA) performance. On the most challenging PA100K dataset, our method outperforms previous SOTA by 1.69% and achieves the highest mA of 84.92%. © 2025 The Authors

关键词： Contrastive Learning

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Neural Network-Assisted Hybrid Model Based Message Passing for Parametric Holographic MIMO Near Field Channel Estimation

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IEEE Transactions on Wireless Communications 2025年

作者： Yuan, Zhengdao Guo, Yabo Gao, Dawei Guo, Qinghua Wang, Zhongyong Huang, Chongwen Jin, Ming Wong, Kai-Kit Henan Open University Artificial Intelligence Technology Engineering Research Center Zhengzhou450002 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia Zhengzhou University School of Information Engineering Zhengzhou450002 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China University of Wollongong School of Electrical Computer and Telecommunications Engineering NSW2522 Australia Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University College London Department of Electronic and Electrical Engineering Torrington Place WC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul Korea Republic of

Holographic multiple-input and multiple-output (HMIMO) is a promising technology with the potential to achieve high energy and spectral efficiencies, enhance system capacity and diversity, etc. In this work, we address the challenge of HMIMO near field (NF) channel estimation, which is complicated by the intricate model introduced by the dyadic Green's function. Despite its complexity, the channel model is governed by a limited set of parameters. This makes parametric channel estimation highly attractive, offering substantial performance enhancements and enabling the extraction of valuable sensing parameters, such as user locations, which are particularly beneficial in mobile networks. However, the relationship between these parameters and channel gains is nonlinear and compounded by integration, making the estimation a formidable task. To tackle this problem, we propose a novel neural network (NN) assisted hybrid method. With the assistance of NNs, we first develop a novel hybrid channel model with a significantly simplified expression compared to the original one, thereby enabling parametric channel estimation. Using the readily available training data derived from the original channel model, the NNs in the hybrid channel model can be effectively trained offline. Then, building upon this hybrid channel model, we formulate the parametric channel estimation problem with a probabilistic framework and design a factor graph representation for Bayesian estimation. Leveraging the factor graph representation and unitary approximate message passing (UAMP), we develop an effective message passing-based Bayesian channel estimation algorithm. Extensive simulations demonstrate the superior performance of the proposed method. © 2025 IEEE.

关键词： Green's function

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Enhancing Sample Utilization in Noise-robust Deep Metric Learning with Subgroup-based Positive-pair Selection

arXiv

引用

arXiv 2025年

作者： Yu, Zhipeng Xu, Qianqian Jiang, Yangbangyan Sun, Yingfei Huang, Qingming The School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China

The existence of noisy labels in real-world data negatively impacts the performance of deep learning models. Although much research effort has been devoted to improving the robustness towards noisy labels in classification tasks, the problem of noisy labels in deep metric learning (DML) remains under-explored. Existing noisy label learning methods designed for DML mainly discard suspicious noisy samples, resulting in a waste of the training data. To address this issue, we propose a noise-robust DML framework with SubGroup-based Positive-pair Selection (SGPS), which constructs reliable positive pairs for noisy samples to enhance the sample utilization. Specifically, SGPS first effectively identifies clean and noisy samples by a probability-based clean sample selectionstrategy. To further utilize the remaining noisy samples, we discover their potential similar samples based on the subgroup information given by a subgroup generation module and then aggregate them into informative positive prototypes for each noisy sample via a positive prototype generation module. Afterward, a new contrastive loss is tailored for the noisy samples with their selected positive pairs. SGPS can be easily integrated into the training process of existing pair-wise DML tasks, like image retrieval and face recognition. Extensive experiments on multiple synthetic and real-world large-scale label noise datasets demonstrate the effectiveness of our proposed method. Without any bells and whistles, our SGPS framework outperforms the state-of-the-art noisy label DML methods. Code is available at https://***/smuelpeng/SGPS-NoiseFreeDML. © 2025, CC BY.

关键词： Contrastive Learning

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Grating pitch comparator traceable to the Cr atom transition frequency

引用

Measurement: Journal of the International Measurement Confederation 2025年 242卷

作者： Lin, Zichao Deng, Xiao Wu, Yize Feng, Jingtong Zhu, Hongyu Yang, Yaao Yu, Jing Xue, Dongbai Wang, Jianbo Shi, Yushu Jin, Tao Zhang, Wentao Dun, Xiong Cheng, Xinbin Li, Tongbao MOE Key Laboratory of Advanced Micro-Structured Materials Shanghai Frontiers Science Center of Digital Optics Institute of Precision Optical Engineering School of Physics Science and Engineering Tongji University Shanghai200092 China National Institute of Metrology China Beijing100029 China College of metrology and measurement engineering China Jiliang University Hangzhou310018 China Guangxi Key Laboratory of Optoelectronic Information Processing School of Optoelectronic Engineering Guilin University of Electronic Technology Guilin541004 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

Calibration of the grating pitch typically requires metrological instruments or diffraction techniques. The traceability and stability demands for laser wavelength in these techniques present substantial challenges for on-site grating calibration. This paper introduces a novel traceability approach to grating pitch calibration using a grating pitch comparator that utilizes the transition frequency of the chromium. The chromium transition frequency is converted into a chromium grating through atom lithography, establishing a self-traceable length standard dCr = 212.7787 ± 0.0049 nm. Calibration of the test grating's pitch is achieved by comparing the phase shift caused by the simultaneous movement of the chromium grating and the test grating. By using the dCr, this approach combines the high-accuracy of fundamental physical constants with the environmental stability of grating interferometers, eliminating the need for measuring laser wavelength, air refraction, diffraction angles. It significantly enhances the precision and makes more straightforward and portable, crucial for facilitating traceable on-site grating measurements. © 2024

关键词： Comparators (optical)

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MN-Net: Speech Enhancement Network via Modeling the Noise

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language processing 2025年 33卷 1208-1219页

作者： Ying Hu Qin Yang Wenbing Wei Li Lin Liang He Zhijian Ou Wenzhong Yang School of Computer Science and Technology Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China Xinjiang Institute of Electronics Research Shares Company Ltd. Urumqi China Department of Electrical Engineering Tsinghua University Beijing China Beijing National Research Center for information Science and Technology Department of Electronics Tsinghua University Beijing China

Currently, deep learning-based speech enhancement methods generally focus on target speech extraction while neglecting modeling the other sound sources in the mixture. These methods still can't distinguish the target speech from the interference well. In this paper, we present a monaural speech enhancement network via Modeling the Noise (MN-Net), which includes a shared Encoder and three separate Decoders for parallel modeling the magnitude and phase spectrogram of target speech, and the complex spectrogram of noise. Specifically, we propose a Multi-Branch Feature Extractor (MBFE) module to capture the richer contextual information in mixture, and a Spatial Reconstruction Unit (SRU) to remove the redundancy from extracted features. We compared our proposed MN-Net with 18 classical speech enhancement methods on the VoiceBank+DEMAND dataset, and with 9 ones on DNS-Challenge dataset for denoising task, and with 7 ones on the WHAMR! dataset for simultaneous denoising & de-reverberation task. Our proposed MBFE module was applied to two classical speech enhancement methods, DB-AIAT and CMGAN, replacing their DenseBlocks module. The results demonstrate that applying the MBFE module can boost their performances while keeping smaller model size. A series of visualization analysis intuitively verify that modeling the noise can enable the network to distinguish the target speech from noise and other interference more accurately.

关键词： Noise Feature extraction Spectrogram Speech enhancement Decoding Kernel Transformers Noise reduction Data mining Training

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