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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

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Efficient Checkpoint under Unstable Power Supplies on NVM based Devices 24

Efficient Checkpoint under Unstable Power Supplies on NVM ba...

引用

24th IEEE International conference on high performance Computing and Communications, 8th IEEE International conference on Data Science and Systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Liu, Jialin Sha, Edwin Zhuge, Qingfeng Xu, Rui Song, Yuhong School of Computer Science and Technology East China Normal University Shanghai China

ISBN: (纸本)9798350319934

In smart cities, many embedded devices' power supply lacks stability, e.g., solar panel, since they are used outdoors. In these systems, power failures can cause data lost if the data is kept on volatile media like DRAM. Thus, using a backup mechanism to record the program status is necessary for the program going forward. Non-volatile memory (NVM) provides systems with a convenient method to persist the programs' status. However, NVM is notorious for poor write endurance. In this paper, we propose an automatic checkpoint mechanism that can reduce NVM write wear. Firstly, the checkpoint trigger code is instrumented into every basic block to achieve automatic runtime checkpoint activation. Then, when the program triggers checkpoint, the checkpoint procedure incrementally updates program state's backup to reduce redundant NVM writes. In this step, we select what to back up by determining the activated stack frame. Also, our mechanism has two backup copies to avoid power loss corrupting the backup content by interrupting the checkpoint procedure. Finally, we design an update log queue to fine-grained update of global data so that the NVM write can be reduced. Experiments show that our mechanism can effectively reduce total NVM write overhead with acceptable execution overhead. © 2022 IEEE.

关键词： Dynamic random access storage

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A Quantum Temporal Difference Learning Method Based on Quantum World Model

A Quantum Temporal Difference Learning Method Based on Quant...

引用

2024 IEEE Global Communications conference, GLOBECOM 2024

作者： Zeng, Peigen He, Ying Yu, F. Richard Du, Jianbo Shenzhen University College of Computer Science and Software Engineering Shenzhen China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an China Carleton University School of Information Technology Ottawa Canada

ISBN: (纸本)9798350351255

Based on quantum parallelism theory and quantum phenomena such as superposition and entanglement, quantum reinforcement learning (QRL) has the potential to surpass classical reinforcement learning (RL). Although some excellent works have been done on QRL, existing RL methods encounter chanllenges when performing in environments with sparse rewards. In this paper, we provide a new perspective on conducting temporal difference (TD) learning in quantum computing, which can eliminate redundant exploration steps compared to classical methods. Specifically, we first use environment information to construct a world model with quantum circuits, enabling it to interact in a quantum way. Then, we perform the learning process by using the quantum world model and Grover's algorithm to query backwards how to reach the recorded states with high TD-errors. Simulation results show that our proposed method has superior performance compared to classical RL algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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Integration of Li-Fi and IoT for Sixth-Generation (6G) Communication Systems 1

Integration of Li-Fi and IoT for Sixth-Generation (6G) Commu...

引用

1st International conference on Emerging Technologies and Innovation for Sustainability, EmergIN 2024

作者： Sharma, Ajay Xuereb, Peter Albert Garg, Lalit Computer Information System Faculty of Information and Communication Technology University of Malta Msida Malta

ISBN: (纸本)9798350391268

Light Fidelity (LiFi) to enhance future communication solutions along with IoT connectivity is a must to design the Sixth Generation (6G) networks. LiFi, which uses visible light for data communication, offers several advantages over RF-based technologies, including a high signaling rate, better security, and reduced interference. This paper aims at analyzing LiFi’s possibilities of integrating with IoT to solve the problems of poor coverage and sensitivity to ambient light and interconnections with existing RF networks. The proposed network structure to integrate both LiFi and RF does provide a path for smooth transition between LiFi and RF to maintain the connection and performance. New modulation techniques and enhanced security schemes are incorporated to overcome technical constraints and gain better communication with security. In projecting how LiFi can enhance green technology, the study shows that through using energy-efficient LED, LiFi is capable of complementing green technology for energy-efficient communication of IoT via 6G networks. © 2024 IEEE.

关键词： Data communication systems

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Generalizing Vehicle Manoeuvre Prediction Across Diverse Datasets 2

Generalizing Vehicle Manoeuvre Prediction Across Diverse Dat...

引用

2nd IEEE International conference on Emerging Research in Computational Science, ICERCS 2024

作者： Venkatarao, Marella Reddy, Dodda Venkata Rao, S. Siva Nageswara Ul Hassan Khan, Ameen Avinash, Golla Koushik, Pendela Chenchu Narasaraopeta Engineering College Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798331534950

Due to the complex nature of automotive components and sensor data, predictive maintenance is essential to ensure the reliability and safety of the vehicle. This work introduces a new predictive algorithm for automotive engine health, designed as it will provide higher accuracy and faster decisions in detecting potential engine failures, linear Through discriminant analysis, Gaussian naive edges, support vector machines, decision trees, random forests, gradient enhancement, and AdaBoost, the program displays patterns and abnormalities that may indicate impending engine *** data set undergoes extensive preprocessing steps such as standardization, handling missing values, and feature engineering to improve model performance. The evaluation criteria used include accuracy, precision, and confusion matrix, with special attention to prevent overfitting through regularization and the early stop method. In the developed model, the group method, especially stacked model 1, obtains impressive results with a model accuracy of 0.99. This high accuracy highlights the effectiveness of the ensemble approach in managing forecasts. The model's ability to deliver real-time analysis and early warning can help significantly reduce maintenance costs, prevent failures, and enhance vehicle safety, resulting in improved vehicle engine health during the maintenance process. © 2024 IEEE.

关键词： Support vector machines

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ASIMSA: Advanced Semantic Information Guided Multi-Scale Alignment Framework for Medical Vision-Language Pretraining 9

ASIMSA: Advanced Semantic Information Guided Multi-Scale Ali...

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9th IEEE International conference on Computational Intelligence and Applications, ICCIA 2024

作者： Xiao, Shuai Zhang, Yangyang Jiang, Liming Wang, Zhengxia School of Computer Science and Technology Hainan University China School of Information and Communication Engineering Hainan University China Software Co. Ltd. China

ISBN: (纸本)9798350352214

Medical Visual Language Pretraining (MVLP) utilizes textual reports for weak supervision to improve the learning of medical visual representations, showing promise in various medical image analysis tasks. While previous studies have focused on image and report correspondences within the same instances, they have overlooked high-level semantic associations, such as disease-level semantic correspondences, across different instances. To address this issue, we propose an Advanced Semantic Information Guided Multi-Scale Alignment Framework (ASIMSA) with two main modules: 1) Image-Report Alignment (IRA) module for learning shared representations between image and report of the same instance, and 2) Disease-Entity Alignment (DEA) module for learning disease entity correspondences across different instances. Extensive experiments on zero-shot classification, fine-tune classification, and semantic segmentation tasks validate the effectiveness of our proposed method, demonstrating its stable and superior performance. © 2024 IEEE.

关键词： Semantic Segmentation

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Deep Neural Network Enhanced Chip-on-board Hybrid-Packaged Photonic-Electrical Silicon Transceiver for high-speed Optical Interconnects

Deep Neural Network Enhanced Chip-on-board Hybrid-Packaged P...

引用

CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of conference on Lasers and Electro-Optics

作者： Qin, Jun Yang, Qipeng Han, Changhao Zhou, Yan Zhang, Yunhao Wu, Yichen Sun, Yu Lu, Junde Liu, Siming Li, Yueqin Sun, Jian Min, Miao Hu, Weiwei He, Zhixue Wang, Lei Yu, Shaohua Shu, Haowen Wang, Xingjun Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing100192 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Peking University Yangtze Delta Institute of Optoelectronics Nantong226010 China Peng Cheng Laboratory Shenzhen518055 China State Key Laboratory of Information Photonics and Optical Communications BUPT Beijing100876 China

We explore implementing a multilevel deep neural network to enhance the performance of a 4-channel photonic-electrical hybrid-packaged silicon transceiver. Stable transmission and reception of 150 Gbps/λ PAM4 signals... 详细信息

关键词： Optical interconnects

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Multi-focus image fusion based on edge feature interaction and global reconstruction 4

Multi-focus image fusion based on edge feature interaction a...

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4th International conference on Signal Image Processing and Communication, ICSIPC 2024

作者： Li, Qing Huang, Man College of Computer and Information Science Chongqing Normal University Chongqing401331 China Chongqing Transsion Communication Technology Co. Ltd. Chongqing401120 China

ISBN: (纸本)9781510682467

Multi-focus image fusion aims to synthesize key features of different images to enhance visual performance and produce high-quality images with higher resolution and finer details to enrich overall visual perception. However, existing methods mainly focus on feature extraction without paying much attention to the feature reconstruction process. In this paper, we propose a fusion method based on edge feature interaction and global reconstruction. By combining spatial attention and channel attention mechanisms, image features are effectively extracted at the encoder stage. And the edge information is detected using the canny operator, and subsequently an interactive edge feature fusion block is designed to facilitate the edge information fusion of two images detected by the canny operator. In the decoder stage, a self-attention mechanism is used for image reconstruction to further improve the quality of image fusion. Extensive experiments show that our model achieves state-of-the-art level and performs well in both subjective perception and objective evaluation. © 2024 SPIE.

关键词： Image fusion

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YPMNet: A lightweight drowning detection algorithm 24

YPMNet: A lightweight drowning detection algorithm

引用

4th International conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2024

作者： Lei, Xigui Jiao, Ge Liu, Hai Hu, Yuxin College of Computer Science and Technology Hengyang Normal University Hengyang Hunan China

ISBN: (纸本)9798400710247

In recent years, there has been a rise in drowning accidents in swimming pools. As a result, there is a growing interest in using deep learning methods to detect drowning incidents. However, current research has identified several issues with existing drowning detection methods, including poor real-time performance, a high number of parameters, and extensive calculations. To address these problems, a lightweight and real-time drowning detection algorithm, YPMNet, was developed. This algorithm utilizes the MobileNetV3 lightweight network to reconstruct the YOLOPose backbone network, resulting in faster feature extraction. Additionally, the SE attention mechanism is incorporated to balance the trade-off between model accuracy and lightweight design. To further optimize the algorithm for mobile devices, the h-swish activation function is used to enhance the model's nonlinear mapping power and robustness. The experimental results demonstrate that YPMNet achieves a detection accuracy of 90% and a detection speed of 32fps, meeting the real-time requirements for drowning detection. © 2024 Copyright held by the owner/author(s).

关键词： Feature extraction

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Spatially Coupled 5G LDPC Codes via Superposition

Spatially Coupled 5G LDPC Codes via Superposition

引用

2025 IEEE Wireless Communications and Networking conference, WCNC 2025

作者： Yang, Jiayi Wang, Qianfan Tan, Zhiyuan Li, Congduan Ma, Xiao School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Sun Yat-sen University Guangdong Key Laboratory of Information Security Technology Guangzhou510006 China Huawei Technologies Co. Ltd. Shanghai China School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen518107 China

ISBN: (纸本)9798350368369

We propose in this paper to enhance the 5G low-density parity-check (LDPC) codes by transmitting the codewords in a block Markov superposition transmission (BMST) manner, resulting in a class of spatially coupled LDPC codes. We present a generalized extrinsic information transfer (EXIT) chart for performance analysis, showing that the decoding thresholds can be improved by increasing the memory size (coupled width) m. However, for m > 1, the receiver requires a relatively large window size for the sliding window decoding (SWD) algorithm, potentially causing unacceptable decoding latency. To address this issue, we propose an adaptive sliding window decoding (ASWD) algorithm, in which the decoding window size depends on the decoding state of the BMST system. The proposed EXIT chart analysis can also effectively guide the setting of the maximum decoding window in the ASWD algorithm, as well as the setting of the superposition fractions for the BMST-5G-LDPC system. Simulation results show that: 1) the ASWD algorithm can effectively reduce the average decoding window size in the medium to high signal-to-noise ratio (SNR) region, without the performance loss compared to the conventional SWD algorithm;2) the proposed BMST-5G-LDPC code can achieve about 0.4, 0.5 and 1.7 dB performance gains compared to the 5G LDPC code over the AWGN channel, the fast fading channel, and the quasi-static block fading channel, respectively. © 2025 IEEE.

关键词： Decoding

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EP-Net: Automatic Artery/Vein Classification With Evidential Probability Map

EP-Net: Automatic Artery/Vein Classification With Evidential...

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2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhao, Rongchang Xu, Bo Jia, Xiaoliang Liu, Jin Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350386226

Abnormal retinal vascular morphology is commonly associated with cardiac, cerebrovascular, and systemic diseases. Hence, automated artery/vein(A/V) classification is crucial for the diagnosis of ophthalmic and systemic diseases. However, existing methods still face limitations in A/V classification and are prone to errors especially in microvessels and in noisy backgrounds. To alleviate these problems, this paper proposes an Evidence Probability Network (EP-Net) to achieve accurate A/V classification. Concretely, the multi-scale feature module in the EP-Net learns various vessel features, and the evidence probability module measures uncertainty and evidence for each pixel to overcome misclassification because of over-/under-confidence. Experiments on two public fundus image datasets demonstrate the superiority of the proposed EP-Net over state-of-the-art A/V classification methods. © 2024 IEEE.

关键词： Lung cancer

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