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Offloading Algorithm for Edge Computing Tasks Based on Energy Optimization 8

Offloading Algorithm for Edge Computing Tasks Based on Energ...

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8th IEEE International Conference on Smart Cloud, SmartCloud 2023

作者： Gao, Xing Gu, Haonan Liu, Fang Hu, Wei Wuhan City College Wuhan China College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (纸本)9798350313505

Current research in computation offloading encompasses various aspects, including task partitioning and allocation, resource management, communication, and data management. This paper focuses on developing new algorithms and techniques to enhance the efficiency and effectiveness of the offloading process while addressing challenges arising from dynamic and heterogeneous mobile environments. Researchers are also examining the trade-off between offloading benefits like improved performance and energy efficiency, and the associated overhead costs such as communication and data management. This paper proposes an optimization strategy for computational offloading to enhance the Grey Wolf algorithm. The solution aims to quickly optimize system energy consumption while considering the arithmetic power constraint of the mobile edge computing server and time delay constraints. Simulation experiments demonstrate that the algorithm in this paper significantly outperforms the traditional Grey Wolf and PSO algorithms in terms of both performance and computational speed. ©2023 IEEE.

关键词： information management

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Implicit Learning of Rotational Decoupling Detection Heads Applied to Remote Sensing Object Detection

Implicit Learning of Rotational Decoupling Detection Heads A...

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2023 Asia-Pacific Conference on Image processing, Electronics and Computers, IPEC 2023

作者： Song, Chengkun Ding, Sheng Chen, Yingying School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Kagoshima University Kagoshima Japan

ISBN: (纸本)9781665457033

Remote sensing image features complicated background, big image size, small object pixels, and variable object direction due to the arbitrary shooting angle. Conventional object detector has poor detection effect on remote sensing image, which is prone to missed detection and false detection. In view of this, an improved YOLOv5 object detection algorithm was proposed for remote sensing image. First, the C3 module was improved. The output feature information of each convolutional module was transmitted to the deep network, so as to preserve more fine-grained feature information and enhance the small object detection capability. The improved downsampling mechanism integrates convolution downsampling with maximum pooling downsampling mechanism in downs amp ling to separate foreground and background information while retaining feature information. A lightweight rotatory decoupling detection head with implicit learning was proposed to address the conflict between regression and classification, thus improving the network detection capability for objects in any direction. In the meantime, an implicit learning matrix was added to guarantee the lightweight detection head and improve the network detection accuracy for multiple tasks. According to experimental results, under the same model size, compared with the original YOLOv5 detection method, the detection accuracy and detection speed of the proposed method were improved by 8% and 15 frames, or 14.7%, respectively on DOTA-v1.5 data set, effectively improving the detection accuracy and speed in remote sensing image detection. The detection accuracy was increased by 3.5% on coco data set in natural scenario. The method herein can still effectively improve the detection accuracy in natural scenario, proving effectiveness of the modified YOLOv5 method. © 2023 IEEE.

关键词： Object detection

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Scheduling Algorithm for Low Energy Consumable Parallel Task Application Based on DVFS 1

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Liu, Xun Zhao, Hui College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Henan University Kaifeng475000 China

ISBN: (数字)9783031281242

ISBN: (纸本)9783031281235

With the continuous improvement of various high-performance computing systems, various data centers had also been fully expanded. Energy consumption and actual performance measurement were very important indicators, which were also key issues in how to judge parallel calls for some tasks in high-performance computer systems. Modern processors were basically equipped with software control functions such as DVFS (Dynamic Voltage Frequency Scaling), in the actual system operation to ensure that the system could ensure the reasonable operation of the system while reducing energy consumption indicators. This paper considered how the designed scheduling algorithm first divides tasks reasonably to ensure that the maximum completion time and energy consumption of the processor were sufficiently reduced when the directed acyclic graph was executed. Then considered making reasonable adjustments to the processor frequency using DVFS technology to adapt to the task while ensuring the critical path of the task. At the end of the article, make sure that the experimental verification algorithm could ensure that the task was completed and could reduce the energy consumption during task execution as much as possible. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy utilization

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Exploring the potential of ultra-low latency spiking neural network in remote sensing image classification

Exploring the potential of ultra-low latency spiking neural ...

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IET International Radar Conference (IRC 2023)

作者： Jiahao Li Ming Xu He Chen Can Li Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing People's Republic of China

ISBN: (数字)9781837240982

With the rapid advancement of artificial intelligence, the depth of artificial neural networks (ANN) has increased, resulting in higher power consumption and resource utilization. To address these concerns, researchers have begun to explore spiking neural networks (SNN) as an alternative. SNN offers the potential for improved energy efficiency while achieving comparable performance. In recent years, there has been a growing interest in applying SNN to remote sensing classification scenarios. However, one major challenge hindering its widespread adoption is the high latency associated with real-time processing. To address this challenge, we adopt a novel hybrid training approach that combines ANN-SNN conversion with backpropagation through time. We evaluate the effectiveness of our approach on three publicly available datasets: MSTAR, AID and UC-Merced. Our experiment results demonstrate that the proposed ultra-low latency SNN achieves classification accuracy rates of 99.05%, 91.36%, and 92.62%, respectively, with a single time step. To the best of our knowledge, this is the first time to apply ultra-low latency SNN to remote sensing image classification tasks. Our findings suggest that this innovative approach has significant potential for future research in the deployment of these models for on-board processing.

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A TRACK ASSOCIATION ALGORITHM BASED ON MOTION DYNAMICS FEATURES IN ENTOMOLOGICAL RADAR

A TRACK ASSOCIATION ALGORITHM BASED ON MOTION DYNAMICS FEATU...

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IET International Radar Conference 2023, IRC 2023

作者： Li, Biao Liu, Hanzhe Zhang, Tianran Cai, Jiong Wang, Rui The Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China Beijing Institute of Electronic System Engineering Beijing100854 China

ISBN: (纸本)9781839539954

When tracking densely distributed targets such as insects, the traditional trajectory association algorithm exhibits poor correlation performance, leading to a decline in multi-target tracking efficiency. In this paper, based on the behavioral characteristics of insects gathering and co-orienting during aerial flight, a motion dynamic feature-assisted multi-target tracking method is proposed. Firstly, a three-dimensional biological distribution state matrix is constructed through spatial gridization. Then, a three-dimensional circular convolution is employed to estimate the target's motion dynamic feature, assisting in multi-target trajectory initialization. Finally, the improvement effect of the method on trajectory tracking accuracy is validated through simulations and migration insect data. © The Institution of Engineering & technology 2023.

关键词： Radar tracking

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Lightweight NPU Method Based on Hyper-Threading technology

Lightweight NPU Method Based on Hyper-Threading Technology

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Ao Zhang Yongrui Li Wenlong Li Yizhuang Xie Zhihan Zhang Zhu Yang Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Beijing Racobit Electronic Information Technology Co. Ltd Beijing China Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Racobit Electronic Information Technology Co.Ltd

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

With the rapid development of deep learning, the processing power of Neural processing Unit(NPU) continues to improve. However, this leads to an increasing scale of logical resources for NPU, making it challenging to deploy NPU on Field-Programmable Gate Array(FPGA). To address this issue and enable the deployment of high-computational-power NPU on FPGA, this paper proposes a lightweight NPU method based on hyper-threading technology, considering the characteristics of NPU hardware architecture. This method effectively reduces the logical resources usage of the NPU while ensuring only a minimal decrease in computational power, thus enabling successful FPGA deployment. The overall experiment is based on the Virtex UltraScale+ HBM VCU128 FPGA platform. After testing, it was found that the NPU, previously undeployable, could be successfully deployed after lightweight processing. The resource usage ratio of LUTs decreased by about 10%, and the computational power only decreased by 4%. In other words, by using this method, the scale of logical resource usage of the NPU was effectively reduced while ensuring a minimal decrease in computational power, improving the deployment situation of the NPU on FPGA. This approach has certain reference value and significance for the lightweight design of NPU.

关键词： Deep learning Computer architecture Logic gates Data processing Hardware Table lookup Field programmable gate arrays Signal resolution Logic arrays Testing

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DD-Net: Dynamic Network Architecture for Optimized Curve Segmentation and Reduce Computational Redundancy 27th

DD-Net: Dynamic Network Architecture for Optimized Curve Seg...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Cao, Yunxiang Chen, Li Wang, Yubo Feng, Zhida Tian, Jing School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Institute of Systems Science National University of Singapore Singapore Singapore

ISBN: (纸本)9783031783821

Curvilinear Structure segmentation has numerous applications in various fields, including providing a better understanding of defects such as cracks on roads or walls, thereby assessing the structural safety of buildings. Numerous curve segmentation methods based on Convolutional Neural Networks(CNN) have been developed in recent years. However, preserving feature integrity remains a challenge. To address this issue, this paper introduces a double dynamic network, called DD-Net, which consists of a dynamic structure for training and dynamic masking for inference. Firstly, to enhance the nonlinearity of the model, a paradigm to auto-adjust the CNN structure via a trainable module is formed for better fine-grained feature extraction. This dynamic structure (DS) paradigm enables a trade-off to extract or discard the feature data in the model training. Due to the data heterogeneity between the training and inference data, models may contain some useless nodes that are less effective during inference. The dynamic masking (DM) is proposed to omit the useless nodes based on the score difference between the train and inference feature statistics, thereby reducing redundant computations. To further improve the model’s performance on thin-curve segmentation and to preserve feature integrity, we introduce a non-curve suppression (NCS) module. This module focuses on background information while considering foreground prediction to address noisy conditions. The experimental results show that our DD-Net achieves promising results on three benchmark datasets and outperforms state-of-the-art curve segmentation models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Open Set Radar HRRP Recognition Using Confidence through Neural Weight Proximity 2

Open Set Radar HRRP Recognition Using Confidence through Neu...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Liu, Yichen Liu, Ping-An Zhang, Liang Wang, Yanhua Bao, Zengdi Xu, Yinhui Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Xi'an Modern Control Technology Research Institute Xi'an710065 China Zhengzhou Research Institute Beijing Institute of Technology Zhengzhou450000 China

ISBN: (纸本)9798331515669

High-resolution range profiles (HRRP) have become increasingly important in radar automatic target recognition (RATR) due to their ability to capture detailed structural features of targets. In practical RATR scenarios, the operational environment is open and dynamic, often exposing models to targets belonging to previously unseen categories. This underscores the necessity for open-set recognition (OSR) methods that can effectively distinguish both known and unknown targets. Theoretically, there exists a distributional discrepancy between the feature representations of known and unknown categories in deep learning models. By designing a suitable confidence calculation method that leverages this discrepancy, it becomes possible to accurately identify whether a given sample belongs to a known or an unknown category. Inspired by the Neural Collapse theory, we observe that deep features representing known categories tend to cluster tightly around the weight vectors of the final layer, whereas features from unknown categories are typically more scattered and lie farther away. Building on this observation, we propose a confidence measure that is determined by the proximity of a feature to the weight vectors of the final classification layer. Samples with confidence values below a defined threshold are classified as unknown, which forms the fundamental mechanism of our open-set detector. Our approach is versatile, making it applicable to a variety of network architectures and adaptable to different feature distance metrics. Extensive experiments conducted on HRRP data confirm the efficacy of our proposed method for open-set recognition, demonstrating its ability to reliably distinguish between known and unknown targets under diverse conditions. © 2024 IEEE.

关键词： Radar target recognition

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User Behavior-Based Collaborative Filtering Recommendation Algorithm and Visualization in Second-Hand Trading Platform 2

User Behavior-Based Collaborative Filtering Recommendation A...

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2nd IEEE International Conference on Sensors, Electronics and Computer Engineering, ICSECE 2024

作者： Qiao, Rui Wang, Xin Zhong, Xiaoben School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan China Yunnan College of Business Management Anning650301 China

ISBN: (纸本)9798350373646

In the backdrop of the digital era, the demand for second-hand trading within campuses has seen a significant rise. This project aims at designing a second-hand trading platform that merges the latest technological trends, providing a secure and convenient trading environment for students. The system utilizes Spring Boot for backend development and Vue along with ElementUI for constructing the user interface, ensuring efficient operation and enhancing user interaction experience. The server employs Spring Boot's built-in Tomcat, simplifying the deployment process. For data persistence, Mybatis framework is used for flexible and efficient interactions with the MySQL database. The platform introduces a collaborative filtering algorithm based on user behavior for smart product recommendations, further improving the user experience. The system integrates core modules such as user management, product browsing, shopping cart functionalities, and order processing, innovatively introducing an offline transaction confirmation mechanism to ensure transaction security and reliability while avoiding legal risks associated with online payments. With its B/S architecture, the platform operates more smoothly, making it more accessible to users. Experimental results demonstrate that the platform has a user-friendly interface and efficient data processing capabilities, receiving widespread recognition and praise from users, proving its practicality and innovativeness. © 2024 IEEE.

关键词： Collaborative filtering

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Named Entity Recognition of Electronic Medical Records based on BERT-BiLSTM-Biaffine Model

Named Entity Recognition of Electronic Medical Records based...

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2023 Asia-Pacific Conference on Image processing, Electronics and Computers, IPEC 2023

作者： Wang, Peng Gu, Jinguang Institute of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Prov. Key Laboratory of Intelligent Information Processing and Real Time Industrial Systems Wuhan University of Science and Technology Wuhan China

In the case of the specific task of identifying named entities within electronic medical record, it is hard to determine the boundary of nested entities, and existing NER systems have insufficient decoding performance. Based on the pre training model BERT, this paper introduces a novel network structure called Biaffine Layer using a bidirectional LSTM layer. The network uses a dual affine attention mechanism for semantic information learning, which can better interact with the semantic information of entity heads and entity tails, thereby achieving better recognition results for entities. Due to the sparsity of named entity datasets and the uneven distribution of entity categories, traditional binary cross entropy loss functions require multiple rounds of training to decode entities. In this paper, we have modified the binary cross entropy loss function to make the proposed model faster decode the entities that need to be identified. The model performs well, according to the experimental findings. The approach suggested in this paper offers a fresh approach to the NER issue raised by electronic medical records, and it is anticipated to considerably boost the effectiveness and caliber of clinical medical research. © Published under licence by IOP Publishing Ltd.

关键词： Decoding

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