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16th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Wang, Jie-pei Tang, Lei-yu Fan, Jian-cong Liu, Guo-qiang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China College of Mathematics and System Science Shandong University of Science and Technology Qingdao China Provincial Key Lab for Information Technology of Wisdom Mining of Shandong Province Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9789811912528

In recent years, deep learning methods, especially convolutional neural networks (CNNs), have shown powerful discriminative ability in the field of remote sensing change detection. However, the multi-temporal remote sensing images often have a long time interval, which leads to the geographical objects with the same semantic may have different spectral representations in different spatial and temporal locations, thus affecting the performance of network detection. Exploring the latent connections between different objects helps promote the discrimination of feature maps, so as to suppress the generation of pseudo changes because the geographical objects are not isolated. In this paper, we propose a novel change detection network focused on the object relations (ORFNet), which can capture relevant context by exploring the relations between objects in dual spatio-temporal scenes, so as to enhance the discrimination of original image features. Experiments on CDD data sets show that our method only increases 0.4M parameters compared with the baseline, and improves F1 by 3.6%. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Change detection

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Resource Allocation for MEC in Ultra-dense Networks

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Journal of computers (Taiwan) 2025年第1期36卷 143-162页

作者： Ma, Huan Li, Shuyun Wang, Zhihui Dou, Xubing Zhang, Xinchang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Shandong Normal University Jinan China

In the complex context of 5G ultra-dense networks, the unprecedented growth in traffic and computing demand has made the interaction between edge devices and edge servers extremely intricate due to the time-varying nature of the network environment and resource heterogeneity. The combination of UDN and Mobile Edge Computing not only significantly improves network performance but also expands application scenarios and enhances user experience, marking it as one of the crucial trends in future network development. In this paper, we review recent results and research progress on MEC-based resource allocation in 5G ultra-dense networks. We analyze resource allocation strategies from multiple dimensions, including user association, interference management, energy efficiency optimization, delay reduction, access selection, and user mobility. Finally, we discuss the challenges and open issues that need to be addressed for future development in this area. © 2025 Codon Publications. All rights reserved.

关键词： 5G mobile communication systems

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An Encoder-Decoder Model Based On Spiking Neural Networks For Address Event Representation Object Recognition

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IEEE Transactions on Cognitive and Developmental systems 2025年

作者： Du, Sichun Zhu, Haodi Zhang, Yang Hong, Qinghui Hunan University College of Computer Science and Electronic Engineering Changsha418002 China Shenzhen University Computer Vision Institute School of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

Address event representation (AER) object recognition task has attracted extensive attention in neuromorphic vision processing. The spike-based and event-driven computation inherent in the spiking neural network (SNN) provides an energy-saving solution for AER object recognition. However, SNN with spike timing dependent plasticity (STDP) learning rule has not achieved satisfying AER object recognition performance. This work proposes an SNN-based encoder-decoder model to improve the recognition performance of AER objects. An STDP-based locally connected spiking neural network (LC-SNN) is proposed as an encoder to extract rich spatiotemporal features from AER event flows more flexibly. After the encoder extracts and learns primary features, we propose a fully connected spiking neural network (FC-SNN) based on the reward-modulated spike-timing-dependent plasticity (R-STDP) learning rule as a decoder to learn higher-level features for classification. In addition, we improved the winner-take-all (WTA) mechanisms and R-STDP learning rule in the decoder based on the reward and punish decision, enabling the network to perform better. The experiments are performed on the N-MNIST, MNIST-DVS, and the DVS Gesture datasets, improving the accuracy of the best existing plasticity-based SNN by 0.19%, 0.27% and 1.35%, respectively. © 2016 IEEE.

关键词： Decoding

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Power Control and Interference Pricing Strategies for Heterogeneous Networks With Renewable Energy Sources

Power Control and Interference Pricing Strategies for Hetero...

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作者： Zhou, Chengcheng Xu, Haitao Tang, Runze Lin, Ruijie Guo, Chao Han, Zhu University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Beijing Institute of Electronic System Engineering Beijing China China Academy of Information and Communications Technology Beijing China Beijing Electronics Science and Technology Institute Department of Electronic and Communication Engineering Beijing China University of Houston HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

In this article, the power control and interference pricing problems between the macro base station (MBS) and small cell base stations (SBSs) in Heterogeneous networks (HetNets) are investigated. To optimally allocate power resources and effectively manage the power resources to reduce interference in HetNets, an innovative Stackelberg differential game approach is proposed. In the proposed Stackelberg game, the SBSs control their transmit power to achieve cost minimization based on the interference price offered by the MBS. Given the optimal power control strategies of SBSs, the MBS can adjust the interference price to realize the objective optimization. Renewable energy sources are introduced into the proposed HetNets, and differential equations formulate the SBSs' dynamic energy. Meanwhile, the MBS's state is given by the dynamic spectrum resources. Then the differential game is imported to achieve optimal power control and interference pricing. The open-loop Nash equilibrium solutions are first analyzed for the MBS and SBSs. Then the feedback Nash equilibrium solutions are given. Under the feedback pattern, the proposed model's performances in the non-cooperative and cooperative situations are discussed. Numerical simulations are given to show the convergence and effectiveness of the proposed game approach. © 1967-2012 IEEE.

关键词： Power control

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Convolutions of String Data Structures: A Case Study of Finding Number of Palindrome Substrings in a String 5

Convolutions of String Data Structures: A Case Study of Find...

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5th International Conference on Advancement in Data Science, E-Learning and information systems, ICADEIS 2023

作者： Kumar, Kamal Jain, Anuj Kumar Tiwari, Raj Gaurang Jain, Nitin Witarsyah, Deden Misra, Alok National Institute of Technology Uttarakhand Dept. of Computer Science and Engineering Uttarakhand Srinagar India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Manav Rachna International Institute of Research and Studies Career Development Centre Haryana Faridabad India Telkom University Information System Departement Bandung Indonesia

ISBN: (纸本)9798350303414

In computer science, substring search or string Matching is a vulnerable problem when text resources are very large. Productivity of diverse scraping applications depend on the effectiveness of searching algorithms. Palindrome string finding is one of these problems which has its use in many applications. The problem is about finding the number of palindrome substrings within a string. This paper discusses a naive and dynamic programming-based algorithm for finding several palindrome strings. The time complexity of algorithm has also been analyzed and an effort has been made to improve it. © 2023 IEEE.

关键词： Dynamic programming

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Taxonomy of Multi-Agent systems Attacks and their Defense Tactics in Certifying Security of Cyber Physical systems

Taxonomy of Multi-Agent Systems Attacks and their Defense Ta...

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Advances in Computation, Communication and information technology (ICAICCIT), International Conference on

作者： Garima Jain N. R Solomon Jebaraj Department of Computer Science & Business System Noida Institute Of Engineering and Technology Greater Noida Uttar Pradesh India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India

The utilization of multi-agent systems has been increasingly prevalent across various sectors, owing to their notable efficacy in execution. However, a multitude of hazards exist that possess the capability to undermine the integrity of the agent and pose a threat to the overall security of the system. Therefore, it is crucial to carefully evaluate and address security vulnerabilities during the process of creating Multi-Agent systems (MAS). This survey assesses different models that aim to guarantee the security of Multi-Agent systems (MAS). These models were developed based on principles related to the agents' functionality and communication. This study offers a comprehensive examination and categorization of the prevailing threats targeting Multi-Agent systems (MASs). Following this, we conduct a comprehensive analysis and assessment of diverse security strategies described in scholarly sources, classifying them into prevention, detection, and resiliency approaches based on their established credibility and reliability. In conclusion, we offer a suggestion for the optimal security approach to address particular types of threats, considering the most recent breakthroughs in the field of study.

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Facial Expression Recognition Using Reversible Neural Network

SSRN

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SSRN 2023年

作者： Barman, Asit Dutta, Paramartha Dept. of Computer Science & Engineering and Information Technology Siliguri Institute of Technology Darjeeling734009 India Dept. of Computer & System Sciences Visva-Bharati University Santiniketan731235 India

When identifying facial expressions using a set of salient features, reversible neural network plays a crucial role. In order to create a prominent feature set, these salient features are extracted from a face image utilizing distance and shape signatures, corresponding stability indices, and statistical measurements. The collected feature set is fed into the Reversible Neural Network (RNN), which maps it to various emotions. To carry out our experiment and verify the efficacy of facial expression recognition, the suggested network was applied to the MMI, CK+, and JAFFE data sets. © 2023, The Authors. All rights reserved.

关键词： Face recognition

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Handwritten Digit Classification based on Ensemble Majority Voting Classifier

Handwritten Digit Classification based on Ensemble Majority ...

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International Conference on systems and Control (ICSC)

作者： Mariam Alsharif Amina Abdo Kenz A Bozed Faculty of Information Technology University of Benghazi Benghazi Libya Departemnt of Artificial Intelligence Collage of Computer Technology-Benghazi Benghazi Libya Department of Computer System Design University of Benghazi Benghazi Libya

Handwritten digit recognition is a significant challenge in the field of machine learning, particularly for pattern recognition and computer vision applications. It has found applications in various areas, such as identifying digits on utility maps, postal mail zip codes, bank check amounts, and more. Offline handwritten digits possess distinct characteristics, including size, orientation, position, and thickness. Each person's handwriting is unique, making the classification process more difficult. Additionally, the high similarity between certain digits and the over fitting problem with high-dimensional data can increase computational time and cost. Consequently, numerous researchers have developed and implemented different machine learning algorithms to effectively address the issue of recognizing handwritten digits. This paper introduces a novel approach to improve the classification performance and accuracy of handwritten digit recognition. The proposed method utilizes an ensemble majority vote classifier, which combines three classifiers in each experiment by utilizing a range of different algorithms; Naïve Bayes (NB), Adaptive Boosting Algorithm (AdaBoost), K-Nearest Neighbors (K-NN), Multi-layer Perceptron Classifier (MLP), eXtreme Gradient Boosting algorithm (XGBoost), and Decision Tree Algorithm (DT) to form a single ensemble classifier. To validate the approach, we used the MINST free public handwritten dataset, to ascertain the classifier with the highest rate of accuracy in this study. The results obtained from the experiments demonstrated that the full performance of Ensemble model classifiers MLP, XGBOOST, and DT is superior to AdaBoost, K-NN, and NB. It achieved the highest accuracy level, reaching 98% in the experiment. The experimental results of this research showed that the recognition accuracy was improved compared to previous works.

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CorcPUM: Efficient Processing Using Cross-Point Memory via Cooperative Row-Column Access Pipelining and Adaptive Timing Optimization in Subarrays 23

CorcPUM: Efficient Processing Using Cross-Point Memory via C...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Chengning Wang Dan Feng Wei Tong Jingning Liu Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China (School of Computer Science and Technology Huazhong University of Science and Technology) Wuhan China

ISBN: (纸本)9798350323481

Emerging cross-point memory can in-situ perform vector-matrix multiplication (VMM) for energy-efficient scientific computation. However, parasitic-capacitance-induced row charging and discharging latency is a major performance bottleneck of subarray VMM. We propose a memory-timing-compliant bulk VMM processing-using-memory design with row access and column access co-optimization from rethinking of read access commands and μ-op timing. We propose row-level-parallelism-adaptive timing termination mechanism to reduce tail latency of tRCD and tRP by exploiting row nonlinear charging and bulk-interleaved row-column-cooperative VMM access mechanism to reduce tRAS and overlap CL without increasing column ADC precision. Evaluations show that our design can achieve 5.03× performance speedup compared with an aggressive baseline.

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DRCTL: A Disorder-Resistant Computation Translation Layer Enhancing the Lifetime and Performance of Memristive CIM Architecture

DRCTL: A Disorder-Resistant Computation Translation Layer En...

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IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Heng Zhou Bing Wu Huan Cheng Jinpeng Liu Taoming Lei Dan Feng Wei Tong Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology School of Computer Science and Technology Huazhong University of Science and Technology Ministry of Education of China

ISBN: (数字)9798350350579

ISBN: (纸本)9798350350586

The memristive Computing-in-Memory (CIM) sys-tem can efficiently accelerate matrix-vector multiplication (MVM) operations through in-situ computing. The data layout has a significant impact on the communication performance of CIM systems. Existing software-level communication opti-mizations aim to reduce communication distance by carefully designing static data layouts, while wear-leveling (WL) and error mitigation methods use dynamic scheduling to enhance system reliability, resulting in randomized data layouts and increased communication overhead. Besides, existing CIM compilers di-rectly map data to physical crossbars and generate instructions, which causes inconvenience for dynamic scheduling. To address these challenges of balancing communication performance and reliability while coordinating existing CIM compilers and dy-namic scheduling, we propose a disorder-resistant computation translation layer (DRCTL), which improves system lifetime and communication performance through co-optimization of data layout and dynamic scheduling. It consists of three parts: (1) We propose an address conversion method for dynamic scheduling, which updates the addresses in the instruction stream after dynamic scheduling, thereby avoiding recompilation. (2) Dynamic scheduling strategy for reliability improvement. We propose a hierarchical wear-leveling (HWL) strategy, which reduces communication by increasing scheduling granularity. (3) Communication optimization for dynamic scheduling. We propose data layout-aware selective remapping (LASR), which helps dynamic scheduling methods improve communication lo-cality and reduce latency by exploiting data dependencies. The experiments demonstrate that HWL extends lifetime by 100.3-205.9 x compared to not using WL. Even with a slight lifetime decrease compared to the state-of-the-art WL (TIWL), it still supports continuous neural network training for 7 years. After applying LASR to HWL, the number of execution cycles, energy consumption

关键词： Training Energy consumption Processor scheduling Layout Dynamic scheduling In-memory computing Common information Model (computing) system-on-chip Reliability Optimization

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