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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Kumar, Boddu Manoj Rajeswari, G. Thanush, B.V. Krishna, Vamsi Sree, E. Divija Balakrishnan, Arunkumar Sathyabama Institute of Science and Technology School of Computer Science Engineering with Blockchain Technology Tamil Nadu Chennai600119 India Sathyabama Institute of Science and Technology School of Computer Science Engineering Tamil Nadu Chennai600119 India VIT-AP University School of Computer Science Engineering India Crm Institute of Technology School of Computer Science Engineering Karnataka Bengaluru India Sree Vidyanikethan Engineering College Rangampeta School of Computer Science Engineering with Artificial Intelligence Tirupati517102 India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Information Technology Manipal India

ISBN: (纸本)9798331527822

The integration of blockchain technology into the grocery purchasing process offers a transformative approach to enhancing transparency, security, and efficiency. This paper presents a comprehensive framework for a blockchain-based grocery system, detailing each step from user registration and identity verification to product listing, smart contract creation, and real-time inventory management. Consumers can place orders with confidence, knowing that payments are securely processed and supply chain logistics are fully transparent. The use of IoT devices ensures quality control during transportation, while smart contracts automate delivery and receipt confirmation. Additionally, the system supports consumer feedback and dispute resolution through immutable records and predefined arbitration mechanisms. This innovative application of blockchain technology aims to revolutionize the grocery sector, fostering trust and accountability among all stakeholders involved. Future research will explore the potential for AI integration and address scalability and interoperability challenges. © 2024 IEEE.

关键词： Smart contract

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Data-driven electrical resistance tomography for robotic large-area tactile sensing

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science China(Information sciences) 2024年第9期67卷 336-337页

作者： Wendong ZHENG Huaping LIU Xiaofeng LIU Fuchun SUN School of Electrical Engineering and Automation Tianjin University of Technology Department of Computer Science and Technology Tsinghua University Key Laboratory of Maritime Intelligent Cyberspace Technology (Hohai University) Ministry of Education

Tactile sensing plays a crucial role in enabling robots to safely interact with objects in dynamic environments [1].Given that potential physical contact can occur at any location during robot interaction, there is a ... 详细信息

关键词： Robot applications

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A Review of Device-Free Indoor Positioning for Home-Based Care of the Aged:Techniques and Technologies

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computer Modeling in engineering & sciences 2023年第6期135卷 1901-1940页

作者： Geng Chen Lili Cheng Rui Shao Qingbin Wang Shuihua Wang College of Electronic and Information Engineering Shandong University of Science and TechnologyQingdao266590China School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo454000China

With the development of urbanization,the problem of neurological diseases brought about by population aging has gradually become a social problem of worldwide *** leads to gradual degeneration of the central nervous system,shrinkage of brain tissue,and decline in physical function in many elderlies,making them susceptible to neurological diseases such as Alzheimer’s disease(AD),stroke,Parkinson’s and major depressive disorder(MDD).Due to the influence of these neurological diseases,the elderly have troubles such as memory loss,inability to move,falling,and getting lost,which seriously affect their quality of *** and positioning of elderly with neurological diseases and keeping track of their location in real-time are necessary and crucial in order to detect and treat dangerous and unexpected situations in *** that the elderly with neurological diseases forget to wear a positioning device or have mobility problems due to carrying a positioning device,device-free positioning as a passive positioning technology that detects device-free individuals is more suitable than traditional active positioning for the home-based care of the elderly with neurological *** paper provides an extensive and in-depth survey of device-free indoor positioning technology for home-based care and an in-depth analysis of the main features of current positioning systems,as well as the techniques,technologies andmethods they employ,fromthe perspective of the needs of the elderly with neurological ***,evaluation criteria and possible solutions of positioning techniques for the home-based care of the elderly with neurological conditions are ***,the opportunities and challenges for the development of indoor positioning technology in 6G mobile networks for home-based care of the elderly with neurological diseases are *** review has provided comprehensive and effective tracking and positioning techniques,technologies

关键词： Home-based care device-free neurological diseases indoor positioning positioning techniques and technologies positioning accuracy

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Joint Optimization of Service Caching and Task Scheduling in Energy-Efficient Mobile Edge Computing 4

Joint Optimization of Service Caching and Task Scheduling in...

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4th International Conference on Intelligent technology and Embedded Systems, ICITES 2024

作者： Chen, Bin Huang, Wenxuan Wang, Linhui Zhang, Yi Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

ISBN: (纸本)9798331506292

This paper studies an energy-efficient task scheduling problem that takes into account the cooperation among service caching-enabled mobile edge computing (MEC) servers. We consider a MEC system consisting of multiple sub-networks of MEC nodes and formulate the studied problem as a constrained optimization model that makes decisions on energy-efficient assignment of task requests. To cope with the formulated optimization problem, we propose a metaheuristic approach that is built upon the searching mechanism of the squirrel search algorithm (SSA) but involves improved operators and name the metaheuristic approach ISSA. By using an offloading sequence (i.e., a permutation of all computation tasks) to represent the scheduling solution, ISSA uses a cooperative task assignment heuristic for evaluating the fitness value of each candidate solution. This heuristic is responsible for dispatching each task in the sequence to a certain MEC node within the sub-network or outsourcing the task to the cloud in case of insufficient resources, such that the total energy consumption can be calculated. In addition, when updating the squirrel positions to facilitate the metaheuristic search, we employ a new updating rule to enhance ISSA's capability in exploring the solution space. We perform extensive simulations to demonstrate the advantage of our proposed ISSA over other metaheuristic approaches in reducing the total energy consumption. © 2024 IEEE.

关键词： Constrained optimization

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Collaborative Learning for Multi-Source Domain Adaptative Object Detection 4

Collaborative Learning for Multi-Source Domain Adaptative Ob...

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4th International Conference on Neural Networks, Information and Communication engineering, NNICE 2024

作者： Cheng, Yansong University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China

ISBN: (纸本)9798350394375

Multi-Source domain adaptation is a more general scenario than single-source domain adaptation, however, it encounters challenges arising from the problem of model effect degradation caused by distribution differences between multiple source domains. To address this issue, we propose a novel approach, namely collaborative learning(CL). CL can retain public intra-domain knowledge and improve the generalization ability of the model. Extensive experiments on different adaptation scenarios demonstrate the effectiveness of the proposed model. © 2024 IEEE.

关键词： Object detection

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One Cycle With N Messages: A Secure and Lightweight Key Agreement Scheme for WSNs 21

One Cycle With N Messages: A Secure and Lightweight Key Agre...

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21st Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2024

作者： Gui, Shenglin Zheng, Meng Liu, Yangyang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331519186

In recent years, wireless sensor networks (WSNs) have been widely used in various critical applications. Secure and efficient mutual authentication and key agreement (AKA) schemes under resource-constrained WSNs are crucial to guarantee security. A wealth of AKA schemes have been proposed, yet most of them are often found to be either insufficient in security or poor in performance. To design a scheme with robust security and better performance in both single-gateway and multi-gateway WSNs, we propose a novel secure and lightweight three-factor AKA scheme under one cycle with N messages mode, where its communication process between N participants requires only N messages in one cycle. Furthermore, we adopt enhanced Chebyshev polynomials to reduce computation overheads while ensuring computational difficulties against attacks and to provide secure access between remote users and sensor nodes with forward secrecy and multi-factor security. The security of this scheme is proved under the random-oracle model and the informal security analysis further demonstrates that our scheme succeeds in meeting all evaluation criteria in the face of the state-of-the-art threat model. At last, we conduct adequate comparisons respectively with single-gateway and multi-gateway schemes. Compared to sixteen state-of-the-art schemes in terms of security and performance, to our best knowledge, our scheme achieves all the security requirements with minimum communication overheads among all competitors and minimum computation overheads among all multi-gateway schemes. We believe that our work takes a substantial step towards a better trade-off between security and efficiency for AKA schemes in WSNs. © 2024 IEEE.

关键词： Chebyshev polynomials

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Optimizing Spiking Neural Networks with Transformer-Based Spatial Attention 4

Optimizing Spiking Neural Networks with Transformer-Based Sp...

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4th International Symposium on computer technology and Information science, ISCTIS 2024

作者： Zheng, Peixiang Shen, Zhao University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China

ISBN: (纸本)9798350354560

This study introduces SpikeNetTransformer, a novel approach to developing Spiking Neural Networks (SNNs) by integrating the Transformer architecture. The proposed model optimizes the interplay between self-attention mechanism and SNN features to enhance performance across various datasets. A key contribution of this research is the introduction of the Temporal Spike-Scaled Attention (TSSA) module, which is an optimization of the existing Sparse Spatial Attention (SSA) structure. The TSSA module efficiently utilizes spike-shaped Queries, Keys, and Values to effectively blend temporal features. Moreover, a scaling factor is incorporated into the TSSA module to deal with vanishing and exploding gradients during training. This modification not only stabilizes the learning process, but also accelerates both the training and inference phases while reducing the memory consumption by 20 percent. SpikeNetTransformer demonstrates superior performance compared to existing SNN models, achieving competitive Top-1 accuracy rates on static datasets such as CIFAR-10 and CIFAR-100. On CIFAR-10, our model achieves a Top-1 accuracy of 93.34%, outperforming most state-of-the-art SNN models. Similarly, on CIFAR-100, our model achieves Top-1 accuracy of 76.22%, outperforming many existing SNN architectures. This study highlights the potential of refined SNNs in combination with Transformer techniques for efficient and accurate neural computation. © 2024 IEEE.

关键词： Spatio-temporal data

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An Enhanced RNN-LSTM Model for Fundus Image Classification to Diagnose Glaucoma

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SN computer science 2024年第5期5卷 514页

作者： Veena, H.N. Patil, Kavita K. Vanajakshi, P. Ambore, Anilkumar Gowda, Naveen Chandra Department of Computer Science and Engineering SJB Institute of Technology Bengaluru India Department of Information Science and Engineering Global Academy of Technology Bengaluru India Department of Information Science and Engineering Vivekananda Institute of Technology Bengaluru India School of CSE REVA University Bengaluru India School of Computer Science and Engineering REVA University Bengaluru India

In the current medical implications, one of the leading ocular diseases is Glaucoma which majorly damage the Optic Nerve Head (ONH) of the eye retina. The intraocular pressure of the eye leads to glaucoma, which may lead to complete or partial vision loss. Regular screening and early detection is the only solution to avoid further vision loss. Due to the laborious and manual procedure in diagnosis, an automatic system is needed to diagnose glaucoma. This paper presents a novel method using deep learning-based RNNLSTM classification model to develop an automatic approach to predict and classify the images to be as healthy or glaucomatous. The RNN and LSTM with dense, dropout, and batch normalization layers are used for training and testing the proposed prediction model. The RNN model is used for training the model and to overcome the problems that occur during training, the LSTM model is applied to increase the performance of the model. The proposed model achieved an accuracy of 97.4%, specificity of 97.9% and sensitivity of 97%in classifying the images. We have made use of the DRISHTI-GS database for training and testing the proposed model. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Glaucoma Long short-term memory (LSTM) Recurrent neural network (RNN)

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SmokerBeacon: Hybrid Real-Time Smoking Detection Using Object Detection and Gas Sensor

SmokerBeacon: Hybrid Real-Time Smoking Detection Using Objec...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Harshavardhan, S. Upender, Patri Ganesh, N. Giftsy, Sharon Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Electronics Engineering Chennai India

ISBN: (纸本)9798331529833

Enforcing smoking bans in public areas remains a challenge due to limitations in conventional monitoring methods. This paper introduces SmokerBeacon, an innovative system for real-time detection of smoking activities in restricted zones. SmokerBeacon uses gas sensors and advanced image classification - YOLO to identify smoking activities. The system catego-rizes detection scenarios into three types: (i) instances where smoking actions are detected by YOLO without corresponding signals from gas sensors, (ii) cases where YOLO fails to detect smoking actions despite gas sensors detecting smoking-related substances, and (iii) situations where both smoking activities and corroborating signals from gas sensors confirm smoking. Each scenario triggers specific actions to improve enforcement and foster healthier environments. By generating timely alerts and enabling proactive intervention, SmokerBeacon mitigates risks associated with smoking in prohibited areas, leveraging IoT technology. © 2025 IEEE.

关键词： Chemical sensors

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Robust Node Embedding Based on Subspace Attribute Complement Topology 4

Robust Node Embedding Based on Subspace Attribute Complement...

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4th IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Kang, Jiahua Deng, Ziling Xi, Hongqing Wang, Xiaoxiao School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350359589

Graph Neural Networks (GNNs) have been proven to be powerful tools for graph analysis. However, despite their success, existing GNNs are often sensitive to the quality of the input graph. Real-world graphs are typically noisy, often represented topologically as containing some edges irrelevant to the task or missing some edges, which can lead to sub-optimal generalization performance of the learned GNNs models. To address this issue, we introduce a robust node representation algorithm for attribute networks based on the concept of subspace (STSAI) algorithm. Adequate experiments were also done on the real-world dataset to verify the validity of this work. © 2024 IEEE.

关键词： GNNs robust subspace

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