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International Conference on Communications and Signal Processing

作者： V. Sridevi H. Azath R. Vijayakumar N. Anbuselvan V. Amirthalingam S Arunkumar Electrical and Electronics Engineering Academy of Maritime Education and Training Deemed to be University Chennai Tamil Nadu India School of Computing Science and Engineering VIT Bhopal University Bhopal India Department of Electronics and Communication Engineering Mahendra Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Electrical and Electronics Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering Vinayaka Missions Kirupananda Variyar Engineering College Vinayaka Missions Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Computer Applications Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

The research focuses on how augmented reality (AR) and the Internet of Things (IoT) might work together to create a novel retail environment. This integrated system revolutionizes how customers engage with products by fusing the digital and real worlds. Smartphones and other devices with AR capabilities allow customers to virtually try on products before purchasing. Smart mirrors and sensor-enabled shelving are just two examples of the IoT-powered interactive displays available today. These fusions of AR and the IoT improve the retail experience and facilitate choice. Capturing data from the physical environment, sensors allow for customized experiences. The extensive product data stored by cloud services paves the way for rapid data processing. However, there are obstacles, such as ensuring data privacy and security and coordinating AR-IoT smoothly. Finding a happy medium between technological complexity and ease of use is essential for a satisfying buying experience. The combination of AR and the IoT broadens the retail experience and has the potential to revolutionize the way people purchase.

关键词： Cloud computing Technological innovation Wireless networks Signal processing User experience Complexity theory Internet of Things

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Timing-Driven High-Level Synthesis for Continuous-Flow Microfluidic Biochips

Timing-Driven High-Level Synthesis for Continuous-Flow Micro...

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IEEE International Symposium on Quality Electronic Design

作者： Zhengyang Ye Zhisheng Chen Youlin Pan Genggeng Liu Wenzhong Guo Tsung-Yi Ho Xing Huang College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China School of Informatics Xiamen University Xiamen China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China School of Computer Science Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798350309270

ISBN: (纸本)9798350309287

As continuous-flow microfluidic biochips technology rapidly advances, its automated synthesis methods require adapting to increasingly complex and stringent biochemical applications. In particular, with the introduction of time-sensitive bioassays, in this work, we present a timing-driven high-level synthesis method to optimize the resource efficiency of bioassays considering the time constraints. Specifically, we investigate the existing forms of time constraints and propose a corresponding flexible modeling approach based on reaction-control which can achieve downward compatibility for physical design. Additionally, we also analyze the impact of storage and wash on time constraints. The experimental results show that our method achieves efficient execution and low devices cost while satisfying time constraints.

关键词： Costs Biochips Time factors Microfluidics Physical design

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A Framework of FAS-RIS Systems: Performance Analysis and Throughput Optimization

arXiv

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arXiv 2024年

作者： Yao, Junteng Lai, Xiazhi Zhi, Kangda Wu, Tuo Jin, Ming Pan, Cunhua Elkashlan, Maged Yuen, Chau Wong, Kai-Kit The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China The School of Computer Science Guangdong University of Education Guangdong Guangzhou China The School of Electrical Engineering and Computer Science Technical University of Berlin Berlin10623 Germany The School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom The Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom The Yonsei Frontier Laboratory the School of Integrated Technology Yonsei University Seoul03722 Korea Republic of

In this paper, we investigate reconfigurable intelligent surface (RIS)-assisted communication systems which involve a fixed-antenna base station (BS) and a mobile user (MU) that is equipped with fluid antenna system (FAS). Specifically, the RIS is utilized to enable communication for the user whose direct link from the base station is blocked by obstacles. We propose a comprehensive framework that provides transmission design for both static scenarios with the knowledge of channel state information (CSI) and harsh environments where CSI is hard to acquire. It leads to two approaches: a CSI-based scheme where CSI is available, and a CSI-free scheme when CSI is inaccessible. Given the complex spatial correlations in FAS, we employ block-diagonal matrix approximation and independent antenna equivalent models to simplify the derivation of outage probabilities in both cases. Based on the derived outage probabilities, we then optimize the throughput of the FAS-RIS system. For the CSI-based scheme, we first propose a gradient ascent-based algorithm to obtain a near-optimal solution. Then, to address the possible high computational complexity in the gradient algorithm, we approximate the objective function and confirm a unique optimal solution accessible through a bisection search method. For the CSI-free scheme, we apply the partial gradient ascent algorithm, reducing complexity further than full gradient algorithms. We also approximate the objective function and derive a locally optimal closed-form solution to maximize throughput. Simulation results validate the effectiveness of the proposed framework for the transmission design in FAS-RIS systems. © 2024, CC BY.

关键词： Antennas

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Graph partitioning and sparse matrix ordering using reinforcement learning and graph neural networks

The Journal of Machine Learning Research

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The Journal of Machine Learning research 2022年第1期23卷 13675-13702页

作者： Alice Gatti Zhixiong Hu Tess Smidt Esmond G. Ng Pieter Ghysels Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA LinkedIn Corporation Sunnyvale CA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA

We present a novel method for graph partitioning, based on reinforcement learning and graph convolutional neural networks. Our approach is to recursively partition coarser representations of a given graph. The neural network is implemented using SAGE graph convolution layers, and trained using an advantage actor critic (A2C) agent. We present two variants, one for finding an edge separator that minimizes the normalized cut or quotient cut, and one that finds a small vertex separator. The vertex separators are then used to construct a nested dissection ordering to permute a sparse matrix so that its triangular factorization will incur less fill-in. The partitioning quality is compared with partitions obtained using METIS and SCOTCH, and the nested dissection ordering is evaluated in the sparse solver SuperLU. Our results show that the proposed method achieves similar partitioning quality as METIS, SCOTCH and spectral partitioning. Furthermore, the method generalizes across different classes of graphs, and works well on a variety of graphs from the SuiteSparse sparse matrix collection.

关键词： graph partitioning sparse matrix ordering graph neural networks reinforcement learning machine learning for scientific computing

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Non-Convex Potential Games for Finding Global Solutions to Sensor Network Localization

Non-Convex Potential Games for Finding Global Solutions to S...

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European Control Conference (ECC)

作者： Gehui Xu Guanpu Chen Yiguang Hong Baris Fidan Thomas Parisini Karl H. Johansson Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering Tongji University Shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Department of Electrical and Electronic Engineering Imperial College London London UK Department of Engineering and Architecture University of Trieste Trieste Italy

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Sensor network localization (SNL) problems require determining the physical coordinates of all sensors in a network. This process relies on the global coordinates of anchors and the available measurements between non-anchor and anchor nodes. Attributed to the intrinsic non-convexity, obtaining a globally optimal solution to SNL is challenging, as well as implementing corresponding algorithms. In this paper, we formulate a non-convex multi-player potential game for a generic SNL problem to investigate the identification condition of the global Nash equilibrium (NE) therein, where the global NE represents the global solution of SNL. We employ canonical duality theory to transform the non-convex game into a complementary dual problem. Then we develop a conjugation-based algorithm to compute the stationary points of the complementary dual problem. On this basis, we show an identification condition of the global NE: the stationary point of the proposed algorithm satisfies a duality relation. Finally, simulation results are provided to validate the effectiveness of the theoretical results.

关键词： Location awareness Coordinate measuring machines Simulation Current measurement Noise Europe Games

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AoI-Sensitive Data Forwarding with Distributed Beamforming in UAV-Assisted IoT

arXiv

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arXiv 2025年

作者： Lang, Zifan Liu, Guixia Sun, Geng Li, Jiahui Sun, Zemin Wang, Jiacheng Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

This paper proposes a UAV-assisted forwarding system based on distributed beamforming to enhance age of information (AoI) in Internet of Things (IoT). Specifically, UAVs collect and relay data between sensor nodes (SNs) and the remote base station (BS). However, flight delays increase the AoI and degrade the network performance. To mitigate this, we adopt distributed beamforming to extend the communication range, reduce the flight frequency and ensure the continuous data relay and efficient energy utilization. Then, we formulate an optimization problem to minimize AoI and UAV energy consumption, by jointly optimizing the UAV trajectories and communication schedules. The problem is non-convex and with high dynamic, and thus we propose a deep reinforcement learning (DRL)-based algorithm to solve the problem, thereby enhancing the stability and accelerate convergence speed. Simulation results show that the proposed algorithm effectively addresses the problem and outperforms other benchmark algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Unraveling Attacks in Machine Learning-based IoT Ecosystems: A Survey and the Open Libraries Behind Them

arXiv

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arXiv 2024年

作者： Liu, Chao Chen, Boxi Shao, Wei Zhang, Chris Wong, Kelvin K.L. Zhang, Yi The Department of Electrical and Computer Engineering University of Maryland Baltimore CountyMD United States The Department of Research Deep Red Future Technology Co. Ltd. Shenzhen China The data61 CSIRO Australia The Department of Mechanical Engineering University of Saskatchewan Saskatchewan Canada Machine Intelligence Laboratory College of Computer Science Sichuan University Sichuan China

The advent of the Internet of Things (IoT) has brought forth an era of unprecedented connectivity, with an estimated 80 billion smart devices expected to be in operation by the end of 2025. These devices facilitate a multitude of smart applications, enhancing the quality of life and efficiency across various domains. Machine Learning (ML) serves as a crucial technology, not only for analyzing IoT-generated data but also for diverse applications within the IoT ecosystem. For instance, ML finds utility in IoT device recognition, anomaly detection, and even in uncovering malicious activities. This paper embarks on a comprehensive exploration of the security threats arising from ML’s integration into various facets of IoT, spanning various attack types including membership inference, adversarial evasion, reconstruction, property inference, model extraction, and poisoning attacks. Unlike previous studies, our work offers a holistic perspective, categorizing threats based on criteria such as adversary models, attack targets, and key security attributes (confidentiality, availability, and integrity). We delve into the underlying techniques of ML attacks in IoT environment, providing a critical evaluation of their mechanisms and impacts. Furthermore, our research thoroughly assesses 65 libraries, both author-contributed and third-party, evaluating their role in safeguarding model and data privacy. We emphasize the availability and usability of these libraries, aiming to arm the community with the necessary tools to bolster their defenses against the evolving threat landscape. Through our comprehensive review and analysis, this paper seeks to contribute to the ongoing discourse on ML-based IoT security, offering valuable insights and practical solutions to secure ML models and data in the rapidly expanding field of artificial intelligence in IoT. © 2024, CC0.

关键词： Internet of things

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Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects

arXiv

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arXiv 2025年

作者： Ma, Chenxiang Chen, Xinyi Li, Yanchen Yang, Qu Wu, Yujie Li, Guoqi Pan, Gang Tang, Huajin Tan, Kay Chen Wu, Jibin Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Institute of Automation Chinese Academy of Sciences Beijing100045 China State Key Laboratory of Brain-Machine Intelligence College of Computer Science and Technology MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University Hangzhou310027 China

Temporal processing is fundamental for both biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses. Spiking Neural Networks (SNNs) excel in handling such data with high efficiency, owing to their rich neuronal dynamics and sparse activity patterns. Given the recent surge in the development of SNNs, there is an urgent need for a comprehensive evaluation of their temporal processing capabilities. In this paper, we first conduct an in-depth assessment of commonly used neuromorphic benchmarks, revealing critical limitations in their ability to evaluate the temporal processing capabilities of SNNs. To bridge this gap, we further introduce a benchmark suite consisting of three temporal processing tasks characterized by rich temporal dynamics across multiple timescales. Utilizing this benchmark suite, we perform a thorough evaluation of recently introduced SNN approaches to elucidate the current status of SNNs in temporal processing. Our findings indicate significant advancements in recently developed spiking neuron models and neural architectures regarding their temporal processing capabilities, while also highlighting a performance gap in handling long-range dependencies when compared to state-of-the-art non-spiking models. Finally, we discuss the key challenges and outline potential avenues for future research. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Religion andwomen's intimate health: Towards an inclusive approach to healthcare 21

Religion andwomen's intimate health: Towards an inclusive ap...

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2021 CHI Conference on Human Factors in computing Systems: Making Waves, Combining Strengths, CHI 2021

作者： Mustafa, Maryam Zaman, Kimia Tuz Ahmad, Tallal Batool, Amna Ghazali, Masitah Ahmed, Nova Department of Computer Science Lahore University of Management Sciences Lahore Pakistan Department of Electrical and Computer Engineering North South University Bangladesh School of Information University of Michigan United States UTM ViCubeLab Research Group Universiti Teknologi Malaysia and School of Computing Malaysia

ISBN: (纸本)9781450380966

We present fndings from a three country study exploring the intersection between female intimate health and religious beliefs. Through a qualitative study with Muslim female populations in Pakistan, Bangladesh and Malaysia, three diferent Muslim majority contexts,we examine the deep impact Islamic beliefs have on female intimate health and well-being. Our study investigates the perceptions, attitudes and behaviours of Muslim women to their own intimate and sexual bodies through their experiences of menarche, marriage and reproduction and menopause. The intersection of religion and female sexual bodies and health is a neglected area within HCI and we highlight how inextricably specifc Islamic values are linked with women's reproductive health in Muslim communities. We further discuss the opportunities and challenges of designing technologies for religious, non-secular beliefs and values with the aim to improve intimate health practices amongst Muslim women and to broaden the scope of health design within HCI. © 2021 ACM.

关键词： Health

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Segmentation and Classification of Gastric Cancer from Endoscopic Image Dataset with the Aid of Artificial Intelligence

Segmentation and Classification of Gastric Cancer from Endos...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Cuddapah Anitha Vemu Srinivas Rao Ramyadevi. R Maya. R Subhasish Debroy Mahaveerakannan R School of Computing Mohan Babu University Sree Vidyanikethan Engineering College Tirupati Andhra Pradesh Shri Vishnu Engineering College for Women Bhimavaram Andhra Pradesh Department of Computer Science & Engineering Saveetha Engineering College Department of Electrical & Electronics Engineering Chennai Institute of Technology & Applied Research Dr. B.C. Roy Engineering College Durgapur West Bengal Department of Computer Science and Engineering Saveetha School of Engineering SIMETs Chennai Tamil Nadu India

In order to detect early stomach malignancies, upper GI endoscopy is commonly used. An object detection model, a form of deep learning, was projected as a means of automating the diagnosis of early stomach cancer using endoscopic pictures. Yet there were difficulties in reducing false positives in the results that were detected. Thus, this research proposes an automatic classification method for stomach cancer classification using a (CNN) that has been pre-trained. To classify cancer in endoscopic pictures automatically, our method is superior to those that rely on traditional, manual features. Thirteen convolutional layers with small 33 size kernels and three fully linked layers make up the suggested model. We use the learning approach, which involves pre-training the weight of fine-tuning the weight of layers with the Slime Mould Algorithm, to deal with the data scarcity (SMA). Experiments employing 1208 photos from healthy subjects and 533 photographs from patients with stomach cancer examined detection performance using the 5-fold cross validation approach. These findings suggest the projected strategy will be effective for automating the diagnosis of early stomach cancer in endoscopic pictures.

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