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IEEE Conference on Global Communications (GLOBECOM)

作者： Chao Fang Hang Xu Yingshan Li Haofei Xie Shu-Ming Tseng Zheng Yang Deze Zeng Faculty of Information Technology Beijing University of Technology Beijing China Purple Mountain Laboratories Nanjing China Key Laboratory of Industrial Internet of Things & Networked Control Chongqing University of Posts and Telecommunications Chongqing China Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Normal University Fuzhou China School of Computer Science China University of Geosciences Wuhan China

To satisfy differentiated service requirements of delay-sensitive and computing-intensive tasks in satellite communication networks, we propose a cache-assisted low-latency content distribution scheme in multi-cell multi-carrier nonorthogonal multiple access (MCMC-NOMA)-based satellite-edge-terminal cooperation environments. In the paper, a delay minimization problem is formulated to achieve optimal content distribution by jointly optimizing the allocation of subchannels and transmit power, which is decomposed and solved in each slot by using many-to-one matching and successive convex approximation, respectively. Simulation results show that the proposed model significantly improve network latency and content distribution in comparison to its existing counterparts in cloud-edge-terminal cooperation networks.

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Suboptimal Safety-Critical Control for Continuous Systems Using Prediction-Correction Online Optimization

arXiv

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arXiv 2022年

作者： Wang, Shengbo Wen, Shiping Yang, Yin Cao, Yuting Shi, Kaibo Huang, Tingwen The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Australian AI Institute Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia College of Science and Engineering Hamad Bin Khalifa University Doha5855 Qatar School of Information Science and Engineering Chengdu University Chengdu611040 China Science Program Texas A & M University at Qatar Doha23874 Qatar

This paper presents an innovative efficient safety-critical control scheme for nonlinear systems by combining techniques of control barrier function (CBF) and online time-varying optimization. The idea lies in that when directly calling the complete optimization solvers used in the CBF method, such as CBF-based quadratic programming (CBF-QP), is computationally inefficient for complex tasks, the suboptimal solutions obtained from online learning techniques can be an alternatively reliable choice for ensuring both efficiency and control performance. By using the barrier-based interior point method, the original optimization problem with CBF constraints is reduced to an unconstrained one with approximate optimality. Then, Newton- and gradient-based continuous dynamics are introduced to generate alternative cheap solutions while ensuring safety. By further considering the lag effect of online tracking, a prediction term is added to the dynamics. In this way, the online cheap solutions are proven to exponentially converge to the time-varying suboptimal solutions of the interior point method. Furthermore, the safety criteria are established, and the robustness of the designed algorithms is analyzed theoretically. Finally, the effectiveness is illustrated by conducting two experiments on obstacle avoidance and anti-swing tasks. © 2022, CC BY-NC-ND.

关键词： Quadratic programming

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Distributed Feedback Laser (DFB) for Signal Power Amplitude Level Improvement in Long Spectral Band

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Journal of Optical Communications 2023年第s1期44卷 S125-S130页

作者： Samanta, Debabrata Sivaram, M. Rashed, Ahmed Nabih Zaki Boopathi, C.S. Amiri, I.S. Yupapin, P. Computational Optics Research Group Advanced Institute of Materials Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Applied Sciences Ton Duc Thang University Ho Chi Minh City Viet Nam Bangalore Bangalore India Department of Computer Networking Lebanese French University Kurdistan Region Erbil Iraq Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Department of EEE SRM Institute of Science and Technology Chennai India

This study outlines the distributed feedback laser for signal power amplitude level improvement in the long spectral band of 1550 nm wavelength within supporting pumped wavelength of 1480 nm. The bias and modulation peak currents based distributed feedback laser are varied in order to test the signal power level, peak signal amplitude variations after the fiber-optic channel and light detectors. The signal power level, peak signal amplitude is measured against spectral wavelength and time bit period variations. The study emphasis the signal power level, peak signal amplitude are enhanced for the best selection values of both a bias current at 45 mA and modulation peak current at 0.5 mA. © 2023 Walter de Gruyter GmbH. All rights reserved.

关键词： Distributed feedback lasers

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An application of WSN in smart aquaculture farming 4th

An application of WSN in smart aquaculture farming

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4th International Conference on Research in Intelligent and Computing in engineering, RICE 2019

作者： Huy, Thong Nguyen Tuan, Khanh Nguyen Trung, Thanh Tran Department of Global Production Engineering and Management Faculty of Engineering Vietnamese-German University Thu Dau MotBinh Duong Viet Nam Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (纸本)9789811527791

Aquaculture farming carries one of the most important roles in the Vietnam economy. This paper focuses on the implementation of wireless sensor network (WSN) in shrimp farms, including the automation, remote control, and monitoring of the pond parameters. Using embedded computer Raspberry Pi 3, wireless communication modules Xbee S2c, the system can help farmers control pumps, actuator, and light via their web browser. Besides, parameters of the pond such as temperature and illuminating power are updated on the Internet via cloud service. The implementation of this project is expected to reduce energy consumption, human resource, and fast reaction to the change of the production pond, especially for large farms with multiple ponds. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Wireless sensor networks

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A nonlinear neural network based on an analog DNA toehold mediated strand displacement reaction circuit

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Nanoscale 2022年第17期14卷 6585-6599页

作者： Chengye Zou Qiang Zhang Changjun Zhou Wenyu Cao School of Mathematics and Statistics Anyang Normal University Anyang 455000 China. Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian 116024 China. zhangq@***. Informedia Electronic Co. Ltd Dalian University of Technology Dalian 116024 China. College of Mathematics and Computer Science Zhejiang Normal University Jinhua 321000 China. zhou-chang231@***. Institution of Information Management and Information system Dalian University of Technology Dalian 116024 China.

The DNA toehold mediated strand displacement reaction is one of the semi-synthetic biology technologies for next-generation computers. In this article, we present a framework for a novel nonlinear neural network based on an engineered biochemical circuit, which is constructed by several reaction modules including catalysis, degradation and adjustment reaction modules. The proposed neural network possesses an architecture that is similar to that of an error back propagation neural network, and is built of an input layer, hidden layer and output layer. As a proof of concept, we utilize this nonlinear neural network based on an analog DNA toehold mediated strand displacement reaction circuit to learn the standard quadratic form function and analyze the robustness of the nonlinear neural network toward DNA strand concentration detection, DNA strand displacement reaction rate and noise. Unlike in error back propagation neural networks, the adaptive behavior of this DNA molecular neural network system endows it with supervised learning capability. This investigation will highlight the potential of analog DNA displacement reaction circuits for implementing artificial intelligence.

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Secure Beamforming Design for Rate-Splitting Multiple Access in Multi-antenna Broadcast Channel with Confidential Messages

arXiv

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arXiv 2022年

作者： Xia, Huiyun Mao, Yijie Zhou, Xiaokang Clerckx, Bruno Han, Shuai Li, Cheng School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China School of Information Science and Technology ShanghaiTech University Shanghai China Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Electrical and Computer Engineering Faculty of Engineering and Applied Science Memorial University St. John’sA1B 3X5 Canada

As physical layer security evolves to multi-user systems, multi-user interference (MUI) becomes an unavoidable issue. Recently, rate-splitting multiple access (RSMA) emerges as a powerful non-orthogonal transmission framework and interference management strategy with high spectral efficiency. Unlike most works fully treating MUI as noise, we take all users’ secrecy rate requirements into consideration and propose an RSMA-based secure beamforming approach to maximize the weighted sum-rate (WSR), where MUI is partially decoded and partially treated as noise. User messages are split and encoded into common and private streams. Each user not only decodes the common stream and the intended private stream, but also tries to eavesdrop other users’ private streams. A successive convex approximation (SCA)-based approach is proposed to maximize the instantaneous WSR under perfect channel state information at the transmitter (CSIT). We then propose a joint weighted minimum mean square error and SCA-based alternating optimization algorithm to maximize the weighted ergodic sum-rate under imperfect CSIT. Numerical results demonstrate RSMA achieves better WSR and is more robust to channel errors than conventional multi-user linear precoding technique while ensuring all users’ security requirements. Besides, RSMA can satisfy all users’ secrecy rate requirements without introducing WSR loss thanks to its powerful interference management capability. Copyright © 2022, The Authors. All rights reserved.

关键词： Beamforming

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Security & Privacy Issues in IoT Using Blockchain and ML

Security & Privacy Issues in IoT Using Blockchain and ML

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International Conference on Image Information Processing

作者： Muna Muhammad Ahthasham Sajid Gunjan Chhabra Hamed Taherdoost Inam Ullah Khan Keshav Kaushik Engineering and Management Sciences Balochistan University of Information Technology Balochistan Pakistan Faculty of Computing Capital University of Science and Technology Islamabad Pakistan Department of CSE Graphic Era Hill University Dehradun Uttarakhand India Q Minded | Quark Minded Tehnology Inc. University Canada West Vancouver Canada Department of Electronic Engineering School of Engineering and Applied Sciences (SEAS) Isra University Islamabad Campus Pakistan School of Computer Science University of Petroleum and Energy Studies Dehradun India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

As Internet of Things (IoT) devices are being involved in abundant applications, security, and privacy of the IoT users have been of more concern. The security threats are rapidly growing making the solutions of security and privacy not inadequate. Thereby, each thing on the internet can be a point of target for hackers. Accordingly, ML algorithms are utilized for producing accurate outputs by using large and complex databases in which the produced outputs could be used in the prediction and detection of weaknesses in IoT systems. Additionally, BC methods are also being used for providing security and privacy solutions in IoT networks. Many existing studies have been carried out on BC techniques or ML algorithms or both. Though, these existing studies focus on either providing privacy or security issues by using BC techniques or ML algorithms, therefore, give rise to need for aa combined survey on solutions made recently which address both privacy and security issues by using BC techniques and ML algorithms. In this paper, we give a summarized survey of research works that happened in the last four years by addressing privacy and security issues by using BC techniques and ML algorithms in IoT networks. Different literatures have been categorized on privacy and security of BC techniques and ML algorithms in IoT. Then at last, some research challenges and future works are highlighted by using BC techniques and ML algorithms for addressing security and privacy issues in IoT networks.

关键词： Surveys Threat modeling Privacy Databases computer hacking Information processing Prediction algorithms

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Reinforcement Learning Based Joint Resource Allocation and User Fairness Optimization in mmWave-NOMA HetNets

Reinforcement Learning Based Joint Resource Allocation and U...

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Iranian Conference on Electrical engineering (ICEE)

作者： Sima Sobhi-Givi Mahdi Nouri Mahrokh G. Shayesteh Hashem Kalbkhani Zhiguo Ding Electrical and Computer Engineering Department Urmia University Urmia Iran Elect. Eng. Dep. Sharif university of technology Mobile Telecommunication Company of Iran (MCI) International Telecommunication Union (ITU) Tehran Iran ACRI Elec. Eng. Dep. Wireless Research Lab Sharif University of Technology Tehran Iran Faculty of Elec. Eng. Urmia University of technology Urmia Iran School of Electrical and Electronic Engineering The University of Manchester Manchester UK

In this paper, we propose heterogeneous network (HetNet) with mmWave and hybrid non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) transmission, where uplink (UL) of macrocell users (MCUs) and downlink (DL) of small cell users (SCUs) share the same resource block (RB) to increase the network capacity. Also, an imperfect successive interference cancelation (I-SIC) decoding is considered due to hardware impairment of the real-world NOMA systems. We formulate the problem of joint power and resource block (RB) allocation to maximize rate-fairness and sum-rate by considering minimum quality of service (QoS) requirements. Reinforcement learning (RL) named Q-learning (QL) and deep Q-learning network (DQN) algorithms are employed to solve these problems. The results indicate that the proposed QL and DQN algorithms increase the fairness and sum-rate while low number of iterations is required.

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Boundary-Assisted Region Proposal Networks for Nucleus Segmentation 23rd

Boundary-Assisted Region Proposal Networks for Nucleus Segme...

引用

23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Chen, Shengcong Ding, Changxing Tao, Dacheng School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China UBTECH Sydney AI Centre School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia

ISBN: (纸本)9783030597214

Nucleus segmentation is an important task in medical image analysis. However, machine learning models cannot perform well because there are large amount of clusters of crowded nuclei. To handle this problem, existing approaches typically resort to sophisticated hand-crafted post-processing strategies;therefore, they are vulnerable to the variation of post-processing hyper-parameters. Accordingly, in this paper, we devise a Boundary-assisted Region Proposal Network (BRP-Net) that achieves robust instance-level nucleus segmentation. First, we propose a novel Task-aware Feature Encoding (TAFE) network that efficiently extracts respective high-quality features for semantic segmentation and instance boundary detection tasks. This is achieved by carefully considering the correlation and differences between the two tasks. Second, coarse nucleus proposals are generated based on the predictions of the above two tasks. Third, these proposals are fed into instance segmentation networks for more accurate prediction. Experimental results demonstrate that the performance of BRP-Net is robust to the variation of post-processing hyper-parameters. Furthermore, BRP-Net achieves state-of-the-art performances on both the Kumar and CPM17 datasets. The code of BRP-Net will be released at https://***/csccsccsccsc/brpnet. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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A Hybrid Spiking Model for Anomaly Detection in Multivariate Time Series

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Expert Systems 2025年第8期42卷

作者： Wei Zhang Ping He Shengrui Wang Fan Yang Ying Liu School of Electronic and Information Engineering Hebei University of Technology Tianjin China School of Artificial Intelligence Hebei University of Technology Tianjin China Faculty of Sciences University of Sherbrooke Sherbrooke Québec Canada School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

Deep neural networks have exhibited preeminent performance in anomaly detection, but they struggle to effectively capture changes over time in multivariate time-series data and suffer from resource consumption issues. Spiking neural networks address these limitations by capturing the change in time-varying signals and decreasing resource consumption, but they sacrifice performance. This paper develops a novel spiking-based hybrid model incorporated a temporal prediction network and a reconstruction network. It integrates a unique first-spike frequency encoding scheme and a firing rate based anomaly score method. The encoding scheme enhances the event representation ability, while the anomaly score enables efficient anomaly identification. Our proposed model not only maintains low resource consumption but also improves the ability of anomaly detection. Experiments on publicly real-world datasets confirmed that the proposed model acquires state-of-the-art performance superior to existing approaches. Remarkably, it costs 5.04× lower energy consumption compared with the artificial neural network version.

关键词： anomaly detection multivariate time series prediction reconstruction spiking neural network

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