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Cooperative Backscatter Communications with Reconfigurable Intelligent Surfaces: An APSK Approach

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arXiv 2024年

作者： Li, Qiang Feng, Yehuai Wen, Miaowen Wen, Jinming Alexandropoulos, George C. Basar, Ertugrul Vincent Poor, H. The College of Information Science and Technology Jinan University Guangzhou510632 China The School of Electronics and Information Engineering South China University of Technology Guangzhou510640 China The Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens15784 Greece The Department of Electrical and Electronics Engineering Koç University Istanbul34450 Turkey The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

In this paper, a novel amplitude phase shift keying (APSK) modulation scheme for cooperative backscatter communications aided by a reconfigurable intelligent surface (RIS-CBC) is presented, according to which the RIS is configured to modulate backscatter information onto unmodulated or PSK-modulated signals impinging on its surface via APSK. We consider both passive and active RISs, with the latter including an amplification unit at each reflecting element. In the passive (resp. active) RIS-CBC-APSK, backscatter information is conveyed through the number of RIS reflecting elements being on the ON state (resp. active mode) and their phase shift values. By using the optimal APSK constellation to ensure that reflected signals from the RIS undergo APSK modulation, a bit-mapping mechanism is presented. Assuming maximum-likelihood detection, we also present closed-form upper bounds for the symbol error rate (SER) performance for both passive and active RIS-CBC-APSK schemes over Rician fading channels. In addition, we devise a low-complexity detector that can achieve flexible trade-offs between performance and complexity. Finally, we extend RIS-CBC-APSK to multiple-input single-output scenarios and present an alternating optimization approach for the joint design of transmit beamforming and RIS reflection. Our extensive simulation results on the SER performance corroborate our conducted performance analysis and showcase the superiority of the proposed RIS-CBC-APSK schemes over the state-of-the-art RIS-CBC benchmarks. © 2024, CC BY-NC-ND.

关键词： Economic and social effects

Smart FIR: Securing e-FIR Data through Blockchain within Smart Cities 91

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Smart FIR: Securing e-FIR Data through Blockchain within Sma...

91st IEEE Vehicular Technology Conference, VTC Spring 2020

作者： Khan, Nasir D. Chrysostomou, Chrysostomos Nazir, Babar Frederick University Department of Electrical and Computer Engineering and Informatics Nicosia Cyprus Comsats University Islamabad Department of Computer Science Abbottabad Pakistan

ISBN: (纸本)9781728152073

Electronic First Information Report (e-FIR) is a basic document filed to the police stations by a victim or someone on his/her behalf when a cognizable offense such as murder, kidnapping, rape, theft, etc. is committed. In the e-FIR database, the offense's record can be compromised due to its centralized nature, and further the intentional registration of false e-FIR can occur. Thus, data integrity and transparency are key concerns in e-FIR database. In this paper, e-FIR data integrity and false registration appended with police stations in a centralized database are addressed via a consensus-based distributed blockchain solution, as an integral part of a smart city environment. Specifically, a smart contract based intelligent framework has been utilized to explore the potential of Ethereum blockchain in providing integrity to e-FIR data stored in a police station's database. Local database is interfaced with Ethereum blockchain using Web3 Remote Procedure Call (RPC) protocol. Multiple simulations have been performed to evaluate the performance of the proposed framework. Our results show a trade-off between different hashing algorithm security level for the offenses data and number of transactions stored in a single block on blockchain ledger. © 2020 IEEE.

关键词： Smart contract

Observing Generator Performance by Finite Element Analysis Considering Coil Numbers

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Observing Generator Performance by Finite Element Analysis C...

Technology and Policy in Energy and Electric Power (ICT-PEP), International Conference on

作者： Muhammad Afnan Habibi Mochamad Farhan Ali Irfani Langlang Gumilar Arya Kusumawardana Aripriharta Angger Baskoro Study Program of Power Plant Engineering Technology Universitas Negeri Malang Malang Indonesia Department of Electrical Engineering and Informatics Universitas Negeri Malang Malang Indonesia Dept. Electrical Engineering and Computer Sciences National Yang Ming Chiao Tung University Hsinchu Taiwan

This paper conducts a comprehensive analysis of electrical generator performance using Finite Element Analysis (FEA), with a specific emphasis on the role of coil numbers in influencing generator efficiency and functionality. In the context of modern energy systems, where efficient power generation is paramount, this research aims to elucidate the relationship between the number of coils within a generator and its overall performance, including power output and electromagnetic behavior. Through systematic FEA simulations that vary coil numbers while keeping other parameters constant, this study provides valuable insights into the trade-offs associated with increased coil numbers and enhanced efficiency. These findings have significant implications for optimizing generator designs across various applications, from renewable energy systems to industrial power generation, ultimately advancing our understanding of generator dynamics and contributing to more sustainable and efficient power generation technologies.

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Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization

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arXiv 2023年

作者： Balaskas, Konstantinos Karatzas, Andreas Sad, Christos Siozios, Kostas Anagnostopoulos, Iraklis Zervakis, Georgios Henkel, Jörg The Department of Physics Aristotle University of Thessaloniki Thessaloniki54124 Greece Karlsruhe Institute of Technology Karlsruhe76131 Germany The School of Electrical Computer and Biomedical Engineering Southern Illinois University Carbondale CarbondaleIL62901 United States The Dept. of Computer Engineering & Informatics University of Patras Patras26504 Greece

Deep Neural Networks (DNNs) have shown significant advantages in a wide variety of domains. However, DNNs are becoming computationally intensive and energy hungry at an exponential pace, while at the same time, there is a vast demand for running sophisticated DNN-based services on resource constrained embedded devices. In this paper, we target energy-efficient inference on embedded DNN accelerators. To that end, we propose an automated framework to compress DNNs in a hardware-aware manner by jointly employing pruning and quantization. We explore, for the first time, per-layer fine- and coarse-grained pruning, in the same DNN architecture, in addition to low bit-width mixed-precision quantization for weights and activations. Reinforcement Learning (RL) is used to explore the associated design space and identify the pruning-quantization configuration so that the energy consumption is minimized whilst the prediction accuracy loss is retained at acceptable levels. Using our novel composite RL agent we are able to extract energy-efficient solutions without requiring retraining and/or fine tuning. Our extensive experimental evaluation over widely used DNNs and the CIFAR-10/100 and ImageNet datasets demonstrates that our framework achieves 39% average energy reduction for 1.7% average accuracy loss and outperforms significantly the state-of-the-art approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

Sensors and healthcare 5.0:transformative shift in virtual care through emerging digital health technologies

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Global Health Journal 2021年第4期5卷 169-177页

作者： Elliot Mbunge Benhildah Muchemwa Sipho'esihle Jiyane John Batani Department of Computer Science Faculty of Science and EngineeringUniversity of EswatiniKwaluseniManziniEswatini Department of Information Technology Faculty of Accounting and InformaticsDurban University of TechnologyP.O.Box 1334Durban 4000South Africa Faculty of Engineering and Technology Botho UniversityMaseru 100Lesotho

Emerging digital technologies continues to evolve posing unprecedented opportunities in health systems globally to improve healthcare services *** has been significant progress in ***,the lack of emotive recognition coupled with a dearth of personalized and pervasive health applications and emotive smart devices calls for the integration of intelligent sensors health systems through emerging *** there has been significant progress in smart and connected health care,more research innovation,dissemination and technologies are needed to unbundle new opportunities and move towards healthcare *** is at the dawn of a paradigm change to reach the new era of smart disease control and detection,virtual care,smart health management,smart monitoring,and ***,this study discusses the roles and capacities of sensors,their capabilities and other emerging technologies such as nanotechnology,5 G technologies,drone technology,blockchain,robotics,big data,internet of things,artificial intelligence,and cloud *** 5.0 provides healthcare services including patient remote *** and virtual clinics,emotive telemedicine,ambient assisted living,smart self-management,wellness monitoring and control,smart treatment reminders,compliance and adherence,and personalized and connected health ***,building resilience and robust healthcare 5.0 is not immune to *** challenges,technological and infrastructural barriers,lack of legal and regulatory frameworks and e-health policies,individual perceptions,misalignment with hospitals'strategy,lack of funding,religious and cultural bariers are identified as potential barriers to the successful implementation of healthcare ***,there is a need for building resilient technology-driven healthcare *** achieve this,there is a need for expanding technological infrastructure,provision of budgetary support based o

关键词： Healthcare 5.0 Sensors Smart devices Virtual care Emerging technologies Coronavirus disease 2019(COVID-19)

Cybersecurity of Sensors on Smart Vehicles: Review of Threats and Solutions

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Cybersecurity of Sensors on Smart Vehicles: Review of Threat...

International Conference of computer and informatics engineering (IC2IE)

作者： Prasetyo Adi Wibowo Putro Fetty Amelia Jan Pidanic Heru Suhartanto Imam Arif Rahardjo Ejo Imandeka Faculty of Computer Science Universitas Indonesia Depok Indonesia Cryptographic Hardware Engineering Politeknik Siber dan Sandi Negara Bogor Indonesia Faculty of Electrical Engineering and Informatics University of Pardubice Pardubice Czech Republic Department of Electrical Engineering Universitas Negeri Jakarta Jakarta Indonesia

The use of sensors in smart vehicles brings benefits and vulnerabilities. Different kinds of sensors in smart vehicles are vulnerable to cyber-attack. Until now, the investigation of challenges and solutions for in-vehicle cybersecurity hasn't discussed various sensor objects and their correlation. In this study, we studied the cyber security problems of sensors in smart vehicles and how to overcome them. The research was designed as Systematic Literature Review (SLR) using the Kitchenham methodology with modification in the filtering phase using the artificial intelligence application, Elicit, to identify the problems, conclusions, and methodology description. Seventeen publications from 2016 until 2023 were gained from five databases. As a result, we find that the most discussed object related to cybersecurity sensors on smart vehicles are Electronic Control Units. Spoofing and jamming is still the most addressed threat, and machine learning is the most utilized solution to be implemented in detection systems. Advanced detection systems are incorporating updated attack models. We also suggest using updated attack models and machine learning algorithms to ensure the safety and security of smart vehicle technology. All identified sensor technology correlated using mind maps under the Intelligent Transport System theory.

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A cross-domain recommender system using deep coupled autoencoders

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arXiv 2021年

作者： Gkillas, Alexandros Kosmopoulos, Dimitrios Department of Electrical and Computer Engineering Patras University Patras Greece The Faculty of Department of Computer Engineering and Informatics Patras University Patras Greece

Long-standing data sparsity and cold-start constitute thorny and perplexing problems for the recommendation systems. Cross-domain recommendation as a domain adaptation framework has been utilized to effectively address these challenging issues, by exploiting information from multiple domains. In this study, an item-level relevance cross-domain recommendation task is explored, where two related domains, that is, the source and the target domain contain common items. Additionally, a user-level relevance scenario is considered, where the two related domains contain common users. In light of these scenarios, two novel coupled autoencoder-based deep learning methods are proposed for cross-domain recommendation. The first method aims to simultaneously learn a pair of autoencoders in order to reveal the intrinsic representations in the source and target domains, along with a coupled mapping function to model the non-linear relationships between these representations. The second method is derived based on a new joint regularized optimization problem, which employs two autoencoders to generate in a deep and non-linear manner the user and item-latent factors, while at the same time a data-driven function is learnt to map the latent factors across domains. Extensive numerical experiments are conducted illustrating the superior performance of our proposed methods compared to several state-of-the-art cross-domain recommendation frameworks. Copyright © 2021, The Authors. All rights reserved.

关键词： Recommender systems

Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis

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arXiv 2022年

作者： Yu, Jun Kong, Zhaoming Zhan, Liang Shen, Li He, Lifang Department of Computer Science and Engineering Lehigh University BethlehemPA United States Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States Department of Biostatistics Epidemiology and Informatics The Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States

The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task. Recent studies have demonstrated that combination of multi-modality imaging techniques can better reflect pathological characteristics and contribute to more accurate diagnosis of AD and MCI. In this paper, we propose a novel tensor-based multi-modality feature selection and regression method for diagnosis and biomarker identification of AD and MCI from normal controls. Specifically, we leverage the tensor structure to exploit high-level correlation information inherent in the multi-modality data, and investigate tensor-level sparsity in the multilinear regression model. We present the practical advantages of our method for the analysis of ADNI data using three imaging modalities (VBM-MRI, FDG-PET and AV45-PET) with clinical parameters of disease severity and cognitive scores. The experimental results demonstrate the superior performance of our proposed method against the state-of-the-art for the disease diagnosis and the identification of disease-specific regions and modality-related differences. The code for this work is publicly available at https://***/junfish/BIOS22. © 2022, CC BY-NC-SA.

关键词： Tensors

Secure IoT-Based Health Monitoring with Cloud-Based Machine Learning Analytics

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Secure IoT-Based Health Monitoring with Cloud-Based Machine ...

Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Ram Deshmukh Arshini Gubbala B Pravallika Amit Dutt Ahmed Sabah Ahmed AL-Jumaili Manjunatha School of Engineering SR University Warangal Telangana India Department of Electronics and Computer Engineering Vignan's Institute of Information Technology (Autonomous) Visakhapatnam Institute of Aeronautical Engineering Dundigal Hyderabad Lovely Professional University Phagwara Department of Business Information Technology (BIT) College of Business informatics University of information Technology and Communications Baghdad Iraq Department of Mechanical Engineering New Horizon College of Engineering Bangalore

The development and deployment of an efficient and trustworthy Internet of Things-based health monitoring system are presented in this study. The system uses biometric authentication, a PKI framework, TLS, and MQTT for secure communication, as well as identification of users to guarantee data confidentiality. Real-time health data analysis with predictive analytics as well as anomaly detection is made possible by cloud-based machine learning analytics on platforms such as AWS. Evaluation in a range of healthcare environments demonstrates just how flexible and resilient the system is. Despite the achievements, there are still obstacles to overcome, such as problems with the current healthcare infrastructure's interoperability in addition to worries about the scalability of resource-intensive machine learning algorithms. It is imperative that these issues be resolved in order to continue enhancing and enhancing the system and ensuring that it has the capacity to completely transform healthcare procedures.

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