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Integrated Sensing and Communications With Reconfigurable Intelligent Surfaces: From signal modeling to processing

IEEE Signal Processing Magazine 2023年第6期40卷 41-62页

作者： Chepuri, Sundeep Prabhakar Shlezinger, Nir Liu, Fan Alexandropoulos, George C. Buzzi, Stefano Eldar, Yonina C. Indian Institute of Science Department of Electrical and Computer Engineering Bengaluru560012 India Ben-Gurion University of the Negev School of Electrical and Computer Engineering Be'er Sheva84105 Israel Southern University of Science and Technology Shenzhen518055 China School of Sciences National and Kapodistrian University of Athens Department of Informatics and Telecommunications Athens15784 Greece University of Cassino and Lazio Meridionale Cassino03043 Italy Weizmann Institute of Science Department of Mathematics and Computer Science Rehovot7610001 Israel

Integrated sensing and communications (ISAC) are envisioned to be an integral part of future wireless networks, especially when operating at the millimeter-wave (mm-wave) and terahertz (THz) frequency bands. However, establishing wireless connections at these high frequencies is quite challenging, mainly due to the penetrating path loss that prevents reliable communication and sensing. Another emerging technology for next-generation wireless systems is reconfigurable intelligent surface (RIS), which refers to hardware-efficient planar structures capable of modifying harsh propagation environments. In this article, we provide a tutorial-style overview of the applications and benefits of RISs for sensing functionalities in general, and for ISAC systems in particular. We highlight the potential advantages when fusing these two emerging technologies, and identify for the first time that 1) joint sensing and communications (S&C) designs are most beneficial when the channels referring to these operations are coupled, and that 2) RISs offer the means for controlling this beneficial coupling. The usefulness of RIS-aided ISAC goes beyond the obvious individual gains of each of these technologies in both performance and power efficiency. We also discuss the main signal processing challenges and future research directions that arise from the fusion of these two emerging technologies. © 1991-2012 IEEE.

关键词： Signal processing

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Optimizing Magnetic Induction Sensors for Non-Obtrusive Vital Signs Monitoring: Impact of Current Control on Operational Quality 46

Optimizing Magnetic Induction Sensors for Non-Obtrusive Vita...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Momeni, Mahdi Radomski, Adrian Guler, Ulkuhan Teichmann, Daniel University of Southern Denmark Sdu Health Informatics and Technology The Maersk Mc-Kinney Moller Institute Denmark Worcester Polytechnic Institute Department of Electrical and Computer Engineering WorcesterMA United States

ISBN: (纸本)9798350371499

This paper investigates the advancement of magnetic induction-based heart and respiration rate sensing by actively controlling the coil current. This is realized through the implementation of a current-starved inverter mechanism. Experiments show a notable level of accuracy of the proposed circuit in measuring heart and respiration activity when compared to a reference sensor. The direct manipulation of current levels was found to have a direct impact on the signal strength. Incrementing the overall current within the proposed circuit from 60 mA to 100 mA resulted in an augmentation of the output amplitude of the heart rate signal from 8.5 mV to 27 mV, accompanied by a marginal enhancement in beat-to-beat interval accuracy. Moreover, the proposed sensor demonstrates noteworthy precision in monitoring the respiratory rate when compared with the reference sensor under different current values, exhibiting the same trend in signal strength. This finding offers valuable insight for the development of future power-optimized magnetic induction sensors with enhanced robustness. © 2024 IEEE.

关键词： Contact sensors

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The Calibration of Single Beam Distance Sensors based on Machine Learning Methods

The Calibration of Single Beam Distance Sensors based on Mac...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Biernacki, Piotr Ziebinski, Adam Syu, Jia-Hao Lin, Jerry Chun-Wei Silesian University of Technology Institute of Theoretical and Applied Informatics Polish Academy of Sciences Institute of Informatics Gliwice Poland Silesian University of Technology Institute of Informatics Gliwice Poland National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324457

Smart cities require the use of many different types of sensors to make the communication, and distance sensors are one of the most commonly used elements in transportation systems and related infrastructures. The introduction of increasingly advanced autonomous systems in many areas of smart cities requires high measurement precision of the sensors used. High precision is essential for proper operation, long-term use, and safety in machine-to-machine or machine-to-human interactions. This paper presents a comparison of the accuracy of distance measurements for two commercially available single-beam LiDARs and two ultrasonic sensors. The aim of the research was to develop a calibration method in order to improve the accuracy of distance sensors. Based on the collected distance measurements, the sensors were calibrated using selected machine learning algorithms. The results of the experiments show the effectiveness of the proposed calibration methods, which yield an average mean absolute error (MAE) of 1.76 E-05 meters (m) and a root mean square error (RMSE) of 1.33 E-04 m for the tested sensors. © 2023 IEEE.

关键词： machine learning sensors calibration single-beam distance sensors

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Survey on Cryptoprocessors Advances and Technological Trends 3rd

Survey on Cryptoprocessors Advances and Technological Tren...

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3rd International Conference on Innovations in Computing research, ICR 2024

作者： Kakoulli, Elena Zacharioudakis, Eleftherios Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Paphos Cyprus Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine

ISBN: (纸本)9783031655210

Cryptoprocessors play a pivotal role in enhancing the security of modern computing systems by accelerating cryptographic operations and fortifying data protection. This survey delves into the world of cryptoprocessors, encompassing their historical development, architectural features, and various types based on their applications. The paper explores the crucial security considerations surrounding cryptoprocessors, covering both physical and logical aspects, as well as potential threats and countermeasures. Furthermore, the survey navigates through the latest technological trends and advancements in cryptoprocessor design. This includes discussions on post-quantum cryptography, secure enclaves, and mitigating side-channel attacks, all of which are shaping the future of cryptoprocessors. Real-world use cases and applications in domains such as secure communications, the Internet of Things (IoT), and data centers are presented, showcasing the practical significance of cryptoprocessors. However, the paper also highlights the persistent challenges faced by cryptoprocessor designers and provides insights into future research directions aimed at addressing emerging security and performance issues. As the digital landscape continues to evolve, the role of cryptoprocessors becomes increasingly vital in safeguarding sensitive information and fortifying the foundations of cybersecurity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Internet of things

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Implementation of Machine Learning and Ensemble Learning Techniques to Proactively Predict Students' academic Success in the Realm of Higher Education 4

Implementation of Machine Learning and Ensemble Learning Tec...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Devkishan, Taneja Sanjay Singh, Sanjay Kumar Bharti, Ajay Kumar Amity Institute of Information Technology Amity University Uttar Pradesh Lucknow India United College of Engineering and Research Department of Computer Science and Engineering Prayagraj India

ISBN: (纸本)9798350387490

Predicting and analyzing student performance is a huge boon for both students and teachers. A number of analytical methodologies have been utilized in recent studies to forecast students' success;the CGPA is the end product of these analyses, which have been conducted for both the internal assessment as well as for the external assessment. Few well known machine learning algorithms have been suggested. Ensemble Learning along with Decision Trees, Support Vector Machine and Random Forest -obtained perfect 100s for Precision Value, Recall Value, as well as in F1 Scores in the evaluation of performances. Naive Bayes has worse performance in comparison, with F1 Scores of 66, Recall Value of 65, and Precision of 68. When contrasted with decision tree models, Support Vector Machines, Multi-Layer Perceptron, Ensemble Enabled Learning, and Random Forest, Naive Bayes, AdaBoost, and K-Nearest Neighbors all perform worse in terms of accuracy. The Naive Bayes classifier models the input distributions across classes and is very good in text categorization because it is a generative method. Since it fails to identify critical features for class differentiation, it cannot be distinguished from discriminative classifiers. © 2024 IEEE.

关键词： Adversarial machine learning

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Fault-Tolerant ST-Diameter Oracles 50

Fault-Tolerant ST-Diameter Oracles

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50th International Colloquium on Automata, Languages, and Programming, ICALP 2023

作者： Bilò, Davide Choudhary, Keerti Cohen, Sarel Friedrich, Tobias Krogmann, Simon Schirneck, Martin Department of Information Engineering Computer Science and Mathematics University of L’Aquila Italy Department of Computer Science and Engineering Indian Institute of Technology Delhi India School of Computer Science Tel-Aviv-Yaffo Academic College Israel Hasso Plattner Institute Universität Potsdam Germany Faculty of Computer Science Universität Wien Austria

ISBN: (纸本)9783959772785

We study the problem of estimating the ST-diameter of a graph that is subject to a bounded number of edge failures. An f-edge fault-tolerant ST-diameter oracle (f-FDO-ST) is a data structure that preprocesses a given graph G, two sets of vertices S, T, and positive integer f. When queried with a set F of at most f edges, the oracle returns an estimate Db of the ST-diameter diam(G−F, S, T), the maximum distance between vertices in S and T in G− F. The oracle has stretch σ ≥ 1 if diam(G−F, S, T) ≤ Db ≤ σ diam(G−F, S, T). If S and T both contain all vertices, the data structure is called an f-edge fault-tolerant diameter oracle (f-FDO). An f-edge fault-tolerant distance sensitivity oracles (f-DSO) estimates the pairwise graph distances under up to f failures. We design new f-FDOs and f-FDO-STs by reducing their construction to that of all-pairs and single-source f-DSOs. We obtain several new tradeoffs between the size of the data structure, stretch guarantee, query and preprocessing times for diameter oracles by combining our black-box reductions with known results from the literature. We also provide an information-theoretic lower bound on the space requirement of approximate f-FDOs. We show that there exists a family of graphs for which any f-FDO with sensitivity f ≥ 2 and stretch less than 5/3 requires Ω(n3/2) bits of space, regardless of the query time. © Davide Bilò, Keerti Choudhary, Sarel Cohen, Tobias Friedrich, Simon Krogmann, and Martin Schirneck.

关键词： Data structures

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Urban Pluvial Flood Prediction Using Machine Learning Models 4

Urban Pluvial Flood Prediction Using Machine Learning Models

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4th International Conference on Innovations in computer Science, ICONICS 2024

作者： Waseemullah Hassan, Tahir Majeed, Sajid Memon, Muhammad Qasim NED University of Engineering and Technology Department of Computer Science & Information Technology Karachi Pakistan Institute of Space Technology Department of Remote Sensing & Geo-Informatics Karachi Pakistan University of Sufism and Modern Sciences Department of Information and Computing Bhitshah Pakistan

ISBN: (纸本)9798331504342

Urban pluvial flooding causes threats to human lives, economy, infrastructure, and ecosystems around the world, particularly in developing countries. Pakistan is among those developing countries that have become vulnerable to urban flooding due to global warming, climate change risks, rapid urbanization, and the occurrence of extreme and unusual rainfall events in the monsoon season. To effectively mitigate the adverse effects of pluvial flooding, there is a pressing need for an advanced and accurate predictive model that is capable of predicting urban pluvial flooding. The aim of this research is to utilize the potential of machine learning (ML) techniques and algorithms to accurately predict the occurrence of urban pluvial flooding, for this study we have worked on the monsoon rainfall data of multiple cities of Sindh province in Pakistan. We made an attempt to use multiple ML models especially logistic regression (LR), random forest (RF), k nearest neighbor (KNN), and support vector machine (SVM) to propose the best and accurate predictive model. The developed models were evaluated and assessed using various performance evaluation metrics which includes accuracy, recall, precision and F1 score. The best among proposed models was created using LR which achieved an accuracy of 97%, while the accuracy of other models which were created using SVM, KNN and RF were 96%, 93%, and 92% respectively. © 2024 IEEE.

关键词： Prediction models

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Exploring Music Similarity through Siamese CNNs using Triplet Loss on Music Samples

Exploring Music Similarity through Siamese CNNs using Triple...

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2023 International research Conference on Smart Computing and Systems engineering, SCSE 2023

作者： Kasif, Gibran Thondilege, Comganesha University of Westminster London School of Computer Science and Engineering United Kingdom Informatics Institute of Technology Department of Computing Colombo06 Sri Lanka

ISBN: (纸本)9798350341454

In the rapidly evolving digital music landscape, identifying similarities between musical pieces is essential to help musicians avoid unintended copyright infringement and maintain the originality of their work. However, detecting such similarities remains a complex and computationally challenging problem. A novel approach to address this issue is a song similarity detection system that utilizes a Siamese Convolutional Neural Network (CNN) with Triplet Loss for effective audio input comparison. The model is trained on a custom dataset from WhoSampled, an extensive database of information on sampled music, cover songs, and remixes. The dataset comprises pairs of audio samples and interpolations, making it suitable for the Siamese CNN approach. Incorporating Triplet Loss enhances the model's performance by learning discriminative features for improved comparison. The performance of this system is assessed using a confidence interval-based metric, achieving a 96.86% accuracy at a 99.7% confidence level in determining the similarity between music samples. The solution provides a helpful tool for musicians to actively compare their creations with existing songs, helping to reduce the likelihood of unintentional plagiarism and possible legal issues. © 2023 IEEE.

关键词： Music

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Resilient Routing Table Computation Based on Connectivity Preserving Graph Sequences 42

Resilient Routing Table Computation Based on Connectivity Pr...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Tapolcai, Janos Babarczi, Peter Ho, Pin-Han Ronyai, Lajos Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Telecommunications and Media Informatics BudapestH-1111 Hungary University of Waterloo Deptartment of Electrical and Computer Engineering Canada Institute for Computer Science and Control Eötvös Loránd Research Networkand Department of Algebra Hungary Institute of Mathematics Budapest University of Technology and Economics BudapestH-1111 Hungary

ISBN: (纸本)9798350334142

Fast reroute (FRR) mechanisms that can instantly handle network failures in the data plane are gaining attention in packet-switched networks. In FRR no notification messages are required as the nodes adjacent to the failure are prepared with a routing table such that the packets are re-routed only based on local information. However, designing the routing algorithm for FRR is challenging because the number of possible sets of failed network links and nodes can be extremely high, while the algorithm should keep track of which nodes are aware of the failure. In this paper, we propose a generic algorithmic framework that combines the benefits of Integer Linear Programming (ILP) and an effective approach from graph theory related to constructive graph characterization of k-connected graphs, i.e., edge splitting-off. We illustrate these benefits through arborescence design for FRR and show that (i) due to the ILP we have great flexibility in defining the routing problem, while (ii) the problem can still be solved very fast. We demonstrate through simulations that our framework outperforms state-of-the-art FRR mechanisms and provides better resilience with shorter paths in the arborescences. © 2023 IEEE.

关键词： Graph theory

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A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

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computer Modeling in engineering & Sciences 2025年第1期142卷 49-90页

作者： Vajratiya Vajrobol Geetika Jain Saxena Amit Pundir Sanjeev Singh Akshat Gaurav Savi Bansal Razaz Waheeb Attar Mosiur Rahman Brij B.Gupta Institute of Informatics and Communication University of DelhiDelhi110021India Maharaja Agrasen College University of DelhiDelhi110096India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Research and Innovation Insights2TechinfoJaipur302001India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia CCRI&Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing *** aims to alleviate the workload on healthcare professionals and aid *** applications have been developed to support the challenges in intelligent healthcare ***,because mental health data is sensitive,privacy concerns have *** learning has gotten some *** research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare *** explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health *** research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security *** survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

关键词： Depression emotional recognition intelligent healthcare systems mental health federated learning stress detection sleep behaviour

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