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IEEE Conference on Decision and Control

作者： Amirreza Silani Giacomo Casadei Michele Cucuzzella Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen AG Groningen The Netherlands Control & Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Laboratoire Ampere Dpt. EEA of the Ecole Centrale de Lyon Université de Lyon Ecully France Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this letter, we propose a control scheme for regulating the voltage in Direct Current (DC) power networks. In contrast with other works in the literature where the loads are assumed to be constant, we consider uncertain time-varying loads described as the outputs of nonlinear dynamical exosystems with uncertain parameters. Based on the output regulation methodology, the proposed control scheme ensures the robust stability of the overall network and achieves voltage regulation in presence of impedance (Z), current (I), and power (P) loads. The simulation results illustrate excellent performance of the proposed control scheme.

关键词： Robust stability Simulation Regulation Impedance Voltage control

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Sex-specific differences in the efficacy of traditional low frequency versus high frequency spinal cord stimulation for chronic pain

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Bioelectronic Medicine 2022年第1期8卷 1页

作者： Conic, Rosalynn R. Z. Caylor, Jacob Cui, Christina L. Reyes, Zabrina Nelson, Eric Yin, Sopyda Lerman, Imanuel Department of Family Medicine and Public Health University of California San Diego La JollaCA United States Department of Anesthesiology Center for Pain Medicine University of California San Diego School of Medicine La JollaCA United States Northwest Pain Care PS SpokaneWA United States Division of Vascular and Endovascular Surgery Department of Surgery Duke University Medical Center DurhamNC United States School of Medicine University of California San Diego San DiegoCA United States College of Osteopathic Medicine of the Pacific Western University of Health Sciences PomonaCA United States Department of Electrical and Computer Engineering University of California San Diego La JollaCA United States VA Center of Excellence for Stress and Mental Health VA San Diego Healthcare System La JollaCA United States San DiegoCA92161 United States

Introduction: Spinal cord stimulation (SCS), an FDA-approved therapy for chronic pain, uses paresthesia (low frequency SCS (LF-SCS)) or paresthesia-free (such as high-frequency SCS (HF-SCS)) systems, providing analgesia through partially-elucidated mechanisms, with recent studies indicating a sexual dimorphism in pain pathogenesis (Bretherton et al., Neuromodulation, 2021;Paller et al., Pain Med 10:289–299, 2009;Slyer et al., Neuromodulation, 2019;Van Buyten et al., Neuromodulation 20:642–649, 2017;Mekhail et al., Pain Pract, 2021). We aim to evaluate SCS therapy sex effects based on paradigm, utilizing visual analog scores (VAS), perceived pain reduction (PPR), and opioid use. Methods: A retrospective cohort study of SCS patients implanted between 2004 and 2020 (n = 237) was conducted. Descriptive statistics and linear mixed methods analyses were used. Results: HF-SCS (10 kHz) was implanted in 94 patients (40 females, 54 males), and LF-SCS in 143 (70 females, 73 males). At 3 months and 6 months, HF-SCS (p © 2022, The Author(s).

关键词： Spinal cord stimulator Outcomes HF-SCS LF-SCS VAS Opioid Sex

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Towards Mutual Trust-Based Matching For Federated Learning Client Selection

Towards Mutual Trust-Based Matching For Federated Learning C...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Osama Wehbi Omar Abdel Wahab Azzam Mourad Hadi Otrok Hoda Alkhzaimi Mohsen Guizani Mohammad Bin Zayed University of Artificial Intelligence Abu Dhabi UAE Department of CSM Cyber Security Systems and Applied AI Research Center Lebanese American University Lebanon Department of Computer and Software Engineering Polytechnique Montréal Montreal Quebec Canada Division of Science New York University Abu Dhabi UAE Department of EECS Center of Cyber-Physical Systems (C2PS) Khalifa University Abu Dhabi UAE Division of Engineering New York University Abu Dhabi UAE

Federated Learning (FL) is a revolutionary privacy-preserving distributed learning framework that allows a small group of users to cooperatively build a machine-learning model using their own data locally. Smart cities are areas that can generate high volume and critical data, which has the potential to revolutionize federated learning. Nevertheless, it is highly challenging to select a trustworthy group of clients to collaborate in model training. The utilization of a random selection technique would pose many threats due to malicious clients’ targeted and untargeted attacks. Such vulnerability may cause attacks and poisoning in the produced model. To address this problem, we present a mutual trust client-server selection approach based on matching game theory and bootstrapping mechanisms for federated learning in smart cities. Our solution entails the creation of: (1) preference functions for federated servers and smart devices (i.e., IoT/IoV) that enables them to sort each other based on trust score, (2) light feedback-base technique that leverages the cooperation of multiple client devices to assign trust value to the newly connected federated server, and (3) intelligent matching algorithms consider trust preferences of both parties in their design. According to our simulation results, our technique outperforms the baseline selection approach VanillaFL in terms of increasing the trust level and hence the global accuracy of the federated learning model and optimizing the number of untrusted selected clients.

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On Quantized Sampled–Data Event–Triggered Consensus Tracking of Nonlinear Multi–Agent Time–Delay Systems

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IFAC-PapersOnLine 2022年第36期55卷 276-281页

作者： M. Di Ferdinando P. Pepe S. Di Gennaro Department of Information Engineering Computer Science and Mathematics University of L'Aquila Via Vetoio Loc. Coppito 67100 L'Aquila Italy Center of Excellence for Research DEWS University of L'Aquila Via Vetoio Loc. Coppito 67100 L'Aquila Italy

In this paper, the quantized sampled-data leaderless consensus tracking problem of nonlinear time–delay multi–agent systems (MASs) is studied. In particular, a new methodology for the design of quantized sampled–data event–triggered controllers achieving the leaderless consensus tracking of nonlinear MASs affected by state–delays is proposed. Firstly, the notion of steepest descent consensus feedback (SDCF), induced by a general class of Lyapunov–Krasovskii functionals, is introduced. Then, it is proved that there exist a suitably accurate quantization of the input channels and a suitably small maximum inter–sampling time such that: the digital implementation of SDCFs (continuous or not), with an event-triggered mechanism to update the control law only when necessary, achieves the leaderless consensus tracking in a semi–global practical sense of the related closed–loop MAS. The stabilization in the sample-and-hold sense theory is used as a tool for proving the results. The possible non–uniform quantization of the input channels is considered. Moreover, the case of time–varying sampling intervals as well as the stability analysis of the inter–sampling system behaviour is included in the theory here developed. An example is studied in order to validate the results.

关键词： Nonlinear Multi–Agent Time–Delay Systems Leaderless Consensus Tracking Quantized Sampled–Data Event–Triggered Control Control Lyapunov–Krasovskii Functionals

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Audio-Based Deep Learning Frameworks for Detecting COVID-19

arXiv

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arXiv 2022年

作者： Ngo, Dat Pham, Lam Hoang, Truong Kolozali, Şefki Jarchi, Delaram School of Computer Science and Electronic Engineering University of Essex United Kingdom Center for Digital Safety & Security Austrian Institute of Technology Austria Solution Technology Unit Department FPT Software Company Limited Viet Nam

This paper evaluates a wide range of audio-based deep learning frameworks applied to the breathing, cough, and speech sounds for detecting COVID-19. In general, the audio recording inputs are transformed into low-level spectrogram features, then they are fed into pre-trained deep learning models to extract high-level embedding features. Next, the dimension of these high-level embedding features are reduced before fine-tuning using Light Gradient Boosting Machine (LightGBM) as a back-end classification. Our experiments on the Second DiCOVA Challenge achieved the highest Area Under the Curve (AUC), F1 score, sensitivity score, and specificity score of 89.03%, 64.41%, 63.33%, and 95.13%, respectively. Based on these scores, our method outperforms the state-of-the-art systems, and improves the challenge baseline by 4.33%, 6.00% and 8.33% in terms of AUC, F1 score and sensitivity score, respectively. Copyright © 2022, The Authors. All rights reserved.

关键词： Spectrographs

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Digital Continuity Guarantee Approach of Electronic Record Based on Data Quality Theory

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computers, Materials & Continua 2020年第6期63卷 1471-1483页

作者： Yongjun Ren Jian Qi Yaping Cheng Jin Wang Osama Alfarraj School of Computer and Software Engineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia

Since the British National Archive put forward the concept of the digital continuity in 2007,several developed countries have worked out their digital continuity action ***,the technologies of the digital continuity guarantee are still *** first,this paper analyzes the requirements of digital continuity guarantee for electronic record based on data quality theory,then points out the necessity of data quality guarantee for electronic ***,we convert the digital continuity guarantee of electronic record to ensure the consistency,completeness and timeliness of electronic record,and construct the first technology framework of the digital continuity guarantee for electronic ***,the temporal functional dependencies technology is utilized to build the first integration method to insure the consistency,completeness and timeliness of electronic record.

关键词： Electronic record digital continuity data quality

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Learning Self-Target Knowledge for Few-Shot Segmentation

SSRN

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SSRN 2023年

作者： Chen, Yadang Chen, Sihan Yang, Zhi-Xin Wu, Enhua Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China State Key Laboratory of Internet of Things for Smart City Department of Electromechanical Engineering University of Macau 999078 China State Key Laboratory of Computer Science Institute of Software University of Chinese Academy of Sciences Beijing100190 China

Few-shot semantic segmentation uses a few annotated data of a specific class in the support set to segment the target of the same class in the query set. Most existing approaches fail to perform well when there are significant intra-class variances. This paper alleviates the problem by concentrating on mining the query image and using the support set as supplementary information. First, it proposes a Query Prototype Generation Module to generate a query foreground prototype from the query features. Specifically, we use both prototype-level and pixel-level similarity matching to generate two complementary initial prototypes, which we then integrate to create a discriminative query foreground prototype. Second, we propose a Support Auxiliary Refinement Module to further guide the final precise prediction of the query image by leveraging the target category information of the support set through step-by-step mining. Specifically, we generate a query-support mixture prototype based on the support prototype representation obtained using the attention mechanism. Then we generate a support supplement prototype to complement the missing information by encoding over the foreground regions that the query-support mixture prototype fails to segment out. Extensive experiments on PASCAL-5i and COCO-20i demonstrate that our model outperforms the prior works of few-shot segmentation. © 2023, The Authors. All rights reserved.

关键词： Semantics

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Interplay between Physical Layer Security and Blockchain Technology for 5G and Beyond: A Comprehensive Survey

TechRxiv

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TechRxiv 2022年

作者： Ghourab, Esraa M. Jaafar, Wael Bariah, Lina Naser, Shimaa Muhaidat, Sami Yanikomeroglu, Halim The KU Center for Cyber-Physical Systems Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi127788 United Arab Emirates The Department of Software and Information Technology Engineering Ecole de Technologie Supérieure MontrealQCH3C 1K3 Canada Abu Dhabi9639 United Arab Emirates The Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada The Department of Systems and Computer Engineering Carleton University OttawaON Canada

Fifth-generation and beyond (5G and xG) wireless networks are envisioned to meet the requirements of vertical applications like high traffic throughput, ultra-massive connectivity, extremely low latency, and high quality of service. Disruptive technologies, such as massive multiple-input multiple-output, millimeter wave, and multiple access are being deployed to meet these requirements. However, their deployment poses several challenges, including a lack of network transparency, management decentralization, and reliability. Moreover, the heterogeneity of future networks raises security concerns, e.g., confidentiality, privacy, and trustworthiness. Indeed, due to the emergence of novel paradigms, e.g., quantum computing, traditional security approaches are no longer sufficient to protect over-the-air communications. Hence, 5G/xG networks must consider smart security techniques to operate seamlessly and efficiently. Within this context, physical layer security (PLS) and blockchain represent promising solutions to complement existing methods. By exploiting the characteristics of wireless links, PLS can enhance the security of communications, while blockchain may enable networks’ decentralization, integrity, and trustworthiness. Motivated by these advancements, we provide an in-depth review of the existing PLS and blockchain literature. Then, for the first time in the literature, we present a framework to integrate PLS with blockchain in 5G/xG systems. We first provide a thorough discussion about the potential of PLS and blockchain for 5G/xG systems. Then, we present our vision of a cross-layer architecture that leverages PLS and blockchain. Through a case study, we demonstrate the high potential of cross-layer design to improve the security of vehicular networks. Finally, we identify related challenges and shed light on future research directions. © 2022, CC BY.

关键词： MIMO systems

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AI-optimized electrochemical aptasensors for stable, reproducible detection of neurodegenerative diseases, cancer, and coronavirus

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Heliyon 2025年第1期11卷 e41338页

作者： Tayfour Ahmed, Amira Elsir Dhahi, Th.S. Attia, Tahani A. Elhassan Ali, Fawzia Awad Elobaid, Mohamed Elshaikh Adam, Tijjani Gopinath, Subash C.B. Department of Information System College of Science & Arts King Khalid University Asser Mohyel Saudi Arabia Health and Medical Technicals College Southern Technical University Basrah Iraq Department of Computer Engineering College of Computer Science and Engineering University of Ha'il Saudi Arabia DEEE Faculty of Engineering University of Khartoum Sudan Imam Abdulrahman Bin Faisal University Applied College Computer Department Saudi Arabia Faculty of Electronic Engineering & Technology Universiti Malaysia Perlis Perlis Arau 02600 Malaysia Institute of Nano Electronic Engineering Universiti Malaysia Perlis Perlis Kangar 01000 Malaysia Micro System Technology Centre of Excellence (CoE) Universiti Malaysia Perlis (UniMAP) Perlis Malaysia Center for Global Health Research Saveetha Medical College & Hospital Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Tamil Nadu Chennai 602 105 India Faculty of Chemical Engineering & Technology Universiti Malaysia Perlis (UniMAP) Perlis Arau 02600 Malaysia

AI-optimized electrochemical aptasensors are transforming diagnostic testing by offering high sensitivity, selectivity, and rapid response times. Leveraging data-driven AI techniques, these sensors provide a non-invasive, cost-effective alternative to traditional methods, with applications in detecting molecular biomarkers for neurodegenerative diseases, cancer, and coronavirus. The performance metrics outlined in the comparative table illustrate the significant advancements enabled by AI integration. Sensitivity increases from 60 to 75 % in ordinary aptasensors to 85–95 %, while specificity improves from 70-80 % to 90–98 %. This enhanced performance allows for ultra-low detection limits, such as 10 fM for carcinoembryonic antigen (CEA) and 20 fM for mucin-1 (MUC1) using Electrochemical Impedance Spectroscopy (EIS), and 1 pM for prostate-specific antigen (PSA) with Differential Pulse Voltammetry (DPV). Similarly, Square Wave Voltammetry (SWV) and potentiometric sensors have detected alpha-fetoprotein (AFP) at 5 fM and epithelial cell adhesion molecule (EpCAM) at 100 fM, respectively. AI integration also enhances reproducibility, reduces false positives and negatives (from 15-20 % to 5–10 %), and significantly decreases response times (from 10-15 s to 2–3 s). These advancements improve data processing speeds (from 10 to 20 min per sample to 2–5 min) and calibration accuracy (<2 % margin of error compared to 5–10 %), while expanding application scope to multi-target biomarker detection. This review highlights how these advancements position AI-optimized electrochemical aptasensors as powerful tools for personalized treatment, point-of-care testing, and continuous health monitoring. Despite a higher cost ($500-$1,500/unit), their enhanced portability and diagnostic performance promise to revolutionize healthcare, environmental monitoring, and food safety, ultimately improving public health outcomes. © 2024

关键词： AI Aptasensors Cancer Coronavirus Electrochemical Integrated biosensor Molecular cells Neurodegenerative

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Deep reinforcement learning for closed-loop blood glucose control: two approaches

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IFAC-PapersOnLine 2022年第40期55卷 115-120页

作者： Francesco Di Felice Alessandro Borri Maria Domenica Di Benedetto Scuola Superiore Sant'Anna Istituto di Intelligenza Meccanica Pisa Italy CNR-IASI Biomathematics Laboratory National Research Council of Italy Rome Italy Department of Information Engineering Computer Science and Mathematics Center of Excellence for Research DEWS University of L'Aquila Italy

Reinforcement learning, thanks to the observation-action approach, represents a useful control tool, in particular when the dynamics are characterized by strong non-linearity and complexity. In this sense, it has a natural application in the biological systems field where the complexity of the dynamics makes the automatic control particularly challenging. This paper presents a combined application of neural networks and reinforcement learning, in the so-called field of deep reinforcement learning, for the glucose regulation problem in patients with diabetes mellitus. The glucose control problem is solved through the Deep Deterministic Policy Gradient (DDPG) and the Soft Actor-Critic (SAC) algorithms, where the environment exploited for the agent's interactions is represented by a glucose model that is completely unknown to agents. Preliminary results show that the DDPG and SAC agents can suitably control the glucose dynamics, making the proposed approach promising for further investigations. The comparison between the two agents shows a better behaviour of SAC algorithm.

关键词： Adaptive Learning Systems Modelling Control of Biomedical Systems Reinforcement learning control Numerical simulation

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