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9th International Conference on Communication and Electronics systems, ICCES 2024

作者： Yasotha, B. Vijayanandh, T. Thirumoorthy, P. Priya, M.Sangeetha Sasidaran, S. Prakalya, S.B. Srmist Department of Data Science and Business Systems Kattankulathur India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Avadi Tamil Nadu Chennai India Dhanalakshmi College of Engineering Department of Electrical and Electronics Engineering Chennai India Chennai India Panimalar Engineering College Department of Information Technology Chennai600123 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350377972

Industrial Internet of Things can improve critical infrastructure in energy, transportation, and manufacturing. However, IIoT device and system integration opened security weaknesses that bad actors may exploit, causing huge disruptions and financial losses. IoT network security and privacy must be prioritized to protect key infrastructure. IIoT security issues and solutions are examined in this research. First, we take a look at the state of security and the unique risks that industrial IoT devices are up against, including hacking, illegal access, and cyber-physical assaults. Then, to lessen the impact of these dangers, we investigate cutting-edge security solutions, such as authentication, encryption, and access control systems. Building a robust security architecture specifically for IIoT applications is one of the main goals of the project. To improve safety and anonymity, this system uses cutting-edge innovation like blockchain, AI, and edge computing. We also talk about how privacy-preserving methods like differential privacy and anonymization are important for keeping sensitive information safe while allowing data sharing for analysis and operations. We assess the efficacy of the suggested security protocols and procedures using a battery of case studies and simulations. Reducing the attack surface and improving the resilience of IIoT systems can be achieved by a multi-layered security approach that includes effective privacy protections. We conclude by outlining our research's larger implications, which include the importance of standardization, regulatory compliance, and stakeholder collaboration in ensuring the safe and privacy-protecting deployment of IIoT technologies in vital infrastructure. Protecting vital infrastructure from cyber threats is our ultimate goal, and our work helps build IIoT systems that are more secure and dependable. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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Monolithically Integrated 940 nm VCSELs on Bulk Ge Substrates

Monolithically Integrated 940 nm VCSELs on Bulk Ge Substrate...

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2024 IEEE Silicon Photonics Conference, SiPhotonics 2024

作者： Wan, Zeyu Yang, Yun-Cheng Chen, Wei-Hsin Chiu, Chih-Chuan Zhao, Yunlong Feifel, Markus Chrostowski, Lukas Lackner, David Wu, Chao-Hsin Xia, Guangrui The University of British Columbia Department Of Materials Engineering Vancouver Canada Fraunhofer Institute For Solar Energy Systems Heidenhofstrasse Freiburg Germany The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada National Taiwan University Graduate Institute Of Electronics Engineering Taipei Taiwan National Taiwan University Graduate Institute Of Photonics And Optoelectronics Taipei Taiwan National Taiwan University Graduate School Of Advanced Technology Taipei Taiwan

ISBN: (纸本)9798350394047

This work successfully developed an independent Ge-VCSEL epitaxy and fabrication technology route. This is the second successful Ge-VCSEL technology reported worldwide, and the first Ge-VCSEL technology with key details disclosed, including Ge substrate specification, transition layer structure and composition, and fabrication process etc. Compared with the GaAs counterparts, after epitaxy optimization, the Ge-VCSEL wafer has a 40% lower surface root-mean square roughness and 72% lower average bow-warp. Additionally, the Ge-VCSEL has a 10% lower threshold current density and a 19% higher maximum slope efficiency than the GaAs-VCSEL. © 2024 IEEE.

关键词： Surface emitting lasers

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Seer: A Framework for Optimizing Traffic Camera Placement and Deep Learning Inference at the Edge for Vehicle Path Reconstruction

Seer: A Framework for Optimizing Traffic Camera Placement an...

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IEEE/ACM Symposium on Edge Computing (SEC)

作者： Siddhant Jain Kunal Jain Arun Ravindran Suresh Purini Computer Systems Group International Institute of Information Technology Hyderabad India Department of Electrical and Computer Engineering The University of North Carolina Charlotte USA

ISBN: (数字)9798350378283

ISBN: (纸本)9798350378290

Integrating traffic cameras with deep learning facilitates real-time multi-camera vehicle path reconstruction, benefiting smart city initiatives such as traffic management and public safety. The high demands on latency, bandwidth, and privacy underscore the necessity of edge computing platforms. However, the continuous operation of compute-intensive deep learning algorithms on round-the-clock camera streams can exceed the compute and power capacities of edge data centers. Therefore, it is critical to strategically deploy and activate camera streams to balance compute demands with performance requirements. Currently, there is a lack of tools to support this planning process. This paper presents Seer, a comprehensive suite of tools and algorithms that aids traffic planners in optimizing traffic camera placement and deep learning model deployment, considering compute and budgetary constraints. Seer encompasses (1) a graph-based algorithm for efficient camera placement incorporating road network topology, (2) a collaborative algorithm for vehicle path reconstruction that enables the use of less accurate but resource-efficient deep learning models, and (3) a scalable traffic simulation framework derived from open-source projects that model city road networks. Evaluations of Seer on two real-world road networks, each with thousands of intersections and simulated vehicle paths, show a 6.3x reduction in compute requirements. This is achieved through a combination of sparse camera deployment and the use of lightweight models, with an average 55% increase in the Hausdorff distance for the path reconstruction algorithm, translating to an absolute increase of 135 to 195 meters.

关键词： Deep learning Smart cities Computational modeling Roads Traffic control Reconstruction algorithms Cameras Real-time systems Streams Edge computing

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Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity

Straggler-Resilient Federated Learning: Leveraging the Inter...

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作者： Reisizadeh, Amirhossein Tziotis, Isidoros Hassani, Hamed Mokhtari, Aryan Pedarsani, Ramtin Massachusetts Institute of Technology Laboratory for Information and Decision Systems CambridgeMA02139 United States The University of Texas at Austin Department of Electrical and Computer Engineering AustinTX78712 United States University of Pennsylvania Department of Electrical and Systems Engineering PhiladelphiaPA19104 United States University of California at Santa Barbara Department of Electrical and Computer Engineering Santa BarbaraCA93106 United States

Federated learning is a novel paradigm that involves learning from data samples distributed across a large network of clients while the data remains local. It is, however, known that federated learning is prone to multiple system challenges including system heterogeneity where clients have different computation and communication capabilities. Such heterogeneity in clients' computation speed has a negative effect on the scalability of federated learning algorithms and causes significant slow-down in their runtime due to slow devices (stragglers). In this paper, we propose FLANP, a novel straggler-resilient federated learning meta-algorithm that incorporates statistical characteristics of the clients' data to adaptively select the clients in order to speed up the learning procedure. The key idea of FLANP is to start the training procedure with faster nodes and gradually involve the slower ones in the model training once the statistical accuracy of the current participating nodes' data is reached, while the final model for each stage is used as a warm-start model for the next stage. Our theoretical results characterize the speedup provided by the meta-algorithm FLANP in comparison to standard federated benchmarks for strongly convex losses and i.i.d. samples. For particular instances, FLANP slashes the overall expected runtime by a factor of O((Ns)) , where N and s denote the total number of nodes and the number of samples per node, respectively. In experiments, FLANP demonstrates significant speedups in wall-clock time -up to 6 - compared to standard federated learning benchmarks. © 2020 IEEE.

关键词： Learning algorithms

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The Impact of Blockchain and AI in the Healthcare 3

The Impact of Blockchain and AI in the Healthcare

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3rd IEEE International Conference on electrical Power and Energy systems, ICEPES 2024

作者： Thatikonda, Ramya Kempanna, M. Tatikonda, Reshmi Bhuvanesh, A. University of the Cumberlands PhD in Information Technology Williamsburg United States Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India University of the Central Missouri Masters in Computer Information Systems Warrensburg United States PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350390728

Integrating blockchain and artificial intelligence (AI) in healthcare presents a transformative opportunity. Therefore, this can revolutionize patient care by improving data management and medical research. Decentralization and invulnerability granted by blockchain technology foster improved data consistency and interoperability. AI enhances blockchain by streamlining data examination, automatic tasks, and decision-making processes, resulting in more precise medical diagnoses and tailored therapy programs. The intersection of blockchain and AI is explored, along with their potential benefits and practical applications in healthcare. The document examines the hurdles encountered when integrating innovations, including scalability, interoperability, and regulatory adherence issues. Additionally, the study investigates moral questions surrounding data ownership and patient consent, thereby protecting confidentiality and trust. The synergy between blockchain, AI, and healthcare will lead to transformative advancements. It pledges to fortify data safety and expedite information sharing. Consequently, this will result in an improved, price-effective, and patient-focused healthcare environment. Recommendations were made for additional research and practical implementation in healthcare technology. The study should concentrate on creating scalable and compatible blockchain systems. A vital factor for extensive use is tackling regulatory and moral issues. Hybrid blockchain designs and federated learning strategies will bolster information privacy and utility. © 2024 IEEE.

关键词： Data consistency

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Self-Healing First-Order Distributed Optimization with Packet Loss

arXiv

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arXiv 2023年

作者： Donato Ridgley, Israel L. Freeman, Randy A. Lynch, Kevin M. Northwestern University EvanstonIL60208 United States Department of Electrical & Computer Engineering Department of Mechanical Engineering Northwestern Institute on Complex Systems United States Center for Robotics and Biosystems United States

We describe SH-SVL, a parameterized family of first-order distributed optimization algorithms that enable a network of agents to collaboratively calculate a decision variable that minimizes the sum of cost functions at each agent. These algorithms are self-healing in that their convergence to the correct optimizer can be guaranteed even if they are initialized randomly, agents join or leave the network, or local cost functions change. We also present simulation evidence that our algorithms are self-healing in the case of dropped communication packets. Our algorithms are the first single-Laplacian methods for distributed convex optimization to exhibit all of these characteristics. We achieve self-healing by sacrificing internal stability, a fundamental trade-off for single-Laplacian methods. © 2023, CC BY-SA.

关键词： Cost functions

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Vision-based FDM Printing for Fabricating Airtight Soft Actuators

Vision-based FDM Printing for Fabricating Airtight Soft Actu...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Yijia Wu Zilin Dai Haotian Liu Lehong Wang Markus P. Nemitz Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Department of Computer Science Worcester Polytechnic Institute Worcester MA USA Department of Mechanical and Materials Engineering Worcester Polytechnic Institute Worcester MA USA Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA USA

Pneumatic soft robots are typically fabricated by molding, a manual fabrication process that requires skilled labor. Additive manufacturing has the potential to break this limitation and speed up the fabrication process but struggles with consistently producing high-quality prints. We propose a low-cost approach to improve the print quality of desktop fused deposition modeling by adding a webcam to the printer to monitor the printing process and detect and correct defects such as holes or gaps. We demonstrate that our approach improves the air-tightness of printed pneumatic actuators while reducing the need for fine-tuning printing parameters. Our approach presents a new option for robustly fabricating airtight, soft robotic actuators.

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Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning

arXiv

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arXiv 2024年

作者： Salazar, Teresa Gama, João Araujo, Helder Abreu, Pedro Henriques The Centre for Informatics and Systems Department of Informatics Engineering University of Coimbra Coimbra3030-790 Portugal INESC TEC The Faculty of Economy University of Porto Porto4200-465 Portugal The Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra3004-531 Portugal

In the evolving field of machine learning, ensuring fairness has become a critical concern, prompting the development of algorithms designed to mitigate bias in decision-making processes. However, achieving fairness in the presence of group-specific concept drift remains an unexplored frontier, and our research represents pioneering efforts in this regard. Group-specific concept drift refers to situations where one group experiences concept drift over time while another does not, leading to a decrease in fairness even if accuracy remains fairly stable. Within the framework of Federated Learning, where clients collaboratively train models, its distributed nature further amplifies these challenges since each client can experience group-specific concept drift independently while still sharing the same underlying concept, creating a complex and dynamic environment for maintaining fairness. The most significant contribution of our research is the formalization and introduction of the problem of group-specific concept drift and its distributed counterpart, shedding light on its critical importance in the realm of fairness. Additionally, leveraging insights from prior research, we adapt an existing distributed concept drift adaptation algorithm to tackle group-specific distributed concept drift which uses a multi-model approach, a local group-specific drift detection mechanism, and continuous clustering of models over time. The findings from our experiments highlight the importance of addressing group-specific concept drift and its distributed counterpart to advance fairness in machine *** Codes 68T01 © 2024, CC BY.

关键词： Machine learning

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Direct Biologically-Inspired Sliding-Mode Control of a Rotorcraft System 32

Direct Biologically-Inspired Sliding-Mode Control of a Rotor...

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32nd Mediterranean Conference on Control and Automation, MED 2024

作者： Alvarez-Munoz, J.U. Escareno, J. Garcia Carrillo, L.R. Scola, I. Rubio Labbani-Igbida, O. Institut Polytechnique de Sciences Avancées Ivry-sur-Seine94200 France XLIM Research Institute UMR-CNRS University of Limoges Robotics and Mechatronics Department 87060 France Klipsch School of Electrical and Computer Engineering New Mexico State University Las CrucesNM88003 United States INTI CONICET and UNR Department of Industrial Products Engineering Rosario Argentina

ISBN: (纸本)9798350395440

In the context of nonlinearity and uncertainty in the control systems, this work presents a novel control strategy based on a biologically-inspired learning robust controller for the attitude stabilization of a Vertical-Take-Off and Landing (VTOL) rotorcraft. Initially, the attitude's VTOL model based on quaternion formalism is detailed to subsequently develop the corresponding control law. Such controller consists of a Brain Emotional Learning-Based Intelligent Control (BELBIC) mechanism, combined with a Super-Twisting Sliding-Mode Control (STSMC) law. By using this architecture, the nonlinear sliding-mode equivalent control term is estimated via the BELBIC model, which allows the controller not to be fully dependent on the system model. Furthermore, closed loop stability is verified through Lyapunov's theory. Finally, for validation and assessment purposes of the overall control strategy, an extensive numerical simulation stage is conducted. © 2024 IEEE.

关键词： Sliding mode control

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Synthesis of Proactive Sensor Placement In Probabilistic Attack Graphs

Synthesis of Proactive Sensor Placement In Probabilistic Att...

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American Control Conference (ACC)

作者： Lening Li Haoxiang Ma Shuo Han Jie Fu Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Department of Electrical & Computer Engineering University of Florida Gainesville FL USA Department of Electrical & Computer Engineering University of Illinois at Chicago Chicago IL USA

This paper studies the deployment of joint moving target defense (MTD) and deception against multi-stage cyber-attacks. Given the system equipped with MTD that randomizes between different configurations, we investigate how to allocate a bounded number of sensors in each configuration to optimize the probability of detecting the attack before the attacker achieves its objective. Specifically, two types of sensors are considered: intrusion detectors that are observable by the attacker and stealthy sensors that are not observable to the attacker. We propose a two-step optimization-based approach: Firstly, the defender allocates intrusion detectors assuming the attacker will best respond to evade detection. Secondly, the defender will allocate stealthy sensors, given the best response attack strategy computed in the first step, to further reduce the attacker’s chance of success. We illustrate the effectiveness of the proposed methods using a cyber defense example.

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