We present an information-theoretic lower bound for the problem of parameter estimation with time-uniform coverage guarantees. Via a new a reduction to sequential testing, we obtain stronger lower bounds that capture ...
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The exponential increase in IoT device usage has spawned numerous cyberspace *** devices,sensors,and actuators bridge the gap between physical processes and the cyber network in a cyber-physical system(CPS).Cyber-phys...
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The exponential increase in IoT device usage has spawned numerous cyberspace *** devices,sensors,and actuators bridge the gap between physical processes and the cyber network in a cyber-physical system(CPS).Cyber-physical system is a complex system from a security perspective due to the heterogeneous nature of its components and the fact that IoT devices can serve as an entry point for *** adversaries design their attack strategies on systems to gain an advantage at a relatively lower cost,whereas abusive adversaries initiate an attack to inflict maximum damage without regard to cost or *** this paper,a sensor spoofing attack is modelled as a malicious adversary attempting to cause system failure by interfering with the feedback control *** is accomplished by feeding spoofed sensor values to the controller and issuing erroneous commands to the *** on a Simulink-simulated linear CPS support the proof of concept for the proposed abusive ideology,demonstrating three attack *** impact of the evaluations stresses the importance of testing the CPS security against adversaries with abusive settings for preventing ***,the research concludes by highlighting the limitations of the proposed work,followed by recommendations for the future.
Network Intrusion Detection System (NIDS) serves as a essential component in data protection by monitoring computer networks for threats that can bypass conventional defenses such as malware and hackers. Deep learning...
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The increase in global average life expectancy over the past century has been largely attributed to medical science developments. In this study, we demonstrate a polymer-based capacitive pressure sensor for measuring ...
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This study explores the development of a self-driving car using a combination of deep learning (DL), machine learning (ML), computer vision (CV), and convolutional neural networks (CNN). The proposed system aims to si...
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This article presents a compact temperature detection system using amorphous-InGaZnO thin-film transistor (TFT) technology. The proposed system is fabricated on a 30-μ m-thick polymide substrate. This system consists...
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— In recent years, significant efforts have been dedicated to detect human emotions. This interest primarily stems from the fact that emotions influence individuals' reactions and behaviors. Understanding these i...
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The solution of a sparse system of linear equations is ubiquitous in scientific applications. Iterative methods, such as the preconditioned conjugate gradient (PCG) method and the generalized minimal residuals (GMRES)...
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Near-field tides prediction for tsunami detection in the coastal area is a significant problem of the cable-based tsunami meter system in north Sipora, Indonesia. The problem is caused by its shallow water condition a...
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We consider the multiobjective optimization problem for the resource allocation of the multiagent network, where each agent contains multiple conflicting local objective functions. The goal is to find compromise solut...
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We consider the multiobjective optimization problem for the resource allocation of the multiagent network, where each agent contains multiple conflicting local objective functions. The goal is to find compromise solutions minimizing all local objective functions subject to resource constraints as much as possible, i.e., the Pareto optimums. To this end, we first reformulate the multiobjective optimization problem into one single-objective distributed optimization problem by using the weighted Lppreference index,where the weighting factors of all local objective functions are obtained from the optimization procedure so that the optimizer of the latter is the desired Pareto optimum of the former. Next, we propose novel predefined-time algorithms to solve the reformulated problem by time-based generators. We show that the reformulated problem is solved within a predefined time if the local objective functions are strongly convex and smooth. Moreover, the settling time can be arbitrarily preset since it does not depend on the initial values and designed parameters. Finally, numerical simulations are presented to illustrate the effectiveness of the proposed algorithms.
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