Non-geostationary (NGSO) satellite communications systems have attracted a lot of attention both from industry and academia, over the past several years. Beam placement is among the major resource allocation problems ...
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Safe overtaking, especially in a bidirectional mixed-traffic setting, remains a key challenge for Connected Autonomous Vehicles (CAVs). The presence of human-driven vehicles (HDVs), behavior unpredictability, and blin...
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This paper presents an optimal modulation and systematic filter design approach for a single-stage dual-active-bridge (DAB) based dc-ac microinverter to achieve improved differential-mode (DM) noise performance for el...
This paper presents an optimal modulation and systematic filter design approach for a single-stage dual-active-bridge (DAB) based dc-ac microinverter to achieve improved differential-mode (DM) noise performance for electromagnetic interference (EMI) tests. As DM filters contribute significantly to the overall converter volume, the main objective of this work is to leverage the degrees of freedom in the DAB converters to effectively attenuate the EMI noise. In addition, the DM filter design method needs to ensure near unity power factor converter operation. To achieve these targets, this paper analyzes three modulation strategies based on fixed or variable switching frequency operation where the different control modulation variables are varied to find the simulated DM noise spectrum. Based on the required DM attenuation, a constrained optimization problem is formulated to determine minimal DM filter parameters. Simulation results show that a spread spectrum approach with variable switching frequency is shown to minimize the DM EMI attenuation effort by spreading the noise profile. A fully GaN 400 W hardware prototype demonstrating the spread-sprectrum approach.
This work investigates cyber attacks targeting cyber-physical systems in the framework of discrete event systems. From intruders’ perspective, we propose a concept called k-step attackability to explore attack scenar...
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Network slicing enables the deployment of multiple dedicated virtual sub-networks, i.e. slices on a shared physical infrastructure. Unlike traditional one-size-fits-all resource provisioning schemes, each network slic...
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Signal propagation in a particular region differs from another due to differences in atmospheric, climatic and environmental properties, distinct terrain and clutter features. Adequate analysis is essential to underst...
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Much recent research in person re-identification has focused on improving the accuracy of matching query images from one camera view to candidates from another camera view. However, in a practical scenario, real-world...
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In this paper, we study unconstrained distributed optimization strongly convex problems, in which the exchange of information in the network is captured by a directed graph topology over digital channels that have lim...
In this paper, we study unconstrained distributed optimization strongly convex problems, in which the exchange of information in the network is captured by a directed graph topology over digital channels that have limited capacity (and hence information should be quantized). Distributed methods in which nodes use quantized communication yield a solution at the proximity of the optimal solution, hence reaching an error floor that depends on the quantization level used; the finer the quantization the lower the error floor. However, it is not possible to determine in advance the optimal quantization level that ensures specific performance guarantees (such as achieving an error floor below a predefined threshold). Choosing a very small quantization level that would guarantee the desired performance, requires information packets of very large size, which is not desirable (could increase the probability of packet losses, increase delays, etc) and often not feasible due to the limited capacity of the channels available. In order to obtain a communication-efficient distributed solution and a sufficiently close proximity to the optimal solution, we propose a quantized distributed optimization algorithm that converges in a finite number of steps and is able to adjust the quantization level accordingly. The proposed solution uses a finite-time distributed optimization protocol to find a solution to the problem for a given quantization level in a finite number of steps and keeps refining the quantization level until the difference in the solution between two successive solutions with different quantization levels is below a certain pre-specified threshold. Therefore, the proposed algorithm progressively refines the quantization level, thus eventually achieving low error floor with a reduced communication burden. The performance gains of the proposed algorithm are demonstrated via illustrative examples.
In this study, we implemented our entropy-based swarm model to an autonomous waypoint navigation application for a group of multi-rotor Unmanned Aerial Vehicles (UAVs) through a set course in free space. Multi-UAVs of...
In this study, we implemented our entropy-based swarm model to an autonomous waypoint navigation application for a group of multi-rotor Unmanned Aerial Vehicles (UAVs) through a set course in free space. Multi-UAVs of multiple group sizes were run with variations in parameters, and the path lengths traveled were measured to determine the most efficient configurations, and we investigated the impact of varying parameters on the swarm behavior performance. The simulation of the UAV kinematics and environment was performed in AirSim. The results show that the swarm model with different parameter setup operates successfully and the effects of the parameter selection on our multi-UAV swarm model are discussed.
Wireless Sensor Networks (WSNs) have been widely deployed, and it is crucial to enhance their network longevity and transmission efficiency. This work comprehensively considers communication interference, transmission...
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ISBN:
(数字)9798350358513
ISBN:
(纸本)9798350358520
Wireless Sensor Networks (WSNs) have been widely deployed, and it is crucial to enhance their network longevity and transmission efficiency. This work comprehensively considers communication interference, transmission rate constraints, and motion constraints of Unmanned Aerial Vehicles (UAVs). We propose an energy-efficient UAV-assisted WSN framework with big data transmission. To minimize the weighted sum of UAV’s and WSNs’ energy consumption, we jointly optimize sensor clustering, path planning, and time window allocation by an improved intelligent optimization algorithm assisted by deep learning named Variational Multidimensional Optimization (VMO) with co-evolved Multiple Subpopulations, noted as VMOMS for short. The effectiveness of the proposed VMOMS algorithm for solving high-dimensional problems is demonstrated through numerical analysis and simulation results. These findings highlight the efficiency and practicality of the designed UAV-assisted hierarchical architecture of WSNs, thereby showcasing its potential to enable reliable data transmission from remote WSNs to a centralized cloud server.
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