Advanced Driver Assistance System (ADAS) uses various sensors that exist in the vehicle to collect information about the vehicle and its surroundings. One of the most commonly used ADASs is the one designed to recogni...
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Adaptive mesh refinement (AMR) is a classical technique about local refinement in space where needed, thus effectively reducing computational costs for HPC-based physics simulations. Although AMR has been used for man...
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Two different types of planar nano-scale (4-5 nm) field emitters, bowtie and diode devices, were characterized for use as electron sources for vacuum nano-transistors. I-V measurements were performed before and after ...
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Large information datasets often impose an immense number of features where many are found redundant and thus inessential for statistical analysis. In the past, a data preprocessing phase was formalized to cope with t...
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This study demonstrates the utilization of Machine Learning (ML) for network slice prediction, enabling the optimization of resources for diverse network slices. Traditional methods for network slice prediction often ...
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Parkinson's disease is an extremely debilitating condition where the brain is not producing enough dopamine to accurately coordinate movement. One symptom of Parkinson's disease, freezing of gait, prevents the...
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Autonomous driving has been significantly advanced in todays society, which revolutionized daily routines and facilitated the development of the Internet of Vehicles (IoV). A crucial aspect of this system is understan...
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Autonomous driving has been significantly advanced in todays society, which revolutionized daily routines and facilitated the development of the Internet of Vehicles (IoV). A crucial aspect of this system is understanding traffic density to enable intelligent traffic management. With the rapid improvement in deep neural networks (DNNs), the accuracy of density estimation has markedly improved. However, there are two main issues that remain unsolved. Firstly, current DNN-based models are excessively heavy, characterized by an overwhelming number of training parameters (millions or even billions) and substantial computational complexity, indicated by a high number of FLOPs. These requirements for storage and computation severely limit the practical application of these models, especially on edge devices with limited capacity and computational power. Secondly, despite the superior performance of DNN models, their effectiveness largely depends on the availability of large-scale data for training. Growing privacy concerns have made individuals increasingly hesitant to allow their data to be publicly used for model training, particularly in vehicle-related applications that might reveal personal movements, which leads to data isolation issues. In this paper, we address these two problems at once with a systematic framework. Specifically, we introduce the Proxy Model Distributed Learning (PMDL) model for traffic density estimation. PMDL model is composed of two main components. First, we introduce a proxy model learning strategy that transfers fine-grained knowledge from a larger master model to a lightweight proxy model, i.e., a proxy model. Second, we design a distributed learning strategy that trains multiple proxy models with privacy-aware local data and seamlessly aggregates these models via a global parameter server. This ensures privacy protection while significantly improving estimation performance compared to training models with limited, isolated data. We tested
Drones are valuable assets across industries for a variety of purposes, including but not limited to surveillance, transportation, delivery, smart agriculture, etc. However, drone-related studies may require significa...
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The accelerated move toward adopting the Smart Grid paradigm has resulted in numerous drawbacks as far as security is concerned. Traditional power grids are becoming more vulnerable to cyberattacks as all the control ...
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The proliferation of Internet of Things (IoT) devices poses potential challenges in the fast-developing field of smart cities, especially in cybersecurity. This work is an attempt to present an extensive comparative a...
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