The advancement of renewable energy sources (RESs) and the integration of electrical vehicles (EVs) into the traditional power grid have both dramatically grown in recent years. The control and management of the inter...
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Multimedia data systems manage complex data types such as text, images, audio, and video. Extensive research and development efforts in multimedia data management systems have been carried out since the1980s. Furtherm...
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ISBN:
(纸本)9798350323856
Multimedia data systems manage complex data types such as text, images, audio, and video. Extensive research and development efforts in multimedia data management systems have been carried out since the1980s. Furthermore, significant progress has been made on securing such systems. For example, techniques for multilevel security for such systems as well as enforcing access control policies have been developed. More recently, with the advent of big data technologies, data science and Artificial Intelligence, there is a need for managing, analyzing, and securing massive data systems with heterogeneous data types. This paper first describes the progress that has been made on securing multimedia data systems and then describes our vision for securing such systems in the era of Artificial Intelligence.
In the era of centralized clouds, multi-cloud, and edge computing, adaptable observability is essential to efficiently manage the growing volume of metrics data. Observability Volume Manager (OVM) addresses the comple...
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Electric vehicles represent one of the most promising technological innovations in promoting smart cities and smart transport systems concepts. However, the charging infrastructure is the main drawback to increasing t...
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ISBN:
(纸本)9798350369458;9798350369441
Electric vehicles represent one of the most promising technological innovations in promoting smart cities and smart transport systems concepts. However, the charging infrastructure is the main drawback to increasing the adoption of electric vehicles. The limited number of charging stations in the cities is an obstacle to the wide adoption of electric vehicles. Therefore, this work proposes to study solutions for the location and installation of charging stations in the context of smart cities. The paper presents three solutions: (i) random, being a naive approach for comparison;(ii) pseudo-random, where the road map is divided into quadrants and a charging station is installed randomly within each quadrant;and (iii) pseudo-greedy, in which the road map is divided into quadrants and the most visited street by vehicles within the quadrant is selected to install a charging station. These approaches are compared using the TAPAS Cologne map and an urban mobility simulator, which considers electric vehicles and charging during their routes.
Optimal decision-making for trajectory tracking in partially observable, stochastic environments where the number of active localization updates-the process by which the agent obtains its true state information from t...
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ISBN:
(纸本)9798350377712;9798350377705
Optimal decision-making for trajectory tracking in partially observable, stochastic environments where the number of active localization updates-the process by which the agent obtains its true state information from the sensors-are limited, presents a significant challenge. Traditional methods often struggle to balance resource conservation, accurate state estimation and precise tracking, resulting in suboptimal performance. This problem is particularly pronounced in environments with large action spaces, where the need for frequent, accurate state data is paramount, yet the capacity for active localization updates is restricted by external limitations. This paper introduces ComTraQ-MPC, a novel framework that combines Deep Q-Networks (DQN) and Model Predictive control (MPC) to optimize trajectory tracking with constrained active localization updates. The meta-trained DQN ensures adaptive active localization scheduling, while the MPC leverages available state information to improve tracking. The central contribution of this work is their reciprocal interaction: DQN's update decisions inform MPC's control strategy, and MPC's outcomes refine DQN's learning, creating a cohesive, adaptive system. Empirical evaluations in simulated and real-world settings demonstrate that ComTraQ-MPC significantly enhances operational efficiency and accuracy, providing a generalizable and approximately optimal solution for trajectory tracking in complex partially observable environments. [Code](1) [Video](2)
Identifying and locating objects in images and videos, including elements like traffic signs, vehicles, buildings, and people, constitutes a fundamental and demanding task in computer vision, known as object detection...
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ISBN:
(纸本)9783031821523;9783031821530
Identifying and locating objects in images and videos, including elements like traffic signs, vehicles, buildings, and people, constitutes a fundamental and demanding task in computer vision, known as object detection. Due to the higher computing complexity of this technique and the large amount of data carried by the video signal, it is nearly impossible for ordinary general-purpose processors GPPs or CPUs to run these techniques in real-time, especially for embedded systems applications. Therefore, special hardware that can acquire, control, or execute in parallel is required. These specialized hardware systems include Digital Signal Processors DSPs, Field Programmable Gate Arrays FPGAs, Visual Processing Units VPUs, Tensor Processing Units TPUs, Neural Processing Units NPUs or Graphics Processing Units GPUs. This work presents the benefits of accelerating traditional object detection methods on a high-end embedded system, the Jetson Nano Developer Kit. This single computer board is equipped with the Tegra K1 System on Chip SoC, which is composed of a quad-core ARM A15 and 192 cores of Kepler-embedded GPU. computing acceleration was ensured via the use of the CUDA OpenCV library for both the Histogram of Oriented Gradients HOG and the Haar Cascade Classifier. For VGA resolution, results reveal that the GPU implementation on this embedded system is 1.4x faster than the CPU for the HOG method and 2x for the Haar Cascade Classifier method.
The most prevalent object detection algorithms are predominantly developed using NVIDIA's Compute Unified Device Architecture (CUDA) and are optimised for NVIDIA hardware. In view of the growing complexity and vol...
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This research paper presents a novel approach using the grasshopper algorithm to improve the energy efficiency of virtual machine (VM) migration in cloud computing. This methodology reframes resource allocation techni...
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This research paper deals with risk management in various evolutionary computing environments i.e. big data, Internet of things, pervasive and artificially intelligent environments. The presented research helps in ide...
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This study explores the control application of incremental PID control algorithms in an intelligentcontrol experimental platform and verifies its feasibility. Based on the control model of the intelligentcontrol exp...
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