Virtualization of computing resources is becoming increasingly important both for high-end servers and multi-core CPUs. In a virtualized system, the set of resources that constitute a virtual compute entity should be ...
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Traffic lights strongly impact vehicle movement and fuel consumption in cities. If drivers were aware of the traffic light phase schedule, they could predict the traffic light state at arrival time and could reduce fu...
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The goal of this paper is to review the first projects for building automatic computing machines during the first half of the 20th century. The presented timeline shows that 85 years ago - in October 1939 - the first ...
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Nonlinear vibratory energy harvesters often exhibit multiple coexisting attractors, making their control challenging and energy-intensive. Ensuring an effective transition between these attractors while minimizing con...
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Nonlinear vibratory energy harvesters often exhibit multiple coexisting attractors, making their control challenging and energy-intensive. Ensuring an effective transition between these attractors while minimizing control effort is crucial for maximizing power generation and maintaining system stability. This paper introduces a differentiable data-driven control strategy that optimizes energy harvester performance by leveraging a neural network (NN)-based control framework. Unlike traditional fixed-gain or surrogate modeling approaches, the proposed method directly integrates system dynamics and control power consumption into the gain adaptation process, ensuring real-time adaptability. By exploiting the differentiability of neural networks, the controller employs gradient-based optimization to continuously refine control parameters in response to changes in operating conditions. The control framework consists of an offline training phase, where the neural network learns an energy-efficient control strategy through differentiable simulations. During this phase, the neural network is trained using a large dataset of system responses to different control inputs, allowing it to learn the most energy-efficient control strategy. This trained network is then used in the online deployment phase, where the trained controller dynamically adjusts control parameters in real-time. The proposed approach minimizes control energy consumption and efficiently guides the system toward the high-energy attractor, avoiding unnecessary control effort. Our approach significantly outperforms conventional PID-based sliding mode controllers. Simulation results confirm that the differentiable controller considerably enhances energy harvesting efficiency and reduces chattering, ensuring a smooth transition to the high-energy orbit while maintaining robust system stability. The study also highlights the necessity of an adaptive control strategy, stressing the urgency of its implementation for o
High quality video streaming for mobile users is difficult to achieve in some areas of the world due to poor broadband capacity and sparse network coverage. We propose a bandwidth-sharing scheme to allow users with li...
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Cooperative awareness is established by vehicles exchanging their status frequently. In situations where a high number of vehicles access the communication channel with high frequency, communication and cooperative aw...
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Channel assignment is one of the most important problem in the research of the multi-radio multi-channel wireless mesh networks ((MRMC-WMN). Aiming at minimizing the overall network interference and optimizing the usa...
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In classical real-time systems the resources for an application are allocated at system start so that every resource request can be fulfilled in future. This would lead to much internal waste of resources in the case ...
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Efficiency has always been one of the problems of high-level languages like Prolog. Different solutions have been suggested to speed up the execution of Prolog. One alternative is to build dedicated hardware. Another ...
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Current high-performance distributedsystems use a switch-based interconnection network. After the occurrence of a topological change, a management mechanism must reestablish connectivity between network devices. This...
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