With increasing climate variability, extreme weather events like heatwaves pose significant challenges to the operation of interdependent power and gas networks. These events result in surges in electricity and gas de...
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Collective operations that involve multiple or all processes/threads within a parallel program are critical in multicore chips and parallel systems, particularly for data-intensive applications. However, many existing...
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Innovative accelerator architectures aim to play a critical role in future performance improvements under ceilings imposed by the end of Moore’s Law. Analog mesh computers are a class of such accelerators, designed t...
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As the modern bulk power system (BPS) expands and electricity users increase, an ever-growing amount of power data needs to be processed. The recommender system (RS), as an artificial intelligence (AI) algorithm, leve...
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Trevor S. Bird's discussion in IEEE Antennas and Propagation magazine (December 2023) provides an excellent overview of Michael Faraday's role in one of the most important aspects of the development of electro...
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A significant limitation of modern wireless IoT sensors is their battery life. We propose a small-scale radio frequency (RF) energy harvesting rectenna, paired with a low-power smart sensor that can be deployed in exi...
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The fundamental bottleneck in adapting data-driven wireless solutions to real world is the lack of tools for augmenting good quality data which is environment-specific. Indeed, much of the data required for popular da...
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The paper presents a millimeter wave object detection technology for the visually impaired. A non-invasive PCB is designed to be used at shoe level combining with Body Area Networks (BANs) for tripping hazard detectio...
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It is with great enthusiasm that I introduce this focused issue of IEEE Microwave Magazine, focusing on state-of-the-art advances in the field of signal generation. As an active member of the IEEE Microwave Theory and...
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Force field-based classical molecular dynamics(CMD)is efficient but its potential energy surface(PES)prediction error can be very *** functional theory(DFT)-based ab-initio molecular dynamics(AIMD)is accurate but comp...
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Force field-based classical molecular dynamics(CMD)is efficient but its potential energy surface(PES)prediction error can be very *** functional theory(DFT)-based ab-initio molecular dynamics(AIMD)is accurate but computational cost limits its applications to small ***,we propose a molecular dynamics(MD)methodology which can simultaneously achieve both AIMD-level high accuracy and CMD-level high *** high accuracy is achieved by exploiting deep neural network(DNN)’s arbitrarily-high precision to fit *** high efficiency is achieved by deploying multiplication-less DNN on a carefully-optimized special-purpose non von Neumann(NvN)computer to mitigate the performance-limiting data shuttling(i.e.,‘memory wall bottleneck’).By testing on different molecules and bulk systems,we show that the proposed MD methodology is generally-applicable to various MD *** proposed MD methodology has been deployed on an in-house computing server based on reconfigurable field programmable gate array(FPGA),which is freely available at http://***.
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