The integrated circuit (IC) industry has experienced exponential growth in the complexity and scale of hardware designs. To sustain this growth, faster development cycles and cost-effective solutions have been the foc...
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Using Buck converter as the final stage for 48V-To1V conversion currently is still one of the main solutions for CPU and GPU power delivery due to its extremely mature monolithic IC industry, controller development an...
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The integration of gallium oxide Ga2O3 in electronic packaging has gained significant attention in recent research due to its promising properties. Despite this growing interest, the technology remains predominantly a...
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This letter focuses on tackling the challenge of accurately determining the timing of buffalo calving while prioritizing power efficiency. To achieve this, a novel, compact, lightweight and power efficient device is d...
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Most power electronics equipment like solar inverters or solid-state circuit breakers (SSCBs) require short-time overload current requirements, which subsequently necessitate the surge current capability of power semi...
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Nowadays, proper urban waste management is one the biggest concerns for maintaining a green and clean environment. An automatic waste segregation system can be a viable solution to improve the sustainability of the co...
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作者：
Abu-Nassar, Ahmad M.Morsi, Walid G.
Electrical Computer and Software Engineering Department Faculty of Engineering and Applied Science OshawaONL1G 0C5 Canada
Transportation electrification plays an important role in the operation of the smart grid through the integration of the electric vehicle fast charging stations (EVFCSs), which allows the electric vehicles to provide ...
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Generalized-linear dynamical models (GLDMs) remain a widely-used framework within neuroscience for modeling time-series data, such as neural spiking activity or categorical decision outcomes. Whereas the standard usag...
Despite having benefits of privacy preservation and secure computation, federated learning (FL) faces the issue of data heterogeneity. Specifically, the performance of FL systems can be degraded due to sample selectio...
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In video surveillance,anomaly detection requires training machine learning models on spatio-temporal video ***,sometimes the video-only data is not sufficient to accurately detect all the abnormal ***,we propose a nov...
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In video surveillance,anomaly detection requires training machine learning models on spatio-temporal video ***,sometimes the video-only data is not sufficient to accurately detect all the abnormal ***,we propose a novel audio-visual spatiotemporal autoencoder specifically designed to detect anomalies for video surveillance by utilizing audio data along with video *** paper presents a competitive approach to a multi-modal recurrent neural network for anomaly detection that combines separate spatial and temporal autoencoders to leverage both spatial and temporal features in audio-visual *** proposed model is trained to produce low reconstruction error for normal data and high error for abnormal data,effectively distinguishing between the two and assigning an anomaly *** is conducted on normal datasets,while testing is performed on both normal and anomalous *** anomaly scores from the models are combined using a late fusion technique,and a deep dense layer model is trained to produce decisive scores indicating whether a sequence is normal or *** model’s performance is evaluated on the University of California,San Diego Pedestrian 2(UCSD PED 2),University of Minnesota(UMN),and Tampere University of Technology(TUT)Rare Sound Events datasets using six evaluation *** is compared with state-of-the-art methods depicting a high Area Under Curve(AUC)and a low Equal Error Rate(EER),achieving an(AUC)of 93.1 and an(EER)of 8.1 for the(UCSD)dataset,and an(AUC)of 94.9 and an(EER)of 5.9 for the UMN *** evaluations demonstrate that the joint results from the combined audio-visual model outperform those from separate models,highlighting the competitive advantage of the proposed multi-modal approach.
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