Abstract: Normally, three-phase linear Hall sensor-based embedded magnetic encoder (EME) are used in permanent magnet synchronous motors to detect the rotor angle, in which prefilters are used to ...
Abstract: Traffic flow prediction is critical for Intelligent Transportation Systems to alleviate congestion and optimize traffic management. The existing basic Encoder-Decoder Transformer model for ...
Abstract: Although the vision transformer-based methods (ViTs) exhibit an excellent performance than convolutional neural networks (CNNs) for image recognition tasks, their pixel-level semantic ...
Abstract: The promotion of the HEVC standard has significantly alleviated the burden of network transmission and video storage. However, its inherent complexity and data dependencies pose a ...
Abstract: Unsupervised anomaly detection (UAD) aims to recognize anomalous images based on the training set that contains only normal images. In medical image analysis, UAD benefits from leveraging ...
Abstract: This article presents a new deep-learning architecture based on an encoder-decoder framework that retains contrast while performing background subtraction (BS) on thermal videos. The ...
Abstract: This paper presents an absolute capacitive rotary encoder using a sample-and-hold demodulator (SHD) to reduce interference between sine and cosine channels. The capacitive encoder measures ...
Abstract: Distributed acoustic sensing (DAS) has been considered a breakthrough technique in seismic data collection owing to its advantages in acquisition cost and accuracy. However, the existence of ...
Abstract: Owing to the limitations of hyperspectral optical imaging, hyperspectral images (HSIs) have a dilemma between spectral and spatial resolutions. The hyperspectral and multispectral image (HSI ...
Abstract: Speech enhancement (SE) models based on deep neural networks (DNNs) have shown excellent denoising performance. However, mainstream SE models often have high structural complexity and large ...
Abstract: Infrared small target detection (IRSTD) is the challenging task of identifying small targets with low signal-to-noise ratios in complex backgrounds. Traditional methods in the complex ...
Abstract: Benefiting from the powerful feature extraction and feature correlation modeling capabilities of convolutional neural networks (CNNs) and Transformer models, these techniques have been ...
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