Direction-of-arrival (DOA) estimation using sub-Nyquist tensor signals benefits from enhanced performance by extracting structural angular information with multi-dimensional sparse arrays. Although convolutional neura...
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Direction-of-arrival (DOA) estimation using sub-Nyquist tensor signals benefits from enhanced performance by extracting structural angular information with multi-dimensional sparse arrays. Although convolutional neural network (CNN) has been employed to achieve efficient DOA estimation in challenging conditions, conventional methods demand excessive memory storage and computation power to process sub-Nyquist tensor statistics. In this letter, we propose a decomposed CNN for sub-Nyquist tensor-based 2-D DOA estimation, where an augmented coarray tensor is derived and used as the network input. To compress convolutionkernels for efficient coarray tensor propagation, we develop a convolution kernel decomposition approach. This enables the acquisition of canonical polyadic (CP) factors containing compressed parameters. Performing decomposable convolution between the coarray tensor and the CP factors leads to resource-efficient DOA estimation. Our simulation results indicate that the proposed method conserves system resources while maintaining competitive performance.
Oriented object detection is an important research topic in remote sensing. The detection of oriented objects in remote sensing images remains a daunting challenge due to their complex backgrounds, various sizes, dive...
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Oriented object detection is an important research topic in remote sensing. The detection of oriented objects in remote sensing images remains a daunting challenge due to their complex backgrounds, various sizes, diverse aspect ratios, and especially arbitrary orientations. In recent years, keypoint-based anchor-free object detectors have demonstrated outstanding performance in this field. However, in current anchor-free detectors, object keypoints are primarily generated using the Gaussian kernel function, which assumes a circular form. This representation falls short in accurately conveying an object's size and orientation. To address the aforementioned issue, this article proposes a keypoint-based oriented object detector called MRSDet, which innovatively adopts the Tricube kernel, scales, and rotates it, to better generate the center keypoint heatmap of the object. Besides, to improve the model's detection performance on oriented objects and improve its ability to perceive object keypoints and boundary boxes, we also design a large receptive field mask (LRFM) module, which is based on large convolution kernel decomposition and semantic segmentation masks. Taking the box boundary-aware vectors (BBAVectors) method as a baseline, we conduct experiments on multiple types of remote sensing datasets such as HRSC2016, UCAS-AOD, and SSDD+ datasets to verify the effectiveness and generalizability of the proposed method.
With the development of personalized healthcare, tailor-made medications are receiving increasing attention. Solutions of specific concentrations or flow rates need to be acquired before medication can be manufactured...
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With the development of personalized healthcare, tailor-made medications are receiving increasing attention. Solutions of specific concentrations or flow rates need to be acquired before medication can be manufactured. To efficiently and accurately generate solutions with specific concentrations or flow rates, we proposed the design of random variable-width (RVW) microfluidic chips, which perform significantly outperform random equal-width (REW) microfluidic chips, and predict their performance through convolutional Neural Networks (CNN). First, we proposed the design of RVW microfluidic chips to extend the range of concentrations and flow rates. Second, the KD-MiniVGGNet model was designed, which effectively improved the accuracy of predicting the outlet concentrations and flow rates of the RVW microfluidic chips. Finally, a database of 51 032 RVW microfluidic chips was built by the KD-MiniVGGNet, which provided a sufficient number of candidate designs. The results showed that the RVW microfluidic chip could provide broader and better candidate designs, and the prediction accuracy of the outlet fluid behavior could be increased to 93%.
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