A novel millimeter wave sensing antenna array is proposed for application in miniature detection and sensing devices suitable for operating in multiple scenarios, e.g., collecting in the face of video and infrared, an...
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The currently constructed millimeter wave imaging system has the problems of long sampling time and more sampling points of antenna units, and the use of compressed perception algorithm can improve the imaging quality...
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The Affine-Projection Maximum Asymmetric Correntropy Criterion (APMACC) is constructed, drawing upon the fundamental principles of the maximum asymmetric correntropy criterion and an affine-projection scheme. The APMA...
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作者：
Tian, ChanglinZhi, HuiTie, KuiAnhui University
School of Electronic and Information Engineering Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Hefei China CO.
Ltd Bengbu China
In this paper, a RIS-assisted multiuser MIMO communication method based on deep reinforcement learning (RMMC-DRL) is proposed for multiuser scenarios. Our objective is to find the optimal transmit beamforming matrix o...
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It has been widely recognized that the efficient training of neural networks (NNs) is crucial to classification performance. While a series of gradient-based approaches have been extensively developed, they are critic...
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It has been widely recognized that the efficient training of neural networks (NNs) is crucial to classification performance. While a series of gradient-based approaches have been extensively developed, they are criticized for the ease of trapping into local optima and sensitivity to hyperparameters. Due to the high robustness and wide applicability, evolutionary algorithms (EAs) have been regarded as a promising alternative for training NNs in recent years. However, EAs suffer from the curse of dimensionality and are inefficient in training deep NNs (DNNs). By inheriting the advantages of both the gradient-based approaches and EAs, this article proposes a gradient-guided evolutionary approach to train DNNs. The proposed approach suggests a novel genetic operator to optimize the weights in the search space, where the search direction is determined by the gradient of weights. Moreover, the network sparsity is considered in the proposed approach, which highly reduces the network complexity and alleviates overfitting. Experimental results on single-layer NNs, deep-layer NNs, recurrent NNs, and convolutional NNs (CNNs) demonstrate the effectiveness of the proposed approach. In short, this work not only introduces a novel approach for training DNNs but also enhances the performance of EAs in solving large-scale optimization problems.
Missed polyps are the major risk factor for colorectal cancer. To minimize misdiagnosis, many methods have been developed. However, they either rely on laborious instance-level annotations, require labeling of prompt ...
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We propose a robust pipeline detection algorithm for obscuration environments, which includes two improvements: a pre-processing method called the Regional Adaptive Thresh-olding Algorithm (RATA) and a novel clusterin...
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ISBN:
(数字)9798350384437
ISBN:
(纸本)9798350384444
We propose a robust pipeline detection algorithm for obscuration environments, which includes two improvements: a pre-processing method called the Regional Adaptive Thresh-olding Algorithm (RATA) and a novel clustering method based on trend analysis and symmetric matching called as Symmetry Matching Clustering Algorithm (SMCA). This approach is highly effective in extracting targets from low-intensity regions and has been validated through simulation experiments. The gotten results demonstrate significant enhancements in the accuracy and reliability of underground pipeline detection in obscuration environments. Comparative analysis shows that, in some aspects, the proposed method outperforms existing algorithms.
The paper introduces a Kernel Weibull M-transform Least-Mean Square (LMS) (KWMLMS) algorithm aimed to enhance filtering performance for a nonlinear system. By incorporating the Weibull M-transformation into the cost f...
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ISBN:
(数字)9798350350791
ISBN:
(纸本)9798350350807
The paper introduces a Kernel Weibull M-transform Least-Mean Square (LMS) (KWMLMS) algorithm aimed to enhance filtering performance for a nonlinear system. By incorporating the Weibull M-transformation into the cost function of the Kernel LMS (KLMS) algorithm, a weighting updated rule is gotten. Simulation experiments conducted for nonlinear-system identification under impulse noise conditions demonstrate that the created KWMLMS algorithm achieves significantly faster convergence compared to existing methods, indicating its superiority in dealing with impulsive-noises.
The investigation of the optoelectronic characteristics of all-perovskite tandem solar cells holds pivotal significance in surpassing the Shockley-Queisser limit of single-junction perovskite solar cells. Initially, w...
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To effectively improve the frequency selectivity of the filter, a substrate integrated waveguide (SIW) filter with miniaturization and out-of-band rejection was designed. To achieve the desired filter response, symmet...
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