Compressed Sensing Magnetic Resonance Imaging (CS-MRI) provides the possibility to accelerate the acquisition with only a small amount of k-space data. Conventional CS-MRI methods are often time-consuming due to the numerous iterative steps. Recently, deep learning has been introduced to solve CS-MRI problem. In this paper, we propose a Residual Dilated model based on Generative Adversarial Networks, titled RDGAN, for fast and accurate reconstruction. We design a modified U-Net architecture which contains dilated convolutions to aggregate multi-scale information in the MRI. Also, inspired by residual learning, we adopt a short residual connection (SRC) and a long residual connection (LRC) strategies to help features flow into deeper layers directly and stabilise the adversarial training process. The experimental results demonstrate that the proposed RDGAN model achieves the state-of-the-art performance in CS-MRI on MICCAI 2013 grand challenge dataset.
Robust hand detection and classification is one of the most essential tasks in sign language recognition. However, the problem is very challenging due to the complexity of hands in sign language. The performance of existing approaches can be easily affected by the numerous variations of sign language gestures, small and unobtrusive hand areas, and ever changing of hand locations. In this paper, to detect and classify the hands in sign language robustly, such kind of small objects that contain rich information, we propose an improved Faster R-CNN approach, namely Multi-scale Faster RCNN. Our approach extends the framework of the Faster R-CNN and a multi-scale strategy is adopted to incorporate hierarchical convolution feature maps. We evaluate our approach on the self-built sign language dataset and the experimental results demonstrate the effectiveness of our proposed approach.
KEYWORDS: Signal to noise ratio, Smoothing, Monte Carlo methods, Interference (communication), Matrices, Signal processing, Electronics engineering, Machine vision, Computer simulations, Information science
An efficient direction of arrival estimation method for wideband uncorrelated and coherent signals is proposed with a uniform linear array. Firstly, uncorrelated signals are resolved by applying a test of orthogonality of frequency subspaces method (TOFS). Secondly, at each frequency bin, the contributions of uncorrelated signals and noise are eliminated and a new matrix is formed. Then, forward spatial smoothing technique is performed on the new matrices and coherent signals are resolved with TOFS. The method can resolve more wideband signals than the array elements. Simulation results demonstrate the effectiveness and efficiency of the method.
In order to improve the performance of spectrum sensing in cognitive radio networks, certain parameters of cooperative spectrum sensing need to be optimized. Aiming at maximizing the capacity of cognitive radio network, a target function for determining the optimal sensing parameters is established. A novel algorithm of adopting a numerical searching method to optimize this established target function is proposed to achieve the optimization of the sensing time and the fusion rule of hard decision. The simulation results show that the cognitive radio network capacity can be maximized when the parameters of the sensing time and the fusion rule are jointly optimized by the proposed algorithm.
An adaptive algorithm is presented for extracting the flux of the fiber spectrum from a two-dimensional
image observed by LAMOST (Large Sky Area Multi-Object Fiber Spectroscopic Telescope). The new
algorithm is based on RBF (Radial basis function) neural network, employing the Gaussian basis
function to approximate the profile of the spectrum in the spatial direction. In this study, an experiment
is performed with the simulated data. The experimental results show that the new algorithm can highly
enhance the computing speed while preserving the accuracy in the flux extraction. A feasible approach
is thus offered for extracting the flux of the fiber spectrum for LAMOST.
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