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Sparse representation of signals has been successfully applied in signal processing. Most of existing methods for sparse representation are based on the synthesis model, in which the dictionary is over complete. This paper addresses the dictionary learning and sparse representation with the so-called analysis model. Based on this model, the analysis dictionary multiplying the signals can lead to a...
Inspired by nature, we combine the easy decoration of polydopamine with the attractive biomimetic silification to develop a facile synthetic route toward raspberry-like nanocomposite particles, and further lead to the superhydrophobic and superoleophilic surfaces by mimicking the lotus leaf surface structures in the usage of these particles. In this approach, monodisperse polystyrene (PS) particles...
We propose a novel optimal data placement technique considering not only the data locality but also the global data access cost to improve the performance of MapReduce in cloud data centers. We first conducted analytical and experimental study to identify the performance issues of MapReduce in data center and show that MapReduce tasks which are involved in unexpected remote data access take much more...
A SoC design of H.264 Video Encoding system is implemented based on FPGA in this paper. Intra prediction algorithm and baseline profile is selected, and H.264 encoder algorithm is designed as an IP core and embedded to the SoC through the interconnect interface AMBA AXI bus. The SoC is implemented on Xilinx Zynq-7000 FPGA and each functional module is simulated by Modelsim and tested within the SoC...
This paper presents an improved and efficient algorithm for overcomplete, nonnegative dictionary learning for nonnegative sparse representation (NNSR) of signals. We adopt the Itakura-Saito (IS) divergence as the error measure, which is quite different from the conventional dictionary learning methods using the Euclidean (EUC) distance as the error measure. In addition, for enforcing the sparseness...
Recently, sparse representations via an overcomplete dictionary has become a major field of research in signal processing. Much efforts have been focused on the development of dictionary learning algorithms so that the sparse representation of signals can be efficiently performed. In this paper, we propose a method for learning a signal dependent overcomplete dictionary. This is accomplished by posing...
A SoC design of dynamic image edge detection system is implemented based on the LEON3 open source soft-core processor in this paper. Sobel edge detection algorithm is selected and designed as an IP core and embedded to the SoC through the interconnect interface AMBA AHB bus. A D5M camera interface IP core is also designed to collect and transfer dynamic image data. The SoC is implemented on FPGA and...
In this paper, we propose an overcomplete, nonnegative dictionary learning method for sparse representation of signals, which is based on the nonnegative matrix factorization (NMF) with 1/2-norm as the sparsity constraint. By introducing the 1/2-norm as the sparsity constraint into NMF, we show that the problem can be cast as sequential optimization problems of quadratic functions and quartic functions...
We study a scheme that an auxiliary qubit is introduced for probing the information of the given bipartite quantum state, while the correlations of the probed quantum state and even the probed state itself are not disturbed after the probing, which means nondestructive probing. We find that, in order to guarantee the invariance of the correlations of the probed state, neither quantum entanglement...
Fuzzy reasoning methods have demonstrated their ability to solve different kinds of problems in various applications domains. Currently, there is an increasing interest to augment new fuzzy reasoning principles and methods with speeding and processing capabilities. This paper points out a kind of fuzzy reasoning (CFR: Compensation Fuzzy Reasoning), and its application on DoS attack detection. Because...
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