Abstract As an important application of image encryption, digital medical image encryption plays an important role in the field of medical health and privacy protection. This paper put forwards a fully chaotic and strongly plaintext associated image encryption framework based on an improved chaotic system, block rotation and DNA computing. The algorithm generates multiple chaotic sequences by using different one-dimensional seed chaotic maps under the sine transform framework for subsequent block rotation, DNA dynamic encoding and decoding, generating key images for DNA XOR calculation. Simulation experiments and comparative analysis have shown that this algorithm can achieve fantastic encryption performance, resist various attacks, and have higher security levels and good generalization performance.
There is a significant method provided by CNN in sign language recognition. In order to train the dataset efficiently, the effect of various parameters on training MNIST is discussed in detail at first. Then carry out an analog analysis, with the same method, the most appropriate parameters are selected from a series of pre-training on ASL dataset. After a formal training, a sign language recognize filter is born with an accuracy close to 90%.
Abstract Modern artificial intelligence systems based on neural networks need to perform a large number of repeated parallel operations quickly. Without hardware acceleration, they cannot achieve effectiveness and availability. Memristor-based neuromorphic computing systems are one of the promising hardware acceleration strategies. In this paper, we propose a full-size convolution algorithm (FSCA) for the memristor crossbar, which can store both the input matrix and the convolution kernel and map the convolution kernel to the entire input matrix in a full parallel method during the computation. This method dramatically increases the convolutional kernel computations in a single operation, and the number of operations no longer increases with the input matrix size. Then a bidirectional pulse control switch integrated with two extra memristors into CMOS devices is designed to effectively suppress the leakage current problem in the row and column directions of the existing memristor crossbar. The spice circuit simulation system is built to verify that the design convolutional computation algorithm can extract the feature map of the entire input matrix after only a few operations in the memristor crossbar-based computational circuit. System-level simulations based on the MNIST classification task verify that the designed algorithm and circuit can effectively implement Gabor filtering, allowing the multilayer neural network to improve the classification task recognition accuracy to 98.25% with a 26.2% reduction in network parameters. In comparison, the network can even effectively immunize various non-idealities of the memristive synaptic within 30%.
In this paper, a novel feature extraction approach which can be referred to as moving window function capturing (MWFC) has been proposed to analyze signals of an electronic nose (E-nose) used for detecting types of infectious pathogens in rat wounds. Meanwhile, a quantum-behaved particle swarm optimization (QPSO) algorithm is implemented in conjunction with support vector machine (SVM) for realizing a synchronization optimization of the sensor array and SVM model parameters. The results prove the efficacy of the proposed method for E-nose feature extraction, which can lead to a higher classification accuracy rate compared to other established techniques. Meanwhile it is interesting to note that different classification results can be obtained by changing the types, widths or positions of windows. By selecting the optimum window function for the sensor response, the performance of an E-nose can be enhanced.
Two types of multiweighted coupled memristive neural networks (CMNNs) with adaptive couplings are introduced in this article, and the fixed-time passivity (FXTP) and fixed-time synchronization (FXTS) of such networks are considered. First, under the developed adaptive scheme, a sufficient condition to guarantee the FXTP for multiweighted CMNNs with adaptive couplings is obtained. Second, the FXTP, fixed-time input-strict passivity and fixed-time output-strict passivity for multiweighted CMNNs with adaptive couplings and coupling delays are investigated by devising an appropriate state feedback controller. Third, applying the Lyapunov functional method, it establishes the FXTS criteria for the two kinds of networks presented. Finally, numerical examples are provided to demonstrate the effectiveness of the derived results.
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