Search

This paper proposes a stream cipher algorithm. Its main principle is conducting the binary random dynamic hash with the help of key. At the same time of calculating the hash mapping address of plaintext, change the value of plaintext through bits scrambling, and then map it to the ciphertext space. This encryption method has strong randomness, and the design of hash functions and bits scrambling is flexible and diverse, which can constitute a set of encryption and decryption methods. After testing, the code evenness of the ciphertext obtained using this method is higher than that of the traditional method under some extreme conditions..

This paper proposes a new method of encryption and decryption based on stream cipher. The method completely abandoned the traditional mathematical calculation method. Its main principle is to using the key to get a binary random Hash mapping; the plaintext will be evenly dispersed in a known binary random Hash, and then mapped to the cipher space. This encryption method has a very strong randomness and collision resistance. The algorithm analysis shows that the time and space complexity of the method is the same as the traditional stream cipher algorithm, both of which are o (n). Finally, we test the time efficiency of the method, and the experimental results show that block and multithreading technology will greatly improve the encryption efficiency for large files.

Bit-to-symbol mapping is one of the key issues in multidimensional modulation. In an effort to resolve this issue, a tree structure based bit-to-symbol mapping scheme is proposed. By constructing a tree structure of constellation points, any neighboring constellation points become nearest-neighbor constellation points with minimum Euclidean distance, which in turn, changes the bit-to-symbol mapping problem in multidimensional signal modulation from random to orderly. Then, through the orderly distribution of labels, the minimum Hamming distance between the nearest neighboring constellation points is ensured, eventually achieving bit-to-symbol mapping optimization for multidimensional signals. Simulation analysis indicates that, compared with random search mapping, tree mapping can effectively improve the bit error rate performance of multidimensional signal modulation without multiple searching, reducing the computational cost.

This paper studies the theoretical problems and implementation methods of stream ciphers, discusses some deficiencies of conventional stream cipher algorithms, designs methods and rules for random Dynamic Hash Mapping and Bits Scrambling, applies nonlinear feedback shift register (NLFSR) technology, and a new stream cipher method with higher security strength and suitable for encryption of short information is proposed. The purpose is to apply the proposed new method of stream cipher to obtain higher encryption strength and code uniformity, and make some theories and algorithms in the research results have beneficial effects on information security in the era of big data.

An accurate electrocardiogram (ECG) beat classification can benefit the diagnosis of the cardiovascular disease. Deep convolutional neural networks (CNN) can automatically extract valid features from data, which is an effective way for the classification of the ECG beats. However, the fully-connected layer in CNNs requires a fixed input dimension, which limits the CNNs to receive fixed-scale inputs. Signals of different scales are generally processed into the same size by segmentation and downsampling. If information loss occurs during a uniformly-sized process, the classification accuracy will ultimately be affected. To solve this problem, this paper constructs a new CNN framework spatial pyramid pooling (SPP) method, which solves the deficiency caused by the size of input data. The Massachusetts Institute of Technology-Biotechnology (MIT-BIH) arrhythmia database is employed as the training and testing data for the classification of heartbeat signals into six categories. Compared with the traditional method, which may lose a large amount of important information and easy to be over-fitted, the robustness of the proposed method can be guaranteed by extracting data features from different sizes. Experimental results show that the proposed architecture network can extract more high-quality features and exhibits higher classification accuracy (94%) than the traditional deep CNNs (90.4%).

Although convolutional neural networks (CNNs) can be used to classify electrocardiogram (ECG) beats in the diagnosis of cardiovascular disease, ECG signals are typically processed as one-dimensional signals while CNNs are better suited to multidimensional pattern or image recognition applications. In this study, the morphology and rhythm of heartbeats are fused into a two-dimensional information vector for subsequent processing by CNNs that include adaptive learning rate and biased dropout methods. The results demonstrate that the proposed CNN model is effective for detecting irregular heartbeats or arrhythmias via automatic feature extraction. When the proposed model was tested on the MIT-BIH arrhythmia database, the model achieved higher performance than other state-of-the-art methods for five and eight heartbeat categories (the average accuracy was 99.1% and 97%). In particular, the proposed system had better performance in terms of the sensitivity and positive predictive rate for V beats by more than 4.3% and 5.4%, respectively, and also for S beats by more than 22.6% and 25.9%, respectively, when compared to existing algorithms. It is anticipated that the proposed method will be suitable for implementation on portable devices for the e-home health monitoring of cardiovascular disease.