The major drawback of existing Differential Chaos Shift Keying (DCSK) communication system is low transmission rate. To solve the problem, a Correlation Delay-Differential Chaos Shift Keying (CD-DCSK) communication scheme without inter-signal interference was proposed. At the transmitting side, two orthogonal chaotic signals were generated by an orthogonal signal generator and normalized by the sign function to keep the energy of the transmitted signal constant. Then, two chaotic signals and their chaotic signals with different delay time intervals were respectively modulated by 1 bit data information to form a frame of transmission signal. At the demodulation side, correlation demodulation was used to extract data information and the information bits were recovered by detecting the sign of correlator output. The theoretical Bit Error Rate (BER) performance of system under Additive White Gaussian Noise (AWGN) channel was analyzed by using Gaussian Approximation (GA) method, and was compared with classical chaotic communication systems. The performance analysis and experimental results indicate that, compared with DCSK system, the transmission rate of CD-DCSK system without inter-signal interference increases by 50 percentage points, and the BER performance of the proposed system is better than that of Correlation Delay Shift Keying (CDSK) system.
The feature representation extracted from the functional connection network based on single brain map template is not sufficient to reveal complex topological differences between patient group and Normal Control (NC) group. However, the traditional multi-template-based functional brain network definitions mostly use independent templates, ignoring the potential topological association information in functional brain networks built with each template. Aiming at the above problems, a multi-level brain map template and a method of Relationship Induced Sparse (RIS) feature selection model were proposed. Firstly, an associated multi-level brain map template was defined, and the potential relationship between templates and network structure differences between groups were mined. Then, the RIS feature selection model was used to optimize the parameters and extract the differences between groups. Finally, the Support Vector Machine (SVM) method was used to construct classification model and was applied to the diagnosis of patients with depression. The experimental results on the clinical diagnosis database of depression in the First Hospital of Shanxi University show that the functional brain network based on multi-level template achieves 91.7% classification accuracy by using the RIS feature selection method, which is 3 percentage points higher than that of traditional multi-template method.
Considering the safety problem of mobile objects while traversing through a sensory field, a novel heuristic anti-monitoring path finding algorithm based on local Voronoi Tessellation (VT) was proposed in this paper. First, an approximate estimation model of path exposure based on local Voronoi tessellation was presented, in which, the mobile object could generate the local Voronoi tessellation dynamically based on currently detected sensor nodes information, and approximately estimated the exposure risk of each path corresponding to an edge of the local Voronoi tessellation based on the newly defined exposure risk computation formula. And then, based on the newly given exposure model, a novel heuristic anti-monitoring path finding algorithm was designed, in which, the mobile object could firstly determine its candidate set of next hop location points based on the local Voronoi tessellation, and then selected a location point with the minimum risk cost from its candidate set as its actual next hop location based on the newly defined heuristic cost function, and therefore, moved along the corresponding path with the minimum exposure risk in the local Voronoi tessellation to the selected next hop location. The theoretical analysis and simulation results show that the proposed algorithm has good anti-monitoring performance, and as for a sensory field with total n sensor nodes, the mobile object can select a path with relatively small risk to get to the destination within the time no more than O(n log n).
In recent years, multiscale pedestrian detection received extensive attentions in the field of computer vision. In traditional methods, the input image must be resized with different scales to compute the features, which significantly reduces the detection speed. Color Self-Similarity Feature (CSSF) was presented to overcome this problem. An improved CSSF with lower dimension was proposed for the CSSF whose dimension is too high and time-consuming in the training process of the classifiers. Combined with pedestrian structural similarity, a fixed-size window was defined at first, and then the improved CSSF was extracted by sliding the fixed-size window in different color space. Finally, the pedestrian detection classifier was constructed by combining with AdaBoost algorithm. Test shows that compared with the traditional CSSF whose dimension is ten millions, new feature dimension is only a few thousand, and it can be extracted and trained faster, but detection effect decreases slightly; compared with the Histogram of Oriented Gradient (HOG), feature extraction speed improves 5 times, detection effect is essentially the same. The new method has a good application value in real-time pedestrian detection and monitoring systems.
Concerning the problems of complicated structure and revolution of 2D traditional neighborhood cellular automata, low encrypting efficiency, little key space of 1D cellular automata, low diffusion speed and needing multiple rounds iteration to produce avalanche effect, a new encryption algorithm based on 2D X-type reversible cellular automata and Arnold transformation was proposed. Firstly, the plaintext was evolved by the proposed cellular automata, then it was transformed by Arnold transformation and cyclic shift transformation after every evolution, until the ciphertext was encrypted well enough. The experimental result shows that the key space is increased by 16.8% and has perfect robustness in resisting brute force attack. In addition the diffusion and confusion is so excellent that it can produce higher avalanche effect and resist chosen plaintext attack.
An adaptive multiple colony ant algorithm was presented to solve frenquency assignment problem of mobile communicaiton. Unlike the traditional ant colony algorithm which used only one ant colony, our algorithm used multiple ant colonies. For each ant colony, a coefficient of convergence was defined by which the ants adaptively could choose the path, update their local pheromone and exchange information between colonies. By using the adaptive strategy to update the pheromone, the balance between the diversity and convergence of every ant colony was kept. The simulation results on the fixed frequency assignment problem and minimal span frequency assignment problem show that our algorithm has global convergence and higher speed of optimization.