Voice disguise through pitch shift is commonly used to conceal the identity of speaker. A bunch of voice changers substantially facilitate the application of voice disguise. To simultaneously address the problem of whether a speech signal is pitch-shifted and how it is modified (pitch-raised or pitch-lowered), with the traces of the electronic disguised voice in the signal spectrum especially the high frequency region analyzed, an electronic disguised voice detection method based on statistical moment features derived from Inverted Mel-Frequency Cepstral Coefficient (IMFCC) was proposed. Firstly, IMFCC and its first-order difference of each voice frame were extracted. Then, its statistical mean was calculated. Finally, on the above statistical feature, the design of Support Vector Machine (SVM) multi-classifier was used to identify the original voice, the pitch-raised voice and the pitch-lowered voice. The experimental results on TIMIT and NIST voice datasets show that the proposed method has satisfactory performance on the detection of the original, pitch-raised and pitch-lowered voice signals. Compared with the baseline system using MFCC as feature construction, the method with the proposed features has significantly increased the recognition rate of the disguise operation. And the method outperforms the Convolutional Neural Network (CNN) based framework when limited training data is available. The extensive experiments demonstrate the proposed has good generalization ability on different datasets and different disguising methods.

Aiming at the over-segmentation problem caused by inconsistency of large-format, high-resolution and inconsistency of parcel size in extraction of Unmanned Aerial Vehicle (UAV) remote sensing image of farmland scene, an automatic extraction process for land boundary based on multi-scale segmentation was proposed. In this process, the block segmentation strategy was adopted under the framework of Multi-scale Combinatorial Grouping (MCG) segmentation method. The optimal ground sampling distance was selected by comparing experimental research and optimal segmentation scale was selected by analyzing the variation curve of boundary extraction accuracy with scale, therefore automatic extraction process of parcel boundaries was achieved. Experiments were conducted on the data collected from Xiantao City, Hubei Province. The experimental results show that the most suitable ground sampling distance for extracting land parcel boundary is about 30 cm and the optimal segmentation scale is[0.2,0.4]. The accuracy of land parcel boundary extraction can be more than 90%. In addition, the proposed method can accurately extract large-scale agricultural parcel boundary and also can provide a reference for later aerial program of agriculture UAV.

Focusing on the problem of object structure discontinuity and poor texture detail occurred in image inpainting, an inpainting algorithm based on self-adaptive group was proposed. Different from the traditional method which uses a single image block or a fixed number of image blocks as the repair unit, the proposed algorithm adaptively selects different number of similar image blocks according to the different characteristics of the texture area to construct self-adaptive group. A self-adaptive dictionary as well as a sparse representation model was established in the domain of self-adaptive group. Finally, the target cost function was solved by Split Bregman Iteration. The experimental results show that compared with the patch-based inpainting algorithm and Group-based Sparse Representation (GSR) algorithm, the Peak Signal-to-Noise Ratio (PSNR) and the Structural SIMilarity (SSIM) index are improved by 0. 94-4.34 dB and 0. 0069-0.0345 respectively; meanwhile, the proposed approach can obtain image inpainting speed-up of 2.51 and 3.32 respectively.

The existing boundary prior based saliency algorithm model has the problem of improper selection of reasonable saliency prior region, which leads to the inaccurate foreground region and influence the final result. Aiming at this problem, a salient target detection algorithm based on contrast optimized manifold ranking was proposed. The image boundary information was utilized to find the background prior. An algorithm for measuring the priori quality was designed by using three indexes, namely, saliency expection, local contrast and global contrast. A priori quality design with weighted addition replaced simple multiplication fusion to make the saliency prior more accurate. When the salient regions were extracted from the a priori, the strategy of selecting the threshold was changed, the foreground region was selected more rationally, and the saliency map was obtained by using the manifold ranking, so that the saliency detection result was more accurate. The experimental results show that the proposed algorithm outperforms the similar algorithms, reduces the noise, which is more suitable for human visual perception, and ahead of the depth learning method in processing time.

In order to improve the accuracy and efficiency of collaborative filtering recommendation method, a collaborative filtering recommendation method based on improved heuristic similarity model, namely PSJ, was proposed, which considered the difference of user ratings, the user global rating preferences and the number of common rating items. The Proximity factor of PSJ method used the exponential function to reflect the influence of the difference of user ratings, which avoided the problem of zero divider. The Significance factor of NHSM (New Heuristic Similarity Model) method and the URP (User Rating Preference) factor were merged to build the Significance factor of PSJ method, which makes the computational complexity of the PSJ method be lower than that of NHSM. To improve the recommendation performance in data sparsity conditions, both the variance value of user ratings and user global rating preferences were considered in PSJ method. In experiments, precision and recall of Top- k recommendation were used to evaluate the results. The results show that compard with NHSM, Jaccard algorithm, Adjust COSine similarity (ACOS) algorithm, Jaccard Mean Squared Difference (JMSD) algorithm and Sigmoid function based Pearson Correlation Coefficient method (SPCC), the precision and recall of PSJ method are improved.

When the packets are broadcasted with the fixed power in Vehicular Ad-Hoc NETwork (VANET), the wireless channel may not be allocated reasonable. In order to solve this problem, a power control mechanism adapted to the variation of vehicle density was proposed. It is adaptive to the variation of vehicle density. The direct neighbor list of each node was constructed and updated in a power control period, the power that used to transmit the vehicle status message was adjusted according to the location of the direct neighbor to cover all the direct neighbors, thus wireless channel could be allocated more reasonable and the performance of router could also be optimized. The validity of the proposed mechanism was proved by the simulation results. It is also found that the proposed mechanism is useful for adjusting the transmission power according to the vehicular density, reducing channel busy ratio and enhancing the performance of packet delivery ratio among direct neighbors, which can ensure the effective transmission of the security information.

Considering the antenna oscillation phenomenon, the performance of spotbeam selection in Geostationary Earth Orbit Mobile Radio Interface (GMR-1) system under antenna oscillation was analyzed and an improved spotbeam selection algorithm was proposed. The improved algorithm could dynamically set hysteresis using the distance between the Mobile Earth Station (MES) and its Gateway Station (GS). A simulation model was implemented using OPNET. The simulation results show that the MES at different location is influenced by antenna oscillation to different extent. Besides, the wrong times of spotbeam selection increases with the increase of hysteresis and the maximum amplitude in the traditional algorithm. Finally, the improved algorithm can reduce the wrong times of spotbeam selection and restrain the influence on spotbeam selection under antenna oscillation efficiently.

Traditional Artificial Bee Colony (ABC) algorithms suffer from the problem of slow convergence and easy stagnation in local optima. An improved ABC algorithm based on cloud model, was proposed to solve the problem. By calculating a candidate food source through the normal cloud particle operator and by reducing the radius of the local search space, the proposed algorithm can enhance the convergence speed and exploitation capability. In order to maintain diversity, a new selection strategy that makes the inferior individual have more chances to be selected was introduced. In addition, the best solution found over time was used to explore a new position in the algorithm. A number of experiments on composition functions show that the proposed algorithm has been improved in terms of convergence speed and solution quality, and is better than some recently proposed improved ABC algorithms.

Frequency-hopping communication was combined with the Low-Density Parity-Check (LDPC) code to improve anti-jamming performance of frequency hopping communications. By simplifying the complexity of coding algorithm in the “greedy algorithm”, an offset layered quantization decoding called Layered Belief Propagation-Offset Min-Sum (LBP-OMS) algorithm was applied to improve the performance of error correction code words. The simulation results show that when certain frequency bands are covered by strong noise, the anti-interference ability of broadband frequency-hopping communications is improved by using the improved channel coding method.

MMDR(Machine Method for Discovering Regularities) is a method in Data Minning field which can get knowledge from data. In this paper, author designed a wide-first approximative regularity dual layer growing algorithm to implement MMDR, and introduced how to apply this algorism in stock price forecasting and building individual models in stock market simulation with the rules produced in the process that the algorithm runs.

The cepstrum method of speech pitch detection was analyzed carefully, and several shortcomings were pointed out when the method was realized digitally. In order to overcome these shortcomings, a pitch detection algorithm based on digital spectral analysis was presented. It used the power spectrum reprocessing results of speech to extract the pitch contour. The presented method not only overcame the shortcomings of cepstrum method, but also improved the computational speed and the accuracy of the estimated pitch contour.