In bamboo strip surface defect detection, the bamboo strip defects have different shapes and messy imaging environment, and the existing target detection model based on Convolutional Neural Network (CNN) does not take advantage of the neural network when facing such specific data; moreover, the sources of bamboo strips are complicated and there exist other limited conditions, so that it is impossible to collect all types of data, resulting in a small amount of bamboo strip defect data that CNN cannot fully learn. To address these problems, a special detection network aiming at bamboo strip defects was proposed. The basic framework of the proposed network is CenterNet. In order to improve the detection performance of CenterNet in less bamboo strip defect data, an auxiliary detection module based on training from scratch was designed:when the network started training, the CenterNet part that uses the pre-training model was frozen, and the auxiliary detection module was trained from scratch according to the defect characteristics of the bamboo strips; when the loss of the auxiliary detection module stabilized, the module was intergrated with the pre-trained main part by a connection method of attention mechanism. The proposed detection network was trained and tested on the same training sets with CenterNet and YOLO v3 which is currently commonly used in industrial detection. Experimental results show that on the bamboo strip defect detection dataset, the mean Average Precision (mAP) of the proposed method is 16.45 and 9.96 percentage points higher than those of YOLO v3 and CenterNet, respectively. The proposed method can effectively detect the different shaped defects of bamboo strips without increasing too much time consumption, and has a good effect in actual industrial applications.

Pedestrian Re-IDentification (Re-ID) is a hot issue in the field of computer vision and mainly focuses on “how to relate to specific person captured by different cameras in different physical locations”. Traditional methods of Re-ID were mainly based on the extraction of low-level features, such as local descriptors, color histograms and human poses. In recent years, in view of the problems in traditional methods such as pedestrian occlusion and posture disalignment, pedestrian Re-ID methods based on deep learning such as region, attention mechanism, posture and Generative Adversarial Network (GAN) were proposed and the experimental results became significantly better than before. Therefore, the researches of deep learning in pedestrian Re-ID were summarized and classified, and different from the previous reviews, the pedestrian Re-ID methods were divided into four categories to discuss in this review. Firstly, the pedestrian Re-ID methods based on deep learning were summarized by following four methods region, attention, posture, and GAN. Then the performances of mAP (mean Average Precision) and Rank-1 indicators of these methods on the mainstream datasets were analyzed. The results show that the deep learning-based methods can reduce the model overfitting by enhancing the connection between local features and narrowing domain gaps. Finally, the development direction of pedestrian Re-ID method research was forecasted.

Concerning the bimodal distribution characteristics of the range boundary presented by the traditional Chebyshev mapping, in order to meet the requirements of homogenized distribution of sequences in optimization theory, the mathematical equation was given by using the probability density function of Chebyshev mapping, and a new system was constructed by combining with the original mapping into a new system. The comparative study shows that the system has good homogenized distribution characteristic, ergodic characteristic, balance and low complexity, and the random error of the generated sequences is small and the similarity is high. Finally, the system is applied to the initialization population stage of the optimization algorithm, and it is further shown that the homogenized distribution system has a significant effect on improving the homogenized distribution characteristic of the original mapping.

Focused on the problem of traditional object recognition algorithm that the artificially designed features were more susceptible to diversity of object shapes, illumination and background, a deep convolutional neural network algorithm was proposed for object recognition. Firstly, this algorithm was trained with NYU Depth V2 dataset, and single depth information was transformed into three channels. Then color images and transformed depth images in the training set were used to fine-tune two deep convolutional neural networks, respectively. Next, color and depth image features were extracted from the first fully connected layers of the two trained models, and the two features from the resampling training set were combined to train a Linear Support Vector Machine (LinSVM) classifier. Finally, the proposed object recognition algorithm was used to extract super-pixel features in scene understanding task. The proposed method can achieve a classification accuracy of 91.4% on the test set which is 4.1 percentage points higher than SAE-RNN (Sparse Auto-Encoder with the Recursive Neural Networks). The experimental results show that the proposed method is effective in extracting color and depth image features, and can effectively improve classification accuracy.

Identity-based signatures are the groundwork of many cryptographic protocols. This paper analyzed GU KE et al.'s (GU KE, JIA WEIJIA, JIANG CHUNLIANG. Efficient and secure identity-based signature scheme. Journal of Software,2011,22(6):1350-1360) efficient identity-based signature scheme. Two equivalent signature generating algorithms were proposed and it was pointed out that Gu et al.'s scheme could not satisfy the basic security properties. In other words, any attacker could use the equivalent secret key and signature generating algorithms proposed in this paper to forge a valid secret key of a user and a valid signature on any message with respect to any identity in their scheme. Furthermore, the reason that the scheme is insecure was also analyzed and it was pointed out that designing a more efficient identity-based signature scheme than the classical one is almost impossible.

A new reversible data hiding algorithm for medical images was proposed. The hidden information was embedded into Region Of Interest (ROI) and non-interest respectively. In ROI, an adaptive integer transform scheme was employed to enhance the embedding capacity and control distortions. And in Region of Non-Interest (RONI), the classical Least Significant Bit (LSB) method was used to keep the marked image’s quality. The experimental results show that, compared with previous works, the performance of the proposed method has been significantly improved in terms of capacity and image quality. The proposed method’s embedding capacity is between 1.2bpp and 1.7bpp, while the Peak Signal-to-Noise Ratio (PSNR) can maintain the 43dB or so. Moreover, the proposed method with high run efficiency can be applied into the practical hospital information system.

Direct towards anonymity and high dynamic of online user in P2P TV, a P2P TV crawler, called TVCrawler was developed and deployed which enabled to launch an active measurement on several popular large scale P2P TV systems. The authors conducted a comparative research on time evolution and geographic distribution of online users in these different P2P TV systems. First, while intuitively researching the time evolution of online users in P2P TV channel, the method of MultiScale Entropy (MSE) analysis was introduced to investigate the complexity in time series of the number of online users. Second, the authors made a study on the regular pattern of online users' geographic distribution, and made a Google map-based visual representation about online users. Then, by analyzing the relationship between geographic distribution of online Chinese users and provincial economic development level of China, it is discovered that significant linear decreasing correlation exists between the two of them.

In the Case-Based Reasoning (CBR) case retrieving and matching, different cases are usually composed by different features. But most of the traditional CBR engines adopt fixed feature weights mode, which makes matching rate of whole system very low. To solve this problem, this paper proposed a CBR engine model with variable feature weights and brought interactive mode into feature weights calculating module. It calculated subjective weight based on group decisionmaking theory and proposed an adjustment method which used differences between a single expert and his group. It used similarity rough set theory to calculate objective weight in order to make results calculating more objective and accurate. At last, it designed composite weights adjustment algorithm which calculated the distance between the subjective weight and objective weight, considered the deviation degree of those two weights, then deduced weights adjustment coefficient, and get the final weight adjustment results. The calculation example and simulation analysis of network attack cases validate the effectiveness of the proposed method and prove this method has much better performance in different performance indexes.