In this paper we propose a method to use features of an individual object to locate and recognize this object concurrently in a static image with Multi-feature fusion based on multiple objects sample library. This method is proposed based on the observation that lots of previous works focuses on category recognition and takes advantage of common characters of special category to detect the existence of it. However, these algorithms cease to be effective if we search existence of individual objects instead of categories in complex background. To solve this problem, we abandon the concept of category and propose an effective way to use directly features of an individual object as clues to detection and recognition. In our system, we import multi-feature fusion method based on colour histogram and prominent SIFT (p-SIFT) feature to improve detection and recognition accuracy rate. p-SIFT feature is an improved SIFT feature acquired by further feature extraction of correlation information based on Feature Matrix aiming at low computation complexity with good matching rate that is proposed by ourselves. In process of detecting object, we abandon conventional methods and instead take full use of multi-feature to start with a simple but effective way-using colour feature to reduce amounts of patches of interest (POI). Our method is evaluated on several publicly available datasets including Pascal VOC 2005 dataset, Objects101 and datasets provided by Achanta et al.

Convolutional neural networks (CNNS) have a strong ability to understand and judge images. However, the enormous parameters and computation of CNNS have limited its application in resource-limited devices. In this letter, we used the idea of parameter sharing and dense connection to compress the parameters in the convolution kernel channel direction, thus greatly reducing the number of model parameters. On this basis, we designed Shared and Dense Channel-wise Convolutional Networks (SDChannelNets), mainly composed of Depth-wise Separable SD-Channel-wise Convolution layer. The advantage of SDChannelNets is that the number of model parameters is greatly reduced without or with little loss of accuracy. We also introduced a hyperparameter that can effectively balance the number of parameters and the accuracy of a model. We evaluated the model proposed by us through two popular image recognition tasks (CIFAR-10 and CIFAR-100). The results showed that SDChannelNets had similar accuracy to other CNNs, but the number of parameters was greatly reduced.

During recent years, there has been a rapid growth in deployment of gossip-based protocol in many multicast applications. In a typical gossip-based protocol, each node acts as dual roles of receiver and sender, independently exchanging data with its neighbors to facilitate scalability and resilience. However, most of previous work in this literature seldom considered cheating issue of end users, which is also very important in face of the fact that the mutual cooperation inherently determines overall system performance. In this paper, we investigate the dishonest behaviors in decentralized gossip-based protocol through extensive experimental study. Our original contributions come in two-fold: In the first part of cheating study, we analytically discuss two typical cheating strategies, that is, intentionally increasing subscription requests and untruthfully calculating forwarding probability, and further evaluate their negative impacts. The results indicate that more attention should be paid to defending cheating behaviors in gossip-based protocol. In the second part of anti-cheating study, we propose a receiver-driven measurement mechanism, which evaluates individual forwarding traffic from the perspective of receivers and thus identifies cheating nodes with high incoming/outgoing ratio. Furthermore, we extend our mechanism by introducing reliable factor to further improve its accuracy. The experiments under various conditions show that it performs quite well in case of serious cheating and achieves considerable performance in other cases.

The correlation filter-based trackers with an appearance model established by single feature have poor robustness to challenging video environment which includes factors such as occlusion, fast motion and out-of-view. In this paper, a long-term tracking algorithm based on multi-feature adaptive fusion for video target is presented. We design a robust appearance model by fusing powerful features including histogram of gradient, local binary pattern and color-naming at response map level to conquer the interference in the video. In addition, a random fern classifier is trained as re-detector to detect target when tracking failure occurs, so that long-term tracking is implemented. We evaluate our algorithm on large-scale benchmark datasets and the results show that the proposed algorithm have more accurate and more robust performance in complex video environment.

An imbalance difference model has been developed to estimate the common-mode radiated emission of a PCB with an attached cable. This model, however, requires significant computation time for full-wave simulation, especially if the attached cable is long, even with a powerful computer configuration. To solve this problem, a method that approximates the imbalance difference model as an equivalent asymmetrical dipole antenna is proposed in this paper. The common-mode radiated emission can be predicted using a line integration of the common-mode current distribution which is directly estimated by the asymmetrical antenna model. Unlike existing methods, the proposed method avoids the circuit construction normally used to measure the common-mode current, and is still able to accurately predict the maximum common-mode radiation. The effectiveness of the proposed method is verified by comparing the predicted results with the 3D full-wave simulation and the measured data gathered in an anechoic chamber.

For suppressing inter symbol interference, the support vector machine mutliuser detector (SVM-MUD) was adopted as a nonlinear method in direct sequence code division multiple access (DS-CDMA) signals transmitted through multipath channels. To solve the problems of the complexity of SVM-MUD model and the number of support vectors, based on recursive least squares support vector machine (RLS-SVM) and Riemannian geometry, a new algorithm for nonlinear multiuser detector is proposed. The algorithm introduces the forgetting factor to get the support vectors at the first training samples, then, uses Riemannian geometry to train the support vectors again and gets less improved support vectors. Simulation results illustrated that the algorithm simplifies SVM-MUD model at the cost of only a little more bit error rate and decreases the computational complexity. At the same time, the algorithm has an excellent effect on suppressing multipath interference.

The original single-shot multibox detector (SSD) algorithm has good detection accuracy and speed for regular object recognition. However, the SSD is not suitable for detecting small objects for two reasons: 1) the relationships among different feature layers with various scales are not considered, 2) the predicted results are solely determined by several independent feature layers. To enhance its detection capability for small objects, this study proposes an improved SSD-based algorithm called proportional channels' fusion SSD (PCF-SSD). Three enhancements are provided by this novel PCF-SSD algorithm. First, a fusion feature pyramid model is proposed by concatenating channels of certain key feature layers in a given proportion for object detection. Second, the default box sizes are adjusted properly for small object detection. Third, an improved loss function is suggested to train the above-proposed fusion model, which can further improve object detection performance. A series of experiments are conducted on the public database Pascal VOC to validate the PCF-SSD. On comparing with the original SSD algorithm, our algorithm improves the mean average precision and detection accuracy for small objects by 3.3% and 3.9%, respectively, with a detection speed of 40FPS. Furthermore, the proposed PCF-SSD can achieve a better balance of detection accuracy and efficiency than the original SSD algorithm, as demonstrated by a series of experimental results.