Semiblind Image Deconvolution with Spatially Adaptive Total Variation Regularization

A semiblind image deconvolution algorithm with spatially adaptive total variation (SATV) regularization is introduced. The spatial information in different image regions is incorporated into regularization by using the edge indicator called difference eigenvalue to distinguish flat areas from edges. Meanwhile, the split Bregman method is used to optimize the proposed SATV model. The proposed algorithm integrates the spatial constraint and parametric blur-kernel and thus effectively reduces the noise in flat regions and preserves the edge information. Comparative results on simulated images and real passive millimeter-wave (PMMW) images are reported

Performance Analysis of Backhaul-Aware User Association in 5G Ultradense Heterogeneous Networks

In 5G ultradense heterogeneous networks, wireless backhaul, as one of the important base station (BS) resources that affect user services, has attracted more and more attention. However, a user would access to the BS which is the nearest for the user based on the conventional user association scheme, which constrains the network performance improvement due to the limited backhaul capacity. In this paper, using backhaul-aware user association scheme, semiclosed expressions of network performance metrics are derived in ultradense heterogeneous networks, including coverage probability, rate coverage, and network delay. Specifically, all possible access and backhaul links within the user connectable range of BSs and anchor base stations (A-BSs) are considered to minimize the analytical results of outage probability. The outage for the user occurs only when the access link or backhaul link which forms the link combination with the optimal performance is failure. Furthermore, the theoretical analysis and numerical results evaluate the impact of the fraction of A-BSs and the BS-to-user density ratio on network performance metric to seek for a more reasonable deployment of BSs in the practical scenario. The simulation results show that the coverage probability of backhaul-aware user association scheme is improved significantly by about 2× compared to that of the conventional user association scheme when backhaul is constrained

A Surveillance System of Android Smartphone with Context-awareness

Video surveillance is a basic mean for fields monitoring, but in uncertain plots for emergency, it is usually impossible to construct a delivery channel for video surveillance. With the wide spreading of 3G network and smart phones equipped with powerful processor and context-aware sensors, it is possible to build a context-aware based mobile surveillance. This work provided a mobile video surveillance solution combined with mobile context-awareness realized on Android smartphones. The present paper discussed a context-awareness framework of smart phone and relevant technology firstly. Then, it introduced mobile surveillance architecture and the realization process. Experiment result and use case showed the methods for video collection and delivery and QoS were feasible. This proposed system is constructed with inner sensors of smartphones and no need to construct a fixed delivery channel. Compared with traditional video surveillance system, the system realized with our solution is more flexible, pervasive, and cheaper

An algorithm for highway vehicle detection based on convolutional neural network

Abstract In this paper, we present an efficient and effective framework for vehicle detection and classification from traffic surveillance cameras. First, we cluster the vehicle scales and aspect ratio in the vehicle datasets. Then, we use convolution neural network (CNN) to detect a vehicle. We utilize feature fusion techniques to concatenate high-level features and low-level features and detect different sizes of vehicles on different features. In order to improve speed, we naturally adopt fully convolution architecture instead of fully connection (FC) layers. Furthermore, recent complementary advances such as batch-norm, hard example mining, and inception have been adopted. Extensive experiments on JiangSuHighway Dataset (JSHD) demonstrate the competitive performance of our method. Our framework obtains a significant improvement over the Faster R-CNN by 6.5% mean average precision (mAP). With 1.5G GPU memory at test phase, the speed of the network is 15 FPS, three times faster than the Faster R-CNN