We propose a deep bilinear model for blind image quality assessment (BIQA) that works for both synthetically and authentically distorted images. Our model constitutes two streams of deep convolutional neural networks (CNN), specializing in the two distortion scenarios separately. For synthetic distortions, we first pre-train a CNN to classify the distortion type and level of an input image, whose ground truth label is readily available at a large scale. For authentic distortions, we make use of a pretrain CNN (VGG-16) for the image classification task. The two feature sets are bilinearly pooled into one representation for a final quality prediction. We fine-tune the whole network on target databases using a variant of stochastic gradient descent. Extensive experimental results show that the proposed model achieves state-of-the-art performance on both synthetic and authentic IQA databases. Furthermore, we verify the generalizability of our method on the large-scale Waterloo Exploration Database, and demonstrate its competitiveness using the group maximum differentiation competition methodology.

The development of intelligent transportation systems brings many requirements to the current vehicular networks. For instance, to ensure secure communications between vehicles, low latency, high connectivity, and high data rate are required for vehicular networks. To meet such requirements, the 5G communication systems for vehicular networks should be improved accordingly. This article proposes a communication scheme for 5G vehicular networks, in which moving access points are deployed on vehicles to facilitate the access of vehicle users. Moreover, adjacent vehicle- installed moving access points may cooperatively communicate with vehicle users by joint transmission and reception. In this way, vehicle users communicate with one or more unspecified cooperative access points in a cell-less manner instead of being associated with a single access point. To manage such cell-less networks, local moving software-defined cloudlets are adopted to perform transmission and scheduling management. Simulation results show that the proposed scheme significantly reduces latency while improving the connectivity of vehicular networks, and can be considered as a research direction for the solution to 5G vehicular networks.

Based on the connectivity and energy consumption problems in wireless sensor networks, this paper proposes a kind of new network algorithm called the connectivity and energy efficiency (CEE) algorithm to guarantee the connectivity and connectivity probability, and also to reduce the network energy consumption as much as possible. Under the premise that all sensors can communicate with each other in a specific communication radius, we obtained the relationship among the connectivity, the number of sensor nodes, and the communication radius because of the theory of probability and statistics. The innovation of the paper is to maximize the network connectivity and connectivity probability, by choosing which types of sleeping nodes to wake up. According to the node’s residual energy and the relative value of distance, the algorithm reduces the energy consumption of the whole network as much as possible, and wakes up the number of neighbor nodes as little as possible, to improve the service life of the whole network. Simulation results show that this algorithm combines the connectivity and the energy efficiency, provides a useful reference value for the normal operation of the sensors networks.

Fingerprint image quality assessment is important because the good performance of the minutiae‐based matching algorithm is heavily dependent on fingerprint images with high quality. Many efforts have been made in existing methods, but most methods either use full fingerprint images or use local areas and involve subjective judgments. Unlike previous methods, the proposed method considers both local and global assessments. Local feature vectors are extracted from the fingerprint image block for hierarchical clustering, and the results are used as target outputs of the back‐propagation (BP) neural network without any subjective judgments. Global feature vectors based on the local quality assessment results are used for hierarchical clustering and fed into the BP neural network that calculates the overall error rate of genuine and imposter errors to achieve global quality assessment. Furthermore, the minutiae quality assessment method is also proposed and incorporated into the minutiae‐based matching algorithm. The experimental results based on the FVC2002 and FVC2004 databases show that the proposed methods can effectively assess the quality of fingerprint images and ensure the overall improvement of matching performance.