To drive upgrades of Depth-Image-Based Rendering (DIBR) algorithms, depth image refinement, etc., quality assessment models for DIBR-synthesized images in 3D video systems are developed. However, most of these models could not effectively evaluate distortion due to irregular stretching (e.g., crumbling), which is more complex and common than black holes and regular stretching (e.g., horizontal stretching) in synthesized images. To make an attempt at this issue, a new quality assessment method is proposed for DIBR views. First, feature point matching and affine transformation are adopted to remove and compensate for the global object shift between reference and synthesized view images. Second, multi-scale discrete wavelet transform is utilized to extract multi-scale structure distortion; gradient magnitude similarity is further integrated to highlight the distortion features; morphological open operation and median filtering are adopted to exclude perceptually unimportant features. Third, scores are obtained by standard deviation pooling on distortion feature maps for each wavelet scale and sub-band. Experimental results demonstrate that our proposed model outperforms the state-of-the-art handcrafted feature-based DIBR-synthesized image quality assessment models on IETR database, and performs the best on average on IETR and IRCCyN/IVC databases. The source code will be available athttps://github.com/House-yuyu/DIBR_IQA.

To promote energy and spectrum efficient communications in multiantenna channels, broadcast can provide a substantial gain in system throughput. However, the hardware constraints and strong Line of Sight (LoS) limit the implementation and performance of multiantenna broadcast in the Internet of unmanned aerial vehicles (UAVs). Based on the pseudo-Doppler principle, we propose a joint precoding and antenna array design to reduce the number of radio frequency (RF) chains required by the broadcast and free the LoS path from the interstream interference. The reduction of RF chains is realized by designing the precoding matrix that makes at least one of the transmit antennas have null inputs during any broadcast and, moreover, the LoS path is formed to match the obtained precoding matrix through antenna array design at the broadcasting UAV. The algorithms with low computational complexity for optimizing this design are developed to minimize the transmit power within the UAV broadcast paradigms. Theoretical formulation and numerical results in the metrics of sum data rate and bit error rate substantiate the validity of our proposed design, specifically in the Internet of UAVs with strong LoS.

In this paper, a decode-and-forward (DF) short-packet relaying model is developed to achieve timely status updates for intelligent monitoring within the Internet of Things (IoT), where the status updates generated at an IoT device are delivered to a remote server with the aid of a relay in both half-duplex (HD) and full-duplex (FD) modes. To characterise the data freshness of status updates, we exploit the age of information (AoI) as a metric, which is defined as the time elapsed since the generation of the latest successfully decoded status update. The average AoI is formulated and minimised for both HD-DF and FD-DF relaying IoT networks in finite blocklength regime. For the HD-DF relaying, we introduce a perfect approximation of the average AoI to solve the problem of average AoI minimisation with the optimal blocklengths in two phases. For the FD-DF relaying, we propose an iterative algorithm to solve the problem of average AoI minimisation by optimising the relay’s transmit power and the blocklength. Illustrative numerical results not only substantiate the validity of our proposed algorithms, but also provide useful references for the IoT monitoring network design, specifically for the transmit power thresholds at the IoT device and the relay.

Quantum neural network (QNN) is a promising area in the field of quantum computing and quantum information processing. A model of feedback quantum neuron (FQN) is proposed and its property is analyzed. A novel multi-user detector based on FQN and quantum register is proposed. An analysis is given to the theoretical basis and practical performance of the proposed detector. The simulation results show that the proposed detector has more powerful properties both in bit error rate and near-far resistance than Hopfield neural network based detector. A multi-user detector based on improved FQN is also proposed and the simulation results have shown that the improved FQN multi-user detector is obviously superior to the FQN multi-user detector. Its performance is closer to optimal, compared with the other multi-user detector.