In the downlink transmission scenario, power allocation and beamforming design at the transmitter are essential when using multiple antenna arrays. This paper considers a multiple input-multiple output broadcast channel to maximize the weighted sum-rate under the total power constraint. The classical weighted minimum mean-square error (WMMSE) algorithm can obtain suboptimal solutions but involves high computational complexity. To reduce this complexity, we propose a fast beamforming design method using unsupervised learning, which trains the deep neural network (DNN) offline and provides real-time service online only with simple neural network operations. The training process is based on an end-to-end method without labeled samples avoiding the complicated process of obtaining labels. Moreover, we use the ’APoZ’-based pruning algorithm to compress the network volume, which further reduces the computational complexity and volume of the DNN, making it more suitable for low computation-capacity devices. Finally, experimental results demonstrate that the proposed method improves computational speed significantly with performance close to the WMMSE algorithm.

The newly developed HEVC video coding standard can achieve higher compression performance than the previous video coding standards, such as MPEG-4, H.263 and H.264/AVC. However, HEVC's high computational complexity raises concerns about the computational burden on real-time application. In this paper, a fast pyramid motion divergence (PMD) based CU selection algorithm is presented for HEVC inter prediction. The PMD features are calculated with estimated optical flow of the downsampled frames. Theoretical analysis shows that PMD can be used to help selecting CU size. A k nearest neighboring like method is used to determine the CU splittings. Experimental results show that the fast inter prediction method speeds up the inter coding significantly with negligible loss of the peak signal-to-noise ratio.

Human emotions recognition (HERO) is considered one of important techniques for realizing intelligent Internet of Things (IIoT). The demand for a robust and precise facial expression recognition algorithm is urgent for HERO. In this paper, we propose a deep recognition algorithm based on the ensemble deep learning model. The proposed algorithm consists of three sub-networks with different depths. Each sub-network is comprised of CNNs and trained independently. The sub-network with more convolutional layers recognizes emotions by extracting local details such as the features of eyes and mouth, while the sub-network with less convolutional layers focuses on the macrostructure of the input image. The three sub-networks are assembled together to constitute the whole model. The experiment is based on the Kaggle facial expression recognition challenge database (FER2013), the Japanese female facial expression database (JAFFE) and the AffectNet database. The experimental results show the proposed algorithm achieves a test accuracy of 71.91%, 96.44% and 62.11% better than other competitors, and increases the test accuracy by approximately 2-3% than unique sub-networks.

Traditional surveillance systems for observing water levels are often complex, costly, and time-consuming. In this study, we developed a low-cost unmanned surveillance system consisting of remote measuring stations and a monitoring center. The system uses a map-based web service, as well as video cameras, water level analyzers, and wireless communication routers necessary to display real-time water level measurements of rivers and reservoirs on a web platform. With the aid of a wireless communication router, the water level information is transmitted to a server connected to the Internet via a cellular network. By combining complex water level information of different river basins, the proposed system can be used to forecast and prevent flood disasters. In order to evaluate the proposed system, we conduct experiments using three feasible methods, including the difference method, dictionary learning, and deep learning. The experimental results show that the deep learning-based method performs best in terms of accuracy and stability.

The newly developed High Efficiency Video Coding (HEVC) Standard has improved video coding performance significantly in comparison to its predecessors. However, more intensive computation complexity is introduced by implementing a number of new coding tools. In this paper, a fast coding unit (CU) decision based on Markov random field (MRF) is proposed for HEVC inter frames. First, it is observed that the variance of the absolute difference (VAD) is proportional with the rate-distortion (R-D) cost. The VAD based feature is designed for the CU selection. Second, the decision of CU splittings is modeled as an MRF inference problem, which can be optimized by the Graphcut algorithm. Third, a maximum a posteriori (MAP) approach based on the R-D cost is conducted to evaluate whether the unsplit CUs should be further split or not. Experimental results show that the proposed algorithm can achieve about 53% reduction of the coding time with negligible coding performance degradation, which outperforms the state-of-the-art algorithms significantly.

The emerging high efficiency video coding (HEVC) standard has improved compression performance significantly in comparison with H.264/AVC. However, more intensive computational complexity has been introduced by adopting a number of new coding tools. In this paper, a fast inter CU decision is proposed based on the latent sum of absolute differences (SAD) estimation. Firstly, a two-layer motion estimation (ME) method is designed to take advantage of the latent SAD cost. The new ME method can obtain the SAD costs for both the upper CU and its sub-CUs. Secondly, a concept of motion compensation rate- distortion (R-D) cost is defined, and an exponential model is proposed to express the relationship between the motion compensation R-D cost and the SAD cost. Then, a fast CU decision approach is designed based on the exponential model. The fast CU decision is implemented by comparing a derived threshold with the SAD cost difference between the upper and sub SAD costs. Experimental results show that the proposed algorithm achieves an average of 52% and 58.4% reductions of the coding time at the cost of 1.61% and 2% bit-rate increases under the low delay and random access conditions, respectively.