The joint video expert team (JVET) is currently developing a new video coding standard called H.266/Versatile Video Coding (VVC). Compared with High Efficiency Video Coding (HEVC), VVC has added a variety of coding tools. These tools have greatly improved video compression efficiency and maintained a high level video quality. However, due to the increase in computational complexity, the encoding time is much longer than HEVC. We propose a prediction tool based on DenseNet (a convolutional neural network) to decrease the VVC coding complexity. We predict the probability that the edge of 4 ×4 blocks in each 64 ×64 block is the division boundary by Convolutional Neural Networks (CNN). Then, we skip the unnecessary rate distortion optimization (RDO) and speed up the coding by probability vectors in advance. The proposed method can reduce the coding complexity of 46.10% in VTM10.0 intra coding, while Bjøntegaard delta bit rate (BDBR) only increases by 1.86%. In the sequence with a resolution greater than 1080P, the acceleration efficiency can be at 64.81%, the BDBR loss only increased by 1.92%.

There is much debate about whether the junction temperature or phosphor temperature is higher in white light-emitting diodes (LEDs). The main reason is that the junction and phosphor temperatures cannot be measured directly using traditional temperature measurement tools. In this study, a magnetic nanoparticle thermometer, a nondestructive and precise temperature measurement tool, is introduced and described in detail. The model, measurement principle, and experimental setup are described. The temperature of the phosphor layer and the top surface of the P-N junction in white LEDs was measured directly using the magnetic nanoparticle thermometer, and the results show that the phosphor layer temperature was higher than the chip top surface temperature at different input voltages, providing a reference for relative researchers.

This paper implements a deep learning‐based modulation pattern recognition algorithm for communication signals using a convolutional neural network architecture as a modulation recognizer. In this paper, a multiple‐parallel complex convolutional neural network architecture is proposed to meet the demand of complex baseband processing of all‐digital communication signals. The architecture learns the structured features of the real and imaginary parts of the baseband signal through parallel branches and fuses them at the output according to certain rules to obtain the final output, which realizes the fitting process to the complex numerical mapping. By comparing and analyzing several commonly used time‐frequency analysis methods, a time‐frequency analysis method that can well highlight the differences between different signal modulation patterns is selected to convert the time‐frequency map into a digital image that can be processed by a deep network. In order to fully extract the spatial and temporal characteristics of the signal, the CLP algorithm of the CNN network and LSTM network in parallel is proposed. The CNN network and LSTM network are used to extract the spatial features and temporal features of the signal, respectively, and the fusion of the two features as well as the classification is performed. Finally, the optimal model and parameters are obtained through the design of the modulation recognizer based on the convolutional neural network and the performance analysis of the convolutional neural network model. The simulation experimental results show that the improved convolutional neural network can produce certain performance gains in radio signal modulation style recognition. This promotes the application of machine learning algorithms in the field of radio signal modulation pattern recognition.

3D High Efficiency Video Coding (3D-HEVC) is the latest High Efficiency Video Coding (HEVC) standard development to improve the compression performance of multi-view video plus depth (MVD) format. It adopts several additional coding tools for better representation of the dependent texture and depth data. These tools achieve high compression efficiency, but require high computational complexity. In this paper, we introduce a fast 3D-HEVC method for the complexity reduction of MVD. A simulation analysis is performed to study the coding information correlations of inter-view, spatio-temporal and texture-depth. The proposed fast algorithm decides to skip some treeblocks of texture and depth map at the early stage without a normal coding process, which includes fast depth level, early SKIP/Merge mode, adaptive early termination, based on the correlations of inter-view, spatio-temporal, and texture-depth. Experimental results show that the proposed fast scheme can save 70.2% encoding runtime of 3D-HEVC, with negligible loss of rate-distortion (RD) performance.

Based on the characteristics of time domain and frequency domain recognition theory, a recognition scheme is designed to complete the modulation identification of communication signals including 16 analog and digital modulations, involving 10 different eigenvalues in total. In the in‐class recognition of FSK signal, feature extraction in frequency domain is carried out, and a statistical algorithm of spectral peak number is proposed. This paper presents a method to calculate the rotation degree of constellation image. By calculating the rotation degree and modifying the clustering radius, the recognition rate of QAM signal is improved significantly. Another commonly used method for calculating the rotation of constellations is based on Radon transform. Compared with the proposed algorithm, the proposed algorithm has lower computational complexity and higher accuracy under certain SNR conditions. In the modulation discriminator of the deep neural network, the spectral features and cumulative features are extracted as inputs, the modified linear elements are used as neuron activation functions, and the cross‐entropy is used as loss functions. In the modulation recognitor of deep neural network, deep neural network and cyclic neural network are constructed for modulation recognition of communication signals. The neural network automatic modulation recognizer is implemented on CPU and GPU, which verifies the recognition accuracy of communication signal modulation recognizer based on neural network. The experimental results show that the communication signal modulation recognizer based on artificial neural network has good classification accuracy in both the training set and the test set.

High efficiency video coding screen content coding (HEVC-SCC) extension is the latest HEVC development to improve the compression performance of screen content (SC) video. Similar to HEVC, the intra mode selection in HEVC-SCC is performed all the coding unit (CU) partitions to find the least rate distortion (RD) cost. Furthermore, additional intra tools are introduced to improve HEVC-SCC coding efficiency. However, these new tools could cause high computation complexity which restricts HEVC-SCC from ongoing applications. To solve the problem, an efficient intra mode decision for HEVC-SCC that adaptively utilizes the texture complexity of SC treeblock is proposed. The texture complexity of a SC treeblock is first analyzed according to the variation degree of the luminance value. And then, two efficient approaches are proposed based on the constructed model, which are early CU depth level determination and adaptive intra mode selection. Experimental results demonstrate that the proposed method can save 48.5% encoder runtime while keeping nearly the same coding efficiency as the HEVC-SCC encoders.