The Long Term Evolution based Vehicle to Everything (LTE-V2X) standard follows the LTE standard's frame format and uses a block-type pilot assisted Single-Carrier Frequency-Division Multiple Access (SC-FDMA) system for channel estimation. However, due to the time-varying characteristics of the V2X channel, large technical challenges are brought to the channel estimation at the receiver. Therefore, a time-varying channel estimation method based on sliding window filtering and polynomial fitting was designed. Aiming at the noise problem at pilot symbols, based on Least Squares (LS) method, an adaptive-length sliding window filtering was adopted for noise reduction, so as to ensure the channel estimation accuracy of pilot symbols. Furthermore, according to the size of the Doppler frequency shift of data symbols, an adaptive-order polynomial fitting method was designed to track the channel changes at data symbols. The simulation results show that the proposed method has a good denoising effect based on LS method. In the case of low-speed movement, the estimation accuracy of the proposed method is between those of LS method and Linear Minimum Mean Square Error (LMMSE) method. In the case of high-speed movement, the proposed method can fit the time-varying channel characteristics better, and its performance exceeds that of the channel estimation method of LMMSE method combined with linear interpolation. The above results show that the proposed method has better adaptability than the comparison methods and is suitable for LTE-V2X communication scenarios with different channel noises and terminal moving speeds.

In order to effectively improve the Bit Error Rate (BER) performance of communication system without significantly increasing the computational complexity, a deep learning based joint channel estimation and equalization algorithm named V-EstEqNet was proposed for Cellular-Vehicle to Everything (C-V2X) communication system by using the powerful ability of deep learning in data processing. Different from the traditional algorithms, in which channel estimation and equalization in the communication system reciever were carried out in two stages respectively, V-EstEqNet considered them jointly, and used the deep learning network to directly correct and restore the received data, so that the channel equalization was completed without explicit channel estimation. Specifically, a large number of received data were used to train the network offline, so that the channel characteristics superimposed on the received data were learned by the network, and then these characteristics were utilized to recover the original transmitted data. Simulation results show that the proposed algorithm can track channel characteristics more effectively in different speed scenarios. At the same time, compared with the traditional channel estimation algorithms (Least Squares (LS) and Linear Minimum Mean Square Error (LMMSE)) combining with the traditional channel equalization algorithms (Zero Forcing (ZF) equalization algorithm and Minimum Mean Square Error (MMSE) equalization algorithm), the proposed algorithm has a maximum BER gain of 6 dB in low-speed environment and 9 dB in high-speed environment.

In order to realize accurate management of yaks during the process of yak breeding, it is necessary to recognize the identities of the yaks. Yak face recognition is a feasible method of yak identification. However, the existing yak face recognition algorithms based on neural networks have the problems such as too many features in the yak face dataset and long training time of neural networks. Therefore, based on the method of transfer learning and combined with the Visual Geometry Group (VGG) network and Convolutional Neural Network (CNN), a Parallel CNN (Parallel-CNN) algorithm was proposed to identify the facial information of yaks. Firstly, the existing VGG16 network was used to perform transfer learning to the yak face image data and extract the yaks' facial information features for the first time. Then, the dimensional transformation was performed to the extracted features at different levels, and the processed features were inputted into the parallel-CNN for the secondary feature extraction. Finally, two separated fully connected layers were used to classify the yak face images. Experimental results showed that Parallel-CNN was able to recognize yak faces with different angles, illuminations and poses. On the test dataset with 90 000 yak face images of 300 yaks, the recognition accuracy of the proposed algorithm reached 91.2%. The proposed algorithm can accurately recognize the identities of the yaks, and can help the yak farm to realize the intelligent management of the yaks.

In order to further improve the user experience of power system customers, and aiming at the problems of poor optimization ability, lack of compactness and difficulty in solving the optimal number of clusters, a Gaussian mixture clustering algorithm combining elbow method and Expectation-Maximization (EM) was proposed, which can mine the potential information in a large number of customer data. The good clustering results were obtained by EM algorithm iteration. Aiming at the shortcoming of the traditional Gaussian mixture clustering algorithm that needs to obtain the number of user clusters in advance, the number of customer clusters was reasonably found by using elbow method. The case study shows that compared with hierarchical clustering algorithm and K-Means algorithm, the proposed algorithm has the increase of both FM (Fowlkes-Mallows) and AR (Adjusted-Rand) indexes more than 10%, and the decrease of Compactness Index (CI) and Degree of Separation (DS) less than 15% and 25% respectively. It can be seen that the performance of the algorithm is greatly improved.

In recent years, the complexity of conducting schedulability experiments increases with the rapid development of real-time scheduling research. In general, schedulability experiments are time-consuming in the absence of standardized and modularized experiment tools. Moreover, since the source codes are not publicly available, it is difficult to verify the reported results in the literature, and to reuse and extend the experiments. In order to reduce the repeative work and help the vertification, a basic schedulability experiment framework was proposed. This experiment framework generated task systems through random distribution, and then tested their schedulability, and based on the framework, a novel open-source schedulability platform called SET-MRTS (Schedulability Experiment Toolkit for Multiprocessor Real-Time Systems) was designed and realized. The platform adopted the modular architecture. SET-MRTS consisted of the task module, the processor module, the shared resource module, the algorithm library, the configuration module and the output module. The experimental results show that, SET-MRTS supports uni- and multi-processor real-time scheduling algorithms and synchronization protocol analyses, which can correctly perform the schedulability test and output intuitive experimental results, and support the expansion of the algorithm library. Compared with algorithms in the algorithms library implemented in the experiment, SET-MRTS has good compatibility and expansibility. SET-MRTS is the first open source platform to support a complete experimental process, including algorithmic implementation, parameter configuration, result statistics, charting, and so on.

With the rapid development of the domestic high-speed railway construction, customer demand for mobile office and entertainment on high-speed railway is growing rapidly. While both of the existing cellular mobile communication and proprietary communication network for Global System for Mobile communication-Railway (GSM-R) cannot satisfy customer demand for Quality of Service (QoS) of broadband wireless communication. High-speed railway will experience all kinds of complex scenarios during the actual driving, and U-shaped groove scene is a common one. However, there is not a full research on time-varying channel modeling of the U-shaped groove scenario under high-speed mobile environment. Therefore, a U-shaped groove time-varying channel modeling method under high-speed mobile environment was proposed and simulated. Firstly, the geometric random distribution theory was used to established geometric distribution model for high-speed railway scenario under U-shaped groove, and the change law of scatterers was analyzed. Besides, the parameters' closed mathematical expressions such as line-of-sight distribution, time-varying angle spread, time-varying Doppler spread were deduced, and the closed solution of the channel impulse response was given. Secondly, the time-variant space-time cross-correlation function, time-variant auto-correlation function and time-variant space-Doppler power spectrum density were analyzed. Finally, the simulations of statistical performance were carried out to verify the proposed model. The simulation results show that the proposed model has the properties of time-varying and high correlation, which verifies the non-stationary of high-speed wireless channel and satisfies the characteristics of high-speed wireless channel.

To improve the reliability of the Wireless Sensor and Actuator Network (WSAN), an optimal design method based on Genetic Algorithm (GA) for WSAN fault detection filter was proposed. In system modeling, the influence of the wireless network transmission delay on network control system was modeled as an external noise, the composite optimization index which is composed of sensitivity and robustness was made as the design goal of fault detection filter, and the optimization objective was made as the core of GA—the fitness function. At the same time, according to the numerical characteristics of optimization objective in WSAN, the corresponding real coding, uniform mutation, arithmetic crossover and other processing methods were selected to speed up the convergence rate, meanwhile taking the accuracy of the calculation results into account. The optimized filter design mentioned herein, not only restrains the noise signal, but also amplifies the fault signal. Finally, the effectiveness of the proposed design is demonstrated by the results of Matlab/OMNET++ hybrid simulations.

In this paper, a fast blind restoration algorithm for motion blurred image was proposed, using a robust algorithm based on Radon transform-domain to determine the blur kernel function, then a modified total variation algorithm was used to restore the blurred images. Its cost function is the sum of three terms corresponding to total variation l2-norm regularization, least squares fidelity term and l1-norm fidelity term. Compared with Fergus' and Levin' algorithm, the experiment results show that the algorithm for a class of motion blurred image caused by the linear movement parallel to the lens has higher speed and good recovery effect.

The sparse representations of signal theory has been extensively and deeply researched in recent years, and been widely applied to image processing. For the huge computation of over-complete dictionary structure and sparse decomposition, a new self-adaptive method for image denoising based on sparse decomposition and clustering was proposed. Firstly, an overcomplete dictionary was designed by training samples with a modified K-means clustering algorithm. In the training process, atoms of the dictionary were updated adaptively in every iterative step to better fit the sparse representation of the samples. Secondly, the sparse representation of the test image was obtained by using the dictionary combined with Orthogonal Matching Pursuit (OMP) algorithm, so as to achieve image denoising. The experimental results show that in terms of image denoising and computational complexity, the performance of the proposed method is better than the traditional dictionary training algorithm.

To solve the insufficiency of Stream Control Multiple Access (SCMA) protocol that it fails to consider both the link Quality of Service (QoS) and the channel access scheduling policy of Multiple Input Multiple Output (MIMO) stream, this paper put forward a SCMA/QA protocol. This protocol fully considered the channel states of different streams in each link and built up a discrete Markov chain model based on channel state of stream. Moreover, it took the request of QoS in each link into consideration and adopted fixed Request to Send/Clear to Send (RTS/CTS) to exchange the information of link QoS and taking the link QoS weight as a primary case in link decision, made the link scheduling problem based on QoS of MIMO modeled as an optimization problem, which led to the result of optimal link and the number of communication streams under the Karush-Kuhn-Tucker (KKT) conditions. Finally, according to the numerical analysis which set throughput as the parameter of the QoS, the results show that under the same network environment the SCMA/QA improves the system throughput much better than SCMA and QoS-aware Cooperation SCMA (QCSCMA).