Sleep apnea affects quality of life and health seriously. PolySomnoGraphy （PSG） is the “gold standard” for diagnosis of sleep apnea， but it is expensive and inconvenient for long-term monitoring. Based on the above， a new method based on universal smart wristband was proposed to detect sleep apnea conveniently. In the method， by analyzing heart rate， blood oxygen saturation， and sleep state data collected by the wristband， an adaptive physiological data reconstruction method and a data interpolation method were used to achieve noise filtering； in feature engineering， continuous physiological variables and categorical variables were fused to extract sleep state features deeply； in the classification module， a lightweight Gated Recurrent Unit （GRU） model was used to simplify the training process and reduce the risk of overfitting. Experimental results show that the proposed method obtains 93.68% accuracy and 93.97% recall on a 23-person dataset. Correlation analysis shows that blood oxygen saturation， body mass index， and age are confirmed as key features for sleep apnea detection. Compared with PSG， the proposed method is more suitable for long-term monitoring in a home environment.

To solve the problem that the current graph summarization methods have high compression ratios and the graph compression algorithms cannot be directly used in downstream tasks， a fusion algorithm of graph summarization and graph compression was proposed， which called Graph Summarization algorithm based on Node Similarity grouping and graph Compression （GSNSC）. Firstly， the nodes were initialized as super nodes， and the super nodes were grouped according to the similarity. Secondly， the super nodes of each group were merged until the specified number of times or nodes were reached. Thirdly， super edges and corrected edges were added between the super nodes for reconstructing the original graph. Finally， for the graph compression part， the cost of compressing and summarizing the adjacent edges of each super node were judged， and the less expensive one in these two was selected to execute. Experiments of graph compression ratio and graph query were conducted on six datasets such as Web-NotreDame， Web-Google and Web-Berkstan. Experimental results on six datasets show that， the proposed algorithm has the compression ratio reduced by at least 23 percentage points compared with SLUGGER （Scalable Lossless sUmmarization of Graphs with HiERarchy） algorithm， and the compression ratio decreased by at least 13 percentage points compared with SWeG （Summarization of Web-scale Graphs） algorithm. Experimental results on Web-NotreDame dataset show that the degree error of the proposed algorithm is reduced by 41.6% compared with that of SWeG algorithm. The above verifies that the proposed algorithm has better graph compression ratio and graph query accuracy.

Aiming at the problem that it is difficult to capture the temporal information during the dynamic change of video popularity, a Deep Attention video popularity prediction model Fusing Content and Temporal information (DAFCT) was proposed. Firstly, according to the users' feedback information, an Attention mechanism based Long Short-Term Memory network (Attention-LSTM) model was constructed to capture the popular trend and mine the temporal information. Secondly, Neural Factorization Machine (NFM) was used to process multi-modal content features and embedding techniques were adopted to reduce the computational complexity of the model by reducing the dimension of sparse high-dimensional features. Finally, the concatenate method was employed to fuse the temporal information and content features, and a Deep Attention Video Popularity Prediction (DAVPP) algorithm was designed to solve the proposed DAFCT. Experimental results show that compared with Attention-LSTM model and NFM model, the recall of DAFCT is improved by 10.82 and 3.31 percentage points, and the F1 score was improved by 9.80 and 3.07 percentage points, respectively.

Aiming at the problem of coordinated control within urban traffic sub-areas and boundary intersections, a traffic signal control model based on Hierarchical multi-granularity and Macroscopic Fundamental Diagram (HDMF) was proposed. First, the hierarchical multi-granularity characteristic of the urban traffic system and the rough set theory were used to describe the real-time states of the traffic elements. Then, combined with the distributed intersection signal control based on backpressure algorithm and the dynamic characteristics of the traffic elements, the pressures of the intersection phases were calculated and the phase decision was made. Finally, Macroscopic Fundamental Diagram (MFD) was used to achieve the maximum total flow of vehicles driving out of the area and the optimal number of vehicles in each sub-area. Experimental results showed that HDMF model had the average queue length reduced by 6.35% and 10.01% respectively, and had the average travel time reduced by 6.55% and 11.15% respectively compared with EMP (Extended cooperative Max-Pressure control) model and HGA model based on MFD and hybrid genetic simulated annealing algorithm. It can be seen that the propsed HDMF model can effectively relieve interior and boundary traffic congestions of sub-areas and maximize the traffic flow of the whole road network.

In order to minimize the expected logistics cost of the supplier alliance, the Inventory Routing Problem (IRP) of multiple suppliers and multiple products under random fluctuations of demand was studied. Based on the horizontal collaboration strategy, a reasonable share method of vehicle distribution costs among the members of the supplier alliance was designed. By considering the retailer's distribution soft and hard time windows and inventory service level requirements, a heterogeneous vehicle inventory routing mixed-integer stochastic programming model of multiple suppliers and multiple products was established, and the inverse function of demand cumulative distribution was employed to transform this model into a deterministic programming model. Then an improved genetic algorithm was designed to solve the programming model. The results of example analysis show that the use of heterogeneous vehicles for distribution can reduce the total cost of supplier alliance by 8.3% and 11.92% respectively and increase the loading rate of distribution vehicles by 24% and 17% respectively, compared with the use of homogeneous heavy-duty and light-duty vehicles. The sensitivity analysis results indicate that no matter how the proportion of suppliers' supply to the total supply of the alliance and the variation coefficient of retailers' commodity demand change, the total cost of the supplier alliance can be effectively reduced by using heterogeneous vehicles for distribution; and the greater the demand variation coefficient is, the more obvious the advantage of using heterogeneous vehicles for distribution has.

Aiming at the inaccuracy of traditional abnormal flow detection model in the industrial control system, an abnormal flow detection model based on Convolutional Neural Network (CNN) was proposed. The proposed model was based on CNN algorithm and consisted of a convolutional layer, a full connection layer, a dropout layer and an output layer. Firstly, the actual collected network flow characteristic values were scaled to a range corresponding to the grayscale pixel values, and the network flow grayscale map was generated. Secondly, the generated network traffic grayscale image was put into the designed convolutional neural network structure for training and model tuning. Finally, the trained model was used to the abnormal flow detection of the industrial control network. The experimental results show that the proposed model has a recognition accuracy of 97.88%, which is 5 percentage points higher than that of Back Propagation (BP) neural network with the existing highest accuracy.

Phased time series data is common in daily life. It describes an event that contains a number of state transitions. Each state has a time attribute, and there are multiple paths between state transitions. Aiming at the problem that the existing visualization techniques are not sufficient in visualizing the transition of each phase or the time variation of paths between states, a novel visualization model based on spiral graph was proposed. In the proposed model, each state was represented by a circle and the states of an event were represented by a set of concentric circles, and the reachable paths between neighboring states were represented by spirals. The start point of each spiral depended on its start time and the start states, and the end point of each spiral depended on its end time and the end states. To solve the overlapping problem caused by large amount of paths, the transparency adjustment algorithm based on long-tailed function was applied on the paths. The transparency of each path was assigned according to the number of intersections of this path and other paths. Flexible interactive facilities such as path filtering, highlighting, bomb box and zooming were provided to support efficient data exploration. The proposed model was implemented on China railway data, the experimental result shows that the model can effectively display trains of any running duration in limited space and is able to reduce the chaos caused by paths overlapping when confronted with large amount of trains as well as keep the information of trains and provide decision support for the user route choice, which validates the effectiveness of the proposed model in visualizing phased time series data.

Aiming at the illumination consistency of complex scenes in Augmented Reality (AR) system and analyzing the marker images by High-Dynamic Range (HDR) technology, an improved measurement algorithm for illumination direction based on the analysis of halo in HDR images was proposed. In order to improve the immersion and reality of virtual objects, after researching and analyzing the existing illumination recovery algorithms, a camera calibration method was proposed by utilizing the projection invariance of the quadratic curve pair. In order to get detailed light information, HDR was used to process marker image to improve accuracy. Refering to Lambert illumination model, the light information of image was analyzed to classify the shooting angle, and the improvement of traditional light source direction measuring was realized, part of the directions of the light sources outside of the photography ball reflection range was measured. The shooting 1 and shooting 2 of the single point light source were tested and analyzed. The experimental results show that this method is simple, robust, and can measure the direction of partial illumination outside the mirror ball reflection range no matter whether the marker is partially shaded or not.

Loop program has a significant amount of execution time in digital signal processing software, temporary storage of loop code with instruction buffer can reduce the number of program memory access to improve the performance of processor. A loop instruction buffer was added in the instruction pipeline. It could store and dispatch instructions of loop program in the software pipelining manner. The instructions of loop program needed to be accessed from program memory only once but executed many times, so the number of memory access was reduced. During the loop instructions were dispatched from buffer, the program memory could be signaled to sleep to reduce the power consumption of processor. In the typical application program, the instruction pipeline can be idle above 90%, and the performance of processor is improved about 10%, the overhead of loop buffer is 9% of the instruction pipeline.

Concerning that the conventional color image coding algorithm does not take advantage of the dependency of the RGB color components, a new algorithm of set-partitioning RGB color components synchronously based on the Set Partitioning In Hierarchical Trees (SPIHT) algorithm was proposed. In this algorithm, RGB color components were treated as a whole, partition was sorted and set at the same time by using the same list of LIS. The color embedded bit-stream generated by this algorithm can stop at any point of the bit-stream and reconstruct the color image. The simulation results show the Peak Signal-to-Noise Ratio (PSNR) of the new algorithm on test images is about 0.1dB to 0.70 dB higher than JPEG2000s.

The data scheduling algorithm in data-driven overlay network is identified as one of the most influential factors affecting system performance of P2P streaming media. Considering the fact that the current algorithm fails to make use of the data blocks and nodes efficiently, which leads to low-quality streaming media services, a new method for data scheduling algorithm was proposed in this study based on both priority of data blocks and capacity of nodes. This algorithm could get priority value according to the scarcity and urgency of blocks. It also could get the capacity of the nodes according to uplink-bandwidths, time-online and relative distance of the nodes. With the utilization of this algorithm, higher priority blocks and higher capacity nodes were requested, and the waiting time to play was decreased. The simulations in the OPNET network indicate that the algorithm can efficiently reduce start-up delay of streaming media playing system and the server load.

This paper proposed a new Multi-Space Multi-Resolutin Markov Random Field Model (MS-MRMRF). Concerning the inadequate description of the color images in a single RGB space, the proposed model firstly transformed images from the RGB color space to the HSV color space and combined these two color spaces as a multi-space feature; then a new multi-resolution Markov model was designed to segment the image based on the multi-space feature, which estimated the parameters by fuzzy theory. The experiments of the color images demonstrate that the segmentation results of MS-MRMRF model have a higher segmentation accuracy compared with the segmentation results of multi-resolution MRF with a single RGB space.

Robot soccer moves in dynamic environment which exists unknown outside interference. In the process of competition, the soccer often collides with obstacles such as robots, thus causing change of position and direction. Aiming at the above, an algorithm based on calculation of strong tracking filter(STF) and H∞ filter is proposed to predict the position of football. By introducing a time-varying fading factor, it can both improve the tracking ability of state mutation and avoid making assumptions on the interference signals. In the middle size league of robot soccer competition platform, experiments were conducted to verify the effectiveness of the proposed algorithm.

The conventional light radiation intensity estimation of K-Nearest Neighbor (K-NN) algorithm can only be improved by increasing the density of photons. The authors replaced the K-NN algorithm with Variable Kernel (VK) method which inherited the properties of smoothing kernel, and estimated the light radiation intensity for different surface point adaptively by calculating the ratio of the assigned radius of each photon to the distance between the photon and the surface point. The experimental results show that the VK algorithm is faster than K-NN algorithm and it can improve image quality without shooting a great number of photos.

The combat capability of the weapon system-of-systems is an important factor of the unit combat capability, so it is a commonly used key index in analyzing the structure of the unit and evaluating the unit’s capability of completing a certain mission. This system used hierarchical, composable, and entity-oriented data structure to store the weapon system-of-systems, then calculated the capabilities layer by layer of the weapon component, weapon system and weapon system-of-system based on the fire power, maneuver, supply, defense, and intelligence ability (for short "five kind capabilities") and the quantification model and aggregation model. The system preserves the numerical analysis method’s virtue of easy usage and rapid calculation, and gives the solution of aggregating and comparing different weapons’ capability. Its results can be used as reference data for formulating weapon development plan and designing combat training simulation system.