To solve traffic congestion problems, a new road condition judgment model was proposed. Firstly, the model was based on YOLOv3 target detection algorithm. Then, according to the eigenvalue matrix corresponding to the picture, the difference between adjacent frames was made by the eigenvalue matrix, and the difference value was compared with preset value to determine whether the current road was in a congested state or a normal traffic state. Secondly, the current calculated road state was compared with previous two calculated road states. Finally, the state statistics method in the model was used to calculate the duration of a state (congestion or patency) of road. The proposed model could analyze the states of three lanes of a road at the same time. Through experiments, the average accuracy of model to judge the state of single lane could reach 80% or more, and it was applicable to both day and night roads.

Some problems of the current load balancing algorithms for distributed systems include:1) The role of each node in the system is fixed, and the system has no adaptability. 2) The load balancing algorithm is not universal. 3) The migration task is too large, and the load balance cycle is too long. To solve these problems, a hybrid load balancing algorithm was proposed. Firstly, a distributed receiving system model was designed, by which the system tasks were divided into three parts:receiving level, handling level and storing level. In receiving level, a home-made transmission protocol was used to improve the reception capability of the system. And then, in the load balancing algorithm, random load migration strategy was used. According to the status of the nodes, the tasks of load were randomly migrated. The problems of long load balance cycle and load moving back were solved by this strategy. Finally, the distributed control node selecting strategy was adopted to make the nodes adaptable. The experimental results show that the average delay in each layer of the system is in milliseconds, and the system load balancing takes less than 3 minutes, which proves that the load balancing mechanism has short load balance cycle and fast response, and can improve the reception capability of the distributed system.

To solve the Access Control List (ACL) strategic audit problem in multi-layer firewalls, the policy anomalies in single firewall and between multi-layer firewalls were analyzed based on time. Then the Anomaly Detection based on Backtracking Algorithm (ADBA) was proposed by constructing the tree structure according to the topology of firewalls. First, the ACL policy of each firewall was analyzed and the data format was unified to the database. Second, the tree structure of firewall was built based on the topology of the firewall and the anomaly would be detected in a single firewall. Finally, the data in the database and the tree structure was used in ADBA to detect and record the abnormal strategy. The experimental results show that compared with the Semi-isomorphic Marked Firewall Decision Diagram (SMFDD) algorithm, the proposed ADBA can reduce the execution time of anomaly detection by 28.01% and reduce the miscalculation of anomaly detection according to the time factor. The ADBA can be implemented effectively at multi-layer firewalls ACL audit to improve detection accuracy and reduce detection time.

Focusing on low counting accuracy caused by serious occlusions and abrupt illumination variations, a new real-time statistical method based on Gaussian Mixture Model (GMM) and Scale-Invariant Feature Transform (SIFT) features for video crowd counting was proposed. Firstly, the moving crowd were detected by using GMM-based motion segment method, and then the Gray Level Co Occurrence Matrix (GLCM) and morphological operations were applied to remove small moving objects of background and the dense noise in non-crowd foreground. Considering the high time-complexity of GMM algorithm, a novel parallel model with higher efficiency was proposed. Secondly, the SIFT feature points were acted as the basis of crowd statistics, and the execution time was reduced by using feature exaction based on binary image. Finally, a novel statistical analysis method based on crowd features and crowd number was proposed. The data sets with different level of crowd number were chosen to train and get the average feature number of a single person, and the pedestrians with different densities were counted in the experiment. The algorithm was accelerated by using multi-stream processors on Graphics Processing Unit (GPU) and the analysis about efficiently scheduling the tasks on Compute Unified Device Architecture (CUDA) streams in practical applications was conducted. The experimental results indicate that the speed is increased by 31.5% compared with single stream, by 71.8% compared with CPU.

To improve the safety of vehicles while driving, a computer vision-based inter-vehicle distance estimation and warning method was proposed in this paper. First, shadow detection method was applied to detect shadow of cars ahead, and inter-vehicle distance estimation function was built based on the distance between shadow and vision center of a frame. Then, estimation equations for non-threatened background optical flow was built, and by judging optical flow with the estimation equations, the abnormal objects could be separated from others, thus the overtaking event could be recognized. Based on the inter-vehicle distance and detection of overtaking event, the driver could be timely warned of the potential safety hazard. The experimental results prove that the proposed method can estimate inter-vehicle distance and detect overtaking event accurately. Finally, NVIDIA GeForce GTX680 GPU (Graphic Processing Unit) was used to accelerate the algorithm on Compute Unified Device Architecture (CUDA) platform and achieve the processing speed of 48.9 ms per frame which basically meets the real-time processing demand.

Since the segmentation performance of existing binary segmentation algorithm for video is excessively low, a binary probability segmentation algorithm in real-time based on Graphics Processing Unit (GPU) was proposed. The algorithm implemented a probabilistic segmentation based on the Quadratic Markov Measure Field (QMMF) model by regularizing the likelihood of each pixel of frame belonging to forground class or background class. In this algorithm, first two kinds of likelihood models, Static Background Likelihood Model (SBLM) and Unstable Background Likelihood Model (UBLM) were proposed. Secondly, the probability of each pixel belonging to background was computed by tonal transforming, cast shadow detecting and camouflage detecting algorithm. Finally, the probability of background which makes the energy function have a minimum value was computed by Gauss-Seidel model iteration and the binary value of each pixel was calculated. Moreover, illumination change, cast shadow and camouflage were included to improve the accuracy of segmentation algorithm. In order to fulfill the real-time requirement, a parallel version of our algorithm was implemented in a NVIDIA GPU. The accuracy and GPU execution time of the segmentation algorithm were analyzed. The experimental results show that the average missing rate and false detection rate of ViBe+ and GMM+ are 3 and 6 times those of QMMF, the average execution time of GPU of ViBe+ and GMM+ is about 1.3 times that of QMMF. Moreover, the average speedup of algorithm was computed and it is about 76.8.

At present, in the field of target recognition, the highest accuracy algorithm is the Deformable Part Model (DPM) for human detection. Aiming at the disadvantage of large amount of calculation, a parallel solution method based on Graphics Processing Unit (GPU) was proposed. In this paper, with the GPU programming model of OpenCL, the details of the whole DPM algorithm were implemented by the parallel methods,and optimization of the memory model and threads allocation was made. Through the comparison of the OpenCV library and the GPU implementation, under the premise of ensuring the detection effect, the execution efficiency of the algorithm was increased by nearly 8 times.

It is difficult for users to find the needed items from a large-scale project resource repository because the project resources in it are disordered, so a parallel fuzzy partition algorithm based on MapReduce was proposed. The algorithm firstly abstracted and standardized characteristic attributes of original project resource. Then a similarity matrix was established based on the standardized characteristic attributes of the project, and it was segmented by using block matrix. MapReduce was used to process the block matrix and merge the results. Finally, the algorithm obtained the partition results according to the threshold. The contrast experiment among the proposed algorithm, K-means algorithm and genetic algorithm shows that the proposed algorithm has higher accuracy and recall, it can achieve better speedup in large-scale data calculation and divide project resources effectively and accurately.

To solve the problem of losing edge and texture information in the existing image denoising algorithms based on fractional-order integral, an image denoising algorithm using fractional-order integral with edge compensation was presented. The fractional-order integral operator has the performance of sharp low-pass. The Cauchy integral formula was introduced into digital image denoising, and the image numerical calculation of fractional-order integral was achieved by the method of slope approximation. In the process of iterative denoising, the algorithm built denoising mask by setting higher tiny fractional-order integral order at the rising stage of image Signal-to-Noise Ratio (SNR); and the algorithm built denoising mask by setting lower small fractional-order integral order at the declining stage of image SNR. Additionally, it could partially restore the image edge and texture information by the mechanism of edge compensation. The image denoising algorithm using fractional-order integral proposed in this paper makes use of different strategies of the fractional-order integral order and edge compensation mechanism in the process of iterative denoising. The experimental results show that compared with traditional denoising algorithm, the denoising algorithm proposed in this paper can remove the noise to obtain higher SNR and better visual effect while appropriately restoring the edge and texture information of image.

In the Wireless Sensor Network (WSN) routing, if the node does not fully consider the path node residual energy and link status of the route, some nodes will be dead early, hence the lifetime of the network will be shorten seriously. To resolve this problem, a uneven clustering routing algorithm for wireless sensor network was proposed based on ant colony optimization algorithm. Firstly, the method clustered nodes using uneven clustering algorithm which considered the node energy. Then considering the node need to transmit data as source node, the sink node as destination node, ant colony optimization algorithm was used to do multipath searching, and the searching process fully considered the factors such as transmission energy consumption, path minimum residual energy, transmission distance and transmission hops, time delay and bandwidth of selected link. Several optimal paths that met the conditions were given to complete the information transmission between source and the destination nodes at last. The experimental results show that the lifetime of WSN can be effectively prolonged while fully considering the path transmission energy consumption, path minimum residual energy and transmission hops.

To solve the system integration problem caused by heterogeneous sensor networks, this paper proposed a heterogeneous sensor networks integration system ISHSN (Integration System for Heterogeneous Sensor Network). ISHSN consisted of the gateway of the Internet of Things (IoT) and the access Agent. The gateway converted the data to the same format and converted the command to the customized format according the target sensor network. The access Agent dealt with data collection, link merge and command forward, and balance loading of the access Agents with the scheduling algorithm. The experiment proves that the ISHSN has good scalability and availability in sensor networks data collection and sensor network control.

A data distribution strategy and a communication-efficient parallel algorithm for sorting integers sequence were proposed on the heterogeneous cluster with multi-core machines. The presented data distribution model properly utilized different computation speed, communication rate and memory capacity of each computing node to dynamically compute the size of the data block to be assigned to each node to balance the loads among nodes. In the proposed parallel sorting algorithm, making use of the characteristic of integers sequence, master node distributed the data blocks to the salve nodes and received the sorted subsequences with two-round mode, each salve node returned its sorted subsequence to master node by bucket-packing method, and master node linked its received sorted subsequences to form directly a final sorted sequence by the bucket mapping in order to reduce the data merge operations with large communication cost. The analysis and experimental results on the heterogeneous cluster with multi-core machines show that the presented parallel sorting integers sequence algorithm is efficient and scalable.

To preserve more image texture information while obtaining better denoising performance, the Riemann-Liouville (R-L) fractional integral operator was described in signal processing. The R-L fractional integral theory was introduced into the digital image denoising, and the method of ladder approximation was used to achieve numerical calculation. The model constructed the corresponding mask of image denoising by setting a tiny integral order to achieve local fine-tuning of noise image, and it could control the effect of image denoising by the way of iteration to get better denoising results. The experimental results show that, compared with the traditional image denoising algorithms, the image denoising algorithm based on R-L fractional integral proposed in this paper can enhance the Signal-to-Noise Ratio (SNR) of image, the SNR of denoising image with the algorithm proposed in this paper can reach 18.3497dB, and the lowest growth rate compared to the traditional denoising algorithms increases about 4%. In addition, the proposed algorithm can better retain weak image edge and texture details information of image.

Wireless Sensor Network (WSN) spreads widely, and with the expansion of WSNs there will be a large number of sensors which produce massive sensor data. To store the data from large-scale WSNs, this paper proposed a two-tier storage architecture based on HBase storing sensor data from different regions and global data management directory, which achieved a near real-time storage system. The experimental results show that this system with high scalability, storage and query efficiency can solve the massive sensor data storage.

For high sensitivity and operation efficiency in weak signal acquisition of Global Positioning System (GPS) software receiver, a differential coherent accumulated acquisition algorithm based on Fast Fourier Transform (FFT) was proposed. The limitation of coherent integration time was overcome by block accumulation of demodulated GPS intermediate frequency data. Based on FFT frequency shift characteristics, a Doppler circular frequency search was used to achieve low computation instead of frequency compensation search. The loss in frequency was resolved by different down conversions. Compared to the original incoherent accumulation, Signal-to-Noise Ratio (SNR) was improved by differential coherent accumulation of coherent results. The weak signal in a -39dB poor SNR environment was successfully acquired in experiments. High sensitivity and operation efficiency of the proposed algorithm were confirmed by the experimental results.

Considering the non-linearity of TCP model, uncertainty of Round Trip Time (RTT) and fluctuation of network load, an Active Queue Management (AQM) scheme based on Variable Structure Controller (VSC) using single neuron adaptive learning was proposed. The nonlinear VSC was used to guarantee the swiftness and robustness of queue response at router. However, the jitter of VSC would cause the queue fluctuation and performance degradation. Therefore, a single neuron was introduced to adjust the parameters of the VSC in order to alleviate the effect of jitter and modeling uncertainty. The proposed scheme can reduce the jitter and enhance the robustness for AQM control system greatly. Finally, the simulation results show the effectiveness of the proposed algorithm through NS-2 simulator.

In older to solve the problem of the deviation accumulation caused by the restoring process of exemplar-based image inpainting algorithm, and to improve the accuracy of the algorithm, this paper focused on the revision of the priority computation formula, by leading an α factor to enhance the proportion of data. Therefore, the algorithm is more sensitive to the detail of image texture during the inpainting procedure. The average energy of texture feature was quantified by the wavelet coefficients. Thus the α factor was made to be self-adaptive so that the images with different energy could be inpainted adaptively through appropriate strategies. It is proved that the image quality can be enhanced effectively by this improved exemplar-based image inpainting algorithm, and the optical connectivity requirements of human beings can be satisfied by the inpainted effects.

To build a common user model platform which can share data and provide more comprehensive and accurate user information to all sites accessed to the platform, the platform provided data interfaces and algorithm interfaces to interact with the third-party sites, focused on how to solve the conflict of user data from different data sources in order to form a unified user model, finally achieved sharing of algorithms, models and data. The experimental results show that it is more accurate and comprehensive to build user model on this platform.