Aiming at the shortcoming of large access delay in industrial Internet of Things （IoT）， a sink location algorithm of Power Domain NonOrthogonal Multiple Access （PD-NOMA） for real-time industrial IoT was proposed. In this algorithm， based on the PD-NOMA technology， the location of the sink was used as an optimization method to minimize access delay by realizing power division multiplexing among users as much as possible. Firstly， for any two users， an assertion that the decodable area of the qualified sink must be a circle if parallel transmissions are successful was proven， and therefore， the decodable area set of the sink was able to be obtained by combining all of the combinations of two users， and every minimal intersection of the area set must be a convex region. So， the optimal location of the sink must be included in these minimal intersection areas. Secondly， for each minimal intersection area where the sink was deployed， the minimum number of chain partition of the network generation graph in the area was computed and used as the metric for evaluating the access delay. Finally， the optimal location of the sink was determined by comparing these minimum number of chain partitioning. Experimental results show that when the decoding threshold is 2 and the number of users is 30， the average access delay of the proposed algorithm is about 36.7% of that of the classic time division multiple access， and besides， it can be decreased almost linearly with the decrease of the decoding threshold and the increase of the channel decay factor. The proposed algorithm can provide reference from the access layer perspective for massive ultra-reliable low-latency communications.

A Cumulative Running Work (CRW) based task scheduler CRWScheduler was proposed to effectively process tasks without any prior knowledge for big data analytics platform like Hadoop and Spark. The running job was moved from a low-weight queue to a high-weight one based on CRW. When resources were allocated to a job, both the queue of the job and the instantaneous resource utilization of the job were considered, significantly improving the overall system performance without prior knowledge. The prototype of CRWScheduler was implemented based on Apache Hadoop YARN. Experimental results on 28-node benchmark testing cluster show that CRWScheduler reduces average Job Flow Time (JFT) by 21% and decreases JFT of 95th percentile by up to 35% compared with YARN fair scheduler. Further improvements can be obtained when CRWScheduler cooperates with task-level schedulers.

Focused on the issue that Point-Of-Interest (POI) recommendation has low recommendation accuracy and efficiency, with deep analysis of the influence of social factors and geographical factors in POI recommendation, a POI recommendation algorithm combining location influence was presented. Firstly, in order to solve the sparseness of sign-in data, the 2-degree friends were introduced into the collaborative filtering algorithm to construct a social influence model, and the social influence of the 2-degree friends on the users were obtained by calculating experience and friend similarity. Secondly, by deep consideration of the influence of geographical factors on POI, a location influence model was constructed based on the analysis of social networks. The users' influences were discovered through the PageRank algorithm, and the location influences were calculated by the POI sign-in frequency, obtaining overall geographical preference. Moreover, kernel density estimation method was used to model the users' sign-in behaviors and obtain the personalized geographical features. Finally, the social model and the geographic model were combined to improve the recommendation accuracy, and the recommendation efficiency was improved by constructing the candidate POI recommendation set. Experiments on Gowalla and Yelp sign-in datasets show that the proposed algorithm can quickly recommend POIs for users, and has high accuracy and recall rate than Location Recommendation with Temporal effects (LRT) algorithm and iGSLR (Personalized Geo-Social Location Recommendation) algorithm.

In order to choose better model and acquire more accurate bounding-box when using the Convolutional Neural Network (CNN) in pedestrian detection, an improved pedestrian detection method based on CNN was proposed. The improvements include two aspects:how to determine the iterative learning number of training CNN samples and how to merge multiple responses of an object. Firstly, on the solution of the first improvement, multiple candidate CNN classifiers were learned from different training samples in different training iterations. And a new strategy was proposed to select the model with better generalization ability. Both the accuracy on the validation set and the stability of the accuracies during the iterative training procedure were considered by the proposed strategy. On the improvement of combining multiple responses, an enhanced refined bounding-box combination method was proposed which was different from the Non-Maximum Suppression (NMS) method. The coarse bounding-box of CNN detection procedure output was taken as the input for obtaining the one-to-one refined bounding-box. Then, the CNN accurate positioning process was used for each coarse bounding-box to get the corresponding refined bounding-box. Finally, the multiple refined bounding-boxes were merged by considering the correction probability of each bounding-box. Exactly, the final output bounding-box was obtained by the weighted average of multiple relevant refined bounding boxes with respect to their correction probabilities. To investigate the proposed two improvements, the comprehensive experiments were conducted on well-recognized pedestrian detection benchmark dataset-ETH. The experimental results show that, the two proposed improvements have effectively improved the detection performance of the system. Compared with the benchmark method of Fast Region proposals with CNN (R-CNN), the detection performance of the proposed method with the fusion of two improvements has greatly improved by 5.06 percentage points under the same test conditions.

For the vacancies on digital watermarking technology based on 2D-vector animation, this paper proposed a blind watermarking scheme which made full use of vector characteristics and the timing characteristics. This scheme adopted color values of adjacent frames in vector animation changed elements as embedded target. And it used Least Significant Bit(LSB) algorithm as embedding/extraction algorithm, which embedded multiple group watermarks to vector animation. Finally the accurate watermark could be obtained by verifying the extracted multiple group watermarks. Theoretical analysis and experimental results show that this scheme is not only easy to implement and well in robustness, but also can realize tamper-proofing. What's more, the vector animation can be played in real-time during the watermark embedding and extraction.

Aggregate nearest neighbor query involves many query points, so it is more complicated than traditional nearest neighbor query, and the distribution characteristic of query set implies the region where its aggregate nearest neighbor exists. Taking full account of the distribution characteristic of query set, a method by utilizing distribution characteristic to direct the way of aggregate nearest neighbor searching was given. Based on the method, a new algorithm named AM was presented for aggregate nearest neighbor query. AM algorithm can dynamically capture and use the distribution characteristic of query set, which enables it to search data points in a right order, and avoid unnecessary searching to data points. The experimental results show the efficiency of the algorithm.