Thread-Level Speculation (TLS) technology can tap the parallel execution potential of programs and improve the utilization of multi-core resources. However, the current TACLeBench kernel benchmarks are not effectively analyzed in TLS parallelization. In response to this problem, the loop-level speculative execution analysis scheme and analysis tool were designed. With 7 representative TACLeBench kernel benchmarks selected, firstly, the initialization analysis was performed to the programs, the program hot fragments were selected to insert the loop identifier. Then, the cross-compilation was performed to these fragments, the program speculative thread and the memory address related data were recorded, and the maximun potential of the loop-level parallelism was analyzed. Finally, the program runtime characteristics (thread granularity, parallelizable coverage, dependency characteristics) and the impacts of the source code on the speedup ratio were comprehensively discussed. Experimental results show that:1) this type of programs is suitable for TLS acceleration, compared with serial execution results, under the loop structure speculative execution, the speedup ratios for most programs are above 2, and the highest speedup ratio in them can reach 20.79; 2) by using TLS to accelerate the TACLeBench kernel programs, most applications can effectively make use of 4-core to 16-core computing resources.

For the monitoring dead angles of fixed surveillance cameras installed in large quantities and low hardware performance of mobile devices, an urban management case target recognition algorithm that can run on IOS mobile devices with low performance was proposed. Firstly, the number of channels of input and output images and the number of feature maps generated by each channel were optimized by adding new hyperparameters to MobileNet. Secondly, a new recognition algorithm was formed by combining the improved MobileNet with the SSD recognition framework and was transplanted to the IOS mobile devices. Finally, the accurate detection of the common 8 specific urban management case targets was achieved by the proposed algorithm, in which the camera provided by the mobile device was used to capture the scene video. The mean Average Precision (mAP) of the proposed algorithm was 15.5 percentage points and 10.4 percentage points higher than that of the prototype YOLO and the prototype SSD, respectively. Experimental results show that the proposed algorithm can run smoothly on low-performance IOS mobile devices, reduce the dead angles of monitoring, and provide technical support for urban management team to speed up the classification and processing of cases.

To efficiently detect outliers in massive hydrologic sensor data, an anomaly detection method for hydrological time series based on SparkR was proposed. Firstly, a sliding window and Autoregressive Integrated Moving Average (ARIMA) model were used to forecast the cleaned data on SparkR platform. Then, the confidence interval was calculated for the prediction results, and the results outside the interval range were judged as anomaly data. Finally, based on the detection results, K-Means algorithm was used to cluster the original data, the state transition probability was calculated, and the anomaly data were evaluated in quality. Taking the data of hydrologic sensor obtained from the Chu River as experimental data, experiments on the detection time and outlier detection performance were carried out respectively. The results show that the millions of data calculation by two slaves costs more time than that by one slave, but when calculating the tens of milllions of data, the time costed by two slaves is less than that by one slave, and the maximum reduction is 16.21%. The sensitivity of the evaluation is increased from 5.24% to 92.98%. It shows that under big data platform, the proposed algorithm which is based on the characteristics of hydrological data and combines forecast test and cluster test can effectively improve the computational efficiency of hydrologic time series detection for tens of millions data and has a significant improvement in sensitivity.

Concerning unsatisfactory classification accuracy and low efficiency of the existing pattern-based classification methods for model training, a concept of density-aware pattern and an algorithm for biological sequence classification based on density-aware patterns, namely BSC (Biological Sequence Classifier), were proposed. Firstly, frequent sequence patterns based on density-aware concept were mined. Then, the mined frequent sequence patterns were filtered and sorted for designing the classification rules. Finally, the sequences without classification were classified by classification rules. According to a number of experiments conducted on four real biological sequence datasets, the influence of BSC algorithm parameters on the results were analyzed and the recommended parameter settings were provided. Meanwhile, the experimental results showed that the accuracies of BSC algorithm were improved by at least 2.03 percentage points compared with other four pattern-based baseline algorithms. The results indicate that BSC algorithm has high biological sequence classification accuracy and execution efficiency.

As a widely used recommendation algorithm in the industry, collaborative filtering algorithm can predict the likely favorite items based on the user's historical behavior records. However, the traditional collaborative filtering algorithms do not take into account the drifting of user interests, and there are also some deficiencies when the recommendation's timeliness is considered. To solve these problems, the measure method of similarity was improved by combining with the characteristics of user interests change with time. At the same time, an enhanced time attenuation model was introduced to measure the predictive value. By combining these two ways together, the concept drifting problem of user interests was solved and the timeliness of the recommendation algorithm was also considered. In the simulation experiment, predictive scoring accuracy and Top N recommendation accuracy were compared among the proposed algorithm, UserCF, TCNCF, PTCF and TimesSVD++ algorithm in different data sets. The experimental results show that the improved algorithm can reduce the Root Mean Square Error (RMSE) of the prediction score, and it is better than all the compared algorithms on the accuracy of Top N recommendation.

The original K-means clustering algorithm is seriously affected by initial centroids of clustering and easy to fall into local optima. To solve this problem, an improved K-means clustering algorithm based on Adaptive Cuckoo Search (ACS), namely ACS-K-means, was proposed, in which the search step of cuckoo was adjusted adaptively so as to improve the quality of solution and boost speed of convergence. The performance of ACS-K-means clustering was firstly evaluated on UCI dataset, and the results demonstrated that it surpassed K-means, GA-K-means (K-means based on Genetic Algorithm), CS-K-means (K-means based on Cuckoo Search) and PSO-K-means (K-means based on Particle Swarm Optimization) in clustering quality and convergence rate. Finally, the ACS-K-means clustering algorithm was applied to the development of heat map of urban management cases of Qingxiu district of Nanning city, the results also showed that the proposed method had better quality of clustering and faster speed of convergence.

Focusing on the safety analysis of the 3D block cipher, a new method on this algorithm against the meet-in-the-middle attack was proposed. Based on the structure of the 3D algorithm and the differential properties of the S-box, the research reduced the number of required bytes during structuring the multiple sets in this attack and constructed a new 6-round meet-in-the-middle distinguisher. According to extending the distinguisher 2-round forward and 3-round backward, an 11-round meet-in-the-middle attack of the 3D algorithm was finally achieved. The experimental results show that:the number of required bytes on constructed the distinguisher is 42, the attack requires a data complexity of about 2⁴⁹⁷ chosen plaintexts, a time complexity of about 2^325.3 11-round 3D algorithm encryption and a memory complexity of about 2³⁴² bytes. The new attack shows that the 11-round of the 3D algorithm is not immune to the meet-in-the-middle attack.

To meet the application demand of high speed scanning and massive data transmission in industrial Computed Tomography (CT) of low-energy X-ray, a system of high-speed data acquisition and transmission for low-energy X-ray industrial CT was designed. X-CARD 0.2-256G of DT company was selected as the detector. In order to accommodate the needs of high-speed analog to digital conversion, high-speed time division multiplexing circuit and ping-pong operation for the data cache were combined; a gigabit Ethernet design was conducted with Field Programmable Gate Array (FPGA) selected as the master chip,so as to meet the requirements of high-speed transmission of multi-channel data. The experimental result shows that the speed of data acquisition system reaches 1MHz, the transmission speed reaches 926Mb/s and the dynamic range is greater than 5000. The system can effectively shorten the scanning time of low energy X-ray detection, which can meet the requirements of data transmission of more channels.

Aiming at the nonlinear modeling of Power Amplifier (PA), an improved Radial Basis Function Neural Networks (RBFNN) model was proposed. Firstly, time-delay of cross terms and output feedback were added in the input. Parameters (weigths and centers) of the proposed model were extracted using the Orthogonal Least Square (OLS) algorithm. Then Doherty PA was trained and validated successfully by 15MHz three-carrier Wideband Code Division Multiple Access (WCDMA) signal, and the Normalized Mean Square Error (NMSE) can reach -45dB. Finally, the inverse class F power amplifier was used to test the universality of the model. The simulation results show that the model can more truly fit characteristics of power amplifier.

To deal with the problem that the process of Gas Chromatography-Mass Spectrography (GC-MS) data is complex and time consuming which delays the whole experimental progress, taking the alignment of multiple samples as an example, a parallel framework for processing GC-MS data on Sector/Sphere was proposed, and an algorithm of aligning multiple samples in parallel was implemented. First, the similarity matrix of all the samples was computed, then the sample set was divided into small sample sets according to hierarchical clustering and samples in each set were aligned respectively, finally the results of each set were merged according to the average sample of the set. The experimental results show that the error rate of the parallel alignment algorithm is 2.9% and the speedup ratio reaches 3.29 using the cluster with 4 PC, which can speed up the process at a high accuracy, and handle the problem that the processing time is too long.

Lower Energy Adaptive Clustering Hierarchy (LEACH) protocol randomly and circularly selects the cluster-head node and evenly distributes network energy consumption to each sensor node, but it does not consider the remaining energy of each node. In order to avoid premature death of the less energy node that was selected as the cluster-head node, an advanced algorithm named LEACH-New was proposed,which was based on the energy probability to select those nodes with more energy as cluster-head and to determine the optimal number of the cluster-head nodes. The cluster-head node collected, fused, then sent the data to the base station by the combined mode of single-hop and multi-hop. This algorithm resolved the problem that less energy node was selected to be cluster-head and cluster-heads energy overloaded in LEACH protocol, so it can prolong the lifetime of whole network. The simulation results show that the improved algorithm effectively reduces the network energy consumption and ensure network load balance.

Local features measuring image clarity are studied. Energy of Laplacian (EOL) is considered as the optimal feature. An novel intelligent image fusion algorithm based on EOL is proposed. A set of registered images is firstly segmented, then local EOLs of segmented image blocks are computed. EOLs are input into neural network and the target vectors are automatically obtained by comparing values of EOLs. Test images are segmented and their EOLs are put into trained network and the rough fusion images are generated. Final fusion results will be obtained by consistence verification. Experimental results demonstrated the good fusion performance on different source images.

The time series of Pulse Coupling Neural Network (PCNN) is widely used in the image retrieval and identification, but it cannot embody the shape and characteristics of the image, which results in the failure of image evaluation. In this paper, the local time series of cross visual cortex was proposed to solve the problem. Fist, the image was divided into blocks; then, the time series of each block was extracted; last, the local time series were linked to global time series. The proposed algorithm was compared with the basic time series and the time series added with edges information. The experimental results demonstrate that the proposed method can effectively and efficiently solve the problems existing in the basic time series.