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.

In order to ensure the high speed of image display and storage in real-time, the image captured by the popular driving recorder usually shows a low resolution, which has a serious impact on effective image information acquisition under unexpected situation. To solve this problem, a perspective transformation based on self-examples of the images and high-frequency compensation were used to reconstruct the city street images with low resolution. Perspective transformation was added to the affine transformation to match image patches, match image patch and high frequency compensation was used to recover the lost high frequency information of each matched image patch when image pyramid was constructed. The image pyramid was searched by non-local multi-scale method to get the matched patches, which were synthesized to obtain the images of high resolution. Many low resolution street view images were used to verify the effectiveness of this algorithm. Compared it to existing typical algorithms such as ScSR (Sparse coding Super-Resolution), Upscaling, SCN (Sparse Coding based Network), the experimental results show that the algorithm in several blind evaluation indices is better than other algorithms and it can improve the image resolution while keeping the edges and details of the image.

The original Rival Penalized Competitive Learning (RPCL) algorithm ignores the influence of the geometry structure of a dataset on the weight variation of its nodes. A new RPCL algorithm proposed by Wei Limei et al. (WEI LIMEI, XIE WEIXIN. A new competitive learning algorithm for clustering analysis. Journal of Electronics, 2000, 22(1): 13-18) overcame the drawback of the original RPCL by introducing the density of samples to adjust the weights of nodes, while the density was not much objective. This paper defined a new density for a sample according to the pattern distribution of samples in a dataset, and introduced the density into the adjusting for the weights of nodes in RPCL to overcome the disadvantages of the available RPCL algorithms. The authors' improved RPCL algorithm was tested on some well-known datasets from UCI machine learning repository and on some synthetic data sets with noisy samples. The accuracy of determining the number of clusters of a dataset and the run time and the clustering error of the algorithms were compared. The Rand index, the Jaccard coefficient and the Adjust Rand index were used to analyze the performance of the algorithms. The experimental results show that the improved RPCL algorithm outperforms the original RPCL and the new RPCL proposed by WEI LIMEI et al. greatly, and achieves much better clustering results and has a stronger anti-interference performance for noisy data than that of the other two RPCL algorithms. All the analyses demonstrate that the improved RPCL algorithm can not only determine the right number of clusters for a dataset according to its sample distribution, but also uncover the suitable centers of clusters and advance the clustering accuracy as well as approximate the global optimal clustering result as fast as possible.

As a criterion of feature selection, F-score does not consider the influence of the different measuring dimensions on the importance of different features. To evaluate the discrimination of features between classes, a new criterion called D-score was presented. This D-score criterion not only has the property as the improved F-score in measuring the discrimination between more than two sets of real numbers, but also is not influenced by different measurement units for features when measuring their discriminability. D-score was used as a criterion to measure the importance of a feature, and Sequential Forward Search (SFS) strategy, Sequential Forward Floating Search (SFFS) strategy, and Sequential Backward Floating Search (SBFS) strategy were, respectively, adopted to select features, while Support Vector Machine (SVM) was used as the classification tool, so that three new hybrid feature selection methods were proposed. The three new hybrid feature selection methods combined the advantages of Filter methods and Wrapper methods where SVM played the role to evaluate the classification capacity of the selected subset of features via the classification accuracy, and leaded the feature selection procedure. These three new hybrid feature selection methods were tested on nine datasets from UCI machine learning repository and compared with the corresponding algorithms with F-score as criterion of the discriminability of features. The experimental results show that D-score outperforms F-score in evaluating the discrimination of features, and can be used to implement the dimension reduction without compromising the classification capacity of datasets.

The multicast routing method has been applied into the Network on Chip (NoC) since traditional unicast communication cannot meet the increasingly rich application requirements of NoC. Three kinds of path-based multicast routing algorithms including XY routing, UpDown routing and SubPartition routing algorithms were applied to 2D Mesh or Torus NoC. The congestion control strategy was proposed. The simulation results show multicast routing algorithms have shorter average latency and higher throughput and balanced applied load compared with unicast routing algorithms. SubPartition routing algorithm was confirmed to have a more stable and better performance as the network size increases. Finally, multicast congestion control techniques for NoC were employed to make multicast communications more efficient and enhance the NoC performance.