Focusing on the issues that the effectiveness and efficiency of existing Adaptive Random Testing (ART) algorithms are not as good as Random Testing (RT) for point failure pattern, an improved ART algorithm based on the concept of crowding level of failure region, namely CLART, was proposed to improve the traditional ART algorithm: Fixed Sized Candidate Set (FSCS) and Restricted Random Testing (RRT), etc. Firstly, the main crowding level was estimated according to the input region to determine the local search region. Secondly, some Test Cases (TCs) were generated by traditional ART algorithms in the local region. Finally, if no failure was found, a new local region was re-selected and some TCs were generated again until the first failure was found. The simulation results show that the effectiveness of the proposed CLART algorithm is about 20% higher than that of FSCS algorithm, and the efficiency is about 60% higher than that of FSCS algorithm. The experimental results indicate that the CLART algorithm can quickly locate the concentrated failure regions by searching several regions one by one to improve the effectiveness and efficiency.

In Wireless Sensor Network (WSN), disjoint multipath routing can lead to the long-path problem, and braided multipath routing can lead to the weakening of fault-tolerant performance. To address these issues, a multipath braided model and a fault-tolerant routing scheme based upon the model were proposed. Firstly, the intersection of multiple paths were quantified from the source to the destination by establishing corresponding multipath braided model, and then a probability model of fault tolerance was proposed to build the relationship between path interactivity and fault tolerance. Secondly, a fault-tolerant routing scheme was designed based on local intersection adjustment. Experimental results show that, when using the proposed model and its scheme in typical multipath routing schemes—Sequential Assignment Routing (SAR) and Energy Efficient Fault-tolerant Multipath Routing (EEFTMR), the data transfer success rate can be improved effectively. In addition, it also has good performance in the network throughput and energy consumption.

Since Markov chain model based software testing does not consider the software structural information and has low ability of path coverage and fault detection, a new software testing model called software hierarchical testing model was proposed based on the combination of statistical testing and Markov chain model based testing. The software hierarchical testing model contains the interaction between software and external environment, and also describes the internal structural information of software. Besides, the algorithm for generating test data set was put forward:firstly, the test sequences conforming to the actual usage of software were generated; then the input data which covered software internal structure was generated for the test sequences. Finally, in the comparison experiments with software testing based on Markov chain, the new model satisfies the software testing sufficiency and improves the test data set's ability of path coverage and fault detection.

To solve the Oracle problem of method sequence call in object-oriented software testing, a method of metamorphic relations constructing for object-oriented software testing based on algebraic specification was proposed. Firstly, metamorphic relations constructing criteria for object-oriented testing was defined based on the algebraic specification. Then the normal form metamorphic relations constructing method in the Generating a Finite number of Test cases (GFT) algorithm was improved according to these criteria. Finally, the improved method was verified through constructing IntStack class metamorphic relations. The experimental results showed that, compared with the normal form metamorphic relations constructing method, the metamorphic relations redundancy was reduced by 66% at the same mutation score. The results indicate that the new method has a low metamorphic relations redundancy and improves the efficiency of software testing.

To solve the problem of detecting human hand in complex background based on traditional camera, a fast, automatic method was proposed which can accurately detect and track foreground human fingertips by using Kinect camera. This method firstly used a combined vision-based information to roughly extract the hand region, then, by taking advantage of depth information, a bare hand could be successfully segmented without connecting to background. Subsequently, the fingertips of that bare hand could be extracted by using minimum circle and curvature relationship on the hand boundary. Finally, to improve the detecting accuracy, the fingertips were optimized by using Kalman filter. The experimental results show that compared with existing method the algorithm can successfully track the 3D locations of fingertips under multiple hand poses and with much lower error rate.

To effectively capture the dynamic information of the gait and accelerate the authentication and identification, a novel gait recognition algorithm was presented in this paper, which employed the row mass vector of the Frame Difference Energy Image (FDEI) as the gait features. The gait contour images were extracted through the object detection, binarization, morphological process and connectivity analysis of the original images. Using the width of the contour images sequence, the quasi-periodicity analysis and the row mass vector of the frame difference image were obtained, then the Continuous Hidden Markov Model (CHMM) was employed to train and recognize the parameters of model. The proposed algorithm was applied to Central Asia Student International Academic (CASIA) gait database. The experimental results show that it can easily extract the features of the gait with low dimension, achieving fast recognition speed and high recognition rate, so it can be used for real-time gait recognition.

To realize automatic and quantitative assessment in home-based upper extremity rehabilitation for stroke, an Extreme Learning Machine (ELM) based prediction model was proposed to automatically estimate the Fugl-Meyer Assessment (FMA) scale score for shoulder-elbow section. Two accelerometers were utilized for data recording during performance of 4 tasks selected from shoulder-elbow FMA and 24 patients were involved in the study. Accelerometer-based estimation was obtained by preprocessing raw sensor data, extracting data features, selecting features based on Genetic Algorithm and ELM. Then 4 single-task models and a comprehensive model were built individually using the selected features. Results show that it is possible to achieve accurate estimation of shoulder-elbow FMA score from the analysis of accelerometer sensor data with a root mean squared prediction error value of 2.1849 points. This approach breaks through the subjective and time-consuming property of traditional outcome measures which rely on clinicians at hand and can be easily utilized in the home settings.