Concerning low representation ability and long learning time for complex and variable environmental factors in greenhouses, a prediction method based on improved Deep Belief Network (DBN) combined with Empirical Mode Decomposition (EMD) and Gated Recurrent Unit (GRU) was proposed. Firstly, the temperature environment factor was decomposed by EMD, and then the decomposed intrinsic mode function and residual signal were predicted at different degrees. Secondly, glia was introduced to improve DBN, and the decomposition signal was used to multi-attribute feature extraction combined with illumination and carbon dioxide. Finally, the signal components predicted by GRU were added together to obtain the final prediction result. The simulation results show that compared with empirical decomposition belief network (EMD-DBN) and glial DBN-glial chains (DBN-g), the prediction error of the proposed method is reduced by 6.25% and 5.36% respectively, thus verifying its effectiveness and feasibility of predictions in greenhouse time series environment with strong noise and coupling.

As a new density-based clustering algorithm, clustering by fast search and find of Density Peaks (DP) algorithm regards each density peak as a potential clustering center when dealing with a single cluster with multiple density peaks, therefore it is difficult to determine the correct number of clusters in the data set. To solve this problem, a mixed density peak clustering algorithm namely C-DP was proposed. Firstly, the density peak points were considered as the initial clustering centers and the dataset was divided into sub-clusters. Then, learned from the Clustering Using Representatives algorithm (CURE), the scattered representative points were selected from the sub-clusters, the clusters of the representative point pairs with the smallest distance were merged, and a parameter contraction factor was introduced to control the shape of the clusters. The experimental results show that the C-DP algorithm has better clustering effect than the DP algorithm on four synthetic datasets. The comparison of the Rand Index indicator on real datasets shows that on the dataset S1 and 4k2_far, the performance of C-DP is 2.32% and 1.13% higher than that of the DP. It can be seen that the C-DP algorithm improves the accuracy of clustering when datasets contain multiple density peaks in a single cluster.

In view of the problems that upper limit of radiated peak power is low for continuous wave laser radar, which limits the maximum measurement range in the application of range and speed measurement, a waveform of modulated signal based on Golomb series was proposed, and the feasibility of simultaneously measuring target's range and speed in road environments by the method was studied. Firstly, the problem of low transmitted signal peak power that exists in continuous wave modulating method was analyzed by using a quasi-continuous, i.e., Pseudo random Noise (PN) code modulation as an example. Characteristics of Golomb series were discussed, and a modulation method based on Golomb series was proposed for raising the peak power of transmitted pulse. Then, a method for analyzing spectrum of Doppler signal modulated by Golomb series was discussed, as well as a data accumulation method for locating signal delay time, such that range and speed could be measured simultaneously. Finally, within the range of Doppler frequency that is generated by moving road targets in road environment, simulations were performed to verify the correctness of the proposed method. The experimental results show that Fast Fourier Transform (FFT) can be used for obtaining the frequency of Doppler signal even when the sampling frequency provided by the pulse series is much lower than the Nyquist frequency, thus largely increasing the peak power of single pulse under the condition that average transmission power keeps unchanged. Furthermore, data accumulation method can be used for locating laser pulse flight time by exploiting the non-equal interval property of Golomb series, ensuring both target range measurement and speed measurement from the same signal.

Aiming at the problem that the current classification algorithm has low generalization ability and insufficient precision, a combination classification model combining Adaboost algorithm and Back-Propagation (BP) neural network was proposed. Multiple neural network weak classifiers were constructed and weighted, which were linearly combined into a strong classifier. The improved Adaboost algorithm aimed to optimize the normalization factor. The sample weight update strategy was adjusted during the lifting process, to minimize the normalization factor, increasing the number of weak classifiers while reducing the error upper bound estimate was ensured, and the generalization ability and classification accuracy of the final integrated strong classifier was improved. A daily precipitation model of 6 sites in Jiangsu province was selected as the experimental data, and 7 precipitation models were established. Among the many factors influencing the rainfall, 12 attributes with large correlation with precipitation were selected as the forecasting factors. The results show that the improved Adaboost-BP combination model has better performance, especially for the site 58259, and the overall classification accuracy is 81%. Among the 7 grades, the prediction accuracy of class-0 rainfall is the best, and the accuracy of other types of rainfall forecast is improved. The theoretical derivation and experimental results show that the improvement can improve the prediction accuracy.

Aiming at the problems that the purpose of the meteorological observation data acquisition is weak, the redundancy of data is high, and the number of single values in the observation data interval is large, the precision of equivalence partitioning is low, an attribute reduction algorithm for Meteorological Observation data Interval-value based on Genetic Algorithm (MOIvGA) was proposed. Firstly, by improving the similarity degree of interval value, the proposed algorithm could be suitable for both single value equivalence relation judgment and interval value similarity analysis. Secondly, the convergence of the algorithm was improved by the improved adaptive genetic algorithm. Finally, the simulation experiments show that the number of the iterations of the proposed algorithm is reduced by 22, compared with the method which operated AGAv (Adaptive Genetic Attribute reduction) algorithm to solve the optimal value. In the time interval of 1 hour precipitation classification, the average classification accuracy of the MOIvGA (λ-Reduction in Interval-valued decision table based on Dependence) algorithm is 6.3% higher than that of RIvD algorithm; the accuracy of no rain forecasting is increased by 7.13%; at the same time, the classification accuracy can be significantly impoved by the attribute subset received by operating the MOIvGA algorithm. Therefore, the MOIvGA algorithm can increase the convergence rate and the classification accuracy in the analysis of interval value meteorological observation data.

Since the research on testing technology for JavaScript browser compatibility problems is insufficient, based on mutation testing method and the analysis on the compatibility of JavaScript in Web applications in major browsers, eighteen mutation operators was designed, and an automated testing tool named Compatibility Mutator was implemented. Compatibility Mutator analyze JavaScript syntax with Abstract Syntax Tree (AST), calls various browsers with Selenium WebDriver to run mutation testing automatically and concurrently. The experiments on 7 widely-used JavaScript frameworks showed that the proposed mutation operators could generate a certain amount of mutants, the mutation scores got from mutation testing on jQuery and YUI were 43.06% and 7.69% respectively. Experimental results prove that the proposed operators can trigger the compatibility issues effectively, and evaluate the completeness of test suite effectively in finding the browser compatibility issues.

To solve the problems of high cost and low echo signal utilization efficiency in recently proposed laser radar systems integrated with double optical receivers for measuring range and speed of road targets, a pulsed Doppler laser radar system based on Pseudo-random Noise (PN) code modulation was proposed. The possibility of measuring range and speed simultaneously by the system integrated with single optical receiver was studied. The performance of the proposed method was verified by computer simulation. Firstly, the system model of vehicle laser radar was demonstrated and the existing problem for measuring range and speed by the model was analyzed when it works in pulsed mode. Then, the schematic diagram method for range and speed measurement by analyzing electric signal output from single optical heterodyne receiver was discussed, i.e. the correlation function of the electric signal and local modulation codes was computed for obtaining light flight time, and then target range; the spectrum of the electric signal was computed by non-uniformly sampled signal spectrum analysis method for obtaining Doppler frequency, and then the target speed. Finally, the stability of the proposed method for range and speed measurement was verified by computer simulation. The experimental results show that the method achieves stable range and speed measurement in road environments. Compared to direct detection system, the sensitivity of measurement is improved over 10 dB, which has no relation to echo arrival time and amount of Doppler frequency.

With the stringent resources and distributed nature in wireless sensor networks, fault diagnosis of sensor nodes faces great challenges. In order to solve the problem that the existing approaches of diagnosing sensor networks have high false alarm ratio and considerable computation redundancy on nodes, a new fault detection mechanism based on Cumulative Sum Chart (CUSUM) and neighbor-coordination was proposed. Firstly, the historical data on a single node were analyzed by CUSUM to improve the sensitivity of fault diagnosis and locate the change point. Then, the fault nodes were detected though judging the status of nodes by the data exchange between neighbor nodes. The experimental results show that the detection accuracy is over 97.7% and the false alarm ratio is below 2% when the sensor fault probability in wireless sensor networks is up to 35%. Hence, the proposed algorithm has a high detection accuracy and low false alarm ratio even in the conditions of high fault probabilities and reduces the influence of sensor fault probability clearly.

To solve the problem of excessive communication energy consumption caused by large number of sensor nodes and high redundant sensor data in wireless meteorological sensor network, a Data Preprocessing Model based on Joint Sparsity (DPMJS) was proposed. By combining the meteorological forecast value with every cluster head's value in Wireless Sensor Network (WSN), DPMJS was used to compute a common portions to process sensor data. A data collection framework based on distributed compressed sensing was also applied to reduce data transmission and balance energy consumption in cluster network; data measured in common nodes was recovered in sink node, so as to reduce data communication radically. A suitable method to sparse the abnormal data was also designed. In simulation, using DPMJS can enhance the data sparsity by exploiting spatio-temporal correlation efficiently, and improve data recovery rate by 25%; compared with compressed sensing, data recovery rate is improved by 46%; meanwhile, the abnormal data processing can recovery data successfully by high probability of 96%. Experimental results indicate that the proposed data preprocessing model can increase efficiency of data recovery, reduce the amount of transmission significantly, and prolong the network lifetime.

Due to the space-time continuity of the physical attributes, such as temperature and illumination, high spatio-temporal correlation exists among the sensed data in the high-density Wireless Sensor Network (WSN). The data redundancy produced by the correlation brings heavy burden to network communication and shortens the networks lifetime. A Clustered Data Collection Framework (CDCF) based on prediction model was proposed to explore the data correlation and reduce the network traffic. The framework included a time series prediction model based on curve fitting least square method and an efficient error control strategy. In the process of data collection, the clustered structure considered the spatial correlation, and the time series prediction model investigated the temporal correlation existing in sensed data. The experimental simulation proves that CDCF used only 10%—20% of the amount of raw data to finish the data collection of the networks in the relatively stable environment, and the error of the data restored in sink is less than the threshold value which defined by user.

The suitability analysis of matching area plays a significant role in the field of vision-based navigation. There are many feature indexes that can only unilaterally describe the suitability of matching area. An algorithm was proposed to integrate several feature indexes to solve conflicts among different feature indexes and provide a kind of method that can measure the suitable confidence and unsuitable confidence of a feature.And then the confidences were fused by using the Dempster-Shafer (DS) rules. At last the algorithm was verified by simulation experiment.

The Dezert-Smarandache Theory(DSmT) is more desirable than the D-S Theory in the case of solving conflicting evidence.However,the mass function of the main focal element is difficult to converge in many cases while applying DSmT.The new mass values were reconstructed to solve this problem.An improved DSmT was proposed so that the mass value of main element could quickly converge.Simulation results of target recognition based on 2D sequence images of airplanes demonstrate that the revised mass value of main focal element has better convergence to the desired threshold and consequently the task of target recognition is accomplished more precisely.