In the two components of hybrid image segmentation algorithm, the initial segmentation cannot form the over-segmentation region sets with low wrong segmentation rate, while region merging lacks the label selection mechanism for region merging and the method of determining region merging stopping moment in this component commonly does not meet the scenario requirements. To solve the above problems, a Multi-level Region Information fusion based Hybrid image Segmentation algorithm (MRIHS) was proposed. Firstly, the improved Markov model was used to smooth the superpixel blocks, so as to form initial segmentation regions. Then, the designed region label selection mechanism was used to select the labels of the merged regions after measuring the similarity of the initial segmentation regions and selecting the region pairs to be merged. Finally, an optimal merging state was defined to determine region merging stopping moment. To verify MRIHS performance, comparison experiments between this algorithm with Multi-dimensional Feature fusion based Hybrid image Segmentation algorithm (MFHS), Improved FCM image segmentation algorithm based on Region Merging (IFRM), Inter-segment and Boundary Homogeneities based Hybrid image Segmentation algorithm (IBHHS), Multi-dimensional Color transform and Consensus based Hybrid image Segmentation algorithm (MCCHS) were carried out on Visual Object Classes (VOC), Cambridge-driving labeled Video database (CamVid) and the self-built river and lake inspection (rli) datasets. The results show that on VOC and rli datasets, the Boundary Recall (BR), Achievable Segmentation Accuracy (ASA), recall and dice of MRIHS are at least increased by 0.43 percentage points, 0.35 percentage points, 0.41 percentage points, 0.84 percentage points respectively and the Under-segmentation Error (UE) of MRIHS is at least decreased by 0.65 percentage points compared with those of other algorithms; on CamVid dataset, the recall and dice of MRIHS are at least improved by 1.11 percentage points, 2.48 percentage points respectively compared with those of other algorithms.

An Ultra WideBand (UWB)/ Pedestrian Dead Reckoning (PDR) fusion positioning algorithm with adaptive coefficient adjustment based on UWB error prediction was proposed in order to improve the UWB performance and reduce the PDR accumulative errors in the indoor Non-Line-Of-Sight (NLOS) positioning scenes and solve the UWB performance degradation caused by environmental factors. On the basis of the creative proposal of predicting the UWB positioning errors in complex environment by Support Vector Machine (SVM) regression model, UWB/PDR fusion positioning performance was improved by adding adaptive adjusted parameters to the conventional Extended Kalman Filter (EKF) algorithm. The experimental results show that the proposed algorithm can effectively predict the current UWB positioning errors in the complex UWB environment, and increase the accuracy by adaptively adjusting the fusion parameters, which makes the positioning error reduced by 18.2% in general areas and reduced by 48.7% in the areas with poor UWB accuracy compared with those of the conventional EKF algorithm, so as to decrease the environmental impact on the UWB performance. In complex scenes of both Line-Of-Sight (LOS) and NLOS including UWB, the positioning error per 100 meters is reduced from meter scale to decimeter scale, which reduces the PDR errors in NLOS scenes.

In the process of indoor positioning and navigation, a Bayesian network-based floor localization algorithm was proposed for the problem of large error of floor localization when only the pedestrian height displacement considered. Firstly, Extended Kalman Filter (EKF) was adopted to calculate the vertical displacement of the pedestrian by fusing inertial sensor data and barometer data. Then, the acceleration integral features after error compensation was used to detect the corner when the pedestrian went upstairs or downstairs. Finally, Bayesian network was introduced to locate the pedestrian on the most likely floor based on the fusion of walking height and corner information. Experimental results show that, compared with the floor localization algorithm based on height displacement, the proposed algorithm has improved the accuracy of floor localization by 6.81%; and compared with the detection algorithm based on platform, the proposed algorithm has improved the accuracy of floor localization by 14.51%. In addition, the proposed algorithm achieves the accuracy of floor localization by 99.36% in the total 1247 times floor changing experiments.

Aiming at the problem that the existing 3D model information hiding algorithms cannot effectively resist uneven compression, a multi-carrier low-density information hiding algorithm based on multiple fusion states was proposed. Firstly, multiple 3D models were positioned, oriented and stereotyped by translation and scaling. Secondly, the 3D models were rotated at different angles and merged by using the center point as merging point to obtain multiple fusion states. Thirdly, local height and Mean Shift clustering analysis were used to divide the energy of the vertices of the fusion state model, obtaining the vertices with different energies. Finally, by changing the vertex coordinates, the secret information changed by Arnold scrambling was quickly hidden in multiple fusion states and 3D models. Experimental results show that the proposed algorithm is robust against uneven compression attacks and has high invisibility.

Aiming at the problem that some 3D model-based information hiding algorithms are incompetent against combined attacks, a new strategy based on feature point labeling and clustering was proposed. Firstly, edge folding was adopted to achieve mesh simplification and all the vertexes were labeled in order by their energy level. Secondly, the ordered vertexes were clustered and re-ordered by using local height theory and Mean Shift clustering analysis. Lastly, hidden information and cover model carrier information were optimized, matched and modified by Logistic chaos mapping scrambling and genetic algorithm, completing the final hiding. The data in hiding areas were labeled and screened locally and globally according to the energy weight, which is good for the robustness and transparency of the algorithm. The experimental results show that, compared with 3D information hiding algorithms based on inscribed sphere and outer skeleton, the robustness of the proposed algorithm against single or joint attacks is significantly improved, and it also has the same degree of invisibility.

In the traditional indoor pedestrian positioning algorithm based on dead reckoning and Kalman filtering, there is a problem of cumulative error in the heading angle, which makes the positional error continue to accumulate continuously. To solve this problem, a pedestrian heading particle filter algorithm with indoor environment constraints was proposed to correct direction error. Firstly, the indoor map information was abstracted into a structure represented by line segments, and the map data was dynamically integrated into the mechanism of particle compensation and weight allocation. Then, the heading self-correction mechanism was constructed through the correlation map data and the sample to be calibrated. Finally, the distance weighting mechanism was constructed through correlation map data and particle placement. In addition, the particle filter model was simplified, and heading was used as the only state variable to optimize. And while improving the positioning accuracy, the dimension of state vector was reduced, thereby the complexity of data analysis and processing was reduced. Through the integration of indoor environmental information, the proposed algorithm can effectively suppress the continuous accumulation of directional errors. The experimental results show that, compared with the traditional Kalman filter algorithm, the proposed algorithm can significantly improve the pedestrian positioning accuracy and stability. In the two-dimensional walking experiment with a distance of 435 m, the heading angle error is reduced from 15.3° to 0.9°, and the absolute error at the end position is reduced from 5.50 m to 0.87 m.

The limited number and high similarity of learning clauses lead to limited historical information and imbalanced search tree. In order to solve the problems, a dynamic branching strategy of award and punishment was proposed. Firstly, the variables of every unit propagation were punished. Different penalty functions were established according to whether the variable generated a conflict and the conflict interval. Then, in the learning phase, the positive variables for the conflict were found according to the learning clauses, and their activities were nonlinearly increased. Finally, the variable with the maximum activity was chosen as the new branching variable. On the basis of the glucose3.0 algorithm, an improved dynamic algorithm of award and punishment named Award and Punishment 7 (AP7) was completed. The experimental results show that, compared with the glucose3.0 algorithm, the rate of cutting branches of AP7 algorithm is improved by 14.2%-29.3%, and that of a few examples is improved up to 51%. The running time of the improved AP7 algorithm is shortened more than 7% compared with the glucose3.0 algorithm. The branching strategy can efficiently reduce the size of search tree, make the search tree more balanced and reduce the computation time.

In order to reduce the cost and simplify the installation of Variable Refrigerant Volume (VRV) air-conditioning system, a communication system was constructed based on Power Line Carrier Communication (PLCC) technology. First, a solution based on narrowband PLCC technology was proposed according to the communication requirement of the system control. Then, the four-layer communication protocol including physical layer, Medium Access Control (MAC) layer, network layer and application layer was designed mainly from two aspects of channel access control and networking control. Three kinds of channel access algorithms based on CSMA/CA (Carrier Sense Multiple Access with Collsion Avoidance) design idea and a kind of network algorithm for star network topology were put forward. Last, the networking test, system communication test and anti-disturbance test for a 11-node VRV system were given. The test results show that the proposed algorithms and solution can satisfy the real-time control of VRV system and has strong ability of anti-disturbance. In addition, with the openness of the designed communication protocol, it can be modified according to different requirements and be applied to a variety of real-time control areas in short distance without relay such as smart home.

In order to fully use the structural information of the data, and compress the image data, the sparse constraints of the subspace (feedback) were applied to the object function of non-negative tensor factorization. Then this algorithm was used to reduce the dimension of the image sets. Finally, image classification was realized. The experimental results on the handwritten digital image database show that the proposed algorithm can effectively improve the accuracy of the image classification.