In view of the difficulties in real-time monitoring of component contents in rare earth extraction process and the high time consumption and memory consumption of existing component content detection methods, a dynamic monitoring system for element component content based on time sequence characteristics of solution images was designed. Firstly, the image acquisition device was used to obtain the time sequence image of the extraction tank solution. Considering the color characteristics of the extracted liquid and the incompleteness of single color space, the time sequence characteristics of the image were extracted in the color space of the fusion of HSI (Hue, Saturation, Intensity) and YUV (Luminance-Bandwidth-Chrominance) by using Principal Component Analysis (PCA) method, and combined with the production index, the Whale Optimization Algorithm (WOA) based Least Squares Support Vector Machine (LSSVM) classifier was constructed to judge the status of the working condition. Secondly, when the working condition was not optimal, the color histogram and color moment features of the image were extracted in HSV (Hue, Saturation, Value) color space, and an image retrieval system was developed with the linear weighted value of the mixed feature difference between solution images as the similarity measurement to obtain the value of component content. Finally, the test of the mixed solution of the praseodymium/neodymium extraction tank was carried out, and the results show that this system can realize the dynamic monitoring of element component content.

For the problems that third party counting institution does not meet the decentralization and de-trusting characteristics of blockchain and is lack of credibility,a blockchain electronic counting scheme based on the Practical Byzantine Fault Tolerance (PBFT) algorithm was proposed. Firstly,the centerless counting model was built in the distributed environment,and the counting node was determined by the credibility level of the node. Secondly,the consensus of pending ballots was formed based on PBFT. Thirdly,the minimum number of honest nodes in PBFT was set as the threshold for threshold signature,and the threshold signature was only formed by results satisfying the threshold. Finally, the results satisfying the trusted state were recorded in the blockchain account book. Test and analysis results show that only when the honest nodes exceed two-thirds,the PBFT is satisfied,and the obtained counting result is credible.

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.

For the routing problem that nodes in mobile wireless sensor network, based on random moving model, a low energy consumption routing strategy named DTVC (Data Transmission based on Virtual Currency) was proposed. When two nodes met each other, the buyer and the seller determined the price of data message and selected relay node according to node attributes and data message attributes. To improve the network performance, the number of the data message's replicas was controlled according to node type and data messages in the queue were sorted according to each message's delay tolerance. The nodes in the network were divided into source nodes and relay nodes for each data message and only the source node could copy it. The smaller the delay tolerance was, the greater the priority was. In order to reduce the energy consumption in the network, the data message in the storage queue that had been transmitted successfully was deleted according to the message broadcast by the sink node. The simulation results on Matlab showed that the data delivery rate of DTVC was increased by at least 2.5%, and the average number of replicas was reduced by at least 25% than those of FAD (the message Fault tolerance-based Adaptive data Delivery scheme), FLDEAR (Fuzzy-Logic based Distance and Energy Aware Routing protocol) and a routing algorithm based on energy consumption optional evolution mechanism.

Concerning the fairness problem of multiple Device-to-Device (D2D) users reusing the spectrum resources allocated to cellular subscribers, a power allocation algorithm based on max-min fairness was proposed under the premise of guaranteeing the rate of cellular users. First, the nonconvex optimization problem was transformed into a Difference between Convex functions (DC) programming problem, then the global optimization algorithm of convex approximation and the bisection algorithm were used to achieve power optimization of D2D. Simulation results show that compared with the global optimization algorithm which only uses convex approximation, the proposed algorithm has better convergence and maximizes the bottleneck rate of D2D users.

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 issues of failing to pinpoint the edge information accurately and lacking texture detail of image by using integer order differential or 0-1-order fractional differential mask operators in digital image processing, a new 1-2-order edge detection operator based on Laplacian operator was proposed. Deduced from the definition of Riemann-Liouville (R-L),the 1-2-order fractional differential had the advantage in enhancing high-frequency signal and reinforcing medium frequency signal. The simulation results demonstrate that the proposed operator can take an higher recognition rate on the subjective recognition, and it's better at extracting the edge information, especially for the image with rich texture detail in the smooth region with little change of gray scale. Objectively, the integrated location error rate is 7.41% which is less than that of integer order differential operators (a minimum of 10.36%) and 0-1-order differential operator (a minimum of 9.97%). Quantitative indicators show the new fractional operator can effectively improve the positioning accuracy of the edge, and the proposed operator is particularly suitable for edge detection with high frequency information.

When drift-scan CCD shooting the sky where bright stars are in the filed of view, because of the frame transfer feature, the trailing smear will appear throughout the star image. A fast smear trailing elimination algorithm was proposed by analyzing the imaging mechanism. The method firstly decreased the background non-uniformity by fitting the background, then located smear trailing by calculating the mean gray value of every column in star image and comparing the mean gray values before and after fitting, finally eliminated smear trailing by setting the trailing pixel with the mean gray value after fitting. The experimental results show that the smear trailing is removed completely and the mean deviation of background is apparently reduced, moreover the consuming time of this method is only 20% of that of traditional smear elimination method, which proves the validity of the method.

To overcome the limitations of traditional face recognition methods for single sample, an improved gradient face algorithm named Orthogonal Gradient Binary Pattern (OGBP), which is robust to variations of illumination, face expression and posture, was proposed. Firstly, the features of the image samples were extracted by orthogonal gradient binary pattern. Then the feature vectors of each direction were concatenated into the general feature vector for face recognition. Finally the Principle Component Analysis (PCA) method was used to reduce dimensions and the nearest neighbor classifier was used for face image classification and recognition. Experimental results on YALE and AR face database indicate that the proposed method is simple, effective and better than the original gradient face algorithm, and also has better performance in face description for single sample.

Concerning the low accuracy of face recognition systems, a face recognition algorithm based on Histograms of Nonsubsampled contourlet Oriented Gradient (HNOG) was proposed. Firstly, a face image was decomposed with Non-Subsampled Contourlet Transform (NSCT) and the coefficients were divided into several blocks. Then histograms of oriented gradient were calculated all over the blocks and used as face features. Finally, multi-channel nearest neighbor classifier was used to classify the faces. The experimental results on YALE , ORL and CAS-PEAL-R1 face databases show that the descriptor HNOG is discriminative, the feature dimension is small and the feature is robust to variations of illumination, face expression and position.

In order to denoise image more effectively, an improved Contourlet transform denoising algorithm based on gray relational degree was proposed. On one hand, considering the gray relational degree and inter-scale from the high-frequency sub-band and low frequency sub-band by Contourlet transform, the Bayes threshold was improved; On the other hand, in order to achieve the purpose of adaptive denoising, the characteristics of Contourlet coefficients were used to improve the compromising threshold function. The experimental results show that the proposed algorithm can denoise image effectively, get higher PSNR and better visual quality, and has a good practicability.

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.

Concerning the problem of limited recognition rate caused by variations in position, illumination and expression in face recognition, an efficient face recognition method based on Nonsubsampled Contourlet Transform (NSCT) and Local Binary Pattern (LBP) was proposed. Firstly, a face image was decomposed with NSCT, and NSCT coefficients in different scales and various orientations were obtained; LBP operator was then used to get LBP feature maps by extracting local neighboring relationship from NSCT coefficients; Finally, feature maps were respectively divided into several blocks, the concatenated histograms, which were calculated over each block, were used as the face features. The experimental results using multi-channel nearest neighbor classifier based on Euclidean distance show that, the proposed method can improve the recognition rate effectively, and the extracted feature is robust to variations of illumination, face expression and position.

Concerning the defects of multi-scale threshold on directional information using Contourlet transform,a new adaptive threshold denoising algorithm was proposed, which was based on average energy of neighboring window. According to the distribution of the coefficients energy, the Contourlet coefficients were divided up into three areas. The noise could be reduced obviously by adjusting the threshold of these areas with different variables. In contrast with the wavelet threshold, Contourlet threshold and multi-scale threshold using Contourlet transform, the experimental results demonstrate that the new algorithm has superiority in Peak Signal-to-Noise Ratio (PSNR) and visual effect, which can maintain effectively the edges of the image.

To dynamically adjust the bandwidth price according to the change of channel quality so as to change the users' profit, an improved spectrum sharing model considering user's mobility was proposed．The improved model comprehensively considers the impact on the price of the offered bands by all secondary users' bandwidth requirement and channel quality; at the same time, the requested bandwidth gets constrained by price, and thus users' profit change with position. The simulation results indicate that users can gain maximum profit by choosing a optimal position in the moving process.

In the current research and design of spectrogram based on Field Programmable Gate Array (FPGA), the direct indication of the spectrogram is not able to reflect the detail variation of spectrum. To solve this problem, a method for the display and enhancement of spectrogram based on FPGA was proposed in this paper. With nonlinear transformation, the high-resolution gray image was compressed to low gray-resolution image, so the detail variation of spectrum would be better reflected. Meanwhile，human vision is less sensitive to the difference between gray-scale pixels than that of colors, so with pseudo-color processing of the gray images, the results are displayed through Video Graphics Array (VGA). The experimental results show that more detail variation of spectrum can be obtained by the method.

To realize the building and controlling of workflow patterns involving multiple instances, a solution was proposed from the perspective of network partition. The implementing method was discussed based on RTWD net proposed by HU Fei-hu, et al. in Patent China 201010114083.9. First, the sub-workflows involving multiple instances should be divided into a subnet. Then the related parameters of multiple instances were defined, and multiple instances were controlled based on it. The paper discussed the controlling of sequential, synchronous and asynchronous parallel workflow patterns involving multiple instances based on the method. Because the divided subnet keeps consistent with the definition of workflow model, multiple instances can be scheduled by original workflow engine, which simplifies the realization of multiple instance patterns.