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The study proposes a new algorithm for change detection of SAR images based on segmentation to improve the accuracy of the SAR image change detection. The ratio method is used to acquire the difference image (DI). Then, the global dictionary is applied to address the image denoising problem. Finally, change mask is obtained by pulse-coupled neural network (PCNN). The results of the experiment show that the proposed method improves accuracy.

In the TD-SCDMA system, there is a problem that non-emergency voice services probability of dropped call is not ideal when we use the dynamic allocation algorithm. To solve this problem, we propose a new algorithm, which is called protect channel reservation queue movable boundary dynamic channel allocation. This algorithm set aside channels for dropped calls of non-emergency voice services service, in order to keep blocking probability for service, the introduction of no patient customers, combined with the data packet to borrow algorithms. Simulation results show that, comparing to the movable boundary algorithm, the proposed algorithm reduce the drop probability for non-emergency voice services, and maintain a variety of service blocking probability, reducing the packet loss probability data services.

In order to solve the problem of noise amplification, low contrast and image distortion in the process of medical image enhancement, a new algorithm is proposed which combines NSCT nonsubsampled contourlet transform and improved fuzzy contrast. The image is decomposed by NSCT. Firstly, linear enhancement method is used in low frequency coefficients; secondly the improved adaptive threshold function is used to deal with the high frequency coefficients. Finally, the improved fuzzy contrast is used to enhance the global contrast and the Laplace operator is used to enhance the details of the medical images. Experimental results show that the proposed algorithm can improve the image visual effects, remove the noise and enhance the details of medical images. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 7-14, 2015

In order to improve the prediction accuracy of busy telephone traffic which is influenced by multiple factors, this paper proposes a combined forecasting model which takes the influence of multiple factors into consideration and combines three models ——wavelet transform, autoregressive integrated moving average (ARIMA) model and least squares support vector machines (LSSVM) model, LSSVM is optimized by particle swarm optimization (PSO). Correlation analysis is firstly applied to the busy telephone traffic data to obtain the key factors which influence the busy telephone traffic. Then wavelet transform is used to decompose and reconstruct the telephone traffic data to get the low-frequency and high-frequency components. The low-frequency component is loaded into ARIMA model to predict, while the high-frequency component and the obtained key factors are loaded into PSO-LSSVM model to predict. Finally the forecasting result is achieved by the superposition of predictive values. The simulation results show that the proposed model has higher prediction accuracy and strong generalization ability.

This study presents a busy telephone traffic prediction model that combines wavelet transformation and least squares support vector machine. Firstly, decompose preprocessed telephone traffic data with Mallat algorithm and get low frequency component and high frequency component. Secondly, reconfigure each component and use LS_SVM model to predict each reconfigure one. Then the traffic can be achieved. The results of experiments have testified higher prediction accuracy and stability of this combined traffic prediction model.

In this paper, a technique is presented for the fusion of Panchromatic (PAN) and low spatial resolution multispectral (MS) images to get high spatial resolution of the latter. In this technique, we apply PCA transformation to the MS image to obtain the principal component (PC) images. A NSCT transformation to PAN and each PC images for N level of decomposition. We use FOCC as criterion to select PC. And then, we use the relative entropy as criterion to reconstruct high-frequency detailed images. Finally, we apply inverse NSCT to selected PC's low-frequency approximate image and reconstructed high- frequency detailed images to obtain high spatial resolution MS image. The experimental results obtained by applying the proposed image fusion method indicate some improvements in the fusion performance.

In order to get the change detection image.An unsupervised change detection algorithm for multi-temporal satellite image based on NSCT (non-subsampling contourlet transform) and k-means clustering is proposed in this paper. For each pixel in the log-ratio image, multi-scale and multi-direction feature vector is extracted by NSCT and the reconstruction of the log-ratio image is obtained. The threshold is produced by using the k-means clustering algorithm and can distinguish between the unchanged and the change region. Finally, the change detection map is achieved. Some satellite images are used to verify the proposed method and the results shows that it has a higher stability and accuracy against Gaussian and speckle noise than traditional algorithms.

A new method for image denoising based on the free distributed hypothesis test threshold (FDR) and the non-sub-sampled contourlet transform(NSCT) is proposed in this paper. This method firstly acquires the free distributed false discovery rate hypotheses test in statistics to set the threshold in the NSCT domain, and then removes the noise through soft threshold function, which doesn’t depend on the length of signal. The experimental results show that the proposed method can more effectively reduce Gaussian noise and improve the peak value signal-to-noise ratio in the remote sensing image; Meanwhile, this method utilizes the shift invariant of NSCT transform to inhibit the pseudo Gibbs distortion effect, and integrally preserves the texture and edge etc.. details’ information of the image, thus obviously ameliorate the visual effect of the image.

In this study, a new handoff strategy to improve the performance of wireless mobile networks is presented. It has been found form this study that the dropping probability of handoff calls is drastically reduced compared to the existing method of channel reservation strategy of handoff calls and the performance of new calls can be improved. The strategy is that the new call should be delayed then it can use the last idle channel. The time that all the channels are holding has been shortened. Opportunities of the new call and handoff call occupancy channel are increased; the scheme also takes into account the priority of different data types, and only when the high-priority data packets queue is empty, the data packets in low-priority data queue can be transmitted. Simulation results show that, to let the new voice call delay in the allowable range, can effectively reduce the dropping probability of handoff call and the blocking probability of high-priority data while improving the probability of new call to enter into the systems

Change detection techniques attempt to be used for remote sensing monitoring of invasive plants. A novel change detection method based on direction feature and RFLICM (an improved fuzzy C-means clustering) is proposed. Firstly, the difference image is acquired from multitemporal images. The pixel values of the difference image ​​are updated using the template of directional neighborhood based on direction feature. The final change detection map is achieved by clustering the pixel values of the difference image using RFLICM algorithm into two disjoint classes: changed and unchanged. The results obtained by experiment are compared with some other existing state of the art methods. It is observed that the proposed method outperforms the other methods. Finally, the proposed change detection technique is applied to remote sensing monitoring of invasive plants.

The frequency resources of mobile communication network are limited. In order to improve utilization of wireless resource, we used the cooperative hybrid artificial bee colony algorithm to solve the wireless channel assignment problem. The proposed algorithm used the dynamic step to balance local and global search capability, the dynamic step was cosine rule with the increasing times of iteration; The artificial bee colony were divided into two subgroups with different evolutionary strategy co-evolution, the algorithm was easy to jump out of local optimal solution in this way; The introduction of the single-individual selective mutation increased diversity of population and the speed of convergence. Simulation results show that: the proposed algorithm can be better to solve the wireless channel assignment.

In order to forecast mobile communication traffic quickly and accurately, Hidden Markov Models (HMM) is used to forecast busy hour traffic of wireless mobile communication forecasting in this paper. Because of the model having rigorous mathematical structure, reliable computing performance and the characteristics of describing the event, HMM becomes the ideal model for describing the traffic sequence. The experimental results shows that HMM model have higher precision and better stability compared with the method of SVM with DE-strategy in the area of forecasting mobile communication traffic.

Holiday traffic prediction is the foundation of the whole communication network planning. In order to predict the busy traffic accurately and ensure the stability of the network, a support vector regression machine (SVR) combined with the improved particle swarm optimization algorithm (MPSO) is proposed, an inertia weight and shrinkage factor is introduced in the algorithm. The proposed algorithm is used to predict the busy traffic in Mid-autumn day. Simulation result shows that, compared with SVR algorithm and the basic particle swarm optimization optimize SVR (PSO-SVR) method, MPSO-SVR algorithm has a higher prediction precision.

The prediction of holiday’s traffic has the characteristics of small historical sample size and strong nonlinear, which result in low prediction accuracy. Genetic algorithm (GA) is adopted in this paper to optimize the support vector regression machine (SVR) to forecast the busy traffic of Xinjiang in holidays and compared with the traditional SVR and the BP neural network. The result shows that the GA-SVR has a higher forecast precision and a less time-consuming, which is an effective method of busy traffic prediction.

In this paper, the characteristics of Xinjiang mobile communication network are analyzed, the results show that for a reliable mobile communication network, BS (base station) and base station control BSC (base station controller) monitoring system, cable and fiber optic cable monitoring system and mobile communications troubleshooting and dispatching system should be built. Combined with GIS, GPS, GSM and geographic database technology, positioning technology for different targets and detection on fault point are studied, the results indicate that precise positioning of static target can accurately locate the failure telecom equipment in the electronic map and provide data source for the realization of real-time monitoring and management system for telecom equipment based on GIS & wireless sensor network technology. GSM-based wireless messaging communications and real-time fast positioning of dynamic target is the key to the emergency repair vehicles monitoring and dispatch system. The proposed fault determination and fault point location method can provide fast accurately fault devices location with detailed geography information and prompt the efficiency of fault management and improve the reliability of telecom network services. The above research results are important basis on the design and implementation of the system.

Telephone traffic of busy hour is one of indicators of load capacity of telecommunication network, which has a significant meaning to dilate and modify the network. A good performance of predicting the monthly busy hour traffic load is cared about by the mobile operators. As a promising learning theory,support vector machine (SVM) has been studied and applied in a wide area, such as financial markets and weather forecast. In this paper, we use SVM to forecast monthly busy hour traffic load of two regions in Xinjiang. A good result has been achieved via an improved grid search method for the search of hyper-parameter of SVM.

Fuzzy Kohonen clustering networks (FKCN) are well known for clustering analysis (unsupervised learning and self-organizing). This classification of FKCN algorithm is a set of iterative procedures that suffer some major problems, for example its constringency rate is not too fast for a large amount of datasets. To overcome these defects, an efficient fuzzy Kohonen network algorithm is proposed in this paper, which can significantly reduce the computation time required to partition a dataset into desired clusters. By introducing the threshold values and fuzzy convergence operators in the network learning procedure to adjust the learning rates dynamically, the network convergence rate is greatly improved and the error rates of dataset cluster are significantly decreased. Experimental results show the new algorithm is on average three times faster than the original FKCN algorithm. We also demonstrate that the quality of the improved FKCN is better than the original FKCN algorithm.

In order to solve the problems of unreasonable cell carrier frequency configuration (CCFC) and insufficient wireless resources utilization in GSM network, the conception of the best CCFC number is introduced. By the statistical analysis of the large number of traffic data, find out the relationship between the CCFC and the busy traffic congestion rate, and then obtain the best carrier frequency configuration number in each traffic interval. According to the best carrier frequency configuration number, the best cell wireless utilization rate is obtained. This standard reflects the demand number of cell actual carrier frequency configuration, which provides a theoretical basis for the CCFC and the current network cell wireless utilization in the future.