Abstract The use of complex network analysis has gathered momenta in both theoretical and empirical studies. Network clustering plays an important role in network analysis. This paper models the network clustering task as an optimization problem. A novel discrete particle swarm optimization algorithm is introduced to solve the modeled optimization problem. Particle swarm optimization is a stochastic searching algorithm, and it cannot avoid... The use of complex network analysis has gathered momenta in both theoretical and empirical studies. Network clustering plays an important role in network analysis. This paper models the network clustering task as an optimization problem. A novel discrete particle swarm optimization algorithm is introduced to solve the modeled optimization problem. Particle swarm optimization is a stochastic searching algorithm, and it cannot avoid prematurity. To improve the performance of the algorithm, a new particle status update principle is defined, a novel turbulence operation is proposed to improve exploration, and a novel local search strategy is developed to enhance exploitation. Extensive experiments on both synthetic and real-world networks are carried out. Several state-of-the-art network-clustering approaches are compared with the proposed method. The experiments demonstrate that the proposed method is effective and promising. Read More Read Less

Abstract A large number of noise are usually carried in the original RFID data and need to be cleaned up before further processing. Outlier detection is an effective method for RFID data cleaning. In this paper, a point probability data model was proposed to describe the uncertain RFID data streams. The wavelet density threshold was incorporated in this method to adaptively detect the... A large number of noise are usually carried in the original RFID data and need to be cleaned up before further processing. Outlier detection is an effective method for RFID data cleaning. In this paper, a point probability data model was proposed to describe the uncertain RFID data streams. The wavelet density threshold was incorporated in this method to adaptively detect the outliers in the sliding window by utilizing the multi-scale and multi-granularity characteristics of wavelet density estimation. The process of outlier detection for RFID data streams was discussed in depth. It was shown that, compared with the existing kernel density estimation algorithm, our method had higher efficiency and precision for the uncertain data streams of RFID data cleaning. Read More Read Less

Abstract This paper proposes a new ARGWishart( Adaptive Region Growing-Wishart) classification algorithm for digital images. It integrates the adaptive region growing algorithm and Wishart maximum likelihood classification algorithm for difficulties that arise from selecting training samples, and instability of the final classification accuracy on PolSAR(Polarimetric Synthetic Aperture Radar) image. At first, the main diagonal elements of the polarimetric coherency matrix are extracted from... This paper proposes a new ARGWishart( Adaptive Region Growing-Wishart) classification algorithm for digital images. It integrates the adaptive region growing algorithm and Wishart maximum likelihood classification algorithm for difficulties that arise from selecting training samples, and instability of the final classification accuracy on PolSAR(Polarimetric Synthetic Aperture Radar) image. At first, the main diagonal elements of the polarimetric coherency matrix are extracted from the image. A few are presentative and clear sample data are selected manually. An improved adaptive region growing algorithm is developed by introducing the OTSU method for the image. After this, we can obtain enhanced training samples by which each class center can be calculated. Then, unknown samples are classified by the Wishart maximum likelihood classification algorithm. Thus, we can finish the classification on the polarimetric SAR image. For comparison, we use PALSAR(Phased Array Type L-Band Synthetic Aperture Radar) and RADARSAT-2 images as data sources and apply SVM(Support Vector Machine), the Wishart supervised classifier, and the ARG-Wishart classifier to conduct comparative research on two experimental plots, namely, Lishui County in Jiangsu Province and Jiangning District in Nanjing City. The results show that the ARG-Wishart classifier can achieve better results than the SVM and the Wishart classifier in terms of overall classification accuracy and Kappa coefficient. Read More Read Less

Abstract In this study,mean shift algorithm and region merging were combined to automatically segment a digital feather image and remove the noise in digital images more effectively for segmentation of a feather quill and a feather leaf. First, the mean shift algorithm employed to calculate the convergence value of each pixel can obtain a filtered smoothened image; then, setting region merging criteria were... In this study,mean shift algorithm and region merging were combined to automatically segment a digital feather image and remove the noise in digital images more effectively for segmentation of a feather quill and a feather leaf. First, the mean shift algorithm employed to calculate the convergence value of each pixel can obtain a filtered smoothened image; then, setting region merging criteria were used to merge filtered images. Finally, the threshold segmentation algorithm was used to extract the feather quill. Experimental results indicated that the mean shift algorithm could be used to denoise the digital feather images, thereby achieving a high value of evaluation index and improved visual quality.Moreover, this algorithm can accurately segment texture image and effectively filter out unwanted background texture information to segment texture image with unclear color information. Read More Read Less