A new particle optimization algorithm with dynamic topology is proposed based on small world network. The technique imitates the dissemination of information in a small world network by dynamically updating the neighborhood topology of the Particle Swarm Optimization (PSO). In comparison with other four classic topologies and two PSO algorithms based on small world network, the proposed dynamic neighborhood strategy is more effective in coordinating the exploration and exploitation ability of PSO. Simulations demonstrated that the convergence of the swarms is faster than its competitors. Meanwhile, the proposed method maintains population diversity and enhances the global search ability for a series of benchmark problems.

VANETs are becoming more and more popular as a way to increase the traffic safety and comfort. The IEEE 802.11p standard, especially the 802.11p MAC protocol, has attracted much attention as part of the WAVE protocol in VANETs. Safety applications, as one of the main categories of applications in VANETs, is very challenging for the design of a MAC protocol due to their low latency and high reliability requirements. The CCH interval is also a key parameter for the 802.11p MAC protocol since it can affect the performance of safety message delivery significantly. In this paper, a simulation based evaluation is proposed to evaluate the performance of the 802.11p MAC protocol with various vehicle densities and CCH interval settings. The evaluation results indicate that the 802.11p MAC protocol can be improved via extending the CCH interval. However, the reliability is still very challenging due to high collision rates.

In this paper, geometrical analysis is used to extract novel straight line segment features from the wings around the peaks of the Hough Transform (HT). Based on these features, a practical segment detection method is proposed which has the ability to determine complete straight line segment parameters including the location of the center, length, slope and the Euclidean distance to the origin. The proposed method does not rely on edge point verification in the image space, i.e., the complete set of segment features are determined only using the information embodied in the HT data. The proposed method can distinguish between highly collinear straight line segments. Segment detection is robust to disturbing edge points, especially ones collinear with the object. A predefined collinear segment resolution that provides a theoretical criterion to determine straight line contiguity is derived. Image processing and analysis experiments show consistent robust performance.

Africa has witnessed an incredible boom in the number of mobile subscribers in mobile networks across Africa. With the rise in demand for capacity in cellular networks, greater pressure is being placed on the network planner. Customer segmentation has been traditionally used in cellular network planning to better understand customer demands and needs. By developing more accurate profiling methods, operators are in a better position to market products and forecast future demand more accurately. This work looks at the extraction of frequency patterns from traffic signals originating from a typical mobile network using multi-scale analysis. By studying the features extracted, the classification of typical subscribers in the network can be conducted more efficiently and with greater granularity.

The distance between a straight line and a straight line segment in the image space is proposed in this paper. Based on this distance, the neighborhood of a straight line segment is defined and mapped into the parameter space to obtain the parameter space neighborhood of the straight line segment. The neighborhood mapping between the image space and parameter space is a one to one reversible map. The mapped region in the parameter space is analytically derived and it is proved that it can be efficiently approximated by a quadrangle. The proposed straight line segment neighborhood technique for the HT outperforms conventional straight line neighborhood methods currently used with existing HT variations. In contrast to the straight line neighborhoods used in existing HT variations, the proposed straight line segment neighborhood has several advantages including: 1) the detection error of the proposed neighborhood is not affected by the length of the straight line segments; 2) a precision requirement in the image space described using the proposed distance can be explicitly resolved using the proposed formulation; 3) the proposed neighborhood has the ability to distinguish between segments belonging to the same straight line. A variety of experiments are executed to demonstrate that the proposed neighborhood has a variety of interesting properties of high practical value.

A new active fire event detection algorithm for data collected with the Spinning Enhanced Visible and Infrared Imager (SE-VIRI) sensor, based on the extended Kalman filter, is introduced. Instead of using the observed temperatures of the spatial neighbours of a pixel to detect anomalous temperatures, the new algorithm only considers previous observations at the current pixel. The algorithm harnesses the Kalman filter to obtain a prediction of the expected brightness temperature at a given location, which is then compared to the actual SE-VIRI observation. An adaptive threshold is used to determine whether the observed difference is indicative of a potential fire event. Initial tests show that the performance of this method is comparable to that of the EUMETSAT FIR product.

Texture features derived using Haralick's Gray-Level Cooccurrence Matrix (GLCM) are by far the most popular in urban remote sensing research - but are they the best features for every application? In order to select the most appropriate texture algorithm for an automated informal settlement classification system, we performed an experiment to compare the performance of the GLCM with that of other texture features. The performance of a texture feature is measured by computing the classification accuracy achieved on a supervised set of images spread over 8 settlement classes, focusing on informal and low-cost housing. The results show that GLCMs perform very well, but that Local Binary Pattern texture features have a small advantage in this classification problem.

This paper presents a unifying review of a learning-based framework for kernel-based attributed graph matching. The framework, which includes as special cases the RKHS Interplator-Based Graph Matching (RIGM) and Interpolator-Based Kronecker Product Graph Matching (IBKPGM) algorithms, incorporates a general approach where no assumption is made about the adjacency structure of the graphs to be matched. Corresponding pairs of attributed adjacency matrices and attribute vectors of an input and reference graph are used as the input–output training set of a constrained multi-input multi-output multi-variable mapping to be learned. It is shown that a Reproducing Kernel Hilbert Space (RKHS) based interpolator can be used to infer this mapping. Partially constraining the inferred mapping by the generation of additional consistency input–output training pairs and the use of polynomial feature augmentation lead to improved performance. The proposed learning-based framework avoids the explicit calculation of compatibility values.

A novel Projections Onto Convex Sets (POCS) graph matching algorithm is presented. Two-way assignment constraints are enforced without using elaborate penalty terms, graduated nonconvexity, or sophisticated annealing mechanisms to escape from poor local minima. Results indicate that the presented algorithm is robust and compares favorably to other well-known algorithms.

We present an algorithm for performing attributed graph matching. This algorithm is derived from a generalized framework for describing functionally expanded interpolators which is based on the theory of reproducing kernel Hilbert spaces (RKHS). The algorithm incorporates a general approach to a wide class of graph matching problems based on attributed graphs, allowing the structure of the graphs to be based on multiple sets of attributes. No assumption is made about the adjacency structure of the graphs to be matched.