Collision-free spatial hash functions for structural analysis of billion-vertex chemical bond networks

State-of-the-art molecular dynamics (MD) simulations generate massive datasets involving billion-vertex chemical bond networks, which makes data mining based on graph algorithms such as K-ring analysis a challenge. This paper proposes an algorithm to improve the efficiency of ring analysis of large graphs, exploiting properties of K-rings and spatial correlations of vertices in the graph. The algorithm uses dual-tree expansion (DTE) and spatial hash-function tagging (SHAFT) to optimize computation and memory access. Numerical tests show nearly perfect linear scaling of the algorithm. Also a parallel implementation of the DTE + SHAFT algorithm achieves high scalability. The algorithm has been successfully employed to analyze large MD simulations involving up to 500 million atoms.     

Specialized supply firms, property rights and firm boundaries

The proper specification of intellectual property rights (IPRs)is a delicate and controversial matter. In this paper, we considerone specialized context in which IPRs can add to efficiency.We build on contributions of both ‘firm capabilities’scholars (e.g. Teece, Pisano et al.) and ‘property rights’economists (e.g. Hart) to show that IPRs can affect efficiencyby influencing the location of technological innovation. Usinga simple set up, where the key choice is whether a technology-intensiveinput will be supplied by an independent firm or produced in-house,we analyze how the choice is affected by the strength of IPRsand by the existence (and nature) of information spillovers.Specifically, we show that when the supply relationship is likelyto produce new information of value to the supplier, strongerproperty rights favor independent suppliers over vertical integration.An important implication of our model (backed by empirical casestudies) is that strong IPRs therefore encourage investmentsin specialized firms with strong ‘firm capabilities’in the area of innovative input supply. IPRs therefore may playa role—along with multiple other factors—in thelocation of firm boundaries in some cases. This contributionto the viability of small, specialized firms, with their superiorability to innovate in some cases, must be taken into accountin evaluating recent criticisms of over-fragmented IPR ownership(i.e. the ‘anticommons’ problem). It also contributesto an understanding of IPRs in the ‘post-Chandlerian’economy, where smaller, specialized firms play a prominent role.     

Satellite imagery based adaptive background models and shadow suppression

Accurate segmentation of foreground objects in video scenes is critical for assuring reliable performance of vision systems for object tracking and situational awareness in outdoor scenes. Most existing techniques for background modeling and shadow suppression require that a number of parameters be “hand-tuned” based on environmental conditions. This paper presents two contributions to overcome such limitations. First, we develop and demonstrate a satellite imagery based approach for selecting appropriate background and shadow models. It is shown that the illumination conditions (i.e. cloud cover) of a scene can be reliably inferred from visible satellite images in the local region of the camera. The second contribution presented in the paper is introduction and evaluation of a Hybrid Cone-Cylinder Codebook (HC3) model which combines an adaptive efficient background model with HSV-color space shadow suppression into a single coherent framework. The structure of the HC3 model allows for seamless fusion of the satellite data. We are thereby able to exploit the fact that, for example, shadows are more pronounced on sunny days than cloudy days, allowing for more sensitive detection. The paper presents a set of experiments using day long sequences of videos from an operational surveillance system testbed. Results of these experimental analyses quantitatively illustrate the benefits of using satellite imagery to inform and adaptively adjust background and shadow modeling.     

Change detection for moving object segmentation with robust background construction under Wronskian framework

Although background subtraction techniques have been used for several years in vision systems for moving object detection, many of them fail to provide good results in presence of noise, illumination variation, non-static background, etc. A basic requirement of background subtraction scheme is the construction of a stable background model and then comparing each incoming image frame with it so as to detect moving objects. The novelty of the proposed scheme is to construct a stable background model from a given video sequence dynamically. The constructed background model is compared with different image frames of the same sequence to detect moving objects. In the proposed scheme the background model is constructed by analyzing a sequence of linearly dependent past image frames in Wronskian framework. The Wronskian based change detection model is further used to detect the changes between the constructed background scene and the considered target frame. The proposed scheme is an integration of Gaussian averaging and Wronskian change detection model. Gaussian averaging uses different modes which arise over time to capture the underlying richness of background, and it is an approach for background building by considering temporal modes. Similarly, Wronskian change detection model uses a spatial region of support in this regard. The proposed scheme relies on spatio-temporal modes arising over time to build the appropriate background model by considering both spatial and temporal modes. The results obtained by the proposed model is found to provide accurate shape of moving objects. The effectiveness of the proposed scheme is verified by comparing the results with those of some of the existing state of the art background subtraction techniques on public benchmark databases. We found that the average F-measure is significantly improved by the proposed scheme from that of the state-of-the-art techniques.     

Multi-objective optimal reactive power dispatch considering voltage stability in power systems using HFMOEA

This paper proposes a new hybrid fuzzy multi-objective evolutionary algorithm (HFMOEA) based approach for solving complex multi-objective, mixed integer nonlinear problems such as optimal reactive power dispatch considering voltage stability (ORPD-VS). In HFMOEA based optimization approach, the two parameters like crossover probability (P_C) and mutation probability (P_M) are varied dynamically through the output of a fuzzy logic controller. The fuzzy logic controller is designed on the basis of expert knowledge to enhance the overall stochastic search capability for generating better pareto-optimal solution. Two detailed case studies are presented: Firstly, the performance of HFMOEA is tested on five benchmark test problems such as ZDT1, ZDT2, ZDT3, ZDT4 and ZDT6 as suggested by Zitzler, Deb and Thiele; Secondly, HFMOEA is applied to multi-objective ORPD-VS problem. In both the case studies, the optimization results obtained from HFMOEA are analysed and compared with the same obtained from two versions of elitist non-dominated sorting genetic algorithms such as NSGA-II and MNSGA-II in terms of various performance metrics. The simulation results are promising and confirm the ability of HFMOEA for generating better pareto-optimal fronts with superior convergence and diversity.     

Land‐use classification of multispectral aerial images using artificial neural networks

During the past decade, there have been significant improvements in remote sensing technologies, which have provided high‐resolution data at shorter time intervals. Considerable effort has been directed towards developing new classification strategies for analysing this imagery, but the use of artificial intelligence‐based analysis techniques has been somewhat limited. The aim of this study was to develop an artificial neural network (ANN)‐based technique for the classification of multispectral aerial images for land use in agricultural and environmental applications. The specific land‐use classes included water, forest, and several types of agricultural fields. Multispectral images at a 1‐m resolution were obtained for the state of Georgia, USA from a Geographic Information Systems (GIS) data clearinghouse. These false‐colour images contained green, red and infrared true‐colour information. Three approaches were used for the preparation of the inputs to the ANN. These included histograms of the pixel intensities, textural parameters extracted from the image, and matrices of the pixels for spatial information. A probabilistic neural network was used. Seven hundred images were used for model development and 175 for independent model evaluation. The overall accuracy for the evaluation data set was 74% for the histogram approach, 71% for the spatial approach and 89% for the textural approach. The evaluation of ANNs based on various combinations of all three approaches did not show an improvement in accuracy. We also found that some approaches could be used selectively for certain classes. For example, the textural approach worked best for forest classes. For future studies, edge detection prior to classification, with more careful selection of each class, should be included for land‐use classification of multispectral images.     

Centered inclined slot coupling between waveguides with coplanar axes

The article presents the method of finding the complete equivalent circuit of two waveguides with coplanar axes coupled through a centered inclined slot in the common broad wall. The variational method based on dyadic Green's function is used for finding the parameters of the equivalent circuit. A cosinusoidal aperture field distribution is assumed. Considerable mathematical simplification is resulted from replacing the centered inclined slot by an equivalent magnetic dipole. Coupling slot characteristics are deduced, including resonant length, dominant mode scattering in both the waveguides. Numerical and experimental results for resonant lengths and scattering parameters are presented over a range of tilt angles, frequencies, and waveguide dimensions. These results have significant applications in linear waveguide arrays and coupler design. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2011.     

On the Roles of Eye Gaze and Head Dynamics in Predicting Driver's Intent to Change Lanes

Driver behavioral cues may present a rich source of information and feedback for future intelligent advanced driver-assistance systems (ADASs). With the design of a simple and robust ADAS in mind, we are interested in determining the most important driver cues for distinguishing driver intent. Eye gaze may provide a more accurate proxy than head movement for determining driver attention, whereas the measurement of head motion is less cumbersome and more reliable in harsh driving conditions. We use a lane-change intent-prediction system (McCall et al., 2007) to determine the relative usefulness of each cue for determining intent. Various combinations of input data are presented to a discriminative classifier, which is trained to output a prediction of probable lane-change maneuver at a particular point in the future. Quantitative results from a naturalistic driving study are presented and show that head motion, when combined with lane position and vehicle dynamics, is a reliable cue for lane-change intent prediction. The addition of eye gaze does not improve performance as much as simpler head dynamics cues. The advantage of head data over eye data is shown to be statistically significant (p < 0.01) 3 s ahead of lane-change situations, indicating that there may be a biological basis for head motion to begin earlier than eye motion during "lane-change"-related gaze shifts.