A performance evaluation framework for association mining in spatial data

The evaluation of the process of mining associations is an important and challenging problem in database systems and especially those that store critical data and are used for making critical decisions. Within the context of spatial databases we present an evaluation framework in which we use probability distributions to model spatial regions, and Bayesian networks to model the joint probability distribution and the structural relationships among spatial and non-spatial predicates. We demonstrate the applicability of the proposed framework by evaluating representatives from two well-known approaches that are used for learning associations, i.e., dependency analysis (using statistical tests of independence) and Bayesian methods. By controlling the parameters of the framework we provide extensive comparative results of the performance of the two approaches. We obtain measures of recovery of known associations as a function of the number of samples used, the strength, number and type of associations in the model, the number of spatial predicates associated with a particular non-spatial predicate, the prior probabilities of spatial predicates, the conditional probabilities of the non-spatial predicates, the image registration error, and the parameters that control the sensitivity of the methods. In addition to performance we investigate the processing efficiency of the two approaches.     

Carbohydrate moieties on the in vitro immunoreactivity of soy β-conglycinin

β-Conglycinin is a functional glycoprotein and one of the most important soybean allergens. The aim of the present research was to investigate the role of the N-glycans moieties of β-conglycinin on its in vitro immunoreactivity. The soy allergen was obtained by isoelectric precipitation from commercial soy protein isolate and was enzymatically deglycosylated by PNGase F (Peptide N-Glycosidase F EC 3.5.1.52). In order to optimize deglycosylation conditions different reaction times and allergen concentrations were tested. The extent of deglycosylation was estimated by SDS–PAGE, CZE, RP-HPLC, and MALDI-TOF MS analyses, which provided information related to changes in protein structure. The antigenicity of both native β-conglycinin and its deglycosylated form was evaluated by western-blotting and indirect ELISA employing polyclonal rabbit anti-soybean sera and horseradish peroxidase-labeled goat anti-rabbit IgG while the in vitro allergenicity was assessed by means of indirect competitive inhibition ELISA employing human sera (IgE) of soy allergics. β-Conglycinin was effectively deglycosylated by PNGase F. Data on immunological tests suggested that glycosyl moieties forming this glycoprotein might be involved in its immunoreactivity.     

A circulatory system approach for wireless sensor networks

One of the challenges in a military wireless sensor network is the determination of an information collection infrastructure which minimizes battery power consumption. The problem of determining the right information collection infrastructure can be viewed as a variation of the network design problem, with the additional constraints related to battery power minimization and redundancy. The problem in its generality is NP-hard and various heuristics have been developed over time to address various issues associated with it. In this paper, we propose a heuristic based on the mammalian circulatory system, which results in a better solution to the design problem than the state of the art alternatives.     

A regenerative multiple zone model for HCCI combustion

A new conserved scalar approach, the so-called regenerative multiple zone (RMZ) model, is introduced to simulate combustion in homogeneous charge compression ignition (HCCI) engines with significant products of combustion. In this approach, two conserved scalars are introduced, the mixture fraction Z and the initial exhaust gas fraction J, to determine uniquely the state of the reactive system as a function of the two conserved scalars and time. For the numerical solution of the HCCI combustion, the conserved scalar plane is divided into different zones, which represent homogeneous reactors with constant initial exhaust gas level. Particularly, the zones are created based on the distribution of the initial exhaust gases and are mixed and regenerated at every time step during combustion in order to account for the history effects which are due to the finite rate chemistry. A proper methodology to create and initialize the new zones during the combustion, the so-called zone creation strategy (ZCS), is also proposed. For validation, the RMZ model is implemented in the 2DRD code, which is a computational fluid dynamics code that solves the governing equations for a two-dimensional reaction-diffusion problem. Initially, the consistency of the new model is validated in a one-dimensional reaction-diffusion (RD) case. Subsequently, the necessity for a proper zone creation strategy is demonstrated by a two-dimensional RD case. Next, a parametric study is performed to investigate the sensitivity of the new model on the maximum number of zones that is used. Finally, the limitations as well as the advantages of the RMZ model are discussed.     

Novel Approaches to Probabilistic Neural Networks Through Bagging and Evolutionary Estimating of Prior Probabilities

In this contribution, novel approaches are proposed for the improvement of the performance of Probabilistic Neural Networks as well as the recently proposed Evolutionary Probabilistic Neural Networks. The Evolutionary Probabilistic Neural Network’s matrix of spread parameters is allowed to have different values in each class of neurons, resulting in a more flexible model that fits the data better and Particle Swarm Optimization is also employed for the estimation of the Probabilistic Neural Networks’s prior probabilities of each class. Moreover, the bagging technique is used to create an ensemble of Evolutionary Probabilistic Neural Networks in order to further improve the model’s performance. The above approaches have been applied to several well-known and widely used benchmark problems with promising results.      

Visual tracking using the Earth Mover's Distance between Gaussian mixtures and Kalman filtering

In this paper, we demonstrate how the differential Earth Mover's Distance (EMD) may be used for visual tracking in synergy with Gaussian mixtures models (GMM). According to our model, motion between adjacent frames results in variations of the mixing proportions of the Gaussian components representing the object to be tracked. These variations are computed in closed form by minimizing the differential EMD between Gaussian mixtures, yielding a very fast algorithm with high accuracy, without recurring to the EM algorithm in each frame. Moreover, we also propose a framework to handle occlusions, where the prediction for the object's location is forwarded to an adaptive Kalman filter whose parameters are estimated on line by the motion model already observed. Experimental results show significant improvement in tracking performance in the presence of occlusion.     

Distributional-based texture classification using non-parametric statistics

Texture classification is an important problem in image analysis. In the present study, an efficient strategy for classifying texture images is introduced and examined within a distributional-statistical framework. Our approach incorporates the multivariate Wald–Wolfowitz test (WW-test), a non-parametric statistical test that measures the similarity between two different sets of multivariate data, which is utilized here for comparing texture distributions. By summarizing the texture information using standard feature extraction methodologies, the similarity measure provides a comprehensive estimate of the match between different images based on graph theory. The proposed “distributional metric” is shown to handle efficiently the texture-space dimensionality and the limited sample size drawn from a given image. The experimental results, from the application on a typical texture database, clearly demonstrate the effectiveness of our approach and its superiority over other well-established texture distribution (dis)similarity metrics. In addition, its performance is used to evaluate several approaches for texture representation. Even though the classification results are obtained on grayscale images, a direct extension to color-based ones can be straightforward.

Fast and effective characterization for classification and similarity searches of 2D and 3D spatial region data

We propose a method for characterizing spatial region data. The method efficiently constructs a k-dimensional feature vector using concentric spheres in 3D (circles in 2D) radiating out of a region's center of mass. These signatures capture structural and internal volume properties. We evaluate our approach by performing experiments on classification and similarity searches, using artificial and real datasets. To generate artificial regions we introduce a region growth model. Similarity searches on artificial data demonstrate that our technique, although straightforward, compares favorably to mathematical morphology, while being two orders of magnitude faster. Experiments with real datasets show its effectiveness and general applicability.     

Comparative analysis of a twin-class M-QAM transmission system for wireless video applications

This paper presents a unique set of techniques to support reliable and efficient video transmission over mobile channels. The transmission system is comprised of an M level Quadrature Amplitude Modulation (QAM) technique. A twin class uniform and non-uniform partitioned M-QAM system is used to transport a compressed video bitstream which is partitioned to match the bit-error sensitivity of the transmitted symbol in terms of mapping in the constellation diagram and picture quality. Video partitioning based on a separation of the Variable Length Coded (VLC) Discrete Cosine Transforms (DCT) coefficients within each block is considered for constant bitrate transmission (CBR). Various scenarios for splitting the bitstream are investigated and their results are compared and analysed thoroughly. The performance of the transmission system is evaluated under Additive White Gaussian Noise (AWGN) conditions. The simulation results showed that the video partition strategy results in a significantly higher quality of the reconstructed video data.