Balakrishna Gokaraju

Change detection analysis of tornado disaster using conditional copulas and Data Fusion for cost-effective disaster management

The up-to-date results are presented from an ongoing study of the Data Fusion of multi-temporal and multi-sensor satellite datasets for near real time damage and debris assessment after a tornado disaster event. The space-borne sensor datasets comprising of: (i) C-band SAR dataset from RADARSAT-2; (ii) Multi-Spectral (MS) optical dataset including NIR from RapidEye; (iii) MS and panchromatic dataset of Advanced Linear Imaging (ALI), are studied for multi-sensor data fusion. A combined approach of multi-polarized radiometric and textural feature extraction, and statistical learning based feature classification is devised for fine tuning of the complex and generalized change detection model. We also investigated the use of multi-variate conditional copula as a classifier technique, by formulating the change and no-change as a binary-class classification problem in this study. The classification results from the above technique are used for assessment of damage and debris cover after the tornado disaster event. The performance of the above approach yields a very significant Kappa accuracy up to 75%. A 10-fold cross validation strategy is used for quantitative analysis of the performance of the classification model. This study will be further extended for modelling the effect of incidence angle discrepancies or climatic condition variances, which will address the heterogeneity factor in terms of local statistics of the dataset.

Data fusion of multi-source satellite data sets for cost-effective disaster management studies

A common approach to multisource data fusion is to aggregate the information in a stacked vector and treat it as a unique dataset. The statistical classifiers used in these data fusion approaches are also transformed by integrating the contextual information from neighboring pixels, to improve the accuracy of a fuzzy-logic-based fusion scheme. A decision level fusion approach was developed by Gokaraju et. al, 2012, which combines statistical methods and machine learning techniques. Here, each data sample is integrated through separate classifiers such as empirical methods and support vector machines (SVMs) and then used a Probabilistic Neural Network (PNN) to fuse the decisions for a unified consensus decision. The data fusion approach consists of either pixel-level or feature-level data fusion in combination with machine learning techniques for classification. The intermediate results of the disaster management studies, such as levee land-slide and tornado debris assessment using data fusion techniques, are presented in this paper. For levee landslide studies, we used the multi-temporal datasets of air-borne synthetic aperture radar sensor (UAVSAR). For Tornado disaster studies, we used multi-source and multi-temporal datasets of both synthetic aperture radar sensor (RADARSAT-2) and multispectral sensor (RapiEye) datasets. The results of data fusion approach outperformed the non-data fusion techniques in both studies with kappa accuracies of 82.8% and 72%.

Intelligent Approaches for Modeling and Optimizing HVAC Systems’ Energy Use

Advanced energy management control systems (EMCS), or building automation systems (BAS), offer an excellent means of reducing energy consumption in heating, ventilating, and air conditioning (HVAC) systems while maintaining and improving indoor environmental conditions. This can be achieved through the use of computational intelligence and optimization. This research will evaluate model-based optimization processes (OP) for HVAC systems utilizing MATLAB, genetic algorithms and self-learning or self-tuning models (STM), which minimizes the error between measured and predicted performance data. The OP can be integrated into the EMCS to perform several intelligent functions achieving optimal system performance. The development of several self-learning HVAC models and optimizing the process (minimizing energy use) will be tested using data collected from the HVAC system servicing the Academic building on the campus of NC A&T State University. Intelligent approaches for modeling and optimizing HVAC systems are developed and validated in this research. The optimization process (OP) including the STMs with genetic algorithms (GA) enables the ideal operation of the building’s HVAC systems when running in parallel with a building automation system (BAS). Using this proposed optimization process (OP), the optimal variable set points (OVSP), such as supply air temperature (Ts), supply duct static pressure (Ps), chilled water supply temperature (Tw), minimum outdoor ventilation, reheat (or zone supply air temperature, Tz), and chilled water differential pressure set-point (Dpw) are optimized with respect to energy use of the HVAC’s cooling side including the chiller, pump, and fan. HVAC system component models were developed and validated against both simulated and monitored real data of an existing VAV system. The optimized set point variables minimize energy use and maintain thermal comfort incorporating ASHRAE’s new ventilation standard

Gabor filter based entropy and energy features for basic scene recognition

Our research objective is to develop a supervised learning based hierarchical classification framework built upon Gabor features. Specifically, we experimented on the Oliva Tor alba data-set from the Corel stock photo library. This data set consists of 2688 natural and artificial scene color images, of size (256X256X3) each, from 8 sub-categories. In this paper, we restrict our goal to categorization of images into natural and artificial groups. The methodology consists of feature extraction and binary classification stages. In this, we propose to use the complex Gabor (CG) filter based global features (depending on the overall layout and scene structure, but invariant to object details) from each image. Initially, in the feature extraction process, a 20-CG filter is applied to images for producing Gabor output images in terms of spatial frequency and orientation. Guided by Gabor uncertainty principle, we choose the resolution of spatial frequencies and orientations. At each of these coordinates, energy and entropy features are computed from images real and imaginary components. We investigate the viability.ofusing the global features with a SVM classifier for basic scene categorization. Next, after applying the PCA based dimensionality reduction, a 2 class SVM discriminant function based on quadratic kernel is applied to the Gabor features for image classification. By using a 10-fold cross validation, we obtained a classification accuracy of 95.79 % and kappa accuracy of 0.9148.

Homeland security education: Managerial versus nonmanagerial market perspectives of an academic program

ABSTRACT The authors discuss the findings of a market study that preceded the offering of an academic program in homeland security. The university disseminated a mail survey to gain data for analysis of variance testing of several hypotheses regarding market perceptions of the intended homeland security program offering. Stratification involved segregating responses into categories of managers versus nonmanagers. Four statistically significant findings were observed regarding perceptions of course titles, academic program characteristics, degree types, and degree titles. The outcomes of the study are described within the article, and recommendations regarding the implications of the market study are discussed.

Comprehensive review of evolution of satellite sensor specifications against speedup performance of pattern recognition algorithms in remote sensing

The objectives of this study are as follows: (i) Discuss the necessity of HPC in remote sensing community towards contemporary scientific solution requirements; (ii) Investigate the speedup in performance of the template matching algorithm with FFT parallelization using hybrid Central Processing Units (CPUs)/Graphics Processing Units (GPUs); (iii) Apply the speedup algorithms for detection of real-time man-made structures such as buildings from remote sensing datasets, for constructing a 3-Dimensional city modelling.