Advances in Pattern Recognition Algorithms, Architectures, and Devices

Over the last decade, tremendous advances have been made in the general area of pattern recognition techniques, devices, and algorithms. We have had the distinct pleasure of witnessing this remarkable growth as evidenced through their dissemination in the previous Optical Engineering special sections we have jointly edited— January 1998, March 1998, May 2000, and January 2002. Twenty-six papers were finally accepted for this latest special section, encompassing the recent trends and advancements made in many different areas of pattern recognition techniques utilizing algorithms, architectures, implementations, and devices. These techniques include matched spatial filter based recognition, hit-miss transforms, invariant pattern recognition, joint transform correlator JTC based recognition, morphological processing based recognition, neural network based recognition, wavelet based recognition, fingerprint and face recognition, data fusion based recognition, and target tracking, as well as other techniques. These papers summarize the work of 70 researchers from eight countries

Smart Energy Management of Multiple Full Cell Powered Applications

In this research project the University of South Alabama research team has been investigating smart energy management and control of multiple fuel cell power sources when subjected to varying demands of electrical and thermal loads together with demands of hydrogen production. This research has focused on finding the optimal schedule of the multiple fuel cell power plants in terms of electric, thermal and hydrogen energy. The optimal schedule is expected to yield the lowest operating cost. Our team is also investigating the possibility of generating hydrogen using photoelectrochemical (PEC) solar cells through finding materials for efficient light harvesting photoanodes. The goal is to develop an efficient and cost effective PEC solar cell system for direct electrolysis of water. In addition, models for hydrogen production, purification, and storage will be developed. The results obtained and the data collected will be then used to develop a smart energy management algorithm whose function is to maximize energy conservation within a managed set of appliances, thereby lowering O/M costs of the Fuel Cell power plant (FCPP), and allowing more hydrogen generation opportunities. The Smart Energy Management and Control (SEMaC) software, developed earlier, controls electrical loads in an individual home to achieve load management objectives such that the total power consumption of a typical residential home remains below the available power generated from a fuel cell. In this project, the research team will leverage the SEMaC algorithm developed earlier to create a neighborhood level control system

Multiclass Object Detection with Single Query in Hyperspectral Imagery Using Class-Associative Spectral Fringe-Adjusted Joint Transform Correlation

We present a deterministic object detection algorithm capable of detecting multiclass objects in hyperspectral imagery (HSI) without any training or preprocessing. The proposed method, which is named class-associative spectral fringe-adjusted joint transform correlation (CSFJTC), is based on joint transform correlation (JTC) between object and nonobject spectral signatures to search for a similar match, which only requires one query (training-free) from the object\u27s spectral signature. Our method utilizes class-associative filtering, modified Fourier plane image subtraction, and fringe-adjusted JTC techniques in spectral correlation domain to perform the object detection task. The output of CSFJTC yields a pair of sharp correlation peaks for a matched target and negligible or no correlation peaks for a mismatch. Experimental results, in terms of receiver operating characteristic (ROC) curves and area-under-ROC (AUROC), on three popular real-world hyperspectral data sets demonstrate the superiority of the proposed CSFJTC technique over other well-known hyperspectral object detection approaches

A Robust Fringe-Adjusted Joint Transform Correlator for Efficient Object Detection

The fringe-adjusted joint transform correlation (FJTC) technique has been widely used for real-time optical pattern recognition applications. However, the classical FJTC technique suffers from target distortions due to noise, scale, rotation and illumination variations of the targets in input scenes. Several improvements of the FJTC have been proposed in the literature to accommodate these problems. Some popular techniques such as synthetic discriminant function (SDF) based FJTC was designed to alleviate the problems of scale and rotation variations of the target, whereas wavelet based FJTC has been found to yield better performance for noisy targets in the input scenes. While these techniques integrated with specific features to improve performance of the FJTC, a unified and synergistic approach to equip the FJTC with robust features is yet to be done. Thus, in this paper, a robust FJTC technique based on sequential filtering approach is proposed. The proposed method is developed in such a way that it is insensitive to rotation, scale, noise and illumination variations of the targets. Specifically, local phase (LP) features from monogenic signal is utilized to reduce the effect of background illumination thereby achieving illumination invariance. The SDF is implemented to achieve rotation and scale invariance, whereas the logarithmic fringe-adjusted filter (LFAF) is employed to reduce the noise effect. The proposed technique can be used as a real-time region-of-interest detector in wide-area surveillance for automatic object detection. The feasibility of the proposed technique has been tested on aerial imagery and has observed promising performance in detection accuracy

Multiple Object Detection in Hyperspectral Imagery Using Spectral Fringe-Adjusted Joint Transform Correlator

Hyperspectral imaging (HSI) sensors provide plenty of spectral information to uniquely identify materials by their reflectance spectra, and this information has been effectively used for object detection and identification applications. Joint transform correlation (JTC) based object detection techniques in HSI have been proposed in the literatures, such as spectral fringe-adjusted joint transform correlation (SFJTC) and with its several improvements. However, to our knowledge, the SFJTC based techniques were designed to detect only similar patterns in hyperspectral data cube and not for dissimilar patterns. Thus, in this paper, a new deterministic object detection approach using SFJTC is proposed to perform multiple dissimilar target detection in hyperspectral imagery. In this technique, input spectral signatures from a given hyperspectral image data cube are correlated with the multiple reference signatures using the classassociative technique. To achieve better correlation output, the concept of SFJTC and the modified Fourier-plane image subtraction technique are incorporated in the multiple target detection processes. The output of this technique provides sharp and high correlation peaks for a match and negligible or no correlation peaks for a mismatch. Test results using real-life hyperspectral data cube show that the proposed algorithm can successfully detect multiple dissimilar patterns with high discrimination

Smart Energy Management and Control for Fuel Cell Based Micro-Grid Connected Neighborhoods

Fuel cell power generation promises to be an efficient, pollution-free, reliable power source in both large scale and small scale, remote applications. DOE formed the Solid State Energy Conversion Alliance with the intention of breaking one of the last barriers remaining for cost effective fuel cell power generation. The Alliance’s goal is to produce a core solid-state fuel cell module at a cost of no more than $400 per kilowatt and ready for commercial application by 2010. With their inherently high, 60-70% conversion efficiencies, significantly reduced carbon dioxide emissions, and negligible emissions of other pollutants, fuel cells will be the obvious choice for a broad variety of commercial and residential applications when their cost effectiveness is improved. In a research program funded by the Department of Energy, the research team has been investigating smart fuel cell-operated residential micro-grid communities. This research has focused on using smart control systems in conjunction with fuel cell power plants, with the goal to reduce energy consumption, reduce demand peaks and still meet the energy requirements of any household in a micro-grid community environment. In Phases I and II, a SEMaC was developed and extended to a micro-grid community. In addition, an optimal configuration was determined for a single fuel cell power plant supplying power to a ten-home micro-grid community. In Phase III, the plan is to expand this work to fuel cell based micro-grid connected neighborhoods (mini-grid). The economic implications of hydrogen cogeneration will be investigated. These efforts are consistent with DOE’s mission to decentralize domestic electric power generation and to accelerate the onset of the hydrogen economy. A major challenge facing the routine implementation and use of a fuel cell based mini-grid is the varying electrical demand of the individual micro-grids, and, therefore, analyzing these issues is vital. Efforts are needed to determine the most appropriate means of implementing micro-grids and the costs and processes involved with their extended operation. With the development and availability of fuel cell based stand-alone power plants, an electrical mini-grid, encompassing several connected residential neighborhoods, has become a viable concept. A primary objective of this project is to define the parameters of an economically efficient fuel cell based mini-grid. Since pure hydrogen is not economically available in sufficient quantities at the present time, the use of reforming technology to produce and store excess hydrogen will also be investigated. From a broader perspective, the factors that bear upon the feasibility of fuel cell based micro-grid connected neighborhoods are similar to those pertaining to the electrification of a small town with a localized power generating station containing several conventional generating units. In the conventional case, the town or locality would also be connected to the larger grid system of the utility company. Therefore, in the case of the fuel cell based micro-grid connected neighborhoods, this option should also be available. The objectives of this research project are: To demonstrate that smart energy management of a fuel cell based micro-grid connected neighborhood can be efficient and cost-effective;To define the most economical micro-grid configuration; and, To determine how residential micro-grid connected fuel cell(s) can contribute to America's hydrogen energy future