Khalid J. Siddiqui

Microsoft Excel Software Usage for Teaching Science and Engineering Curriculum

In this article, our main objective is to present the use of Microsoft Software Excel 2007/2003 for teaching college and university level curriculum in science and engineering. In particular, we discuss two interesting and fascinating examples of interactive applications of Microsoft Excel targeted for undergraduate students in: 1) computational physics and physics education; and 2) computer and medical sciences. We demonstrate the tremendous amount of computational power of the most recent Microsoft Excel 2007 Software, which is employed here to perform simulations of a projectile (may be a missile) launched from an airplane so as to hit a target on the ground using the simplest assumption of no air resistance during the projectile motion in air and that of rolling of nine dice with six surfaces. For projectile motion, kinematic equations based on Newtons laws of uniform motion have been employed to simulate projectile trajectory. We are going to prove through a plot of vertical distance as a function time that the projectile motion undergoes a parabolic path. However, modification in the simulation equations of the projectile motion, which includes the effect of air resistance, change in gravitational force with altitude, curvature in the surface of Earth, Coriolis force due to Earths spin motion about its axis of rotation, and wind speed, will be discussed in very near future. We will also plot a graph of normalized total score versus the maximum score for rolling of nine dice like in casino games.

Knowledge based system for weather information processing and forecasting

Using physical observations from the satellite imagery and the meteorological information a knowledge based weather information processing and forecasting system (KB/WIS) is designed the characteristic features extracted from the weather images are used to represent various weather patterns. The system is statistically trained to characterize, interpret, and forecast weather patterns. The system proposed is a composite of five components, namely: image acquisition, image preprocessing and enhancement, feature extraction and selection, weather knowledge base, and weather INference engine (WINE). Complete architecture of the KB/WIS system including its application and algorithms is described.

Feature selection using a proximity-index optimization model

Abstract One of the recurring issues in pattern recognition problems has been feature extraction and selection. This paper addresses this issue from a different pespective. Without assuming any particular classificaiton algorithm, it first suggests that one extract as much information as conveniently possible in several pattern-information domains. This paper later suggests applying the proposed Proximity-Index method, to select a significantly smaller, yet optimal feature subset. This method is formally described and is successfully applied to a waveform classification problem. The features selected by the algorithm are used to classify ten signal classes and produce a very encouraging recognition performance of 87.00% on 200 samples. This method is computationally inexpensive and particularly useful for large data set problems.

Pattern recognition and chemometrics for spectral classification

The broader definition of chemometrics includes methods such as pattern recognition (PR) and signal/image processing for noninvasive analysis and interpretation of data. These methods are among the most powerful tools currently available for noninvasively examining spectroscopic and other chemical data. Using spectral data, these systems have found a variety of applications employing analytical techniques for gas chromatography, fluorescence IR or NMR spectroscopy, etc. An advantage of PR approaches is that they make no a priori assuniption regarding the stmcture of the spectra. However, a majority of these systems rely on hunianjudgment for parameter selection and classification of spectra. Generally a spectral pattern recognition (SPR) problem is considered as a group of several subproblems. We considered a SPR problem as a group of five subproblems: spectra acquisition, feature extraction, feature selection, spectra organization, and spectra classification. One of the basic issues in PR approaches is to determine and measure the discriminatory features useful for successful classification. A spectral pattern classification system, combining spectral feature extraction and selection, and decision-theoretic approaches, is developed. It is shown how such a system can be used for analysis of large data analysis, warehousing, and interpretation.

Distributed-knowledge-based spectral processing and classification system for instruction and learning

This paper develops a distributed knowledge-based spectral processing and classification system which functions in one of two modes, executive and assistant. In the executive mode the system functions as a stand-alone system, automatically performing all the tasks from spectral enhancement, feature extraction and selection, to spectral classification and interpretation using the optimally feasible algorithms. In the assistant mode the system leads the user through the entire spectral processing and classification process, allowing a user to select appropriate parameters, their weights, knowledge organization method and a classification algorithm. Thus, the latter mode can also be used for teaching and instruction. It is shown how novice users can select a set of parameters, adjust their weights, and examine the classification process. Since different classifiers have various underlying assumptions, provisions have been made to control these assumptions, allowing users to select the parameters individually and combined, and providing facilities to visualize the interrelationships among the parameters.

Pattern recognition and image processing for environmental monitoring

Pattern recognition (PR) and signal/image processing methods are among the most powerful tools currently available for noninvasively examining spectroscopic and other chemical data for environmental monitoring. Using spectral data, these systems have found a variety of applications employing analytical techniques for chemometrics such as gas chromatography, fluorescence spectroscopy, etc. An advantage of PR approaches is that they make no a prior assumption regarding the structure of the patterns. However, a majority of these systems rely on human judgment for parameter selection and classification. A PR problem is considered as a composite of four subproblems: pattern acquisition, feature extraction, feature selection, and pattern classification. One of the basic issues in PR approaches is to determine and measure the features useful for successful classification. Selection of features that contain the most discriminatory information is important because the cost of pattern classification is directly related to the number of features used in the decision rules. The state of the spectral techniques as applied to environmental monitoring is reviewed. A spectral pattern classification system combining the above components and automatic decision-theoretic approaches for classification is developed. It is shown how such a system can be used for analysis of large data sets, warehousing, and interpretation. In a preliminary test, the classifier was used to classify synchronous UV-vis fluorescence spectra of relatively similar petroleum oils with reasonable success.

Educational Technology Eroding National Borders

Businesses are continuously forced to reeducate and train their new workforce before they can be productive. The existing employees also need to improve their skills or retrain for promotion/growth. To cater to this breed of students ”schooling business” is also faced with an inescapable demand to design new programs/courses. The growth in educational technology is also influencing academic institutions to redefine their endeavors in terms of producing learning while providing instructions. With the growing demand for higher education such endeavors are crossing the physical boundaries of many countries. We are using online and hybrid learning models to attract traditional, distance and international students. A major component of this model is the web-based course management system that provides an environment for teaching/learning and interaction 24/7. This model optimally combines interactive-classroom, web-based lectures and traditional instructions and has successfully offered courses at SUNY-Fredonia with students registering from around the globe. This model is presented and the opportunities and challenges of web technologies in education are discussed.

Modeling Mendel's Laws on Inheritance in Computational Biology and Medical Sciences

The current research article is based on a simple and practical way of employing the computational power of widely available, versatile software MS Excel 2007 to perform interactive computer simulations for undergraduate/graduate students in biology, biochemistry, biophysics, microbiology, medicine in college and university classroom setting. To accomplish this important motive, we developed the necessary computer algorithm, which used a built-in pseudo-random number generating function in MS Excel 2007, to computer model two basic Mendels Laws of heredity for plant and animal species. We performed more than 18,000 computer simulations to investigate the behavior of dominant and recessive genes to verify two basic Mendels Laws of heredity. Our simulation work corroborates the experimental observations of Mendels research on inheritance in Pisum hybrid species. When we compare our results of simulated data with that of experiments done on Drosophila melanogaster, fruit fly extensively being used as a model organism to study genetics and development, an exceedingly good agreement between the simulated and the experimental data has been observed for the F2 generation.

Classification of synchronous fluorescence of petroleum oils

A pattern classification system for the identification of UV-visible synchronous fluorescence of petroleum oils is developed. The system is a composite of three phases, namely, feature extraction, feature selection and pattern classification. These phases are briefly described, focusing particularly on the classification method. A method called successive feature elimination process (SFEP) is used for feature selection and a proximity index classifier (PIC) is developed for classification. The feature selection method extracts as many features from spectra as conveniently possible and then applies the SFEP process to remove the redundant features. From the remaining features a significantly smaller feature subset is selected that enhances the recognition performance of the PIC classifier. The SFEP and PIC methods are formally described. These methods are successfully applied to the classification of UV-visible synchronous fluorescence spectra. The features selected by the algorithm are used to classify twenty different sets of petroleum oils. The system was trained on the design set on which the recognition performance was 100%. The performance on the testing set was over 93% by successfully identifying 28 out of 30 samples in six classes. This performance is very encouraging. In addition, the method is computationally inexpensive and is equally useful for large data set problems as it always partitions the problem into a set of two class problems.

Environmental control using multiple-criteria decision making

Various aspects of controlling the environment can be structured in a form of multiple-criteria decision making. The combination ofniathematical modeling, decisioninaking, and the use ofpattern recognition in the environmental modeling and management of complex systems is discussed from a historical perspective. Of particular interest is the use of objective space technology in modeling multiple criteria decisionmaking. This paper presents a model and solution algorithm for such type ofdecision problem using a multiple objective linear program. Several applications for environmental control can be modeled using this methodology.