Impedance studies of polycrystalline tin oxide

Polycrystalline SnO₂ samples prepared in the laboratory were irradiated with 2 MeV He ions having doses of 3.62 × 10¹⁵ ion/cm². The ac small-signal electrical data acquired for the polycrystalline SnO₂ in the frequency (f) range 100 Hz ≤ f ≤ 1 MHz and temperature (T) range 26°C ≤ T ≤ 100°C revealed one semicircular relaxation in the impedance plane for the He implanted sample. However, two semicircular relaxations were obtained in the same plane for the samples without He implantation. The He implantation indicated enhancement in the donor density in the polycrystalline SnO₂ as depicted via terminal conductance (or resistance). An erratum to this article can be found at     

High-dimensional MRI data analysis using a large-scale manifold learning approach

A novel manifold learning approach is presented to efficiently identify low-dimensional structures embedded in high-dimensional MRI data sets. These low-dimensional structures, known as manifolds, are used in this study for predicting brain tumor progression. The data sets consist of a series of high-dimensional MRI scans for four patients with tumor and progressed regions identified. We attempt to classify tumor, progressed and normal tissues in low-dimensional space. We also attempt to verify if a progression manifold exists—the bridge between tumor and normal manifolds. By identifying and mapping the bridge manifold back to MRI image space, this method has the potential to predict tumor progression. This could be greatly beneficial for patient management. Preliminary results have supported our hypothesis: normal and tumor manifolds are well separated in a low-dimensional space. Also, the progressed manifold is found to lie roughly between the normal and tumor manifolds.     

Feature selection for medical diagnosis : Evaluation for cardiovascular diseases

Machine learning has emerged as an effective medical diagnostic support system. In a medical diagnosis problem, a set of features that are representative of all the variations of the disease are necessary. The objective of our work is to predict more accurately the presence of cardiovascular disease with reduced number of attributes. We investigate intelligent system to generate feature subset with improvement in diagnostic performance. Features ranked with distance measure are searched through forward inclusion, forward selection and backward elimination search techniques to find subset that gives improved classification result. We propose hybrid forward selection technique for cardiovascular disease diagnosis. Our experiment demonstrates that this approach finds smaller subsets and increases the accuracy of diagnosis compared to forward inclusion and back-elimination techniques.     

Enhancing biometric recognition with spatio-temporal reasoning in smart environments

We discuss smart environments that identify and track their occupants using unobtrusive recognition modalities such as face, gait, and voice. In order to alleviate the inherent limitations of recognition, we propose spatio-temporal reasoning techniques based upon an analysis of the occupant tracks. The key idea underlying our approach is to determine the identity of a person based upon information from a track of events rather than a single event. We abstract a smart environment by a probabilistic state transition system in which each state records a set of individuals who are present in various zones of the smart environment. An event abstracts a recognition step, and the transition function defines the mapping between states upon the occurrence of an event. We express two forms of spatio-temporal reasoning in the form of transition functions: a track-based transition function and an error-correcting transition function. We also define the concepts of ‘precision’ and ‘recall’ to quantify the performance of the smart environment and provide experimental results to clarify the performance improvements from spatio-temporal reasoning. Our conclusion is that the state transition system is an effective abstraction of a smart environment and the application of spatial-temporal reasoning enhances the overall performance of a biometric recognition system.     

Hydrogen Effects in Metal Catalysts

The interaction of hydrogen with metals is the cause or basis of a host of phenomena ranging from the chemisorption of hydrogen on the surface, its dissolution in the metal, catalytic reactions involving hydrogen as a reactant or as an astoichiometric component, etc., to the formation of metal hydrides. Hydrogen -induced corrosion and hydrogen embrittlement of steel are well known in chemical process industry and metallurgy. Catalytic reactions on metal or metal-oxide catalysts in the presence of hydrogen, often under pressure, are some of the major chemical, petroleum, and petrochemical processes of today, e.g., ammonia, methanol, Fischer-Tropsch, Oxo and other syntheses, hydrogenation of oils and fats, catalytic reforming, hydrode-sulfurization/hydrotreating, hydrocracking, and hydrogenation/ dehydrogenation.     

Microstructure and abrasive wear study of (Ti,W)C-reinforced high-manganese austenitic steel matrix composite

A high-manganese austenitic steel matrix composite (Fe-1.42%C-16.02%Mn-0.447%Si-0.288%Al-0.34%Cr-0.028%S-0.25%P-3.02%Ti-3.0%W, all in wt.%) reinforced with in-situ (Ti,W)C was synthesized by conventional melting and casting route. It has been found that the concentration of Ti decreases whereas the concentration of tungsten increases from core to the periphery of the (Ti,W)C particulates. The abrasive wear resistance of as-cast (Ti,W)C-reinforced composite is better than that of the as-cast high-manganese austenitic steel matrix material.     

Peak Load Management in Electrolytic Process Industries

Electrolytic process, employed for manufacturing basic chemicals like caustic soda and chlorine, is highly energy intensive. Due to escalating costs of fossil fuels and capacity addition, the electricity cost has been increasing for the last few decades. Electricity intensive industries find it very difficult to cope up with higher electricity charges particularly with time-of-use (TOU) tariffs implemented by the utilities with the objective of flattening the load curve. Load management programs focusing on reduced electricity use at the time of utility's peak demand, by strategic load shifting, is a viable option for industries to reduce their electricity cost. This paper presents an optimization model and formulation for load management for electrolytic process industries. The formulation utilizes mixed integer nonlinear programming (MINLP) technique for minimizing the electricity cost and reducing the peak demand, by rescheduling the loads, satisfying the industry constraints. The case study of a typical caustic-chlorine plant shows that a reduction of about 19% in the peak demand with a corresponding saving of about 3.9% in the electricity cost is possible with the optimal load scheduling under TOU tariff.     

Intelligent Interactive Tutoring Systemfor Engineering Mechanics

Engineering education has traditionally been imparted through the lecture-tutorial-laboratory paradigm. Education technology in the last few years has tried to make teaching more effective by supplementing the chalkboard teaching with audiovisual aids like overhead∕slide projectors and videos. These are, however, passive teaching tools. Recent advancement in computer multimedia has brought in a new teaching medium. Multimedia education has several advantages; most noticeably, the instructions can be interactive and self-paced to meet the needs of each individual student. The writers have developed an Intelligent Interactive Tutoring System for engineering mechanics for the first-year engineering students at the Nanyang Technological University, Singapore. The main objective of this system was to supplement classroom teaching via intelligent interaction with the computer-based tutoring system. This system is designed to be a self-paced virtual tutor with intelligent, user-friendly interactions. It is intended to be different from the many commercially available computerized textbooks. The prototype system was tried out with about 200 students who had earlier failed the course in the first semester and repeated it in the second semester of academic year 1997–1998. The system received good response from the students. The full version was implemented on the Nanyang Technological University Intranet and the Internet for use by the regular class of more than 1,600 students in both academic years 1998–1999 and 1999–2000.     

Tungsten Lamps as an Affordable Light Source for Testing of Photovoltaic Cells

An improved Tungsten light source system for photovoltaic cell testing made from low-cost, commercially available materials is presented as an alternative to standard expensive testing equipment. In this work, spectral correction of the Tungsten light source is achieved by increasing the color temperature to ??5200 K using inexpensive commercially available filters. Spectral measurements of the enhanced light source reveal that a better spectrum match towards the solar spectrum is achieved than what has been previously demonstrated. Specifically, the improved solar spectrum match is achieved by substantial filtering of the infrared range. The proposed setup is used to evaluate the performance of both silicon and organic based photovoltaic cells.