State University of New York System

About: State University of New York System is a education organization based out in Albany, New York, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 54077 authors who have published 78070 publications receiving 2985160 citations.

Stochastic Finite Elements: A Spectral Approach

Abstract: Representation of stochastic processes stochastic finite element method - response representation stochastic finite element method - response statistics numerical examples.

The Fatigue Severity Scale: Application to Patients With Multiple Sclerosis and Systemic Lupus Erythematosus

Abstract: • Fatigue is a prominent disabling symptom in a variety of medical and neurologic disorders. To facilitate research in this area, we developed a fatigue severity scale, subjected it to tests of internal consistency and validity, and used it to compare fatigue in two chronic conditions: systemic lupus erythematosus and multiple sclerosis. Administration of the fatigue severity scale to 25 patients with multiple sclerosis, 29 patients with systemic lupus erythematosus, and 20 healthy adults revealed that the fatigue severity scale was internally consistent, correlated well with visual analogue measures, clearly differentiated controls from patients, and could detect clinically predicted changes in fatigue over time. Fatigue had a greater deleterious impact on daily living in patients with multiple sclerosis and systemic lupus erythematosus compared with controls. The results further showed that fatigue was largely independent of self-reported depressive symptoms and that several characteristics could differentiate fatigue that accompanies multiple sclerosis from fatigue that accompanies systemic lupus erythematosus. This study demonstrates (1) the clinical and research applications of a scale that measures fatigue severity and (2) helps to identify features that distinguish fatigue between two chronic medical disorders.

Optimal Filtering

Abstract: Estimation theory has had a tremendous impact on many problem areas over the past two decades. Beginning with its original use in the aerospace industry, its applications can now be found in many different areas such as control and communjcations, power systems, transportation systems, bioengineering, image processing, etc. Along with linear system theory and optimal control, a course in estimation theorycan be found in the graduate system and control curriculum,of most schools in the country. In fact, it is probably one of the most,salable courses as far as employment is concerned. However, despite its economic value and the amount of activities in the field, very few books on estimation theory have appeared recently. This book helps to fill the void in the market and does that in a superb manner. Although the book is called OptimalFiltering, the coverage is restricted to discrete time filtering. A more appropriate title would thus be Optimal Discrete Time ,Filtering. The authors’ decision to concentrate on discrete time f lters is due to “recent technological developments as well as the easier path offered students and instructors.” This is probably a wise move since a thorough treatment of continuous time filtering will require a better knowledge o f stochastic processes than most graduate students or engineers will have. As it stands now, the text requires little background beyond that of linear system theory and probability theory. Written by active researchers, in the area, the book covers the standard topics such as Kalman filtering, innovations processes, smoothing, and adaptive and nonlinear estimation. Much of the material in the book has been around for a long time and has been widely used, by practitioners in the area: Some results are more recent. However,-it .has been difficult to locate all of them presented in a n organized manner within a single text. This is especially true of the chapters dealing with the computation aspects and nonlinear and adaptive estimation. After a short introductory chapter, Chapter 2 introduces the mathematical model to be used throughout most of the book. The discrete time Kalman filter is 1 hen presented in Chapter 3, along with some applications. Chapter 4 contains a treatment