Clemson University

About: Clemson University is a education organization based out in Clemson, South Carolina, United States. It is known for research contribution in the topics: Population & Control theory. The organization has 20556 authors who have published 42518 publications receiving 1170779 citations. The organization is also known as: Clemson Agricultural College of South Carolina.

A breadth-first survey of eye-tracking applications

Abstract: Eye-tracking applications are surveyed in a breadth-first manner, reporting on work from the following domains: neuroscience, psychology, industrial engineering and human factors, marketing/advertising, and computer science. Following a review of traditionally diagnostic uses, emphasis is placed on interactive applications, differentiating between selective and gaze-contingent approaches.

Synthesis and Optical Properties of Silver Nanoparticles and Arrays

Abstract: This Minireview systematically examines optical properties of silver nanoparticles as a function of size. Extinction, scattering, and absorption cross-sections and distance dependence of the local electromagnetic field, as well as the quadrupolar coupling of 2D assemblies of such particles are experimentally measured for a wide range of particle sizes. Such measurements were possible because of the development of a novel synthetic method for the size-controlled synthesis of chemically clean, highly crystalline silver nanoparticles of narrow size distribution. The method and its unique advantages are compared to other methods for synthesis of metal nanoparticles. Synthesis and properties of nanocomposite materials using these and other nanoparticles are also described. Important highlights in the history of the field of metal nanoparticles as well as an examination of the basic principles of plasmon resonances are included.

Missing data: a conceptual review for applied psychologists

Abstract: There has been conspicuously little research concerning missing data problems in the applied psychology literature. Fortunately, other fields have begun to investigate this issue. These include survey research, marketing, statistics, economics, and biometrics. A review of this literature suggests several trends for applied psychologists. For example, listwise deletion of data is often the least accurate technique to deal with missing data. Other methods for estimating missing data scores may be more accurate and preserve more data for investigators to analyze. Further, the literature reveals that the amount of missing data and the reasons for deletion of data impact how investigators should handle the problem. Finally, there is a great need for more investigation of strategies for dealing with missing data, especially when data are missing in nonrandom or systematic patterns.

Pattern Recognition: Statistical, Structural and Neural Approaches

Abstract: STATISTICAL PATTERN RECOGNITION (StatPR). Supervised Learning (Training) Using Parametric and Nonparametric Approaches. Linear Discriminant Functions and the Discrete and Binary Feature Cases. Unsupervised Learning and Clustering. SYNTACTIC PATTERN RECOGNITION (SyntPR). Overview. Syntactic Recognition via Parsing and Other Grammars. Graphical Approaches to SyntPR. Learning via Grammatical Inference. NEURAL PATTERN RECOGNITION (NeurPR). Introduction to Neural Networks. Introduction to Neural Pattern Associators and Matrix Approaches. Feedforward Networks and Training by Backpropagation. Content Addressable Memory Approaches and Unsupervised Learning in NeurPR. Appendices. References. Permission Source Notes. Index.