ESMD Space Grant Faculty Project

No abstract availabl

The Use of Fuzzy Measures in Pain Relief Control

Many people suffer from a continuous strong pain which is caused solely by the malfunction of the pain mechanism itself. One way to ease their pain is to electrically stimulate the spinal cord. Since the equations of pain are not known, we must use heuristic methods to find the optimal pain relief stimulation. In this paper, we show how fuzzy measures and similar nonlinear models can be used in pain relief control: they can be used to determine the parameters of the model which describes the dependence of the pain relief on the applied stimulation. Thus, fuzzy measures lead to the determination, for a given pain distribution, of the optimal pain relief stimulation

Application of Soft Computing in Coherent Communications Phase Synchronization

The use of soft computing techniques in coherent communications phase synchronization provides an alternative to analytical or hard computing methods. This paper discusses a novel use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for phase synchronization in coherent communications systems utilizing Multiple Phase Shift Keying (MPSK) modulation. A brief overview of the M-PSK digital communications bandpass modulation technique is presented and it's requisite need for phase synchronization is discussed. We briefly describe the hybrid platform developed by Jang that incorporates fuzzy/neural structures namely the, Adaptive Neuro-Fuzzy Interference Systems (ANFIS). We then discuss application of ANFIS to phase estimation for M-PSK. The modeling of both explicit, and implicit phase estimation schemes for M-PSK symbols with unknown structure are discussed. Performance results from simulation of the above scheme is presented

Application of ANFIS to Phase Estimation for Multiple Phase Shift Keying

The paper discusses a novel use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for estimating phase in Multiple Phase Shift Keying (M-PSK) modulation. A brief overview of communications phase estimation is provided. The modeling of both general open-loop, and closed-loop phase estimation schemes for M-PSK symbols with unknown structure are discussed. Preliminary performance results from simulation of the above schemes are presented

Nhập môn trí tuệ tính toán = Computational Intelligence : Introduction

328 tr. ; 24 c

Nhập môn trí tuệ tính toán = Computational Intelligence : Introduction

328 tr. ; 24 c

A GEOMETRIC APPROACH TO CLASSIFICATION OF TRASH IN GINNED COTTON

This paper discusses the use of geometric approach to classify different types of trash (non-lint, non-fiber material) in ginned cotton. Pieces of trash can have complicated shapes, so we would like to find a good approximating family of sets. Which approximating family is the best? We reduce the corresponding optimization problem to a geometric one: namely, we show that, under some reasonable conditions, an optimal family must be shift-, rotation- and scale-invariant. We then use this geometric reduction to conclude that the best approximating low-dimensional families consist of sets with linear or circular boundaries. This result is in good agreement with the existing empirical classification of trash into bark1, bark2, leaf, and pepper trash

Some Practical Applications of Soft Computing and Data Mining

. Traditional data mining techniques mainly deal with a search for patterns in traditional databases, where data consists of numbers and words. In many application areas, however, data is more complicated: real-life data is often obtained as an image from a camera rather than a few measurements. Furthermore, this image can also change dynamically. In this paper, we present several examples of how soft computing is related to mining such data. Keywords: data mining; soft computing; image analysis; cotton; tracking high-speed targets 1 Introduction Traditional data mining techniques (see, e.g., [4,6,15,18,27,28]) mainly deal with a search for patterns in traditional databases, where data consists of numbers and words. In many application areas, however, data is more complicated: real-life data is often obtained as an image from a camera (see, e.g., [2,3]). Each image contains much more information than a typical data record, so we cannot simply use traditional methods to mine through ..