The Trace transform proposed, a generalization of the Radon transform, consists of tracing an image with straight lines along which certain functionals of the image function are calculated. Different functionals that can be used may be invariant to different transformations of the image. The paper presents the properties the functionals must have in order to be useful in three different applications of the method: construction of invariant features to rotation, translation and scaling of the image, construction of sensitive features to the parameters of rotation, translation and scaling of the image, and construction of features that may correlate well with a certain phenomenon we wish to monitor.

The Trace transform and its applications.

A new approach based on the implementation of k-means clustering is presented for automated detection of erythemato-squamous diseases. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. The studied domain contained records of patients with known diagnosis. The k-means clustering algorithm's task was to classify the data points, in this case the patients with attribute data, to one of the five clusters. The algorithm was used to detect the five erythemato-squamous diseases when 33 features defining five disease indications were used. The purpose is to determine an optimum classification scheme for this problem. The present research demonstrated that the features well represent the erythemato-squamous diseases and the k-means clustering algorithm's task achieved high classification accuracies for only five erythemato-squamous diseases.

Automatic Detection of Erythemato-Squamous Diseases Using k-Means Clustering

This paper presents a neural network based novel automated system that can analyze vehicle mounted video data for improving road safety. There are video data collection systems currently available although no tools exist which could be used to automatically analyze vehicle mounted video data and estimate future crash sites. The main aim of the research presented in this paper is to develop a technique to segment roadside data obtained from vehicle mounted video into regions of interest, classify roadside objects and estimate the risk factor based on roadside conditions and objects for various crashes. A clustering technique for segmentation of roadside frames into regions of interest and a neural network to classify the regions of interest into objects are investigated. The preliminary segmentation and classification results on a small dataset taken from Transport and Main Roads’ vehicle mounted video data collection are promising.

An Automated System for the Analysis of the Status of Road Safety Using Neural Networks

An Artificial Intelligence (AI) based Estimator is robust to parameter variations and noise and it avoids the use of mathematical models. Such a system is not restricted by the many assumptions used in the conventional methods and is capable of mapping any degree of non linearity. It can also yield the results more quickly. By the application of minimum configuration it is possible to obtain cost effective simple solutions using FPGA. The conventional methods are direct synthesis from state equations, Model Reference Adaptive System (MRAS) and Flux Observers. All these techniques use complex mathematical model of the motor which includes many assumptions. The estimation is not robust to parameter variations. The time taken for computation is also long. In this paper to develop AI based Estimators to Estimate the Speed, Torque and Flux of an Induction Motor for DTC drives.

M. Sasikumar

Papers

Parameter Estimation in High Performance Sensor Less Vector Controlled Drives