The aim of this work is to discriminate various milk samples using Linear Discrimination Analysis (LDA) detection technique of pure milk, pure milk adulterated with urea, tap water etc. by forming samples phase data into matrix form at different frequencies in Constant phase (property of electrode-electrolyte interface of Polymer coated copper probe dipped in ionic solution) region as well as data taken at different time. Euclidean distance from any unknown sample with the given known samples has been calculated using LDA algorithm and the sample with shortest distance will show the best maximal characteristics of the unknown sample. The whole calculation and discrimination plotting has been done in MATLAB software.

Discrimination of various Milk adulterated sample using Linear Discrimination Analysis(LDA)

Dependable operation of brain computer interface (BCI) needs accurate classification of EEG Application based on environment control system for disable people need comfortable and simple switching modes This paper studies about eye close (EC) and eye open (EO) conditions of EEG for communication tool for severely disable users For this purpose, Multivariate Gaussian Distribution analysis is applied to check the discrimination of two classes Power Spectral Density and Central Tendency Measurement are used as features These features used in Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Fisher's Linear Discriminant Analysis (FLDA) to observe the classification performance in linear and nonlinear environment The experiment results show that EEG signals can be a reliable media for a switching paradigm of ECS

Toalha Mohammad Tosrif

Papers

Discrimination analysis of EEG signals at eye open and eye close condition for ECS switching system