In this paper, we summarize the human emotion recognition using different set of electroencephalogram (EEG) channels using discrete wavelet transform. An audio-visual induction based protocol has been designed with more dynamic emotional content for inducing discrete emotions (disgust, happy, surprise, fear and neutral). EEG signals are collected using 64 electrodes from 20 subjects and are placed over the entire scalp using International 10-10 system. The raw EEG signals are preprocessed using Surface Laplacian (SL) filtering method and decomposed into three different frequency bands (alpha, beta and gamma) using Discrete Wavelet Transform (DWT). We have used "db4" wavelet function for deriving a set of conventional and modified energy based features from the EEG signals for classifying emotions. Two simple pattern classification methods, K Nearest Neighbor (KNN) and Linear Discriminant Analysis (LDA) methods are used and their performances are compared for emotional states classification. The experimental results indicate that, one of the proposed features (ALREE) gives the maximum average classification rate of 83.26% using KNN and 75.21% using LDA compared to those of conventional features. Finally, we present the average classification rate and subsets of emotions classification rate of these two different classifiers for justifying the performance of our emotion recognition system.

In bin picking applications, robots are required to pick up an object from a pile of stacked or scattered objects placed in a bin. To perform such tasks, identification of the objects to be picked using a vision system is indispensable. In this paper, a stereo vision based automated bin picking system is proposed which identifies the topmost object from a pile of occluded objects and computes its location. The proposed bin picking process consists of two modules namely object segmentation module and object localization module. In the segmentation module, an 'Acclimatized Top Object Threshold' [ATOT] algorithm is proposed for segmentation of topmost object and in the localization module, the location of the object is estimated by computing the 'x', 'y', 'z' coordinates of the object midpoint using a unified stereo imaging algorithm. The validity of the algorithms is experimentally verified for object pick and place operations using the object location coordinates. The developed stereo vision system was implemented and validated for bin pick and place operations on an Adept Cobra 600 Robot.