Ramadoss Janarthanan

Tuning PID and PI /λ D δ Controllers using the Integral Time Absolute Error Criterion

Particle swarm optimization (PSO) is extensively used for real parameter optimization in diverse fields of study. This paper describes an application of PSO to the problem of designing a fractional-order proportional-integral-derivative (PIlambdaDdelta) controller whose parameters comprise proportionality constant, integral constant, derivative constant, integral order (lambda) and derivative order (delta). The presence of five optimizable parameters makes the task of designing a PIiquestDiquest controller more challenging than conventional PID controller design. Our design method focuses on minimizing the integral time absolute error (ITAE) criterion. The digital realization of the deigned system utilizes the Tustin operator-based continued fraction expansion scheme. We carry out a simulation that illustrates the effectiveness of the proposed approach especially for realizing fractional-order plants. This paper also attempts to study the behavior of fractional PID controller vis-a-vis that of its integer-order counterpart and demonstrates the superiority of the former to the latter.

Neural Network Based Gesture Recognition for Elderly Health Care Using Kinect Sensor

A simple method to detect gestures revealing muscle and joint pain is described in this paper. Kinect Sensor is used for data acquisition. This sensor only processes twenty joint coordinates in three dimensional space for feature extraction. The recognition part is achieved using a neural network optimized by Levenberg-Marquardt learning rule. A high recognition rate of 91.9% is achieved using the proposed method. This is also better than several algorithms previously used for elder person gesture recognition works.

Single channel electrooculogram(EOG) based interface for mobility aid

Human computer interfacing (HCI) technology has emerged as a new pathway towards the improvement of different rehabilitative aids. In this paper, new approach to control the motorized human computer interface using electrooculogram (EOG) is proposed. A mobility interface controlled by eye movements has been developed to help the disabled individuals with motor impairment who cannot even speak. Electrooculogram(EOG) is the potential generated in due the movement of the eyeballs and can be acquired from the surrounding region of eye socket. The signal is easy to acquire noninvasively and has a simple pattern. A low cost data acquisition system for EOG is designed. Horizontal electrooculographic signal is recorded by placing electrodes at the outer region of the orbit of eyes, and a reference electrode at neck. Using different combinations of eye movements in right and left direction a simple control strategy has been developed to drive motors. Control signals have been first generated using 8051 microcontroller. To meet the problems occurred while using 8051, ATMEGA microcontroller has been adapted. Directional movements of a small prototype of mobility aid (a toy car) with DC motors in right, left and forward is controlled and start and stop of movement is also implemented with ATMEGA. These control signals can be further used to command rehabilitative assistive device with eye movement sequences.

Interval type-2 fuzzy model for emotion recognition from facial expression

The paper proposes a new approach to emotion recognition from facial expression of a subject by constructing an Interval type-2 fuzzy model. An interval type-2 fuzzy face-space is first constructed with the background knowledge of facial features of different subjects for different emotions. The fuzzy face-space thus created comprises primary membership distributions for m facial features, obtained from n subjects, each having

Artificial Bee Colony Based Feature Selection for Motor Imagery EEG Data

\textit{l}

A support vector machine classifier of emotion from voice and facial expression data

-instances of facial expression for a given emotion. Second, the emotion of an unknown facial expression is determined based on the consensus of the measured facial features with the fuzzy face-space.The classification accuracy of the proposed method is as high as 88.66 %.

Multi-robot cooperative box-pushing problem using multi-objective Particle Swarm Optimization technique

Brain-computer Interface (BCI) has widespread use in Neuro-rehabilitation engineering. Electroencephalograph (EEG) based BCI research aims to decode the various movement related data generated from the motor areas of the brain. One of the issues in BCI research is the presence of redundant data in the features of a given dataset, which not only increases the dimensions but also reduces the accuracy of the classifiers. In this paper, we aim to reduce the redundant features of a dataset to improve the accuracy of classification. For this, we have employed Artificial Bee Colony (ABC) cluster algorithm to reduce the features and have acquired their corresponding accuracy. It is seen that for a reduced features of 200, the highest accuracy of 64.29 %. The results in this paper validate our claim.

Multi-Robot box-pushing using non-dominated sorting bee colony optimization algorithm

The paper provides a novel approach to emotion recognition from facial expression and voice of subjects. The subjects are asked to manifest their emotional exposure in both facial expression and voice, while uttering a given sentence. Facial features including mouth-opening, eye-opening, eyebrow-constriction, and voice features including, first three formants: F1, F2, and F3, and respective powers at those formants, and pitch are extracted for 7 different emotional expressions of each subject. A linear Support Vector Machine classifier is used to classify the extracted feature vectors into different emotion classes. Sensitivity of the classifier to Gaussian noise is studied, and experimental results confirm that the recognition accuracy of emotion up to a level of 95% is maintained, even when the mean and standard deviation of noise are as high as 5% and 20% respectively over the individual features. A further analysis to identify the importance of individual features reveals that mouthopening and eye-opening are primary features, in absence of which classification accuracy falls off by a large margin of more than 22%.

EEG controlled remote robotic system from motor imagery classification

The present work provides a new approach to solve the well-known multi-robot co-operative box pushing problem as a multi objective optimization problem using modified Multi-objective Particle Swarm Optimization. The method proposed here allows both turning and translation of the box, during shift to a desired goal position. We have employed local planning scheme to determine the magnitude of the forces applied by the two mobile robots perpendicularly at specific locations on the box to align and translate it in each distinct step of motion of the box, for minimization of both time and energy. Finally the results are compared with the results obtained by solving the same problem using Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The proposed scheme is found to give better results compared to NSGA-II.

Emotion Recognition System by Gesture Analysis Using Fuzzy Sets

The paper provides a new approach to multi-robot box pushing using a proposed Non-dominated Sorting Bee Colony (NSBC) optimization algorithm. The proposed scheme determines time-, energy- and friction-optimal solution to the box-pushing problem. The performance of the developed NSBC algorithm is compared to NSGA-II in connection with the box-pushing problem and the experimental results reveal that the NSBC outperforms NSGA-II in all the experiments.