Sunil Kumar Singla

Artifact Removal from Biosignal using Fixed Point ICA Algorithm for Pre-processing in Biometric Recognition

Abstract In the modern world of automation, biological signals, especially Electroencephalogram (EEG) and Electrocardiogram (ECG), are gaining wide attention as a source of biometric information. Earlier studies have shown that EEG and ECG show versatility with individuals and every individual has distinct EEG and ECG spectrum. EEG (which can be recorded from the scalp due to the effect of millions of neurons) may contain noise signals such as eye blink, eye movement, muscular movement, line noise, etc. Similarly, ECG may contain artifact like line noise, tremor artifacts, baseline wandering, etc. These noise signals are required to be separated from the EEG and ECG signals to obtain the accurate results. This paper proposes a technique for the removal of eye blink artifact from EEG and ECG signal using fixed point or FastICA algorithm of Independent Component Analysis (ICA). For validation, FastICA algorithm has been applied to synthetic signal prepared by adding random noise to the Electrocardiogram (ECG) signal. FastICA algorithm separates the signal into two independent components, i.e. ECG pure and artifact signal. Similarly, the same algorithm has been applied to remove the artifacts (Electrooculogram or eye blink) from the EEG signal.

OPTIMIZING THE ROTATION AND TRANSLATION OF FINGERPRINT IMAGES USING GENETIC ALGORITHM

In the minutiae-based fingerprint authentication system, the minutiae in the query image are required to be matched with the minutiae of the reference image that is stored in the database. Ideally, the minutiae extracted from the different impressions of the same fingerprint must match with each other, but practically, because of displacement, rotation, and other linear/ nonlinear distortions, minutiae extracted from different impressions of the same fingerprint do not match with each other. In order to maximize the number of matching minutiae, the alignment of the two fingerprints is required. Correctly aligning the fingerprints requires the translation and rotation to be recovered exactly. In this article, a new genetic-algorithm (GA)-based relative alignment algorithm for the alignment of reference and query fingerprint images is proposed. With the proposed algorithm there is no need to find the reference core or delta point because reliable detection of these reference points is a difficult task. In the proposed algorithm, all the three parameters x, y (translation), and θ (rotational) have been optimized separately. In order to improve the processing time, two acceleration steps have also been implemented. The experiments conducted on the FVC2002/Db1_a database reveal that a high accuracy has been achieved with the proposed method.

Stabilization of mobile inverted pendulum using fuzzy PID controllers

A mobile inverted pendulum (MIP) is an under actuated system with two degrees of freedom. The control objective is to stabilize the MIP in vertical upward position by applying appropriate control signal. In this paper fuzzy logic based PID controllers have been designed for stabilizing the mobile inverted pendulum. A two loop fuzzy based PID control scheme has been proposed for stabilizing the MIP. One loop controls the angle while the other loop controls the position of the base. The proposed scheme has been compared with the conventional PID scheme with fixed gains. Simulation results affirm the effectiveness of the proposed controllers as compared with the conventional PID controllers in terms of various performance parameters such as maximum overshoot, maximum undershoot and rise time etc.