Department Publications

Brain-Computer Interaction (BCI) is a technology developed with the purpose of building a pathway between the brain and computer which is independent of neuromuscular functions. Potential applications in rehabilitation of patients with motor disabilities and video gaming make BCI an important field of research. A task like controlling a prosthetic limb using BCI is challenging. Performing this with readily available consumer grade EEG devices complicates the matters further due to lower accuracies. This paper presents the work related to an online classifier for imagined hand movement implemented using Emotiv Epoc for EEG data capturing. The system attempts to discriminate between left and right hand movement imagination by analysing the recordings of two electrodes placed over the motor-cortex. Auto-Regression (AR) based signal processing techniques are employed to derive features that enable …

Listening to the heart sounds is a common practice in identifying cardiac malfunctions. Since this method has many limitations, tools that aid physicians in their diagnosis of heart diseases are very useful. This paper presents a software tool to predict cardiac abnormalities which can be identified using heart sounds. Both heart sound information and symptoms are used in disease prediction. First audio inputs at four clinically important locations on the chest are acquired using an electronic stethoscope and entered to a database with symptoms for each patient. After de-noising, prominent features and statistical parameters needed for disease detection are extracted from the heart sound samples using several algorithms. Then the disease classification is performed to find out possible disease and murmur types. The software tool reported in this paper is capable of identifying normal heart sounds and abnormal heart …

The natural sciences have been transformed with the incorporation of advanced technologies, and the current technological wave of change is also revolutionizing biological science. With the wide use of herbal medicines in traditional medical systems, the demand for medicinal plants has increased enormously. Though the current trend has made the use of medicinal plants more popular, we still require better methods to distinguish between medicinal plants and plants which do not possess medicinal value. Manual methods of plant recognition rely on a plants flower information. However, these manual processes of flower recognition are not straightforward for lay persons unless expert guidance is provided. Motivated by this reasoning, an intelligent flower analyzing system has been developed to recognize medicinal plants. Various tests have been performed on 160 images taken from four types of flowers, to recognize flowers based on their colour and shape features. The experiment was carried out using Support Vector Machine (SVM) and Principal Component Analysis (PCA) methodologies respectively for colour and shape extraction. Acquiring an average of 65% recognition rate implies that the applicability PCA and SVM in the specified domain is a valuable step forward.

We present a novel method where smart phone sensors are used to sense the prevailing traffic situation and to share extracted information among a community. Instead of using the power-hungry GPS receiver in the mobile device for detecting congestion through vehicular motion, we use the accelerometer along with a novel algorithm. We show that traffic congestion situations can be identified with 78% accuracy with our technique. Vehicular traffic has become a severe problem, particularly in the less developed countries. Therefore pre awareness of congestion situations is very important for an efficient lifestyle.