Informatics Publishing Limited

M. Rathi ¹, A. Rohini ¹, R. Surya ¹, K. Jeyanthi ¹

Affiliations
1 KPR Institute of Engineering and Technology, Coimbatore, IN

Abstract

In this work, the effectiveness of bispectral invariant features in diagnostic classification of breast thermal images into malignant, benign and normal classes is evaluated and a phase-only variant of these features is proposed. Boundaries are extracted using Canny edge detection and elimination of inner edges. Breast images are then segmented using fuzzy c-means clustering and the hottest regions are selected for feature extraction. Bispectral invariant features are extracted from Radon projections of these images. An Adaboost classifier is used to select and fuse the best features during training and then classify unseen test images into malignant, benign and normal classes. A data set comprising 9 malignant, 12 benign and 11 normal cases is used for evaluation of performance. Malignant cases are detected with 95% accuracy. A variant of the features using the normalized bispectrum, which discards all magnitude information, is shown to perform better for classification between benign and normal cases, with 83% accuracy compared to 66% for the original.

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L. Sivachandran , R. Ravikumar , K. Jeyanthi

Abstract

This paper describes system identification, estimation and control of translational motion and heading angle for a cost effective open-source quadcopter — the MikroKopter. The dynamics of its built-in sensors, roll and pitch attitude controller, and system latencies are determined and used to design a computationally inexpensive multi-rate velocity estimator that fuses data from the built-in inertial sensors and a low-rate onboard laser range finder. Control is performed using a nested loop structure that is also computationally inexpensive and incorporates different sensors. Experimental results for the estimator and closed-loop positioning are presented and compared with ground truth from a motion capture system.

Keywords

UAV, QUADCOPTER, System Identification

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M. Vimalraj , Sakthivel , K. Jeyanthi

Abstract

In this paper a complete system is designed and implemented, in which the motion of a quadcopter is stably controlled based on visual feedback and measurements of inertial sensors. We focus on developing a cost effective and easy-to-setup vision system. Active markers were finely designed to improve visibility under various perspectives as well as robustness towards disturbances in the image-based pose estimation. Moreover, position- and heading controllers for the quadcopter were implemented to show the system’s capabilities. The performance of the controllers was further improved by the use of inertial sensors of the quadcopter. A closed-loop control system is successfully conducted.

Keywords

Vision System, Motion Estimation, Multiple Sensing System, UAV/MAV Control.

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P. Padmapriya ¹, K. Manikandan ¹, K. Jeyanthi ¹, V. Renuga ¹, J. Sivaraman ¹

Affiliations
1 Department of Biomedical Engineering, Vel Tech Multitech, Chennai – 600062, Tamil Nadu, IN

Abstract

Aim: This paper proposes the automatic support system for detecting the tumor cells by analyzing the scalp EEG by means of RBF technique. Objectives: To acquire the EEG signal from the various electrodes. The artificial neural network will be focused to split up the EEG signal whether cyst i.e. tumor or regular. Methods: The EEG signal is been acquired from the subject using EEG scalp electrodes. The various features such as mean, variance, co-variance, Eigen values and Eigen vectors are extracted from those signals using the Principal Component Analysis. Radial Basis Function (RBF) networks are feed-forward networks which uses a supervised training algorithm are used for function approximation, time series prediction and system control. The RBF is used to train and classify the signal whether the subject is normal or suffering from abnormalities. Results: The features are been extracted using the Principal Component Analysis and the features are skilled. Thus the acquired signals are been classified as cyst or normal. Conclusion: Thus in this paper an automatic system is been developed for diagnosing the tumor cells by means of analyzing EEG signal which is non-invasive method. It can also extend for analyzing other diseases seizures of epilepsy, Alzheimer's disease.

Keywords

EEG, Neural Network, Principal Component Analysis, Radial Basis Function

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S. Vinurajkumar ¹, V. Renuga ¹, K. Nithyakalyani ¹, K. Jeyanthi ¹, N. Malathy ², J. Sivaraman ¹

Affiliations
1 Department of Biomedical Engineering, Vel Tech Multitech, Chennai - 600062, Tamil Nadu, IN
2 Department of Electronics and Instrumentation Engineering, RMK Engineering College, Chennai - 601206, Tamil Nadu, IN

Abstract

Objectives: This study involves the measurement of obstructions in the blood flow of an arterial system. An attempt is made to find the correlation between the pressure and flow of the blood. Methods: The obstructed area produces vascular sounds which are acquired through disturbed blood flow patterns. The measurements are carried over by audio frequency analysis of the sound observed during arterial stenosis. These sounds can be measured using piezoelectric film sensor by placing it in the wrist and mid arm in the blood flow path. Results:The sounds that initiate in the narrowed arteries is measured by non-invasive, sensitive piezoelectric film sensor that captures the vibration caused by the flow of blood inside arteries and generate appropriate waveform for different flow conditions. Conclusion: The waveforms are analyzed in spectrum analyzer and compared with the normal flow patterns and if the values deviate from normal it is concluded for occlusion or clot that happened in the blood flow passage.

Keywords

Arterial Stenosis, Blood Flow, Occlusion, Piezoelectric Film Sensor

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