K. Manikandan ¹, E. Chandra ²

Affiliations
1 Department of Computer Science, PSG College of Arts and Science, Coimbatore – 641015, Tamil Nadu, IN
2 Department of Computer Science, Bharathiar University, Coimbatore – 641046, IN

Abstract

Objectives: The proposed speaker identification using a novel prosody with fuzzy based hierarchical decision tree approach and is used to modifying the limitations of existing traditional methods. It improves the performance of speaker identification in given population under noisy environments. Methods/Statistics: The key idea of this approach is to achieve an enhanced efficiency speaker cluster group using prosody features with fuzzy clustering at each level to construct the hierarchical decision tree. At each level, a speaker at belong to same groups are constructed. The proposed method has novelty of prosody as pitch and loudness with fuzzy clustering are used. Findings: An experimental result shows that the proposed model using prosody features outperforms the efficiency of speaker accuracy rate of 93.75 when compare to vocal source accuracy rate of 81.25 under noisy environments. Applications: Gender and age identification, banking and smart voice based technology operation.

Keywords

Fuzzy Clustering, Large Population Speaker Identification, Prosody Feature Extraction, Prosody with Fuzzy based Hierarchical Decision Tree.

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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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K. Manikandan ¹, Logesh Kumar ¹, J. Sivaraman ¹, P. Padmapriya ¹, C. Vijayalakshmi ¹

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

Abstract

Objective: The purpose of the study is to develop a system for measuring the pressure level at several points on the foot sole. Methodology: The pressure sensors placed on the pressure plate develops a voltage for the corresponding exerted pressure and the voltages are amplified to a considerable value and fed into the multiplexer for digitization by using A/D convertor for further processing. All the data are normalized to obtain an appropriate image. The image colour intensities indicate different pressure distributions of the foot. Findings: The diabetes patients are made to walk on the pressure device and the distributions of pressure are simulated with various color coding using Visual Basic (VB) and the range of pressure from minimum to maximum is validated. Applications/Improvements: The pressure distribution of a patient is found in the foot sole and an appropriate foot wear can be designed to avoid foot ulcers and other deformations.

Keywords

Capacitive Sensor, Contact Area, Diabetes, Pressure Level

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P. Vimal Prabhu ¹, J. Sivaraman ², S. Sathish ², S. Vinurajkumar ², K. Manikandan ¹

Affiliations
1 Department of Biomedical Engineering, Indian Institute of Technology, Hyderabad - 502285, Telangana, IN
2 Department of Biomedical Engineering, Vel Tech MultiTech, Chennai - 600062, Tamil Nadu, IN

Abstract

Objective: This study analyzes the dilation and constriction (elastic) properties of blood vessels using a non invasive Photoplethysmography method. The dilation and constriction of blood vessel changes as a function of aging which can be analyzed using the shape of Phototplethsymogram. Methods: A preliminary test is conducted in twenty sinus rhythm subjects in the range between 20 to 73 years. Findings: The parameters like peak ratio, time ratios, valley to peak ratio, stiffness index and augmentation index are the indices to determine the dilation and constriction properties of the blood vessels. Applications/Improvements: The above parameters are evaluated with the help of appropriate hardware and software. Neural networks is trained using these parameters to classify the level of elasticity, using which the abnormalities such as Atherosclerosis, aortic stenosis, aortic regurgitation can be diagnosed.

Keywords

Atherosclerosis, Dicrotic Notch, Peak Ratio, Photoplethysmography, Sinus Rhythm

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