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Heart murmur detection and classification using wavelet transform and Hilbert phase envelope 21

Heart murmur detection and classification using wavelet tran...

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2015 21st National Conference on Communications, NCC 2015

作者： Nivitha Varghees, V. Ramachandran, K.I. Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham University Tamilnadu641112 India

ISBN: (纸本)9781479966196

Detection and classification of heart murmurs play an important role in accurate diagnosis of different types of heart dysfunctions. In this paper, we present a noise-robust method for detection and classification of heart murmurs using stationary wavelet transform (SWT) and Hilbert phase envelope. The proposed method consists of five major stages: SWT based PCG signal decomposition for identifying heart sound (HS) including S1, S2, S3 and S4, and heart murmur(HM) subbands, Hilbert phase envelope based boundary determination, temporal feature extraction, murmur detection and classification rule. The boundaries of local acoustic HS segments are determined using the positive slope of instantaneous phase waveform of the smooth absolute envelope. The temporal features such as amplitude, duration, zerocrossing rate, interval, onset and offset time-instants of the detected HS and HM segments are used at the classification stage. The performance of the proposed method is tested and validated using a wide variety of normal and pathological signals containing different patterns of heart sounds and murmurs. The method achieves a probability of correctly detecting HM segments Pms=100%, a probability of correctly detecting HS segments Phs=97.33% and probability of falsely detecting segments Pfs=1.33% for SNR value of 15 dB, and murmur classification accuracy ranging from 82.76% to 100%. © 2015 IEEE.

关键词： Probability

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Aerial image classification using GURLS and LIBSVM

Aerial image classification using GURLS and LIBSVM

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2016 International Conference on Communication and Signal Processing, ICCSP 2016

作者： Ajay, Aleena Deepa Merlin Dixon, K. Sowmya, V. Soman, K.P. Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu India

ISBN: (纸本)9781509003969

Image classification using kernels have very great importance in remote sensing data. The goal of this work is to efficiently classify the large set of aerial images into different classes. This paper introduces a kernel based classification for aerial images. It uses Grand Unified Regularized Least Square (GURLS) and library for support vector machines (LIBSVM). This paper compares the performance of different kernel methods used in GURLS and LIBSVM. The experiment is performed on three sets of aerial image data sets which are obtained from electrical engineering department of Banja Luka University under the DSP laboratory, funded by the WUSAUSTRIA project of the European Union. From the experiment performed, it can be deduced that GURLS library is better compared to LIBSVM in terms of its prediction accuracy. The advantage of GURLS library package over LIBSVM is its automatic parameter selection. © 2016 IEEE.

关键词： Support vector machines

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Spatial preprocessing based multinomial logistic regression for hyperspectral image classification

Spatial preprocessing based multinomial logistic regression ...

引用

IUTAM Symposium on the Dynamics of Extreme Events Influenced by Climate Change (2013)

作者： Nidhin Prabhakar, T.V. Xavier, Gintu Geetha, P. Soman, K.P. Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India

The paper presents a fast, reliable and efficient method for improving hyperspectral image classification aided by segmentation. The Multinomial Logistic Regression(MLR) algorithm can be extended to a semi-supervised learning of the posterior class distribution using unlabeled samples actively selected from the dataset. Classification results obtained from regression model is improved by performing a maximum a posteriori segmentation as it considers the spatial information of the hyperspectral image. The addition of the spatial processing step prior to the above mentioned classification scheme improves the overall accuracy of the process. The accuracies obtained before and after applying the preprocessing are compared. © 2015 The Authors. Published by Elsevier B.V.

关键词： Image segmentation

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Impact of orbital perturbations on LEO solar power satellite formation in a wireless broadband over power system

Impact of orbital perturbations on LEO solar power satellite...

引用

2016 International Conference on Communication and Signal Processing, ICCSP 2016

作者： Aswathi, M. Shanmugha Sundaram, G.A. Soman, K.P. Centre for Excellence Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu641112 India

ISBN: (纸本)9781509003969

Increasing power requirements lead the way for the development of new innovative ideas to generate electricity like the introduction of solar power satellites. This system consists of a number of satellites such that the maximum solar energy is collected. This energy is then converted to electromagnetic beams and transmitted to Earth wirelessly. Simultaneous wireless power and data transmission is performed for better efficiency. There are some external forces which act on the satellite formation, as a result of which they are deviated from their orbit. The orbital elements of the formation and the perturbing forces which they are acted upon are determined. A periodic variation on the orbital elements is observed. The receiver on the Earth station, called the rectenna, is designed to collect both wireless power and data simultaneously. A novel design for the solar power satellite formation in a low earth orbit has been proposed. © 2016 IEEE.

关键词： Data transfer

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AMRITA-CEN@FIRE-2014: Morpheme extraction and lemmatization for Tamil using machine learning 6

AMRITA-CEN@FIRE-2014: Morpheme extraction and lemmatization ...

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6th International Workshop of the Forum for Information Retrieval Evaluation, FIRE 2014

作者： Anand Kumar, M. Soman, K.P. Centre for Excellence in Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore641 112 India

ISBN: (纸本)9781450337557

This paper presents the method of Morpheme Extraction and lemmatization for Tamil language in Morpheme Extraction Task (MET) of FIRE-2014. Tamil is a morphologically rich and agglutinative language. Such a language needs deeper analysis at the word level to capture the meaning of the word from its morphemes and its categories. In this attempt, the methodology employed to extract Tamil morphemes and lemmas are based on a supervised machine learning algorithm for nouns and verbs and simple suffix stripping for pronouns and proper nouns. Morphemes are extracted for other Part-of-Speech categories using Tamil Part of Speech tagger. In supervised learning, Morphological analyzer problem is redefined as a classification problem. We decompose the problem of noun and verb morpheme extraction into two sub-problems: learning to perform morpheme identification of words in a text, and learning to perform morpheme tagging. In addition to the Morpheme extraction task results of FIRE-2014, we have carried out different experiments to show the effectiveness of the proposed method. © 2015 ACM.

关键词： Extraction

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X-ray image classification based on tumor using GURLS and LIBSVM

X-ray image classification based on tumor using GURLS and LI...

引用

2016 International Conference on Communication and Signal Processing, ICCSP 2016

作者： Pooja, A. Mamtha, R. Sowmya, V. Soman, K.P. Centre for Excellence in Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham Amrita Nagar Coimbatore India

ISBN: (纸本)9781509003969

In today's world, X-ray imaging is the low cost diagnostic technique when compared with all other medical imaging techniques. In this paper, the proposed method is to classify X-ray images based on tumor. The features are extracted using Singular Value Decomposition (SVD) and classified using different kernels in Library for Support Vector Machine (Lib-SVM) and Grand Unified Regularized Least Squares (GURLS). The proposed method is experimented on X-ray image dataset which is approved by an Oncologist. The effectiveness of proposed method is validated based on classification parameters. The experiment result analysis shows that Gaussian-ho in GURLS provides 95% classification accuracy which is 5% higher than RBF kernel in LibSVM. The performance of the proposed system is validated by an Oncologist. © 2016 IEEE.

关键词： Singular value decomposition

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Compressive sensing based image acquisition and reconstruction analysis

Compressive sensing based image acquisition and reconstructi...

引用

2014 IEEE International Conference on Green Computing, Communication and Electrical engineering, ICGCCEE 2014

作者： Ravindranath, Sabbisetti Ram, S.R Nishanth Subhashini, S. Reddy, A.V. Sesha Janarth, M. Aswathvignesh, R. Gandhiraj, R. Soman, K.P. Department of Electronics and Communication Engineering Amrita VishwaVidyapeetham Coimbatore India Centre for Excellence in Computational Engineering and Networking Amrita VishwaVidyapeetham Coimbatore India

ISBN: (纸本)9781479949823

Compressive sensing is a technique by which images are acquired and reconstructed from a relatively fewer measurements than what the Nyquist rate suggests. Compressive sensing is applicable when the signals under consideration are sparse, and most of the images are sparse in wavelet or frequency domain. In this paper, the mathematical formulation of compressive sensing is explained where in various notations and parameters like measurement matrices and sparsity-inducing matrices are dealt in detail. A deterministic measurement matrix, known as chess measurement matrix is implemented in an aperture assembly. Several reconstruction algorithms are analysed and the images reconstructed with PSNR plotted for every case. Based upon the results, it is proved that OMP is the efficient reconstruction algorithm among all. © 2014 IEEE.

关键词： Compressed sensing

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Performance comparison of Variational Mode Decomposition over Empirical Wavelet Transform for the classification of power quality disturbances using Support Vector Machine

Performance comparison of Variational Mode Decomposition ove...

引用

IUTAM Symposium on the Dynamics of Extreme Events Influenced by Climate Change (2013)

作者： Aneesh, C. Kumar, Sachin Hisham, P.M. Soman, K.P. Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore Campus Tamilnadu641112 India

This work considers the classification of power quality disturbances based on VMD (Variational Mode Decomposition) and EWT (Empirical Wavelet Transform) using SVM (Support Vector Machine). Performance comparison of VMD over EWT is done for producing feature vectors that can extract salient and unique nature of these disturbances. In this paper, these two adaptive signal processing methods are used to produce three Intrinsic Mode Function (IMF) components of power quality signals. Feature vectors produced by finding sines and cosines of statistical parameter vector of three different IMF candidates are used for training SVM. Validation for six different classes of power qualities including normal sinusoidal signal, sag, swell, harmonics, sag with harmonics, swell with harmonics is performed using synthetic data in MATLAB. Classification results using SVM shows that VMD outperforms over EWT for feature extraction process and the classification accuracy is tabled. © 2015 The Authors. Published by Elsevier B.V.

关键词： Variational mode decomposition

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A low cost implementation of multi-label classification algorithm using mathematica on raspberry Pi

A low cost implementation of multi-label classification algo...

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IUTAM Symposium on the Dynamics of Extreme Events Influenced by Climate Change (2013)

作者： John, Neethu Surya, R. Ashwini, R. Sachin Kumar, S. Soman, K.P. Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore Campus Tamilnadu641112 India

Implementation of data mining algorithms with low cost is one of the challenging tasks in the present world of massively increasing data. The key idea of this paper is to utilize the functionalities of Mathematica which is freely accessible on Raspberry Pi for the purpose of implementing Multi-label classification algorithm with low cost. With the facilities available in Mathematica software for Raspberry Pi, the line of code required for implementing data mining algorithms can be reduced sufficiently. Use of Random Kitchen Sink algorithm improves the accuracy of Multi-label classification and brings improvement in terms of memory usage for large dataset. © 2015 The Authors. Published by Elsevier B.V.

关键词： Data mining

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Comparative study of recent compressed sensing methodologies in astronomical images

Comparative study of recent compressed sensing methodologies...

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International Conference on Eco-Friendly Computing and Communication Systems, ICECCS 2012

作者： Nidhin Prabhakar, T.V. Hemanth, V.K. Sachin Kumar, S. Soman, K.P. Soman, Arun Centre for Excellence in Computational Engineering and Networking Amrita University Coimbatore 641112 India Department of Information Technology Rajagiri School of Engineering and Technology India

ISBN: (纸本)9783642321115

Compressed sensing(CS) which serves as an alternative to Nyquist sampling theory, is being used in many areas of applications. In this paper, we applied recent compressed sensing algorithm such as DALM, FISTA and Split-Bregman on astronomical images. In astronomy, physical prior information is very crucial for devising effective signal processing methods. We particularly point out that CS-based compression scheme is flexible enough to account for such information. We try to compare these algorithms using objective measures like PSNR, MSE et al. With these measures we intend to verify the image quality of reconstructed and original images. © 2012 Springer-Verlag.

关键词： Compressed sensing

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