S. Abhishek ¹, S. Veni ², K. A. Narayanankutty ¹

Affiliations
1 Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore – 641112, Tamil Nadu, IN
2 Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore – 641112, Tamil Nadu

Abstract

Background/Objectives: Compressed Sensing (CS) is an efficient sensing paradigm which guarantees reasonable reconstruction with less number of samples. We aim to increase the reconstruction quality of signals in CS. Methods/ Statistical Analysis: The behavior of random matrices is analyzed and an efficient method for improving the reconstruction quality is developed in CS based ECG reconstruction applications. The method is compared against Biorthogonal wavelet based approaches. Findings: Our analysis reveals that introduction of a modified column vector in the reconstruction matrix, which contains the sum of all columns of random matrix increases the reconstruction quality in CS applications. This idea was applied to different sparsifying domains and the results are very encouraging. We studied the effect of doing this on the singular values and both unitary matrices U and V. The first singular value (Σ) shot up making the condition number high, however there was not much change in the other singular values. The matrix U seems to remain random unitary matrix, where as matrix V has one value becoming unity in its rank space. Application/Improvements: Compared to wavelet based approaches the method shows reasonable improvement in Percentage Root Square Deviation (PRD).

Keywords

Compressed Sensing, ECG, PRD, Singular Values, Splines

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S. P. Harisubramanyabalaji ¹, C. A. Sribalaji ¹, A. Vivek ¹, G. Vigneshwaran ¹, S. Abhishek ¹, R. Balamurugan ¹, R. Panneer ¹

Affiliations
1 School of Mechanical Engineering, SASTRA University, Thanjavur - 613401, Tamil Nadu, IN

Abstract

Objectives: To develop a theoretical model to predict the surface Roughness (R_a) of different metallic surfaces using acoustic signals. Methods: Acoustic signals are generated with the help of dry friction contact between two metallic surfaces. In this work, Cast iron and Mild Steel Samples from different machining processes are collected and the dry friction contact is made with HSS and Tungsten Carbide Tools to generate the acoustic signals. The acoustic signals obtained through a microphone are processed using MATLAB. Number of Samples versus Amplitude is plotted and then the resulting output is plotted as Time versus Amplitude. Subsequently the external noises from the acoustic signals are removed to get a reliable roughness value. Findings: The maximum amplitudes of the samples are tabulated and used for the deriving model for the surface roughness prediction. Theoretical model of HSS with various machining processes samples is y = 1.6865 ln(x) + 8.9978. Theoretical model of Tungsten carbide with various machining processes samples is y = 6.302 ln(x) + 27.337. Both the models are correlating with a trend line of 0.9. Application: This theoretical model can be used to predict the surface roughness (R_a) of metallic surfaces. This approach can be implemented in surface finish measuring devices.

Keywords

Acoustic Signals, Amplitude, Microphone, Metallic Surfaces, Surface Roughness.

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