Terahertz imaging in transmission mode using Terahertz to thermal converter D Sharath, CH Anagha, M Menaka, B Venkatraman International Journal of Applied Electromagnetics and Mechanics, 2026 In this paper a Terahertz imaging technique in transmission mode based on the photothermal conversion of Terahertz to thermal radiation is discussed. The theoretical background of the Terahertz to Thermal Converter and detailed experimental aspects of the imaging technique is discussed. The feasibility of this technique to evaluate dielectric materials is presented. The paper demonstrated the ability of Terahertz to Thermal Converter based transmission imaging technique for applications like, non-destructive evaluation, security, medical etc.
The influence of defect thickness on defect characterization using pulsed thermography: a numerical study on stainless steel material D Sharath, M Menaka, B Venkatraman Engineering Research Express, 2025 The combined effect of defect thickness and lateral size on defect characterization in AISI 316 L Stainless Steel material using Pulsed Thermography method is studied numerically. As the defect volume (thickness and lateral width) decreases for the same depth, the magnitude of change in surface temperature decreases, deteriorating the defect visibility. The change in the surface temperature can be related to the apparent reduction in the Reflection Coefficient, R, of the material and defect interface. The finite difference analysis is used to simulate the Pulsed Thermography experiment on steel material. A least-square-based optimization technique is used to estimate the R as a function of defect depth and lateral size to establish a relationship between them. The contrast derivative and log 2nd derivative methods are used for depth quantification. To carry out the defect visibility study, a noise matrix was generated using noise data obtained from an actual IR camera and superimposed on the numerical results to create a dataset that closely resembles actual experimental data. The study concluded that the defect thickness has the least effect on defect visibility and depth quantification if the thickness of the defect is greater than 500 μm for Stainless Steel. This work provides useful insight into the impact of defect volume on the precise measurement of defect depth and its visibility.
Downscaling of wind fields on the east coast of India using deep convolutional neural networks and their applications in storm surge computations Siva Srinivas Kolukula, P. L. N. Murty, Balaji Baduru, D. Sharath, Francis P. A. Journal of Water and Climate Change, 2024 Downscaling is reconstructing data from low to high resolution, capturing local effects and magnitudes. Widely employed methods for downscaling are dynamic and statistical methods with pros and cons. With ample data, machine learning (ML) and deep learning (DL) techniques can be employed to learn mapping from low to high resolution. This article investigates convolutional neural network capabilities for downscaling winds. The speed and direction of the wind are guided by a complex relation among pressure, Coriolis force, friction, and temperature, which leads to highly nonlinear wind patterns and poses a significant challenge for downscaling. The problem can be formulated as a super-resolution technique called a super-resolution convolutional neural network (SRCNN) for data reconstruction. Few variations of SRCNN are studied for wind downscaling. Six years of European Centre for Medium-Range Weather Forecasts (ECMWF) wind datasets along the east coast of India are used in the current study and are downscaled up to four times. Downscaled winds provide better results than traditional interpolation methods. Simulations for an extreme event are conducted with SRCNN downscaled winds and are compared against interpolation methods and original data. The numerical simulation results show that DL-based methods provide results closer to the ground truth than interpolation methods.
Deep learning with filtering for defect characterization in pulsed thermography based non-destructive testing Sethu Selvi Selvan, Sharath Delanthabettu, Menaka Murugesan, Venkatraman Balasubramaniam International Journal of Electrical and Computer Engineering, 2024 Pulsed thermography is widely used for non-destructive testing of various materials. The temperature profile obtained after pulse heating is used to characterize the underlying defects in an object. In this paper, the automation of the process of defect visualization and depth quantification in pulsed thermography through various deep learning algorithms is reported. Stainless steel plate with artificial defects is considered for analysis. The raw temperature data is smoothed using moving average, Savitzky-Golay and quadratic regression filters to reduce noise. Thermal signal reconstruction, the conventional method to eliminate noise, is also used for generating filtered datasets. Defect visualization refers to identifying and locating the defects in an image sample and Mask region convolutional neural network (Mask R-CNN) is considered for not just detecting the defects but also locating them on the image. The located defects are utilized for depth estimation using the following networks-multi-layer perceptron (MLP), long short-term memory (LSTM) and gated recurrent units (GRU). The input to the networks is the temperature contrast characteristics which symbolizes the difference in temperature over defective and non-defective areas measured over 250 time points and output of the networks is the estimated depth. The study shows that LSTM based approach provides the least percentage error of 5.5% and is a very suitable approach for automation of defect characterization in pulsed thermography.
Characterization of drilling damage in glass laminate composites using lock-in thermography nondestructive evaluation method: a feasibility study Sharath D, Mohandas K N, Sunith Babu L Australian Journal of Mechanical Engineering, 2024 The drilling of composite structure leads to its damage, near the peripheral areas of the drilled hole, due to the laminar structure of the composites. The goodness of a drilling process can be evaluated by measuring the extent of the damage caused by it. In the present study, lock-in thermography non-destructive inspection method is proposed to characterise the drilling damages in glass fiber–reinforced polymer panel. A semi-automated image processing methodology is proposed to calculate damage parameters, namely delamination area and size, and delamination factor. The effect of excitation frequency on the damage characterisation is studied to decide the optimum frequency range to measure the damage parameters through signal-to-noise ratio and damage visibility. The damage parameters are measured at optimum frequency. The study showed that the lock-in thermography technique has the potential to characterise drilling damage.
Breast cancer lesion detection from cranial-caudal view of mammogram images using statistical and texture features extraction Kavya N, Sriraam N, Usha N, Bharathi Hiremath, Anusha Suresh, Sharath D, Venkatraman B, Menaka M Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention, 2022 Breast cancer is the most common cancer among women in the world today. Mammography screening gives vital information about normal and abnormal regions. The task is to detect the lesion in mammograms using computer-aided diagnosis techniques. The automated detection of cancer decreases the mortality rate and manual error. In this work, the statistical (mean, variance, skewness, kurtosis, energy and entropy) and tamura features (coarseness, contrast and directionality) were extracted from the Cranial-Caudal (CC) view of mammogram images collected from the M.S. Ramaiah Memorial Hospital, Bangalore. The support vector machine was used for classification. Different support vector machine kernels were used and results were tabulated. The highest accuracy was obtained for linear and quadratic kernels with 95.7% with sensitivity of 100% and specificity of 91%.
A deep learning approach based defect visualization in pulsed thermography Sethu Selvi Selvan, Sharath Delanthabettu, Menaka Murugesan, Venkatraman Balasubramaniam, Sathvik Udupa, Tanvi Khandelwal, Touqeer Mulla, Varun Ittigi Iaes International Journal of Artificial Intelligence, 2022 <span lang="EN-US">Non-destructive evaluation (NDE) is very essential in measuring the properties of materials and in turn detect flaws and irregularities. Pulsed thermography (PT) is one of the advanced NDE technique which is used for detecting and characterizing subsurface defects. Recently many methods have been reported to enhance the signal and defect visibility in PT. In this paper, a novel unsupervised deep learning-based auto-encoder (AE) approach is proposed for enhancing the signal-to-noise ratio (SNR) and visualize the defects clearly. A detailed theoretical background of AE and its application to PT is discussed. The SNR and defect detectability results are compared with the existing approaches namely, higher order statistics (HOS), principal component thermography (PCT) and partial least square regression (PLSR) thermography. Experimental results show that AE approach provides better SNR at the cost of defect detectability.</span><br /><div id="ext-mouse-move" style="display: none;"> </div><div id="ext-mouse-down" style="display: none;"> </div><div id="ext-mouse-up" style="display: none;"> </div>
Breast cancer lesion detection from cranial-caudal view of mammogram images using statistical and texture features extraction Kavya N, Sriraam N, Usha N, Bharathi Hiremath, Anusha Suresh, Sharath D, Venkatraman B, Menaka M Research Anthology on Medical Informatics in Breast and Cervical Cancer, 2022 Breast cancer is the most common cancer among women in the world today. Mammography screening gives vital information about normal and abnormal regions. The task is to detect the lesion in mammograms using computer-aided diagnosis techniques. The automated detection of cancer decreases the mortality rate and manual error. In this work, the statistical (mean, variance, skewness, kurtosis, energy and entropy) and tamura features (coarseness, contrast and directionality) were extracted from the Cranial-Caudal (CC) view of mammogram images collected from the M.S. Ramaiah Memorial Hospital, Bangalore. The support vector machine was used for classification. Different support vector machine kernels were used and results were tabulated. The highest accuracy was obtained for linear and quadratic kernels with 95.7% with sensitivity of 100% and specificity of 91%.
Terahertz imaging in transmission mode using Terahertz to thermal converter D Sharath, CH Anagha, M Menaka, B Venkatraman International Journal of Applied Electromagnetics and Mechanics … , 2025 2025
The influence of defect thickness on defect characterization using pulsed thermography: a numerical study on stainless steel material D Sharath, M Menaka, B Venkatraman Engineering Research Express 7 (1), 015553 , 2025 2025
A retrospective study on breast mammogram-clinical and quantitative assessment N Sriraam, P Shetty, D Sharath, B Hiremath, B Venkatraman, M Menaka Computer Science Engineering, 23-35 , 2024 2024
Characterization of drilling damage in glass laminate composites using lock-in thermography nondestructive evaluation method: a feasibility study D Sharath, KN Mohandas, S Babu Australian Journal of Mechanical Engineering 22 (3), 615-624 , 2024 2024 Citations: 1
Downscaling of wind fields on the east coast of India using deep convolutional neural networks and their applications in storm surge computations SS Kolukula, PLN Murty, B Baduru, D Sharath, F PA Journal of Water and Climate Change 15 (4), 1612-1628 , 2024 2024 Citations: 6
Deep learning with filtering for defect characterization in pulsed thermography based non-destructive testing SS Selvan, S Delanthabettu, M Murugesan, V Balasubramaniam Int J Electr Comput Eng (IJECE) 14 (1), 1027-1040 , 2024 2024 Citations: 3
Breast cancer lesion detection from cranial-caudal view of mammogram images using statistical and texture features extraction N Kavya, N Sriraam, N Usha, D Sharath, B Venkatraman, M Menaka Research Anthology on Medical Informatics in Breast and Cervical Cancer, 632-644 , 2023 2023 Citations: 19
Image definition and defect sizing in lock-in thermography: an experimental investigation D Sharath, M Menaka, B Venkatraman Experimental Techniques 46 (5), 811-822 , 2022 2022 Citations: 4
A deep learning approach based defect visualization in pulsed thermography SS Selvan, S Delanthabettu, M Murugesan, V Balasubramaniam, ... IAES International Journal of Artificial Intelligence 11 (3), 949 , 2022 2022 Citations: 5
Qualitative and Quantitative Evaluation of Breast Images-Comparative Study of Mammogram and Thermogram MM N Sriraam, Praneethi K, Kavya N, Usha N, Sharath D, Prabha Ravi, Bharathi ... INTERNATIONAL JOURNAL OF SYSTEMS APPLICATIONS, ENGINEERING & DEVELOPMENT 16 … , 2022 2022 Citations: 1
Quantitative Analysis of Breast Thermograms Using BM3D Denoising Method and Features Extraction N Sriraam, N Kavya, N Usha, D Sharath, B Venkatraman, M Menaka ICDSMLA 2020: Proceedings of the 2nd International Conference on Data … , 2021 2021 Citations: 1
Ocular surface temperature measurement in diabetic retinopathy B Chandrasekar, AP Rao, M Murugesan, S Subramanian, D Sharath, ... Experimental Eye Research 211, 108749 , 2021 2021 Citations: 34
Asymmetrical analysis of breast thermal images for detection of breast cancer N Kavya, N Sriraam, N Usha, D Sharath, P Ravi, B Hiremath, ... Advances in Non-destructive Evaluation: Proceedings of NDE 2019, 249-260 , 2021 2021 Citations: 2
Feature selection using neighborhood component analysis with support vector machine for classification of breast mammograms N Kavya, N Sriraam, N Usha, D Sharath, B Hiremath, M Menaka, ... International Conference on Communication, Computing and Electronics Systems … , 2020 2020 Citations: 8
Comparison of pulsed and lock-in thermography techniques for debond detection in Ni-B coatings D Sharath, M Menaka, B Venkatraman Materials Evaluation 77 (12), 1450-1462 , 2019 2019 Citations: 4
Automated Defect Detection and Characterization on Pulse Thermography Images Using Computer Vision Techniques MMBV Meghana V, Megha P. Arakeri, Sharath D Journal of ICT Research and Applications 13 (1), 63-79 , 2019 2019
Feature selection and classification for analysis of breast thermograms N Usha, N Sriraam, N Kavya, D Sharath, R Prabha, B Hiremath, ... 2019 2nd international conference on signal processing and communication … , 2019 2019 Citations: 6
Breast cancer detection using non invasive imaging and cyber physical system N Kavya, N Usha, N Sriraam, D Sharath, P Ravi 2018 3rd international conference on circuits, control, communication and … , 2018 2018 Citations: 12
Evaluation of coating thickness and debond in thermal barrier coatings using pulsed thermography D Sharath 2017
A comparison between finite element modeling and various thermographic non-destructive testing techniques for the quantification of the thermal integrity of macro-brush plasma … SP Pandya, SN Pandya, YV Patil, DS Krishnan, M Murugesan, D Sharath, ... Review of Scientific Instruments 87 (2) , 2016 2016 Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
Medical thermography: a diagnostic approach for type 2 diabetes based on non-contact infrared thermal imaging S Sivanandam, M Anburajan, B Venkatraman, M Menaka, D Sharath Endocrine 42 (2), 343-351 , 2012 2012 Citations: 107
Defect Characterization Using Pulsed Thermography D Sharath, M Menaka, B Venkatraman Journal of Nondestructive Evaluation 32 (2), 134-141 , 2013 2013 Citations: 61
Defect depth quantification using lock-in thermography S Delanthabettu, M Menaka, B Venkatraman, B Raj Quantitative InfraRed Thermography Journal 12 (1), 37-52 , 2015 2015 Citations: 47
Evaluation of Mammary Cancer in 7, 12-Dimethylbenz (a) anthracene-Induced Wister Rats by Asymmetrical Temperature Distribution Analysis Using Thermography: A Comparison with … SP Angeline Kirubha, M Anburajan, B Venkataraman, R Akila, D Sharath, ... Journal of Biomedicine and Biotechnology 2012 , 2012 2012 Citations: 40
Estimation of blood glucose by non-invasive infrared thermography for diagnosis of type 2 diabetes: An alternative for blood sample extraction S Sivanandam, M Anburajan, B Venkatraman, M Menaka, D Sharath Molecular and Cellular Endocrinology 367 (1-2), 57-63 , 2012 2012 Citations: 37
Ocular surface temperature measurement in diabetic retinopathy B Chandrasekar, AP Rao, M Murugesan, S Subramanian, D Sharath, ... Experimental Eye Research 211, 108749 , 2021 2021 Citations: 34
Effect of defect size on defect depth quantification in pulsed thermography VB Sharath D, M Menaka Measurement Science and Technology 24, 125205 , 2013 2013 Citations: 22
Breast cancer lesion detection from cranial-caudal view of mammogram images using statistical and texture features extraction N Kavya, N Sriraam, N Usha, D Sharath, B Venkatraman, M Menaka Research Anthology on Medical Informatics in Breast and Cervical Cancer, 632-644 , 2023 2023 Citations: 19
Breast cancer detection using non invasive imaging and cyber physical system N Kavya, N Usha, N Sriraam, D Sharath, P Ravi 2018 3rd international conference on circuits, control, communication and … , 2018 2018 Citations: 12
Biofilm formation and thermographic evaluation of fly ash concrete in sea water V Vishwakarma, RP George, D Ramachandran, M Menaka, D Sharath, ... Concrete Research Letters 3 (2), 426-438 , 2012 2012 Citations: 11
Feature selection using neighborhood component analysis with support vector machine for classification of breast mammograms N Kavya, N Sriraam, N Usha, D Sharath, B Hiremath, M Menaka, ... International Conference on Communication, Computing and Electronics Systems … , 2020 2020 Citations: 8
Downscaling of wind fields on the east coast of India using deep convolutional neural networks and their applications in storm surge computations SS Kolukula, PLN Murty, B Baduru, D Sharath, F PA Journal of Water and Climate Change 15 (4), 1612-1628 , 2024 2024 Citations: 6
Feature selection and classification for analysis of breast thermograms N Usha, N Sriraam, N Kavya, D Sharath, R Prabha, B Hiremath, ... 2019 2nd international conference on signal processing and communication … , 2019 2019 Citations: 6
A deep learning approach based defect visualization in pulsed thermography SS Selvan, S Delanthabettu, M Murugesan, V Balasubramaniam, ... IAES International Journal of Artificial Intelligence 11 (3), 949 , 2022 2022 Citations: 5
Image definition and defect sizing in lock-in thermography: an experimental investigation D Sharath, M Menaka, B Venkatraman Experimental Techniques 46 (5), 811-822 , 2022 2022 Citations: 4
Comparison of pulsed and lock-in thermography techniques for debond detection in Ni-B coatings D Sharath, M Menaka, B Venkatraman Materials Evaluation 77 (12), 1450-1462 , 2019 2019 Citations: 4
Deep learning with filtering for defect characterization in pulsed thermography based non-destructive testing SS Selvan, S Delanthabettu, M Murugesan, V Balasubramaniam Int J Electr Comput Eng (IJECE) 14 (1), 1027-1040 , 2024 2024 Citations: 3
Asymmetrical analysis of breast thermal images for detection of breast cancer N Kavya, N Sriraam, N Usha, D Sharath, P Ravi, B Hiremath, ... Advances in Non-destructive Evaluation: Proceedings of NDE 2019, 249-260 , 2021 2021 Citations: 2
Defect depth detectability in Austenitic stainless steel by lock in thermography M Menaka, D Sharath, B Venkratraman, B Raj Proc. 12th QIRT, Bordeaux , 2014 2014 Citations: 2
Defect Depth Quantification Using Pulsed Thermography D Sharath, M Menaka, B Venkatraman Advanced Materials Research 585, 72-76 , 2012 2012 Citations: 2
