Publications:

Prof. BVVSN Prabhakar Rao

Journal Papers

1. Priyanka BG, Plava Kattamuri, BVVSN Prabhakar Rao, Memristor Based Image Fusion Architecture Using Iterative Kernel PCA"(submitted to International Journal of Information Technology)

2. Dheerendranath Battalapalli, BVVSN Prabhakar Rao, P. Yogeeswari, C. Kesavadas, Rajagopalan.V,  “Fractal Dimension: Analyzing its Potential as a Neuroimaging Biomarker for Brain Tumor Diagnosis” (submitted to Journal of Neuroscience Methods)

3. Souvik Kundu, Priyanka B. Ganganaik, Jeffry Louis, Hemanth Chalamalasetty, and BVVSN Prabhakar Rao. "Memristors Enabled Computing Correlation Parameter In-Memory System: A Potential Alternative to Von Neumann Architecture." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 30, no. 6 (2022): 755-768.  

4. Malayappan, Balasubramanian, U. Poorna Lakshmi, BVVSN Prabhakar Rao, Kannan Ramaswamy, and Prasant Kumar Pattnaik. "Sensing Techniques and Interrogation Methods in Optical MEMS Accelerometers: A Review." IEEE Sensors Journal (2022).

5. Dheerendranath Battalapalli, BVVSN Prabhakar Rao, P. Yogeeswari, C. Kesavadas, Rajagopalan.V “An optimal brain tumor segmentation algorithm for clinical MRI dataset with low resolution and non-contiguous slices”  BMC Medical Imaging , 22, Article Number 89 (2022).

6. Aditya Viswakumar, Priyanka B Ganganaik, P. Michael Preetham Raj, Prof. BVVSN Prabhakar Rao, Dr. Souvik Kundu, "Memristor-Based In-Memory Processor for High Precision Semantic Text Classification", Computers and Electrical Engineering Journal (Elsevier) 92 (2021) 107160

7. Balasubramanian M, Poornalakshmi U, Kannan Ramaswamy, BVVSN Prabhakara Rao and Prasant Kumar Pattnaik, “Enhancing Functionality of Free-Space and Guided-Wave Optical MEMS Devices by Integration with Photonic Circuits”, Optik, Vol.243, 166955, October 2021. 

8. Priyanka B Ganganaik, G. Abhijith, P Michael Preetam Raj, H. Renuka, Prof. BVVSN Prabhakar Rao,  Dr. Souvik Kundu, "Realization of Memristive State Machine for Smart Edge Detector Applications", IETE Journal of Research (Taylor & Francis), 2020

9. Mary Salve, Aurnab Mandal, Khairunnisa Amreen, BVVSN Prabhakar Rao, Prasant Kumar Pattnaik, Sanket Goel, “Portable 3D printed Electrochemiluminescence Platform with Pencil Graphite Electrodes for Point of Care multiplexed analysis with Smartphone based Read-out”, IEEE Transactions on Instrumentation and Measurement, 70 (2020)

10. Bharathwaj Suresh, Pavan Kumar Reddy Boppidi, B V V S N Prabhakar Rao, Souri Banerjee and Souvik Kundu Realizing spike-timing dependent plasticity learning rule in Pt/Cu:ZnO/Nb:STO memristors for implementing single spike based denoising autoencoder;; Journal of Micromechanics and Microengineering, 29 (2019) 085006 (9pp)

11. P Joshna, S R Gollu, P Michael Preetam Raj, B V V S N Prabhakar Rao, Parikshit Sahatiya and Souvik Kundu,, Plasmonic Ag nanoparticles arbitrated enhanced photodetection in p-NiO/n-rGO heterojunction for future self-powered UV photodetectors, Nanotechnology 30 (2019) 365201 (11pp)

12. Manu Gupta, Dr.Venkateswaran Rajagopalan, Prof. B.V.V.S.N.Prabhakar Rao, "Glioma grade classification using wavelet transform-local binary pattern based statistical texture features and geometric measures extracted from MRI," Journal of Experimental & Theoretical Artificial Intelligence (Taylor & Francis), Vol. 31:1, (2018) pp. 57-76.      

13. Paramkusham, Spandana, Kunda MM Rao, and BVVSN Prabhakar Rao. "Comparison of rotation invariant local frequency, LBP and SFTA methods for breast abnormality classification." International Journal of Signal and Imaging Systems Engineering, vol.11, no. 3, (2018) pp: 136-150, 

14. Manu Gupta, Dr.Venkateswaran Rajagopalan, Dr.Erik P Pioro, Prof. B.V.V.S.N. Prabhakar  Rao, " Volumetric analysis of MR images for glioma classification and their effect on brain tissues" Signal, Image and Video Processing (Springer), 2017.

15. Madhuri Bayya, Dr. BVVSN Prabhakar Rao, Rao, UM, Dr. N. Moorthy Muthukrishnan, Battery State Estimation using AC analysis, Elsevier Energy Procedia, Vol.117, (2017), pp. No. 739-744.  

16. P. Spandana, KMM Rao, BVVSN Prabhakar Rao, "Comparison of Rotation Invariant Local Frequency, LBP and SFTA methods for Breast abnormality classification" International Journal of signal and imaging systems Engineering, Inderscience Publishers, 2017 

Conference Papers

1. Manu Gupta, Prof. B.V.V.S.N.Prabhakar Rao, Dr.Venkateswaran Rajagopalan, "Brain Tumor Detection in conventional MR Images based on Statistical Texture and Morphological Features",  15th  IEEE International Conference on Information Technology (ICIT) 2016, IIIT Bhubaneswar, India, December 22-24 (2016).

2. Rahul Dubey, Sasikal Nath, K Harsha, D. Rama Si Vinay, Madhuri Bayya, Dr. BVSSN Prabhakara Rao, U.M.Rao, Dr.N.Moorthy Muthukrishnan, “Smart Home power Management”, IEEE HTC 2016, Dec 21-23, (2016), Agra, India.

3. Spandana Paramkusham, Mmrao Kunda and Prabhakar Rao B.V.V.S.N “Novel technique for the detection of abnormalities in Mammograms using texture and geometric features” In Microwave, Optical and Communication Engineering (ICMOCE), 2015 International Conference on  Dec 18 (2015) (pp. 150-153). IEEE.

4. Spandana Paramkusham, Shivakshit Patri, Mmrao Kunda and Prabhakar Rao B.V.V.S.N, “A New Features Extraction Method based on Polynomial Regression for the assessment of Breast Lesion Contours”, IEEE Int. Conf. on Industrial Instrumentation and Control, Pune, India, May, (2015(

5. Manu Gupta, B.V.V.S.N.Prabhakar Rao, Venkateswaran Rajagopalan, Abhijit Das, C.Kesavadas “Volumetric Segmentation of Brain Tumor Based on Intensity Features of Multimodality Magnetic Resonance Imaging”, IEEE Int. Conf. on Computers, communication and control, Indore, India, September (2015)

Book Chapter(s) 

1. Priyanka B.Ganganaik, Omkar Mukul Gowaikar, V. Jeffry Louis, Rajesh K. Tripathy, Venkateswaran Rajagopalan, BVVSN Prabhakar Rao, and Souvik Kundu. "Implementation of Binary Particle Swarm Optimization for Image Thresholding using Memristor Crossbar Array." In Advances in Electrical and Computer Technologies, pp. 915-936. Springer, Singapore, 2022.

2. Dheerendranath B, BVVSN Prabhakar Rao, P. Yogeeswari, C. Kesavadas, Venkateswaran Rajagopalan, “A review on Brain Tumor Segmentation Algorithms using recent deep neural network architectures and a gentle introduction to deep neural network concepts”, 2020 ( in Book “Microelectronics and Signal Processing”, Taylor & Francis)

3. Manu Gupta, Dr.Venkateswaran Rajagopalan, Prof. B.V.V.S.N.Prabhakar Rao, "Non-invasive Biomarker," Higher Education ,Times of India, pp.161-162, Feb 2017.

Prof. R.Venkateswaran

Journal Articles

1. Sreejith. V, BVVSN. Prabhakar Rao, Yogeeswari. P, Kesavadas C. Rajagopalan.V, “ Deep Learning classifies Low and High-grade glioma patients with high accuracy, sensitivity, and specificity based on their brain white matter networks derived from diffusion tensor imaging, “ Diagnostics 2022, 12 (12), 3216; https://doi.org/10.3390/diagnostics12123216 (Impact factor 3.99)

2. Rajagopalan.V, Erik. P. Pioro “Hypometabolic and hypermetabolic brain regions in patients with ALS-FTD show distinct patterns of grey and white matter degeneration: A pilot multimodal neuroimaging study”, European Journal of Radiology, 158 (1-2), 2023, 110616, Doi, 10.1016/j.ejrad.2022.110616  (impact factor 4.53)

3. Rajagopalan.V, Erik. P. Pioro “Graph theory network analysis provides brain MRI evidence of a partial continuum of neurodegeneration in patients with UMN-predominant ALS and ALS-FTD”  Neuroimage Clinical,  8 May, 2022,  103037,  https://doi.org/10.1016/j.nicl.2022.103037

(impact factor 4.88)

4. Dheerendranath Battalapalli, BVVSN Prabhakar Rao, P. Yogeeswari, C. Kesavadas, Rajagopalan.V “An optimal brain tumor segmentation algorithm for clinical MRI dataset with low resolution and non-contiguous slices”  BMC Medical Imaging , 22, Article Number 89, 2022 ( impact factor 1.93)

5. Aditya Viswakumar, Rajagopalan. V, Tathagata Ray, Pranitha Gottipati and Chandu Parimi, “ Development of a Robust, Simple, and Affordable Human Gait Analysis System using Bottom-up Pose Estimation with a Smartphone Camera”,  Front. Physiol 05 January 2022 doi:10.3389/fphys.2021.784865 (impact factor 4.75)

6. Rajagopalan.V, Erik. P. Pioro “Corticospinal tract and related grey matter morphometric shape analysis in ALS phenotypes: A fractal dimension study” Brain Sciences, 2021, 11(3), 371; https://doi.org/10.3390/brainsci11030371 (registering DOI) (impact factor 3.3)

7. Rajagopalan.V, Erik. P. Pioro. “Degeneration of grey and white matter differs between hypometabolic and hypermetabolic brain regions in a patient with ALS-FTD: A longitudinal MRI-PET multimodal study” Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, 2020 Sep 12;1-6 (impact factor 3.52)

8. Rajagopalan.V, Erik. P. Pioro, “18F-FDG PET, cortical thickness, and white matter graph network abnormalities in ALS-FTD patient brains suggest early neuronopathy rather than axonopathy” European Journal of Neurology, 2020, 27 (10), 1904-1912, (impact factor 6.28)

9. Rajagopalan.V, Erik P. Pioro “Unbiased MRI Analyses Identify Micropathologic Differences Between Upper Motor Neuron-Predominant ALS Phenotypes”  Frontiers in Neuroscience, Front. Neurosci., 12 July 2019 https://doi.org/10.3389/fnins.2019.00704 (impact factor 5.15)

10. Rajagopalan.V, Erik P. Pioro “Longitudinal 18F-FDG PET and MRI Reveal Evolving Imaging Pathology That Corresponds to Disease Progression in a Patient With ALS-FTD”  Frontiers in Neurology, Front. Neurol., 19 March 2019  https://doi.org/10.3389/fneur.2019.00234 (impact factor 4.0) 

11. Manu Gupta, Rajagopalan.V, B.V.V.S.N.Prabhakar Rao, Glioma grade classification using wavelet transform-local binary pattern based statistical texture features and geometric measures extracted from MRI, Journal of experimental & theoretical artificial intelligence, 2019, 31:1, 57-76 (impact factor 1.73) 

12. Rajagopalan.V and Abhijit Das Luduan Zhang,, Frank Hillary, Glenn R Wylie, Guang H Yue Fractal dimension brain morphometry: A novel approach to quantify white matter in Traumatic Brain Injury,  Brain Imaging and Behavior  2018 Jun 16. doi: 10.1007/s11682-018-9892-2. [Epub ahead of print]  Springer (impact factor 3.22)

13. Sheela kumari R , Rajagopalan.V, Anuvitha C, Tinu Varghese, Luduan Zhang, Guang H Yue, P.S. Mathuranath, C.Kesavadas, Quantitative analysis of grey matter degeneration in FTD patients using fractal dimension analysis, Brain Imaging and Behavior 2017 Oct 30. doi: 10.1007/s11682-017-9784-x. [Epub ahead of print]  Springer (impact factor 3.22)

14. Manu Gupta, Rajagopalan.V,Erik P. Pioro, B. V. V. S. N. Prabhakar Rao, "Volumetric analysis of MR images for glioma class Impact Factorization and their effect on brain tissues", Signal, Image and Video Processing  2017 pp 1–9 (eprint) Springer (impact factor 1.102)

15. Rajagopalan V, Zhiguo Jiang,  Jelena Stojanovic-Radic, Guang H Yue, Erik P Pioro, Glenn R Wylie, and Abhijit Das "A Basic Introduction to Diffusion Tensor Imaging Mathematics and Image Processing Steps" Brain Disord Ther 2017, 6:229 (impact factor 1.0)  Highly Accessed

16. Rajagopalan V , Erik P. Pioro “Differential involvement of corticospinal tract fibers in ALS phenotypes: A diffusion tensor tractography and imaging study ”Neuroimage Clinical, Volume 14, 2017, Pages 574–579. (impact factor 4.88)

17. Rakesh B. Pilkar, Mathew Yarossi, Arvind Ramanujam, Rajagopalan V, Meghan Mitchell,Stephen Canton and Gail Forrest, “Application of Empirical Mode Decomposition Combined with Notch Filter to Interpret the Surface Electromyograms during Functional Electrical Stimulation” IEEE Trans Neural Syst Rehabil Eng. 2016 Nov 3. [Epub ahead of print] (impact factor 4.52)

18. Rajagopalan V, Erik P. Pioro “Brain Parenchymal fraction: A simple useful measure to distinguish ALS phenotypes clinically” Biomed Res Int. 2015;2015:693206. doi: 10.1155/2015/693206 (impact factor 2.13)

19. Rajagopalan V, Pioro EP  “Disparate voxel based morphometry (VBM) results between SPM and FSL softwares in ALS patients with frontotemporal dementia: which VBM results to consider?” BMC Neurol.(Part of Springer Nature) 2015 Dec;15(1):274. doi: 10.1186/s12883-015-0274-8. Epub 2015 Mar 13.  (impact factor 2.23)  Highly Accessed 

20. Rajagopalan V , Erik P. Pioro “Comparing brain structural MRI and metabolic FDG-PET changes in patients with ALS-FTD: “The chicken or the egg? question” J Neurol Neurosurg Psychiatry. 2015 Sep;86(9):952-8. doi: 10.1136/jnnp-2014-308239. Epub 2014 Dec 17 (impact factor 13.65)

21. Genova HM, Rajagopalan V, Nancy Chiaravalloti, Binder A, Deluca J, Jeannie Lengenfelder, “Facial Affect Recognition Linked to Damage in Specific White Matter Tracts in Traumatic Brain Injury” Soc Neurosci. 2015 Feb;10(1):27-34. doi: 10.1080/17470919.2014.959618. Epub 2014 Sep 16. (impact factor 2.87)

22. Cai B, Allexandre D, Rajagopalan V, Jiang Z, Siemionow V, Ranganathan VK, Davis MP, Walsh D, Dai K, Yue GH "Evidence of SignImpact Factoricant Central Fatigue in Patients with Cancer-Related Fatigue during Repetitive Elbow Flexions till Perceived Exhaustion" PLoS One. 2014 Dec 22;9(12):e115370. doi: 10.1371/journal.pone.0115370. eCollection 2014. (impact factor 2.13)

23. Rajagopalan V, Erik P. Pioro “Distinct patterns of grey matter atrophy in brains of ALS patients with or without dementia: A voxel based morphometry study” Amyotroph Lateral Scler Frontotemporal Degener. 2014 Jun;15(3-4):216-25. doi: 10.3109/21678421.2014.880179. Epub 2014 Feb 20 (impact factor 3.52)

24. Rajagopalan V, Guang H. Yue, and Erik P. Pioro “Do preprocessing algorithms and statistical models influence VBM results in ALS patients: A systematic comparison of two VBM analytical methods ” J Magn Reson Imaging. 2014 Sep;40(3):662-7. doi: 10.1002/jmri.24415. Epub 2013 Oct 31. (impact factor 5.11, an ISMRM Journal)

25. Genova HM, Rajagopalan V, Deluca J, Das A, Binder A, Arjunan A, Chiaravalloti N, Wylie G “Examination of cognitive fatigue in multiple sclerosis using functional magnetic resonance imaging and diffusion tensor imaging” PLoS One. 2013 Nov 1;8(11):e78811. doi: 10.1371/journal.pone.0078811. eCollection 2013

26. Rajagopalan V, Zao Liu , Didier Allexandre, Erik P Pioro, Luduan Zhang ,  Guang H Yue “Brain white matter shape changes in amyotrophic lateral sclerosis (ALS): A Fractal dimension study PLoS One. 2013 Sep 9; 8(9):e73614. doi: 10.1371/journal.pone.0073614 

27. Glenn Wylie, Venkateswaran Rajagopalan, Abhijit Das, Luduan Zhang, Guang Yue. “Fractal Dimension Assessment of Brain Morphometry as A Novel Biomarker in Traumatic Brain Injury” Journal of Head Trauma Rehabilitation 28(5):E58-E58 September 2013

28. Rajagopalan V ,Guang H. Yue, Erik P. Pioro “Brain white matter dImpact Factorfusion tensor metrics from clinical 1.5T MRI distinguish between ALS phenotypes”  ” Journal of Neurology. 2013, Vol 260,Issue 10, Page 2532-2540 (impact factor 6.68) 

29. Rajagopalan V, Mark J. Lowe, Erik B. Beall, Guang H. Yue, Erik P. Pioro “T2 relaxometry measurements in low spatial frequency brain regions dImpact Factorfer between fast spin-echo and multiple echo spin echo sequences” Magnetic Resonance Materials in Physics, Biology and Medicine 2013, Vol 26, issue 5, 443-450. (impact factor 2.53)

30. Rajagopalan V, Didier Allexandre, Guang H Yue, Erik P Pioro “Diffusion Tensor Imaging Evaluation of corticospinal tract hyperintensity in Upper motor neuron predominant ALS patients ” J Aging Res. 2011;2011:481745 doi: 10.4061/2011/481745 Epub 2011 Oct 26

 Book Chapters

1. B Dheerendranath, BP Rao, P Yogeeswari, C Kesavadas, V Rajagopalan A Review on Brain Tumor Segmentation Algorithms Using Recent Deep Neural Network Architectures and a Gentle Introduction to Deep Neural Network Concepts, Microelectronics and Signal Processing, 227-244

2. Souvik Kundu Priyanka B G, Omkar Mukul Gowaikar, Jeffry LouisJeffry Louis, Rajesh Tripathy, Venkateswaran Rajagopalan, BVVSN Prabhakar Rao, Implementation of Binary Particle Swarm Optimization for Image Thresholding using Memristor Crossbar Array, Advances in Electrical and Computer Technologies, Select Proceedings of ICAECT 2021, 915–936

Conferences

1. EP Pioro, V Rajagopalan Multimodal Neuroimaging Reveals Differential Gray and White Matter Degeneration in Hypo-and Hypermetabolic Brain Regions of Patients with ALS-FTD (P1-1. Virtual), American Academy of Neurology Conference, Published in Neurology, 2022,  98 (18 Supplement), Impact Factor 9.901

2. E Pioro, V Rajagopalan, Graph Theory Network Analysis Distinguishes Regional Brain Changes in UMNpredominant ALS Patients With and Without Corticospinal Tract Hyperintensity (P4. 440)

American Academy of Neurology Conference, Neurology, 2018, 90 (15 Supplement), Impact Factor 9.901

3. Aditya Viswakumar, Venkateswaran Rajagopalan, Tathagata Ray, Chandu Parimi , A Robust, Simple, and Affordable Human Gait Analysis System using Bottom-up Pose Estimation with a Smartphone Camera ( accepted for presentation at International Conference on. Image Information Processing (ICIIP -2019). November 15 - 17 , 2019. Jaypee University of Information Technology) 

4. Venkateswaran Rajagopalan, Erik P Pioro, "Widespread Brain Changes Differentiated by Graph Theory Network Analysis in UMN-predominant ALS Patients With and Without Corticospinal Tract Hyperintensity" , International Symposium on ALS-MND , Glasgow, UK, Dec 7-9, 2018   

5. Venkateswaran Rajagopalan, Erik P Pioro, "UMN predominant ALS patients with or without cortical spinal tract hyperintensity are distinct phenotypes: Evidence from Graph Theory Network Analysis" American Academy of Neurology (AAN), Los Angeles, USA, April 21-27, 2018,

6. Chandu Parimi, Venkateswaran R, Sanketh V Sai, Vishal B Athreya, Prakash Shrivastava and Tathagata Ray  ," MS Kinect a potential gait analysis system: Clothing Effect on the gait kinematics" Presented in International Society of Biomechanics Brisbane, Australia,  July 2017 

7. Manu Gupta, B.V.V.S.N.PrabhakarRao, Venkateswaran Rajagopalan Brain Tumor Detection in conventional MR Images based on Statistical Texture and Morphological Features",   15th  IEEE International Conference on Information Technology (ICIT) 2016, IIIT Bhubaneswar, India, December 22-24, 2016 

8. Manu Gupta, K.S.Gayatri, K.Harika, Dr. B.V.V.S.N.PrabhakarRao, Venkateswaran Rajagopalan, Dr. Abhijit Das, Dr.C.Kesavadas, " Brain Tumor Segmentation by Integrating Symmetric Property with Region Growing Approach", IEEE INDICON 2015, Jamia Millia Islamia, New Delhi, India, 17-20 December 2015.

9. Venkateswaran Rajagopalan, Erik P Pioro, "Sequential PET and MRI in a patient with ALS-FTD reveal worsening brain metabolism and cortical thinning with disease progression" ALS/MND Symposium, Miami, Florida, USA, Dec 2015 

10. Manu Gupta, B.V.V.S.N. Prabakar Rao, Venkateswaran Rajagopalan, Abhijit Das, C.Kasavadas, " Volumetric Segmentation of Brain Tumor Based on Intensity Features of Multimodality Magnetic Resonance Imaging" (accepted for presentation) at IEEE International Conference on Computer, Communication and Control, MGI Indore, INDIA. September 10 -12, 2015. Paper identification number (IC4_5214)

11. Venkateswaran Rajagopalan, Abhijit Das, Luduan Zhang, Glenn R Wylie, and Guang H Yue “What's in a shape? Fractal dimension assessment of brain morphometry as a biomarker to predict cognitive outcome in Traumatic Brain Injury” (Presented at Society for Neuroscience Meeting, Nov, 2013) 

12. Didier Allexandre, Bin Cai, Venkateswaran Rajagopalan, Zhiguo Jiang, Guang Yue “Understanding the neuromuscular mechanism of cancer-related fatigue-a preliminary study” (Presented at Society for Neuroscience Meeting, Nov, 2013 )

13. David A Cunningham, Andre Machado,  Venkateswaran Rajagopalan, Mark J Lowe, Stephen Jones, Erik Beall, Ken Sakaie, and Ela B Plow “DTI versus fMRI: accuracy and reliability in predicting response to TMS in Stroke  (American Neurological Academy,  Nov 2013)

14. Glenn R Wylie, Venkateswaran Rajagopalan, Luduan Zhang, Guang H Yue, Abhijit Das “Fractal dimension assessment of brain morphometry as a novel biomarker in Traumatic Brain Injury (Presented at North American Brain Injury Society, Sep 2013) 

15. Venkateswaran Rajagopalan, Luduan Zhang, Glenn R.Wylie, Guang H Yue  "Reduced complexity level of brain white matter structure in traumatic brain injury"  ISPRM conference China, June, 2013 (shortlisted among 9 finalists for young investigator award) 

16. Venkateswaran  Rajagopalan,  Guang  H.  Yue,  Erik  P.  Pioro  "Differential  Involvement of Corticospinal  Tract  Fibers in UMN-predominant  ALS  Patients"  Presented  at  3 rd   Neuroimaging society in ALS, 8-9 December 2012, Miami, Florida. 15. 

17. Venkateswaran  Rajagopalan,  Guang  H.  Yue,  Erik  P.  Pioro  "Differential  Involvement of Corticospinal  Tract  Fibers in UMN-predominant  ALS  Patients:  A  Diffusion  Tensor  Imaging and Tractography Study " Presented at 23rd International Symposium on ALS/MND, 5-7 December 2012 Chicago

18. Bonnett C, Cunningham D, Wyant A, Bayram MB, Janini D, Rajagopalan V, Mamone B, Hou J, Siemionow V, Plow EB, Yue GH. Central Neural Adaptations Underlying Voluntary Muscle Strengthening in Healthy Aged Individuals. Soc. Neurosci. Abstr., 582.22, 2012 

19. Venkateswaran Rajagopalan, Guang H. Yue, Erik P. Pioro "Cortical grey matter atrophy in ALS  patients  with  or  without  cognitive  impairment:  A  voxel  based  morphometry  study"  poster presented at Society for Neuroscience 2011 Washington DC USA. 

20. Zao Liu, Venkateswaran Rajagopalan, Guang  H. Yue, Erik P. Pioro " Fractal dimension analysis  of  brain  white  matter  complexity  in  amyotrophic  lateral  sclerosis  (ALS)  patients" poster presented at Society for Neuroscience 2011 Washington DC USA

21. Venkateswaran  Rajagopalan,  S  Radhakrishnan "Acquisition  and  Analysis  of  Electro-Oculogram  in  Normal  and  Best's  Dystrophy  Patients",  published  in  21 st   Southern  Bio-Medical Engineering Conference, Washington D.C., September 2002. 

22. Venkateswaran Rajagopalan, S Radhakrishnan "Classification of Night Blindness disorders by Electroretinogram signal analysis", JIPMER, Pondichery, October 2002.

23. Venkateswaran  Rajagopalan,  S  Radhakrishnan  "Analysis  and  classification  of Electroretinogram in Normals and Diseased Subjects" ICBME, Singapore 2002

24. Venkateswaran  Rajagopalan,  S  Radhakrishnan  "Classification  of  Central  Retinal  Vein Occlusion and Foveal Schisis by Electroretinogram signal Analysis ", IEEE Tencon, Bangalore 2003.

Dr. Rajesh Kumar Tripathy

Journal Papers

Year 2023

1. Chauhan, Chhaviraj, Rajesh Kumar Tripathy, and Monika Agrawal. "Patient specific higher order tensor based approach for the detection and localization of myocardial infarction using 12-lead ECG." Biomedical Signal Processing and Control 83 (2023): 104701.

2. Bhaskarpandit, Sathvik, Anurag Gade, Shaswati Dash, Dinesh Kumar Dash, Rajesh Kumar Tripathy, and Ram Bilas Pachori. "Detection of Myocardial Infarction From 12-Lead ECG Trace Images Using Eigendomain Deep Representation Learning." IEEE Transactions on Instrumentation and Measurement 72 (2023): 1-12.

3. Chauhan, Chhaviraj, Rajesh Kumar Tripathy, and Monika Agrawal. "Tensor-Domain Machine Learning Based Cardiac Diseases Detection Using 12-lead ECG." In 2023 National Conference on Communications (NCC), pp. 1-5. IEEE, 2023.

4. Dash, Shaswati, Rajesh Kumar Tripathy, Dinesh Kumar Dash, Ganapati Panda, and Ram Bilas Pachori. "Multiscale Domain Gradient Boosting Models for the Automated Recognition of Imagined Vowels Using Multichannel EEG Signals." IEEE Sensors Letters 6, no. 11 (2022): 1-4.

Year 2022

5. Sresth Gupta, Anurag Singh, Abhishek Sharma, RK Tripathy, ‘dSVRI: A PPG-based Novel Feature for Early Diagnosis of Type-II Diabetes Mellitus’, IEEE Sensors Letters, 2022.  

6. SK Ghosh, RN Ponnalagu, RK Tripathy, Ganapati Panda, RB Pachori, "Automated Heart Sound Activity Detection from PCG Signal using Time-frequency Domain Deep Neural Network", IEEE Transactions on Instrumentation and Measurement, 2022. 

7. Nabanita Sinha, RK Tripathy, Arpita Das, "ECG beat Classification based on Discriminative Multilevel Feature Analysis and Deep Learning Approach", Biomedical Signal Processing and control, Elsevier, 2022.  

8. ME Zayed, VP Katekar, AH Elsheikh, RK Tripathy, SS Deshmukh, "Predicting the yield of stepped corrugated solar distiller using kernel-based machine learning models, Applied Thermal Engineering, Elsevier, 2022. 

9. RK Tripathy, Shaswati Dash, Adyasha Rath, Ganapati Panda, Ram Bilas Pachori, "Automated Detection of Pulmonary Diseases from Lung Sound Signals using Fixed Boundary based Empirical Wavelet Transform", IEEE Sensors Letters, 2022. 

10. Jay Karhade, Shaswati Dash, SK Ghosh, DK Dash, RK Tripathy, "Time-Frequency Domain Deep Learning Framework for the Automated Detection of Heart Valve Disorders using PCG Signals", IEEE Transactions on Instrumentation and Measurement, 2022. https://ieeexplore.ieee.org/document/9744120

11. RK Tripathy, MA Paternina, JA De La O Serna, "Editorial: Machine Learning and Deep Learning for Physiological Signal Analysis", Frontiers in Physiology, 2022.  https://www.frontiersin.org/articles/10.3389/fphys.2022.887070/full

12. Neha Muralidharan, Shaurya Gupta, Manas Prusty, RK Tripathy, "Detection of COVID19 from X-ray Images using Multiscale Deep Convolutional Neural Network", Applied Soft Computing, Elsevier, 2022. www.sciencedirect.com/science/article/pii/S1568494622001119

13. Shaswati Dash,  RK Tripathy, Ganapati Panda, Ram Bilas Pachori, "Automated Recognition of Imagined Commands from EEG Signals using Multivariate Fast and Adaptive Empirical Mode Decomposition based Method", IEEE Sensors Letters, 2022. ieeexplore.ieee.org/abstract/document/9681253 

14. Phattarapong Sawangjai, Manatsanan Trakulruangroj, Chiraphat Boonnag, Maytus Piriyajitakonkij, RK Tripathy, Thapanun Sudhawiyangkul, Therawitt Wilaiprasitporn, "EEGANet: Removal of Ocular Artifact from the EEG Signal Using Generative Adversarial Networks", IEEE Journal of Biomedical and Health Informatics, 2022. ieeexplore.ieee.org/document/9627782

15. Rajesh Reddy Yakkati, Rakesh Reddy Yakkati, RK Tripathy, Linga Reddy Cenkeramaddi, "Radio Frequency Spectrum Sensing by Automatic Modulation Classification in Cognitive Radio System using Multiscale Deep CNN", IEEE Sensors Journal, 2022. ieeexplore.ieee.org/document/9615184 code available at github.com/yrajeshreddy914/AMC_EWT

Year 2021

16. Jay Karhade, SK Ghosh, Pranjali Gajbhiye, RK Tripathy, UR Acharya, "Multichannel Multiscale Two-stage Convolutional Neural Network for the Detection and Localization of Myocardial Infarction using Vectorcardiogram Signal", MDPI Applied Sciences, 2021. https://www.mdpi.com/2076-3417/11/17/7920. code available at https://github.com/tripathy12345/MI_VCG_DL.

17. Tejas Radhakrishnan, Jay Karhade, SK Ghosh, PR Mudali, RK Tripathy*, UR Acharya, "AFCNNet: Automated detection of AF using Chirplet transform and Deep Convolutional Bidirectional Long Short Term Memory Network with ECG signals", Computers in Biology and Medicine, Elsevier, 2021, https://www.sciencedirect.com/science/article/pii/S0010482521005771. Code available at https://github.com/tripathy12345/AF_paper_code

18. Abhishek Varshney, SK Ghosh, Sibasankar Padhy, RK Tripathy, UR Acharya,"Automated Classification of Mental Arithmetic Tasks Using Recurrent Neural Network and Entropy Features Obtained from Multi-channel EEG Signals", MDPI Electronics, 2021. www.mdpi.com/2079-9292/10/9/1079 (IF-2.39)

19. D Maheswari, SK Ghosh, RK Tripathy*, M Sharma, UR Acharya, "Automated Accurate Emotion Recognition System using Rhythm-Specific Deep Convolutional Neural Network Technique with Multi-Channel EEG Signals", Computers in Biology and Medicine, Elsevier, 2021, code available at github.com/tripathy12345/multi-channel-eeg-deep-cnn-emotion-recognition. (IF-4.58)

20. SK Ghosh, RK Tripathy, RN Ponnalagu, "A Transform domain Approach for the Compression of Fetal Phonocardiogram Signal", IEEE Sensors Letters, 2021.ieeexplore.ieee.org/document/9411736

21. Pranjali Gajbhiye, N Mingchinda, W Chen, SC Mukhopadhyay, T Wilaiprasitporn*, RK Tripathy*, "Wavelet Domain Optimized Savitzky-Golay Filter for the Removal of Motion Artifacts from EEG Recordings", IEEE Transactions on Instrumentation and Measurement, Vol. 70, No. 4002111, 2021. doi.org/10.1109/TIM.2020.3041099,  code available at github.com/tripathy12345/WOSGfilteringIEEETIM (IF-4.01)

22. Abhijeet Bhattacharyya, RK Tripathy, L. Garg, RB Pachori, "A novel multivariate-multiscale approach for computing EEG spectral and temporal complexity for human emotion recognition", IEEE Sensors Journal, Vol. 21, issue-3, 2021. ieeexplore.ieee.org/document/9207950 (IF-3.30)

23. Rohan Panda, Sahil Jain, RK Tripathy*, RR Sharma, RB Pachori, "Sliding Mode Singular Spectrum Analysis for the Elimination of Cross-terms in Wigner-Ville Distribution", Circuits, Systems & Signal Processing, Springer, vol. 40, pp1207–1232, 2021. (*corresponding author) link.springer.com/article/10.1007/s00034-020-01537-0?wt_mc=Internal.Event.1.SEM.ArticleAuthorOnlineFirst, code available at github.com/tripathy12345/SMSSA_WVD (IF-2.22)

Year 2020

24. Samit Kumar Ghosh, RN Ponnalagu, RK Tripathy*, UR Acharya, "Deep Layer Kernel Sparse Representation Network for the Detection of Heart valve Ailments from the Time-Frequency Representation of PCG Recordings", Biomed Research International, Hindawi, 2020. www.hindawi.com/journals/bmri/2020/8843963/ (IF-3.41)

25. Piyush Jain, Pranjali Gajbhiye, RK Tripathy*, UR Acharya, "A two-stage CNN Architecture for the classification of Low-risk and High-risk hypertension classes using Multichannel ECG Signals", Informatics in Medicine Unlocked, Elsevier, 2020. www.sciencedirect.com/science/article/pii/S2352914820306304

26. RK Tripathy*, SK Ghosh, Pranjali Gajbhiye, UR Acharya*, "Development of automated sleep stages classification system using multivariate projection-based fixed boundary empirical wavelet transform and entropy features extracted from multichannel EEG signals", Entropy, MDPI Journal, 2020. www.mdpi.com/1099-4300/22/10/1141

27. Rohan Panda, Sahil Jain, RK Tripathy*, UR Acharya, "Detection of Shockable Ventricular Cardiac Arrhythmias from ECG Signals using FFREWT Filter-bank and Deep Convolutional Neural Network", Computers in Medicine and Biology, Elsevier, 2020. (*corresponding author) (IF: 4.58) https://www.sciencedirect.com/science/article/pii/S0010482520302742

28. RK Tripathy*, Pranjali Gajbhiye, UR Acharya, "Automated Sleep Apnea Detection from Cardio-Pulmonary Signal using Bivariate Fast and Adaptive EMD Coupled with Cross Time-Frequency Analysis ", Computers in Medicine and Biology, Elsevier, Vol. 120, May 2020, 103769 , 2020. (*corresponding author) (IF: 4.58) https://www.sciencedirect.com/science/article/abs/pii/S0010482520301414

29. SK Ghosh, RK Tripathy*, MRA Paternina, Juan J Arrieta, A. Z. Mendez, GR Naik, "Detection of Atrial Fibrillation from Single Lead ECG Signal using Multirate Cosine Filter bank and Deep Neural Network", Journal of Medical Systems, Springer, Vol. 44 (144) 2020. (*corresponding author) (IF: 4.46) https://link.springer.com/article/10.1007/s10916-020-01565-y

30. Samit Kumar Ghosh, RN Ponnalagu, RK Tripathy, UR Acharya, "Automated Detection of Heart Valve Diseases using Chirplet Transform and Multiclass Composite Classifier with PCG Signals ", Computers in Medicine and Biology, Elsevier, 2020. (IF-4.58) https://www.sciencedirect.com/science/article/pii/S0010482520300305

31. Sahil Jain, Rohan Panda, RK Tripathy*, "Multivariate Sliding Mode Singular Spectrum Analysis for the Decomposition of Multisensor Time series ", IEEE Sensors Letters, Vol. 4 (6), pp. 1-4, 2020.  (*corresponding author) https://ieeexplore.ieee.org/document/9097414

32. Himali Singh, RK Tripathy, RB Pachori, "Detection of sleep apnea from heart beat interval and ECG derived respiration signals using sliding mode singular spectrum analysis", Digital Signal Processing, Elsevier, 2020. (Q2 rated, IF: 2.87) https://www.sciencedirect.com/science/article/pii/S105120042030141X

33. T Siddharth, Pranjali Gajbhiye, RK Tripathy*, RB Pachori, "EEG based Detection of Focal Seizure Area using FBSE-EWT rhythm and SAE-SVM Network", IEEE Sensors Journal, 2020. (IF-3.30) (*corresponding author) https://ieeexplore.ieee.org/document/9096344

34. J. A. de la O Serna, MRA Paternina, A. Z. mendez, RK Tripathy*, RB Pachori, "EEG-Rhythm Specific Taylor-Fourier filter bank Implemented with O-splines for the Detection of Epilepsy using EEG Signals", IEEE Sensors Journal, 2020. (*corresponding author) (IF-3.30) https://ieeexplore.ieee.org/document/9017999

35. Pranjali Gajbhiye, RK Tripathy*, RB Pachori, "Elimination of Ocular Artifacts from single channel EEG Signals using FBSE-EWT based rhythms", IEEE Sensors Journal, 2020. (*corresponding author) (IF-3.30) https://ieeexplore.ieee.org/document/8932609

36. M. Srirangan, RK Tripathy*, RB Pachori, "Time-Frequency Domain Deep Convolutional Neural Network for the Classification of Focal and Non-Focal EEG Signals, IEEE Sensors Journal, 2020. (*corresponding author) (IF-3.30) https://ieeexplore.ieee.org/abstract/document/8913620

37. Pavan B, V. Jeffry Louis, A. Subramaniam, RK Tripathy, S. Banerjee and S. Kundu, "Implementation of Fast ICA Using Memristor Crossbar Arrays for Blind Image Source Separations", IET Circuits, Devices & Systems, 2020. (Q4 rated, IF: 1.27) https://digital-library.theiet.org/content/journals/10.1049/iet-cds.2019.0420

Year 2019

38. Viswabhargav Ch.S.S.S, RK Tripathy*, U R Acharya, "Automated Detection of Sleep Apnea using Sparse Residual Entropy Features with Various Dictionaries extracted From Heart rate and EDR signals", Computers in Biology and Medicine, Elsevier, Vol. 108, pp: 20-30, 2019. (*corresponding author) (IF-4.58) https://www.sciencedirect.com/science/article/pii/S0010482519300897

39. RK Tripathy*, Mario R. A. Paternina, Juan G. Arrieta, Alejandro Zamora-M endez, and Ganesh R. Naik, "Automated Detection of Congestive Heart Failure from Electrocardiogram Signal using Stockwell Transform and Hybrid Classification Scheme ", Computer Methods and Programs in Biomedicine , Elsevier, Vol. 173, pp: 53-65, 2019. (*corresponding author) (IF-5.42) https://www.sciencedirect.com/science/article/pii/S0169260718302980

40. T Siddharth, RK Tripathy*, RB Pachori, "Discrimination of Focal and Non-focal Seizures from EEG Signals using Sliding Mode Singular Spectrum Analysis ", IEEE Sensors Journal, Vol. 19 (24), pp: 12286 - 12296, 2019. (IF-3.30) (*corresponding author) https://ieeexplore.ieee.org/document/8826440

41. RK Tripathy*, Abhijeet Bhatacharyya, RB Pachori, "Localization of Myocardial Infarction from Multi-Lead ECG Signals using Multiscale Analysis and Convolutional Neural Network ", IEEE Sensors Journal, Vol. 19 (23), pp: 11437 - 11448 , 2019. (IF-3.30) (*corresponding author) https://ieeexplore.ieee.org/document/8801852

42. Pranjali Gajbhiye, RK Tripathy*, Abhijeet Bhatacharyya, RB Pachori, "Novel Approaches for the Removal of Motion Artifact from EEG recordings", IEEE Sensors Journal, Vol. 19 (22), pp: 10600 - 10608 , 2019. (IF-3.30) (*corresponding author) https://ieeexplore.ieee.org/document/8784397

43. RK Tripathy*, Abhijeet Bhattacharyya, RB Pachori, "A Novel Approach for Detection of Myocardial Infarction from ECG Signals of Multiple Electrodes", IEEE Sensors Journal, Vol. 19 (12), pp: 4509 - 4517, 2019. (*corresponding author) (IF-3.30)  https://ieeexplore.ieee.org/document/8630000

44. MRA Paternina, RK Tripathy, AZ Mendez, D Dotta, "Identification of Electromechnical Modes using Variational Mode Decomposition", Electric Power System Research (EPSR), Elsevier, 2019. (SCI, Scopus) Link: https://www.sciencedirect.com/science/article/pii/S0378779618303328?dgcid=rss_sd_all (Q2 rated, IF-3.21)

45. HP Tripathy, Priyabrata Pattanaik, DK Mishra, SK Kamilla, RK Tripathy, "A Model-based Approach to Validate the Aluminium Nitride Material based Ultrasonic MEMS Transceiver for Temperature Sensing", IET Micro and Nano Letters, Vol. 14 (3), pp. 280 – 285, 2019. (SCI, Scopus)Link: http://digital-library.theiet.org/content/journals/10.1049/mnl.2018.5266 (Q4 rated, IF-0.975)

46. Samit Kumar Ghosh, RK Tripathy*, R N Ponnalagu, RB Pachori, "Automated Detection of Heart Valve Disorders from PCG Signal using Time-Frequency Magnitude and Phase Features ", IEEE Sensors Letters, Vol. 3 (12), Dec, 2019. (*corresponding author) https://ieeexplore.ieee.org/document/8883037

Year 2018

47. R. K. Tripathy, A. Z. mendez, J. A. de la O Serna, MRA Paternina, J. G. Arrieta, G. R. Naik, "Detection of Life Threatening Ventricular Arrhythmia using Digital Taylor Fourier Transform", Frontiers in Physiology, 2018. (SCI-E, Scopus) Link: https://www.frontiersin.org/articles/10.3389/fphys.2018.00722/full (JCR Q1 rated, IF-3.367)

48. Daniel Guillen, Mario. R. Arrieta Paternina, Jose Ortiz-Bejar, R. K. Tripathy, Alejandro Zamora, Ruben Tapia-Olvera, Eric S. Tellez, "Fault detection and classification in transmission lines based on a power spectral density index", IET Generation, Transmission and Distribution, 2018 . (SCI, SCI-E, Scopus) Link: http://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2018.5062 (IF-3.22)

49. R. K. Tripathy*, U. R. Acharya, "Use of features from RR-time series and EEG signals for Automated Classification of Sleep Stages in Deep Neural Network Framework", Biocybernetics and Biomedical Engineering, Elsevier, 2018. (*corresponding author) Link: https://www.sciencedirect.com/science/article/pii/S0208521618300949 (IF-4.31)

50. H. P. Tripathy, D. Bej, P. Pattanaik, D.P Mishra, S. K. Kamilla, R. K. Tripathy, "Measurement of Zone Temperature Profile of a Resistive Heating Furnace Through RVM Model", IEEE Sensors Journal, 18(11), pp. 4429 - 4435, 2018. Link: https://ieeexplore.ieee.org/document/8337727/ (IF-3.30)

51. R. K. Tripathy*, "Application of intrinsic band function technique for automated detection of sleep apnea using HRV and EDR signals", Biocybernetics and Biomedical Engineering, Elsevier, 38(1), pp. 136-144, 2018. (*corresponding author) Link: https://www.sciencedirect.com/science/article/pii/S0208521617303133 (IF-4.31)

52. A. Chetan, R. K. Tripathy*, S. Dandapat, "A Diagnostic System for Detection of Atrial and Ventricular Arrhythmia Episodes from Electrocardiogram", Journal of Medical and Biological Engineering, Springer, 38(2), pp. 304-315, 2018. (SCI-E, Scopus) (*corresponding author) Link: https://link.springer.com/article/10.1007/s40846-017-0294-5 (SJR Q4 rated, IF-1.55)

53. LM Satapathy, RK Tripathy, P Das, "A Combination of Variational Mode Decomposition and Histogram Equalization for Image Enhancement ", National Academy Science Letters, Springer, 2018. (SJR Q4 rated, IF-0.416)Link: http://link-springer-com-443.webvpn.jxutcm.edu.cn/article/10.1007%2Fs40009-018-0742-y

54. S. Bagha, R.K. Tripathy, P. Nanda, C. Preetam, D. P. Das, "Understanding Perception of Active Noise Control System through Multichannel EEG Analysis", IET Healthcare Technology Letters, 2018 (Accepted) (ESCI, Scopus) Link: http://digital-library.theiet.org/content/journals/10.1049/htl.2017.0016 (SNIP-0.85)

Year 2017

55. R. K. Tripathy*, S. Dandapat, "Detection of Myocardial Infarction from Vectorcardiogram using Relevance Vector Machine", Signal, Image and Video Processing, Springer, 2017. (*corresponding author) (SCI-E, Scopus) Link: https://link.springer.com/article/10.1007/s11760-017-1068-9 (IF-2.15)

56. R. K. Tripathy*, Mario R. A. Paternina, Juan J. Arrieta, P. Pattnaik, "Detection of Atrial Fibrillation using Two-stage VMD and Atrial Fibrillation Diagnosis Index", Journal of Mechanics in medicine and biology, World Scientific, 17(8), 1-20, 2017. (*corresponding author) (9IF=0.859)  Link: https://www.worldscientific.com/doi/abs/10.1142/S0219519417400449 (IF-0.89)

57. R. K. Tripathy*, Mario R. A. Paternina, P. Pattnaik, "A new method for detection of diabetes from heart rate signal", Journal of Mechanics in medicine and biology, World Scientific, 17(7), 1-12, 2017. (*corresponding author) (9 IF=0.89) Link: https://www.worldscientific.com/doi/abs/10.1142/S0219519417400012

58. H. P. Tripathy, P. Pattanaik, S. K. Kamilla, R. K. Tripathy*, "A Simulation Approach to Study the Effect of Ultrasonic MEMS-based Receiver for Blood Glucose Sensing Applications", IEEE Sensors Letters, 1(5), 2017. (*corresponding author) Link: https://ieeexplore.ieee.org/document/8003313/

59. R. K. Tripathy*, S. Dandapat, "Automated Detection of Heart Ailments from 12-lead ECG using Complex Wavelet Subband Bispectrum Features", IET Healthcare Technology Letters, 4(2), 57-63, 2017. (*corresponding author) (ESCI, Scopus) Link: http://digital-library.theiet.org/content/journals/10.1049/htl.2016.0089 (SNIP-0.85)

60. R. K. Tripathy*, S. Deb, S. Dandapat, "Analysis of Physiological Signal using State Space Correlation Entropy", IET Healthcare Technology Letters, 4(1), 30-33, 2017. (*corresponding author) (ESCI, Scopus) Link: http://digital-library.theiet.org/content/journals/10.1049/htl.2016.0065 (SNIP-0.85): code available at github.com/tripathy12345/SSCE_measure

Year 2016

61. R. K. Tripathy*, S. Dandapat, "Detection of Cardiac Abnormalities from Multilead ECG using Multiscale Phase Alternation Features", Journal of Medical Systems, Springer, 40(6):1-9, 2016. (SCI-E, Scopus) (*corresponding author) Link: https://link.springer.com/article/10.1007%2Fs10916-016-0505-6 (IF-4.46)

62. R. K. Tripathy*, L. N. Sharma, S. Dandapat, "Detection of Shockable Ventricular Arrhythmia using Variational mode decomposition", Journal of Medical Systems, Springer, 40(4):1-13, 2016. (SCI-E, Scopus) (*corresponding author) Link: https://link.springer.com/article/10.1007/s10916-016-0441-5 (IF-4.46)

63. R. K. Tripathy*, L. N. Sharma, S. Dandapat, "Diagnostic measure to quantify the loss of Clinical Components in Multilead Electrocardiogram", IET Healthcare Technology Letters, 3(1): 1-6, 2016. (ESCI, Scopus) (*corresponding author) Link: http://digital-library.theiet.org/content/journals/10.1049/htl.2015.0047 (SNIP-0.85)

Year 2015

64. L. N. Sharma, R. K. Tripathy, S. Dandapat, "Multiscale energy and eigenspace approach to detection and localization of myocardial infarction", IEEE Transactions on Biomedical Engineering, 62(7):1827-1837, 2015. (IF-4.56) Link: https://ieeexplore.ieee.org/document/7047810/ (IF-4.49)

Year 2014

65. R. K. Tripathy, S. Mahanta, and S. Paul, "Artificial intelligence-based classification of breast cancer using cellular images", RSC Advances, Royal Society (UK), 4(18), 9349-9355, 2014. (SCI-E, Scopus) Link: http://pubs.rsc.org/en/content/articlelanding/2014/ra/c3ra47489e#!divAbstract (IF-3.04)

66. R. K. Tripathy*, L. N. Sharma, S. Dandapat, "A New way of Quantifying Diagnostic Information from Multilead Electrocardiogram for Cardiac Disease Classification", IET Healthcare Technology Letters, 1(4): 98-103, 2014. (*corresponding author) (ESCI, Scopus) Link: http://digital-library.theiet.org/content/journals/10.1049/htl.2014.0080 (SNIP-0.85)

67. D kumar, R. K. Tripathy, A. Acharya, "Least square support vector machine based multiclass classification of EEG signals", WSEAS Transactions on Signal Processing, Volume 10, Issue 1, Pages 86-94, 2014. (Scopus) https://www.scopus.com/record/display.uri?eid=2-s2.0-84896943041&origin=resultslist&sort=plf-f&src=s&sid=328fb23d0358fa9237f2cd07ed608149&sot=autdocs&sdt=autdocs&sl=18&s=AU-ID%2856035676800%29&relpos=30&citeCnt=5&searchTerm=

Year 2013

68. S. Behera, R. K. Tripathy, and S. Mohanty. "Least Square Support Vector Machine Modeling of Breakdown Voltage of Solid Insulating Materials in the Presence of Voids", Journal of The Institution of Engineers, Springer, 94(1), 21-27, 2013. (Scopus) Link: https://link.springer.com/article/10.1007/s40031-013-0039-y

Conference Papers

1. Manda, Haarika, Shaswati Dash, and Rajesh Kumar Tripathy. "Time-Frequency Domain Modified Vision Transformer Model for Detection of Atrial Fibrillation using Multi-lead ECG Signals." In 2023 National Conference on Communications (NCC), pp. 1-5. IEEE, 2023.

2. Shaswati Dash, SK Ghosh, RK Tripathy, Ganapati Panda, RB Pachori, "Fourier Bessel Domain based Discrete Stockwell Transform for the Analysis of Non-Stationary Signals", IEEE Indiscon, KIIT University, Bhubaneswar, India, 2022.  

3. SK Ghosh, RN Ponnalagu, RK Tripathy, "Classification of PCG Signals Using Fourier-Based Synchrosqueezing Transform and Support Vector Machine", IEEE Sensors International Conference, 2021. https://ieeexplore.ieee.org/abstract/document/9639687  

4. K Prudviraj, Sandip Deshmukh, RKTripathy, K. Supradeepan, Praveen Tandon, Pramod Kumar Jha, “Machine Learning-based Approach for the Prediction of an Orifice size of Aerospace Vehicle RCS Thrusters during Cold Flow Calibration”, 2021 IEEE 6th International Conference on Computing, Communication and Automation (ICCCA)

5. PB Ganganaik, OM Gowaikar, VJ Louis, RK Tripathy, V Rajagopalan, BP Rao, S Kundu, "Implementation of Binary Particle Swarm Optimization for Image Thresholding using Memristor Crossbar Array", Third International Conference on Advances in Electrical and Computer Technologies 2021 (ICAECT 2021).

6. Samit Kumar Ghosh, RN Ponnalagu, RK Tripathy, "Evaluation of Performance Metrics and Denoising of PCG Signal Using Wavelet Based Decomposition", INDICON, NSUT, Delhi, 2020. ieeexplore.ieee.org/document/9342464

7. Alejandro Zamora-Mendez, Daniel Dotta, Joe H. Chow, R. K. Tripathy, Mario R. Arrieta Paternina, "Data-Driven Modal Features Extraction Through the Variational Mode Decomposition Method ", IEEE PES INNOVATIVE SMART GRID TECHNOLOGY CONFERENCE - LATIN AMERICA, 2019. Link: https://ieeexplore.ieee.org/document/8895351

8. M.R.A. Paternina, Daniel Guillen, R. K. Tripathy, A. Zamora, J.C. Silva, J. C. Rosas-Caro, "Phasor, Frequency and ROCOF Measurements in Microgrids: A practical approach", IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC 2017), Ixtapa, Mexico, 2017. Link: https://ieeexplore.ieee.org/document/8261690/

9. J. M. Ramirez, M.R.A. Paternina, A.Z. Mendez, J.R. Caro, P.G. Vite, R. K. Tripathy, D. Dotta, R.A.A Lopez, "A General Framework for Micro-Grids", IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC 2017), Ixtapa, Mexico, 2017. (Special Session Organizer)

10. R. K. Tripathy, S. Dandapat, "Multiresolution Inter-sample and Inter-lead Eigen Error Features for Detection of Cardiac Ailments", Twenty-Second National Conference on Communications (NCC), IIT Guwahati, 2016. Link: https://ieeexplore.ieee.org/abstract/document/7561157/

11. A. Chetan, R. K. Tripathy, S. Dandapat, "Classification of Cardiac Arrhythmia from Multilead ECG using Multiscale Non-linear analysis", IEEE Conference on Electrical, Computer and Electronics, IIIT Allahabad, 2015. Link: https://ieeexplore.ieee.org/abstract/document/7456698/

12. R. K. Tripathy, L. N. Sharma, S. Dandapat, "Detection of Cardiac Ailments from multilead ECG using Diagnostic Eigen Error Features", IEEE Conference on Power, Communication and Information Technology, Bhubaneswar, 2015. Link: https://ieeexplore.ieee.org/document/7438157/

Book Chapters

1.   SK Ghosh, RN Ponnalagu, RK Tripathy, "A Study on Time-Frequency analysis of Phonocardiogram Signals", Microelectronics and Signal Processing, CRC Press , Taylor and Francis, 2021.

2. SK Ghosh, RN Ponnalagu, RK Tripathy, "Heart Sound Data Acquisition and Preprocessing Techniques A Review ", Advancement of Artificial Intelligence in Healthcare Engineering , IGI Global, doi: 10.4018/978-1-7998-2120-5 , Feb, 2020. https://www.igi-global.com/book/advancement-artificial-intelligence-healthcare-engineering/235693

3.  S. Dandapat, L. N. Sharma, R. K. Tripathy, "Quantification of diagnostic information from electrocardiogram signal: A review", In Advances in communication and computing, Lecture notes on Electrical Engineering, Springer, pp. 17-39, 2015. Link: https://link.springer.com/chapter/10.1007/978-81-322-2464-8_2

4.  R. K. Tripathy, S. Dandapat, "Quantifying Clinical Information in MECG Using Sample and Channel Convolution Matrices", In Advances in communication and computing, Lecture notes on Electrical Engineering, Springer, pp. 73-80, 2015. Link: https://link.springer.com/chapter/10.1007/978-81-322-2464-8_5