| Contact Person Name | : | Dr Jambunath S Baligar |
|---|---|---|
| Description | : | Cadence Research Software licences for 40 users |
Electronics and Communication Engineering Research
- Cryptography and Network Security
- Wireless Communication
- Machine Learning and Computer Vision
- Microwave Engineering
- VLSI & Embedded Systems
- Image Processing
- Artificial Intelligence
- Signal Processing
- Wireless Sensor Networks
- Digital VLSI design
Research Faculty Details
| S.No | Research Supervisor | Specialization | Awarded University | Year of Award |
|---|---|---|---|---|
| 1. | Dr. Manjula N | VLSI | VTU | 2025 |
| 2. | Dr. Pushpalatha G S | Cryptography | VTU | 2025 |
| 3. | Dr. Mala Swadi | Signal Processing | VTU | 2024 |
| 4. | Dr. Hemalatha K N | Digital VLSI Design | VTU | 2023 |
| 5. | Dr. Divya A | Image Processing | Bangalore University | 2023 |
| 6. | Dr. Swamy TN | VLSI | VTU | 2023 |
| 7. | Dr. Nayana R Shenoy | Image Processing | VTU | 2021 |
| 8. | Dr. Meenakshi L Rathod | Communication | VTU | 2021 |
| 9. | Dr. Hemantaraj M Kelagadi | Wireless Sensor Networks | VTU | 2020 |
| 10. | Dr. Tanuja P | Wireless Communication and Machine Learning | VTU | 2019 |
| 11. | Dr. RANGASWAMY Y | Image Processing | Jawaharlal Nehru Technological University | 2018 |
| 12. | Dr. Shilpa K C | VLSI and Artificial Intelligence | VTU | 2018 |
| 13. | Dr. Chetan S | VLSI and Image Processing | Jain University | 2018 |
| 14. | Dr.Shivaputra | Image Processing, Digital Communication and Signal Processing | Jain University | 2017 |
| 15. | Dr.H Umadevi | Wireless Communication | Bangalore University | 2014 |
| 16. | Dr. S Ramesh | Cryptography and Network Security | Dr. M.G.R. Educational and Research Institute | 2013 |
| 17. | Dr.Mahalinga V Mandi | Cryptography, CDMA, Digital Communication | Dr. M.G.R. Educational and Research Institute | 2013 |
| 18. | Dr. J S Baligar | RFID, Wireless Sensors | Bangalore University | 2003 |
Research Scholars
| S.No | Guide Name | Student Name | Field of Study | Scholar Type | Research Type | Year of Admission | Status | Year of Completion |
|---|---|---|---|---|---|---|---|---|
| 1. | Dr. Meenakshi L Rathod | Renuka N | Image Processing | Part Time | Ph.D | 2025 | Ongoing | 0 |
| 2. | Dr Shwetha N | Chaitra H N | Image processing | Part Time | Ph.D | 2024 | Ongoing | 0 |
| 3. | Dr. Meenakshi L Rathod | Spoorthi P A | AI | Part Time | Ph.D | 2021 | Ongoing | 0 |
| 4. | Dr. Shivaputra | Venkatesh S N | Design and development of efficient algorithm for chaotic based encryption an decryption for multimedia application | Part Time | Ph.D | 2021 | Ongoing | 0 |
| 5. | Dr.Tanuja P | Ripal Patel | Crowd Behavior Analysis using Video surveillance | Part Time | Ph.D | 2021 | Ongoing | 0 |
| 6. | Dr Rangaswamy Y | Vidyashree C | IOT and Deep Learning for Agriculture based applications | Part Time | Ph.D | 2021 | Ongoing | 0 |
| 7. | Dr. H Umadevi | Soumay Padukone | DNA microwave array analysis in forensic science using optical sensor with image processing | Part Time | Ph.D | 2017 | Completed | 2023 |
| 8. | Dr. H Umadevi | G Srinivasalu | Noval Technique for efficient power management in WLSN | Part Time | Ph.D | 2016 | Completed | 2025 |
| 9. | Dr. H Umadevi | Kavithadevi C S | Design and implementation of Microstrip filters course work yet to complete | Part Time | Ph.D | 2016 | Completed | 2023 |
| 10. | Dr. Shivaputra | Sunitha S V | Improving the intelligibility of Dysarthric speech | Part Time | Ph.D | 2016 | Completed | 2023 |
| 11. | Dr. Shivaputra | Soumyashree M Panchal | Object segmentation using Adaptive composite filter | Part Time | Ph.D | 2016 | Completed | 2023 |
| 12. | Dr. S Ramesh | Pushpalatha G S | Digital encryption of speech signal using chaotic based stream cipher system for secure voice communication | Part Time | Ph.D | 2015 | Completed | 2025 |
| 13. | Dr. J S Baligar | Sudha B S | Broadband microstrip antennas for mobile communication application | Part Time | Ph.D | 2015 | Ongoing | 0 |
| 14. | Dr. S Ramesh | Prem Kumar | An efficient key distribution and management method for wireless sensor networks | Part Time | Ph.D | 2014 | Completed | 2020 |
| 15. | Dr. Mahalinga V Mandi | Dilip D | Cryptography | Part Time | Ph.D | 2014 | Completed | 2020 |
| 16. | Dr. Mahalinga V Mandi | Chidananda Murthy | Generation of chaotic binary sequences for cryptographic applications | Part Time | Ph.D | 2014 | Completed | 2020 |
| 17. | Dr. J S Baligar | Shobha I Hugar | Design and Implementation of reconfigurable band pass microstrip filters | Part Time | Ph.D | 2013 | Completed | 2022 |
| 18. | Dr. J S Baligar | Kubendra K | Optimization of clock circuits for SoC | Part Time | Ph.D | 2012 | Ongoing | 0 |
| 19. | Dr. J S Baligar | Shanthi | Linearization of MMIC power Amplifier | Part Time | Ph.D | 2011 | Completed | 2019 |
| Contact Person Name | : | Dr Mahalinga V Mandi |
|---|---|---|
| Description | : | Matlab Software for unlimited usage across the campus. |
|
Project Title : Digital Encryption of Speech signal using chaotic based stream cipher system for secure voice communication Sanctioned Year : 2018 Amount : 20,00,000 Funding/Sanction agency : VGST Status : Ongoing Principal Investigators : Dr. S Ramesh |
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Project Title : Dual mode reconfigurable microstrip filters Sanctioned Year : 2011 Amount : 12,52,000 Funding/Sanction agency : VTU Status : Completed Principal Investigators : Dr. J S Baligar |
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Project Title : Multimedia Security based on Non-Linear Stream Cipher Sanctioned Year : 2022 Amount : 15 Lakhs Funding/Sanction agency : VGST Status : Ongoing Principal Investigators : Dr. Mahalinga V Mandi |
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Project Title : Voltage Controllers for Stand Alone Systems using IG Sanctioned Year : 2013 Amount : 24.14 Lakhs Funding/Sanction agency : AICTE Status : Completed Principal Investigators : Dr G V jayaramaiah |
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Student Name : S.V. Sunitha Title of the Paper : Comparative landmark detection on stops of dysarthric speech Research Description : Biomedical Signal Processing and Control, ISSN: 1746-8094 , Volume 79, Part 1, January 2023, 104125 Faculty Coordinator : Shivaputra Reference Link: https://doi.org/10.1016/j.bspc.2022.104125 |
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Student Name : S.V. Sunitha Title of the Paper : Improving Intelligibility of Dysarthric Speech using Landmark Detection and Modifications on Distorted Stops Research Description : TELEMATIQUE, ISSN: 1856-4194 , Volume 21, pp-4556 – 4576, 2022 Faculty Coordinator : Shivaputra Reference Link: https://www.provinciajournal.com/index.php/telematique/article/view/641 |
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Student Name : Soumyashree M Panchal Title of the Paper : A Novel Approach of Hyperspectral Imaging Classification using Hybrid ConvNet Research Description : (IJACSA) International Journal of Advanced Computer Science and Applications, ISSN:2156-5570, Volume 13, pp-273-281, 2022 Faculty Coordinator : Shivaputra Reference Link: https://doi.org/10.14569/IJACSA.2022.0130334 |
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Student Name : Soumyashree M Panchal Title of the Paper : Precise identification of objects in a hyperspectral image by characterizing the distribution of pure signatures Research Description : International Journal of Electrical and Computer Engineering (IJECE), ISSN: 2088-8708, Volume 12, pp-6068-6078, 2022 Faculty Coordinator : Shivaputra Reference Link: http://doi.org/10.11591/ijece.v12i6.pp6068-6078 |
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Student Name : Soumyashree M Panchal Title of the Paper : Object Classification from a Hyper Spectral Image Using Spectrum Bands with Wavelength and Feature Set Research Description : International Conference, ISBN 978-3-030-77442-4, Springer , Software Engineering and Algorithms section of the10th Computer Science On-line Conference 2021 (CSOC 2021), held on 1st April 2021 at Czech Republic. Faculty Coordinator : Shivaputra Reference Link: https://doi.org/10.1007/978-3-030-77442-4_29 |
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Student Name : Chaitra HN Title of the Paper : Dynamic graph convolution with comprehensive pruning and GNN classification for precise lymph node metastasis detection Research Description : Early and accurate detection of lymph node metastases is crucial for improving breast cancer patient outcomes. However, current clinical practices, including CT, PET imaging, and microscopic examination, are time-consuming and prone to errors due to low tissue contrast, varying lymph node sizes, and complex workflows. To address the limitations of existing approaches in lymph node segmentation, feature embedding, and classification, this study proposes a novel framework Graph-Pruned Lymph Node Detection Framework (GPLN-DF) that integrates a Dynamic Graph Convolution (DGC) autoencoder with Node Attribute-wise Attention (NodeAttri-Attention) for accurate lymph node segmentation. This segmentation is further refined using Comprehensive Graph Gradual Pruning (CGP) to reduce unnecessary parameters and computational costs. After segmentation, Hessian-based Locally Linear Embedding (HLLE) is applied for effective feature extraction and dimensionality reduction, preserving the geometric structure of lymph node regions. Finally, a Graph Neural Network (GNN) classifier enhanced with CGP is used to classify the segmented lymph nodes as metastatic or non-metastatic based on the extracted features. This comprehensive framework addresses challenges such as small lymph node size, shape variability, low contrast in medical imaging, and high computational burden. The model was evaluated on the CAMELYON17 dataset, achieving a classification accuracy of 98.65%, surpassing existing models in segmentation precision and classification performance. Faculty Coordinator : Dr.Shwetha N Reference Link: https://pmc.ncbi.nlm.nih.gov/articles/PMC12913665/ |
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Student Name : Spoorthi Pothaganahalli Akkalappa Title of the Paper : WES-HOA-based ReScaleX-CNN for Plant Leaf Disease Classification, JOURNAL OF COMMUNICATIONS SOFTWARE AND SYSTEMS, VOL. 22, NO. 4, DECEMBER 2026 Research Description : Plant leaf disease classification involves identification and classification of different diseases based on indicators of plant leaves, which plays a crucial role in managing crop health. However, classifying plant leaf diseases is challenging due to wide variation in leaf shape, texture, and color, which leads to overlapping symptoms and inaccurate classification. In this research, the Wombat Escape Strategy-Hippopotamus Optimization Algorithm-based Recalibrated Multi-Scale Squeeze and Excitation Convolutional Neural Network (WES-HOA-based ReScaleX-CNN) is proposed to classify plant leaf disease accurately. In HOA, WES is incorporated to select the most appropriate features that enhance global searchability by guiding agents away from local optima. ReScaleX-CNN enhances the model’s ability to concentrate on informative features by emphasizing significant spatial and channel-wise information. The multiscale approach captures disease patterns at various resolutions, leading to robust performance. Hence, the proposed method obtains a high accuracy of 99.92% on PlantVillage dataset in comparison with existing methods, such as DeepPlantNet. Faculty Coordinator : Dr. Shivaputra & Dr. Meenakshi Laxman Rathod Reference Link: https://doi.org/10.24138/jcomss-2025-0194 |
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