| Course Title | Material URL |
|---|---|
| Generative AI | View Material |
| From Date | To Date | Designation | Organization | Organization Address |
|---|---|---|---|---|
| 2011-06-05 | 2012-07-12 | Asst. Professor | SOSPS | Nagothane, Roha |
| 2014-05-07 | 2016-06-15 | Visitor Lecturer | Gov. Polytechnics | Pen, Raigad |
| 2016-08-04 | 2018-04-11 | Asst. Professor | PNP Education Society | Veshwi |
| 2023-12-26 | 2024-11-20 | Lecturer | STEAM Trainer | Thrissur, Keral |
| 2024-11-23 | 0000-00-00 | Asst. Professor | SIES GST | Nerul |
| Date | Title | Type | Level | Academic Year |
|---|---|---|---|---|
| 2015-12-15 | Verification of Network Theorems for Linear Electrical Circuits with Lossy Passive Elements using Fractional-Order Modelling | I | III | 2012-15 |
| Date | Title | Presented/Published | Level | Academic Year | Organizer / Venue |
|---|---|---|---|---|---|
| 2025-02-21 | Hybrid Model for video Enhancement | ICICN | 2025 | Institution Innovation Council Thakur College of Engineering & Technology , Kandivali(E), Mumbai |
|
| a Hybrid Model for Video En hancement that combines several methods to enhance video quality for surveillance, medical imaging, and content production applications. The model combines low-light enhancement through HSVcolor space transformation and wavelet-based fusion, Super Resolution GAN (SRGAN) for super-resolution of low-resolution frames, and frame interpolation for more smooth motion. A Reinforcement Learning-based GAN (RL-GAN) also optimizes video enhancement through structural detail preservation and motion coherence. The system also includes image-to-video syn thesis, slow and fast motion, and quality upscaling to restore downgraded material. Tested for PSNR and SSIM, the model demonstrates substantial improvements in sharpness, motion f luidity, and subjective quality. The model is constructed with TensorFlow and PyTorch and uses GPU acceleration to offer high-performance processing. The method offers an end-to end solution for video enhancement, with future developments expected to enhance temporal consistency further using GANs and RNNs. | |||||
| 2015-12-22 | VERIFICATION OF NETWORK THEOREMS FOR LINEAR ELECTRICAL CIRCUITS WITH LOSSY PASSIVE ELEMENTS USING FRACTIONAL-ORDER MODELING | IJEEE | 2015-16 | TCET Association Bharathi Vidyapeeth ,Pune |
|
| Network analysis is very important while doing any network related parameter operation which may include circuit design, simplification. But, there are many circuits can not be analyzed or simplified by any conventional circuit simplification method such as Kirchhoff’s Voltage Law and Kirchhoff’s Current Law, so we need to use some techniques which are very much important for simplification which further help to find actual output or response of circuit. For that we have Network Theorem using it circuit simplification gets very much faster so analysis becomes faster instead of getting we do not get actual output or response of circuit, which contains some losses due to circuit elements that circuit elements may be inductors and capacitors. For that we have to study about lossy components and their losses such as eddy current loss, iron loss and core loss. All the inductors and capacitors have losses. If we wish to consider this losses, then the resulting model is very bulky. So fractional order model of lossy inductor and for capacitor has been proposed. This project is an attempt to contribute part in linear network circuits containing lossy passive elements. Using this fractional order we are verifying our network theorem. Considering lossy elements we are going to implement the fractional equation for each of circuit elements which make circuit reliable which helps to reduce its disadvantages and contribute to many bigger circuits to reduce their losses. This project will helps many circuit designing and contributing to society as in many improved circuit performance. | |||||
