Abhimanyu Bhowmik

Hi there 👋 Welcome to my page! I’m a Master’s thesis student at the Autonomous Robots Lab, NTNU (Norwegian University of Science and Technology), working under the supervision of Prof. Kostas Alexis and Prof. Martin Ludvigsen. My current research focuses on uncertainty-aware mapping for vision-based underwater robots.

I’m in the second year of the Erasmus Mundus Joint Master’s in Marine and Maritime Intelligent Robotics (MIR), specializing in Safe Autonomous Subsea Operations. During summer 2024, I worked on the DriX USV at Exail Robotics SAS in France. In my first year of masters, I studied at the SeaTech - Ecole d’ingénieurs at Université de Toulon . I graduated with B.Tech in Artificial Intelligence from Amity University Kolkata, India.

Feel free to reach out if you’d like to collaborate, or just talk about robots 🤖!

News

Jan 15, 2025	Started Masters thesis at Autonomous Robots Lab 🐳
Aug 12, 2024	Arrive at NTNU Trondheim, Norway. Joined IMT for my second year of masters.
Aug 8, 2024	Finished summer internship at Exail. Great people , fun experience 😁 and happy summer days at Côte d’Azur. 🇫🇷
Jun 11, 2024	Attended my first MIR Symposium at Lisbon. 🇵🇹

Projects

Robotics

Object Detection and Tracking using Turtle Bot

Parallel object tracking

Pan-Tilt Camera-IMU

Developed a compact, open-source eye-inspired pan-tilt gimbal for UAVs

Autonomous Behaviour in BlueROV

Autonomous pool inspection with heading and depth control

Geometric and kinematic modeling of Hexapod Leg

Forward Kinematics using DH/Khalil formalism

Image-Based Visual Servoing Control (IBVS)

Object detection and tracking of bouy with BlueROV in varying degrees of freedom in underwater

EKF SLAM on Simulated Landmarks

Benchmarking consistency for JCBB with EKF SLAM

Computer Vision

Underwater Depth Estimation

LoRA on DepthAnything for accurate monocular depth estimation

Satellite Image Super Resolution

AutoEn-GAN outperforms state-of-the-art satellite super-resolution methods

Horizon Segmentation for ASV

Trained variants of U-Net for semantic segmentation on Horizon dataset

Selected Publications

CloudAISim: A toolkit for modelling and simulation of modern applications in AI-driven cloud computing environments

Abhimanyu Bhowmik, Madhushree Sannigrahi, Deepraj Chowdhury, and 2 more authors

BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 2023

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There is a very significant knowledge gap between Artificial Intelligence (AI) and a multitude of industries that exist in today’s modern world. This is primarily attributable to the limited availability of resources and technical expertise. However, a major obstacle is that AI needs to be flexible enough to work in many different applications, utilising a wide variety of datasets through cloud computing. As a result, we developed a benchmark toolkit called CloudAISim to make use of the power of AI and cloud computing in order to satisfy the requirements of modern applications. The goal of this study is to come up with a strategy for building a bridge so that AI can be utilised in order to assist those who are not very knowledgeable about technological advancements. In addition, we modelled a healthcare application as a case study in order to verify the scientific reliability of the CloudAISim toolkit and simulated it in a cloud computing environment using Google Cloud Functions to increase its real-time efficiency. A non-expert-friendly interface built with an interactive web app has also been developed. Any user without any technical knowledge can operate the entire model, which has a 98% accuracy rate. The proposed use case is designed to put AI to work in the healthcare industry, but CloudAISim would be useful and adaptable for other applications in the future.
@article{bhowmik2023cloudaisim, title = {CloudAISim: A toolkit for modelling and simulation of modern applications in AI-driven cloud computing environments}, author = {Bhowmik, Abhimanyu and Sannigrahi, Madhushree and Chowdhury, Deepraj and Dey, Ajoy and Gill, Sukhpal Singh}, journal = {BenchCouncil Transactions on Benchmarks, Standards and Evaluations}, volume = {3}, number = {4}, pages = {100150}, year = {2023}, publisher = {Elsevier}, }
DBNex: Deep Belief Network and Explainable AI based Financial Fraud Detection

Abhimanyu Bhowmik, Madhushree Sannigrahi, Deepraj Chowdhury, and 2 more authors

In 2022 IEEE International Conference on Big Data (Big Data), 2022

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The majority of financial transactions are now conducted virtually around the world. The widespread use of credit cards and online transactions encourages fraudulent activity. Thus, one of the most demanding real-world challenges is fraud detection. Unbalanced datasets, in which there are a disproportionately high number of non-fraud samples compared to incidents of fraud, are one of the key obstacles to effective fraud detection. A further factor complicating the learning process for cutting-edge machine learning classifiers is how quickly fraud behaviour changes. Thus, in this study, we suggest an efficient fraud detection methodology. We propose a unique nonlinear embedded clustering to resolve imbalances in the dataset, followed by a Deep Belief Network for detecting fraudulent transactions. The proposed model achieved an accuracy of 94% with a 70:30 ratio of training-validation dataset.
@inproceedings{bhowmik2022dbnex, title = {DBNex: Deep Belief Network and Explainable AI based Financial Fraud Detection}, author = {Bhowmik, Abhimanyu and Sannigrahi, Madhushree and Chowdhury, Deepraj and Dwivedi, Ashutosh Dhar and Mukkamala, Raghava Rao}, booktitle = {2022 IEEE International Conference on Big Data (Big Data)}, pages = {3033--3042}, year = {2022}, organization = {IEEE}, }