Year-2025Year-2025http://repository.iiitd.edu.in/xmlui/handle/123456789/18082026-04-14T10:00:12Z2026-04-14T10:00:12ZExploring XR-based tangible interactionsIndora, RohanSrivastava, Anmol (Advisor)Johry, Aakash (Advisor)Eden, Grace (Advisor)http://repository.iiitd.edu.in/xmlui/handle/123456789/18792026-04-13T22:00:07Z2025-07-28T00:00:00ZExploring XR-based tangible interactions
Indora, Rohan; Srivastava, Anmol (Advisor); Johry, Aakash (Advisor); Eden, Grace (Advisor)
This project presents a novel tangible user interface that merges the physical act of sculpting with the creative power of generative AI. By leveraging an Augmented Reality (AR) Sandbox as a direct, physical input for a real-time diffusion model, the system allows users to shape landscapes in sand and witness them instantly transform into vivid, AI-generated biomes. This work explores the potential of tangible interaction to provide a more intuitive, expressive, and accessible means of controlling complex AI systems, bridging the gap between the digital and physical realms. The prototype successfully demonstrates a functional pipeline for real-time visual synthesis, validating the core concept of using tangible interaction to guide generative processes. This exploration aims to revolutionize how users interact with and visualize data, enabling rapid prototyping, interactive manipulation, and enhanced creative expression.
2025-07-28T00:00:00ZGeneralized-rank action selection in multi-robot belief space planning with limited connectivityShandilya, BhanuKundu, Tanmoy (Advisor)http://repository.iiitd.edu.in/xmlui/handle/123456789/18782026-04-13T22:00:25Z2025-07-20T00:00:00ZGeneralized-rank action selection in multi-robot belief space planning with limited connectivity
Shandilya, Bhanu; Kundu, Tanmoy (Advisor)
Effective coordination among autonomous robots in dynamic, communication-constrained environ- ments remains a significant challenge in multi-robot systems. This thesis addresses such coordination problems in real-world scenarios, such as search-and-rescue missions, where communication failures and inconsistent beliefs hinder effective collaboration. We build upon the decentralized algorithm VerifyAC[1], which verifies consistency in multi-robot coordination and triggers communication only when required. However, VerifyAC is restricted to rank-1 action preferences, which incurs both high communication and computational costs. To address this, we previously introduced VerifyAC-Gen[2], a decentralized variant that generalizes rank selection via backward reasoning, pruning, and heuristic-based ambiguity resolution. While VerifyAC-Gen effectively reduces unnecessary communication, its extension to multiple agents introduces scalability and complexity issues. In this thesis, we present two extended strategies to scale our framework to environments involving N > 2 robots: a decentralized Min-Heap Tree approach and a centralized cluster registry protocol. The decentralized Min-Heap Tree method reduces communication complexity by assigning non- leader robots to clusters based on KL divergence from entropy-minimized leaders. Communication is hierarchically structured, significantly reducing overhead while preserving coordination integrity. Complementarily, the centralized registry approach maintains a global cluster-to-robot mapping, enabling dynamic reconfiguration upon leader failure or agent arrival. It utilizes entropy-based leader selection, KL-divergence-based clustering, and min-heap structures to ensure optimal com- munication and reallocation of robots across clusters. Together, these strategies extend the scalability, robustness, and efficiency of our coordination framework under dynamic and uncertain environments. Ongoing experimental validation focuses on communication reduction, computational efficiency, and adaptability in real-time applications.
2025-07-20T00:00:00ZMatlab CST interfacing for metasurface unit cell and coding diffuse metasurface designSingh, AngadKosta, Pragya (Advisor)Kundu, Debidas (Advisor)http://repository.iiitd.edu.in/xmlui/handle/123456789/18772026-04-13T22:00:25Z2025-07-23T00:00:00ZMatlab CST interfacing for metasurface unit cell and coding diffuse metasurface design
Singh, Angad; Kosta, Pragya (Advisor); Kundu, Debidas (Advisor)
This thesis explores the design and simulation of a coding diffusion metasurface to achieve wideband radar cross-section (RCS) reduction using MATLAB–CST interfacing. Inspired by the principle of phase cancellation through binary coding, the work focuses on the development of two distinct unit cells with a reflection phase difference of approximately 180° ± 37°, constructed on a dielectric substrate. These unit cells form the basis of an 8×8 metasurface array governed by a randomly generated binary matrix. The methodology involves automating unit cell modeling in CST Microwave Studio through MAT- LAB scripting and integrating the geometry into a single simulation environment. The results demonstrate successful phase-based scattering diffusion, with characteristic multi-lobe patterns confirming RCS suppression across a wide frequency band. This work contributes to the field of electromagnetic stealth and programmable metasurfaces by offering a complete modeling-to- simulation workflow capable of generating custom-coded designs with reduced specular reflections.
2025-07-23T00:00:00ZBiomedical radar for foreign object depth estimationAnwar, Md SarfarazKumar, PankajRam, Shobha Sundar (Advisor)http://repository.iiitd.edu.in/xmlui/handle/123456789/18762026-04-13T22:00:25Z2025-07-18T00:00:00ZBiomedical radar for foreign object depth estimation
Anwar, Md Sarfaraz; Kumar, Pankaj; Ram, Shobha Sundar (Advisor)
Accurate localization of metallic fragments embedded within biological tissues is critical in med- ical and trauma-related scenarios. This work presents a simulation-driven approach that com- bines Finite-Difference Time-Domain (FDTD) modeling with machine-learning (ML) techniques to estimate fragment depth using a 60GHz wideband radar system. A two-dimensional FDTD model was developed to simulate electromagnetic propagation through layered tissue structures containing metallic and vascular inclusions. Operating across a 4GHz bandwidth centered at 60GHz, the radar setup captured reflected Ez-field waveforms at multiple observation points to emulate realistic returns. Time-domain signals were transformed into high-dimensional feature vectors via spectrograms and Fast Fourier Transforms (FFT). These features trained ML regressors for depth prediction. A Random Forest (CPU) established a low-cost, interpretable baseline, while a GPU-accelerated XGBoost (RGBoost) model exploited parallelism to handle larger, more complex datasets and shorten training times. Both models achieved high localization accuracy, validating the end-to-end simulation pipeline. Future work will extend the framework to larger anatomical regions (hand, abdomen, thigh, shoulder, head), incorporate full-body modeling, and integrate a real 60GHz radar for experimental validation. The study lays the groundwork for a real-time, non-invasive imaging system with biomedical and defense applications.
2025-07-18T00:00:00Z