arXiv subjects

Mainak Mondal

Publications and source records attributed to Mainak Mondal.

8 recordsLinked to original sources

arXiv2026

MIND-CAVs: Multi-Intelligence Negotiation and Decision System for CAVs based on Intent-Driven Autonomy

Modern autonomous vehicles largely operate as isolated agents: they rely on on-board perception and decision modules and broadcast Basic Safety Messages (BSMs) that expose only low-level kinematic state. While existing cooperative driving frameworks enable limited sensor sharing, they rarely communicate high-level maneuver intentions, and edge computing is primarily used for content delivery rather than decision arbitration. As a result, current connected autonomy lacks a principled mechanism for making globally consistent, intent-aware coordination decisions across vehicles. To address this gap, we propose MIND-CAVs, a Multi-Intelligence Negotiation and Decision framework for connected autonomous vehicles (CAVs) based on intent-driven autonomy. Each vehicle abstracts raw sensor observations into structured intent representations, exchanges them over V2X links, and receives globally consistent coordination plans from roadside edge servers. Edge agents combine learned and rule-based arbitration mechanisms to negotiate conflicting intents among vehicles, while a cloud platform records decisions for auditing and continual retraining. We implement MIND-CAVs in a CARLA-based AI-in-the-loop platform and evaluate it in multi-lane highway scenarios involving conflicting maneuvers and route-constrained exits. Experimental results show improved maneuver completion time and reduced unsafe proximity and unnecessary braking compared with isolated autonomy, first-come-first-served arbitration, and multi-agent reinforcement learning baselines.

cs.RO↗

arXiv2025

Quantum siphoning of finely spaced interlayer excitons in reconstructed MoSe2/WSe2 heterostructures

Atomic reconstruction in twisted transition metal dichalcogenide heterostructures leads to mesoscopic domains with uniform atomic registry, profoundly altering the local potential landscape. While interlayer excitons in these domains exhibit strong many-body interactions, extent and impact of quantum confinement on their dynamics remains unclear. Here, we reveal that quantum confinement persists in these flat, reconstructed regions. Time-resolved photoluminescence spectroscopy uncovers multiple, finely-spaced interlayer exciton states (~ 1 meV separation), and correlated emission lifetimes spanning sub-nanosecond to over 100 nanoseconds across a 10 meV energy window. Cascade-like transitions confirm that these states originate from a single potential well, further supported by calculations. Remarkably, at high excitation rates, we observe transient suppression of emission followed by gradual recovery, a process we term "quantum siphoning". Our results demonstrate that quantum confinement and competing nonlinear dynamics persist beyond the ideal moire paradigm, potentially enabling applications in quantum sensing and modifying exciton dynamics via strain engineering.

cond-mat.mes-hall↗

arXiv2025

Robust and Safe Multi-Agent Reinforcement Learning with Communication for Autonomous Vehicles: From Simulation to Hardware

Deep multi-agent reinforcement learning (MARL) has been demonstrated effectively in simulations for multi-robot problems. For autonomous vehicles, the development of vehicle-to-vehicle (V2V) communication technologies provide opportunities to further enhance system safety. However, zero-shot transfer of simulator-trained MARL policies to dynamic hardware systems remains challenging, and how to leverage communication and shared information for MARL has limited demonstrations on hardware. This problem is challenged by discrepancies between simulated and physical states, system state and model uncertainties, practical shared information design, and the need for safety guarantees in both simulation and hardware. This paper designs RSR-RSMARL, a novel Robust and Safe MARL framework that supports Real-Sim-Real (RSR) policy adaptation for multi-agent systems with communication among agents, with both simulation and hardware demonstrations. RSR-RSMARL leverages state (includes shared state information among agents) and action representations considering real system complexities for MARL formulation. The MARL policy is trained with robust MARL algorithm to enable zero-shot transfer to hardware considering the sim-to-real gap. A safety shield module using Control Barrier Functions (CBFs) provides safety guarantee for each individual agent. Experimental results on 1/10th-scale autonomous vehicles with V2V communication demonstrate the ability of RSR-RSMARL framework to enhance driving safety and coordination across multiple configurations. These findings emphasize the importance of jointly designing robust policy representations and modular safety architectures to enable scalable, generalizable RSR transfer in multi-agent autonomy.

cs.RO↗

arXiv2024

Optical control of multiple resistance levels in graphene for memristic applications

Neuromorphic computing has emphasized the need for memristors with non-volatile, multiple conductance levels. This paper demonstrates the potential of hexagonal boron nitride (hBN)/graphene heterostructures to act as memristors with multiple resistance states that can be optically tuned using visible light. The number of resistance levels in graphene can be controlled by modulating doping levels, achieved by varying the electric field strength or adjusting the duration of optical illumination. Our measurements show that this photodoping of graphene results from the optical excitation of charge carriers from the nitrogen-vacancy levels of hBN to its conduction band, with these carriers then being transferred to graphene by the gate-induced electric field. We develop a quantitative model to describe our observations. Additionally, utilizing our device architecture, we propose a memristive crossbar array for vector-matrix multiplications.

cond-mat.mes-hall↗

arXiv2024

Quantification of 2D Interfaces: Quality of heterostructures, and what is inside a nanobubble

Trapped materials at the interfaces of two-dimensional heterostructures (HS) lead to reduced coupling between the layers, resulting in degraded optoelectronic performance and device variability. Further, nanobubbles can form at the interface during transfer or after annealing. The question of what is inside a nanobubble, i.e. the trapped material, remains unanswered, limiting the studies and applications of these nanobubble systems. In this work, we report two key advances. Firstly, we quantify the interface quality using RAW-format optical imaging, and distinguish between ideal and non-ideal interfaces. The HS-substrate ratio value is calculated using a transfer matrix model, and is able to detect the presence of trapped layers. The second key advance is identification of water as the trapped material inside a nanobubble. To the best of our knowledge, this is the first study to show that optical imaging alone can quantify interface quality, and find the type of trapped material inside spontaneously formed nanobubbles. We also define a quality index parameter to quantify the interface quality of HS. Quantitative measurement of the interface will help answer the question whether annealing is necessary during HS preparation, and will enable creation of complex HS with small twist angles. Identification of the trapped materials will pave the way towards using nanobubbles for novel optical and engineering applications.

cond-mat.mes-hall↗

arXiv2023

Controlled defect production in monolayer MoS2 via electron irradiation at ultralow accelerating voltages

Control on spatial location and density of defects in 2D materials can be achieved using electron beam irradiation. Conversely, ultralow accelerating voltages (less than or equal to 5kV) are used to measure surface morphology, with no expected defect creation. We find clear signatures of defect creation in monolayer (ML) MoS2 at these voltages. Evolution of E' and A1' Raman modes with electron dose, and appearance of defect activated peaks indicate defect formation. To simulate Raman spectra of MoS2 at realistic defect distributions, while retaining density-functional theory accuracy, we combine machine-learning force fields for phonons and eigenmode projection approach for Raman tensors. Simulated spectra agree with experiments, with sulphur vacancies as suggested defects. We decouple defects, doping and carbonaceous contamination using control (hBN covered and encapsulated MoS2) samples. We observe cryogenic PL quenching and defect peaks, and find that carbonaceous contamination does not affect defect creation. These studies have applications in photonics and quantum emitters.

cond-mat.mes-hall↗

arXiv2022

Optical microscope based universal parameter for identifying layer number in two-dimensional materials

Optical contrast is the most common preliminary method to identify layer number of two-dimensional (2D) materials, but is seldom used as a confirmatory technique. We explain the reason for variation of optical contrast between imaging systems. We introduce a universal method to quantify the layer number using the RGB (red-green-blue) and RAW optical images. For RGB images, the slope of 2D flake (MoS2, WSe2, graphene) intensity vs. substrate intensity is extracted from optical images with varying lamp power. The intensity slope identifies layer number and is system independent. For RAW images, intensity slopes and intensity ratios are completely system and intensity independent. Intensity slope (for RGB) and intensity ratio (for RAW) are thus universal parameters for identifying layer number. A Fresnel-reflectance-based optical model provides an excellent match with experiments. Further, we have created a MATLAB-based graphical user interface that can identify layer number rapidly. This technique is expected to accelerate the preparation of heterostructures, and fulfil a prolonged need for universal optical contrast method.

physics.optics↗

arXiv2021

Effect of electron-irradiation on layered quantum materials

Technological advancement towards the quantum era requires secure communication, quantum computation, and ultra-sensitive sensing capabilities. Layered quantum materials (LQMs) have remarkable optoelectronic and quantum properties that can usher us into the quantum era. Electron microscopy is the tool of choice for measuring these LQMs at an atomic and nanometer scale. On the other hand, electron-irradiation of LQMs can modify various material properties, including the creation of structural defects. We review different types of structural defects, as well as electron elastic- and inelastic-scattering induced processes. Controlled modification of optoelectronic and quantum properties of LQMs using electron-irradiation, including creation of single photon emitters is discussed. Protection of electron-irradiation induced damage of LQMs via encapsulation by other layered materials is encouraged. We finally give insights into challenges and opportunities, including creating novel structures using an electron beam.

cond-mat.mes-hall↗