arXiv subjects

Suresh Sundaram

Publications and source records attributed to Suresh Sundaram.

At least 37 records · Page 2Linked to original sources

arXiv2025

On the importance of Ni-Au-Ga interdiffusion in the formation of a Ni-Au / p-GaN ohmic contact

The Ni-Au-Ga interdiffusion mechanisms taking place during rapid thermal annealing (RTA) under oxygen atmosphere of a Ni-Au/p-GaN contact are investigated by high-resolution transmission electron microscopy (HR-TEM) coupled to energy dispersive X-ray spectroscopy (EDX). It is shown that oxygen-assisted, Ni diffusion to the top surface of the metallic contact through the formation of a nickel oxide (NiOx) is accompanied by Au diffusion down to the GaN surface, and by Ga out-diffusion through the GaN/metal interface. Electrical characterizations of the contact by Transmission Line Method (TLM) show that an ohmic contact is obtained as soon as a thin, Au-Ga interfacial layer is formed, even after complete diffusion of Ni or NiOx to the top surface of the contact. Our results clarify that the presence of Ni or NiOx at the interface is not the main origin of the ohmic-like behavior in such contacts. Auto-cleaning of the interface during the interdiffusion process may play a role, but TEM-EDX analysis evidences that the creation of Ga vacancies associated to the formation of a Ga-Au interfacial layer is crucial for reducing the Schottky barrier height, and maximizing the amount of current flowing through the contact.

cond-mat.mtrl-sci↗

arXiv2024

A Comprehensive Study on Modelling and Control of Autonomous Underwater Vehicle

Autonomous underwater vehicles (AUV) have become the de facto vehicle for remote operations involving oceanography, inspection, and monitoring tasks. These vehicles operate in different and often challenging environments; hence, the design and development of the AUV involving hydrodynamics and control systems need to be designed in detail. This book chapter presents a study on the modelling and robust control of a research vehicle in the presence of uncertainties. The vehicle's dynamic behaviour is modelled using a 6-degree-of-freedom approach, considering the effect of ocean currents. The level flight requirements for different speeds are derived, and the resulting model is decomposed into horizontal and vertical subsystems for linear analysis. The simulation results presented focus on the efficacy of linear controllers within three key subsystems: depth, yaw, and speed. Moreover, level-flight outcomes are demonstrated for a speed of 4 knots. The nonlinear control strategies employed in this study encompass conventional and sliding-mode control (SMC) methodologies. To ensure accurate tracking performance, the controller design considers the vehicle's dynamics with various uncertainties such as ocean currents, parameter uncertainty, CG (Center of Gravity) deviation and buoyancy variation. Both conventional and nonlinear SMC controllers' outcomes are showcased with a lawn-mowing manoeuvre scenario. A systematic comparison is drawn between the robustness of SMC against disturbances and parameter fluctuations in contrast to conventional controllers. Importantly, these results underscore the trade-off that accompanies SMC's robustness, as it necessitates a higher level of complexity in terms of controller design, intricate implementation intricacies, and the management of chattering phenomena.

eess.SY↗

arXiv2024

IndraEye: Infrared Electro-Optical UAV-based Perception Dataset for Robust Downstream Tasks

Deep neural networks (DNNs) have shown exceptional performance when trained on well-illuminated images captured by Electro-Optical (EO) cameras, which provide rich texture details. However, in critical applications like aerial perception, it is essential for DNNs to maintain consistent reliability across all conditions, including low-light scenarios where EO cameras often struggle to capture sufficient detail. Additionally, UAV-based aerial object detection faces significant challenges due to scale variability from varying altitudes and slant angles, adding another layer of complexity. Existing methods typically address only illumination changes or style variations as domain shifts, but in aerial perception, correlation shifts also impact DNN performance. In this paper, we introduce the IndraEye dataset, a multi-sensor (EO-IR) dataset designed for various tasks. It includes 5,612 images with 145,666 instances, encompassing multiple viewing angles, altitudes, seven backgrounds, and different times of the day across the Indian subcontinent. The dataset opens up several research opportunities, such as multimodal learning, domain adaptation for object detection and segmentation, and exploration of sensor-specific strengths and weaknesses. IndraEye aims to advance the field by supporting the development of more robust and accurate aerial perception systems, particularly in challenging conditions. IndraEye dataset is benchmarked with object detection and semantic segmentation tasks. Dataset and source codes are available at https://bit.ly/indraeye.

cs.CV↗

arXiv2024

Distributed Online Life-Long Learning (DOL3) for Multi-agent Trust and Reputation Assessment in E-commerce

Trust and Reputation Assessment of service providers in citizen-focused environments like e-commerce is vital to maintain the integrity of the interactions among agents. The goals and objectives of both the service provider and service consumer agents are relevant to the goals of the respective citizens (end users). The provider agents often pursue selfish goals that can make the service quality highly volatile, contributing towards the non-stationary nature of the environment. The number of active service providers tends to change over time resulting in an open environment. This necessitates a rapid and continual assessment of the Trust and Reputation. A large number of service providers in the environment require a distributed multi-agent Trust and Reputation assessment. This paper addresses the problem of multi-agent Trust and Reputation Assessment in a non-stationary environment involving transactions between providers and consumers. In this setting, the observer agents carry out the assessment and communicate their assessed trust scores with each other over a network. We propose a novel Distributed Online Life-Long Learning (DOL3) algorithm that involves real-time rapid learning of trust and reputation scores of providers. Each observer carries out an adaptive learning and weighted fusion process combining their own assessment along with that of their neighbour in the communication network. Simulation studies reveal that the state-of-the-art methods, which usually involve training a model to assess an agent's trust and reputation, do not work well in such an environment. The simulation results show that the proposed DOL3 algorithm outperforms these methods and effectively handles the volatility in such environments. From the statistical evaluation, it is evident that DOL3 performs better compared to other models in 90% of the cases.

cs.AI↗

arXiv2024

Syn2Real Domain Generalization for Underwater Mine-like Object Detection Using Side-Scan Sonar

Underwater mine detection with deep learning suffers from limitations due to the scarcity of real-world data. This scarcity leads to overfitting, where models perform well on training data but poorly on unseen data. This paper proposes a Syn2Real (Synthetic to Real) domain generalization approach using diffusion models to address this challenge. We demonstrate that synthetic data generated with noise by DDPM and DDIM models, even if not perfectly realistic, can effectively augment real-world samples for training. The residual noise in the final sampled images improves the model's ability to generalize to real-world data with inherent noise and high variation. The baseline Mask-RCNN model when trained on a combination of synthetic and original training datasets, exhibited approximately a 60% increase in Average Precision (AP) compared to being trained solely on the original training data. This significant improvement highlights the potential of Syn2Real domain generalization for underwater mine detection tasks.

cs.LG↗

arXiv2024

Deep Attention Driven Reinforcement Learning (DAD-RL) for Autonomous Decision-Making in Dynamic Environment

Autonomous Vehicle (AV) decision making in urban environments is inherently challenging due to the dynamic interactions with surrounding vehicles. For safe planning, AV must understand the weightage of various spatiotemporal interactions in a scene. Contemporary works use colossal transformer architectures to encode interactions mainly for trajectory prediction, resulting in increased computational complexity. To address this issue without compromising spatiotemporal understanding and performance, we propose the simple Deep Attention Driven Reinforcement Learning (DADRL) framework, which dynamically assigns and incorporates the significance of surrounding vehicles into the ego's RL driven decision making process. We introduce an AV centric spatiotemporal attention encoding (STAE) mechanism for learning the dynamic interactions with different surrounding vehicles. To understand map and route context, we employ a context encoder to extract features from context maps. The spatiotemporal representations combined with contextual encoding provide a comprehensive state representation. The resulting model is trained using the Soft Actor Critic (SAC) algorithm. We evaluate the proposed framework on the SMARTS urban benchmarking scenarios without traffic signals to demonstrate that DADRL outperforms recent state of the art methods. Furthermore, an ablation study underscores the importance of the context-encoder and spatio temporal attention encoder in achieving superior performance.

cs.AI↗

arXiv2024

A Fast Dynamic Internal Predictive Power Scheduling Approach for Power Management in Microgrids

This paper presents a Dynamic Internal Predictive Power Scheduling (DIPPS) approach for optimizing power management in microgrids, particularly focusingon external power exchanges among diverse prosumers. DIPPS utilizes a dynamic objective function with a time-varying binary parameter to control the timing of power transfers to the external grid, facilitated by efficient usage of energy storage for surplus renewable power. The microgrid power scheduling problem is modeled as a mixed-integer nonlinear programmig (MINLP-PS) and subsequently transformed into a mixed-integer linear programming (MILP-PS) optimization through McCormick's relaxation to reduce the computational complexity. A predictive window with 6 data points is solved at an average of 0.92s, a 97.6% improvement over the 38.27s required for the MINLP-PS formulation, implying the numerical feasibility of the DIPPS approach for real-time implementation. Finally, the approach is validated against a static objective using real-world load data across three case studies with different time-varying parameters, demonstrationg the ability of DIPPS to optimize power exchanges and efficiently utilize distributed resources whie shifting the eexternal power transfers to specified time durations.

eess.SY↗

arXiv2024

High-quality hexagonal boron nitride selectively grown on patterned epigraphene by MOVPE

Hexagonal boron nitride encapsulation is the method of choice for protecting graphene from environmental doping and impurity scattering. It was previously demonstrated that metal-organic vapor phase epitaxy (MOVPE) grows epitaxially ordered, uniform BN layers on epigraphene (graphene grown on SiC). Due to graphene non-wetting properties, h-BN growth starts preferentially from the graphene ledges. We use this fact here to selectively promote growth of high-quality flat h-BN on epigraphene by patterning epigraphene microstructures prior to BN growth. Thin h-BN films (down to 6 nm) grown by MOVPE show smooth and pleated surface morphology on epigraphene, while crumpled BN is observed on the SiC. Cross-sectional high-resolution transmission electron microscopy images and fluorescence imaging confirm the higher BN quality grown on the epigraphene. Transport measurements reveal p-doping as expected from hydrogen intercalation of epigraphene and regions of high and low mobility. This method can be used to produce structurally uniform high-quality h-BN/epigraphene micro/nano scale heterostructure.

cond-mat.mtrl-sci↗

arXiv2024

Supervised Image Translation from Visible to Infrared Domain for Object Detection

This study aims to learn a translation from visible to infrared imagery, bridging the domain gap between the two modalities so as to improve accuracy on downstream tasks including object detection. Previous approaches attempt to perform bi-domain feature fusion through iterative optimization or end-to-end deep convolutional networks. However, we pose the problem as similar to that of image translation, adopting a two-stage training strategy with a Generative Adversarial Network and an object detection model. The translation model learns a conversion that preserves the structural detail of visible images while preserving the texture and other characteristics of infrared images. Images so generated are used to train standard object detection frameworks including Yolov5, Mask and Faster RCNN. We also investigate the usefulness of integrating a super-resolution step into our pipeline to further improve model accuracy, and achieve an improvement of as high as 5.3% mAP.

cs.CV↗

arXiv2024

Genetic Algorithm-based Routing and Scheduling for Wildfire Suppression using a Team of UAVs

This paper addresses early wildfire management using a team of UAVs for the mitigation of fires. The early detection and mitigation systems help in alleviating the destruction with reduced resource utilization. A Genetic Algorithm-based Routing and Scheduling with Time constraints (GARST) is proposed to find the shortest schedule route to mitigate the fires as Single UAV Tasks (SUT). The objective of GARST is to compute the route and schedule of the UAVs so that the UAVS reach the assigned fire locations before the fire becomes a Multi UAV Task (MUT) and completely quench the fire using the extinguisher. The fitness function used for the genetic algorithm is the total quench time for mitigation of total fires. The selection, crossover, mutation operators, and elitist strategies collectively ensure the exploration and exploitation of the solution space, maintaining genetic diversity, preventing premature convergence, and preserving high-performing individuals for the effective optimization of solutions. The GARST effectively addresses the challenges posed by the NP-complete problem of routing and scheduling for growing tasks with time constraints. The GARST is able to handle infeasible scenarios effectively, contributing to the overall optimization of the wildfire management system.

cs.RO↗

arXiv2024

A Resource-Efficient Decentralized Sequential Planner for Spatiotemporal Wildfire Mitigation

This paper proposes a Conflict-aware Resource-Efficient Decentralized Sequential planner (CREDS) for early wildfire mitigation using multiple heterogeneous Unmanned Aerial Vehicles (UAVs). Multi-UAV wildfire management scenarios are non-stationary, with spatially clustered dynamically spreading fires, potential pop-up fires, and partial observability due to limited UAV numbers and sensing range. The objective of CREDS is to detect and sequentially mitigate all growing fires as Single-UAV Tasks (SUT), minimizing biodiversity loss through rapid UAV intervention and promoting efficient resource utilization by avoiding complex multi-UAV coordination. CREDS employs a three-phased approach, beginning with fire detection using a search algorithm, followed by local trajectory generation using the auction-based Resource-Efficient Decentralized Sequential planner (REDS), incorporating the novel non-stationary cost function, the Deadline-Prioritized Mitigation Cost (DPMC). Finally, a conflict-aware consensus algorithm resolves conflicts to determine a global trajectory for spatiotemporal mitigation. The performance evaluation of the CREDS for partial and full observability conditions with both heterogeneous and homogeneous UAV teams for different fires-to-UAV ratios demonstrates a $100\%$ success rate for ratios up to $4$ and a high success rate for the critical ratio of $5$, outperforming baselines. Heterogeneous UAV teams outperform homogeneous teams in handling heterogeneous deadlines of SUT mitigation. CREDS exhibits scalability and $100\%$ convergence, demonstrating robustness against potential deadlock assignments, enhancing its success rate compared to the baseline approaches.

cs.RO↗

arXiv2024

An efficient Deep Spatio-Temporal Context Aware decision Network (DST-CAN) for Predictive Manoeuvre Planning

To ensure the safety and efficiency of its maneuvers, an Autonomous Vehicle (AV) should anticipate the future intentions of surrounding vehicles using its sensor information. If an AV can predict its surrounding vehicles' future trajectories, it can make safe and efficient manoeuvre decisions. In this paper, we present such a Deep Spatio-Temporal Context-Aware decision Network (DST-CAN) model for predictive manoeuvre planning of AVs. A memory neuron network is used to predict future trajectories of its surrounding vehicles. The driving environment's spatio-temporal information (past, present, and predicted future trajectories) are embedded into a context-aware grid. The proposed DST-CAN model employs these context-aware grids as inputs to a convolutional neural network to understand the spatial relationships between the vehicles and determine a safe and efficient manoeuvre decision. The DST-CAN model also uses information of human driving behavior on a highway. Performance evaluation of DST-CAN has been carried out using two publicly available NGSIM US-101 and I-80 datasets. Also, rule-based ground truth decisions have been compared with those generated by DST-CAN. The results clearly show that DST-CAN can make much better decisions with 3-sec of predicted trajectories of neighboring vehicles compared to currently existing methods that do not use this prediction.

cs.RO↗

arXiv2024

Variable-Pitch-Propeller Mechanism Design, and Development of Heliquad for Mid-flight Flipping and Fault-Tolerant-Control

This paper presents the design of Variable-Pitch-Propeller mechanism and its application on a quadcopter called Heliquad to demonstrate its unique capabilities. The input-output relationship is estimated for a generic mechanism. Various singularities and actuator sizing requirements are also analyzed. The mechanism is manufactured, and the validated input-output relationship is implemented in the controller of Heliquad. Heliquad is controlled by a unified non-switching cascaded attitude-rate controller, followed by a unique Neural-Network-based reconfigurable control allocation to approximate nonlinear relationship between the control input and actuator command. The Heliquad prototype's mid-flight flip experiment validates the controller's tracking performance in upright as well as inverted conditions. The prototype is then flown in upright condition with only three of its working actuators. To the best of the authors' knowledge, the cambered airfoil propeller-equipped Heliquad prototype demonstrates full-attitude control, including yaw-rate, on three working actuators for the first time in the literature. Finally, the utility of this novel capability is demonstrated by safe recovery and precise landing post-mid-flight actuator failure crisis. Overall, the controller tracks the references well for all the experiments, and the output of the NN-based control allocation remains bounded throughout.

eess.SY↗

arXiv2024

MRFP: Learning Generalizable Semantic Segmentation from Sim-2-Real with Multi-Resolution Feature Perturbation

Deep neural networks have shown exemplary performance on semantic scene understanding tasks on source domains, but due to the absence of style diversity during training, enhancing performance on unseen target domains using only single source domain data remains a challenging task. Generation of simulated data is a feasible alternative to retrieving large style-diverse real-world datasets as it is a cumbersome and budget-intensive process. However, the large domain-specfic inconsistencies between simulated and real-world data pose a significant generalization challenge in semantic segmentation. In this work, to alleviate this problem, we propose a novel MultiResolution Feature Perturbation (MRFP) technique to randomize domain-specific fine-grained features and perturb style of coarse features. Our experimental results on various urban-scene segmentation datasets clearly indicate that, along with the perturbation of style-information, perturbation of fine-feature components is paramount to learn domain invariant robust feature maps for semantic segmentation models. MRFP is a simple and computationally efficient, transferable module with no additional learnable parameters or objective functions, that helps state-of-the-art deep neural networks to learn robust domain invariant features for simulation-to-real semantic segmentation.

cs.CV↗

arXiv2024

Full Attitude Intelligent Controller Design of a Heliquad under Complete Failure of an Actuator

In this paper, we design a reliable Heliquad and develop an intelligent controller to handle one actuators complete failure. Heliquad is a multi-copter similar to Quadcopter, with four actuators diagonally symmetric from the center. Each actuator has two control inputs; the first input changes the propeller blades collective pitch (also called variable pitch), and the other input changes the rotation speed. For reliable operation and high torque characteristic requirement for yaw control, a cambered airfoil is used to design propeller blades. A neural network-based control allocation is designed to provide complete control authority even under a complete loss of one actuator. Nonlinear quaternion based outer loop position control, with proportional-derivative inner loop for attitude control and neural network-based control allocation is used in controller design. The proposed controller and Heliquad designs performance is evaluated using a software-in-loop simulation to track the position reference command under failure. The results clearly indicate that the Heliquad with an intelligent controller provides necessary tracking performance even under a complete loss of one actuator.

eess.SY↗

arXiv2024

Towards Improved Imbalance Robustness in Continual Multi-Label Learning with Dual Output Spiking Architecture (DOSA)

Algorithms designed for addressing typical supervised classification problems can only learn from a fixed set of samples and labels, making them unsuitable for the real world, where data arrives as a stream of samples often associated with multiple labels over time. This motivates the study of task-agnostic continual multi-label learning problems. While algorithms using deep learning approaches for continual multi-label learning have been proposed in the recent literature, they tend to be computationally heavy. Although spiking neural networks (SNNs) offer a computationally efficient alternative to artificial neural networks, existing literature has not used SNNs for continual multi-label learning. Also, accurately determining multiple labels with SNNs is still an open research problem. This work proposes a dual output spiking architecture (DOSA) to bridge these research gaps. A novel imbalance-aware loss function is also proposed, improving the multi-label classification performance of the model by making it more robust to data imbalance. A modified F1 score is presented to evaluate the effectiveness of the proposed loss function in handling imbalance. Experiments on several benchmark multi-label datasets show that DOSA trained with the proposed loss function shows improved robustness to data imbalance and obtains better continual multi-label learning performance than CIFDM, a previous state-of-the-art algorithm.

cs.LG↗

arXiv2023

Graph-based Prediction and Planning Policy Network (GP3Net) for scalable self-driving in dynamic environments using Deep Reinforcement Learning

Recent advancements in motion planning for Autonomous Vehicles (AVs) show great promise in using expert driver behaviors in non-stationary driving environments. However, learning only through expert drivers needs more generalizability to recover from domain shifts and near-failure scenarios due to the dynamic behavior of traffic participants and weather conditions. A deep Graph-based Prediction and Planning Policy Network (GP3Net) framework is proposed for non-stationary environments that encodes the interactions between traffic participants with contextual information and provides a decision for safe maneuver for AV. A spatio-temporal graph models the interactions between traffic participants for predicting the future trajectories of those participants. The predicted trajectories are utilized to generate a future occupancy map around the AV with uncertainties embedded to anticipate the evolving non-stationary driving environments. Then the contextual information and future occupancy maps are input to the policy network of the GP3Net framework and trained using Proximal Policy Optimization (PPO) algorithm. The proposed GP3Net performance is evaluated on standard CARLA benchmarking scenarios with domain shifts of traffic patterns (urban, highway, and mixed). The results show that the GP3Net outperforms previous state-of-the-art imitation learning-based planning models for different towns. Further, in unseen new weather conditions, GP3Net completes the desired route with fewer traffic infractions. Finally, the results emphasize the advantage of including the prediction module to enhance safety measures in non-stationary environments.

cs.AI↗

arXiv2023

Contrastive Learning-Based Spectral Knowledge Distillation for Multi-Modality and Missing Modality Scenarios in Semantic Segmentation

Improving the performance of semantic segmentation models using multispectral information is crucial, especially for environments with low-light and adverse conditions. Multi-modal fusion techniques pursue either the learning of cross-modality features to generate a fused image or engage in knowledge distillation but address multimodal and missing modality scenarios as distinct issues, which is not an optimal approach for multi-sensor models. To address this, a novel multi-modal fusion approach called CSK-Net is proposed, which uses a contrastive learning-based spectral knowledge distillation technique along with an automatic mixed feature exchange mechanism for semantic segmentation in optical (EO) and infrared (IR) images. The distillation scheme extracts detailed textures from the optical images and distills them into the optical branch of CSK-Net. The model encoder consists of shared convolution weights with separate batch norm (BN) layers for both modalities, to capture the multi-spectral information from different modalities of the same objects. A Novel Gated Spectral Unit (GSU) and mixed feature exchange strategy are proposed to increase the correlation of modality-shared information and decrease the modality-specific information during the distillation process. Comprehensive experiments show that CSK-Net surpasses state-of-the-art models in multi-modal tasks and for missing modalities when exclusively utilizing IR data for inference across three public benchmarking datasets. For missing modality scenarios, the performance increase is achieved without additional computational costs compared to the baseline segmentation models.

cs.CV↗