BITS Faculty Publications

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Start date: 2023Subject: search.filters.subject.EEE

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    Stochastic diffusivity with time-varying trajectory in mobile molecular communication: performance analysis and channel modeling
    (IEEE, 2025-04) Joshi, Sandeep
    This work considers a three-dimensional mobile molecular communication (MC) with intra-body disease spread applications. The communicating devices in the considered mobile MC system are point transmitters and passive spherical receiver nano-machines (NMs) with emitted information-carrying molecules following the Gaussian Brownian motion. These NMs can be used to detect the presence of disease spread and for targeted drug delivery. We propose stochastic diffusivity models for both communicating devices and information-carrying molecules. Using the stochastic diffusivity model and considering initial distance as a reference, we derive the probability density function of the relative distance between the communicating devices. We allocate the time-varying trajectory to the information-carrying molecules moving towards receiver NM and obtain its diffusivity distribution. Through the proposed stochastic diffusivity model, we characterize the mobile MC channel by channel impulse response and derive its statistical mean. We consider the discrete-time statistical channel model at a high inter-symbol interference regime and analyze the channel performance in terms of error analysis and receiver operating characteristics. We also derive the channel for the considered system model. We show the degree of accuracy through root mean square error for the Poisson and Gaussian distribution models. Furthermore, the numerical results are verified through particle-based simulations.
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    Nonlinear anisotropic diffusion-based channel estimation in 5G wireless networks
    (IEEE, 2025-03) Joshi, Sandeep
    In the context of the fifth-generation new radio downlink scenario, we introduce an innovative approach for channel estimation in this paper that circumvents the requirement for the prior dataset. We incorporate anisotropic diffusion and bit-plane decomposition to remove the noise in channel estimates. We first pre-process wireless channel coefficients with bit-plane decomposition to partially reduce noise interference and maintain the granularity of the information. In the second stage, anisotropic diffusion is performed based on neighboring coefficients, and the gradient-based denoising takes place without prior training. We assess the mean square error (MSE) across varying noise levels compared to the state-of-the-art method and further explore the impact of key parameters governing anisotropic diffusion. The simulation results indicate that the proposed channel estimation technique achieves a 44.77% reduction in average MSE and a significant reduction in computational complexity compared to the baseline reference technique.
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    Redefining channel estimation in underwater acoustic OFDM systems with deep neural network
    (IEEE, 2025-08) Joshi, Sandeep
    This paper introduces a novel method for channel estimation in underwater acoustic communication in an autonomous underwater vehicular network. The proposed method employs a denoising technique to refine least squares (LS) channel estimates using deep image prior (DIP). By establishing an equivalence between underwater acoustic (UWA) channel estimation and image denoising, we leverage DIP to enhance estimation accuracy. The proposed approach is validated on the Norway continental shelf (NCS1) watermark dataset, demonstrating superior performance with average mean square error reductions of 96.64% and 96.09% compared to LS and the deep denoising convolutional neural network (DnCNN), respectively. Furthermore, the proposed analysis of pilot symbol utilization in the DIP-based estimator shows a 46.47 % error reduction, even when using only 25 % of the pilot symbols. By efficiently utilizing available resources, the proposed method enhances spectral efficiency and enables accurate estimation, even with limited pilot signals.
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    Application mapping onto manycore processor architectures using active search framework
    (IEEE, 2023-02) Sambangi, Ramesh
    Finding an optimal application mapping solution in a manycore processor is an NP-hard problem. Heuristic search techniques have the advantage of finding near-optimal solutions faster than other methods when mapping large-scale applications. However, the majority of the heuristic-based application mapping methods easily fall into local minima. Machine learning (ML) methods can learn heuristics from training data on their own, require minimal assistance from humans, and produce better mapping solutions. Recently, a reinforcement learning-based framework (RLF) has been proposed to generate the initial population for metaheuristics, designed using genetic algorithm (GA) and particle swarm optimization (PSO). The RLF framework does not incorporate reward information while generating mapping solutions. However, the model performance can be improved further by refining the network parameters using the reward information during predictions. To overcome this challenge, we propose an active search framework (ASF). For the first time, we propose a new intellectual property (IP)-core numbering scheme, which will assist ASF in learning the mapping rules more effectively. We demonstrate that REINFORCE with multiple samples (predictions) per data point improves model accuracy and reduces variance by constructing a baseline using these samples. With these, we propose two RL models: active search (ATSR) and active search with pretraining (ATSRP). According to experimental results, both ATSRP and ATSR models produce better mapping solutions compared to RLF and other state-of-the-art methods. The results suggest that the ATSRP model is better suited for performing application mapping onto a 2-D mesh-based manycore processor. Finally, we extend this framework to other performance metrics and 3-D mesh-based manycore processors.
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    Congestion-aware vertical link placement and application mapping onto 3-D network-on-chip architectures
    (IEEE, 2024-02) Sambangi, Ramesh
    3-D Network-on-Chip (NoC) technology has emerged as a compelling solution in modern System-on-Chip (SoC) designs. This NoC technology effectively addresses the escalating need for high-performance and energy-efficient on-chip communication in various applications, including high-performance computing (HPC), graphics processing units (GPUs), and multiprocessor SoCs (MPSoCs). However, the efficient mapping of applications onto 3-D Network-on-Chips (3-D NoC) remains a complex challenge, necessitating the development of improved algorithms to address the issue. In this context, we present a novel neural mapping model with a reinforcement learning (RL) approach (NeurMap3D) to design application-specific 3-D NoC-based IC. Additionally, we propose the neural congestion-aware through-silicon vias (TSVs) placement and application mapping (NCTPAM) approach, which not only addresses application mapping but also incorporates TSVs placement and load balance across the TSVs for the specific application. In order to reduce the CPU execution time of NCTPAM algorithm, we propose incorporating a partial model parameter (θ) update mechanism. Experimental results indicate improved performance in terms of minimizing communication cost, load balancing across TSVs and energy consumption, highlighting the potential of our approach to enhance the efficiency of these synthesized network architectures.
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    Highly sensitive thermal sensor design using a gate-bias-controlled TCR in MoSe2 FET
    (IEEE, 2025-05) Rao, V. Ramgopal
    Temperature coefficient of resistance (TCR) is an important property for the design of thermal sensors. It is calculated as per the relative shift in electrical resistance for every degree of thermal variation. Furthermore, tunable TCR implies controlling the TCR through the manipulation of gate voltage. In this article, we have investigated the TCR tunability of the layered semiconductor material molybdenum diselenide (MoSe2) with gate-bias control. Atomic force microscope (AFM) is used to measure flake height, and Raman spectroscopy is used to characterize the MoSe2 flakes. Their TCR is higher by about two times that of MoS2 and five times that of metallic films, which are typically around 0.5% K −1 . Its TCR can be tuned to about two times higher than its value for 15-nm-thick flake within a gate voltage change of 7 V, with the highest recorded value being −2.75% K −1 . Similarly, 65-nm-thick flake has a TCR tunability of 4.5 times higher than the minimum value. Additionally, the average relative uncertainty in TCR is observed to be 3.8% for the 65-nm devices and 4.6% for the 15-nm devices, respectively.
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    Residual analysis based neutron-gamma pulses segregation of liquid scintillator detector
    (IOP, 2025-01) Bitragunta, Sainath
    An innovative residual analysis based method is proposed for pulse shape discrimination and segregation of neutron and gamma pulses of liquid scintillator-based detector (BC501A). This study develops a simple and efficient algorithm for discrimination of neutrons and gammas from mixed environment and then segregate the neutron and gamma pulses into two label datasets. This method involves analyzing the residuals between the measured pulse and the reference gamma pulse in normalized scale. By examining these residuals, users can identify characteristics unique to each type of radiation. The pulse shape discrimination performance obtained for a 5 inches by 5 inches (length by diameter) liquid scintillator detector using this residual method is found to be 20% better compared to that obtained using traditional charge comparison method.
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    Gauss-ramanujan functions: constructions, properties, and applications in communications and signal processing
    (2025-05) Bitragunta, Sainath
    In this article, I construct a new set of functions based on Ramanujan sequences (RSEs), Gaussian pulse (GP), and its delayed Gaussian pulse (DGP). The motivation for this construction is based on the special properties of RSEs, GP, and DGP. First, I present a procedure for constructing Gauss-Ramanujan (GauRam) functions using selected RSEs. I develop an insightful analysis for deterministic and stochastic overlap between GP and DGP. Specifically, I present exact and closed form approximation expressions for delay-averaged GP and DGP overlap and then evaluate them numerically. Later, I derive and analyze the mathematical (spectral) properties of selected GauRam functions. I extend the analysis by analyzing the Hilbert transform of the first-order GauRam function and validating orthogonality and its usefulness in analytic signal representations. Furthermore, I present insightful applications of these functions in communications and signal processing. Specifically, I present the continuous-wave Gauss-Ramanujan modulation (GRM) scheme, Gauss-Ramanujan Shift Keying (GRSK) scheme, and Gauss-Ramanujan wavelets and their analysis and comparisons with benchmarking. The desirable properties of these novel modulation schemes and wavelets enable their use in next-generation hybrid and energy-efficient communication systems and signal processing.
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    Functional analysis of neutron-gamma pulses and synthetic pulse generation for liquid scintillator
    (IEEE, 2025-08) Bitragunta, Sainath
    An innovative method is proposed to generate a realistic functional neutron and gamma pulses model for a liquid scintillator-based detector. This approach analyzed neutron and gamma pulse shapes, electronic noise and fit the model parameters that include the intrinsic properties of the scintillator and standard deviation of the transit time of the photomultiplier tube. The synthetic data are generated using Monte-Carlo-based statistical methods from the modeled functions, energy distributions of neutrons, gammas, and electronic noise. This work emulates realistic pulses that can be used to calibrate and test scintillation detectors used in nuclear physics experiments. This synthetic data library provides realistic labeled neutron and gamma pulses for liquid scintillators and photomultiplier tubes, which may be used for improving radiation detection through supervised machine learning. This study provides a comprehensive framework for neutron-gamma discrimination, synthetic data generation, and data validation.
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    Semi-supervised machine learning technique for neutron-gamma discrimination and generalized approach for figure of merit
    (IOP, 2025-08) Bitragunta, Sainath
    The discrimination between neutron and gamma radiation pulses is crucial in mixed environment for neutron spectroscopy, particularly in fields such as nuclear science, nuclear safety, environmental monitoring, and radiation imaging. A quantitative measurement is essential to evaluate the discriminatory performance and a generalized yardstick is desirable for all the available methods. This study introduces a semi-supervised machine learning approach utilizing Multi-Layer Perceptron, Convolutional Neural Network, Long Short-Term Memory Network and Transformer encoder-based classifier to perform neutron-gamma pulse discrimination. The proposed model is applied to pulse signals acquired from a liquid scintillator BC501A coupled with a photomultiplier tube R4144, recognized for their high sensitivity and effectiveness in neutron-gamma discrimination tasks. The model's performance is rigorously evaluated against traditional analogue and digital charge comparison discrimination techniques. A generalized method is introduced in terms of figure of merit for equipollent discrimination performance comparison with existing analog and digital-based methods as well as various other machine learning based classification techniques.