BITS Faculty Publications

Permanent URI for this communityhttp://localhost:4000/handle/123456789/1867

Browse

Start date: 2020Subject: search.filters.subject.EEE

Search Results

Now showing 1 - 10 of 887
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
    LPNet: a DNN based latency prediction technique for application mapping in Network-on-Chip design
    (Elsevier, 2021-11) Sambangi, Ramesh
    Analytical models used for latency estimation of Network-on-Chip (NoC) are not producing reliable accuracy. This makes these analytical models difficult to use in optimization of design space exploration. In this paper, we propose a learning based model using deep neural network (DNN) for latency predictions. Input features for DNN model are collected from analytical model as well as from Booksim simulator. Then this DNN model has been adopted in mapping optimization loop for predicting the best mapping of given application and NoC parameters combination. Our simulations show that using the proposed DNN model, prediction error is less than 12% for both synthetic and application specific traffic. More than 108 times speedup could be achieved using DPSO with DNN model compared to DPSO using Booksim simulator.
  • No Thumbnail Available
    Item
    Algorithm and architecture design of random fourier features-based kernel adaptive filters
    (IEEE, 2022-12) Sambangi, Ramesh
    Numerous real-life systems exhibit complex nonlinear input-output relationships. Kernel adaptive filters, a popular class of nonlinear adaptive filters, can efficiently model these nonlinear input-output relationships. Their growing network structure, however, poses considerable challenges in terms of their hardware implementation, making them inefficient for real-time applications. Random Fourier features (RFF) facilitate the development of kernel adaptive filters with a fixed network structure. For the first time, this paper attempts to implement the RFF-based kernel least mean square (RFF-KLMS) algorithm on hardware. To this end, we propose several reformulations of the feature functions (FFs) that are computationally expensive in their native form so that they can be implemented in real-time VLSI. Specifically, we reformulate inner product evaluation, cosine, and exponential functions that appear in the implementation of FFs. With these reformulations, the proposed delayed RFF-KLMS (DRFF-KLMS) is then synthesized using 45-nm CMOS technology with 16-bit fixed-point representations. According to the synthesis results, pipelined DRFF-KLMS architectures require minimal hardware increase over the state-of-the-art conventional delayed LMS architecture while significantly improving estimation performance for the nonlinear model. Our results suggest that the cosine feature function-based DRFF-KLMS is appropriate for applications requiring high accuracy, whereas the exponential function-based DRFF-KLMS may be well suited for resource-constrained applications.
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
    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.