Department of Electrical and Electronics Engineering

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1925

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Author: search.filters.author.Bitragunta, SainathSubject: search.filters.subject.EEE

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Now showing 1 - 10 of 61
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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.
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    A Novel Hybrid Communication Policy Using Network Coding Based Post-Quantum Cryptography and Adaptive Neuro Fuzzy Inference System
    (Springer, 2024-02) Bitragunta, Sainath
    This paper presents a novel hybrid communication policy developed using Hybrid Universal Network Coding Cryptosystem (HUNCC), which can perform post-quantum cryptography at a high information rate. This hybrid scheme (HUNCC) is based on the notion of individual secrecy and computational complexity, where we assume that if the eavesdropper has access to a small part of the communication link (ciphertext) between the sender and receiver, individual secrecy will ensure secure communication. However, If the eavesdropper can access the full link (ciphertext), then computational complexity can guarantee post-quantum security. We extend our study by applying fuzzy logic to HUNCC, helping us gain more insights. Specifically, we propose a novel Adaptive Neuro Fuzzy Inference System that provides a security metric from the input attributes, namely the physical layer (PHY) security attribute and cooperative sensing (CS) security attribute obtained from trusted sources. We assume such attributes are available for the ANFIS to evaluate a security metric that will decide on whether (i). to implement the cryptosystem over a single link in a multi-link system (HUNCC) or (ii). to completely use the notion of individual secrecy alone to encode the system, provided the output from ANFIS guarantees good security. This method can help switch between classical and quantum-secure communication, improving the effective throughput. Using HUNCC with a modified McEliece Cryptosystem that uses Hamming code, we obtain an information rate of 0.9507 bits, illustrating the increase in throughput obtained using the modified HUNCC model when compared to existing PQC techniques. Furthermore, we introduce a novel concept aimed at achieving even higher information rates. This approach relies on the security metric derived from ANFIS and involves dynamic switching between quantum and classical communication, guided by security risk factors. As a result, we achieve an information rate of up to 0.98 bits.
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    Estimation and Analysis of Maximum Energy Harvested in RF-EH Wireless System Over Different Fading Channels
    (Springer, 2024-02) Bitragunta, Sainath
    Parameter estimation is an important problem in practical wireless systems and networks. Specifically, accurate estimation of harvested energies is vital in autonomous, green wireless systems and networks whose essential energy resources are ambient radio frequency (RF) signals. In this paper, I address the problem of estimating the maximum energy harvested among multiple energy harvesting (EH) relays in a double-hop EH relay-assisted cooperative wireless system. In the system considered, multiple EH relays harvest energy from RF signals they receive from the RF source over Rayleigh fading channels. I formally state the maximum harvested energy estimation problem in the presence of additive white noise. I derive analytical expressions for the exact minimum mean squared error (MMSE) estimator and MMSE. Further, I obtain an expression for the number of measurements for a special scenario where MMSE and Cramer-Rao lower bound (CRLB) are equal. I further extend the analysis and simulations for Rayleigh fading with shadowing and Nakagami fading to get more analytical and quantitative insights. Finally, the MMSE of the proposed model is compared with a benchmark model that includes quantization noise and multiplicative noise. The analysis is useful for further investigating order statistics of harvested energies and application in green cooperative energy harvesting Internet of Things (IoT)-enabled wireless systems.
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    Optimal Path-Finding using Quantum Alternating Operator Ansatz with Grover’s Search for Multi-hop Wireless Sensor Networks
    (IEEE, 2024-07) Bitragunta, Sainath
    With the development of wireless sensor networks to combat the problem of reaching places otherwise unreachable for humans, there is a need to keep these remote renewable devices charged. Optimizing the network to utilize the minimum amount of energy becomes paramount. With the advent of powerful Quantum Variational Algorithms suited for the current Noisy Intermediate Scale Quantum (NISQ) era of quantum computers, we can exploit the power of these quantum processors to solve classically hard problems. In this paper, we use the Quantum Alternating Operator Ansatz (QAOA) followed by Grover Searching, which amplifies the possible paths to find the optimal path in a multi-hop network. We perform experiments using quantum simulators to obtain useful insights into the algorithm’s performance with respect to various parameters of interest. Our approach involving QAOA and Grover Searching is a useful benchmark for more general and complex optimization problems in remote renewable wireless networks.
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    Environment-Aware Green UAV-Assisted, CubeSat Communication Network Energy Efficiency and Outage Probability Analysis
    (IEEE, 2024-08) Bitragunta, Sainath
    Rapid advancements in internet-of-things (IoT), unmanned aerial vehicles (UAVs), and energy harvesting (EH) technologies can be leveraged to design and develop green and reliable cooperative Cube satellite communication (CSC) systems and networks. In this work, we propose a novel cooperative CSC system model comprising green UAVs as intelligent relays equipped with IoT sensors, intelligent processing and EH modules, and transceivers. Using a novel and intelligent probabilistic transmission policy (PTP) that we propose, CubeSats can conserve energy by deactivating transmissions in unfavorable weather conditions based on control signals from the smart UAV via a telemetry link. We extend this model to include multiple CubeSats and analyze it by deriving and evaluating network energy efficiency and its lower bound. Our numerical plots show that the proposed PTP significantly outperforms the continuous transmission policy (CTP). At a specific transmission probability of 0.125, PTP is 40 times more energy efficient than CTP. We extend the work and develop a novel and insightful performance analysis for energy efficiency outage (EEO) probability. Specifically, we derive closed-form approximate expressions for EEO probability and present numerical results. Furthermore, we analyze the performance of clustered CSC networks and present numerical results to assess EEO probability, providing valuable insights for future large-scale green CSC network design and deployment.
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    Probabilistic Modeling and Outage Analysis for Smart Microgrid and Electric Vehicles Ecosystem
    (IEEE, 2023) Bitragunta, Sainath; Mishra, Puneet
    In this work, we propose a simple yet novel prob-abilistic model for a renewable smart-grid and electric vehi-cle (EV) ecosystem supported by cellular vehicle-to-grid (C- V2G) infrastructure. Our stochastic model accounts for two key energy components modeled as random variables: the energy available from renewable sources and energy consumed by the EV. We define novel performance measures, desire probability, and threshold for the ratio of two energy components for this stochastic model. For it, we develop an insightful analysis that includes mathematical derivations for obtaining a single integral expression for the desired probability for a stable green grid- EV ecosystem operation. We present various numerical plots with varying model parameters and obtain useful insights to understand the model and suggest the optimum threshold for stable and safe operation. The model and analysis we develop are useful as the theoretical benchmark for other learning-based practical approaches.
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    SEP-optimal adaptive gain and transmit power amplify-and-forward relaying
    (IEEE, 2012) Bitragunta, Sainath
    Amplify-and-forward (AF) relay based cooperation has been investigated in the literature given its simplicity and practicality. Two models for AF, namely, fixed gain and fixed power relaying, have been extensively studied. In fixed gain relaying, the relay gain is fixed but its transmit power varies as a function of the source-relay (SR) channel gain. In fixed power relaying, the relay's instantaneous transmit power is fixed, but its gain varies. We propose a general AF cooperation model in which an average transmit power constrained relay jointly adapts its gain and transmit power as a function of the channel gains. We derive the optimal AF gain policy that minimizes the fading-averaged symbol error probability (SEP) of MPSK and present insightful and tractable lower and upper bounds for it. We then analyze the SEP of the optimal policy. Our results show that the optimal scheme is up to 39.7% and 47.5% more energy-efficient than fixed power relaying and fixed gain relaying, respectively. Further, the weaker the direct source-destination link, the greater are the energy-efficiency gains.