Department of Electronics and Communication Engineering
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Item Design and Fabrication of a Gas Sensor for Food Freshness Detection(Department of Electronics & Instrumentation Engineering, NITS, 2025) Biswas, BedabrataFood spoilage is a serious issue that affects both food safety and the economy, leading to health risks and financial losses. Various factors contribute to food deterioration such as natural chemical processes, biological activity, microbial growth, etc. Thus a food’s texture, taste, color, and smell can be changed. One of the clear indicators of spoilage is the release of specific gases. Various gases such as ammonia and formaldehyde are released which result from bacterial activity and chemical reactions. Detecting these gases can help determine whether food is still fresh. There are different methods to assess food spoilage, ranging from simple sensory eval uations to advanced electronic devices. Among these techniques, gas sensors, biosensors, spectroscopy, and electronic noses are popular and effective. Gas sensors provide real-time detection of spoilage gases, while biosensors, which use biological elements like enzymes, offer highly precise results. Spectroscopy techniques, such as infrared and ultraviolet light analysis, help identify chemical changes in food. Electronic noses combine multiple gas sensors and mimic the human sense of smell to improve detection accuracy. Though these methods are useful, some challenges remain. Continued research is necessary to enhance these technologies for limited accuracy, high costs, and lack of portability. Here, we focused on developing compact, highly sensitive gas sensors capable of quickly detecting spoilage-related gases. By improving sensor technology, this research contributes to better food safety measures. However, there are several challenges in designing better detection systems. Many sensors respond to multiple gases at once, making it difficult to pinpoint spoilage indicators. Some sensors take too long to deliver results, while others are too large or complex for practical use outside laboratory settings. Additionally, many existing sensors struggle to detect very low concentrations of spoilage gases. To address these challenges, this research explores different gas sensor technologies, including metal oxide, electrochemical, optical, and MEMS-based sensors. Their working principles, advantages, and limitations are analyzed in the context of food spoilage detection, highlighting gaps in current research. A key development in this study is a Metal-Insulator Semiconductor (MIS) capacitive sensor designed to detect formaldehyde in fish treated with formalin. Constructed using silver and reduced graphene oxide on a silicon wafer, this sensor achieves high sensitivity (142.1 pF/ppm) and provides results within 85 seconds, offering a reliable solution for real-time food safety monitoring. Another major advancement in this research is a resisto-capacitive sensor developed for ammonia detection, a key spoilage gas in fish. This sensor, featuring a gold electrode with a tin oxide layer, delivers quick response and recovery times of 69 and 135 seconds, respec tively. Experimental results confirm its accuracy in determining fish freshness under various storage conditions. Moreover, the study investigates microheater design and optimization, which play a crucial role in enhancing sensor performance. Different microheater structures, viz. tree-type, ladder-type, U-shaped, and spiral-type designs, are tested, with advanced simulations used to improve their efficiency and reliability. A gas sensor, for enhancing food spoilage detection has been successfully developed in this research to address key challenges in food safety monitoring. Formaldehyde is ef fectively detected by the MIS capacitive sensor. Ammonia is accurately identified using the resisto-capacitive sensor, reducing cross-selectivity issues in assessing fish freshness. Sensor performance is further improved through the integration of microheater technology. Future research will focus on increasing accuracy, minimizing sensor size, incorporating machine learning for smarter analysis, and adding wireless features for real-time food monitoringItem Predictive Forward- Reverse-Hybrid Modeling of Microstrip Antenna Using Machine Learning Based Regression and Classification Techniques(Department of Electronics and Communication Engineering, NITS, 2025) Kumar, AnjaniThis Ph.D. thesis presents the design and development of a new class of printed microstrip antennas optimized for space and satellite communication applications. With the rapid advancement of intelligent computational techniques, particularly machine learning (ML), conventional approaches to antenna design, analysis, and optimization have been significantly improved. In this framework, the thesis proposes an integrated ML-based modeling strategy that combines forward, inverse, and hybrid methodologies utilizing both regression and classification algorithms. The research focuses on accurately predicting key electrical parameters—such as resonance, cut-off, and notched frequencies—and estimating their corresponding geometrical configurations for various antenna architectures, including single port, dual-port, and four-port models. These antenna designs are tailored to operate efficiently across the S-band to X-band frequency spectrum, meeting the high-performance requirements of contemporary satellite communication systems. The forward modelling phase, multiple supervised regression algorithms-such as Support Vector Regression (SVR), Gradient Boosted Decision Trees (GBDT), Ridge, Lasso, Elastic Net, and Deep neural networks (DNNs)-are implemented to predict frequency characteristics from antenna geometries. Conversely, in the reverse modelling approach, the algorithms predict optimal geometrical parameters from known electrical specifications. Hybrid models are further developed by integrating forward and reverse predictions to improve reliability and design efficiency. For antennas incorporating U-shaped slots and Electromagnetic Band Gap (EBG) structures, the ML models effectively learn the complex nonlinear relationships between inputs and outputs. Models are evaluated using data generated from antenna simulator in Ansys HFSS, and in selected cases, validated through fabricated prototypes. Deep learning architectures- artificial neural networks (ANN), convolution neural network (CNN), and recurrent neural network (RNN)-are employed to model more complex multi-output problems involving dual- and quad port antennas, achieving notched frequency responses up to 8.2 GHz. Additionally, the thesis explores classification tasks for recognizing antenna types such as circular, triangular, and rectangular patches using Random Forest and K-Nearest Neighbors (KNN) classifiers. The classifiers achieve accuracies exceeding 97.9%, confirming the robustness of the proposed models. The performance of all machine learning models is meticulously assessed using a comprehensive set of evaluation metrics. For regression tasks, metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Square Error (MSE), R² score, Mean Squared Logarithmic Error (MSLE), and Root Mean Squared Logarithmic Error (RMSLE) are employed. For classification models, evaluation is conducted using accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), and Cohen’s kappa, ensuring a rigorous and holistic performance analysis. The results indicate that ensemble methods and deep learning architectures significantly outperform conventional algorithms, confirming the effectiveness of machine learning in predictive antenna modeling. This study introduces a novel data-driven approach that accelerates the antenna design cycle and improves the functional performance of microstrip antennas, particularly for satellite communication applications spanning low, medium, and geostationary orbits.Item Efficient Image Dehazing Algorithms with Conventional and Deep Learning Based Techniques Using Multi-Spectral Images(Department of Electronics and Communication Engineering, NITS, 2026) Bhavani, Murapaka Dhana LakshmiImage haze is a common problem in photography and imaging that occurs due to particles like dust, smoke, and moisture in the atmosphere. These particles scatter light, which leads to a reduction in visibility and a degradation in image quality. This scattering effect reduces contrast and visual clarity. The impact of haze is particularly significant in real-time applications, such as autonomous driving, surveillance, and aerial photography, where the quality of visual data is crucial for performance and accuracy. In autonomous driving, for instance, clear images are essential for the vehicle’s vision systems to accurately detect and interpret the surrounding environment. Haze can obscure important details like road signs, pedestrians, and other vehicles, potentially leading to unsafe situations. Similarly, in surveillance, clear imagery is critical for the accurate identification of individuals and activities. Hazy images can hinder the effectiveness of these systems, compromising security and safety. To address these challenges, image dehazing techniques have been developed. These techniques aim to restore the clarity and contrast of hazy images, making them more useful for their intended applications. Image dehazing can be approached using conventional methods as well as deep learning methodologies. Conventional dehazing methods are often based on physical models of atmospheric scattering. However, conventional methods have limitations, particularly in handling complex and diverse haze conditions. This thesis mainly focus on the development of dehazing algorithms, both conventional and deep learning-based, has significantly advanced the field of image processing. These algorithms not only improve the visual quality of images but also enhance the performance of vision systems in various applications. By addressing issues related to low contrast and haze, these techniques contribute to safer autonomous driving, more effective surveillance, and higher-quality aerial photography. The first approach proposed in this thesis is a novel methodology for dehazing single images using multi-scale image fusion. The process involves extracting different scale images from a single hazy image, specifically a white-balanced image and a luminance-based image. These images are then fused using their corresponding weight maps. To avoid unwanted enhancements, pyramid decomposition is applied to the weight maps and input images, enhancing contrast and sharpening the image. This approach effectively removes haze, improving both color and texture information, and produces clear images from a single hazy input. A fast and efficient method is developed with an improved U-Net architecture for image dehazing, enhancing images captured under low illumination, fog, and snow conditions. The methodology involves replacing the pooling operations in the traditional U-Net with Discrete Wavelet Transform (DWT) for up and downsampling. Additionally, an attention module is introduced by fusing up and down samples to identify and recover missing low-level features in the up-samples. Another approach is introduced with a DeepNet convolutional network architecture for effective haze removal from RGB images by accurately estimating the transmission map, indicating light transmission at each pixel. The DeepNet model leverages deep learning to predict the initial transmission map, capturing essential features of haze-affected areas. This initial map is then refined using Near-Infrared (NIR) images, which provide additional, non-visible information. A multi-spectral transmission map fusion approach is employed to further enhance accuracy, combining transmission maps from both RGB and NIR images. The fusion process utilizes haze-aware weight generation, assigning appropriate weights to different regions based on their haze levels, thus leveraging the strengths of each spectral domain for a more detailed and accurate transmission map. Finally a novel image dehazing framework is presented using a Generative Adversarial Network (GAN) architecture, featuring a U-Net generator and dual discriminators. The methodology involves leveraging the GAN’s capabilities to transform hazy input images into clear, dehazed versions. The U-Net generator, renowned for its efficiency in image-to-image translation tasks, plays a central role in this process. Also, dual discriminators for two different spectral input (RGB and NIR) images. It enhances the dehazing by effectively learning and removing intricate haze patterns. The framework trains the GAN to generate high-quality, perceptually realistic images, resulting in significantly improved image quality. This advanced approach provides a robust and reliable solution for generating clear, haze-free images from hazy inputs, crucial for outdoor vision applications. The proposed image dehazing architectures were evaluated using various publicly available datasets, including HazySky, I-Haze, O-Haze, See-in-the-Dark (SID), Exclusively Dark Image Dataset (ExDARK), REalistic Single Image DEhazing (RESIDE), and RGB-NIR. Additionally, the performance of low-light image enhancement using an improved U-Net algorithm was analyzed with several real-time images captured on the campus of the National Institute of Technology Silchar. This study presents efficient and optimized approaches for image dehazing.Item Some Investigations on Electromagnetic Interference Rejection in Ultra-Wideband (UWB) Antennas using EBG Technology(Department of Electronic and Communication Engineering, NITS, 2021-10-27) Modak, SumonThe PhD Thesis proposes the design and development of novel class of printed monopole antennas for ultra-wideband (UWB) applications. Firstly, in the context of UWB antennas, comprehensive studies on various configurations of printed antennas and their design approaches for realizing wide operational bandwidth with miniaturized antenna structures are investigated and summarized. Later, a novel configuration of printed monopole antenna for use in ultra-wideband (UWB) systems is conceptualized. As UWB antenna suffers from narrow band interferences, different approaches to procure band-notch characteristics in UWB antennas have also been studied. In the second development, a detailed study on design and analysis of compact Archimedean spiral EBG structure over printed microwave circuits has been carried out. In the first stage, a compact Archimedean spiral electromagnetic bandgap (EBG) structure has been developed and its performance matrices are characterized in terms of transmission and reflection coefficients. Further, it has been investigated for achieving frequency rejection characteristics in a printed monopole-based ultra-wideband (UWB) antenna and bandpass filter (BPF) circuits. The suggested designs exhibit dual band notched characteristics within the same band. Thus, the realization of dual notched band characteristics simultaneously in antenna, as well as filter circuit using EBG structure has not been achieved to date. In the third contribution, the main attention has been turned to develop UWB antennas with multi band rejection features. The literature based on single/dual/triple, and multi band notched UWB antennas are deeply reviewed. The limitations associated with the existing literature are listed out and also as an alternative solution, three different configurations of UWB antenna with multi-filtering features are conceptualized. The first design antenna exhibits quadruple notched band characteristics by utilizing the combination of slotted geometries and EBG structures. Similarly, in the second and third design configurations, penta notched band behaviour has been realized by using the same approaches. Experimental analysis for the reported design has also been carried out for validation purposes. The final part of this thesis comes up with the design and development of planar and cuboidal UWB MIMO antennas. In the context of planar MIMO system, two different configurations of UWB MIMO antennas have been developed. The first design comprises of dual port UWB MIMO antenna, which accomplished quadruple notched band functions using slotted geometry over antenna radiator and EBG structures coupled to the feedline. Similarly, in the second design configuration quad port MIMO antenna with dual filtering features has been designed for UWB applications. The rejection behaviour is achieved by using rectangular split ring resonator (RSRR) and a pair of mushroom type EBG structures over antenna geometry. To improve the performances the suggested quad port MIMO antenna is further modified with a 3D type MIMO antenna by vertically placing each antenna element in a cuboidal pattern. Furthermore, the same concept has been implemented to develop quad port cuboidal UWB MIMO antenna with single band notched functions. For validation purposes, the prototypes of all these designs are fabricated and characterized.Item Comprehensive Design of Nano scale Hetero Junction Dual Gate Vertical TFET: Analytical modelling, Sensing Functionality, and Circuit Level Integration(Department of Electronic and Communication Engineering, NITS, 2025) Nasani, KarthikThe continuous scaling of CMOS technology has brought substantial improvements in performance and integration density. However, the corresponding rise in power consumption presents major challenges for modern electronic systems. Tunnel Field-Effect Transistors (TFETs) have emerged as strong contenders for low-power applications due to their band to-band tunneling (BTBT) mechanism. This thesis proposes and thoroughly investigates a novel device architecture, the Heterojunction Dual Gate Vertical TFET (HJ-DGVTFET), which is designed to overcome limitations in conventional TFETs including low ON-current (ION), high OFF-current (IOFF), large subthreshold swing (SS), and drain-induced barrier thinning (DIBT). The electrical characteristics of the HJ-DGVTFET are optimized using 2D TCAD simulations and validated through an analytical model based on the 2D Poisson equation. The device comprises an InP source and an In₀.₄₇Ga₀.₅₃As channel/drain region, forming a heterojunction that facilitates enhanced tunneling efficiency. Key design parameters such as indium mole fraction, oxide thickness, source doping, and channel length are varied and studied in detail. Results show that increasing indium content and source doping significantly enhance ION and the ION/ IOFF ratio, while optimizing oxide thickness and channel length further improves device performance. The optimized HJ-DGVTFET achieves a subthreshold swing of 36 mV/dec and a ION/ IOFF ratio of 109. Reliability analysis includes the impact of Gaussian and uniform traps, temperature variations, and lateral straggle on device behavior. Lateral straggle is shown to reduce effective channel length, improving tunneling and transconductance, while temperature primarily affects IOFF, leaving ION relatively stable due to its BTBT nature. Noise analysis is conducted for generation-recombination (GR), diffusion, and flicker noise, revealing that GR and flicker noise dominate at low to mid frequencies (0–1 GHz), while GR noise persists at higher frequencies (up to 10 GHz). This thesis also explores the sensor applications of the HJ-DGVTFET. A biosensor is designed and analyzed for detecting biomolecules such as gelatin, streptavidin, glucose oxidase, keratin, bacteriophage T7, and staphylococcal nuclease. The HJ-DGVTFET biosensor outperforms its single-metal-gate counterpart (SMG-HJ-DGVTFET), showing enhanced sensitivity due to reduced energy band gap and improved gate control. The device ix demonstrates excellent response to positive biomolecular charge and significant ON-current modulation. Additionally, the sensor is employed for CerbB-2 antigen detection, showing high sensitivity and design flexibility for array-based bio-screening. A gas sensor variant of the HJ-DGVTFET is developed, employing catalytic gate materials such as silver, cobalt, platinum, and palladium. Changes in work function due to gas adsorption modulate surface potential and electric field, enabling detection of gases like CO, NH₃, H₂, and O₂. Sensitivity is evaluated based on OFF-current variation, with platinum and palladium achieving the highest sensitivity for CO and ammonia detection, respectively. Furthermore, the thesis includes the development and simulation of a HJ-DGVTFET based digital inverter circuit. The inverter is analyzed under varying temperature conditions and interface trap densities, showing stable performance with minimal temperature dependence. Interface traps, however, introduce undershoot in transfer characteristics, highlighting the importance of trap minimization in digital applications. The results confirm the HJ-DGVTFET's suitability for low-power, high-performance logic circuits, particularly under environmental and process-induced variations. Overall, the HJ-DGVTFET presents itself as a robust and versatile solution for future nanoelectronic applications, combining high reliability, excellent low-power performance, and multi-functional capabilities across sensing and logic domains.