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Design and Fabrication of a Gas Sensor for Food Freshness Detection
(Department of Electronics & Instrumentation Engineering, NITS, 2025) Biswas, Bedabrata
Food 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 monitoring
Assessing Attributes of Religious Tourism Experience and Behavioral Intentions
(Department of Management Studies, NITS, 2025-10-24) Das, Anirban
This study investigated the relationships between the experiential attributes and behavioral
outcomes of tourist experiences in the context of religious tourism. While religious tourism
has gained prominence as a growing segment of the tourism industry, driven by the rising
popularity of religious places, there remains a gap in understanding what factors shape
tourists' cognitive and emotional evaluations of experiences in religious places and their
effects on future behavior. This research addresses that gap by developing a comprehensive
conceptual model, which includes the relationships between key religious tourism attributes
(spirituality, authenticity, interactions, service experience, and crowding) and examines how
these factors impact tourist behavioral responses, through the mediating role of place identity
and destination image.
Data were collected from the tourists at the Kamakhya Temple in Guwahati, India—
one of the most prominent Shakti Pithas in South Asia and a major pilgrimage destination.
Using a structured questionnaire, 457 valid responses were gathered and analyzed through a
Partial Least Squares Structural Equation Modeling (PLS-SEM) approach, complemented
by a configurational analysis using fuzzy-set Qualitative Comparative Analysis (fsQCA).
The integration of both variance-based and configurational approaches offers a richer
understanding of both linear and non-linear pathways influencing behavioral intentions in
religious tourism.
This study initially developed a base model using structural model, incorporating
five key experiential predictors—spirituality, authenticity, interactions, service experience,
and crowding—to examine their direct influence on place identity, destination image, and
behavioral intention. To evaluate the presence of non-linear effects in tourist experience
behavior relationships, the base model was specified without quadratic terms to identify
direct linear relationships. Building upon this, a quadratic model was then estimated to
capture potential curvilinear effects, particularly for crowding, which prior literature
suggests may have non-monotonic influences in dense religious settings.
In the quadratic model, spirituality, authenticity, and interactions were found to have
consistently positive and significant effects on both place identity and destination image.
These attributes also positively influenced behavioral intentions, with the exception of
interactions, which, while enhancing place identity and destination image, did not show a
direct impact on future behavioral responses. This suggests that social engagement during
religious tourism experiences enriches cognitive and emotional evaluations but may not
alone drive revisit intentions. Service experience showed a limited influence, significantly
contributing only to destination image. Its effects on place identity and behavioral intention
were not significant, indicating that while service-related elements may shape how tourists
perceive the destination, they are insufficient to create deeper emotional bonds or behavioral
commitment on their own. A notable insight emerged with the construct of crowding, which
exhibited an inverted U-shaped (curvilinear) relationship with all three outcome variables:
place identity, destination image, and behavioral intentions. This result suggests that
moderate levels of crowding may enhance the experience by reinforcing a sense of collective
participation and ritual intensity, while excessive crowding begins to detract from
satisfaction and emotional connection. The non-linear nature of this effect underscores the
importance of managing visitor flow and density in religious destinations to maintain
optimal experiential conditions.
The mediation analysis revealed that place identity served as a significant mediating
pathway for several experiential attributes. Specifically, the effects of spirituality,
authenticity, and interactions on behavioral intention were significantly mediated through
place identity. This confirms that these experiential factors enhance tourists’ emotional and
symbolic attachment to the place, which in turn influences their intention to revisit or
recommend the destination. Additionally, crowding exhibited an indirect negative effect on
behavioral intention via place identity, reinforcing the inverted U-shaped understanding of
crowding's role—where excessive density diminishes place-related emotional connections.
In contrast, destination image did not mediate the relationship between any of the
experiential attributes and behavioral intention in the quadratic model. This suggests that
under conditions of complex experiential intensity—such as those shaped by varying levels
of crowding—the cognitive evaluations captured by destination image are less influential in
shaping future intentions than the affective and identity-based bonds formed through place
identity. Overall, the findings emphasize that place identity is a more robust and consistent
mediator than destination image in high-density religious tourism contexts, particularly
when nonlinear experiential effects are considered. This underscores the importance of
fostering emotional and symbolic place attachments in tourism strategy and design.
To complement the structural model, fsQCA revealed six distinct configurations
(C1–C6) that lead to high behavioral intentions, demonstrating the principle of
equifinality—where different combinations of conditions produce the same outcome. These
configurations illustrate that religious tourism experiences are conjunctural and
interdependent.
Configurations C1 to C3 reflected immersive and socially engaging experiences
(Type I), with spirituality, interactions, and authenticity acting as core drivers in different
combinations. C4 and C5 represented more selective or introspective pathways (Type II),
where high behavioral intentions emerged even when certain experiential components like
service or place identity were weak or absent. Configuration C6 suggested that a service
aesthetic orientation (Type III), characterized by authenticity, service experience, and place
identity, could also lead to strong intentions without necessarily involving spirituality or
crowding.
Crucially, the fsQCA results demonstrated that certain relationships not found to be
statistically significant in the SEM model nonetheless contributed meaningfully within
specific combinations. For example, crowding—although curvilinear in PLS-SEM—was
either absent or peripheral in certain fsQCA solutions, suggesting that its impact is highly
contingent on the presence of other experiential factors. This underscores the value of
employing a configurational lens to reveal complex, context-dependent causal mechanisms
that are not easily captured through linear models.
The study contributes to theory by advancing a dual-method framework that
integrates PLS-SEM and fsQCA, allowing for both generalizable inferences and context
sensitive insights. It enriches the understanding of how tourist experiences in religious
settings give rise to affective attachment (place identity), cognitive evaluations (destination
image), and future behavioral intentions. The findings also provide managerial implications
for religious tourism planners and destination marketers. For example, fostering authentic
and spiritually resonant experiences, managing crowding to an optimal level, and supporting
visitor interactions can enhance both individual satisfaction and destination loyalty.
In conclusion, this research offers a nuanced understanding of the multidimensional
and multi-pathway nature of tourist behavioral outcomes in religious tourism. By
demonstrating both linear and non-linear effects, mediating mechanisms, and multiple
sufficient configurations, the study underscores the importance of designing religious
tourism experiences that are not only operationally efficient but also emotionally, socially,
and spiritually meaningful.
Spatiotemporal Analysis of EEG Signal for Emotion Recognition
(Department of Electronics & Instrumentation Engineering, NITS, 2025-11-04) Kar, Priyam
Emotion plays an important role in the everyday life of a human being, starting from
cognitive abilities to having a general interaction. Therefore, understanding emotion has
significant implications for healthcare, including the treatment of depression, anxiety, and
mood disorders. Moreover, it aids in the assessment of an individual's behavioral and
cognitive abilities. As technology advancements progress, human-machine interaction has
also intensified. Consequently, an emotion-adaptive Brain Computer Interface (BCI) can
enhance general productivity and improve user experience in human-machine interactions,
facilitating various applications such as cognitive ability assessment, interactive gaming
design, experience-based multimedia content delivery, interactive robotics development,
healthcare service provision, and real-time patient monitoring. The basic premise of an
adaptive BCI is to utilize audio-visual or physiological sensors to detect signals sent by the
brain or body. Common instances of audio-visual sensors include human speech and facial
emotions. Although facial expression and speech are effective ways of communicating an
emotional state, it is important to validate the data to prevent misinterpretation, duplication,
or fraudulent manipulation, which may be effectively addressed by the use of physiological
sensors. While there are various physiological sensors available for emotion identification,
such as Galvanic Skin Response (GSR), Electrooculography (EOG), Electromyography
(EMG), Electrocardiogram (ECG), Respiration (RSP), and Electroencephalogram (EEG),
the EEG has attracted significant interest in the field of BCI emotion recognition due to its
non-invasive nature, and superior temporal resolution.
Hence, the thesis concentrates specifically on the EEG-based emotion recognition task for
establishing an emotion-adaptive BCI system and attempts to address some of the existing
challenges, such as subjective experience, high computation cost to deal with large data
volume, and channel overload for mobile practical applications. Moreover, the thesis
evaluates the effectiveness of several affect representation methodologies usually employed
in the EEG emotion recognition framework. Two experiments were performed to compare
the observed emotion clusters based on subjective ratings with the clustering of
corresponding physiological responses based on EEG characteristics (time, frequency, and
time-frequency features). The study aims to investigate whether distinct structures may be
identified in physiological signal characteristics, which are often categorized using
supervised approaches. Furthermore, the study provides insights about the existing current
methods for representing affect and evaluates the efficacy of discrete (assumes that there
are only a few basic emotions, and all the other emotions are a combination of these) and
dimensional models (quantitatively defines emotion and projects emotion in 2D or 3D space)
of affect representation.
Next, the effectiveness of various conventional feature extraction techniques utilized in EEG
based emotion identification systems has been evaluated. The study includes an extensive
range of EEG features from the time, frequency, time-frequency, and spatial domains. The
efficacy of the feature extraction methods has been assessed using three independent
machine learning algorithms and three different EEG emotion datasets. The study offers a
unique and distinct examination of various EEG features that will assist researchers in
selecting EEG features for emotion recognition.
The thesis explores a temporal window localization technique, which uses EEG band power
to identify and locate emotion-rich time periods in EEG data to enhance emotion
classification performance compared to prior studies where the entire data segment is
considered for feature extraction and further emotion classification. The study examines the
impact of the relative power ratio among all EEG bands on the selection of significant EEG
temporal windows. The study furthermore introduces a novel framework for feature
extraction using temporal segmentation. This framework is specifically applied to the
temporally localized EEG segments of importance. The proposed methodologies have been
evaluated via extensive tests on three benchmark datasets across four classification
scenarios, and over three machine learning approaches. The proposed temporal window
localization approach reduces the data volume typically required for feature extraction and
subsequent classification, hence providing a tool to reduce the computation cost.
Furthermore, the thesis presents an unsupervised spatiotemporal emotion localization
framework for selecting emotion-centric temporal windows and channels. A smaller EEG
data segment is used to construct the channel selection framework for faster and more
efficient computing. Additionally, an effective channel-feature extraction technique has been
presented to boost the efficiency of the emotion identification framework. The optimum
channel-feature extraction approach was tested for practical applicability using an EEG
emotion dataset collected over a wireless, portable, and commercially available EEG
headset. Results show great performance with few frontal and occipital lobe channels. The
outcome of the research highlights the proposed methodology's potential to advance the
development of robust, portable, and efficient EEG sensor devices for practical emotion
recognition BCI applications.
Profit Maximization of Wind-HESS based System by Minimizing the Negative Imbalance Cost
(Department of Electrical Engineering, NITS, 2019) Dhruvagupt
Recent decades the rapid development of wind power integration because of wind power generation reduces carbon emissions and extinction. Among all the renewable energy extraction technologies wind is most suitable and cheaper one. The main drawbacks of wind power integration are wind power predictability, optimal offering strategies for wind farms in a day ahead electricity markets. Due to this wind farms injects energy imbalance into the grid. We want to reduce the wind power intermittency nature by using hybrid energy storage system. Main advantages of hybrid energy storage system become more reliable and efficient. Here we proposed logic for coordination of hybrid energy storage system and wind farm in efficient manners to get maximum profit with minimum wind farms power imbalance. This thesis compared the profit before and after the integration of hybrid energy storage system with the wind-thermal integrated power system. Our results highlights that when HESS is supplying power to the wind-thermal based power system deviations are less, spot market compensation cost is less, LMP is also reduces and imbalance cost is also reduces.
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, Anjani
This 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.
Profit Allocation Of Distribution System Considering Distributed Generation & Facts Devices
(Department of Electrical Engineering, NITS, 2019) Rashmi, Divya
This project is about to optimal sizing and siting of Distributed generator in Distribution side. The exponential increase in electricity demand has resulted in the continuous depletion of the traditional power generation sources. The traditional power generation sources are operated centrally and are not suited for environmental and cost issue in today’s liberalized electricity market. After decentralization, electricity provide to their customer in a reliable, decent and cost effective manner. DG proves to be the solution of these issue upto some extent. It is based on decentralization marketing strategy. The line losses are reduced, voltage profile improved and also maximize the economic benefits and share the social benefits amongst network partners when DG connected optimally.
Energy Management in a Microgrid Using Battery Electric Vehicle
(Department of Electrical Engineering, NITS, 2019) Chingakham, Rojita
This project considers an isolated Wind Power Plant based DC microgrid system supported with Battery Electric Vehicles (BEV). The maximum power from the wind energy is extracted using the control methodology of classical Perturbation and Observation (P&O) Algorithm. BEV is used for the purpose of transportation as well as for power transaction between the microgrid and its battery storage system. A comprehensive supervisor controller for connection and disconnection of EV based on the power difference between generation and load (P) and the status of State of Charge (SOC) of the battery of BEV is designed. As the BEV has bidirectional power transfer capability, it is used as a load when there is a large power surplus and as a source when the demand is more than a generation. The transportation activity performed by the EV is controlled by the intelligent based algorithm which is designed by using a fuzzy logic controller.
Fractional-order chaotic systems, its stabilization cum synchronisation and its circuit simulation
(Department of Electrical Engineering, NITS, 2018) Dutta, Maitreyee
Automatic Generation Control of Multi-Area Multi-Source System Using Coyote Optimizer Algorithm based PD–TID Cascade Controller
(Department of Electrical Engineering, NITS, 2019) Kumar, Narisetty Varun
Automatic generation control (AGC) plays a crucial role in the operation and control of power systems. AGC is a supportive scheme that maintains the system frequency under permissible limits. This thesis presents Automatic Generation Control (AGC) of a three unequal area thermal system incorporating solar thermal power plant (STPP) in area-1 and geothermal power plant (GTPP) in area-2. The control areas are provided with single reheat turbine and generation rate constraints (GRC) of 3% / min. A maiden attempt has been made to apply a Proportional derivative–Tilt integral derivative (PD–TID) cascade controller in AGC. Controller gains are optimized using Coyote optimization algorithm (COA) which is population based metaheuristic. The system dynamic performances are studied with 1% step load perturbation in Area1. Performance of classical controllers such as Proportional Integral (PI), Proportional Integral Derivative (PID), Tilt Integral Derivative (TID) controllers are investigated and compared with PD–TID cascade controller. Investigations reveal that PI, PID and TID provide more or less same response where as PD–TID cascade controller provides much better response than the later. Dynamic analysis has also been carried out for the controllers in presence of random load pattern, which reveals the superior performance of the PD–TID cascade controller. Sensitivity analysis reveals that the COA optimized PD–TID Cascade controller parameters obtained at nominal condition of loading, size and position of disturbance and system parameter (Inertia constant, H) are robust and need not to be reset with wide changes in system loading, size, position of disturbance and system parameters.
Comprehensive Seismic Hazard Assessment of Odisha State with Emphasis on Bhubaneswar City and Port Areas
(Department of Civil Engineering, NITS, 2025-11-10) Mishra, Satyaprakash