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VALIDATION OF STANDARD CELLS AND MEMORY DESIGNS
No full textIn the realm of semiconductor design, standard cells are the fundamental building blocks used
to create complex digital circuits. These cells, which include basic logic gates, flip-flops, and
other essential components, are meticulously designed and characterized to ensure they meet
specific performance, power, and area requirements. However, their role becomes even more
crucial when integrated into memory designs, such as SRAM, DRAM, and Flash, where
precision and reliability are paramount.
This report explores the methodologies and tools used for the validation of standard cells and
memory designs. It addresses the critical need for comprehensive methodologies that efficiently
validate these cells to ensure they meet necessary performance, power, and area requirements.
The research highlights the challenges faced in achieving the desired efficiency and reliability
of memory units, which are essential for the overall performance of electronic devices.
The proposed research approach encompasses multiple methodologies and tools, including
RTL generation, simulation, synthesis, post-synthesis simulation, Design for Testability (DFT)
insertion, post-DFT simulation, and Automatic Test Pattern Generation (ATPG). By integrating
these techniques, the research aims to enhance the reliability and performance of memory units
in electronic devices.
Despite significant advancements, several research gaps remain, such as the limited scope of
existing methods, lack of standardization, and inadequate tools for integration. Addressing
these gaps is crucial for guiding future research efforts and developing more robust
methodologies and tools.
The objectives of this research are to develop advanced methodologies for the validation of
standard cells, implement rigorous validation processes, and integrate with other designs and
validation techniques to create a comprehensive framework that enhances the reliability and
efficiency standard cells and memory designs
Development of a Biometric System Using Ear
No full textEar biometrics is gaining recognition as a promising biometric modality due to
the stability and constancy of the outer ear over a person’s lifetime. Unlike facial
features, which can change due to aging, emotional states, or other factors, the
structure of the outer ear remains largely unchanged, making it a reliable feature
for identification. This consistency makes ear biometrics an attractive option
for biometric systems. Recent advances in machine learning, particularly in deep
learning techniques such as Convolutional Neural Networks (CNNs), have accelerated
research in this field. CNNs are especially effective for biometric applications
because of their ability to automatically extract distinguishing features from raw
ear image data, leading to more accurate and efficient identification systems. This
study aims to leverage deep learning approaches to advance human identification
using ear biometrics, focusing on developing new models and techniques to
improve system precision and effectiveness.
The primary objective of this research is to explore the use of deep learning
techniques for ear biometrics and develop robust models that surpass traditional
methods in accuracy. This involves not only the development of new models but
also the use of data augmentation techniques to improve generalization, especially
in cases where datasets are limited. In addition, this study examines multi-modal
fusion techniques, which combine both ear and profile face images to enhance the
identification process. The performance of the proposed models is evaluated using
several key metrics, including accuracy, precision, recall, F1-score, and Cumulative
Match Curves (CMC).
A thorough review of existing research in ear biometrics provides the foundation
for this study. While ear biometrics has historically been less researched than
other biometric modalities, recent developments in deep learning have opened new
possibilities. Multi-modal fusion techniques, for example, have shown promise by
combining ear and facial data to improve identification accuracy. However, a significant
challenge in this field is the lack of large, high-quality datasets dedicated
to ear biometrics, which limits the generalizability of models. To address this
issue, data augmentation strategies are employed to introduce variations into the
training data, making the models more robust to real-world variations.
This research presents three deep learning models designed to address the
challenges of person identification using ear biometrics: DeepBio, CSA-GRU, and
EMF-CNN. The DeepBio model combines CNNs with Bi-directional Long Shortv
Term Memory (BI-LSTM) networks in a hybrid deep learning approach. CNN
layers are used to extract meaningful features from ear images, while BI-LSTM
networks capture sequential dependencies within the data. Data augmentation
techniques such as flipping, rotation, and noise injection are applied to enhance
the model’s robustness. DeepBio is evaluated using recognition rate and F1-score
as performance metrics, demonstrating improved accuracy and robustness.
The second model, CSA-GRU, integrates CNNs with Gated Recurrent Units
(GRUs) and self-attention mechanisms. The CNN layers extract spatial features
from the ear images, while the GRU layers process temporal information. GRUs
are computationally efficient and well-suited for sequential data processing. Selfattention
mechanisms allow the network to focus on the most relevant parts of the
ear images, improving the model’s ability to distinguish subtle differences. Data
augmentation techniques, including Gaussian noise, brightness adjustments, and
color jittering, are used to improve the model’s generalization and performance.
The final model, EMF-CNN, employs a multi-modal fusion framework that
combines ear biometrics with additional features to enhance identification accuracy.
This model uses pre-trained CNN architectures for feature extraction and
enhances them with adaptive Local Phase Quantization (a-LPQ) and weighted Local
Directional Patterns (w-LDP) to capture fine-grained texture information from
ear images. The multi-modal approach, which fuses ear and facial data, improves
the overall accuracy and robustness of the identification system by leveraging
complementary biometric information.
The use of these diverse datasets, such as IITD-I, IITD-II, AWE, AMI, EARVN1,
and UND, ensures that the models are trained on a comprehensive set of ear images,
each captured under varying lighting conditions, angles, and environmental
factors. This variety allows the models to generalize well across different scenarios
and improves their robustness to real-world conditions. In addition to traditional
performance metrics like accuracy, precision, recall, and F1-score, Cumulative
Match Curves (CMC) offer a deeper understanding of the models’ ranking abilities,
especially in security and forensic applications where identifying the top candidate
matches is crucial. These evaluations highlight the strengths and weaknesses of
each model, providing insights into areas for further improvement. Overall, this
testing framework ensures that the proposed models are not only accurate but
also versatile and reliable across diverse biometric applications.
In conclusion, this research presents innovative approaches to ear biometrics,
contributing significantly to the field of biometric identification. The proposed
models—DeepBio, CSA-GRU, and EMF-CNN—demonstrate the potential to envi
hance the accuracy and reliability of ear-based identification systems. By integrating
advanced deep learning techniques, such as CNNs, GRUs, and self-attention
mechanisms, these models outperform traditional methods. Future research could
further explore larger datasets, real-time applications, and additional augmentation
techniques to refine these models. The proposed approaches hold potential
for use in various applications, including security, healthcare, and personalized
identity solutions, paving the way for more effective biometric systems
From Impulse to Intention : The Age and Gender Spectrum of Bystander Intervention during eve-teasing
No full textThis dissertation investigates bystander behaviour in the context of eve-teasing, a prevalent gender-
based harassment. Using a mixed-methods approach, it combines survey-based data with qualitative
information gained from in-depth interviews. The research investigates male witnesses (passive and
active witnesses) and women who have encountered eve-teasing along with bystander roles. 40
participants were recruited: 10 males (18–25), 10 males (35–45), 10 females (17–22), and 10 females
(35–45). Data collection commenced with a Google Forms survey recording demographic information
and personal experiences of eve-teasing. Interviewees were chosen on the basis of the richness of their
answers. Semi-structured interviews (mean duration: 45 minutes) probed emotional responses,
decision-making processes, and socio-psychological determinants of intervention. Primary themes were
social norms, perceived risk, and institutional influences.
Alongside interviews, a subsample of 10 males participated in a third phase under the Repertory Grid
Technique (RGT). This constructivist technique retrieved participants' individual meaning systems by
determining the manner in which they sort and appraise different bystander situations. RGT provided
richer insight into their internal cognitive structures; to break down how personal belief systems
influence intervention choices. All of the interviews were transcribed with the aid of cutting-edge
software and analysed through the use of Atlas.ti, utilizing thematic coding over stages including
observation of the event, taking responsibility, and acting to intervene. The study also critiques existing
legal and institutional reaction limitations and makes specific recommendations.
Ultimately, this work contributes to social psychology and gender studies by offering a nuanced,
multi-layered understanding of bystander intervention during eve-teasing. It advocates for cultural
change and systemic reform to foster proactive bystander behavior and enhance gender-based safety
Graphitic Carbon and Bimetallic Bismuth Oxide composites for Adsorption and Photocatalytic Removal of Toxic Pollutants
No full textChapter-1
The chapter includes the structural and chemical composition of bimetallic bismuth oxide compounds, as well as their adsorptive and photocatalytic properties. The behaviour and changes of BBOs have been addressed in relation to adsorption and photocatalysis. This chapter also covers the literature evaluation, research gaps, objectives, experimental procedures, and characterisation methodologies.
Chapter-2
Photocatalytic degradation has emerged as one of the most efficient methods to eliminate toxic dyes from wastewater. In this context, graphitic nitride (g-C3N4) loaded BiVO4 nanocomposites (5wt.% g-CN@BiVO4 and 10wt.% g-CN@BiVO4) have been fabricated by the wet impregnation method, and their efficiency towards photocatalytic removal of rhodamine B have been investigated under visible light irradiation. These hybrid composites have been characterized by XRD, FESEM, HRTEM, EDS-mapping, UV-Vis DRS, DLS, XPS and BET, etc. The HRTEM images revealed that BiVO4 has a decagonal shape covered by a layered nanosheet-like structure of g-C3N4. BET measurements suggest increasing the proportion of g-C3N4 results enhancement of the specific surface area. Among different photocatalysts, the 10wt.% g-C3N4@BiVO4 hybrid possesses the best catalytic activity with 86% degradation efficiency after 60 minutes of reaction time. The LC-MS studies suggest that the degradation reactions follow the de-ethylation pathway. Even after five cycles, the heterostructure shows only a 14% decrease in photocatalytic activity, confirming its stability. As a result, the binary composite can be regarded as a promising catalyst for the degradation of pollutants due to its ease of preparation, high stability and superior catalytic activity.
Chapter-3
Because of unrestricted disposal, the concentration of reactive dyes in wastewater is gradually increasing. Owing to their eco-toxicity their removal becomes so crucial. In this regard, Bi (0)-doped g-C3N4/Bi2WO6 (g-C3N4/Bi@Bi2WO6) nanocomposites were prepared by wet impregnation followed by calcination. Remarkably, the Bi (0) doping occurs concertedly during the preparation of Bi2WO6 without the addition of any extra reducing agent. The efficacy of the photocatalyst for eliminating reactive orange 16 was evaluated under visible light irradiation. XRD, FESEM, HRTEM, DRS, XPS, BET, etc., were employed to characterize these hybrids. The presence of Bi (0) was confirmed by HRTEM and XPS. Increasing the g-C3N4 content enhances the specific surface and reduces the charge transfer resistance. Among the various photocatalysts, the 20 wt.% g-C3N4/Bi@Bi2WO6 hybrid owned the highest degradation efficiency of 89 % after 300 min of reaction time. The controlled experiments confirm the participation of holes and superoxide anions during the reactions. The various reaction intermediates were detected by HRMS providing the necessary evidence about the mechanism. The heterostructure possesses excellent reusability and stability. Due to enhanced catalytic activity, high stability, and ease of synthesis, the reported composite can be considered as a promising catalyst for the degradation of pollutants.
Chapter-4
Water contamination is a result of the excessive use of antibiotics nowadays. Owing to this environmental toxicity, photocatalytic degradation is the primary approach to non-biological degradation for their removal. In this respect, Bi (0)-doped g-C3N4/Bi2MoO6 [g-C3N4/Bi@Bi2MoO6] ternary nanocomposite was prepared using the wet impregnation method. Surprisingly, the zerovalent Bi is generated simultaneously during the hydrothermal synthesis of Bi2MoO6 without any extra reducing agent. The performance of the synthesised catalyst for the removal of ofloxacin is measured using visible light radiation. Various techniques like XRD, XPS, DRS, HRTEM, FESEM etc characterise the nanocomposite. Additionally, XPS, DRS and HRTEM confirm the presence of zerovalent Bi. The degradation efficiency was recorded to be 82% after 180 min of reaction time for the optimised catalyst. The control experiments validate the role of holes and superoxide radicals in the reaction mechanism. HRMS was used to identify the intermediates and various fragments which support the suggested mechanism. The photocatalyst exhibits outstanding stability and reusability. Due to its stability, easy synthesis, excellent catalytic activity, and reusability, the reported photocatalyst is considered favourable for pollutant degradation
Emotional Intelligence and Leadership Style among Teachers
No full textBackground and aim: The psychological variables of leadership style and emotional
intelligence among teachers across different genders and experience levels have gained
attention in education research. This study aims to explore leadership styles and emotional
intelligence among male and female teachers with different levels of teaching experience (1–
10 years and above 10 years).
Methodology: Teachers were selected from both genders and categorized into two groups
based on their years of experience –Group 1 (1– 10 years) and Group 2 (more than 10 years).
They were administered with two standardised scales: the Multifactor Leadership
Questionnaire (MLQ) to assess leadership styles and an Emotional Intelligence scale to
evaluate emotional intelligence. This study seeks to examine how genders and years of
teaching experience influence these psychological variables.
Results & conclusions: The study found that female teachers scored higher than male in
emotional intelligence and in leadership styles such as Contingent Reward and Individualized
Consideration. Male teachers showed slightly higher traits in Laissez-Faire leadership.
Teaching experience did not significantly influence either emotional intelligence or
leadership styles, and no significant interaction was found between gender and experience
Encapsulation and Stability Analysis of Syzygium Cumini (Jamun) Anthocyanin Using Foam Mat Drying
No full textThis study explores the encapsulation and stabilization of anthocyanins extracted from
Syzygium cumini (Jamun) using the foam mat drying technique. Anthocyanins are known for
their potent antioxidant and therapeutic properties but are highly sensitive to environmental
conditions such as heat, light, and oxygen. To address these limitations, the extracted
anthocyanins were encapsulated using maltodextrin as the wall material, with foaming agents
including glycerol monostearate (GMS) and methyl cellulose (MC) to enhance foam
characteristics. The encapsulated formulations were subjected to three drying methods:
microwave, vacuum, and hot air oven drying. Foam properties such as expansion, density, and
drainage were evaluated to determine optimal combinations for drying performance. Structural
and functional characterization was carried out using techniques such as FTIR, SEM, and
antioxidant assays to assess encapsulation efficiency and compound stability. Among the
drying techniques evaluated, microwave-assisted foam mat drying emerged as the most
effective in preserving anthocyanin integrity and functional properties. The findings suggest
that foam mat drying, particularly when combined with appropriate wall and foaming agents, is
a promising approach for stabilizing thermosensitive bioactive compounds, with potential
applications in the development of functional foods, nutraceuticals, and natural colorant
systems
Methods to Perform Site Specific Seismic Hazard Assessment
No full textThis dissertation, titled "Methods to Perform Site Specific Seismic Hazard Assessment," presents
a comprehensive workflow for evaluating earthquake hazard through both deterministic and
probabilistic approaches. Focusing on the Saraighat Bridge site in Guwahati, Assam, the study details
the collection and homogenization of regional earthquake data, declustering procedures, and
assignment of seismicity parameters to multiple fault sources via the Gutenberg-Richter relationship.
Deterministic Seismic Hazard Analysis (DSHA) utilizes region-specific ground motion prediction
equations to estimate conservative peak ground acceleration (PGA) values for each fault scenario,
while Probabilistic Seismic Hazard Analysis (PSHA) quantifies the combined uncertainties and
effects of all relevant seismic sources, generating hazard curves and uniform hazard spectra (UHS)
across varied exceedance probabilities and design periods. The analysis demonstrates notable faultspecific hazard variability, with the Oldham Fault emerging as the controlling source for design-level
ground motions at the study site. By benchmarking results against regional codes and prior studies,
the research emphasizes the necessity of meticulous site-specific procedures for robust, performancebased seismic design in high-risk regions. The established methodology supports resilient
infrastructure planning and enhanced seismic risk mitigation for critical facilities in Northeast India
Examining the Relationship Between Technology Business Incubators and Incubatees in Northern and Western Regions of India
No full textThe measurement and optimisation of incubator performance is a contentious and
regularly discussed issue among various stakeholders in business incubators. Numerous
scholars have investigated various facets of business incubators, including their
performance; yet, the literature remains deficient in certain areas. The performance of
business incubators encompasses the performance of the incubatees, and both are
mutually dependent for their survival and growth. The primary objective of the present
study is to investigate the dyadic relationship between Technology Business Incubators
(TBIs) and Incubatees. The study investigates the induction of the incubatees via the
use of the selection criteria, different services and facilities offered by the business
incubators to their incubatees, and the incubator sectoral differentiation. The present
research empirically studies the different relationships between the exogenous (i.e.,
selection criteria, managerial skills, services, and facilities) and endogenous (i.e.,
incubator’s performance) constructs of the study. Using the Resource-Based View as
the theoretical framework, it examines the influence of various incubator capital
resource groups – organisational, human, and physical – on the sustained competitive
advantage of the business incubators and how the different sub-resource groups affect
their respective capital resource groups.
The study uses a mix of descriptive, exploratory, and causal research designs. Out of
75 TBIs, respondents from only 34 expressed their willingness to participate in the
study. One hundred responses were collected from the respondents – 41 from the
incubator’s managerial team and 59 from the incubatees. Various measures of
descriptive statistics and three inferential statistical techniques: Exploratory Factor
Analysis (EFA), Partial Least Squares – Structural Equation Modelling (PLS-SEM),
and Kruskal-Wallis test, were used to analyse the collated data.
Through EFA, a varying number of sub-resources were identified for the different
resource groups: four sub-resources for the organisational capital, i.e., incubator’s
incubatee selection criteria; three for the human capital, i.e., incubator’s staff’s skills;
ten for the physical capital, i.e., five each for incubator services and incubator facilities;
and five for the sustained competitive advantage, i.e., incubator’s performance.
Through PLS-SEM, empirical evidence was found that all the different incubator
capital resource groups, to varying degrees, impacted the sustained competitive
advantage of the business incubators, and various sub-resource groups also differently impacted their respective capital resource groups. Physical Capital in the form of
Facilities (0.328) and Services (0.285) has the strongest influence on the business
incubator’s sustained competitive advantage, followed by Organisational Capital
(0.254) and Human Capital (0.232). Through the Kruskal-Wallis test, except for only
two factors, i.e., Incubator’s Basic Facilities and Incubator’s Outreach Facilities, no
sectoral differences were found.
The study outcomes will provide valuable insights for the diverse stakeholders of TBIs
and contribute to advancing theoretical understanding in this area. Using the study
results, the researchers would be able to identify which sub-resource groups make
higher and lower contributions to the overall strength of the construct within each
resource group. Using the study’s findings, the incubation managers can streamline
their incubation delivery and conserve the different scarce incubator capital resources.
Using the study’s results, policymakers can strengthen existing programs or create new
ones to support the continued skill development and capacity building of incubatees
and incubation managers
Low-Rate Flow Table Overflow Detection For SDN
No full textSoftware-Defined Networking (SDN) in 5G has emerged to reconfigure traditional network architectures by offering programmability for dynamic service provisioning, which
is mainly supported by the OpenFlow (OF) protocol. Within an OpenFlow-enabled SDN
framework, the control plane orchestrates packet forwarding by establishing connections
with switches and populating their flow tables with precise flow entries. However, these
flow tables are built using ternary content-addressable memory (TCAM), that have
limited storage capacity. This limitation makes SDN prone to Low-Rate Flow Table
Overflow (LFTO) attacks, slowly degrading the performance and network efficiency by
filling flow tables with malicious flow entries.
To address this vulnerability, we propose various machine learning, deep Learning
and quantum-based detection frameworks that classify LFTO attacks into malicious
and regular traffic by utilizing advanced feature selection techniques, feature scaling,
and addressing data imbalance through Synthetic Minority Over-sampling Technique
(SMOTE). Moreover, the proposed framework was evaluated, including Decision Tree,
Random Forest, Long Short-Term Memory (LSTM) and Quantum Neural Networks
(QNN). The LSTM model achieved 99.14% accuracy and 99.96% recall, while the
Random Forest and Decision Tree models reached 99.27% and 99.02% accuracy, respectively. Additionally, the quantum-based detection model achieved an accuracy of
98.49%. Hence, the results from our analysis illustrate that the proposed framework for
detecting LFTO attacks maintains seamless data packet forwarding and safeguards the
finite capacity of flow table resources within SDN environments