TIET Digital Repository

Thapar Institute of Engineering & Technology (TuDR)

Welcome to Thapar Institute of Engineering & Technology Digital Repository (TuDR).

TuDR is the digital asset management system which integrates the intellectual output in the form of research articles, PhD theses, and M.Tech / M.E. theses. TuDR facilitates the sharing and exchange of intellectual output of the university.

TuDR supports the management of scholarly resources of enduring value to Thapar University. Faculty members, students, and research scholars use TuDR services to share their intellectual work with the global academic community.

Download Archive Request Form: (PDF) | (Word)
Submission Guidelines: Download Submission Guidelines

Facilities at Thapar Institute of Engineering & Technology Digital Repository (TuDR):

The users of TuDR can search, download, and browse the collections of documents.
Publish & share electronic documents.
Provide views & comments.
For creating new Communities or Collections, mail to dspace@thapar.edu

Communities in DSpace

Select a community to browse its collections.

Now showing 1 - 5 of 8

Central Library 6
Departments 4955
Research@TIET 59
Persons or groups engaged in research at TIET
Schools 1740
Sponsored Projects 4

Recent Submissions

Improving Runtime Efficiency in Emulation Models of Server IPs
(2026-08-08) Garg, Saloni; Kumar, Sanjay; Singh, Hari Shankar
This project explores improving runtime efficiency in emulation models of server Intellectual Properties (IPs) for modern Very Large Scale Integration (VLSI) design. Emulation involves mimicking functionality and behavior of real-time hardware systems, operating significantly faster than simulation at clock frequencies of several GHz. This enables execution of extensive test cases in reduced time, uncovering deep corner-case bugs that simulation typically fails to detect. Since emulation utilizes the same Register Transfer Level (RTL) code deployed on actual silicon, comprehensive bug elimination becomes critical for server IP quality. The primary objective is improving runtime efficiency of emulation models by optimizing total time spent by software and hardware components during emulation runs to achieve better resource utilization. The secondary objective focuses on developing Python-based automation scripts to reduce manual effort during validation and improve overall workflow efficiency. The work addresses challenges posed by high costs and limited availability of emulation platforms through systematic optimization of testbench and software components in collaboration with Ultra eXtensible Interconnect Verification Intellectual Property teams. Through systematic optimization phases spanning multiple development cycles, progressive improvements were achieved using data-driven analysis and targeted enhancement strategies. A comprehensive five-component automation validation framework was developed encompassing hardware configuration parsing, memory region analysis, test configuration generation, workflow coordination, and parallel execution management. This approach resulted in a remarkable 41% reduction in runtime, substantially exceeding the initial target of 30% improvement, while establishing scalable automation methodologies that transform validation preparation from manual processes into systematic, repeatable workflows for server IP validation.
Collaborative Story Writing as a Method to Enhance Empathy
(Unpublished Theses, 2026-08-06) Agarwal, Yashika; Singh, Vandana
Empathy has long been used and mentioned in psychology; it is the ability to understand people and consciously try to put oneself in the place of another person to understand their thoughts and feelings. This helps interpersonal skill development and social functioning. Narrative-based approaches have been studied by researchers as a tool to enhance empathy. While researchers have focused on passive forms of narrative, there is still much to explore in narratives as the active form. The present study gives a deeper understanding of this active form of narrative as collaborative story writing. It has also integrated transportation and affect to provide a more comprehensive understanding. The present study aims to explore the relation between story writing and empathy, specifically focusing on collaborative story writing as a method. It examines the difference in individual versus collaborative story writing in enhancing empathy while also assessing the role of narrative transportation. The study has worked on finding the possible factors contributing to the change of empathy. A mixed-method design has been used in the study to give up both practical and theoretical understanding. A group of college students participated in this study. Different tools such as the empathy scale, transportation scale, scale for affect and social desirability along with the collaborative story-writing task were used. The story also used semi-constructed interviews for the qualitative data. The study found a strong relation between narrative and empathy. There was a significant increase in the empathy level after intervention, especially in the group condition. Social desirability and mood effects show significant influence on empathy. The qualitative finding revealed various psychological processes involved in the development of empathy during story writing. Participants demonstrated various aspects such as perspective-taking shifts in identification with multiple characters, moral reasoning, communication awareness and others through the story-writing activity. This shows use in social bonding and grief-related therapy and family therapy. Thus, the study has strong implications in clinical and counselling settings along with the educational field.
Software-Hardware Co-Design of Multi Task Deep Neural Networks for Camera Vision System
(2026-07-31) Karn, Bodh Krishna; Singh, Anil; Bansal, Manu
Real-time camera vision on edge devices must run multiple perception tasks simultaneously while staying within tight power and memory constraints. However, single-task detection networks like YOLO series are unable to do so efficiently. In this thesis, we present a software hardware co-design multi-task deep neural network architecture which performs seven visual perception tasks: detection, classification, semantic & instance segmentation, pose, oriented detection, and age-gender estimation, with a single feature backbone and lightweight task specific heads. An Intelligent Preprocessing Module measures scene complexity and runs only the tasks a given frame actuallyneeds, reducingredundantcomputationandpowerconsumption. The architecture was first developed in Python, trained on Kaggle, and demonstrated on an NVIDIA H100 GPU.Its computational core was then translated into synthesizable Verilog and integrated with the Zynq UltraScale+MPSoC.Thisprovideshardware–softwareco-design, with the Processing System handling control while the custom accelerator runs in the programmable logic. On the H100, the model sustains 177–196 FPS at 5.0–5.6 ms per frame with a 4.7 MB backbone. On hardware, it uses 36,472 LUTs, 71,429 registers, and 59 DSP slices at 100 MHz. It meets all timing constraints (WNS +0.358 ns) and consumes 3.532 W total on-chip power, independently verified as 3.590 W in AMD Power Design Manager.
Relationship of Problematic Social Media Use, Fear of Missing out, Loneliness and Evening Chronotype with Sleep Quality among Young Adults: A Mediation- moderation Analysis
(2026-07-30) Sharma, Aarya; Alreja, Sarika
The present study sought to explore the mediating role of fear of missing out (FoMO) and the moderating role of loneliness in the relationship between problematic social media use (PSMU) and sleep quality. This cross-sectional, correlational study involved a sample of 200 young adults aged 18 to 30 years from a university in Punjab. The data were collected using standardised instruments such as the Pittsburgh Sleep Quality Index, the Berger Social Media Addiction Scale, the Fear of Missing Out Scale (FoMOS), the UCLA Loneliness ULS-8, and the Reduced Morningness-Eveningness Scale (rMEQ). Correlational analysis indicated that problematic social media use and evening chronotype are negatively correlated with sleep quality. Mediation analysis was conducted using Hayes’ PROCESS macro revealed that FoMO significantly mediated the relationship between problematic social media use and sleep quality. Moderation analysis was performed using the Hayes’ PROCESS macro, which indicated that loneliness did not significantly moderate the impact of social media use on sleep.
Correlation Analysis of Air Pollutants and Human Diseases Using Explainable AI for Health Outcomes
(2026-07-23) Aanchal; Sharma, Anamika; Bala, Anju
Air pollution is a prominent global health risk factor responsible for millions of premature deaths annually, with its impact spanning cardiovascular, respiratory, and malignant disease categories. Predicting the health impact of air pollution across diverse national contexts requires modelling complex non-linear interactions between multiple pollutant species and disease-specific outcomes, a task that conventional single-pollutant epidemiological frameworks cannot adequately address. To address this, the present research proposes an Explainable AI-enabled predictive framework integrating EDGAR v8.1 global emission inventories with WHO Global Health Observatory health metrics across 177 countries, 9 pollutant species, and 6 disease categories for the period 2010-2019. Two novel deep learning architectures are introduced: RBN-BiLSTM, which extends the standard BiLSTM with Batch Normalisation and L2 regularisation for short emission panel sequences, and T-RBN-BiLSTM, which further incorporates a temporal attention mechanism to dynamically weight emission years by their predictive relevance. The models are evaluated under both random split and strict temporal split protocols and compared against classical ML baselines, including XGBoost and Extra Trees. The proposed T-RBN-BiLSTM achieves 95.8% temporal accuracy and an AUC of 98.2%, outperforming baseline BiLSTM by 12.6 percentage points. SHAP-based per-disease attribution is applied across all six WHO disease categories to identify pollutant-specific health impact drivers. Results demonstrate that agricultural NH3 and biomass-burning organic carbon are the dominant positive health impact drivers across cardiovascular and respiratory diseases, while NOx and SO2 act primarily as economic confounding signals. PM2.5 ranks last due to multicollinearity with co-emitted combustion species, highlighting the importance of multi-pollutant frameworks for accurate policy attribution. Future work includes incorporating sub-national resolution and socioeconomic covariates to further improve the predictive and explanatory scope of the framework.