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.

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Recent Submissions

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.
The Role of Gratitude and Patience in Forgiveness
(2026-07-20) Batra, Jigyasa; Chowdhury, Ipshita
Forgiveness plays a well-recognized role as a significant psychological process that enables individuals to release feelings of bitterness, reduce hostility and gain emotional balance following conflicts and wrongdoings in relationships. Although previous studies have explored forgiveness in terms of its associations with empathy, apology and personality, minimum research has focused on the role of positive character strengths in promoting forgiveness, particularly gratitude and patience. This study aimed to bridge this gap by exploring how gratitude and patience plays a role in forgiveness. This was done with a correlational study. The study used three scales. One for each variable. Patience was used as a mediator while gratitude served as a criterion variable and forgiveness as an outcome variable. Results showed a positive relationship between all three variables, and mediation analysis confirmed full mediation. Patience completely explained the pathway from gratitude to forgiveness. The findings highlight the importance of targeting patience, not only gratitude. This is especially important and useful for mental health professionals.
Effect Of Imposter Syndrome On Dysfunctional Metacognitive beliefs: Mediating Role Of Rumination
(2026-07-20) Malhotra, Jiya; Chowdhury, Ipshita
Imposter syndrome involves persistent self-doubt, fear of being exposed as a fraud, and attributing one’s achievements to luck rather than ability. It is a common source of psychological distress among university students, yet the cognitive processes that connect it to broader mental functioning remain poorly understood. This study examined rumination as a mediating variable in the relationship between imposter syndrome and dysfunctional metacognitive beliefs , drawing on Wells and Matthews’ S-REF model. It was hypothesized that imposter syndrome would positively predict dysfunctional metacognitive beliefs with rumination partially mediating this relationship. Data were collected from 151 university students (ages 18 to 25) using snowball sampling and an online questionnaire. Three validated measures were used: the Leary Imposter Scale, the Ruminative Response Scale, and the Metacognitions Questionnaire-30. Imposter syndrome was significantly associated with both rumination (r = 0.55, p < 0.001) and dysfunctional metacognitive beliefs (r = 0.66, p < 0.001), accounting for 43.4% of the variance in dysfunctional metacognitive beliefs .Rumination partially mediated the relationship, with both direct and indirect effects remaining significant. These findings point to rumination as a key cognitive pathway linking imposter syndrome to dysfunctional metacognitive beliefs,and suggest that interventions such as Metacognitive Therapy may be helpful in university counseling settings. Limitations include a cross-sectional design and self-report measures.