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.

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

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Now showing 1 - 5 of 8

Recent Submissions

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    Rice Husk Biochar-Based Ag3PO4/g-C3N4 Ternary Photocatalyst for Solar-Light-Driven Tetracycline Degradation: Kinetics, Mechanism and Degradation Pathways
    (2026-09-08) Kaur, Loveneet; Basu, Soumen
    The present study involves the preparation of a Ag3PO4/g-C3N4/Rice husk biochar (CAR) heterojunction photocatalyst by adding 30 wt% biochar to the Ag3PO4/g-C3N4 (AC) binary composite (70 wt%). Different composites were prepared with weight ratio of Ag3PO4 and g-C3N4 (1:1, 1:3, and 3:1), while keeping the amount of biochar constant, for the degradation of tetracycline (TC) from water. The material was characterized using XRD, XPS, FESEM, EDS, HRTEM, BET, BJH, FTIR, UV–Vis DRS, and PL, which confirmed its good crystal structure, high surface area, and strong response to sunlight. Among all the prepared samples, the 1:1 composition (CAR11) showed the best photocatalytic activity. It degraded about 96% (0.03549 min-1) of TC (25 ppm) under sunlight within 75 min, while under visible and UV light, the degradation efficiencies were 47.5% and 65.5%, respectively. The effects of different experimental conditions, such as pH, light intensity, catalyst dosage, and reactive species, were also studied to understand the photocatalytic process. The catalyst showed good stability and could be reused for six cycles, maintaining about 81% of its photocatalytic activity. Radical trapping experiments revealed that •O2⁻ radicals were the main reactive species in TC degradation. The degradation intermediates were identified by HRMS analysis, and TOC (76%) and COD (73.2%) measurements confirmed that most of the dye was mineralized into simpler and less harmful products. These results show that the CAR11 exhibited high efficiency and stability for solar-light-driven pollutant degradation.
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    Polycystic Ovary Syndrome (PCOS) Detection: Using Deep Learning Approaches for Images and Clinical Features
    (NA, 2026-09-04) Madaan, Arushi; Bajaj, Anu; Garhwal, Sunita
    The hormone disorder known as polycystic ovarian syndrome (PCOS) is most prevalent in women nowadays. The ovaries of women are directly impacted by this metabolic disorder, where numerous small sacs of fluid develop around the edges of the ovary, called cysts or follicles. Despite advancements in technology, the exact cause of PCOS still remains unknown. It takes a lot of work for the doctors to manually diagnose it from the ultrasound (US) images. For PCOS identification, a range of machine learning (ML), deep learning (DL) and image processing techniques were employed, that allowed for the identification of follicle counts and follicle size analysis. The current research on PCOS detection for image segmentation and classification, as well as clinical characteristics, was reviewed in this work. For this study, we utilised two different kinds of dataset: image data and clinical feature dataset. We proposed a transfer learning-based DL approach for classifying women with PCOS by using ultrasound images. InceptionV3 and ResNet50 models were used, which achieved accuracy rates of 99.68% and 97.5%, respectively. Numerous DL algorithms were applied on the clinical dataset using various feature selection techniques. The flower pollination algorithm (FPA), cuckoo search algorithm (CSA), and genetic algorithm (GA) are the nature-inspired feature selection algorithms that have been utilised in the study. The results of the proposed GA-based feature selection using an ensemble model were statistically tested with the existing algorithms using Analysis of variance (ANOVA) and the Tukey test, with a 94.58% accuracy, 97.48% precision, 83.76% recall, 90.08% F1-Score, and 99.09% specificity. The interpretability of the results is further ascertained by explainable artificial intelligence (XAI) techniques, i.e., LIME and SHAP. Overall, by using reliable and accurate prediction models, this work advances predictive analytics in PCOS diagnosis with the goal of assisting early diagnosis and well-informed decision-making processes.
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    A Comparative Study of Directed Testing vs UVM-Based Random Verification
    (2026-09-04) Gupta, Konika; Upadhyay, Rahul; Sharma, Ashu; Saxena, Saurabh
    In this thesis, both directed testing and constrained random verification using the Universal Verification Methodology (UVM) approach are considered for verifying the functionality of an Advanced Highperformance Bus Lite (AHB Lite) protocol design. With today’s SoCs being very complex in nature, there is a great need to choose a proper verification methodology to ensure that all aspects comply with the protocol. This research provides a solution for this challenge by implementing both approaches on an AHB Lite slave memory design and then analyzing their performance based on certain quantitative metrics.. The directed test methodology validates basic AHB transactions such as single transactions, WRAP4 burst increments, and write then read transactions with manual stimulus with static address and known data patterns. The methodology based on UVM-Universal Verification Methodology uses a complete verification environment with a transaction level sequence item with protocol constrained, agent with driver and monitor, automation reference model scoreboard, and functional coverage collection from covergroups. Constrained random test stimuli generation covers the legitimate transaction space in different seeds, aiming at scenarios not anticipated in directed tests. Both approaches are looked at using a few different factors like functional coverage percentage, code coverage, simulation throughput, test code volume, debugging complexity, and how well the testbench architecture scales. The results show that directed testing gives us a clear and easy way to debug known scenarios with just a bit of infrastructure. On the other hand, UVM-based constrained random verification manages to cover a lot more cases with fewer lines of test code, plus it brings to light some corner case behaviors that might get missed in tests that are written by hand. Overall, these findings are pretty useful for verification engineers when they're picking the right method for AHB-based digital IP verification in today's SoC development projects.
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    Constrained Random Verification of a Digital IP Using System Verilog
    (2026-09-04) Sood, Himanshi; Kumar, Ravi; Sharma, Saurabh
    This thesis describes the restricted random verification of a digital IP for an AXI4-Lite-based design with SystemVerilog. As advanced communication protocols are incorporated into contemporary SoC designs, it is crucial to guarantee the functional accuracy of AXI-compliant intellectual property. Conventional directed testing techniques frequently fall short in covering protocol-level interactions and corner cases. In order to increase coverage, scalability, and reusability, a limited random verification methodology is used. Developing a reusable and modular verification environment with SystemVerilog features like classes, randomization, constraints, assertions, functional coverage, and interfaces is the main goal of the effort. In order to validate address, data, and control channel transactions, an AXIbased digital IP is validated by producing random stimuli within specified protocol limitations. While assertions are used to verify protocol compliance and identify violations during simulation, functional coverage models are used to gauge the completeness of verification. The verification environment is designed to achieve great coverage and to find corner case bugs fast. The efficiency of limited random verification in enhancing design resilience and cutting verification time is shown by simulation results. In order to validate complicated AXIbased digital IPs for dependable SoC integration, this work emphasizes the significance of sophisticated verification approaches.
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