VTechWorks
VTechWorks provides global access to Virginia Tech scholarship, including journal articles, books, theses, dissertations, conference papers, slide presentations, technical reports, working papers, administrative documents, videos, images, and more by faculty, students, and staff. Faculty can deposit items to VTechWorks from Elements, including journal articles covered by the University open access policy. Email vtechworks@vt.edu for help.
Open Access Policy
Virginia Tech's open access policy enables researchers to deposit the accepted version of scholarly articles with no embargo.
Theses and Dissertations
Virginia Tech was first in the world to require ETDs in 1997, and continues to add scans of older theses and dissertations.
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More than 40 freely available and openly licensed textbooks are among our most downloaded items.
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Recent Submissions
Virginia Tech Commencement Spring 2025
(Virginia Tech, 2025-05)
The program for the Spring 2025 graduation ceremonies at Virginia Tech.
Democracy and Spyware: The Case of India
Rice, Ahissa Breanna (Virginia Tech, 2025-05-15)
There is troubling contradiction between India's status as the world's largest democracy, with a constitution that enshrines privacy as a fundamental right, and the government's routine engagement in invasive digital surveillance of its own citizens. The Pegasus spyware revelations exposed how Indian authorities exploit systemic flaws, legal loopholes, and lack of oversight to illegally spy on dissidents, journalists, opposition figures, and activists, disregarding constitutional guarantees. This study uses the Most Similar Systems Design (MSSD) method to compare India's surveillance regime with the European Union's GDPR plus associated frameworks. This comparison aims to find the reasons for differing surveillance practices between the two, despite similar legal and constitutional protections. This analysis will examine five key variables: constitution, laws, policies/regulations, diversity of population, and security.
This analysis focuses on weaknesses in India's laws that enable government overreach, focusing on the insufficient oversight and highlighting the need for reforms to adjust surveillance practices with democratic norms. This study which examines the important discrepancy between India's strong privacy rights as outlined in law and its largely unregulated surveillance powers, highlights the urgent need for thorough reforms. These reforms are necessary to limit surveillance powers, to firmly enshrine due process, and to enable independent oversight. A comparative analysis between India and the EU aims to better understand the reasons and factors leading to the misuse of surveillance powers in India, and to also lead towards potential solutions to better safeguard citizens' rights in the digital age. This thesis contributes to the ongoing discussion about how democracies manage the challenges caused by these modern surveillance technologies and how democracies do this while still upholding and protecting both the rule of law and individual privacy rights.
Exploring Engineering Employment Trends: A Decade-long Deep Dive into Skills and Competences Included in Job Advertisements
Alsharif, Abdulrahman Mohammed (Virginia Tech, 2025-05-15)
My dissertation explores how Natural Language Processing (NLP) can support job advertisement discipline classification to help workforce researchers in analyzing labor market trends and relate it back to higher education. In particular, this study investigates how NLP can be used to identify discipline-specific and education-level skill demands from pre-classified large-scale online job advertisements form Burning Glass Technologies. Although engineering education has made long steps in preparing students with foundational knowledge, employers continue to report a misalignment between the skills students acquire in school and the skills needed in practice. A key challenge in addressing this issue is the effective interpretation of semi-structured labor market data such as online job postings, which contain rich but inconsistently labeled skill information. To address this, I developed an NLP classification system that applies pattern-based text classification and flexible regular expression (regex) matching to identify relevant engineering job postings across Civil (CE), Electrical (EE), and Mechanical (ME) Engineering.
The classification framework leverages a dictionary of O*NET job title terms and engineering-specific vocabulary to refine the labeling of jobs originally mapped using Standard Occupational Classification (SOC) codes. To validate the classification accuracy, I evaluated results using confusion matrix metrics (accuracy, precision, recall, F1-score) and performed manual spot-checking of 100 job ads from each discipline. The final classification system achieved high F1-scores across CE (94.2%), EE (91.7%), and ME (93.0%), showing strong alignment with human-judged classifications. This step was essential to ensure accurate discipline-specific labeling for subsequent skill demand analysis.
Guided by the SABER-Workforce Development (SABER-WfD) framework, the study then addresses two additional research questions. The second research question examines how skill demands differ by engineering discipline and by degree level (bachelor's, master's, doctoral). Using skill mention proportions and statistical analyses such as ANOVA and Cohen's d, the study reveals that foundational technical skills like Drafting and Engineering Design, CAD, and Microsoft Office tools are dominant across all three disciplines at the bachelor's level. At the graduate level, postings increasingly emphasize management-oriented competencies such as Project Management, Budgeting, and Scheduling, particularly in civil and mechanical engineering. EE showed a higher graduate-level demand for specialized tools like MATLAB, Python, and Simulation.
The third research question explores how skill requirements have changed over time from 2010 to 2022. Longitudinal analysis shows a growing emphasis on digital and programming tools (e.g., Python, MATLAB) across all disciplines, especially at the graduate level. Simultaneously, demand for traditional skills such as Drafting, Project Management, and Engineering Design has remained steady or increased, signaling that core engineering competencies remain essential. These time-based trends highlight the dual importance of technical depth and managerial fluency in modern engineering roles.
This study demonstrates the potential of NLP-based classification and analysis techniques to extract meaningful trends from complex labor market datasets. In doing so, my dissertation contributes to ongoing discussions about curriculum reform by providing a replicable framework for aligning engineering education with workforce needs. The methodology introduced in this study also offers guidance for researchers and institutional stakeholders aiming to apply NLP in large-scale skill demand analysis, thereby expanding access to labor market insights that support engineering workforce development.
Study on Zero Crossing Detection in CRM Totem-pole PFC Converter
Rajendran, Rahul (Virginia Tech, 2025-05-15)
The totem-pole PFC is a promising candidate for achieving high efficiency and high-power-density power factor correction. Critical conduction mode (CRM) or triangular current mode (TCM) operation of totem-pole PFCs has become popular for achieving zero-voltage switching (ZVS). This enables pushing the converter's switching frequency into the megahertz range to achieve high power density while maintaining high efficiency. Zero-crossing detection (ZCD) of inductor current is important for CRM/TCM operation. The ZCD signal helps synchronize the gate signals and turn off the synchronous switch at the appropriate current to achieve ZVS. A sensing resistor is typically used in conjunction with an amplifier and comparator to generate the ZCD signal trigger.
The key issues associated with zero-crossing detection are detection delay and noise immunity. A delay in turning off the synchronous switch due to ZCD delay results in a large negative current and an overall increase in ripple current, reducing efficiency. ZCD delay is also variable, as it depends on the operating point, making it difficult to compensate for in the control algorithm. One of the main challenges of the ZCD circuit is managing common-mode noise caused by fast-switching GaN devices, which exhibit high dv/dt and di/dt. This noise can lead to false ZCD triggers, affecting PFC operation.
First, this thesis investigates and characterizes sources of delay in ZCD, identifies the sources of variable delay, and examines the effect of sensing parasitic inductance. Based on an understanding of the delay mechanism, ZCD delay compensation techniques are discussed and compared. Second, the coupling mechanism of dv/dt switching noise as common-mode noise in the ZCD circuit is analyzed. Upon understanding this coupling mechanism, a shielding technique is discussed and demonstrated to mitigate common-mode noise issues in the ZCD circuit.
Low-Power Piezoelectric Energy Harvesting Circuit to Power Wind Turbine Sensors
Hong, Ji Wu (Virginia Tech, 2025-05-15)
Monitoring the operation of wind turbines is critical to ensure a system's reliability and performance. Sensors for acceleration, vibration, temperature, and pressure, can be used to collect data and optimize the performance of a wind turbine. To operate these sensors, a power supply is required. However, using external sources are challenging due to their limited lifetime and the difficulty of maintenance. Energy harvesting offers a possible solution to this issue. By attaching a piezoelectric transducer to the blades of the wind turbine, energy can be harvested from vibrations during operation and used to supply power to a wireless sensor that captures data in real time.
This paper focuses on the design of a power management circuit for energy harvesting from piezoelectric sources under low power conditions. The harvested energy is used to power a wireless vibration sensor, eliminating the need for external power sources or batteries. A major challenge in these circuits is the cold start-up issue, where the limited initial energy fails to activate the circuit. The proposed power management circuit includes a full bridge rectifier, an impedance matched buck boost converter, an oscillator, a voltage regulator, and a cold start-up assistance circuit for automated start-up. This work provides a solution for start-up issues with vibration energy harvesting in monitoring status of wind turbine blades.