The key highlight of my degree was being part of the Main Road Australia project Enhanced vehicle detection for smart freeways and intersections to investigate alternative vehicle detection technologies for traffic signal control and smart freeway operations through a comparative desktop analysis and field trials of shortlisted technologies at two locations (intersection and freeway) in order to inform the future enhanced detection installation business case and delivery strategy under the guidance of Prof Farid Boussaid. Apart from that, I learned about constantly evolving fields such as control systems, Instrumentation & Control, Machine Learning, thoroughly enjoying the projects as I love hands-on work. I also enjoyed working with Electrical and Electronic Engineers of WA - EEEWA as a committee member and volunteering with UniMentor UWA to mentor future engineers of similar disciplines. Ever since I was a child, I have had a keen interest in #technology. Gradually, I developed an aspiration to make the world a better place for the community through my technical work and that is why I chose to study # electrical engineering. A few years into the industry, I had the urge to dive deeper into my areas of interest and thus took up a master's degree. I had accomplished 30 projects both working as a team and personal projects during my bachelors and master's degree. In addition to that, my recent project done with Mains Road Australia in collaboration with UWA will be published in a journal later this year.
Gayatri Mehta is a Professor in the department of Electrical Engineering at the University of North Texas. She received her Ph. D in Electrical and Computer Engineering from the University of Pittsburgh in 2009. Her research interests are broadly in the areas of electronic design automation, reconfigurable computing, low-power VLSI design, system-on-a chip design, embedded systems, and portable/wearable computing. She is a senior member of the IEEE and a member of the ACM.
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Sachin Mehta is keeping busy. He has just started an out-of-this-world internship with NASA, is a graduate student at Georgia Tech earning his professional masters in applied systems engineering (PMASE 2017), and is founding his own company, DynamiCoupons.
Georgia Tech's College of Engineering, the division of Professional Education, and the Georgia Tech Research Institute collaboratively designed the Professional Master's in Applied Systems Engineering (PMASE) program for experienced professionals interested in building and expanding their systems engineering expertise. The systems engineering master's program offers a practical, hands-on approach to learning how to successfully integrate systems engineering processes to gain a competitive advantage in any industry.
This master's in systems engineering will help students develop the skills and knowledge necessary to successfully compete in the global market. Students gain real-world experience by collaborating in a team environment. They will learn how to think strategically to determine project plans and identify areas of risks.
During her time at ASU, she served as an Undergraduate Teaching Assistant in a freshmen engineering course, a Fulton Ambassador, a Barrett Mentor for honors students and a Community Mentor in a residential hall to name a few.
Mehta also served as Outreach Director for the Society of Women Engineers where she helped to coordinate events to attract and retain women engineering students. One event, Girl Scouts Engineering Awareness and Retention, known as GEAR Day, brought 150 young girl scouts to campus to learn about careers in science, technology, mathematics and engineering.
By supporting the scholarships, professors and programs for engineering students, you are investing in the future.Your gift can directly change lives and inspire our students and faculty to shape the future for all of us.
This thesis addresses the above challenges by proposing several models, algorithms, and simulation and software frameworks. In the first part, we investigate methods for early detection of short-lived and significant increase in demand for computing resources (also called spikes) which may cause significant degradation in the performance of a distributed application. We make use of adaptive signal processing techniques for early detection of spikes. We then consider trade-offs between parameters such as the time taken to detect a spike and the number of false spikes that are detected. In the second part, we study the resource planning problem where we study the cost benefits of adding new compute resources based on performance requirements for emerging applications. In the third part, we study the problem of allocating resources to applications by formulating as an optimization problem, where the objective is to minimize overall operational cost while meeting the performance targets of applications. We also propose a hierarchical scheduling framework and policies for allocating resources to applications based on performance metrics of both applications and compute resources. In the last part, we propose a framework, Calvin Constrained, for resource-constrained devices, which is an extension of the Calvin framework and supports a limited but essential subset of the features of the reference framework taking into account the limited memory and processing power of the resource-constrained IoT devices.
Calvin is an IoT framework for application development, deployment and execution in heterogeneous environments, that includes clouds, edge resources, and embedded or constrained resources. Inside Calvin, all the distributed resources are viewed as one environment by the application. The framework provides multi-tenancy and simplifies development of IoT applications, which are represented using a dataflow of application components (named actors) and their communication. The idea behind Calvin poses similarity with the serverless architecture and can be seen as Actor as a Service instead of Function as a Service. This makes Calvin very powerful as it does not only scale actors quickly but also provides an easy actor migration capability. In this work, we propose Calvin Constrained, an extension to the Calvin framework to cover resource-constrained devices. Due to limited memory and processing power of embedded devices, the constrained side of the framework can only support a limited subset of the Calvin features. The current implementation of Calvin Constrained supports actors implemented in C as well as Python, where the support for Python actors is enabled by using MicroPython as a statically allocated library, by this we enable the automatic management of state variables and enhance code re-usability. As would be expected, Python-coded actors demand more resources over C-coded ones. We show that the extra resources needed are manageable on current off-the-shelve micro-controller-equipped devices when using the Calvin framework.
This paper proposes an approach to classify faults that commonly occur in a High Voltage Direct Current (HVDC) power system. These faults are distributed throughout the entire HVDC system. The most recently published techniques for power system fault classification are the wavelet analysis, two-dimensional time-frequency representation for feature extraction and conventional artificial neural networks for fault type identification. The main limitation of these systems is that they are commonly designed to focus on a group of faults involved in a specific area of a power system. This paper introduces a framework for fault classification that covers a wider range of faults. The proposed fault classification framework has been initiated and developed in the context of the HVDC power system at Manitoba Hydro, which uses what is known as the TranscanTM system to record and archive fault events in files. Each fault file includes the most active signals (there are 23 of them) in the power system. Testing the proposed framework for fault classification is based on fault files collected and classified manually over a period of two years. The fault classification framework presented in this paper introduces the use of the rough membership function in the design of a neural fault classification system. A rough membership function makes it possible to distinguish similar feature values and measures the degree of overlap between a set of experimental values and a set of values representing a standard (e.g., set of values typically associated with a known fault). In addition to fault classification using rough neural networks, the proposed framework includes what is known as a linear mean and standard deviation classifier. The proposed framework also includes a classifier fusion technique as a means of increasing the fault classification accuracy.
The increasing integration of wind turbines mainly doubly fed induction generators (DFIGs) introduces dynamic interaction with the conventional synchronous generators (SGs) affecting the damping of low-frequency electromechanical oscillations (LFEOs) in the system. This study presents a complete impact analysis of DFIG integration on system damping. The damping ratio sensitivity to inertia is used to evaluate the impact of DFIG integration on the critical modes. This study identifies that the impact could be positive or negative based on the location of DFIG. The results obtained are verified by detailed eigenvalue analysis and time-domain simulations for different operating conditions with three-phase fault. The negative impact on damping is improved by optimally tuned power oscillation dampers (PODs) with input signals obtained from the phasor measurement units. The impact on dynamic performance of the system due to removal of SGs and addition of DFIGs is investigated. The DFIG penetration is increased on the basis of participation of SGs in critical modes and the results of sensitivity analysis. The proposed approach is tested on standard IEEE 39-bus test system. The investigations carried out in this work can be employed for planning studies of power system integrated with DFIG for identifying their suitable locations and improving LFEO damping with their high penetration. 006ab0faaa
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