Courses
Electromagnetic and Electromechanical Component Design
This course focuses on the design of electromagnetic and electromechanical components with power applications. The course includes design optimization methods, modeling techniques for design (as opposed to for simulation or analysis), and the formulation of design problems as optimization problems.
Power Electronic Converters and Systems
Part 1 of the course addresses components and modeling techniques and considers three power converters - a dc/dc converter, an ac/dc power supply, and a dc/ac inverter. Detailed and average-value modeling concepts are presented, and detailed component controls are developed. Part 2 of the course focuses on systems of power electronic converters. In particular, the concept of negative impedance instability is explored using both linear and non-linear analysis methods.
Antennas: Design and Application
Electrically small antennas; Arrays; wire antennas and feeding arrangements; aperture antennas, such as slots, horns, and parabolic reflectors; antennas for multiple frequencies, including log-periodic and other frequency independent types; receiving antennas and the concept of antenna temperature; antenna measurements and evaluation.
Modeling and Simulation of Power System Components
This course is recommended for those interested in learning to use computer simulation to investigate the dynamic and controlled behavior of electrical power components. Beginning with an introduction to MATLAB/SIMULINK, the course goes through the key steps of modeling, implementing, and verifying the simulation of transmission lines, single and three-phase transformers, induction machines and wound-field synchronous machines. Students are expected to implement and verify about eight simulation projects, and also discuss observed behaviors on topics such as inrush current in transformers, motoring, generation and braking operation of machines, and pulsating torque from sub-synchronous resonance. This course is co-taught between 4 instructors in sequential order; Sudhoff, Pekarek, Wasynczuk, Aliprantis
Digital Image Processing I
Deterministic and stochastic modeling of images, linear and nonlinear filtering, and image transformations for coding and restoration. A variety of web based laboratory experiments based on a combination of Matlab and C programming environments will be used.
Modern Automatic Control
The first order of business in the analysis of a real world system is the construction of a mathematical model of that system. In this course, we discuss mathematical modeling of systems from mechanical and electrical engineering, as well as from physics and biology. Nonlinear systems are emphasized to acknowledge the critical role that nonlinear phenomena are playing in science and technology. The models presented are the ones that will be used to design controllers. These models are constructed from the control engineering point of view. Two main types of dynamical systems are common in applications: those for which the time variable is discrete and those for which the time variable is continuous. When the time variable is discrete, the dynamics of such systems are usually modeled using difference equations. In the case when the time is continuous, ordinary differential equations are frequently chosen for modeling purposes. Both types of models are considered in the course.
Advanced IoT Design and Applications
Recent years have witnessed the rise of the Internet of Things (IoT), a newly emerged networking paradigm that connects humans and the physical-world through ubiquitous sensing, computing, and communicating devices. With billions of such connected devices that pervade every corner of the world, IoT is able to benefit a whole spectrum of civilian and military applications with enormous societal and economic impacts, such as smart cities and transportation, healthcare and assisted living, activity and gesture recognition, smart homes and buildings, and environmental monitoring. This course provides the students with a deep and comprehensive understanding of IoT systems by introducing the key IoT technologies from the ground up, including IoT devices programming, wireless network design and optimization, edge-cloud IoT platforms, deep/machine learning, as well as security and privacy preserving mechanisms. In this course, we will also survey recently published algorithms, systems, and applications of Internet of Things, and explore key opportunities as well as challenges emerging in the research of this area.
Big Data for Reliability and Security
This course covers the theoretical aspects of big data for reliability and security and stresses the practical systems aspects of such techniques. There are two challenge programming problems based on large real-world datasets that we have collected and curated. Topics:
1. Foundational material on reliability and security
2. Data analytic techniques for dependability
3. Big data security and insecurity
4. Case studies and challenge problems
1. Foundational material on reliability and security
2. Data analytic techniques for dependability
3. Big data security and insecurity
4. Case studies and challenge problems
Communication for Engineering Leaders
The goal of this course is to upskill talented engineers' communication abilities. By the end of the course, you should have new adeptness at creating and delivering powerful presentations on a short deadline, as well as strategies for leading others effectively.
Epidemic Processes
This course provides a control theory and data science approach to traditional epidemic models. Traditional epidemiological ideas will be explored and combined with probability theory and systems theoretic ideas to be able to capture spread behavior, learn from data, and design mitigation techniques. The course consists of four modules: 1) Group Virus Models, 2) Solutions and Limiting Behavior, 3) Model Parameter Identification, and 4) Mitigation Algorithms.
Networked Epidemic Processes
This course presents a class of epidemic models from a network science, control theoretic, and data science perspective. Networked epidemiological ideas will be explored combined with probability theory and systems theoretic ideas to be able to capture spread behavior, learn the behavior from data, and design mitigation techniques. The course consists of four modules: 1) Group Virus Models, 2) Solutions and Limiting Behavior, 3) Model Parameter Identification, and 4) Mitigation Algorithms.
Fiber Optics Communications
This course will aim to introduce students to the fundamentals of fiber optic communications, which constitute the backbone of the internet. The course will start with a refresher on the operation of key components needed for an effective fiber optic communication system, and then show how these components interact at a system level. Finally, the course will conclude with outlook for future research in extending the capabilities of these networks to higher bandwidths and quantum-secured communications.
Introduction to Mathematical Fundamentals for Systems and Control Theory
This course serves as background for ECE602, Lumped System Theory; ECE695, Epidemic Processes over Networks; and ECE695, Structure and Dynamics of Large-Scale Networks; and other similar courses. The course will make the necessary mathematical background for these courses accessible by decomposing and illustrating difficult concepts with real-world examples and problems. The course consists of five modules: 1) Linear Algebra, 2) Basic Graph Theory, 3) Basic Control Theory, 4) Probability, and 5) Optimization.
Quantum Detectors
Classical detectors and sensors are ubiquitous around us from heat sensors in cars to light detectors in a camera cell phone. Leveraging advances in the theory of noise and measurement, an important paradigm of quantum metrology has emerged. Here, ultra-precision measurement devices collect maximal information from the world around us at the quantum limit. This enables a new frontier of perception that promises to impact machine learning, autonomous navigation, surveillance strategies, information processing, and communication systems. Students in this in- depth course will learn the fundamentals about state-of-the-art quantum detectors and sensors.
Quantum Networks
Applying exotic quantum properties such as entanglement to every-day applications such as communication and computation reveals new dimensions of such applications. Quantum encoding and entanglement distribution provide means to establish fundamentally secure communication links for transfer of classical and quantum data. Generation, transmission and storage of quantum optical information are basic processes required to establish a quantum optical network. This course describes the physics behind these processes and overviews various implementation approaches. Technologies including quantum key distribution, quantum repeaters, quantum memories and quantum teleportation will be discussed and their engineering challenges will be evaluated.
Foundations of Engineering Education
This course gives students entering the graduate program in engineering education (ENE) opportunities to explore their roles within the field of engineering education, to create a learning plan that maps to program requirements, and to develop habits of mind to support their ongoing professional development. In particular, students will refine their ability to write clearly and coherently in an academic context. The course also provides new graduate students with dedicated time to explore research trends and faculty interests so they can make informed choices about advising and program opportunities.
History and Philosophy of Engineering Education
Aligned with these objectives are a set of "core ideas" or standpoints we hope participants take away:
1. The definition and boundaries of "engineering" are not given or fixed but negotiated over time. 2. Big questions about education - who should be educated, how should they be educated, for what reasons, and who should pay - need to be asked and answered by each generation (and often multiple times). 3. Who we define as an engineer, and who we educate to be engineers is a gendered, raced, and classed process, which is deeply embedded into our very notion of what an engineer is. 4. The content and philosophy of engineering is defined by people who participate in it, and by people who make decisions to not participate in it based on those definitions. 5. The history of engineering as a field is extensive, and across the globe. 6. Engineering education has a varied history in the US and is done differently across the globe. 7. Engineering education research may feel like a new field, but it is neither new nor centered on the US. Those entering this field should know their 'roots' as they take on roles in shaping the field. 8. Writing and reading are critical to your future work (graduate study and beyond), and there are genres or ways of writing you need to develop skills in - in both reading and writing.
1. The definition and boundaries of "engineering" are not given or fixed but negotiated over time. 2. Big questions about education - who should be educated, how should they be educated, for what reasons, and who should pay - need to be asked and answered by each generation (and often multiple times). 3. Who we define as an engineer, and who we educate to be engineers is a gendered, raced, and classed process, which is deeply embedded into our very notion of what an engineer is. 4. The content and philosophy of engineering is defined by people who participate in it, and by people who make decisions to not participate in it based on those definitions. 5. The history of engineering as a field is extensive, and across the globe. 6. Engineering education has a varied history in the US and is done differently across the globe. 7. Engineering education research may feel like a new field, but it is neither new nor centered on the US. Those entering this field should know their 'roots' as they take on roles in shaping the field. 8. Writing and reading are critical to your future work (graduate study and beyond), and there are genres or ways of writing you need to develop skills in - in both reading and writing.
Engineering Education Inquiry
This graduate level course on inquiry in Engineering Education aims to introduce students to research in engineering education. This course covers a survey of educational research methodologies as well as strategies for locating, documenting, and critically reading literature for the purpose of crafting arguments from evidence. In this course, students will engage in professional and ethical conduct of research through readings, videos, discussions and assignments; define and practice research as crafting arguments from evidence; and 3) explore a rich repertoire of research methodologies used in engineering education. Research literature in engineering education will be analyzed and synthesized to form arguments from evidence while recognizing multiple paradigmatic lenses including positivist, post-positivist, post-modernist, constructivist, and critical theory. Qualitative, quantitative, mixed approaches will be compared in alignment with contemporary academic thought and ways they reveal the complexity of phenomena under study. This course is one of the required core courses for the graduate degree in Engineering Education at Purdue University.