Research activity is a building block for electrical and computer engineering and is an essential part of graduate-level study.
There are ample opportunities in the department for undergraduate and graduate students to be involved in faculty members’ research. Students are encouraged to contact faculty members to inquire about possible opportunities to participate in their research.
Explore our research areas:
The Electrical Engineering Department has an internationally recognized faculty (two IEEE and ACES fellows) in the antennas and wireless communications areas with diverse but closely related interests and expertise in computational electromagnetics, electromagnetic radiation and scattering, antennas and antenna arrays, microwave circuits, radar, remote sensing, electromagnetic measurements, visualization and wireless communications.
Pulse Dispersion in Phased and Timed Arrays
PI: Randy Haupt
Modern communication systems need to transmit and receive very wideband signals. High demands on the antenna performance in modern communication systems call for array beamforming. Classic arrays use phase shifters to collimate the beam, however the term “phased”, implies narrow bandwidth. Wideband signals have a very short time duration that impacts the total signal received by an antenna array. When a pulse is incident on an antenna array, the elements of the array receive the wideband signal at different instances in time. Phase shifters align the phase of the signals at each element such that they add up in phase, however due to the shift in signal envelope the total plus, i.e. the sum of the pulses received by the phased array, exhibit some dispersion. The solution to this problem is to use time-delay-units, as opposed to phase shifters, which shift the signal envelope, providing wide instantaneous signal bandwidth. These types of arrays are referred to as timed arrays. This work is done in conjunction with Prof. Payam Nayeri.
Adaptive Devices for Highly Efficient Wireless Energy Harvesting Systems
PI: Payam Nayeri
The rapid growth of wireless communication systems in recent years, combined with the growing popularity and applications of largescale, sensor-based wireless networks, have created a strong demand to adopt inexpensive, green communication strategies. Most of these devices need to operate without batteries, or have the ability to recharge their battery wirelessly during operation, and as such require an energy harvesting circuit that captures wireless power and converts it to usable DC power. In order to maintain proper functionality in a dynamic environment where direction and polarization of the wireless signal, as well as the surroundings, change continuously, the new generation of wireless energy harvesting systems need adaptive capabilities. The primary focus of this research is on design, analysis, and development of intelligent adaptive devices with reconfigurable beam, polarization, frequency, and impedance matching capabilities for wireless energy harvesting systems that can maximize system efficiency in real-time.
Isoflux Phased Array Design for Cubesats
PI: Atef Z. Elsherbeni
This project aims for the optimization and design process of an isoflux phased array for Cubesats in low Earth orbit (LEO) operating at the upper end of the X-band. At this frequency, the array becomes small enough to fit on the 1U face of a Cubesat based on a compromise between the number of elements needed to create a reasonable isoflux pattern and the aperture size of the array in wavelengths. The isoflux array allows for effective communication over a large portion of Earth’s surface. Moreover, the array does not require any mechanical parts that can fail on launch or over the lifetime of the satellite, thus increasing reliability. An optimization process based on the genetic algorithm is used to create a multi-ring concentric circular phased array that creates a near-isoflux power pattern on the Earth surface. The genetic algorithm yields appropriate element spacing, amplitude, and phase shifts. Simple microstrip patch antennas are used for the array design for proof of concept, with full wave simulation demonstrating the array performance.
ISS is an interdisciplinary research area that encompasses the fields of control systems, signal and image processing, compressive sensing and optimization. This group undertakes fundamental research into the development, characterization and implementation of algorithms for processing and acting upon data sources, as well as research directed towards applications in energy systems, image analysis, communication systems, and robotics.
50-MW Segmented Ultralight Morphing Rotor Wind Turbine
The 50-MW Segmented Ultralight Morphing Rotor wind turbine project is a multi-institutional, multi-disciplinary effort “to conceptualize, design and demonstrate morphing technologies for 50-megawatt wind turbines that can reduce offshore levelized cost of energy by as much as 50% by 2025” (www.sumrwind.com). The University of Virginia leads the research, with Colorado School of Mines (CSM), University of Colorado-Boulder, National Renewable Energy Laboratory (NREL), Sandia National Laboratory, University of Illinois, and University of Texas-Dallas as partners. The CSM team is responsible for control systems design and testing, working iteratively with aerodynamics, structural design/dynamics, and cost of energy experts to achieve significant improvements compared to today’s state-of-the-art wind turbines. The bio-inspired load alignment concept is facilitated by a downwind configuration and advanced control technologies. One highlight of the research will be the demonstration of a scaled version of the wind turbine at NREL during summer 2018.
Distributed Control of Thermal Loads for Aggregated Demand Response
PI: Tyrone Vincent
Residential thermostatically controlled loads (TCLs) such as ACs, heat pumps, water heaters, and refrigerators, represent about 20% of the total electricity consumption in the United States, and thus offer significant potential for actively varying their behavior to help balance electricity supply and demand. TCLs have inherent thermal storage, so their electricity consumption can be modulated while still meeting the desired temperature requirements of the end user, but these variations must be coordinated in order to meet global grid balancing objectives.
Convex Optimization for Blind Inverse Problems
PI: Gongguo Tang
One of the fundamental tasks in processing sensor and imaging data is to solve an inverse problem, determining the nature of some fundamental structure that produced that data. Such problems are often underdetermined, meaning that the number of unknowns exceeds the number of observations, and these problems are even more complicated in the blind setting, where the fundamental structure may undergo some unknown transformation en route to the sensor. This project considers a number of such blind inverse problems, including non-stationary deconvolution, where an unknown point spread function changes over time; multi-band signal identification, where line spectrum estimation is extended to signals with multiple narrow frequency bands; super-resolution radar imaging, where extended and accelerating targets may cause unwanted spreading in the delay-Doppler space; and simultaneous blind deconvolution and phase retrieval. Conventionally, all of these problems have been studied separately. This project investigates all of the problems jointly under a unifying optimization and analysis framework.
Tracking Low-Dimensional Information in Data Streams and Dynamical Systems
PI: Michael Wakin
Recent advances in sensor technology have allowed observation of massive data about complex dynamical systems at an unprecedented scale. Low-dimensional models serve as a useful structure for understanding the information in high-dimensional signals and systems. However, this information often changes over time, and so these models can further be improved by exploiting temporal dynamics. This project is concerned with developing new methods for tracking changing low-dimensional structure in data streams and dynamical systems, particularly in settings where the observations may be missing, incomplete, corrupted, or compressed. This research aims to 1) develop techniques for tracking low-dimensional structure and, in particular, to extend tracking capabilities far beyond conventional signals to much more general data sets with intrinsic low-dimensional structure; 2) develop new tools for tracking low-dimensional structure in systems jointly with estimating the content of time-varying signals and data sets; 3) perform low-dimensional quantitative and qualitative analysis in systems that are too complex and high-dimensional for system identification.
ESPE is a research area that focuses on the design, operation and control of energy systems, from the electric power grid to small-scale systems such as industrial or residential microgrids. We work on solutions to control and integrate renewable energy resources into the power grid, develop optimal algorithms to enhance the operation of energy systems, build models and solutions to analyze and control electric machines, and find ways to reinforce the power grid against natural and man-made hazards.
Design Optimization of AEPS Components Using AI-EM Techniques
PI: Abd Arkadan; funded by Office of Naval Research (ONR), Long Range Scientific and Technology Program
In this project, we developed characterization and design optimization environments utilizing artificial intelligence (AI) techniques in conjunction with Electromagnetic – State Space models for the design optimization problem of Advanced Electric Power Systems (AEPS) components. The AI environment exploits the large degree of uncertainty in system characteristics to achieve fast, accurate, robust, low cost solution based on some knowledge of the performance. This approach is investigated as the design optimization of AEPS, components necessitates the simultaneous solution of the governing electrical/magnetic and mechanical equations due to the coupled nature of the electromagnetic and mechanical forces and the tight coupling between its electrical and magnetic fields as well as the nature of the devices’ nonlinear material and complicated geometries. The developed tools formed basis for detailed studies related to thermal, stress/vibration/acoustic, and Electromagnetic Interference (EMI) issues and for the development of Virtual Test Bed detailed models for Marine based Advanced Electric Power Systems.
Asset-and-User-Aware Energy DIspatch Solution for a Power Distribution System Exposed to Extreme Temperature Events
PI: Salman Mohagheghi; funded by National Science Foundation (NSF), Energy, Power, Control and Networks Program
Extreme temperatures can push various power grid components to their operational limits. The available capacity of most generation resources, transformers, and overhead lines get negatively affected as the temperature increases beyond certain thresholds. This is becoming more important since climate models project an increase in the duration and frequency of heat waves. Unsurprisingly, the temperature-induced reduction in available power generation and power transmission capacities coincides with higher electric demand on the system, mostly attributed to the over-utilization of air-conditioning systems. In this project, we are developing optimal solutions to minimize the operational cost of the system, while at the same time minimizing the loss of life of assets due to overloading or operating under harsh conditions. In addition, we make use of A/C-based demand response as a sustainable solution, but at the same time, we acknowledge the fact that air-conditioning units are essential in maintaining an acceptable indoor temperature for the residents during heat waves. We therefore avoid overexploiting this resource which could otherwise pose risks to the end-users’ health and well-being. Collectively, we model this problem as a multi-objective optimization problem where the different objective functions are optimized while considering their direct impact on one another.
Advanced Power Theories Applications in Renewable-Based Microgrid Design and Control
PI: Marcelo Simoes
We are developing new control frameworks to address the corresponding power quality issues imposed by the inherent uncertainty in the behavior of the renewable energy sources such as wind and solar. We expanded our work based on a specific viewpoint which says that power theories can be interpreted as advanced signal decomposition techniques which are used as the initial step in electrical power signals analysis. We design smart renewable-based Microgrid systems, not only through usage of advance power theories but also by developing new mathematical power theory concepts, such as our new Enhanced Instantaneous Power theory. These signal analysis step forms the fundamental headstock for power electronic interfaces controller design. In this project we also proposed a new intelligent reason-oriented instantaneous islanding detection (a major security challenge in Microgrid technology) strategy by combining the unique specifications of these signal decomposition techniques into artificial intelligence methodologies such as neural networks. We use both software-based and hardware-based platforms to verify and validate those theoretical analysis.
From the Department Head
Between our high-caliber faculty, excellent faculty-to-student ratio, cutting-edge labs and equipment and strong ties to research centers and industry, Mines' electrical engineering programs are a great choice for students who want to be leaders in the field today and in the future
Professor and Department Head
Sponsors and Collaborators
- National Science Foundation (NSF)
- Office of Naval Research (ONR)
- Air Force Office of Scientific Research (AFOSR)
- Department of Energy (DOE)
- Advanced Research Projects Agency – Energy (ARPA-E)
- Defense Advanced Research Projects Agency (DARPA)
- National Renewable Energy Laboratory (NREL)
- Epilog Laser
- University of California
- Rocky Mountain Scientific Laboratory
- ITN Energy Systems, Inc.
- United Launch Alliance
- Lockheed Martin
- Keysight Technologies
- National Institute of Standards and Technology (NIST)
- Carollo Engineers
- Envision Energy
#4 Top 25 Brainiest Colleges, #1 in Colorado (Lumosity, 2019)
6 faculty fellows of the professional organization IEEE
19:1 student-faculty ratio
#2 in combining scholarly research and classroom instruction (Wall Street Journal, 2016)