Individual Research Groups

The mission of Cyber Physical Systems Laboratory (ECL) is to design and implement smart system software and middleware to enable near-real-time distributed computing in the next generation Internet-of-Everything (IoE) operating in the Big-Squared-Data space. Examples of such IoEs include connected ecosystems of Self-Driving Vehicles, Smart Grids in electric power distribution systems, and real-time Data Analytics applications. We also focus on co-designing innovative system architectures with system and application software, and on vertically integrating mobile real-time embedded platforms with Fog and Cloud computing. Of particular interest to us are the development of algorithms for real-time and near-real-time scheduling, and implementing database management systems for Big Data.

Lead Faculty: Ari Mukherjee

Collaborators: Chen Chen (ECE) and Srinivas Pulugurtha (CEE)

Location: EPIC 2128

The Duke Energy Smart Grid Laboratory (DESGL) at the Energy Production and Infrastructure Center (EPIC) primarily supports the education, research and outreach activities to modernize the power grid. Centered in the Charlotte region the lab supports utilities, vendors and agencies in the region, nationally and internationally. The laboratory is a state‐of‐the‐art facility designed to perform advanced testing and evaluation of smart grid enabling technologies and energy systems.

The laboratory includes a real‐time digital simulation test‐bed that can perform smart grid device functional testing, system integration, real‐time power system analysis, under-graduate and post‐graduate education and professional training in grid modernization. Several hardware components for grid level studies such as generator exciters, protective relays, power converters, smart meters, etc. can be tested under real-time grid conditions in this facility. Power amplifiers are used to perform power hardware-in-the loop testing. Control hardware and software devices are used for testing high-speed communications and real‐time system studies. The smart grid laboratory serves as the primary network communications node that bridges a powerful high-performance server backbone and additional power analysis laboratories within the EPIC facility.

Primary Faculty: Robert Cox

Collaborators: Valentina Cecchi, Mahdav Manjrekar, Babak Parkhideh, Badrul Chowdhury, Maciej Noras, Somasundaram Essakiappan, Ehab Shoubaki and Sukumar Kamalasadan

Location: EPIC 1250

The mission of Edge Computing Lab. (ECL) is to bring recent advances in machine learning, deep learning, and data analytics to our communities for enhancing the safety, security, and overall well-being without jeopardizing the privacy of residents. At ECL, we focus on developing novel technologies across the entire computing stack including data analytics and machine learning applications, system software and firmware, real-time distributed computing, data storage, security, and privacy built-in computer systems. With this, our aim is to enable real-time decentralized edge cognitive Intelligence integrated into community environments to enable a broad range of Smart and Connected Communities applications. Few examples are AI for public and pedestrian safety, smart transportation, and smart energy and health care systems. To achieve this broad mission, faculties and students in ECL closely work with domain experts from civil and construction engineering, power electronics, social sciences, and criminal justice, as well as community partners, stakeholders and leading industries, and general public and community residents.

Primary Faculty: Hamed Tabkhi and Arun Ravindran

Collaborators: Shannon Reid (Criminal Justice), Doug Shoemaker (Geography and Earth Sciences) and Srinivas Pulugurtha (CEE), City of Charlotte

Location: EPIC 2128

Website: https://smartsafecharlotte.charlotte.edu/

Electric Machines, Drives, and Protection Laboratory is a multi-faculty, multiple sponsors research laboratory focused on applying the principles of power electronics and energy systems to application space consisting of electrical generation, motion control, and solid-state protection. The topic area of electrical generation includes condition monitoring and diagnostics of large machines and magnetic gearboxes employed to serve power takeoff from off-shore turbine generators. The research projects in this area are predominantly supported by Electric Power Research Institute (EPRI), Coastal Studies Institute (CSI), and the National Science Foundation (NSF). The topic area of motion control includes investigations of advanced methodologies for speed and torque control of various motors employed in residential, commercial and industrial Heating, Ventilation, and Air-Conditioning (HVAC) systems. The research projects in this area are mainly supported by Ingersoll Rand. Lastly, the solid-state protection topic area includes characterization and performance testing of low voltage circuit breakers, and development of medium voltage breakers for Motor Control Centers (MCC). The research projects in this area are supported by Atom Power, Power America, and United State Department of Energy (US DoE).

Primary Faculty: Madhav Manjrekar, Robert Cox and Tiefu Zhao

Collaborators: Ehab Shoubaki, Aidan Browne and Wesley Williams

Location: EPIC G230

The Embedded Systems and Autonomous Vehicle Lab has a long history of university/industry collaboration with companies like Frontline Test Equipment, iRobot, Zapata Engineering, Emerson, Renesas, and National Instruments. Graduated students currently work in embedded systems jobs at Qualcomm, Texas Instruments, Intel, General Dynamics, Dematic, Seagate, and The Mathworks. The lab director, Professor James Conrad, has over 25 years of experience in the embedded systems field in academia and at IBM, Ericsson/Sony Ericsson, and several start-ups.

Current Projects

ZapataBot autonomous All-terrain vehicle; InMoov Humanoid Robot; Robot Localization and Control.

Past Projects

Nekton Research/iRobot underwater vehicles, Stiquito Insectoid Robot, “Professor X” Humanoid robot, Renesas Embedded Microcontroller System Development.

Faculty: James Conrad

Collaborators: Hamed Tahbki (ECE) and Aidan Browne (ETCM)

Location: EPIC 2122/2124

Website: https://webpages.charlotte.edu/~jmconrad/ResearchProjects.html

The Energy Management Systems Laboratory focuses on applications of analytics and controls for efficient energy management in engineered systems. The laboratory includes high-performance computing capabilities for whole-building energy simulation, PV performance, and load modeling. The laboratory also includes advanced test systems for building automation and control.

Primary faculty: Robert Cox

Location: EPIC 1250

The PV Integration Laboratory is a state-of-the-art facility for testing and developing high performance power-electronic systems for photovoltaic integration. The lab features a reconfigurable 21kW PV system, including 14 individual 60-cell modules, a 14-module string, and a 7kW grid-tied system. The laboratory also includes a bi-directional 4-quardant grid emulators, advanced controller hardware-in-the-loop (CHIL) test capabilities, and thermal test chambers for reliability studies. Research in the laboratory focuses on various aspects of power electronic systems, including wideband current sensors, miniaturized passive component structures, reliability of wide-bandgap semiconductors, grid integration studies, and high-performance PV inverter design.

Primary faculty: Babak Parkhideh

Collaborators: Robert Cox

Location: EPIC 2374

For more information, https://coefs.charlotte.edu/bparkhid/pv-integration-laboratory/

The mission of Power, Energy and Intelligent Systems Lab (PEISL) is to conduct advanced research in power and energy systems especially related to smart grid and microgrid and provide hands-on training to both undergraduate and graduate students in modeling, hardware and software integration, real-time simulation and prototyping of power management devices and systems. Main research focus areas are related to power grid modernization such as grid impact of renewable energy integration, real-time modeling, and control of power grid and renewable energy devices, dynamics and stability of renewable energy integrated power grid and advanced topics in power system optimization. Current hardware integration and test studies are in the areas of Phasor Measurement Units (PMUs), power system relaying, device and grid level controllers and renewable energy integration experimental testbeds such as wind generator test bed, PV generator test bed, and uninterruptible power supply and battery management systems.

Primary Faculty: Sukumar Kamalasadan

Collaborators: Tao Han, Valentina Cecchi and Umit Cali (ETCM)

Location: EPIC G232

The focus of the Renewable Energy and Power Electronics Advanced Research Lab (RE-PEARL) is to conduct research in power electronics and power system, provide our graduate students with hands-on training of hardware prototyping and testing. The lab features a distribution voltage regulator testing platform and a wave energy emulation system, which serve as valuable platforms to collaborate with energy industries and conduct research on power electronics and renewable energies. There are currently a few active projects using the lab to conduct research and prototype development.

The lab will bring generated power from soon-to-be installed solar photovoltaic panels on the roof of the EPIC building. Students will be able to perform control and energy management in microgrid settings once the lab is completed.

Current Projects:

1. Reliability oriented control for wave energy systems using heterogenous underwater communication network (sponsor: UNC Coastal Studies Institute)
2. Power electronics assisted distribution voltage regulator (sponsor: CAPER)
3. Wireless power transfer for switcher trains

Primary faculty: Tiefu Zhao and Badrul Chowdhury

Collaborators: Tao Han and Shen-En Chen (CEE)

Location: EPIC G238

With the continuous growth in the complexity of deep learning and machine learning algorithms, the demand for novel computer architecture and system-level design paradigms is a most. At TeCSAR, we focus on transformative design methodologies, system-level modeling and computer hardware design for emerging machine learning and deep learning algorithms. The general roadmap of TeCSAR is algorithm/architecture codesign by offering novel scalable design tools and methodologies to bring algorithm-awareness in hardware design and enable rapid architecture design space exploration and prototyping. At the same time, we develop novel domain-specific computer architecture to reconcile the hardware programming flexibility and execution efficiency. At TeCSAR lab, Faculty, researchers, and students enable high-performance energy-efficient execution of a broad range of data-intensive numerical problems including scientific computing, graph processing, computer vision, and deep learning.

Primary Faculty: Hamed Tabkhi

Collaborators: Arun Ravindran (ECE), Andrew Willis (ECE), Babak Parkhideh (ECE), Omid Shoghli (ETCM), James Conrad (ECE), Amirhossein Ghassemi (MEES) and Gunar Schirner (ECE, Northeastern University)

Location: EPIC 2143

Website: https://coefs.charlotte.edu/htabkhiv/

City centers nationwide are rapidly evolving from a traditional downtown into a vibrant “Central Activity District.” Yet, city leaders, small businesses, and residents lack timely, trustworthy data on how people move through and experience these public spaces. This pilot project will convert existing security cameras into privacy-preserving “urban intelligence sensors.” Instead of transmitting recognizable faces, each camera’s video is processed on a small computer installed on-site, where people are represented only as anonymous motion heatmaps. The resulting insights, crowd-flow patterns, congested walkways, accessibility barriers, and potential safety risks will be shared in real-time with planners, shop owners, event venues, and the public through dashboards and mobile alerts. A strong partnership among UNC Charlotte, Charlotte Center City Partners, Central Piedmont Community College, Law Enforcement, and a network of local businesses ensures the work is co-designed with the very communities it serves. By demonstrating that advanced AI can be deployed responsibly, transparently, and with measurable civic benefit, the pilot offers a replicable model for safer, more walkable downtowns nationwide.

The pilot will make fundamental Artificial Intelligence and Computer Vision innovations that transform raw pixels into high-dimensional thermal “motion tokens,” enabling accurate detection of trajectories, dwell times, queue lengths, and anomalous behaviors without storing personally identifiable information. An online active-learning framework, reinforced by feedback from security staff and site managers, continuously adapts detection thresholds to the unique rhythms of college campuses, art venues, entertainment arenas, retail corridors, and nightlife districts. Real-time statistics are fused into spatial heatmaps, crowd-density graphs, and occupancy forecasts, which are served to stakeholders via a web dashboard and mobile app. Statistical modules aggregate foot traffic trends to guide lighting schedules, signage placement, staffing decisions, and public safety interventions. Research outcomes will include validated algorithms, open latent-motion datasets, and evidence-based policy recommendations, advancing urban informatics, responsible AI, and edge computing while establishing a scalable blueprint for AI-assisted city planning across the United States.

Award #2527312
NSF Project Webpage

The lab conducts research on a wide spectrum of topics in computer vision, machine learning and robotics.

Primary Faculty: Andrew Willis

Location: EPIC 2372

Website: https://visionlab.charlotte.edu/index.php

The Wireless Communications and Networking Lab-2 focuses on wireless sensors research, including protocol development, solutions for long-term sustainability, energy management, and application development for wide-area monitoring. Students work with experimental wireless sensor networking hardware, including MICAz, XBee-Pro, LORA, Powercast, and others.

Current Projects

Current projects include techniques for overcoming shadowing effects in RSSI-based wireless sensor localization; long-range wide-area sensor networks using LORA; and adaptive protocols for rechargeable and intermittently connected wireless sensor networks.

Past Projects

Adaptability to Variations of Renewable Energy in Large Scale Rechargeable Wireless Sensor Networks (NSF, 2011 – 2016), Critical Infrastructure of the Distribution Grid (CAPER, 2015-2016), Wireless Sensor Networ for Valve Monitoring (Burkert, 2015-2016), Wireless Mesh Sensor Network for Power Systems Monitoring (EPRI, 2006-2012), Wireless Mesh Sensor Network for Fossil Plants Monitoring (EPRI, 2008-2009).

Primary Faculty: Asis Nasipuri

Collaborators: Badrul Chowdhury, Robert Cox, James Conrad and Andrew Willis

Location: EPIC 2376