Â鶹ӳ»­

Background:
The electrification of the transportation sector is not only a key pathway to meeting air quality and climate goals, but also is a path toward global independence from oil and securing a sustainable energy future. In future cities, electrification of the transportation sector will be considered an important tool for restructuring how people and goods move sustainably across the urban environment. Electrified transportation appears in the form of electric vehicles (EVs) and hybrid electric vehicles (HEVs). Different configurations of HEVs display different characteristics based on their power train architecture and the different components used within. In general, all these configurations facilitate improved efficiency, emissions, performance and, most importantly, offer sustainable mobility that is not dependent on fossil fuels.

Project description:
The REU student will design and work with a small-scale HEV setup (see picture) to explore different power train configurations of EVs and HEVs, namely series, parallel and complex arrangements. Different drive cycles will be compared and analyzed for each configuration in terms of energy usage and overall efficiency. The advantages of each configuration over others also will be experimentally studied. Finally, the student will develop and implement a control strategy to reduce the overall energy consumption for a given travel range of the small-scale vehicle in order to gain a solid understanding of energy management strategies used in these vehicles.

Faculty advisor:
Electrical & Biomedical Engineering Associate Professor Poria Fajri
Phone: (775) 682-6864
Email: pfajri@unr.edu
Building: 
Mailstop: 0260

Background:
Unmanned aerial vehicles (UAVs) are flexible and fast mobile platforms that can be used for many applications, making them potential key players in the development of future Smart Cities. Batteries often are used onboard UAVs to provide power and to support other auxiliary power requirements. However, UAVs can only carry a limited number of onboard batteries and this restricts their capability and flight time. To reap the full advantages of UAVs, energy-efficient flight control has become a focus of research with an emphasis on different path planning strategies and intelligent flight control designs. Energy-efficient design for UAVs is a critical topic that not only improves their performance but also broadens their usage in applications requiring extended flight time. 

Project description:
The REU student will work with the full-scale octocopter UAV setup in the University’s Unmanned System Lab (see photo) to explore different energy-efficient path planning and flight control designs. Primarily, the REU student will study existing UAV path planning and flight control to understand energy challenges. Then, with a given mission (e.g., Smart City real-time traffic monitoring), the student will develop an energy-efficient operation plan using intelligent flight control. Finally, the student will experimentally implement the developed strategy to evaluate the effectiveness of the design. The hands-on experimental validation step will improve the student’s experience and understanding of energy-efficient UAV development and realization.

Faculty advisor:
Electrical & Biomedical Engineering Associate Professor Hao Xu
Phone: (775) 682-6873
Email: haoxu@unr.edu
Building: 
Mailstop: 0260

Background: 
There is a growing interest in wearable assistive robots to aid the elderly with everyday tasks and to rehabilitate patients with physical impairments. Compared to assistive robots using passive, spring-like elements for energy storage and release, assistive robots using powered actuators show much stronger adaptability and effectiveness in real environments. An important challenge is to design assistive robots that are lightweight, comfortable and simple to use. Existing assistive robots are predominantly actuated by electric motors. As a result, they are rigid, bulky and potentially dangerous. Artificial muscles are compliant materials and structures that can change shape without complex linkages, and are recently being used in assistive robots.

Assistive robotic glove and wrist orthosis

Project description:
In this REU project, the student will work with an assistive robotic glove and wrist orthosis system available in the Smart Robotics Lab (see image) to explore different configurations of artificial muscles as a robotic actuator system. The student will study the fundamentals of design, modeling and control of several artificial muscles, such as super-coiled polymers, twisted strings and coiled spring actuators, and test their performance experimentally. The student then will develop and implement a control strategy for the assistive robot to produce the desired assistance in accurately grasping and moving an object.

Faculty advisor:
Mechanical Engineering Assistant Professor Jun Zhang
Phone: (775) 682-9383
Email: jun@unr.edu
Building: 
Mailstop: 0312

Background: 
Grid-connected converters (GCCs) have majorly contributed to the integration of rooftop photovoltaic solar energies into the utility grid. A critical consideration for solar energy integration with existing smart grids is the control of the GCC, which enables two-way power flow: from the power grid to the customers and from the customers back to the power grid. Recent research in controlling these converters has opened up new possibilities for efficient utilization of household solar energy and will facilitate integration of this renewable energy with future power grids.

Project description:
In this project, students will study and explore embedded control of power converters in real-time for integrating solar energy with the grid. Specifically, students will conduct hands-on experiments which will allow them to i) get familiar with Texas Instruments (TI) grid-tied solar micro inverter operations; ii) learn real-time control using Code Composer Studio (CCS); and iii) implement solar integration using a Digital Signal Processor (DSP). Through these experiments, students will gain knowledge of novel artificial intelligence-based control for solar integration for next-generation smart and renewable power grids.

Faculty advisor:
Electrical & Biomedical Engineering Associate Professor Xingang Fu
Phone: (775) 784-1490
Email: xfu@unr.edu
Building: 
Mailstop: 0260

Background: 
Transitioning from a fossil fuel economy to one that uses green technologies is imperative and is a global responsibility of this generation. The multifaceted application of solar energy makes it an ideal candidate for expansion. Yet there are several challenges to the sustainable use of this resource, including storage, material development, efficiency and awareness on a global scale. With the exception of photovoltaics, applications of solar energy and awareness of those applications need to be improved. Of particular interest is educating the next generation on the interdisciplinary knowledge required to successfully tap into solar power for the future needs of mankind.

Example showing tea as a solar energy harvesting material; it's integration into a solar cell; and it's utilization to power a device

Project description: 
The student participating in this REU project will research the application of solar power in transportation using hydrogen generated by a photoelectrochemical solar cell with waste materials as a primary component (see image). The project will offer insights into i) solar energy conversion using photoactive materials; ii) solar energy storage in the form of chemical energy (H₂); and iii) the photocatalytic pathway to remediation of waste. A key aspect of the project will be to demonstrate that it is possible to build a “smart system” to convert waste to fuel. The student will also be tasked with articulating the challenges involved in such a system, like the need to improve system efficiency.

Faculty advisor:
Chemical & Materials Engineering Associate Professor Ravi Subramanian
Phone: (775) 784-4686
Email: ravisv@unr.edu
Building: LME
Room: 309
Mailstop: 0388

Background:
Connected vehicle technologies can significantly improve traffic safety, mobility and fuel efficiency. A connected-vehicle system provides extended detection distance for drivers, pedestrians and autonomous vehicles to “see” around corners or “through” other vehicles so that threats and changing traffic situations can be perceived earlier. The Center for Advanced Transportation Education and Research (CATER) at the Â鶹ӳ»­ has been performing research to obtain high-resolution data of all road users, connected and unconnected, with 360° roadside light detection and ranging (LiDAR) sensors. The goal of this research is to enhance future traffic infrastructures to detect and broadcast every road user’s real-time status as an intuitive solution to fill the data gap of connected-vehicle deployments. A planned pilot deployment of a new generation of 360° LiDAR sensors (see image) will help Â鶹ӳ»­ and the city of Reno prepare for future connected/autonomous transportation systems. This will help make Â鶹ӳ»­ one of the leading states to implement connected-vehicle technologies.

Portable 360-degree roadside LiDAR sensor that will be used in this project

Project description:
The REU student will work as part of a team to expand the pilot deployment of a new generation of 360° LiDAR sensors at intersections along a one-mile segment of North Virginia Street in Reno to collect real-time data about all road users, including vehicles, pedestrians and bicyclists. The student will help design and improve a traffic signal system that will optimize traffic flows with the extracted real-time LiDAR data. The student also will be involved in simulating the traffic signal system using actual LiDAR data, made possible by the advanced software packages available in the CATER lab.

Faculty advisor:
Civil & Environmental Engineering Associate Professor Hao Xu
Phone: (775) 784-6909
Email: haox@unr.edu
Building: 
Mailstop: 0258

Background: 
With the advent of smart grids and the penetration of renewable energies in residential areas, patterns of household electricity usage are being revolutionized. As a result, classical residential buildings have evolved into smart homes equipped with advanced energy features. Smart homes are small energy systems equipped with batteries and renewable energy sources such as photovoltaic panels, and they have the ability to exchange energy with one or more electric vehicles (EVs) connected to them (see image). Coupled with advances in home area networks, smart home energy management will play a key role in energy conservation in future smart cities. Energy management of smart home resources will provide an opportunity for economic incentives for both homeowners and the grid by synergizing energy production, storage and consumption.

Smart home structure studied for this project

Project description: 
The REU student will work on developing energy management strategies for controlling multiple components connected to a smart home. The objective will be to efficiently utilize all available resources to i) meet smart home load requirements; ii) satisfy desired EV charging rate; and iii) minimize the overall cost of energy imported from the grid. The student will become familiar with the operation of smart homes and will gain valuable insight into prioritization of sources and loads in a multi-source, multi-load system. Finally, the student will experimentally implement the developed energy management strategy using controllable loads and sources available in the lab to emulate a smart home operating condition.

Faculty advisor:
Electrical & Biomedical Engineering Professor and Chair M. Sami Fadali
Phone: (775) 784-6951
Email: fadali@unr.edu
Building: 
Mailstop: 0260

Background: 
Moving people and goods efficiently is essential for any city, and it is projected that smart roads will significantly reduce congestion, decrease collisions and provide the required infrastructure to charge electric vehicles (EVs) as they move. In-motion or dynamic wireless charging of EVs is a concept based on wireless power transfer that allows energy transfer between a transmitter coil and a receiving coil using electromagnetic induction. In dynamic wireless charging (see image), multiple transmitter coils are embedded into the road such that when an EV moves over each transmitter coil, energy is transferred through the magnetic field to the vehicle. A charge-as-you-drive system would allow EVs to drive for an unlimited amount of time without having to stop to recharge, alleviating range concerns associated with these vehicles.

Smart road and the concept of dynamic wireless charging for EVs

Project description: 
In this project, the topic of smart roads will be investigated with a focus on the dynamic wireless charging capability of these roads. The REU student first will explore the concept of smart roads and identify the different technologies that can be incorporated into these roads to make them safer and more efficient while meeting future electrified transportation needs. At the same time, the REU student will research wireless power transfer in EVs and explore dynamic wireless charging, its challenges and infrastructure requirements. Finally, the student will build a low-power demonstration platform resembling the concept of dynamic wireless power transfer on a scaled road. 

Faculty advisor:
Electrical & Biomedical Engineering Associate Professor Poria Fajri
Phone: (775) 682-6864
Email: pfajri@unr.edu
Building: 
Mailstop: 0260

Background:
As the amount of data and information collected increases, it will become more complex to monitor, plan, operate and assess security in smart cities with numerous sensors, interconnected devices and information technology networks. Thus, artificial intelligence and machine learning are becoming standard tools to help operators and planners make informed decisions. However, as these interconnected systems and infrastructures become more complex, they become prone to more errors and more adverse cyber-attacks. Attackers can compromise the delivery of services and can even incite serious incidents, with potentially significant impacts on security, economics and safety.

Robust machine learning application for cyberattack-resilient electricity infrastructure

Project description:
The REU student will work on the Â鶹ӳ»­’s cyber-physical system testbed to study and explore application of machine learning tools in monitoring, decision making and planning of interconnected devices within a Smart City electricity infrastructure. The student also will learn about system vulnerability to cyber-attacks and explore different defense mechanisms to enhance the robustness of machine learning approaches (see image). The REU student will build an iterative attack-defense game framework to assess vulnerability with respect to different attacks and defense mechanisms.

Faculty advisor:
Electrical & Biomedical Engineering Associate Professor Hanif Livani
Phone: (775) 784-6103
Email: hlivani@unr.edu
Building: 
Mailstop: 0260

Background: 
Security and privacy issues exist in different phases of smart manufacturing industries. The Internet of Things (IoT) devices, smart machines and autonomous robots on which advanced manufacturing processes rely can be compromised by both internal and external cyberattacks. Vulnerability analysis can play a significant role in providing resilience against such security threats. However, this can prove to be challenging due to resource-constrained devices, lack of knowledge about penetration testing of advanced manufacturing systems, scalability and oftentimes unencrypted communications. 

Smart City and vulnerability analysis testbed

Project description: 
In this project, the topic of vulnerability analysis in advanced manufacturing systems will be investigated to enhance the security, privacy and resiliency of the manufacturing system against cyberattacks such as unauthorized access, malicious manipulation of data and malware attacks from inside and outside of the systems. The REU student first will explore the concept of static and dynamic vulnerability analysis through malware analysis, traffic analysis and reverse engineering. They also will study and research open-source controls and configuration options suited to develop a secure, sharing and analytic environment that meets the needs of security analysts and protects the privacy of the data. Last but not least, students will research about developing a governance framework that will include both policies and procedures to protect the data. The student will work closely with the University of Reno, Reno’s cybersecurity research lab to explore the vulnerabilities and learn defense mechanisms to enhance the robustness of advanced manufacturing systems against cyberattacks. Finally, the student will experimentally implement and test different defense mechanisms on the University’s Smart City prototype testbed (see image).

Faculty advisor:
Computer Science & Engineering Professor Shamik Sengupta
Phone: (775) 784-6953
Email: ssengupta@unr.edu
Building: 
Mailstop: 0171