Home > REU > 2018 AWaRE REU Results

2018 AWaRE REU Results

Optically-Controlled Tunable Microwave and Millimeter-Wave Devices for Adaptive Wireless Communications

Principal Investigator: Professor Liu

AWaRE REU Researcher: Adrian Siwy, Lewis University

Project Description: This project investigated reconfigurable microwave circuits based on photo-induced electromagnetic band gap (EBG) structures. EBG structures allow the circuit to interact with electromagnetic waves and create stop-bands, pass-bands, and band gaps for wave propagation. EBG structures can be employed for antenna and filter designs and have been researched extensively in the field of wireless communication. The ability to dynamically reconfigure the EBG patterns allows the circuit to be tunable and reconfigurable, and therefore the circuit frequency response can be altered. In this project, the EBG patterns required for reconfigurability can be generated using a novel optical control approach that avoids any complicated fabrication processes. In brief, the approach takes advantage of photo-induced free carriers in a semiconductor, and the EBG patterns can be directly projected onto a Si or Ge wafer to serve as a microstrip transmission line ground plane. By projecting different photo-patterns onto the semiconductor, tunable EBG band-stop filter (BSF) frequency response can be realized. For a prototype demonstration, a tunable/reconfigurable microwave BSF for X band has been designed, simulated, and studied. Both the BSF center frequency and stopband bandwidth can be dynamically tuned.

Finding: The research explored in this project involves the simulation of components utilizing EBG structures using HFSS (High-Frequency Electromagnetic Field Simulation Software); simulations with the optically illuminated EBG on Ge substrates were also investigated. Prototype circuits have been fabricated and assembled for measurements. The knowledge obtained from these simulations along with additional research was used to aid in the development of a new prototype reconfigurable and tunable EBG component. HFSS simulations performed by the student have shown that important characteristics of the BSF, such as the stop-band bandwidth and the center frequency, can be tuned by changing the EBG patterns illuminated on the Ge ground plane.

Using AR to Control a Six-Axis Robot Arm

Principal Investigator: Professor Billo

AWaRE REU Researcher: Daniel Riehm, University of Notre Dame 

Project Description: Augmented Reality (AR) headsets offer a suite of hardware and software tools conducive to real-time, user-friendly manipulation of virtual 3D objects located in persistent locations around the user. This research sought to apply these tools to the programming and control of a six-axis universal robotic arm, providing the ability to either control the arm in real-time or to pre-program a sequence of steps to be performed on command. Using AR to provide input allows users to control the arm with a reasonable degree of precision without a need for extensive technical training. The application utilizes the orientation of the user’s head along with certain recognized hand gestures to allow selection, translation, and rotation of virtual objects. The user controls the robotic arm by manipulating a 3D “cursor” object, which represents the desired location and orientation of the end of the robotic arm. This information is translated into UR Script commands and sent to the robot via a network connection, either as the user moves the cursor or in recorded batches.

Finding: The application sends commands to and reads data from the robot over a local network TCP connection. Using a simple calibration process, the user can align the coordinate axes of the headset with those of the robot, allowing the user to specify exact poses in the space around them and have the arm reach those poses with precision.

Machinery Sensing: Analysis and Optimization

Principal Investigator: Professor Pratt

AWaRE REU Researcher: Loren Hahn, Bethel College

Project Description: Equipment health monitoring and system characterization are important to ensure reliable and safe operation. The Machinery Sensing project involved the investigation of practical methods for achieving these objectives using radio-frequency (RF) non-contact sensors to detect vibrations, debris, and abnormal behaviors in machinery. Sensor data was collected in an industrial plant known as I/N Tek on a machine where conventional health monitoring methods could not easily be employed.

Finding: The project provided evidence of the value of RF-based sensing methods for health monitoring in certain industrial settings. Data analysis revealed a differential performance in gearbox responses, suggesting that one of the gearboxes was in a declining state of health relative to the other gearbox.   

SDR Based Dual-Polarized Radar

Principal Investigator: Professor Pratt

AWaRE REU Researcher: Henadz Krukovich, Gonzaga University

Project Description: This project involved the practical experience of building a prototype system based on off-the-shelf programmable hardware. The specific objective was to build-up a dual-polarization radar. Typical radar systems are single polarization systems with corresponding single channel transmitter and receiver operation. The objective of this project was to assemble a dual-polarized radar node incorporating a new SDR model (Ettus N310). The work involved hardware integration and software and firmware development to enable control with SDR graphical user interfaces.

Finding:  A number of challenges were identified and resolved, including identifying software bugs in the vendor equipment and developing software tools to enable radar operation, including the development of a method to download radar waveforms into the field programmable gate array (FPGA). The outcome of the project was an integrated system with many functions that were successfully tested.

Human-Robot Collaboration 

Principal Investigator: Professor Lin

AWaRE REU Researcher: Jeremiah Yohannan, University of Oklahoma

Project Description: Robots are expected to work alongside humans in a safe, intelligent, and friendly manner in warehouses, homes, and other robot assistant applications. A key component of such a robotic system is the ability to interpret human intention and operate with knowledge of the surrounding environment. As much of human intention is conveyed through movement, we use a Bayesian non-parametric learning approach to create a human model from motion data. Unlike most human modeling methods, which assume that the number of states is defined, our more flexible method identifies the number of human states directly from the data. We also enable intelligent robotic manipulation of the environment with object classification and localization information generated by a Faster Region-based Convolutional Neural Network. In order to achieve goal-oriented human-robot collaboration and optimize overall task performance, we model sequential tasks as Markov Decision Processes. We demonstrate the effectiveness of our framework in a chair assembly task.

Finding: In this project, the undergraduate student developed algorithms to track human movements, collect data from demonstrations, build human models using Bayesian Non-parametric learning algorithms and build high-level task planning algorithms. The demonstration results confirm that using Bayesian non-parametric learning can help to identify the number of human states directly from the data such as the motion of human hands and it also gives the probabilistic description of each human state (intentions) which ensure robots could infer the human intention correctly and enable the robot to behave collaboratively with human partners. The student also used Faster Region-Based Convolutional Neural Network to classify and localize objects. The whole project shows that the framework we proposed gives a more natural and efficient way to design the communication between human and robots through intention inferencing and high-level decision making.

Integrated Task and Motion Planning for Robotic Systems       

Principal Investigator: Professor Lin

AWaRE REU Researcher: Marina Malone, Loyola University

Project Description: Intelligent physical systems capable of interpreting, planning, and executing high-level tasks requires reasoning over discrete actions and continuous motions, and that poses unique computational challenges. A recent trend in solving these challenges is integrated task and motion planning (ITMP), which proposes to synthesize both discrete task plans and continuous motion trajectories for mobile robots simultaneously. Successful intelligent autonomous robots are able to perform ITMP in uncertain environments, and that requires the ability to observe and act upon its environment to develop a plan towards accomplishing some task. Our basic idea is to use a camera for perception and integrating perception in mission and motion planning. This REU project is mainly about developing a demonstration in unmanned ground vehicle robots by combining perception and learning models with ITMP. We aim to develop a control system in which a robot receives a high-level specification on the continuous robot and object state spaces, but with unreliable prior knowledge.

Finding: In this project, the undergraduate student developed a grasping motion planning for unreliable object position using Kalman Filter, Linear Quadratic Regulator (LQR), and our novel ITMP algorithm, iterative deepening Signal Temporal Logic (idSTL). We use a monocular camera and OpenCV toolbox to estimate the object pose. The novelty on these control systems is that we integrate perception, planning, and control; thus, we can gradually increase the uncertainty of the object’s location by giving unreliable locations of the object to the robot and later obstructing it from the robot’s initial view. This would enable us to evaluate the effectiveness of the robot planning and action taking to find more information about the object it is tasked to find.

Wireless Communications Interface and Services for Drone Swarms           

Principal Investigator: Professor Laneman

AWaRE REU Researcher: Bob Schenck, Valparaiso University

Project Description: One of the things that makes drones so popular is that a user can communicate with the drone remotely. These communications send information, such as position or battery level, and commands, which instruct the drone in its movements. Drones become dependent on these communications in order to maintain a safe environment for use. By adding an onboard computing and wireless platform to the drone, we hope to advance and expand upon the communication framework available to the drone. This will include several wireless interfaces. A 4G LTE cellular connection will be established for the drone to communicate to the cloud and ultimately establish drone-to-ground communications. Alongside that, an infrastructure less Wifi network is configured on the computer for drone-to-drone communications. The onboard computer also acts as a communications manager for the drone’s wireless interfaces. This allows the drone to monitor the strength of each wireless network and controls the messages broadcast on each. These interfaces and the data we collect from them will allow us to better operate drone swarms and begin implementation of detect-avoid protocols. We aim to conduct tests of the network to examine the strength, speed, range, and other parameters to each medium of communication.

Finding: Our goal for the AWaRE REU program was to configure a flexible wireless communications manager with multiple interfaces (LTE, WiFi) that would enable both drone-to-ground and drone-to-drone communications experiments. The REU student investigated options for the LTE interface on a Raspberry Pi 3B embedded computer, and prototyped a solution based upon a Verizon LTE USB dongle for drone-to-ground communication in collaboration with a staff software engineer. The team also established a UDP broadcast network over WiFi for drone-to-drone communications. Preliminary measurements were collected on signal quality and packet latency as a function of range. We are expanding upon these measurements in order to prototype a distributed detect-and-avoid protocol in collaboration with our industry partner, InterDigital, and planning to publish the results next Spring. More generally, the software engineer will continue to evolve the control messaging and data streaming software architecture to set the stage for follow-on REU projects.

Passive, Crowd-Sourced WiFi Characterization         

Principal Investigator: Professor Striegel

AWaRE REU Researcher: Spencer Spitz, Colgate University

Project Description: Although WiFi and cellular are nearly everywhere, the performance of said wireless technologies tends to vary dramatically both in time and space.  While many of us have relied on Speedtest or other tools to help assess just how poor the nearby wireless performance tends to be, the challenge is that tools like Speedtest and others tend to be slow and take significant amounts of bandwidth to run.  We have created a recent set of tools called Fast Mobile Network Characterization that lets us do equivalent tests roughly 100x faster and with 10-100x less bandwidth with approaches that both work by actively probing the network and approaches that are completely passive.  While we have largely demonstrated many of the tools in the lab, the next step is to explore how well the tools translate out into the real world.  

Finding: In the technology-driven world of today, WiFi is utilized by billions of people across the globe all the time. So, it is crucial that the WiFi is fast and trustworthy, but bad signals and roaming connections create problems for users. How are we supposed to know where to focus our attention to fix the connectivity issues that arise? The undergraduate REU project focused on developing a proposed solution via an application that generates a lightweight “health assessment” of the WiFi signals in the area.

The developed Android application runs in the background to periodically connect to the web and gather network data via well-structured web requests. With implementations for local and online storage, users may view any data collected in the past as well as data collected by other users. This information is then used to make graphs with visualization as well via Google maps that display the variations of WiFi strength in a creative way. The program runs as a service, gathering network statistics to address connectivity concerns and in turn help to make the world more connected.

Programmable and Reconfigurable Millimeter-Wave Circuits and Antennas  

Principal Investigator: Professor Chisum 

AWaRE REU Researcher: Tristen Lewandowski, University of Notre Dame 

Project Description: As the frequency of distributed microwave circuits corresponds to their physical geometry, there is a need for reconfigurable radios and antennas that operate over a wide range of the electromagnetic spectrum. Currently, advanced RF ICs enable extremely wideband operation, but lack corresponding antennas and other distributed structures. One way to achieve reconfigurability in such distributed structures is with spatial patterning of phase change materials such as vanadium dioxide, or VO2— when cold, it is an insulator; when heated, it is a conductor. However, a disadvantage of the material is that the conductivity of VO2is 105 S/m, two orders of magnitude less than that of most metals, 107 S/m, meaning that VO2 alone dissipates much more energy than metal. The goal of this project is reconfigurable distributed circuits using pixelized metal inclusions on a VO2 layer with a control mechanism of either spatial UV switching or localized thermal switching.

In preliminary measurements, pure VO2 CPW transmission lines with metallic launches resulted in extremely lossy lines. In a study of loss and isolation, a coplanar waveguide including unit cells of aluminum and VO2 was varied in VO2 thickness, unit-cell gap, CPW cross-section (trace width), and unit-cell size. In the transmission line simulations, aluminum launches were included around the unit-cells, and loss and isolation per unit-cell were extracted. Three different simulators were compared (ADS Momentum, ADS TLIN, and Ansys HFSS) in an attempt to show agreement across software and gain confidence in the model and measured values. Simulation results from parametric sweeps showed ADS Momentum (MoM) and ADS TLIN (Circuit) in close agreement, while HFSS predicts similar trends across frequency but with a higher loss. The approach is promising as results show a sufficiently low loss for a distributed circuit of length λ, but it requires care in balancing loss and isolation. High-quality VO2 films at greater thicknesses are necessary, and unit-cell gaps should be small (≤200nm). Loss can be further reduced by trading off programmable resolution by using larger unit-cells. The next steps of this project involve fabricating the lines and comparing real measurements to simulations.

Finding: Over the course of the AWaRE REU we successfully measured 1D (linear) programmable transmissions line test structures to confirm the loss and isolation of such lines. We developed three independent simulation models all with significant agreement to the measurements. Models include full-wave electromagnetic (Finite element and Method of Moments), distributed circuit modes (transmissions lines), and lumped element circuit models. The circuit models enable rapid design iteration while the electromagnetic simulations enable high-fidelity simulations.

RadioHound: A Low-Cost Spectrum Sensor           

Principal Investigator: Professor Hochwald

AWaRE REU Researcher: Chloe Crusan, Bethel College 

Project Description: RadioHound, an ongoing project at Notre Dame Wireless Institute, is the development of low-cost, portable spectrum measurement sensors capable of tuning over a wide range of frequencies commonly used by everything from cellular phones to wireless local area networks, to radios and televisions. One goal is to distribute these sensors over a wide geographical area and thereby crowd-source the real-time measurements to create a “heat-map” of spectrum usage over the area and across frequency. Such a map would be used, for example, to determine where spectrum congestion is dense. We are in the third year of this project.

The project has many hardware and software components and opportunities for students to contribute, depending on their technical software and hardware maturities and skillsets. Basic hardware and laboratory capabilities, and knowledge of C, Python, and networking are a plus, but not required.

In particular, we have openings for two opportunities: (1) laboratory measurement help with the experimental verification of heat-maps that are generated by these sensors. Hence, knowledge of laboratory equipment and practices is advantageous; (2) web-application software development to help with displaying and controlling various aspects of the RadioHound system. Hence, knowledge of web development is advantageous.

Finding: Spectrum mapping is a form of signal processing that creates a visual representation of the presence of a signal across a geographical region. The U.S. Army, Google, and Amazon have a use for the graphical data our sensors can provide. Mapping can be used to track walkie-talkie usage or to map which frequencies are being used for Wi-Fi, TV, radio, or personal communication. When computing the spectral density estimation by way of the Fast Fourier transform (FFT), the signal in the time domain transforms to the frequency domain. After transformation, there is an issue with proper scaling of amplitudes to the final graph. To rectify this, I used applied mathematics of the Welch periodogram method to correct for the amplitude.