Summer Internship

2021 Research Projects

Project Pre-Requisites
Transmitter Identification with machine learning: Future radio spectrum management techniques will use dynamic information to allocate spectrum resources, rather than using a fixed number of bands. Transmitter identification is critical to accuracy gauge demand as well as enforce existing policies. In this project, you will quantify the efficacy of a variety of machine learning approaches to transmitter identification. You will collect training data from a variety of transmitter types in the orbit grid and compare the accuracy of different types of neural networks ability to identify the type and number of transmitters.
OS: Linux
Software: C++, Python
High speed training using binary neural networks: Training machine learning systems is currently very slow. Recent work has shown promise by using simpler representations of numbers than the commonly used floating point ones. In this project, you will create and measure neural networks which use only binary or integer numbers for both training and inference.
OS: Linux
Software: C/C++, Python
Distributed spectrum sensing and channel assignment: use a collection of software defined radios (SDRs) to detect spectrum occupancy and perform channel usage coordination among number of radio systems.
OS: Linux
Software: C/C++, Python
Characterizing 5G Leakage on Passive Weather Sensing Spectrum Bands: Perform RF interference characterization experiments at 23-24 GHz by using mmWave radios and RF measurement equipment
OS: Linux
Software: Matlab, Shell Scripting, C/C++
FPGA implementation of object detection: This project aims to study real-time object detection on the FPGA board. FPGA is a programmable chip that inherently supports parallel processing. Building object detection, which is a key component for autonomous driving and drone, on FPGA can provide task-flexible time-efficient energy-efficient solutions for practical object detection.
OS: Linux
Software: C/C++, VHDL/Verilog
Smart Intersection Situational Awareness: Use inputs from lidars, 2d and 3d cameras and other sensors to create a 3D point-cloud of the intersection area.
OS: Linux
Software: C/C++, Phyton
Educational Virtual Reality Escape Rooms: A game based on a set of virtual rooms in which players will have to solve a STEM related problem before being able to move to the next one – similar to the popular “escape rooms” experience. OS: Windows, Android
Software:C#,C/C++, Java
Real-time machine learning on the embedded devices: This project aims to study efficient algorithm to reduce the computational and storage cost of the state-of-the-art deep learning models to enable real-time energy-efficient deployment on the embedded devices, such as smartphone, microcontroller etc. OS: Android, Linux, Embedded programming
Software: C/C++,Java, Phyton
Self-Driving Vehicular Project: Assemble and train miniature autonomous vehicles to run in the miniature smart city environment. Design and implement self-driving algorithms using machine learning libraries in python. Design behavior that will allow the vehicles to react realistically to other cars and props in the smart city environment, and work with the testbed infrastructure to use external data from the intersection to improve performance. OS: Linux
Software:Python, C/C++
Tracking bee movements with machine learning: This project will investigate how the recent explosion of Radio Frequency (RF) signals in the environment can have biological impacts. Recent work has shown bird navigation is impacted by megahertz frequencies, the investigation of the impacts of possible RF pollution is a concern. Honeybees are one of the few species which have been shown to have electromagnetic detection mechanisms sensitive enough to navigate using the Earth’s magnetic field. In this project, you will develop software to quantify the bees’ motion vectors in a feeder. The motion vectors will be used in a Bayesian analysis to quantify the strength of their response to an RF field in the feeder, as compared with other factors which could influence their direction of motion. OS: Linux, Embedded programming
Software: C/C++, Python
Smart City Traffic Simulator: Work on virtual reality smart city environment using Unity. Connect VR environment to traffic simulation software to generate realistic car behavior in the smart city. Work with physical smart city environment to use real car behavior as input to the simulator. OS: Linux
Software: C/C++, Python
Measuring the proboscis extension reflex for radio frequency radiation: This project investigates possible biological impacts to radio frequency radiation. In this project, you will test if honeybees can associate an RF field with food. The PER reflex has been shown to be associated with odors, colors, and motion. Using a classical conditioning approach, this project will measure the bees’ proboscis extension reflex (PER) to RF fields. OS:
Software:
Quantifying the transformation of Information to Energy: Understanding the lower bounds of energy use in communications and computing is not well understood. In this project, you will build a software version of Maxwell’s demon, which converts information into energy. You will measure the demon’s effectiveness by modeling the relationship between the demon and the physical system as a discrete channel with noise, and measuring the energy extracted as a function of the conditional entropy.
OS:
Software:
Analyzing Social Distancing Based on Sensory Inputs: Use computer vision and other machine learning techniques to identify and measure the effects of social distancing policies on the population in an urban setting
OS: Linux
Software: Python, C/C++
Using Machine Learning for IoT: Develop a framework for building intelligent environment applications through ambient sensing and machine learning
OS: Linux
Software: Python, C/C++
AR Memorial: Develop augmented reality obelisk/memorial that can grow in all dimensions allowing users can contribute paintings, photos, videos or notes and leave them for other visitors
OS: Windows
Software: C#
Adversarial Machine Learning on Voice Assistant Systems and IoT Devices: Driven by the advancement of machine learning, voice-user interfaces in voice assistant systems (e.g., Alexa, Google Assistant, Siri) and IoT devices have widely incorporated machine learning models as building blocks for speech recognition and speaker identification. However, the machine learning models are inherently vulnerable to adversarial examples, which are well-designed perturbations leading to wrong predictions while being imperceptible to human beings. This project aims to study the vulnerability of voice-user interfaces under such adversarial attacks by developing audio adversarial examples.
OS: Linux/Windows
Software: Python
Mobile User Authentication via Deep Learning: User authentication is a critical process in many mobile and IoT applications (e.g., unlocking devices, mobile payment). This project aims to achieve user authentication by applying deep learning techniques to the wireless signals generated by mobile/IoT devices. The insight is that the user’s unique behaviors can lead to distinctive patterns in the wireless signals. By using wireless signals as input, we can develop deep learning techniques to extract unique behavioral biometrics to perform user authentication in real-time.
OS: Linux/Windows
Software: C/C++, Python

2022 Summer Internship Dates

Applications Due: April 3
Notifications: April 15 (postponed to April 19 due to holidays)
Internship Starts: May 31
Internship Ends: Aug 5

Project Pages

Past Research Topics