The following is a list of projects that will be associated with this site. For those intending to apply to the program, it is important to specify a subset of these projects in order of preference in the application form you will fill.

Project 1: Crowdsourced Indoor and Outdoor Mapping
Faculty Mentor: Dr. Vinod Namboodiri
Many emerging applications for indoor environments such as navigation, real-time proximity updates, marketing feeds require high-quality indoor maps. Mapping techniques used outdoors such as imagery from cars driven on streets do not work in indoor environments. Indoor mapping thus tends to typically rely on architectural drawings and individuals or robots moving through the indoor spaces. Crowdsourcing using people moving around the spaces that need to be mapped can be an effective way to create high quality maps. With such efforts only being employed recently, there aren’t enough published results on parameters such as number of participants typically needed to cover an indoor space with adequate coverage, the best ways to incentivize such users to participate and what characteristics of smartphones make them more useful for this exercise, and how best to stitch together user collected imagery to create maps. In this project, REU participants will work on various aspects of creating indoor maps from images collected from networked smartphones in a crowdsourced fashion. They will design and test incentivization schemes for participants, design algorithms to stitch together images to create indoor maps, and study minimum smartphone characteristics/resources and communication protocols required for participation in this crowdsourced activity. Of particular interest would be the application of this research for indoor navigation for the blind and visually impaired. Given that some outdoor areas also lack adequate mapping, this project can be extended to those areas as well.
Qualifications: Strong programming skills in Java/C++, and prior experience using MATLAB.

Project 2: Comparison of Various Bluetooth Low Energy (BLE) Platforms for Indoor Navigation
Faculty Mentor: Dr. Vinod Namboodiri
Bluetooth Low Energy (BLE) beacons are small battery-powered devices with a Bluetooth wireless radio that can send periodic notifications about their presence. Such beacon devices have been recently used for broadcasting advertisements and other real-time updates to smartphones of people passing by. These also have found use in assisting with applications such as indoor navigation. For many of these applications, there are many BLE platforms from which to choose. Each BLE platform varies in its interaction between the beacon, a backend server, and the ease with they can be configured to interact with the end user. REU students will learn to imagine and build novel applications with BLE beacons. Students will study one or more of the following aspects as part of this project: (i) accuracy and timeliness of beacon notifications for specific applications and best practices in beacon placement, (ii) optimal transmission power and beacon notification intervals, and (iii) ease of building applications with various BLE platforms.
Qualifications: Strong programming skills in Java/C++, and prior experience with application development for Android OS or iOS.

Project 3: Secure Cloud Computing for Big Data Computations

Faculty Mentor: Dr. Sergio Salinas
Modern organizations collect huge amounts of data that have great potential to advance scientific and engineering knowledge, and accelerate innovation. For example, biomedical researchers can develop novel treatments by finding patterns in large-scale genomic databases; power engineers can perform real-time analysis like state estimation and power optimization based on the enormous amount of data collected from the electric grid. However, users face a formidable challenge in trying to analyze such huge amounts of data in a timely and cost-effective way. Recently, cloud computing has been proposed as an efficient, and cost-effective way for resource-limited users to analyze large-scale data sets. Although many users recognize the advantages of cloud computing, many of them are reluctant to adopt it due to privacy concerns. Specifically, in many cases, users’ data are very sensitive and should be kept secret from the cloud for ethical, security, or legal reasons. Therefore, to enable scientists and engineers to revolutionize their fields through the analysis of large-scale data, outsourcing tools must be designed that preserve their data privacy. Two fundamental mathematical problems frequently appear in the analysis of large-scale data: linear systems of equations (LSEs) of the form Ax = b, and optimization problems with quadratic objective functions and linear constraints, i.e., quadratic programs (QPs). Some works on secure outsourcing of large-scale computations to the cloud have proposed traditional cryptographic techniques, such as fully homomorphic encryption, to protect the user’s data and analyze them at the cloud. Although this approach offers a strong data privacy guarantee, it requires both the user and the cloud to perform a significant amount of overhead computations, which are impractical for large-scale data sets. In this project, REU students will implement secure outsourcing methods for both LSEs and QPs. Specifically, the students will develop software to implement the algorithms using Hadoop on an Amazon Elastic Compute Cloud (EC2) cluster as the cloud, and a PC as the user. The students will analyze the computational and communications performance of the algorithms under real-world large-scale data sets.
Qualifications: Strong programming skills in C++/Java; familiarity with Hadoop, Linear Algebra, and Network Security principles will be useful.

Project 4: Secure Advanced Manufacturing
Faculty Mentor: Dr. Sergio Salinas
Manufacturing plays a critical role for US national and economic security. Essential services such as health-care and transportation depend on the reliable functioning of manufacturing. For example, hospitals need a constant supply of manufactured pharmaceuticals to provide timely care to patients, and aerospace companies require high-quality parts to build safe aircraft. Moreover, disruptions to manufacturing create shortages of essential goods that significantly increase their prices. To improve the competitiveness of manufacturing in the U.S., industry, government, and academia have proposed advanced manufacturing, which encourages an aggressive adoption of information technologies (IT). By increasing connectivity between machines and computers, new tools and services are enabled. For example, companies can coordinate complex global logistics by remotely operating many factories over the Internet, and operators can reduce manufacturing costs by monitoring and controlling machines in real-time. Although advanced manufacturing offers many benefits, it suffers from cyber vulnerabilities, which can be exploited by sophisticated adversaries, e.g., criminal organizations or nation-states, to halt production, inject defects into manufactured goods, or steal intellectual property. In fact, since manufacturing systems are a high-value target for adversaries, they are constantly under attack. For this reason, it is important that technologies are developed to ensure the reliable operation of manufacturing systems in the presence of sophisticated cyber adversaries. In this project, the REU students will work with graduate students in development of software to launch attacks against an advanced manufacturing testbed. Specifically, the students will implement eavesdropping, denial of service, and defect injection attacks against the testbed’s components, which include 3-D printers, state-of the art networking devices, and a cloud computing service provider that remotely controls the manufacturing process. Besides, they will analyze the performance of the testbed under the attacks.
Qualifications: strong programming skills in C++/Java; familiarity with computer networking and network security principles will be a plus.

Project 5: Secret-key generation with information-theoretic security guarantees
Faculty Mentor: Dr. Remi Chou

The omnipresence of communication networks and the increasing number of connected users and devices create challenges related to information security and data privacy. Most designs of current security solutions are attack-specific and rely on the assumption of computationally limited opponents. Consequently, systems constantly need to be updated to keep up with the increasing computational power offered by computers, as well as, always more creative attacks from hackers. Worse, the approach leaves communication vulnerable against unanticipated strategies of attacks. A paradigm called information-theoretic security aims at obtaining security guarantees that hold for any possible strategy of attack performed by an opponent with unlimited computational power. While several theoretical studies support this paradigm, the development of practical implementations of information-theoretic secure methods is still in its infancy. The goal of this project is the implementation and performance evaluation of information-theoretic methods for a fundamental security primitive in wireless communication networks, namely secret-key generation. The REU students will work on one or more of the following tasks (i) build a testbed made of Universal Software Radio Peripherals (USRPs) to collect measurements, (ii) implement a secret-key generation protocol that relies on a recently invented family of error-correcting codes called polar codes, (iii) evaluate the performance of the protocol developed in (ii) with the measurements obtained in (i).

Qualifications: Strong programming skills in C++, prior experience using MATLAB and Linux, and basic knowledge of digital signal processing. Familiarity with probability theory will also be useful.

Project 6: Wearable Motion Understanding Using Body Sensor Networks

Faculty Mentor: Dr. Hongsheng He

Detection of motion deficits is important for the prevention and diagnose of mobility-related diseases, such as Parkinson's disease and strokes, as well as the monitoring of rehabilitation progress. The project designs a nonintrusive body-movement tracking system using a network of multiple inertial measurement units (IMU), which are attached to human limbs. A learning based method was proposed to understand motion pattens by discovering motion primitives in body motion trajectories. The hypothesis of the research project is that the motion deficits can be discovered and identified by unique motion primitives. The proposed method does not rely on exact placements of wearable inertial sensors and strict calibration, facilitating long-time motion tracking.

REU students will learn and develop sensor fusion and machine learning algorithms for wearable computing. Students will study one or more of the following aspects as part of this project: (i) dynamics measurement using inertial sensors; (ii) body motion tracing using wearable sensor networks; and (iii) wearable motion understanding.

Qualifications: Mathematical skills in linear algebra and programming skills in Python preferred.

Project 7: Natural Human-Robot Interaction Using Natural Languages

Faculty Mentor: Dr. Hongsheng He

One of the critical problems to address in intelligent robotics is a trustworthy architecture that covers the sensing, decision making, and human-machine interaction. The project aims to investigate a robust analytic methodology for modeling, analysis and verification of using human natural language in human-machine interaction and knowledge representation. The key to this goal is to construct a human-machine “common language” that both the human and the machine can understand and share knowledge in the form of rigid formal logic, which mitigates ambiguities common to natural language communication.

REU students will learn and develop robotics and natural-language-processing algorithms for human-robot interaction. Students will have chances to work with an Nao humanoid robot and a Sawyer robot. Students will study one or more of the following aspects as part of this project: (i) natural language processing of human commands; (ii) parsing of human commands into robot actions; and (iii) command grounding through learning.

Qualifications: Mathematical skills linear algebra and programming skills in Python preferred.