(a) SmartMart: IoT-based In-store Mapping for Mobile Devices (Dr. Byron J. Gao)

Quite often, when shopping in a supermarket (e.g. Walmart), shoppers are frustrated at locating the items on the shopping list and no assistance is available. On the other hand, retailers lose about 20% of sales as a result. The objective of this project is to leverage Internet of Things (IoT) technology to make store items "smart" so that they can automatically register and update their location information, allowing shoppers to search, locate, and map them on the store floor plan using mobile devices. Advanced features include recommending routes, computing shortest paths for multiple items on the shopping list, and facilitating exploratory search and personalized search capacities.

(b) Flexible IoT Management Platform (Dr. Anne Ngu)

While Internet of Things (IoT) offers numerous exciting potentials and opportunities, it remains challenging how to effectively manage things to achieve seamless integration of the physical world and the virtual one. In this project, we investigate  a context-aware IoT management framework that smoothly integrates IoT devices from multiple vendors, socialize them through Twitter, Facebook and comprehensively manage them in an effective and user-friendly way. We plan to design and implement a variety of IoT applications such as smart home, smart health, and smart cities prototypes to showcase the context-aware IoT framework. We also plan to provide a workbench with a set of tools, supporting streamlined deployment of such prototypes and their convenient maintenance, development, and extension.

(c) Amazon Picking Challenge (Dr. Yijuan Lu)

Amazon Picking Challenge is a competition to challenge entrants to build their own robot hardware and software that can attempt simplified versions of the general task of picking products from shelves. The robots will be presented with a stationary lightly populated inventory shelf and be asked to pick a subset of the products and put them on a table. The challenge combines object recognition, pose recognition, grasp planning, compliant manipulation, motion planning, task planning, task execution, and error detection and recovery. The robots will be scored by how many items are picked in a fixed amount of time, with $26,000 in prizes In this project, students will focus on the object recognition and will explore deep learning technology to recognize the products on the shelves fast and accurately. A product recognition system will be built up and will be embedded into our Robot system. We wish to construct and bring our own systems to the Amazon Picking Challenge 2016. More information about Amazon Picking Challenge and competition videos can be found at http://amazonpickingchallenge.org

(d) Design and Verification of IoT Systems (Dr. Rodion Podorozhny)

Increasingly, the design of modern reasoning control systems for teams of reconnaissance and combat unmanned vehicles rely on ad-hoc, self-healing networks. The nature of such systems require specialized methods of design and verification to automate and shorten the development time and verify the properties specific to these systems to increase their software reliability.

This project will focus on distributed scheduling as an algorithmic approach for the reasoning component of the control system. Such an approach has certain benefits. For instance, it can allow several robots to coordinate and dynamically adjust their actions to create, as a team, future situations necessary to achieve a certain common goal.

The project will have three main aspects: development and evaluation of the distributed reasoning component for a cyber-physical system with communication via internet, a control system of an individual robot and a hybrid verification system for a scheduling based reasoning component.

The perceived novelty of the suggested hybrid verification system is its ability to verify that scheduling commitments of robots are indeed fulfilled in the future and verify thread safety of critical shared data structures: hierarchical task networks and schedules.

To accomplish these tasks we will use a simulation, a team of two-track rovers and electrically powered model planes. The control system will use Android-based cellphones as embedded devices on board rovers and model planes that will communicate via Wi-Fi.

(e) Large-scale gene network analysis  (Dr. Habil Zare)

In this project, students will learn about some applications of machine learning techniques in bioinformatics. It is a part of a larger project that aims in deriving useful information from large biological datasets including gene expression profiles and DNA-methylation data. 

In a cell, genes can be turned on or off depending on the biological status such as being normal or cancerous. The level of expression of a gene in a body tissue, which can be measured by clinical tests, shows the ratio of cells in which the gene is on. The level of expression of some specific genes can be associated with the disease type. For instance, low expression of androgen receptor (AR) gene is considered as a risk factor for breast cancer because when this gene is off, the tumor can grow uncontrollably. 

Gene expression values are often used for diagnosis and predicting the prognosis of the disease. However, in most complicated diseases such as cancers, hundred of genes are affected. Therefore, sophisticated computational models are required to analyze the expression of all genes together and identify the biological pathways that contribute to the disease. Gene network analysis is a well-established approach to study thousands of genes in one single network and discover the underlying biological phenomena. 

(f) IoT for Human Physiological Data Analysis (Dr. Vangelis Metsis)

Students working with Dr. Metsis will be involved in projects to develop and test methods and tools for acquisition and analysis of behavioral and physiological data, coming from human subjects. The data will be collected using wearable and remote sensing devices, and they will be analyzed using methods coming from the areas of machine learning, data mining and signal processing. Extracting useful information from human physiology and behavior can have multiple applications, from personalized healthcare to fitness and athletic performance. Lately, a number of private initiatives have been moving towards that direction, e.g. Fitbit and Apple smart watches, etc. However, these devices are limited in the type of data that can collect and signals that can analyze. Our effort will expand the possibilities of human-centered data analysis though the use of state-of-the-art research methods and tools. Students will have the opportunity to come in contact with these research methods and acquire new skills that can be directly applied to their future professional career.

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