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Proposed Projects for Winter 2015

If you are interested in pursuing a 4080 project in Summer 2015, please see this course webpage.




Concurrent Data Structures

Supervisor: Eric Ruppert

Required Background: EECS2031 and general EECS4080 prerequisites

Desirable Background: EECS3221

A traditional data structure is designed so that one operation can be performed on it at a time. This is no longer sufficient for the multicore architectures that have become prevalent in the past few years. A concurrent data structure is designed so that many threads can access it simultaneously. This requires some care in ensuring that concurrent operations do not interfere with one another.

The goal of this project is to implement concurrent data structures in C so that performance testing can be carried out on them. In particular, we would like to make use of Intel's Manycore Testing Lab (see https://software.intel.com/en-us/articles/intel-mtl-faq-1) to look at throughput and scalability of the data structures when large numbers of threads access them concurrently. Ultimately, we would also like to examine the possibility of designing special-purpose hardware to make concurrent data structures run faster.




Dynamic Interface Detection and Control Project

Supervisor: Michael Jenkin

Contrary to most industries, fine chemical manufacturing is dominated by batch production methods. Increasing economic, environmental and safety pressures are motivating a turn towards continuous synthesis. Rather than making products in one big flask, continuous synthesis involves performing chemical reactions by flowing reagents through a tube. Working in this way provides more control over the reaction parameters leading to increases in product quality, and process efficiency and safety. The flow chemistry industry for fine chemical production is a relatively new but burgeoning field with a projected market capacity of billions of dollars by 2018.

Extraction of the reaction mixture for purification and/or further processing is an important step in chemical manufacturing. This is a relatively straightforward operation in batch production, but offers several challenges for flowing processes. In order to facilitate continuous liquid extraction we require a sophisticated control system. This project involves designing, constructing and evaluating a pertinent practical problem in the field.

A key step in the process takes place in a clear tube that is mounted vertically. The tube contains two fluids with a boundary between them. During the process material flows into and out of the tube from the top and the bottom. Chemical reactions take place within this tube and It is essential that the position of the boundary be monitored as its position in the tube is used to control the flow of materials into the tube.

One way of solving this problem is to float a marker at the boundary between the two liquids and to monitor this boundary using a video camera. Although this approach solves the problem, it requires the introduction of a specific float within the tube. Can we build a system that monitors the boundary without resorting to the use of an artificial float?

Specific goals of the project include:

- Develop a computer vision system that can detect and monitor the interface between two miscible fluids of different density.

- Evaluate the performance of the system over a range of different (and typical) fluids

- Explore the use of different illuminant/filter choices to simplify the task for specific fluid combinations.

The successful candidate(s) will have the experience of working with a diverse group of scientists and engineers toward the design and implementation of an automated liquid extraction device with applications across many industries. Upon successful prototyping, you will be able to interact with professionals in high-throughput manufacturing and system integration. Based on project success, you may be invited to join the MACOS(TM) team for implementation and process validation, which may involve opportunities in graduate school. You will have the opportunity to interact with the broad audience of MACOS(TM) technology including governmental regulatory agencies and industrial partners. This project will give you a great opportunity to apply your engineering expertise and gain experience in process implementation and technology transfer.

For further information please contact,

Michael Jenkin (jenkin@cse.yorku.ca) or Michal Organ (organ@yorku.ca)




Attentive Sensing for Better Two-Way Communication in Remote Learning Environments

Supervisor: James Elder

Required Background: General CSE408x prerequisites, good programming skills, good math skills, knowledge of C and MATLAB programming languages

One of the challenges in remote learning is to allow students to communicate effectively with the lecturer. For example, when a student asks a question, communication will be more effective if the instructor has a zoomed view of the student’s face, so that s/he can interpret expressions etc. The goal of this project is to apply attentive sensing technology (www.elderlab.yorku.ca) to this problem. This technology is able to monitor a large environment such as a classroom and direct a high-resolution ‘attentive’ sensor to events of interest. In particular, working with a senior graduate student or postdoctoral fellow, the successful applicant will:

  1. Study the problem of detecting hand-raises in the preattentive sensor stream
  2. Implement algorithms for detecting hand-raises based upon this investigation
  3. Evaluate these algorithms in a real-classroom setting, using proprietary attentive sensing technology




Attentive Sensing for Sport Video Recording Markets

Supervisor: James Elder

Required Background: Good programming skills; Good math skills; Knowledge of C and MATLAB programming languages

The goal of this project is to modify York University’s patented attentive sensor technology to the sport video recording market. Specific application domains under investigation include skiing, indoor BMX parks, and horse tracks. The general problem is to use attentive sensing technology (www.elderlab.yorku.ca) to visually detect and track multiple moving agents (e.g., skiers, riders, horses) and to select specific agents for active high-resolution smooth pursuit. The student will work with senior graduate students, postdoctoral fellows and research scientists to help modify the attentive sensing technology to operate in these domains. Specific tasks include: 1. Ground-truth available datasets 2. Evaluate current attentive algorithms on these datasets 3. Modify these algorithms to improve performance on these datasets



Mining Software Repositories Data

Supervisor: Zhen Ming (Jack) Jiang (zmjiang at cse dot yorku dot ca)

Required Background: Good programming skills in Java; Good analytical and communication skills; Knowledge in AI and statistics; Interested in large scale software analysis

Short Description: Software engineering data (e.g., source code repositories and bug databases) contains a wealth of information about a project's status and history. The research on Mining Software Repositories (MSR) aims to transform the data from static record-keeping repositories into knowledge, which can guide the software development process. For example, one can derive correct API usage patterns and flag anomalous (and potentially buggy) API usages by mining the source code across many projects in GitHub and Google Code. In this project, the student(s) will research and develop an efficient infrastructure, where MSR researchers and practitioners can share and analyze such data.




Model-based Design and Development of Embedded Systems with Code Generation Tools

Supervisor: Jia Xu

Required Background: At least a B+ in Embedded Systems (CSE3215), MATLAB, C programming skills, solid experience in using a microcontroller such as Arduino.

Project Description:

Model-based design with code generation tools can be used for simulation, rapid prototyping, and hardware-in-the-loop testing of embedded systems. This project explores model-based design and development of embedded systems on various hardware platforms with code generation tools. The selected student will develop and test embedded systems using model-based design and code generation tools such as MathWorks MATLAB /Simulink Coder.




C2000 Concerto Microcontrollers

Supervisor: Jia Xu

Required Background: At least a B+ in Embedded Systems (CSE3215), strong C programming skills, solid knowledge of microcontrollers

Description: The C2000 Concerto family of microcontrollers combines two cores on a single-chip with on-chip low latency interprocessor communication between the two cores: a C28x 32-bit control core for real-time control with faster/more loops and small sampling window; and an ARM 32-bit Cortex-M3 host core for communications and general purpose. The selected student will evaluate the capabilities of the C2000 Concerto family of microcontrollers through testing and investigating open source software for real-time control applications that runs on C2000 Concerto Microcontrollers.




Real-Time Bidding Platform

Supervisor: Jia Xu

Required Background: At least a B+ in Operating System Fundamentals (CSE3221), strong Ubuntu/Linux, C++ programming, GCC, TCP/IP skills

Description: Real-time bidding (RTB) is a new method of selling and buying online display advertising in real-time one ad impression at a time. Once a bid request has been sent out, all bids must be received within a strict deadline - generally under 100 milliseconds, including network latency. This project explores RTBkit, an open source SDK allowing developers to create customized real time ad bidding systems (for Media Buyers/Bidders).




More project proposals may be added here in the first week of the winter term.




Last modified:
2015/04/13 21:58