CSE4080

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====== Ongoing projects ======

====== Web-based digital signage system ======

**Student**:  Ankit Agrawal

**Supervisor**:  John Amanatides

__Description__

Build a web-based digital signage system for Bethune College. Some of the technologies that you will be expected to learn/use include Javascript, JQuery, HTML, CSS, and ical/CalDAV. We expect to go open source with this software so that others can use it as well. The deliverables will also include an analysis of what it takes to scale this type of signage campus wide, including provisions for campus alerts/emergency announcements and a prototype for such a system.

====== Tandem repeat detection using spectral methods ======

**Student**:  Mohamad Alsabbagh

**Supervisor**:  Suprakash Datta

__Description__

DNA sequences of organisms have many repeated substrings. These are called repeats in Biology, and include both exact as well as approximate repeats. Repeats are of two main types – interspersed repeats (which are spread across a genome) and tandem repeats, which occur next to each other. Tandem repeats play important roles in gene regulation and are also used as markers that have several important uses, including human identity testing.  

Finding tandem repeats is an important problem in Computational Biology. The techniques that have been proposed for it fall into two classes – string matching algorithms and signal processing techniques. In this project, we will explore fast, accurate algorithms for detecting tandem repeats and evaluate the outputs of the algorithms studied by comparing their outputs with those of available packages, including mreps (http://bioinfo.lifl.fr/mreps/), SRF (http://www.imtech.res.in/raghava/srf/) and TRF (http://tandem.bu.edu/trf/trf.html). 

The student will implement the spectral algorithms used in [1] (one based on Fourier Transforms and one on autoregressive models). He will then make changes suggested by the supervisor, and evaluate the effect of the modifications.
Throughout the course, the student is required to maintain a course Web site to report any progress and details about the project.

====== Entropy-based concept drift detection ======

**Student**:  Junjie Guo

**Supervisor**:  Aijun An

__Description__

The student will design and implement an entropy-based concept drift detection method. The method will be an improved version of the entropy-based method described by Vorburger and Bernstein, which is likely to contain flaws. The student will first study the original method, identify problems, implement the method to verify the identified problems, implement a corrected version of the method and evaluate the corrected version on a number of data stream data sets. The student is also expected to improve the corrected version of the entropy-based drift detection method by using the bootstrapping technique to automatically determine the threshold used in the entropy-based method.

====== Computer Security Lab Evaluation ======

**Student**:  Yongcheol Kim

**Supervisor**:  Bil Tzerpos

__Description__

The project will require the student to evaluate a series of computer
secutiry lab exercises. The exercises will be prepared by the
supervisor. The student will evaluate the clarity with which the lab
exercises have been presented, whether the necessary background has been
sufficiently covered, and whether it is feasible to complete the lab
exercise in the space of one week.

A similar evaluation process will take place for the term project. This
will also be provided by the supervisor.

====== Assistive Technology Software: Narratives for Information Delivery and Deployment ======

**Student**:  Sidrah Laldin

**Supervisor**:  Melanie Baljko

__Description__

Assistive technology software refers to a family of software packages and tools that are used by individuals who experience the effects of disability that arise from motor, linguistic, sensory or cognitive disorder or impairment.  Such software is almost always used in conjunction with specialized computer hardware and/or input devices.  For instance, an individual who has quadriplegia might use specialized text entry software in conjunction with a sip’n’puff switch  (a mouth-operated device that emulates two buttons).  

Assistive technology software falls into several categories: closed- vs open-source; free vs. proprietary software; commercially available (aka “payware”) vs shareware vs freeware. Assistive technology software is most often obtained by clinicians (Speech Language Pathologists, Occupational Therapists, Education Specialists).   If the procurement is being done under the Ontario Ministry of Health and Long-Term Care’s Assistive Devices Program (ADP), then the software is “prescribed” (in the medical sense of the word).

A current project underway in the Multimodal Mediated Communcation (MuMeC) Research Lab is the design best practices and the logistical framework for the deployment of assistive technology software that has already been developed.  This is a non-trivial project with not only technical and software aspects, but also commercialization and IP issues.

====== Simulation of a 6dof virtual reality tracker ======

**Student**:  Kapil Patel

**Supervisor**: Wolfgang Stuerzlinger

__Description__

Previous work by the supervisor resulted in a novel and highly accurate Virtual Reality tracking system that matches or exceeds the specifications of all competing systems. However, this system works only in 5 or 6-sided immersive display environment.

This project is the first step towards an adaptation of the technology for more general environments. In particular we target normal rooms and immersive displays with less than 5 screens. The technical work involves adapting the simulation software for the previous device to simulate a new design, and iteratively optimizing that design based on the results obtained.

====== Electronic voting system ======

**Student**:  Nauman Rana

**Supervisor**:  John Amanatides

__Description__

Build a stand-alone electronic voting system. It is to be a web-based, open source system that can have on-line elections of several thousand voters. Multiple elections can be going on at the same time. A web front-end for administrators is also required. the test system will run on a Mac OSX server, and be accessed via the web by both administrators and voters.

====== Cluster visualization using Multi-Core GPUs and CUDA ======

**Student**:  Roozbeh Takhayorie

**Supervisor**:  Suprakash Datta 

__Description__

The development of high-throughput experiments in Biology has made available huge amounts of data, and there is a pressing need for the development of analysis tools for them. In this work we focus on clustering and the visualization of clustered data. While techniques for cluster visualization exist, the computational costs involved result in large running times for large data sets. This project will investigate the use of parallel computing using modern graphics processors (GPU) in speeding up cluster data visualization.

There are many different high-dimensional datasets and many different clustering algorithms available today. While several analytical cluster evaluation methodologies exist, many experimental scientists like to evaluate cluster quality visually. This is standard practice in many fields of Biology, including Flow Cytometry. We would like to develop a visualization tool that takes two clusters input by the user and displays the clusters from the best possible viewpoint. A good candidate for the best possible viewpoint is one that separates the clusters as much as possible. 

The first responsibility of the student in this project is to learn to write programs in the CUDA architecture [1]. Then he will learn to use a few well-known libraries ported to CUDA, especially BLAS/LAPACK for linear algebra and SVM for machine learning. The student will then work with the supervisor to develop cluster visualization tools using CUDA. The project will use some ideas from the Ggobi package [2], but implement them to exploit the parallelism of GPUs. Since this project is quite ambitious, the emphasis will be on the first part – the development of basic tools using CUDA and the use of well-known libraries. 

The supervisor will provide the datasets and the clustering algorithms that can generate clustered data. The student will use them to demonstrate the output of his visualization tool.  

====== Estimating Registration Error ======

**Student**: Lalit Walia

**Supervisor**: Burton Ma

__Description__

A fundamental step in computer-assisted surgery is registration where the anatomy of the patient is matched to an image or model of the anatomy. For some types of orthopaedic procedures, registration is performed by digitizing the locations of points on the surface of a bone and matching the point locations to the surface of a model of the bone. Here, a surgeon uses a pointer that is tracked using an optical tracking system to measure registration point locations on a patient. A registration algorithm is used to compute the transformation that best matches the points to a model of the anatomy.

Virtual navigational information (such as where to drill or cut the bone) can be provided to the surgeon after the registration transformation has been established. Here, a surgeon is using a tracked surgical drill to drill a hole along a pre-operatively defined path. Notice that the surgeon looks at the virtual navigational information instead of the patient when performing this task.

Computer-assisted surgical navigation depends on having an accurate registration. If the estimated registration is inaccurate then the navigational information will also be inaccurate, which may lead to errors in the surgical procedure. It is of great interest to know the accuracy of the estimated registration.

Further details on the project can be found [[http://www.cse.yorku.ca/~burton/4080/4080.html|here]].

====== Programming Multi-Core GPUs with CUDA ======

**Student**: Huajian Wang

**Supervisor**: Franck van Breugel

__Description__

CUDA stands for "compute unified device architecture."  It is an architecture to program multicore graphical processing units (GPUs for short).  In the past, these GPUs were only used for graphics. However, CUDA allows us to use these GPUs for other types of computation. Since today's GPUs have hundreds of cores, algorithms can be parallelized and, hence, run often much faster.

The aim of this project is to get familiar with GPUs and to study how to program them.

More details can be found at: [[http://www.cse.yorku.ca/~franck/projects/cuda.html]]
(this link is only accessible from machines within the domain yorku.ca.) 

====== Low-Cost Three-Dimensional Face Scanning System ======

**Student**: Thomas Young

**Supervisor**:  James Elder

__Description__

Low-cost three-dimensional face-scanning systems have a large range of potential applications in security and retail markets.  Our laboratory at York University has recently developed a prototype face-scanning system that has the potential for very low-cost mass production.  This project involves the development of a second-stage prototype that is one-step closer to commercialization.

The project will involve systems design and development of a specialized real-time 3D face scanner.  A combination of hardware and software design will be required.  The student will work closely with graduate students and postdoctoral fellows at York University, as well as researchers at other institutions involved in the project.  The student will develop skills in both hardware and software design, as well as computer-vision techniques.

For more information on the laboratory: [[http://www.elderlab.yorku.ca]]