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Proposed Projects for Summer 2016

Immersive Virtual Worlds

Supervisor: Robert Allison

Required Background: General CSE408x prerequisites, good programming skills, previous work with computer graphics would be helpful

We have a new and unique fully immersive virtual environment at York. The student would develop interactive 3D virtual worlds to study self motion perception and human computer interaction in a virtual world. In particular, working with a senior graduate student or postdoctoral fellow, the successful applicant would model 3D environments, render them in a virtual reality display and develop/implement interaction methods to control and interact with the simulation. Artistic background or skill would be an asset but is not required.

Virtual Walking Devices

Supervisor: Robert Allison

Required Background: General CSE408x prerequisites, good programming and mechanical skills

Simulating effective walking in an immersive virtual environment is challenging. Working with a senior graduate student, the successful applicant would help to develop a circular treadmill interface to support virtual walking metaphors.

Adapting a 3D Printer for Paste Extrusion

Supervisor: James Smith

3D printers are an enabling technology for engineering design. However most low- to medium-end printers can only extrude plastics through a hot-end extrusion system. A local company has developed a nozzle and pump system for pastes that can be integrated with existing printers. A student group is sought to design a mounting bracket for an existing TAZ 4 printer that can be used to augment its ABS extruder with a Discov3ry paste extruder. The bracket is to be printed with the TAZ and the design is to be released publicly to the open-source community. Sample prints, calibration routines and possible modifications to the printer's software may be required. Experience with programming and hardware are important prerequisites.

For more information, please send email to

Required Background: General CSE408x prerequisites

Peer-to-Peer agent based applications in smart power grids

Supervisor: Hany Farag

Multi-agent systems have been mentioned recently as a potential technology for several operational control objectives in smart power grids. The multi-agents technology allows the rapid and detailed creation of a system model and creates a robust framework for distributed control. The distributed control structure consists of components called control agents. These control agents try, through communication and negotiation with other control agents, to: 1) determine the current state of the system and/or subsystems and 2) make decisions (set their local actuators or communicate with other agents) in such a way that their own objectives are met as closely as possible and any constraints are satisfied.

This project aims to implement formulated distributed constraint optimization (DCOP) in a multi-agent platform for two objectives in smart grids: 1) voltage regulation and 2) electricity market,. There are several Java-based open source platforms for peer-to-peer agent based applications e.g. (JADE, Jadex, Jason,..etc). With the help of the supervisor, the student will choose the platform that suits the required applications. Also, the student will implement the formulated DCOP in the suitable multi-agent platform to test its effectiveness and robustness. Toward that end, an interface between the multi-agent platform in Java environment and the power grid simulator in MATLAB environment is required to transfer the measurements from MATLAB to the multi-agent platform and then transfer the actions from the multi-agent platform to MATLAB in real-time.

The student should be a strong Java and MATLAB programmer. He should have prior knowledge about multi-agent platforms such as JADE. The work involves reading and understanding the formulated DCOP in smart grids and working with the supervisor and master/PhD student(s) to implement the developed algorithms and to measure the performance of the developed algorithm(s) in the multi-agent platform.

For more information, please send email to

Required Background: General CSE408x prerequisites

Clustering High-Dimensional Data Sets

Supervisor: Suprakash Datta

Clustering is a basic technique for analyzing data sets. Clustering is the process of grouping data points in a way that points within a group are more similar to each other than points in other clusters. Many clustering algorithms have been developed over the years. However no single algorithm works well for all data sets. Further, most clustering algorithms have running times of the order of n^2 or n^3, so that they are not feasible for data sets with hundreds of thousands of points. In this project we will design good clustering algorithms for large real data sets. In particular we are interested in Biological data sets.

Our data sets will include those obtained from Flow Cytometry data. Flow Cytometry is a common technique in many areas of Biology, particularly Immunology. Typical usage involves testing a blood sample for 25 attributes on a per-cell basis, and thus typical data sets are arrays of 500,000 points in a 25 dimensional space. The aim is to identify clusters that correspond to a biologist's notion of a cell “population”. In addition to the size of the data, the factors that make this problem difficult are heterogeneity of population sizes and densities and overlapping populations. With the help of collaborators we proposed an accurate algorithm called SWIFT (Cytometry A. 2014 May;85(5):408-33) that successfully finds small cell populations of interest to Immunologists. This project attempts to design and implement algorithms that are faster than SWIFT but at least as accurate. The supervisor has collaborators in Immunology who are experts in interpreting and analyzing Flow Cytometry data. Their expertise will be used to design and validate effective clustering algorithms that can run on large data sets.

No Biology knowledge is required. The student should be a strong programmer. Knowledge of C/C++ is desirable but not essential. The work involves reading and understanding existing algorithms and working with the supervisor to design and implement improved algorithms and to measure the performance of the proposed algorithm(s).

For more information, please send email to

Required Background: General CSE408x prerequisites

Metaheuristic-based Optimization techniques

Supervisor: Suprakash Datta

Optimization is a crucial step in many computational problems. For computational problems that seem (or are known to be) intractable, metaheuristic-based techniques often work well in practice. These are typically randomized algorithms, often inspired by physical or biological systems. Examples of such algorithms include simulated annealing, genetic algorithms and ant colony optimization. In this project we will focus on particle swarm optimization (PSO), a technique inspired by the search for food by flocks of birds or schools of fish. Briefly, a set (or population) of candidate solutions (called particles) are maintained at all times by the algorithm. These particles move in the search-space using simple rules that make use of the best solutions found so far by the particle as well as by the swarm. Movement of particles result in new particles being generated. The process is repeated until some termination criteria are met and the best solution found is output by the algorithm. While there is no guarantee of optimality, PSO has been shown to produce good or very good solutions for many practical problems. Many variants of PSO's have been proposed. In this problem we will study the performance of some PSO variants on both artificial and real optimization problems.

The student should be a strong programmer. A good grasp of algorithms and knowledge of C/C++ are desirable but not essential. The work involves reading and understanding existing algorithms and working with the supervisor to design and implement improved algorithms and to measure the performance of the proposed algorithm(s).

For more information, please send email to

Required Background: General CSE408x prerequisites

Data visualization in Skydive

Supervisor: Jarek Gryz

Skydive is a prototype system designed for database visualization using a concept of the so called data pyramid. The system is composed of three modules (DB - Database Module, D2I - Data-to-Image module, and VC - Visualizaton Client). Each is designed to use a different type of computer memory. The DB module uses disk to store and manage the raw data, and materialized data pyramids. The D2I module works with a small subset of the aggregated dataset, and stores data in main memory (RAM). The VC module uses the graphic card’s capabilities to perform more advanced operations – such as zooming, scaling, panning, and rotation – over the graphical representation of the data. Currently the system support three presentation models implemented within the Visualization Component, namely:

• a 2D heat-map;

• a 2.5 D heat-map by 3D barchart; and

• a 2.5 D terrain (by mesh and UV-mapping).

The goal of the project is to implement two additional ways of data visualization as well as extend some of existing ones, that is:

1. Implement and test functions for data pyramid-based visualization of time series.

2. Implement functions for visualization based on cross-product of data pyramids.

3. Add support for specular and normal maps for 2.5 D terrain presentation model.

Required Background: CSE 3421, Java programming course, (C programming course a plus)

Genome-wide identification of plant micro RNAs

Supervisor: Katalin Hudak

The Hudak Lab in the Biology Department has an opening for a fourth-year Honours student to assist with a bioinformatics project. We study the pokeweed plant, Phytolacca americana, which displays broad-spectrum virus resistance. To evaluate pokeweed gene expression, we recently sequenced the plant’s mRNA and small RNA transcriptomes under jasmonic acid (JA) treatment. JA is a plant hormone that mediates defence against pathogens and insect herbivores. We are interested in learning how pokeweed gene expression is regulated by miRNAs during biotic stress. Please note: no previous knowledge of biology is required.

Working with the support of a PhD student, your project will involve:

1) Prediction of micro RNA (miRNA) targets on the basis of complementary sequence matches

2) Correlation of miRNA and mRNA expression changes to identify genes that are regulated by miRNAs

3) Conducting pathway analysis to determine which biological processes are controlled by miRNAs

4) Construction of a miRNA/target interaction network to visualize predictions This work will contribute to a scientific manuscript on miRNA-mediated gene regulation in pokeweed during response to JA.


1) Pre-requisites as per EECS Calendar

2) Facility with script-writing/modification (in Perl or Python)

3) Preference for students with knowledge of statistics and familiarity with R programming

4) Able to begin in September 2015

Learning outcomes:

1) Manipulate and analyze quantitative biological data

2) Develop and test hypotheses by modifying existing software and writing new script

3) Manage a CentOS computer server to store and facilitate ongoing research

No knowledge of biology is required.

For more information, please see: Hudak Lab website-

RNA sequencing-


DDoS Attack using Google-bots

Supervisor: Natalija Vlajic

Recommended Background: CSE 3213 or CSE 3214, CSE 3482

Not long ago, botnets - networks of compromised computers - were seen as the most effective (if not the only) means of conducting Distributed Denial of Service (DDoS) attacks. However, with the growing popularity and prevalence of application-layer over other types of DDoS attacks, the DDoS execution landscape is becoming increasingly more diverse. An especially interesting new trend is the execution of application-layer DDoS attacks by means of skillfully manipulated Web-crawlers, such as Google-bots. The goal of this project is to design, implement and test a real-world framework consisting of the following: a) the attacker's web-accessible domain specially designed to attract Google-bots and then manipulate them into generating attack traffic towards the target/victim site; b) the victim's Web site set up in Amazon S3 cloud. In addition to the hands-on component, the project will also look into the statistical/numerical estimation of the framework's anticipated 'attack potential' relative to an actual (real-world) target/victim site.

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 ( 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 ( 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


JPF in a Jar

Supervisor: Franck van Breugel

Description: JPF, which is short for Java PathFinder, is an open source tool that has been developed at NASA's Ames Research Center. The aim of JPF is to find bugs in Java code. Instead of using testing to find those bugs, JPF uses model checking. The facts that JPF is downloaded hundreds of times per month and that some of the key papers on JPF have been cited more than a thousand times reflect the popularity of JPF. In fact it is the most popular model checker for Java.

A study done by Cambridge University in 2014 found that the global cost of debugging code has risen to $312 billion annually. Furthermore, on average software developers spend 50% of their programming time with finding and fixing bugs. As a consequence, advocating the use tools, such as JPF, may have significant impact.

Installing JPF is far from trivial. The tool itself has been implemented in Java. Therefore, it should, in theory, be feasible to encapsulate JPF in a Java archive (jar) file. This would make it significantly simplifying the installation process of JPF and, therefore, make the tool more easily accessible to its potential users.

The aim of this project is to attempt to put JPF in a jar. Since JPF relies on a number of configuration files, so-called Java properties files, incorporating these properly into the jar is one of the challenges. Setting JPF's classpath is another challenge. Since JPF changes almost on a daily basis, our modifications to JPF should ideally be limited to only a few classes, yet another challenge.

In this project you may collaborate with graduate students of the DisCoVeri group ( and computer scientists of NASA. For more information, feel free to send email to

Required Background: General CSE408x prerequisites

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.

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

projects.txt · Last modified: 2016/05/03 14:25 by utn