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--- projects [2012/09/20 02:01] – jonathan
+++ projects [2013/04/29 12:27] (current) – mb
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-====== Currently offered Projects, Fall 2012 (updated September 5, 2012)  ======
+====== Available Projects  ======
-(Listed in order received.)
-====== Continuation of a Path Diagram to Syntax Application ======
+Currently offered Projects, Summer 2013
-**Supervisor**: Jeff Edmonds
+(some projects still subject to confirmation)
-**Required Background**: General CSE408x prerequisites
+======Tracking and Activity Recognition Through Consensus in Distributed Camera Networks======
-**Recommended Background**: Java software development
+**Supervisor**: Amir Asif
-Structural equation modeling (SEM) is a statistical technique that is becoming increasingly popular in the educational and behavioral sciences. SEM allows researchers to test the validity of hypothesized models involving complex relationships among multiple variables. Collected data is used to estimate the parameters of the equations and assessing the fit of the model.
+**Required Background: ** Computer Vision or Signal and Systems Course preferred; Matlab; Interest in Signal/Image Processing
-The software required is an application that allows researchers to define their hypothesized models visually and will output the correct syntax for the analytical software of their choosing.
+**Short Description: **Over the past decade, large-scale camera networks have become increasingly popular in a wide range of applications, including: (i) Sports analysis; (ii) Security and surveillance; (iii) disaster response, and; (iv) Environmental modeling, where the objective is to follow the trajectory of a key target, for example, a star player in a soccer game or a suspect in a surveillance environment. In many applications, bandwidth constraints, security concerns, and difficulty in storing and analyzing large amounts of image data centrally at a single location necessitate the development of distributed camera network architectures. In this project, we investigate distributed scene analysis algorithms, where each camera estimates certain parameters of the target using a signal processing algorithm based upon its own set of observations. The local estimates are then shared with the neighboring cameras in an iterative, goosip-type fashion, and a final estimate is computed across the network using consensus algorithms. The selected student will develop Matlab code to apply distributed signal processing algorithms [1,2] that have been developed in the Signal Processing and Communications lab for target tracking and activity recognition in distributed camera networks.
-To date a promising functional application has been developed in JAVA by a Computer Science student as a 4080 project. The existing software allows the user to draw a path diagram and outputs code for the R package sem.  There are a number of improvements to be made (refinements and additions to graphical user interface) and then the application needs to be extended to output syntax appropriate for additional software applications (openMX, MPlus and EQS).  Though this project may not begin at “the first stages” of the software lifecycle, this scenario is likely common in the software development market. In addition, the student will be working with a primary “client” who is far less technically advanced, which is also reflective of real-world situations.
-More details {{:continuation_of_a_path_diagram_to_syntax_application.pdf|here}}.
-====== YUsend Thermal Vacuum (TVAC) Test Manager ======
+[1] A. Mohammadi and A. Asif, Distributed Particle Filter Implementation with Intermittent/Irregular Consensus Convergence, IEEE Transactions on Signal Processing, 2013. http://arxiv.org/abs/1112.2431.
-**Supervisor**: Rob Allison (co-supervised with Hugh Chesser, Space Engineering)
+[2] A. Mohammadi and A. Asif, Decentralized Sensor Selection based on the Distributed Posterior Cramer-Rao Lower Bound, in proceedings of IEEE International Conference on Information Fusion, Singapore, 2012. pp. 1668-1675.
-**Required Background**: General CSE408x prerequisites, familiarity with C++ and Windows software tools
+====== 3D Drawing System with Leap Motion finger tracker ======
-**Description** The YUsend (York University Space Engineering Nanosatellite Demonstration) Lab has procured a Windows XP-based industrial computer and temperature acquisition card (as well as other hardware) for performing TVAC testing of nanosatellites in the CSIL Lab (PSE 003). A “TVAC Test Manager” application written using LabView's G programming language will oversee the acquisition of temperatures (thermal test outputs) and control of IR lamps (thermal test inputs) during the rather long periods (4 or more days, 24 hours a day) of a TVAC test.
+**Supervisor**: Wolfgang Stuerzlinger
-Specific tasks include: 1. Writing temperature acquisition card (OMEGA Engineering CIO-DAS-Temp) drivers for LabView - should be written in Visual C++ or similar and compiled into SubVI format. 2. Write LabView VI's (“Virtual Instrument”) to perform (a) Test set-up activities - checkout of sensor and lamps, assigning neumonics to temperature sensors, setting of alarm conditions for sensors and lamps (b) Acquire and monitor temperature data and control lamp voltage during test, raise operator alarms for temperature or IR lamp anomalous conditions as required © Store temperature and control data for subsequent analysis and reporting. 3. (Optional) Interface the Test Manager with an orbital simulation tool which would be used to compute IR lamp inputs based on a simulation of the nanosatellite's orbital position and attitude (eg - in the sun, lamps on, in eclipse lamps off). The simulation tool is a package called Satellite Toolkit (STK) which has an TCP/IP-based API.
+**Required Background**: 3D computer graphics, C/C++ coding
+The Leap Motion, leapmotion.com, is a new device that lets users control a computer with their fingers. This project creates a new 3D drawing system that enables users to quickly generate 3D solids.
-====== Game Playing Framework in Eiffel ======
+====== 3D Drawing System with 3Gear gesture tracker ======
-NO LONGER AVAILABLE
+**Supervisor**: Wolfgang Stuerzlinger
-**Supervisor**: Jonathan Ostroff
+**Required Background**: 3D computer graphics, C/C++ coding
-**Required Background**: Eiffel software development method
+The 3Gear system, threegear.com, lets users control a computer with their hands and fingers. This project creates a new 3D drawing system that enables users to quickly generate and modify 3D solids.
-**Description** Game playing frameworks in Eiffel such as Eiffelmedia usually wrap C frameworks in Eiffel. The goal of this project is to develop (initially, a simple) framework using pure Eiffel/EiffelVision so that the framework can be used on any platform (Windows, Linux, Mac) without external dependencies. Games provide a rich source for exploring object oriented software construction and design patterns, so that the framework would be useful in software design courses.
-====== Numerical Methods ======
-**Supervisor**: Mike McNamee
+====== Tilt Target Selection on Touchscreen Phones ======
+**Supervisor**: Scott MacKenzie
+**Required Background**: General 4080 prerequisites, CSE3461, and (preferably) CSE4441. Interest in user interfaces and human-computer interaction (HCI).  Students can use their own Android phone for the project or one supplied by the course supervisor.
+Touchscreen mobile devices commonly use a built-in accelerometer to sense movement or tilting actions of the device.  Tilt is commonly used the change the orientation of the display between portrait and landscape.  Gaming is another common use for tilting actions.  However, tilt may also be used for target selection, as a replacement for touch.  This research project will evaluate tilt as an input primitive for target selection on touchscreen mobile devices.
+**Readings**: MacKenzie, I. S., & Teather, R. J. (2012). FittsTilt: The application of Fitts’ law to tilt-based interaction. Proceedings of the Seventh Nordic Conference on Human-Computer Interaction – NordiCHI 2012, pp. 568-577. New York: ACM.
+====== 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:
+  - Study the problem of detecting hand-raises in the preattentive sensor stream
+  - Implement algorithms for detecting hand-raises based upon this investigation
+  - 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:
+.     Ground-truth available datasets
+.     Evaluate current attentive algorithms on these datasets
+.     Modify these algorithms to improve performance on these datasets
+====== Continuation of a Path Diagram to Syntax Application ======
+**Supervisor**: Jeff Edmonds
+**Required Background**: General CSE408x prerequisites
+**Recommended Background**: Java software development
+Structural equation modeling (SEM) is a statistical technique that is becoming increasingly popular in the educational and behavioral sciences. SEM allows researchers to test the validity of hypothesized models involving complex relationships among multiple variables. Collected data is used to estimate the parameters of the equations and assessing the fit of the model.
+The software required is an application that allows researchers to define their hypothesized models visually and will output the correct syntax for the analytical software of their choosing.
+To date a promising functional application has been developed in JAVA by a Computer Science student as a 4080 project. The existing software allows the user to draw a path diagram and outputs code for the R package sem.  There are a number of improvements to be made (refinements and additions to graphical user interface) and then the application needs to be extended to output syntax appropriate for additional software applications (openMX, MPlus and EQS).  Though this project may not begin at “the first stages” of the software lifecycle, this scenario is likely common in the software development market. In addition, the student will be working with a primary “client” who is far less technically advanced, which is also reflective of real-world situations.
+More details {{:continuation_of_a_path_diagram_to_syntax_application.pdf|here}}.
-**Required Background**: Good grade in a Numerical Methods course and good knowledge
-of Fortran, C or similar language.
-**Description**: Write, debug and run several Fortran programs related to solving
-polynomial equations, with a view to comparing different known methods.
 ====== Enabling SaaS access to an experimental AI planner ======