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--- projects [2013/04/19 20:05] – mb
+++ projects [2013/08/23 02:03] – pd
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-====== Currently offered Projects, Winter 2013 (updated January 9, 2013)  ======
+====== Proposed Projects  ======
-(Listed in order received.)
+Current proposed Projects, Fall 2013
+\\
+====== 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
+\\
+\\
+------------------
+\\
+Additional current possible  projects will be listed here by start of fall term.
+\\
+------------------
+\\
+\\
+=====Previous Projects from Summer 2013=====
+======Tracking and Activity Recognition Through Consensus in Distributed Camera Networks======
+**Supervisor**: Amir Asif
+**Required Background: ** Computer Vision or Signal and Systems Course preferred; Matlab; Interest in Signal/Image Processing
+**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.
+[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.
+[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.
 ====== 3D Drawing System with Leap Motion finger tracker ======
@@ Line 18: / Line 81: @@
 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.
-====== Comparison of Finger Tracking systems ======
-**Supervisor**: Wolfgang Stuerzlinger
-**Required Background**: C/C++ coding
-This project compares the Leap Motion and the 3Gear System against each other in a comparative Fitts' law study
 ====== Tilt Target Selection on Touchscreen Phones ======
@@ Line 36: / Line 93: @@
 **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
 ====== Continuation of a Path Diagram to Syntax Application ======
@@ Line 71: / Line 111: @@
-====== Numerical Methods ======
-**Supervisor**: Mike McNamee
-**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 ======