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--- projects [2013/01/09 20:36] – bil
+++ projects [2013/04/29 12:27] (current) – mb
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-====== Currently offered Projects, Winter 2013 (updated January 9, 2013)  ======
+====== Available Projects  ======
-(Listed in order received.)
+Currently offered Projects, Summer 2013
+(some projects still subject to confirmation)
+======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 ======
+**Supervisor**: Wolfgang Stuerzlinger
+**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.
+====== 3D Drawing System with 3Gear gesture tracker ======
+**Supervisor**: Wolfgang Stuerzlinger
+**Required Background**: 3D computer graphics, C/C++ coding
+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.
 ====== Tilt Target Selection on Touchscreen Phones ======
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   - 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
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 More details {{:continuation_of_a_path_diagram_to_syntax_application.pdf|here}}.
-====== YUsend Thermal Vacuum (TVAC) Test Manager ======
-**Supervisor**: Rob Allison (co-supervised with Hugh Chesser, Space Engineering)
-**Required Background**: General CSE408x prerequisites, familiarity with C++ and Windows software tools
-**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.
-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.
-====== 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 ======