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You are here: EECS 4080.03, EECS 4081.06, EECS 4082.06, EECS 4084.06, EECS 4088.06, EECS 4090.06 and EECS 4480.03 » Proposed Projects for Winter 2016
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projects [2015/08/26 21:55] – jarekprojects [2015/08/26 21:59] – jarek
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 ====== Proposed Projects for Fall 2015 ====== ====== Proposed Projects for Fall 2015 ======
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-=====Clustering High-Dimensional Data Sets=====+======Clustering High-Dimensional Data Sets======
  
-**Supervisor: Suprakash Datta**+**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 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
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-=====Metaheuristic-based Optimization techniques=====+======Metaheuristic-based Optimization techniques======
  
-**Supervisor: Suprakash Datta**+**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. 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.
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 Required Background: General CSE408x prerequisites Required Background: General CSE408x prerequisites
  
-=====Data visualization in Skydive=====+\\ 
 + 
 +======Data visualization in Skydive======
  
-**Supervisor: Jarek Gryz**+**Supervisor:** Jarek Gryz
  
 Skydive is a prototype system designed for database visualization using a concept of the so called Skydive is a prototype system designed for database visualization using a concept of the so called
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-=====Genome-wide identification of plant micro RNAs===== 
  
 +======Genome-wide identification of plant micro RNAs======
  
-**Supervisor: Katalin Hudak**+ 
 +**Supervisor:** Katalin Hudak
  
  
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-=====Dynamic Interface Detection and Control Project===== 
  
-**Supervisor: Michael Jenkin**+======Dynamic Interface Detection and Control Project====== 
 + 
 +**Supervisor:** Michael Jenkin
  
  
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 ====== DDoS Attack using Google-bots ====== ====== DDoS Attack using Google-bots ======
  
-**Supervisor**: Ntalija Vlajic+**Supervisor:** Natalija Vlajic
  
 **Recommended Background**: CSE 3213 or CSE 3214, CSE 3482 **Recommended Background**: CSE 3213 or CSE 3214, CSE 3482
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 ====== Attentive Sensing for Better Two-Way Communication in Remote Learning Environments ====== ====== Attentive Sensing for Better Two-Way Communication in Remote Learning Environments ======
  
projects.txt · Last modified: 2016/01/13 20:05 by stevenc