Category: Master OR semester project

Categories
Master OR semester project NSO

DKE Scheduling Project

rp-or-heu-userinterface2

Students: Anton Bulat, Fatimah Mulan Ahmed, Fred Shen, Maxime Laschet
Supervisor: Dr. Matúš Mihalák
Semester 2015-2016

Problem statement and motivation:

The aim of this work is to compute an optimal schedule for the students and teachers of the Department Data Science of Knowledge Engineering at Maastricht University. The research and the mathematically modeling of a solution take a central aspect of this project.
The work has split into two groups: The Integer Linear Programming and the heuristics group. This report will focus on the work of the heuristics group. It will explain the conceptuation of the model including all constraints, the application of Local search and some heuristics approaches and experiments to improve feasible solutions.

Research questions:

In the present project we aimed at answering the following research questions:

  • How an academic timetable can be effectively constructed, using the local search techniques and heuristic approaches?
  • How the application can distinct between a good and a worse schedule?

Major outcomes:

  • The local search algorithm starts with a given solution. Local movements of not feasible events in relation to new start conditions will lead to a feasible solution step by step. Addtionally the ILP group’s output is parsed and translated as input for our algorithm.
  • The second main part is the verification of some rules which are decided to provide a better solution. The algorithm tries to minimize penalty values which are calculated for each event in each possible time point.
  • The user has the possibility to make manual changes on the proposed solution and rerun the algorithm until a satisfying solution is generated.
  • From the user interface the user is able to generate a PDF output of the timetable.

References:

  • Arntzen, Halvard, and Arne Løkketangen. “A local search heuristic for a university timetabling problem.” nine 1.T2 (2003): T45.
  • Rossi-Doria, Olivia. “A local search for the timetabling problem.” Proceedings of the 4th International Conference on the Practice and Theory of Automated Timetabling, PATAT. 2002.
  • Müller, Tomáš, Keith Murray, and Stephanie Schluttenhofer. “University course timetabling & student sectioning system.” Space Management and Academic Scheduling, Purdue University (2007).
  • Ponnalagu, Karthikeyan, Renuka Sindhgatta Rajan, and Bikram Sengupta. “Automatically generating high quality soa design from business process maps based on specified quality goals.” U.S. Patent Application No. 12/885,870.

Download: report

Categories
BMI Data analysis Master OR semester project

OR@Heart: Torso and heart segmentation for ECG imaging

Student(s): Oskar Person, Yanik Dreiling, Justus Schwann, Ullaskrishnan Poikavila;
Supervisor(s): Dr. Pietro Bonizzi, Dr. Joel Karel, Matthijs Cluitmans;
Semester: 2015-2016;

Fig 1. – CT scans.

Problem statement and motivation:

Cardiovascular diseases (CVD), or irregularities in heart and related blood vessels, have led to nearly 17.5 million deaths in 2011 alone, and this number is increasing at a steady rate. This makes CVD the leading cause of deaths worldwide. A majority of these deaths could have been prevented by earlier detection of symptoms. Electrocardiogram Imaging (ECGI) is a technique that helps in quickly detecting the cardiac irregularities and expediting the diagnoses. Electrodes are placed on the torso to record cardiac electrical activity. But the skin and body mass in between the heart and torso dampens these currents leading to an incorrect visualization of cardiac motion. The reverse problem of ECGI tries to reconstruct the true cardiac electric activity using the observed currents on torso electrodes and geometric knowledge of the surfaces of heart and torso. Currently, the reconstruction of heart surface and segmentation of electrodes from the torso surface are done manually. This makes it both time and energy consuming and goes against the whole purpose of ECGI quickening cardiac diagnosis. The aim of this project was to automate the process of reconstructing heart and torso surfaces. CT Scans are taken of the patient with the electrodes on, and they represent the input to the automated segmentation and generation of torso and heart surface. The implemented algorithms try to segment out the electrode strips to help reconstruct the torso surface and also detects the edges to help visualize the heart surface. A GUI is also provided to help the user in running the algorithms. The group achieved successful automated segmentation of the body surface electrode strips, generation of a preliminary torso surface, and preliminary segmentation of the heart surface. Future work will be focused on making the torso model more realistic, improving segmentation of the heart surface, and generating the heart model.

Research questions/hypotheses:

The aim of this project was to automate the process of reconstructing heart and torso surfaces. CT Scans are taken of the patient with the electrodes on, and they represent the input to the automated segmentation and generation of torso and heart surface.

Main outcomes:

The implemented algorithms try to segment out the electrode strips to help reconstruct the torso surface and also detects the edges to help visualize the heart surface. A GUI is also provided to help the user in running the algorithms.