Graduate School of Informatics

To evaluate applications for Cybersecurity Department Department MSc Non-Thesis Program 2017-2018 Fall Semester,

Interviews will be held on 14 July 2017 Friday

Candidates invited to the interview will be announced on our website on 10 July 2017 Monday.

Interview information for the applications of Health Informatics Department Bioninformatics MSc Program 2017-2018 Fall Semester

Interviews will be held on 11 July 2017 Tuesday between 10:00-12:00

Invited applicants have to be ready in front of Conference Hall-2 in Informatics Instute at the specified date

Interview Date:11 July 2017 10:00 - 12:00

Interview Place: Conference Hall-2 in Informatics Instute

In result of the applications for MSc and PhD programs of Cognitive Science Department,

interviews  will be held on 30 June 2017 Friday

You need to be ready for your interviews in the Graduate School of Informatics before the specified interview date

Interviews are scheduled for Thursday, July 13, 2017 with the qualified applicants for the MSc and PhD in Information Systems.

For the relevant instructions and forms on secondment applications and passport procedures, please visit http://pdb.metu.edu.tr/akademik-gorevlendirme

Graduate School of Informatics /Health Informatics

In partial fulfillment of the requirements for the degree of Master of Science Mahdieh Farzin Asanjan will defend his thesis.

Title: SEMI-AUTOMATIC SEGMENTATION OF MITOCHONDRIA ON TRANSMISSION ELECTRON MICROSCOPY IMAGES USING LIVE-WIRE AND SURFACE DRAGGING METHODS

Date: 16th June 2017

Time: 10:30 AM

Place: A-108

Thesis Abstract : Semi-automatic segmentation of mitochondria Segmentation is an integral part of image processing. Contrary to many technical applications the design of fully automated segmentation routines is extremely challenging in the medical context because of the large biological variation. Even if automatic routines do work in normal subjects, they typically fail in pathologic cases, which are often more interesting from a clinical point of view. Segmentation of mitochondria in medical images is essential for studying mitochondrial morphology and computer aided analysis and diagnosis. Since using an automatic segmentation method may leads to the least flexibility and also using the manual methods needs a considerable amount of human effort, automatic detection and segmentation of mitochondria with user interaction is necessary in order to facilitate the analysis of large 3D data sets and user interaction introduces a subjective element to image processing and analysis. So segmentation methods using human interaction to initialize the algorithms can be more helpful. In new method I am trying to make possible to operator interaction although it may be used in a minority of cases, only. And this will help to reduce the failure rates. Two principle modes of user interaction can be distinguished. In the first mode the user interactively selects a region or volume of interest (ROI or VOI) in which subsequently an automated operation is performed. A typical well-known example is the selection of a seed point to start a region growing process. In contrast, the second mode is iterative and requires extended user interaction, for example, an interactive change of a contour.

Interviews are scheduled for Friday, June 30, 2017 with the qualified applicants for the Master of Science in Cognitive Science.

Interviews are scheduled for Monday, July 3, 2017 with the qualified applicants for the Master of Science in Multimedia Informatics.

Graduate School of Informatics /Game Technologies 

In partial fulfillment of the requirements for the degree of Master of Science Umut Demirel will defend his thesis.

Title: CREATING A GENERIC HAND AND FINGER GESTURE RECOGNIZER BY USING FOREARM MUSCLE ACTIVITY SIGNALS

Date: 01th August 2017

Time: 13:30 PM

Place: A-212

Thesis Abstract : Hand and finger gestures are the most natural way of communication without speaking. Therefore using hand and finger gestures as inputs is widely used in human-computer interaction applications. There are variety of applications using gestures as inputs such as: sign language recognition, robot controlling, medical device controlling and video game controlling. This thesis deals with creating a generic hand and finger gesture recognizer by processing muscle activity signals. For data collection, MYO from Thalmic Labs, a portable armband having 8 channel EMG sensors and IMU, is used. Normally, EMG sensors should directly be placed on muscles for each user. However, because MYO is a generic device for anyone to use, EMG sensor placements will differ from user to user. As a result, the recognizer will be calibration based, meaning that it is person and session dependent. Gestures are chosen from the set of expressive gestures used by classical orchestra conductors.13 different hand and finger gestures and 1 rest gesture is performed 5 times, each for 3 seconds by 20 subjects in separate sessions. While preprocessing the collected data, not only the time domain is used by applying filters to raw data, but also the frequency domain is used by representing each gesture with circular harmonic coefficients. Neural Network is trained, validated and tested to recognize 14 different gesture patterns. Cross validation method is performed with 5 data sets for each test subjects. 3 sets are used for training, 1 set is used for validation and 1 set is used for testing. 20 combinations of data sets are fed to 20 different neural network for the cross validation. The proposed generic gesture recognizer system is tested with one of the best Turkish classical orchestra conductor, Orhun Orhon. Before the performance, his forearm EMG data is collected while he performs his own gestures. During the performance, data is continued to be collected and real-time accuracy is tested.