| Name Surname | Deniz Can Köseoğlu |
| Title | Mobile Telecommunication Revenue’s and It’s Gross Domestic Product’s proportion for Countries |
| Abstract | This study explores the modeling of the share of mobile telecommunication revenues in gross domestic product between 1997 to 2011. In this research Germany, Italy, Spain, and Turkey are investigated for the subject. For this research, the fractional calculus and Least Squares Method are used for the mathematical model.With this modelling, we can observe how is the GDP has changed over the years and how it affects countries economically |
| Advisors Name and Surname | Ertuğrul Karaçuha |
| Date | 8.12.2020 |
| Hour | 09:30 |
| Link | https://itu-edu-tr.zoom.us/j/94354902759?pwd=dVBtUHgyWlVsdU1XUzBDOFlKbG8vUT09 |
| |
| Name Surname | Beltus Nkwawir Wiysobunri |
| Title | A Deep Learning Approach to Fault Detection and Classification in Data Centers |
| Abstract | As cloud computing applications have witnessed an exponential growth in recent years, the maintenance of the server uptime has never been more important. A fault in a server due to overload, attack, or a misconfiguration of a cooling system can be of imponderable economicand financial loss to the echelons of global institutions. The advent of thermal cameras has helped to provide fine texture information which can be used for monitoring and providing intelligent thermal management in large data centers. This paper focuses on leveraging the power and effectiveness of modern deep learning techniques for the classification, detection, and diagnosis of faults in data centers using infrared images. |
| Advisors Name and Surname | Dr. Hamza Erden Salih |
| Date | 8.12.2020 |
| Hour | 10:15 |
| Link | https://itu-edu-tr.zoom.us/j/94354902759?pwd=dVBtUHgyWlVsdU1XUzBDOFlKbG8vUT09 |
| |
| Name Surname | Sümeyye Çavaş |
| Title | Anomaly Detection in Compressed Video |
| Abstract | In our daily life, many places such as streets, shops, workplaces aremonitored 24/7 with a camera. Cameras have become part of our lives toensure security. One of the first things that the police examine whenthere is any problem is checking security cameras. Collecting data from cameras at any time causes a lot of data accumulation over time.It takes a long time to examine this huge amount of data with manpowerand causes a lot of workload. In daily life, these videos usually continue as usual. However, insome moments, unusual events can also be observed. For example, in atraffic that travels normally, a pedestrian jumping on the road orcausing a vehicle to cause an accident with high speed is abnormal intraffic. It is also an anomaly for a cyclist to cross the pedestrianpath quickly and through the pedestrians. Our aim is to detect suchanomalies in a video. To put it more clearly, we aim to eliminatelong-term data reviews of people to detect unusual events thanks tothe model we have constructed. We aim to detect anomaly by using Motion vectors in H265 videos. Forthis, we will first use tools that detect motion vectors for theframes of the video. Then, we will construct a model that detectsanomalies using motion vectors we have obtained. |
| Advisors Name and Surname | Behçet Uğur Töreyin |
| Date | 8.12.2020 |
| Hour | 11:00 |
| Link | https://itu-edu-tr.zoom.us/j/94354902759?pwd=dVBtUHgyWlVsdU1XUzBDOFlKbG8vUT09 |
| | |
Name Surname
| Hüseyin Onur Yağar
|
Title
| Compressed Video Action Recognition
|
Abstract
| Action recognition is a process for extracting information from videos. The actions made by actors, poses they are in have meaningful information and these are extracted from videos. The movements of actors, even when swimming, shopping, reading a book or combing their hair can be separated from each other. It is important to watch these videos and convert them to meaningful data. Action recognition can be done over raw videos or compressed videos. Examining the increasingly sized videos in the compressed domain seems like a more rational, effective and fast solution rather than raw videos. Developing deep learning methods are applied to almost every problem of computer vision. But the compressed domain has few research with deep networks. So under these circumstances, researching on compressed domain videos with deep learning architectures is planned.
|
Advisors Name and Surname
| Behçet Uğur Töreyin
|
Date
| 15.12.2020
|
Hour
| 9:30
|
| | https://itu-edu-tr.zoom.us/j/92964037119?pwd=MFh5SWRjMCs2MVdPWXlLV0ZQaTcvdz0
|
| | |
Name Surname
| Nizameddin Ahmet Baştuğ
|
Title
| OFDM Channel State Information Estimation with Deep Learning
|
Abstract
| ORTHOGONAL frequency-division multiplexing (OFDM) is a popular modulation scheme that has been widely adopted in wireless broadband systems to combat frequency-selective fading in wireless channels. Channel state information is vital to coherent detection and decoding
in OFDM systems. Usually, the CSI can be estimated by means of pilots prior to the detection of the transmitted data. With the estimated CSI, transmitted symbols can be recovered at the receiver. The conventional prediction methods like least squares and minimum mean square error, have been used in various conditions. The method of least squares estimation requires no pre-channel statistics, but its performance may be insufficient. The minimum mean square error
prediction has much better performance by utilizing the second order statistics of channels. By introducing a deep learning based solution like convolutional neural networks or multi-layer perceptrons, estimation can be done with much higher accuracy.
|
Advisors Name and Surname
| Lütfiye Durak Ata
|
Date
| 15.12.2020
|
Hour
| 10:15 |
| | https://itu-edu-tr.zoom.us/j/92964037119?pwd=MFh5SWRjMCs2MVdPWXlLV0ZQaTcvdz0
|
| | |
Name Surname
| Semih Aslan SAĞLAMOL
|
Title
| Common Criteria Evaluation
|
Abstract
| The Common Criteria for Information Technology Security Evaluation (CC), and the companion Common Methodology for Information Technology Security Evaluation (CEM) are the technical basis for an international agreement, the Common Criteria Recognition Arrangement
(CCRA), which ensures that: Products can be evaluated by competent and independent licensed
laboratories so as to determine the fulfilment of particular security properties, to a certain extent or assurance; Supporting documents, are used within the Common Criteria certification process to define how the criteria and evaluation methods are applied when certifying specific technologies; The certification of the security properties of an evaluated product can be issued by a number of Certificate Authorizing Schemes, with this certification being based on the result of their evaluation; These certificates are recognized by all the signatories of the CCRA.
The CC is the driving force for the widest available mutual recognition of secure IT products. This web portal is available to support the information on the status of the CCRA, the CC and the
certification schemes, licensed laboratories, certified products and related information, news and events.
|
Advisors Name and Surname
| Eldar Veliyev
|
Date
| 15.12.2020
|
Hour
| 11:00 |
| | https://itu-edu-tr.zoom.us/j/92964037119?pwd=MFh5SWRjMCs2MVdPWXlLV0ZQaTcvdz0
|
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