Süleyman Eken

Registering landsat-8 mosaic images: A case study on the Marmara sea

This paper proposes a feature-based image registering technique for remote sensing satellite images. The system is composed of multiple steps in which various image processing techniques are used. Speed-up Robust Features (SURF) and Scale Invariant Feature Transform (SIFT) can be mounted alternatively to detect and define features on images. We also address the issues of registering multi-spectral and multi-temporal images. The effectiveness of the proposed technique has been studied by registering partially overlapping satellite mosaic images of the Marmara Sea. The images were captured by the recently-launched LandSat-8 satellite.

Analyzing distributed file synchronization techniques for educational data

“Movement of Enhancing Opportunities and Improving Technology”, abbreviated as Fatih, is among the most important educational projects in Turkey, in which students and teachers can use their tablet PCs to obtain educational data (text, images, media, etc.) stored in cloud servers. However, the limited network bandwidth and increase in both the number of users and their educational data sizes degrade the system performance and even negatively affect the overall usability of the system. Proxy server is a solution approach to both decreasing network traffic and increasing the efficiency in data transfers between end users (tablets) and cloud servers. In case of using a proxy server, synchronization problems arise. In this paper, distributed file synchronization approaches such as SyncML, Rsync and CouchDB have been reviewed and compared for their feasibility of employing in this school level proxy server based distributed systems architecture.

Kd-tree and quad-tree decompositions for declustering of 2D range queries over uncertain space

We present a study to show the possibility of using two well-known space partitioning and indexing techniques, kd trees and quad trees, in declustering applications to increase input/output (I/O) parallelization and reduce spatial data processing times. This parallelization enables time-consuming computational geometry algorithms to be applied efficiently to big spatial data rendering and querying. The key challenge is how to balance the spatial processing load across a large number of worker nodes, given significant performance heterogeneity in nodes and processing skews in the workload.

Tiling of Satellite Images to Capture an Island Object

This study proposes a novel tiling approach to capture an image of an entire object. Multi-spectral and multi-temporal satellite images are obtained a priori, and these individual image pieces can then be joined together at a later date to form an image of the entire object. The effectiveness of the proposed technique has been studied by tiling partially overlapping satellite mosaic images of the Island of Cyprus. The images were captured by the recently-launched LandSat-8 satellite.

Distributed and collaborative real-time vehicle detection and classification over the video streams:

Traffic surveillance cameras are widely used in traffic management and information systems. Processing streaming media in real time is resource and time-consuming processes and even impossible to realize in most real-world applications. To overcome the performance problems in such applications, this article introduces a middleware system based on pub/sub messaging protocol and a dispatcher to preprocess the streams in real time. Experimental results show that proposed middleware may be utilized in different areas such as infrastructure planning, traffic management, and prevention of traffic offenses.

Performance Evaluation of Support Vector Machine and Convolutional Neural Network Algorithms in Real-Time Vehicle Type Classification

Intelligent traffic management systems needs to obtain information about traffic with different sensors to control the traffic flow properly. Traffic surveillance videos are very actively used for this purpose. In this paper, we firstly create a vehicle dataset from an uncalibrated camera. Then, we test Tiny-YOLO real-time object detection and classification system and SVM classifier on our dataset and well-known public BIT-Vehicle dataset in terms of recall, precision, and intersection over union performance metrics. Experimental results show that two methods can be used to classify real time streaming traffic video data.

Evaluation of Real-Time Performance for BGSLibrary Algorithms: A Case Study on Traffic Surveillance Video

Video surveillance systems have been used in various applications such as traffic monitoring, detecting military threats and public safety. In this study, we evaluate real-time performance for BGSLibrary (Background Subtraction Library) algorithms on well-known datasets, which are Background Models Challenge (BMC) and ChangeDetection.

A Method for Localization of Computational Node and Proxy Server in Educational Data Synchronization

Localization methods enable location estimation accurately and provide location information about mobile devices, people, cars, data and equipment. Accurate location detection is a vital process for most of location-based applications such as emergency rescue, in-building guidance, security services, and product tracking in hospitals. This paper addresses localization of student/teacher tablets and school level proxy servers for educational data synchronization. For this purpose, locations of proxy servers and tablets are detected using Android Location API. After localization, if tablets are outside of school, they could access cloud server directly to get educational data and if tablets are in school, they could access data via proxy server. Experimental results show that the proposed technique increases the efficiency in data transfers between the end users and cloud servers.

Tracking a single node's availability for communication by means of observing local system resources

“Movement of Enhancing Opportunities and Improving Technology”, abbreviated as Fatih, is among the most important educational projects in Turkey, in which students and teachers can use their tablet PCs to obtain educational data (text, images, media, etc.) stored in cloud services. However, the limited network bandwidth and increase in both the number of users and their educational data sizes degrade the system performance and even negatively affect the overall usability of the system. Using proxy server is an approach to both decreasing network traffic and increasing the efficiency in data transfers between end users and cloud services. In this approach, it is critical that when synchronization process will be performed. In this paper, bandwidth utilization, CPU and memory usage information have been examined for obtaining accurate synchronization time for this school level proxy server based distributed systems architecture. In order to minimize effect of too large and too small CPU and memory usage data, we use min-max normalization. Also, multiple linear regression is used to estimate a nodes availability for communication. Experimental results show that proposed technique is feasible not only for school level proxy server based systems but also for other systems.

CNN Based Traffic Sign Recognition for Mini Autonomous Vehicles

Advanced driving assistance systems (ADAS) could perform basic object detection and classification to alert drivers for road conditions, vehicle speed regulation, and etc. With the advances in the new hardware and software platforms, deep learning has been used in ADAS technologies. Traffic signs are an important part of road infrastructure. So, it is very important task to detect and classify traffic signs for autonomous vehicles. In this paper, we firstly create a traffic sign dataset from ZED stereo camera mounted on the top of Racecar mini autonomous vehicle and we use Tiny-YOLO real-time object detection and classification system to detect and classify traffic signs. Then, we test the model on our dataset in terms of accuracy, loss, precision and intersection over union performance metrics.