LEE- Bilgisayar Mühendisliği-Yüksek Lisans
Bu koleksiyon için kalıcı URI
Gözat
Son Başvurular
1 - 5 / 16
-
ÖgeNext generation wireless networks for social good(Graduate School, 2023-08-18)The advancement of technology and communication systems has yielded beneficial outcomes in everyday life. Including next generation wireless networks is an integral component of this evolutionary process. Consequently, the advancement of technology and evolving needs have led to the enhancement of wireless communication systems by implementing next generation wireless networks, thereby rendering them more powerful and efficient. These technologies, such as mobile communications, industrial applications, and the Internet of Things (IoT), significantly impact our lives. In addition to the factors above, wireless networks have emerged as a pivotal tool in addressing societal challenges. Next generation wireless networks have the potential to manage various critical domains such as natural disasters, environmental concerns, traffic and transportation challenges, and public health issues. Because of these reasons , this thesis has two main objective utilizing wireless networks. Firstly, we propose a wildfire monitoring method. Wildfires have emerged as a significant worldwide concern in today's world. The prevalence and severity of wildfires have increased due to climate change, anthropogenic actions, and natural influences. In response to the prevailing ecological crisis, researchers and professionals in science and engineering are actively exploring a range of technological and supplementary precautions. The findings of this investigation indicate that unmanned aerial vehicles (UAVs) significantly impact combatting forest fires. UAVs have become essential tools in firefighting and monitoring operations due to their notable attributes, including user-friendly interfaces, exceptional maneuvering capabilities, and enhanced availability. Nevertheless, the constrained energy capacity of a singular UAV poses a significant challenge in efficiently surveilling expansive fire zones. To effectively tackle these challenges and enhance the efficiency of firefighting operations, a proposed solution is implementing an advanced monitoring application called "Phoenix." Phoenix provides an advanced fire-tracking monitoring system, which integrates path planning, a graph engine, and modified Traveling Salesman Problem (TSP) algorithms. This system aids the UAV in effectively tracking fire areas and optimizing its trajectory. This capability enables the UAV to conduct a more efficient scanning of the fire area, reducing response time. Consequently, this helps to mitigate the spread of the fire. Phoenix has designed a network architecture that facilitates the prompt transmission of monitoring data to the fire brigade and other firefighting units. This enables the firefighting crews to remain informed about the prevailing conditions at the site and enhance their coordination efforts.The Phoenix application facilitates energy optimization to tackle the energy limitations an individual Unmanned Aerial Vehicle (UAV) faces. Therefore, UAVs can remain airborne for an extended duration and effectively survey more significant geographical regions. This enhances the efficacy of firefighting operations. The application operates by employing elliptical fire modeling and simulation techniques. Additionally, the analysis of critical fire zones incorporates fuel moisture content (fmc) data within the fire zone. This facilitates Phoenix's enhanced ability to respond effectively to real-world situations, thereby augmenting the likelihood of success in firefighting endeavors. Secondly, we propose a blind spot detection method to protect pedestrians, cyclists and motorcyclists in traffic and prevent accidents. Traffic crashes are a significant issue that regrettably results in numerous fatalities and injuries in contemporary times. Traffic accidents are a prominent contributor to global mortality rates, particularly in middle-income nations with high traffic volumes and insufficient or inadequate infrastructure. Despite implementing numerous safety measures to address this issue, a significant level of risk remains, particularly for susceptible road users, including pedestrians, cyclists, and motorcyclists. The significance of vehicle blind spots is a crucial factor in such accidents. Despite the recent introduction of advanced safety systems incorporating costly hardware, detecting vulnerable users remains challenging, particularly in situations where the field of view is obstructed. Furthermore, we utilized ultra-wide-band (UWB) technology to develop this system. UWB is an advantageous wireless communication tool for both cost-effectiveness and widespread availability. We use the Time Difference Of Arrival (TDOA) method to detect the vehicle or pedestrian in the blind spot. We have developed a demo by developing this proposed method. We used four UWB kits and a UWB-supported mobile phone for this demo. We implement the software in the kits used for the demo and the software of the application on the mobile phone ourselves. Apart from that, we compared our method with different methods using simulation. In conclusion, this thesis proposes two next-generation wireless network approaches. First, Phoenix, an advanced monitoring program, powers the suggested wildfire monitoring technique. This novel technology uses UAVs, advanced algorithms, and fire model to revolutionize firefighting, save lives, preserve ecosystems, and reduce wildfire damage. Phoenix shows how technology can safeguard our environment and develop a more resilient and sustainable future as we battle climate change and wildfires. The second stage of this thesis proposes and examines the continuing development and enhancement of road safety technology like blind spot identification, which reduces traffic accidents and saves lives. UWB technology and new algorithms may make roads safer and more inclusive. These road safety applications use technology, legislation, and public awareness to reduce accidents and make roads safer.
-
ÖgeCrowd density map estimation system from aerial images(Graduate School, 2023-07-31)Today, the concept of urbanization, which has emerged with the choice or necessity of people to live in cities is a social and economic transformation. In recent times, the notion of a "smart city" has gained significant popularity due to its ability to incorporate various elements like sustainability, livability, quality of life, competition, branding, governance, participation, social welfare, and digitalization, thereby contributing to the advancement of urban development. Cities of varying sizes across different regions of the globe have been formulating smart city strategies for numerous years. Making a city "smart" emerges as a strategy to alleviate the problems caused by urban population growth and rapid urbanization. In order to provide a smart solution to the increasing traffic density in a big city by making detailed analyzes, to develop an automatic system that does not allow new vehicles to enter when the capacity is full by directing the newly arrived vehicles to the empty spaces according to the total capacity in the parking lots, can be given as a good example. In the earthquake that took place in Kahramanmara¸s, Turkey in 23 February, we saw that a system that can automatically detect the places where earthquake victims are concentrated has already become mandatory. In any natural disaster that may occur like this, it has become very important to be able to quickly identify groups of people in the regions and provide support with the help of drones. Military use cases can be mentioned as another application area for crowd counting. Today, it is very important for unmanned vehicles, developed for military purposes, to process the images in videos or photographs and continue their duty within the framework of an algorithm. In the situations such as smuggling activities at the borders or an illegal immigration, it is becoming a great need to be able to predict people and crowds from images taken from UAVs. Crowd analysis is very important for situations that require visual surveillance such as anomalies and alarm situations. In recent years, many different methods have been proposed to perform crowd density map estimation, and it has now become the most popular method to calculate the crowd density map estimation by processing density maps. These density maps are usually calculated with the help of CNNs. Most of the crowd counting datasets in the literature consist of images collected from surveillance cameras. Such images taken at an oblique and fixed angle, with people occupying the majority of the image, taken at a distance relatively close to the drone footage. The proposed approach in this study is of great importance for emergencies where images are required to be taken by drones in the environments where there are no surveillance cameras. The developed system consists of two stages. In the first stage, we determine whether the image contains any person(s) with the help of a binary classifier. If there are persons in the input image, the crowd estimation algorithm then calculates the density map of people in the given image. This study involves the development of a crowd density map detection system that leveraged the robust feature extraction capabilities of deep CNN architectures. A binary classifier comes into play before running a CNN designed for the crowd counting task in our system. This binary classifier is included to the system to distinguish whether there is a person(s) or not in an image taken from an UAV. In order to test the performance of the proposed system we benefited from VisDrone-CC2020 dataset [1]. We used image inpainting methods on this dataset to create UAV images that do not contain any human. For binary classification, the pretrained ResNet50 model [6] was then fine-tuned on the dataset and %87 accuracy was achieved. In order to perform crowd counting, which is the second stage of this system, we used SGANet [9].SGANet has been designed specifically for this problem. We created a new architecture by adding several layers to this network. To train the network, first, ground truth density maps were created. Ground truth density maps are produced using images and labels provided by the dataset, while output density maps are learned with our SGANet. By comparing the learned density map with the ground truth density map, a loss is evaluated, and this loss is used to train our SGANet. We obtained 8.65 MAE, which is the most used metric in the crowd counting task. We then performed an error analysis for the models trained for both binary classification and crowd counting. In the model used for binary classification, we have deduced that the incorrect outputs for binary classification can be caused by the formed artifacts in the photos after image inpainting. For crowd counting, it has been deduced that small percentage errors in dense scenes affect MAE a lot, so new metrics should be developed for this problem. In addition, in photographs where the distance between the place where the photograph was taken and the ground is greatly increased, we see that the pixels representing any object on the ground and the pixels representing a person are close to each other and are very few in number. Therefore, we have deduced that in these scenes calculations are made as if there were more people than the actual number.
-
ÖgeOrder dispatching via deep reinforcement learning(Graduate School, 2022)In this thesis, the unique order dispatching problem of Getir, a retail and logistics company, has been studied. Getir serves in many cities and multiple countries, and its service area is expanding day by day. Getir, which serves thousands of customers every day in many different fields, is the pioneer of the market in this field. In this thesis, it has been studied on ultra-fast delivery, which is the first and most known service area of the company, which Getir found and started to apply as a first in the world. The aim of Getir's ultra-fast delivery business model is to deliver orders to its customers within minutes. In this business model, orders are fulfilled from the company's warehouses. It is a very challenging goal to complete order delivery in a very short time. Achieving an ultra-fast delivery goal becomes a real problem due to traffic congestion, high numbers of orders at certain times of the day or on certain days of the week. In addition, due to the Covid-19 pandemic and changing customer habits, people increasingly prefer home delivery and shopping method. For this reason, serious changes can be observed in the expected number of orders on a daily and weekly basis. Previously unknown curfews or other restrictions cause changes in the expected number of orders and their content. Therefore, it is not possible to predict these changes with data analysis and estimation methods. For these reasons, an order dispatching algorithm that can adapt to changing conditions is vital. In the ultra-fast delivery model, the goal is to serve as many customers as possible within the predetermined and promised time. Orders can be placed at any time during the working hours of the warehouses in the customer's service zone. It is decided to accept or reject the incoming order according to the order density of the relevant warehouses in the region and the courier shift plans. In the decision-making algorithm here, we recommend using a deep reinforcement learning algorithm instead of a rule-based structure that does not violate constraints. We suggest that an algorithm should be used that can keep up with the growth rate of Getir, which is a fairly fast growing company, and can adapt to the different characteristics of the regions. Before deep reinforcement learning methods that can be applied for this problem, we describe the related problem of Getir and one of the methods used by the company. We discuss the problems, limitations and shortcomings of the method used. We compare and highlight the differences between the proposed method and the current method. We measure the success of the approaches by comparing the proposed methods and the currently used methods over the actual order data. In the ultra-fast delivery business model, it is aimed to deliver the order to the user within 10-15 minutes.
-
ÖgeYabancı dil öğrenimi için otomatik gramer egzersizi üretimi üzerine kullanıcı algılarının değerlendirilmesi(Lisansüstü Eğitim Enstitüsü, 2022-12-28)Morfolojik olarak zengin dillerin yapısı gereği, dilbilgisinin kelime düzeyinde gömülü olması nedeniyle öğrenciler için dil öğrenimi ve öğretmenler için dil alıştırmaları oluşturma süreci oldukça zor hale gelebilmektedir. Kelimelerin, çoklu çekimlemeler ve türetmeler nedeniyle karmaşıklıklıkları artabilmektedir. Yabancı dil öğrenicileri için bu dillerde, bir kelimenin başsözcüğünü bulmak ve sözlükten aramak bile zorlu bir görev olabilmektedir. Bu da başlangıç seviyelerindeki öğreniciler için fazla sayıda alıştırmaya maruz kalarak dilin morfolojik yapısını öğrenme gerekliliğini ortaya çıkarmaktadır. Dil eğitimi programlarında kullanılan ders kitaplarındaki sınırlı sayıdaki alıştırmalar yeterli olmamaktadır. Bu noktada dinamik olarak üretilebilecek alıştırmaların önemi açığa çıkmaktadır. Bu doğrultuda morfolojik olarak zengin diller için sonlu durum dönüştürücüsü tabanlı morfolojik çözümleyiciler ve morfolojik üreticiler önemli bir çözüm imkanı sunmaktadır. Teknolojinin gelişmesiyle birlikte eğitim ortamlarında artan oranda dijital teknolojilerden destek alınmaktadır. Akıllı telefonların giderek daha fazla öğrenme amacıyla kullanılması ile birlikte, mobil uygulamaların eğitimdeki konumu büyümektedir. Bu doğrultuda mobil destekli dil öğrenimi konsepti giderek popüler hale gelmektedir. Ayrıca mobil destekli dil öğreniminde oyunlaştırma yaklaşımları, dil öğrenme uygulamalarının etkililiğini artırmaktadır. Bu çalışmada, karmaşık morfoloji öğrenimi için İTÜ'de geliştirilen sonlu durum dönüştürücüsü tabanlı bir oyunlaştırma yaklaşımının, yabancı dil olarak Türkçe öğrenenler üzerindeki motivasyonel ve davranışsal sonuçları araştırılmaktadır. Karmaşık morfoloji öğrenimi için önerilen oyunlaştırma yaklaşımına yönelik öğrencilerin algıları ölçülerek nicel ve nitel analizler yapılmıştır. Analizler sonucu ortaya çıkan yeni isterler neticesinde, söz konusu mobil uygulama farklı arayüz dillerini ve Türkçeden farklı dilleri destekleyebilecek bir yapıya kavuşturulmuştur. Test amaçlı olarak Fransızca dili için gerçekleme yapılmıştır. Ayrıca uygulamada sunulan alıştırmaların, gerçek hayattan alınan bağlamlar ile genişletilmesi doğrultusunda çalışmalar yürütülmüştür. Öğrenci algılarının ölçülmesi adına yürütülen deney 3 haftalık bir kapsamda sürdürülmüştür. Deneyin katılımcıları, A1 seviyesi Türkçe dil eğitimi sınıfında bulunan yabancı öğrencilerdir. Uygulama, dil eğitiminde yardımcı bir araç olarak kullanılmıştır. Katılımcıların deney süresi içerisinde, müfredatlarına paralel olarak uygulama içerisindeki bazı oyunları oynamaları istenmiştir. Deney süreci içerisinde günlük uygulaması yapılmış, deney sonunda anket ve yarı yapılandırılmış odak grup görüşmesi uygulanmıştır. Sonuçlar, katılımcıların büyük çoğunluğunun uygulama hakkında olumlu bir algıya sahip olduklarını göstermiştir. Öğrencilerin önceki mobil destekli dil öğrenimi deneyimlerine kıyasla önerilen yaklaşım, öğrencilerin müfredata uygun olan dilbilgisi alıştırmaları ihtiyaçlarını karşıladığı için beğenilmiştir. Bulgular, öğrencilerin bu yaklaşımdan sınıf ortamlarında ek materyal olarak yararlanabileceğini göstermektedir. Kullanım istatistikleri ayrıca, oyun ögelerinin öğrenciler arasında rekabeti beslediğini ve statik alıştırmalardan farklı olarak onları birbirinden farklı, dinamik olarak üretilmiş içeriklerle alıştırmaları tekrar etmeye yönlendirdiğini ortaya koymuştur. Türkçe için diğer mobil destekli dil öğrenimi uygulamaları ile deneyim sahibi olan katılımcıların görüşleri ışığında, önerilen yaklaşımın morfolojik olarak zengin diller için dilbilgisinin açık alıştırmalar aracılığı ile öğretimi alanında önemli bir boşluğu doldurduğu görülmektedir.
-
ÖgeMac sublayer protocol design and optimization for aerial swarms(Graduate School, 2023-07-28)The main objective of this thesis is to design and optimize a MAC sublayer protocol for ad hoc networks, with a primary focus on maintaining the reliabile communication. Ad hoc networks, comprising aerial swarms, provide benefits such as easy use and operation in diverse environments, thanks to their simple and economical deployment, along with their remarkable maneuverability. However, the communication standard used in these networks -IEEE 802.11 standard, widely known as Wi-Fi- is primarily designed for networks with limited mobility and minimal changes in network topology. As a result, the existing Wi-Fi standards have limitations in accommodating rapidly changing network topology. This limitation becomes particularly problematic for aerial swarms that require reliable and high-bandwidth multi-hop communication links, ultimately leading to an inability to meet the quality of service (QoS) requirements. Due to the dynamic and contested nature of ad hoc networks, ensuring reliable communication can be challenging at times. To address network management challenges in highly decentralized networks, a self-organizing TDMA-based protocol is proposed. This protocol is designed to tackle communication difficulties in ad hoc networks and optimize the overall communication process by incorporating intelligent topology management, dynamic slot assignment, slot migration, and slot releasing as key components. By integrating these features, the protocol aims to enhance communication reliability and address the specific requirements of ad hoc networks. Implementing this protocol at the data link layer allows for decentralized coordination among nodes, removing the requirement for a central unit and assuring continuous communication even in dynamically changing environments and conditions. In contrast to existing MAC-sublayer protocols, the goal of this research is to present and simulate a protocol that meets ad hoc network's specific requirements. The thesis begins with an examination and modeling of the current situation, which is followed by an outline of services, message formats, procedural rules, and sequence diagrams for the subsequent protocol design stage. The protocol's design incorporates a number of notable abilities, such as slot operations, frame size modifications, topology management, optimization in control packet exchange, and collision avoidance, all of which contribute to the protocol's successful operation. To validate the findings of this thesis, the suggested protocol is evaluated using the OMNeT++ simulation environment. In contrast to previous studies, the proposed S-TDMA protocol is assessed based on four key metrics: energy efficiency, control traffic, packet delivery ratio, and average channel utilization. The evaluation results indicate a substantial enhancement in overall channel utilization, reaching up to 55%, while also reducing control traffic overhead by approximately 13%. These outcomes highlight the effectiveness and benefits of the proposed protocol in improving network performance and resource utilization. The results of simulations provide important insights into the protocol's performance and ability to adapt to changing network conditions.