LEE- Bilgisayar Mühendisliği-Yüksek Lisans
Bu koleksiyon için kalıcı URI
Gözat
Son Başvurular
1 - 5 / 20
-
ÖgeAnthropometric measurements from images(Graduate School, 2023-07-18)In this work, a system that simultaneously estimates several anthropometric measurements (namely, height and the circumferences of the bust, waist, and hip) using only two 2D images of a human subject has been proposed and tested. The proposed system has two components: a customized camera setup with four laser pointers and image analysis software. The camera setup includes an Android smartphone, four laser pointers around the smartphone's camera, and a tripod carrying the hardware. The image analysis software is a web-based application that has not been publicly available. The application takes the images as input, processes them, and yields the aforementioned anthropometric measurements in the unit of centimeters. The pipeline of the proposed system has the following components: 1. Feeding the images to the software, 2. Determining the locations of the body parts that will be measured, 3. Calculating the width of the body part on the specific location in both images (anterior and lateral), 4. Transforming pixel widths into physical units, 5. Estimating the circumference of the body part (or the height). For determining the locations of the body parts that will be measured, the software model applies pre-trained pose estimation and body segmentation models to both input images. For pose estimation, the MediaPipe framework, a tool developed for constructing pipelines based on sensory data, has been used. For body segmentation, BodyPix 2.0 in TensorFlow, a powerful tool that can perform whole-body segmentation on humans in real time, has been adopted. With the help of these models, body parts to be measured has been located on the input images. The width of a body part is measured as the largest distance between the left and right sides of the specific body part on the image. Laser points attached to the camera are leveraged while transforming pixel widths into physical units (i.e., centimeters). The last step of the measurements is converting the width into circumference. It is assumed that the cross-sectional areas of the body parts that are focused on in this research, namely, the bust, waist, and hip, are elliptical, and the circumferences of these body parts correspond to the perimeters of these ellipses. With the axes of the ellipses in hand, it is possible to estimate these anthropometric measurements. In order to evaluate the performance of the model, experiments were done on 19 volunteer human subjects. The actual measurements of these subjects were collected with traditional manual methods. The results obtained from the proposed model were compared with the actual measurements of the subjects, and the relative percentage errors were evaluated. The proposed hardware is a developed version of the prototype that was designed to assess the validity of the idea. The experiments described in this work, include the previous version of the proposed camera setup for better analysis and comparison. During the image collection stage of the experiment, the subjects that participated in the experiments are photographed with both versions of the camera setup, and the images are processed with software that is calibrated for individual camera setups. Finally, collected images are fed to a commercially available system that creates 3D meshes of humans from 2D images. This product can estimate body measurements from these meshes. For comparing the proposed system to a commercial product, this tool is included to the experiments. The images collected from the subjects who participated in the experiment are processed with the three systems mentioned earlier: the initial prototype, the improved version, and the commercially available tool. The results show that the initial prototype's relative errors for the bust, waist, and hip circumferences and height are 7.32%, 9.7%, 7.12%, and 5.0%, respectively. For the improved version, the errors become 15.97%, 9.92%, 2.01%, and 4.43%. The commercial product included in the study has relative errors of 7.8%, 10.69%, 12.43%, and 3.33% for the aforementioned body measurements. The main advantage of the proposed system over the alternative automatic methods is that, unlike the state-of-the-art measuring techniques, our method does not require predefined environmental conditions such as a specific background, a predetermined distance from the camera, or some clothing constraints. The lack of these restrictions makes the proposed system adaptable to various conditions, such as indoor and outdoor environments. The target user profile for this application would be medical practitioners, personal trainers, and individuals who want to keep track of their weight-loss progress since the system is lightweight, easy to use, and adaptable to various environments.
-
ÖgeMQTT-CT: İntelligent MQTT protocol with cloud integration(Graduate School, 2023-06-20)The MQTT protocol, named Message Queuing Telemetry Transport, has become widely recognized as a superior communication protocol in the Internet of Things (IoT) community. However, conventional MQTT protocols described in existing literature have limitations in supporting distributed environments and scalability. To address these limitations, a more advanced MQTT protocol called MQTT-ST has been developed, which offers bridging capabilities within distributed environments, making it an attractive choice for IoT systems. We have created a better version of our MQTT protocol called MQTT-CB. Our upgraded MQTT-ST protocol has added features like intelligence, scalability, and distribution using containers, making it easy to transport and deploy. Moreover, we've made deploying a cloud-based architecture that takes advantage of cloud technology even simpler. Our research focuses on enhancing the MQTT-ST protocol by incorporating intelligence capabilities. We utilize LSTM (Long Short-Term Memory) network, a cutting-edge deep-learning model that can capture intricate patterns over time. In addition, our protocol uses predictive algorithms that enable it to anticipate retransmitted packets, dynamically adjust the number of brokers in real-time, and reduce brokers when clients are inactive. We have extensively tested our protocol MQTT-CB with MQTT-ST. As a result, MQTT-CB performs better than traditional MQTT-ST protocols in reducing latency between subscribers and publishers. This provides better efficiency and responsiveness in IoT systems. Furthermore, our protocol adapts to publication rate changes and provides robustness in dynamic environments. MQTT-CB is a dependable and effective means of communication for IoT applications. Its ability to seamlessly adapt to changing conditions makes it ideal for IoT systems deployed in distributed environments. MQTT-CB opens up new possibilities for IoT solutions that can operate effectively in various scenarios where scalability, intelligence, and distribution capabilities are crucial for success. In summary, MQTT-CB significantly advances MQTT-ST protocols, introducing intelligence, scalability, and distribution to enable efficient and reliable communication between IoT devices. Furthermore, with its integration of the predictive LSTM algorithm, MQTT-CB optimizes the performance of the MQTT-ST protocol, showing the way for enhanced IoT applications with improved responsiveness and adaptability in distributed environments. The content of this thesis, including the methodology and results presented in all sections, is based on my research paper titled "MQTT-CB: Cloud Based Intelligent MQTT Protocol".
-
ÖgeUnveiling the wireless network limitations in federated learning(Graduate School, 2022-05-27)Huge increase in edge devices over the world with powerful processors inspired many researchers to apply decentralized machine learning techniques so that these edge devices can contribute to train deep neural networks. Among those decentralized machine learning schemes, federated learning has gained tremendous sympathy as it grants privacy to the edge devices as well as diminishing communication costs. This is because federated learning does not need to access raw data nor store it, instead, clients would learn from their raw data locally and produce gradient updates. These gradient updates would be aggregated at the server. The raw data is kept at clients untouched, to a degree that only the trained gradient updates are shared with the parameter server. As a matter of fact, the privacy and security issues are mostly scaled down and the ML models instead of raw data would save communication overhead. Considering these issues, federated learning has emerged from distributed and decentralized learning yet it revolutionizes the training as it aggregates the locally trained ML models by edge devices. A typical federated learning scheme which is investigated in the thesis, includes many number of clients who calculate the gradient of the loss function by applying stochastic gradient descent method and it also consists of an aggregator that collects these gradients in each communication round. In each round, only randomly selected number of clients participate in federated learning with their calculated gradient. The gradient descent is estimated according to the local batch size which is the fraction of client's local raw data. Collected gradients by the server are averaged in the server and the averaged gradient is disseminated to the clients back. It is expected to see the convergence after many communication rounds, as many clients are anticipated to contribute and therefore train the model in the server about the data. Yet, the issues related to the network limitations for the federated learning process are not covered in the literature. In such federated learning applications and simulations, the network is assumed to be stable and the limitations that come with unstable network are overlooked. These simulations are mostly written on Python and the essential network settings are implicitly asserted. Quality of Service (QoS) parameters such as packet drop ratio and delay are not considered, however they stand as key factors for federated learning convergence since they can slow down or even prevent the convergence process. In fact, there are federated learning applications proposed in the literature which are real-time such as cache-based popular content prediction applications. Meaning that these applications are sensitive to packet drops and delays that are caused by the network. Therefore, delay and packet drops in the network must be thoroughly examined in order to make such federated learning applications feasible. To this end, an advanced federated learning simulation is introduced and results are shared in this study. The simulation includes not only clients and server which are producing gradient updates, but also a full network backbone which allows the observation of the QoS parameters in the federated learning process. To be able to achieve this, a network which consists of clients and the server of the federated learning is simulated using reputable NS3 (Network Simulator 3). The network is designed as dumbbell topology which includes 100 clients on the left hand side of the dumbbell and server on the right hand side. This makes the left router to be the bottleneck, thus the background traffic in the network causes packet drops there. Additional node to generate background traffic is placed in the same side with clients so that the packet drops are observed and the intensity of the packet drops can be arranged by a hyperparameter which is called the interarrival time of the packets that are generated via background traffic. Poisson distribution based background traffic is produced in the manner of the interarrival time between packets at the traffic generator node. By applying ns3-ai framework which enables NS3 and python processes to communicate, the network and the federated learning process are run simultaneously so that the observations on QoS can be made. Since millions of devices are expected to be involved in a federated learning application in which the speed of converge is essential and not all of the clients updates may increase the convergence, UDP (User Datagram Protocol) is utilized as transport layer protocol. These gradient updates are fragmented to UDP packets and are sent from clients to servers and servers to clients. Thus, whenever a UDP packet that carries client update is dropped, the whole client update must be discarded. As a result, discarded clients reduce the performance of the federated learning and cause significant drawbacks to the application. Initially, the experiment is validated by running countless simulations with different seed values. Validation is carried out by testing the reproducibility of the same experiments by comparing cross entropy error, accuracy of both server and clients and also packet drop rates. For various interarrival time values ranging from 250 milliseconds to 900 milliseconds many simulation scenarios are designed. The replication method is used to evaluate the results. This means that each scenario with different seeds are run 10 times and the results are presented with \%95 confidence interval. Among those scenarios, three of them are picked and are tagged as heavy, medium and light traffic intensity which correspond to 250, 400, 900 milliseconds interarrival time, respectively. The results are presented by giving maximum error rates, average success rates and per round test accuracies. The most erroneous batch that is detected in aggregated gradient at server is presented by maximum error percentage after each communication round. It shows the worst performing model and it is meaningful to demonstrate the unfavorable consequences of the background traffic to the performance. With heavy intensity traffic, maximum error percentage goes up to \%80 after round 90, whereas maximum error percentage is between \%10 and \%20 with light traffic. This shows the federated learning application's early vulnerability to the background traffic. With the assumption of the network being completely stable, then average success percentage of client update delivery becomes \%100. However, it is not realistic and average success percentage reduces and fluctuates according to the traffic intensity. As the traffic gets intense, less client updates are received by the parameter server for a successful aggregation. Finally, the test accuracy of various intensity traffic configurations are presented. Packet drops because of the bottleneck queue capacity overflow causes tremendous decrease for the test accuracy which is crucial for any federated learning application. For at least 200 communication rounds, the decline in the accuracy is evidently visible when the traffic is intense. More specifically, \%90 accuracy is reached over 120 rounds for high intensity traffic, while it is reached around 60 rounds for light traffic. The intensity of the background traffic becomes highly crucial consideration for potential time-critical federated learning applications. Confidence intervals on test accuracy are presented according to the traffic intensity. The convergence is achieved no matter what the traffic intensity is. Wide intervals can be seen in earlier rounds and it gets slightly wider if the intensity is higher. In addition to these, according to the traffic intensity or interarrival time, the amount of traffic data, the number of packets that are produced by the background traffic generator node, the data delivery rate and monitored interarrival time are presented as well. In the light of these results, an adaptive federated learning is proposed in order to cope with heavy intensity traffic. By using network metrics such as upload rate, transmission and queueing delay, the maximum number of clients that can be fit in a communication round is calculated and set as participation rate. This allows server to receive more client updates and increasing the performance of the federated learning under heavy background traffic
-
ÖgeEvent extraction from Turkish Trade Registry Gazette(Graduate School, 2023-05-16)The Turkish Trade Registry Gazette is the official gazette published by The Union of Chambers and Commodity Exchanges of Türkiye. Companies announce crucial events like change in management, change in capital or bankruptcy in the gazette. In many industries, the gazette is used as an important source of information and intelligence. The gazette has a history of almost 70 years. The issues are also publicly available on the internet in image PDF format. This format is both hard to read for humans and hard to process for computers. On top of that, since the gazette has been published in newspaper layout, the text is usually in columns. In later issues of the gazette, some information can be given in tables. Although optical character recognition looks like a viable option for text extraction, it must be supported with image processing. To extract information from the Turkish Trade Registry Gazette, announcements of selected companies between January 2014 and August 2022 were collected. The collected data consists of PDF documents of gazette pages for the selected companies and related metadata. The metadata contains information about issue number, page number and what type of announcement the company has on the given page. Text was extracted using an image processing and optical character recognition pipeline. After the text was extracted, it was manually annotated. Since the text is extracted from the whole document, it contains multiple announcements. Thus, announcement boundaries were annotated. Based on the most important and frequent announcement types encountered in the Turkish Trade Registry Gazette, four event types were defined: Composition with Creditors, Notice to Creditors, Change in Management and Change in Working Capital. Events consist of triggers that signal the occurrence of the event, event arguments that specify general and event-specific entities involved in the events and event roles that define the relations between triggers and arguments. Using these definitions, triggers, arguments and roles were defined and annotated for each of these event types. Using announcement boundaries, an announcement splitting model was trained. After all collected announcements were split using this model, announcements listed in the metadata table were located in the pages and an announcement classification dataset with 16 announcement types was created. Using this dataset, an announcement classification model was trained. Since announcements are documents of varying lengths, the effect of context was observed. The announcement classification model achieves an F1 score of 0.83. For trigger and argument extraction, experiments were carried on in different settings. The effect of IOB tags, an added CRF layer and handling argument and trigger extraction separately were observed. The best performing model was determined to be the two-stage one that does not use IOB tags or a CRF layer, with a micro F1 score of 82.5. For event extraction, a rule-based model and Doc2EDAG [1] were explored. Although the rule-based model performs better on simpler event types, Doc2EDAG was found to be better with a micro F1 score of 73.9 on gold arguments and 54.2 on predicted arguments. Four approaches were proposed to improve the performance. Of these, removing the CRF layer and applying transfer learning yielded improved micro F1 scores of 74.9 and 75.2 over gold arguments and 60.5 and 62.9 over predicted arguments, respectively. The other two proposed methods, namely, turning off path expansion memory and field-aware path expansion yielded poorer results than the baseline.
-
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.