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Conference Object Citation - Scopus: 4Convolution Neural Network (cnn) Based Automatic Sorting of Cherries(Institute of Electrical and Electronics Engineers Inc., 2021) Park,H.; Khan,M.U.Cherries are spring fruits enriched with nutrients, and are easily available in food markets around the world. Due to their excess demand, many enterprises solely focused on their processing. Cherries are especially susceptible to pathological-, physiological-diseases and structural degradation due to their soft outer skin. The post-harvest life of the fruit is limited by various characteristics. The agricultural industry has also been at the forefront to get benefits from the advanced machine learning tools. This study presents an image processing-based system for sorting cherries using the convolutional neural network (CNN). For this study, Prunus avium L cherries of export quality, available in Turkey, tagged as ‘0900 Ziraat’, are used. Surprisingly, there exists no dataset for these cherries; hence, we developed our dataset. Through the proposed approach based upon U-Net, the binary classification accuracy of 99% is achieved. Clear identification is demonstrated by the test results of varying mixture ratios of good and bad cherries. It can therefore be said that for cherry sorting and grading, U-Net can be applied as a reliable and promising machine learning tool. ©2021 IEEEConference Object Comparative Analysis Of Patch Antennas With Rectangular Slots For Laminate And Wearable Materials At 5g Networks(Institute of Electrical and Electronics Engineers Inc., 2024) Hakanoglu, B.G.; Agaya, E.; Gulmez, G.; Yalinsu, S.In this study, new multi-band patch antenna design models are proposed for use in 5G networks. The purpose of the designs is to open rectangular slots on rectangular shaped patch antennas and bring them to the desired operating conditions with parametric analyzes. The designs were carried out by following the same procedure steps using five different dielectric laminate substrate materials, such as RO3003, RT6006, FR4, RO3203, RO6010, and one denim fabric base material. The antennas were compared in terms of return loss, gain and radiation characteristics. Except for the antenna designed with RO3203 at certain values of rectangular slots, radiation at multiple frequencies was obtained at 5G frequencies. With the proposed method, improvement was observed for return loss and bandwidth characteristics in the RO3203 based antenna. This study will be a resource for antenna researchers by revealing the responses of different substrate materials to the same design method for 5G bands in patch antennas. © 2024 IEEE.Conference Object Citation - Scopus: 2Miniaturized 2.4 Ghz Antenna Design for Uav Communication Link;(Institute of Electrical and Electronics Engineers Inc., 2020) Yilmaz,V.S.; Kara,A.; Aydin,E.In many communications applications, unlike conventional antennas, lightweight, flexible, small antennas that can adapt to mechanical and industrial constraints are required. In this study, the results of antenna design operating at 2.4 GHz are presented for use in Unmanned Aerial Vehicle (UAV) tele command links. In the parametric and optimization studies carried out on the antenna, it is aimed to increase the gain while keeping the size as small as possible. The requirements of the industry, such as light, aesthetics, miniature and high gain aspects of the antenna were targeted in the design process. Finally, an antenna of 55.2x88 mm size and 7dB gain was achieved using commercial electromagnetic design tools. The designed antenna become satisfying industrial requirements with these features. © 2020 IEEE.Conference Object Internet-Of Smart Transportation Systems for Safer Roads(Institute of Electrical and Electronics Engineers Inc., 2020) Derawi,M.; Dalveren,Y.; Cheikh,F.A.From the beginning of civilizations, transportation has been one of the most important requirements for humans. Over the years, it has been evolved to modern transportation systems such as road, train, and air transportation. With the development of technology, intelligent transportation systems have been enriched with Information and Communications Technology (ICT). Nowadays, smart city concept that integrates ICT and Internet-of-Things (IoT) have been appeared to optimize the efficiency of city operations and services. Recently, several IoT-based smart applications for smart cities have been developed. Among these applications, smart services for transportation are highly required to ease the issues especially regarding to road safety. In this context, this study presents a literature review that elaborates the existing IoT-based smart transportation systems especially in terms of road safety. In this way, the current state of IoT-based smart transportation systems for safer roads are provided. Then, the current research efforts undertaken by the authors to provide an IoT-based safe smart traffic system are briefly introduced. It is emphasized that road safety can be improved using Vehicle-to-Infrastructure (V2I) communication technologies via the cloud (Infrastructure-to-Cloud - I2C). Therefore, it is believed that this study offers useful information to researchers for developing safer roads in smart cities. © 2020 IEEE.Conference Object Citation - Scopus: 31Improving Text Classification With Transformer(Institute of Electrical and Electronics Engineers Inc., 2021) Soyalp,G.; Alar,A.; Ozkanli,K.; Yildiz,B.Huge amounts of text data are produced every day. Processing text data that accumulates and grows exponentially every day requires the use of appropriate automation tools. Text classification, a Natural Language Processing task, has the potential to provide automatic text data processing. Many new models have been proposed to achieve much better results in text classification. The transformer model has been introduced recently to provide superior performance in terms of accuracy and processing speed in deep learning. In this article, we propose an improved Transformer model for text classification. The dataset containing information about the books was collected from an online resource and used to train the models. We witnessed superior performance in our proposed Transformer model compared to previous state-of-art models such as L S T M and CNN. © 2021 IEEEConference Object Citation - Scopus: 2Statistical Randomness Tests of Long Sequences by Dynamic Partitioning(Institute of Electrical and Electronics Engineers Inc., 2020) Akcengiz,Z.; Aslan,M.; Karabayir,O.; Doganaksoy,A.; Uguz,M.; Sulak,F.Random numbers have a wide usage in the area of cryptography. In practice, pseudo random number generators are used in place of true random number generators, as regeneration of them may be required. Therefore because of generation methods of pseudo random number sequences, statistical randomness tests have a vital importance. In this paper, a randomness test suite is specified for long binary sequences. In literature, there are many randomness tests and test suites. However, in most of them, to apply randomness test, long sequences are partitioned into a certain fixed length and the collection of short sequences obtained is evaluated instead. In this paper, instead of partitioning a long sequence into fixed length subsequences, a concept of dynamic partitioning is introduced in accordance with the random variable in consideration. Then statistical methods are applied. The suggested suite, containing four statistical tests: Collision Tests, Weight Test, Linear Complexity Test and Index Coincidence Test, all of them work with the idea of dynamic partitioning. Besides the adaptation of this approach to randomness tests, the index coincidence test is another contribution of this work. The distribution function and the application of all tests are given in the paper. © 2020 IEEE.Conference Object Citation - Scopus: 48Internet-Of Smart Transportation Systems for Safer Roads(Institute of Electrical and Electronics Engineers Inc., 2020) Derawi,M.; Dalveren,Y.; Cheikh,F.A.From the beginning of civilizations, transportation has been one of the most important requirements for humans. Over the years, it has been evolved to modern transportation systems such as road, train, and air transportation. With the development of technology, intelligent transportation systems have been enriched with Information and Communications Technology (ICT). Nowadays, smart city concept that integrates ICT and Internet-of-Things (IoT) have been appeared to optimize the efficiency of city operations and services. Recently, several IoT-based smart applications for smart cities have been developed. Among these applications, smart services for transportation are highly required to ease the issues especially regarding to road safety. In this context, this study presents a literature review that elaborates the existing IoT-based smart transportation systems especially in terms of road safety. In this way, the current state of IoT-based smart transportation systems for safer roads are provided. Then, the current research efforts undertaken by the authors to provide an IoT-based safe smart traffic system are briefly introduced. It is emphasized that road safety can be improved using Vehicle-to-Infrastructure (V2I) communication technologies via the cloud (Infrastructure-to-Cloud - I2C). Therefore, it is believed that this study offers useful information to researchers for developing safer roads in smart cities. © 2020 IEEE.Conference Object Securing the Internet of Things: Challenges and Complementary Overview of Machine Learning-Based Intrusion Detection(Institute of Electrical and Electronics Engineers Inc., 2024) Isin, L.I.; Dalveren, Y.; Leka, E.; Kara, A.The significant increase in the number of IoT devices has also brought with it various security concerns. The ability of these devices to collect a lot of data, including personal information, is one of the important reasons for these concerns. The integration of machine learning into systems that can detect security vulnerabilities has been presented as an effective solution in the face of these concerns. In this review, it is aimed to examine the machine learning algorithms used in the current studies in the literature for IoT network security. Based on the authors' previous research in physical layer security, this research also aims to investigate the intersecting lines between upper layers of security and physical layer security. To achieve this, the current state of the area is presented. Then, relevant studies are examined to identify the key challenges and research directions as an initial overview within the authors' ongoing project. © 2024 IEEE.Conference Object Citation - Scopus: 5Topic-Controlled Text Generation(Institute of Electrical and Electronics Engineers Inc., 2021) Çağlayan,C.; Karakaya,M.Today, the text generation subject in the field of Natural Language Processing (NLP) has gained a lot of importance. In particular, the quality of the text generated with the emergence of new transformer-based models has reached high levels. In this way, controllable text generation has become an important research area. There are various methods applied for controllable text generation, but since these methods are mostly applied on Recurrent Neural Network (RNN) based encoder decoder models, which were used frequently, studies using transformer-based models are few. Transformer-based models are very successful in long sequences thanks to their parallel working ability. This study aimed to generate Turkish reviews on the desired topics by using a transformer-based language model. We used the method of adding the topic information to the sequential input. We concatenated input token embedding and topic embedding (control) at each time step during the training. As a result, we were able to create Turkish reviews on the specified topics. © 2021 IEEEConference Object Packet Header Classification for Network Intrusion Detection System Based on Fpga(Institute of Electrical and Electronics Engineers Inc., 2022) Dakhil,Y.H.; Ozbek,M.E.; Al-Kaseem,B.R.Network security is becoming a key problem in data communication via the Internet. Classifying the incoming packets on network devices is one of the ways that increases network se-curity. Packet header classification is a major strategy for secure networking and connectivity. An intrusion detection system (IDS) is necessary for network devices to protect the network's traffic. Packet classification is a mechanism used by Internet services and security tools to examine each incoming packet against predetermined rules. This paper introduces a new algorithm for packet header classification based on a field-programmable gate array (FPGA) using the finite state machine (FSM) technique. The introduced algorithm compares each header field of an incoming packet to a predefined rule stored in a block read-only memory (ROM) of the FPGA chip to identify matches and then executes certain snort rules to classify them. The selected FPGA platform in this work exhibited high processing speed, particularly in digital system design. The presented algorithm was written using Verilog programming language and executed in Xilinx Vivado 18.2 software. The final program was uploaded to the Artix-7 FPGA development board. The simulation results demonstrated that the developed algorithm successfully classified the incoming packets as required with a maximum throughput that reached 100 Mbps. © 2022 IEEE.
