Formation Control of Multiple Autonomous Mobile Robots Using Turkish Natural Language Processing

People use natural language to express their thoughts and wishes. As robots reside in various human environments, such as homes, offices, and hospitals, the need for human–robot communication is increasing. One of the best ways to achieve this communication is the use of natural languages. Natural language processing (NLP) is the most important approach enabling robots to understand natural languages and improve human–robot interaction. Also, due to this need, the amount of research on NLP has increased considerably in recent years. In this study, commands were given to a multiple-mobile-robot system using the Turkish natural language, and the robots were required to fulfill these orders. Turkish is classified as an agglutinative language. In agglutinative languages, words combine different morphemes, each carrying a specific meaning, to create complex words. Turkish exhibits this characteristic by adding various suffixes to a root or base form to convey grammatical relationships, tense, aspect, mood, and other semantic nuances. Since the Turkish language has an agglutinative structure, it is very difficult to decode its sentence structure in a way that robots can understand. Parsing of a given command, path planning, path tracking, and formation control were carried out. In the path-planning phase, the A* algorithm was used to find the optimal path, and a PID controller was used to follow the generated path with minimum error. A leader–follower approach was used to control multiple robots. A platoon formation was chosen as the multi-robot formation. The proposed method was validated on a known map containing obstacles, demonstrating the system’s ability to navigate the robots to the desired locations while maintaining the specified formation. This study used Turtlebot3 robots within the Gazebo simulation environment, providing a controlled and replicable setting for comprehensive experimentation. The results affirm the feasibility and effectiveness of employing NLP techniques for the formation control of multiple mobile robots, offering a robust and effective method for further research and development on human–robot interaction

Embedded and Smart Systems Education and Laboratory in Fatih Sultan Mehmet Vakıf University

2000’li yıllarda başlayan teknolojik gelişmeler hepsi programlanabilen daha akıllı sistemlerin ev, iş ve endüstriyel ortamlardan, enerji üretim ve dağıtım alanlarına kadar çeşitli yerlerde yaygın olarak kullanılacağını göstermektedir. Her türlü akıllı nesneden veya sistemden veri alış verişinin, akıllı ortamlar üstünden akıllı şebekelere bağlanarak, taşınan ve kullanılan veri miktarında kısa sürede çok önemli artış olacağı görülmektedir. Ülkemizde bu alanda yetiştirilecek yani açığı kapatacak bir mühendis için kazandırılacak nitelikler ile teorik ve uygulama bilgi-becerisinin dikkatle belirlenmesi gerekmektedir. İş dünyasının üniversitelerden beklentisi, işyerine alacakları mühendislerin güncel teknolojik bilgi ve uygulama becerisi ile kolay uyum sağlaması, yeni teknolojileri araştırması ve geliştirmesi olmaktadır. Bu çalışmada, Fatih Sultan Mehmet Vakıf Üniversitesi, Bilgisayar Mühendisliği Bölümündeki ders programı ile üst düzey teknolojiye sahip laboratuarlarda, Akıllı Sistemler, Akıllı Nesneler, Akıllı Ortamlar, Siber Fiziksel Sistemler, Nesnelerin İnterneti, Nesnelerin Webi gibi kavramlarla isimlendirilen teknolojilere ait teori-uygulama bilgi ve becerisinin kazandırıldığı öğretim yapımız açıklanacaktır. In the 2000s, starting with the technological advances, All Programmable and smarter systems for home, business and industrial environments, energy production and distribution so as to use the common areas of the various places. All kinds of smart object or system data is exchanged, over smart environments, connected through smart grids. The amount of data that is used in a short time would be very significant increase can be seen. In our country, will raise the deficit in this area, so an engineer for theoretical and application information-capability with the traits constituting will also carefully determination is required. New graduate engineers with up-to-date technology information and application ability to easily adapt to new technologies, to provide research and development capability would like to be. In this study, Fatih Sultan Mehmet Vakıf University, Department of Computer Engineering in courses and senior tech laboratories, Intelligent Systems, Smart Objects, Smart Media, Cyber Physical Systems, the object of the Internet, the object of the Web with concepts such as naming the technologies related to the theory-practice knowledge and skills our teaching is imparted structure will be described

Neural Architecture Search Using Metaheuristics for Automated Cell Segmentation

Deep neural networks give successful results for segmentation of medical images. The need for optimizing many hyper-parameters presents itself as a significant limitation hampering the effectiveness of deep neural network based segmentation task. Manual selection of these hyper-parameters is not feasible as the search space increases. At the same time, these generated networks are problem-specific. Recently, studies that perform segmentation of medical images using Neural Architecture Search (NAS) have been proposed. However, these studies significantly limit the possible network structures and search space. In this study, we proposed a structure called UNAS-Net that brings together the advantages of successful NAS studies and is more flexible in terms of the networks that can be created. The UNAS-Net structure has been optimized using metaheuristics including Differential Evolution (DE) and Local Search (LS), and the generated networks have been tested on Optofil and Cell Nuclei data sets. When the results are examined, it is seen that the networks produced by the heuristic methods improve the performance of the U-Net structure in terms of both segmentation performance and computational complexity. As a result, the proposed structure can be used when the automatic generation of neural networks that provide fast inference as well as successful segmentation performance is desired

Differential evolution-based neural architecture search for brain vessel segmentation

Brain vasculature analysis is critical in developing novel treatment targets for neurodegenerative diseases. Such an accurate analysis cannot be performed manually but requires a semi-automated or fully-automated approach. Deep learning methods have recently proven indispensable for the automated segmentation and analysis of medical images. However, optimizing a deep learning network architecture is another challenge. Manually selecting deep learning network architectures and tuning their hyper-parameters requires a lot of expertise and effort. To solve this problem, neural architecture search (NAS) approaches that explore more efficient network architectures with high segmentation performance have been proposed in the literature. This study introduces differential evolution-based NAS approaches in which a novel search space is proposed for brain vessel segmentation. We select two architectures that are frequently used for medical image segmentation, i.e. U-Net and Attention U-Net, as baselines for NAS optimizations. The conventional differential evolution and the opposition-based differential evolution with novel search space are employed as search methods in NAS. Furthermore, we perform ablation studies and evaluate the effects of specific loss functions, model pruning, threshold selection and generalization performance on the proposed models. The experiments are conducted on two datasets providing 335 single-channel 8-bit gray-scale images. These datasets are a public volumetric cerebrovascular system dataset (vesseINN) and our own dataset called KUVESG. The proposed NAS approaches, namely UNAS-Net and Attention UNAS-Net architectures, yield better segmentation performance in terms of different segmentation metrics. More specifically, UNAS-Net with differential evolution reveals high dice score/sensitivity values of 79.57/81.48, respectively. Moreover, they provide shorter inference times by a factor of 9.15 than the baseline methods

Neuropathic Pain Frequency in Neurology Outpatients: A Multicenter Study

Introduction: Neuropathic pain is common, but the frequency of misdiagnosis and irrational treatment is high. The aim of this study is to evaluate the rate of neuropathic pain in neurology outpatient clinics by using valid and reliable scales and review the treatments of patients