Today’s technology allows people to remotely control and monitor many systems. In these technological systems, robots or unmanned vehicles are generally used, which are controlled remotely without human interaction. Unmanned aerial vehicle (UAV), which does not have a pilot on it, is one of the unmanned vehicles capable of flying either remotely or automatically. UAVs are among the systems that are used in many fields for military, civil, and academic purposes and are constantly developing in parallel with the advancement of technology. One of the biggest problems of UAVs that are effectively used in many areas is undoubtedly flight times. To overcome this situation, charging stations are used and allow the UAV batteries to be charged without human intervention. In this article, a review study about the charging stations developed for charging batteries used in UAVs has been made. In this study, the findings obtained as a result of literature research on charging stations were analyzed and the performances of charging stations were compared. The main purpose of this review study is to guide people who will develop a charging station for rotary-wing UAVs by providing a preliminary research opportunity and to help choose one of the wired or wireless charging stations according to the needs in the applications to be developed.

Advancements in artificial intelligence have significantly enhanced communication for individuals with hearing impairments. This study presents a robust cross-lingual Sign Language Recognition (SLR) framework for Turkish, American English, and Arabic sign languages. The system utilizes the lightweight MediaPipe library for efficient hand landmark extraction, ensuring stable and consistent feature representation across diverse linguistic contexts. Datasets were meticulously constructed from nine public-domain sources (four Arabic, three American, and two Turkish). The final training data comprises curated image datasets, with frames for each language carefully selected from varying angles and distances to ensure high diversity. A comprehensive comparative evaluation was conducted across three state-of-the-art deep learning architectures—ConvNeXt (CNN-based), Swin Transformer (ViT-based), and Vision Mamba (SSM-based)—all applied to identical feature sets. The evaluation demonstrates the superior performance of contemporary vision Transformers and state space models in capturing subtle spatial cues across diverse sign languages. Our approach provides a comparative analysis of model generalization capabilities across three distinct sign languages, offering valuable insights for model selection in pose-based SLR systems.

Hava kirliliği, günümüzde karşılaştığımız en büyük problemlerden bir tanesidir. İnsan sağlığı, iklim ve ekosistem üzerinde olumsuz etkilere yol açan hava kirliliğinin artmasında, atmosferdeki kirleticilerin miktarlarındaki artışın yanı sıra iç ortamlardaki hava kalitesininde katkısı olduğu bir gerçektir. Günlük yaşantısının büyük bir kısmını kapalı iç ortamlarda (evlerde, ofislerde, okullarda, hastanelerde, alışveriş merkezlerinde vb.) geçiren insanlar, ortamın havasındaki zararlı gazlardan ve partikül maddelerden etkilenmekte ve sağlık sorunları yaşayabilmektedir. Bu tarz ortamlardaki yaşam standartlarının insan yaşamı için optimum değerlerde tutulması ve insan sağlığını tehdit edebilecek faktörlerin belirlenerek ortadan kaldırılması ya da etkilerinin azaltılması gerekmektedir. Teknolojik gelişmeler sayesinde ortamın hava kalitesinin belirlendiği sistemler ile bu tarz sorunların ortadan kaldırılması mümkündür. Bu çalışma kapsamında, IoT tabanlı iç ortam hava kalitesi izleme sistemi gerçekleştirilmiştir. Oluşturulan sistem içerisinde Raspberry Pi 3 kontrol kartı, sıcaklık, nem, gaz ve ışık sensörleri kullanılmıştır. Sensörler aracılığıyla alınan iç ortam verileri, kontrol kartı aracılığıyla işlenerek elde edilmiş ve veri tabanına kaydedilmiştir. Hazırlanan web arayüzü ile iç ortamdaki yaşam koşullarına ilişkin bilgiler anlık olarak takip edilebilmektedir. Geliştirilen hava kalitesi formülü kullanılarak iç ortam hava kalitesi hesaplanarak kullanıcılara aktarılmaktadır. Taşınabilir donanım platformu ile herhangi bir iç ortamın hava kalitesinin tespit edilmesi sağlanmaktadır.

Elektronik sistemlerin ayrılmaz bir parçası olan bataryalar, güç kaynağı olarak önemli roller oynamaktadırlar. Günümüzde mobil araçlar, elektronik cihazlar ve yedek güç gibi birçok alanda şarj edilebilir bataryalar kullanılmaktadır. Bataryaların şarj veya deşarj işlemi sırasında takip edilmesi, performans açısından bataryanın ömrünün korunmasını ve güvenliğini sağlamaktadır. Batarya Yönetim Sistemleri (BMS: Battery Management Systems), bataryanın sahip olduğu hücreleri, balanslı bir şekilde şarj veya deşarj ederek hücrelerdeki dengenin korunmasını sağlamaktadır. Bu sistemler, bataryaların ömrünü uzatabilme ve optimize edebilme imkânı sağlar. Bu çalışma kapsamında, İnsansız Hava Araçlarında (İHA) kullanılan Lityum Polimer (LiPo) bataryalarının şarj işlemi sırasında takip edilmesine imkân sağlayacak bir sistem gerçekleştirilmiştir. Geliştirilen sistem ile LiPo bataryasının hücre gerilim değerleri, hazırlanan bir arayüz ile gerçek zamanlı olarak takip edilebilmektedir.

Recent advancements in autonomous vehicle technologies are transforming intelligent transportation systems. Artificial intelligence enables real-time sensing, decision-making, and control on embedded platforms with improved efficiency. This study presents the design and implementation of an autonomous radio-controlled (RC) vehicle prototype capable of lane line detection, obstacle avoidance, and navigation through dynamic path planning. The system integrates image processing and ultrasonic sensing, utilizing Raspberry Pi for vision-based tasks and Arduino Nano for real-time control. Lane line detection is achieved through conventional image processing techniques, providing the basis for local path generation, while traffic sign classification employs a You Only Look Once (YOLO) model optimized with TensorFlow Lite to support navigation decisions. Images captured by the onboard camera are processed on the Raspberry Pi to extract lane geometry and calculate steering angles, enabling the vehicle to follow the planned path. In addition, ultrasonic sensors placed in three directions at the front of the vehicle detect obstacles and allow real-time path adjustment for safe navigation. Experimental results demonstrate stable performance under controlled conditions, highlighting the system’s potential for scalable autonomous driving applications. This work confirms that deep learning methods can be efficiently deployed on low-power embedded systems, offering a practical framework for navigation, path planning, and intelligent transportation research.

The progress on today's technologies has brought many advantages. Wearable technologies, telecommunication, robotic systems, 3d printers and many other applications provide great convenience for human life. Sensor technologies are also among these applications. Performing a process by controlling or co-operating the sensors also provides a significant benefit in eliminating difficulties for humans. One of the places where sensors are used is recognition systems. In the recognition process, the user materials are used such as the card, fingerprint, iris, face etc. Digital identification systems are recognition systems that can be done with various sensors and provide various benefits for the users. In the scope of this study, student attendance management system that comprises two stage control processes has been realized. At the first stage, the student ID card is read in the RFid card reader module and then the person information is checked from the database and go to the second stage. At the second stage, the fingerprint of the student is read by fingerprint sensor and the student is given the possibility to transition after the database control. Time and login information are recorded in the database when the student uses the system. Thanks to this system, it is aimed to get rid of some problems, such as sign someone else's, reading the student card someone else's, etc., in the student attendance management system.