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.

Telerehabilitation systems provide some advantages against the classic rehabilitation methods. The ability of the shoulders depends on active motion range of them to do activities in daily life and to do sports. To evaluate the shoulder motions, range of motion (ROM) measurement is a basic method. Clinical goniometer and digital goniometer are the most commonly used measurement tools. However, these measurement tools have some deficiencies and difficulties. In this paper, we consider a Kinect One Sensor (Kinect V2) based measurement system for shoulder motions as an alternative method. The aim of this study is to examine the reliability and validity analyzes of the proposed shoulder measurement system. Three systems were used to evaluate validity of the Kinect V2 to measure shoulder motions: Kinect V2 based system, clinical goniometer and digital goniometer. One expert physical therapist measured shoulder abduction, flexion, external rotation, internal rotation and extension ROM values using a clinical goniometer and a digital goniometer in 40 healthy volunteers (22 males, 18 females, and 19-33 years old). All poses for each shoulder motion were captured with the Kinect V2 based system again and the ROM values were calculated. These procedures were carried out with all of the volunteer participants in three repetitions. In reliability for Kinect V2 based shoulder motion measurement system, we used the intraclass correlation coefficients (ICC), standard error of the measure (SEM), minimal detectable change (MDC). The validity test includes the 95% limits of agreement (LOA) and mean difference between the Kinect V2 based system and the both of the goniometer systems for measuring shoulder motions. The high ICC values show that the Kinect V2 based shoulder motion measurement system has very good intra-rater reliability for abduction, flexion, external rotation, internal rotation shoulder poses. For extension pose, it has good reliability result according to the ICC value. The validity analysis gives good results for all shoulder poses except internal rotation between Kinect V2 and clinical/digital goniometer. As a result, Kinect V2 based measurement system is a reliable and valid alternative telerehabilitation tool for shoulder motions.

This paper presents dynamic neural-network-based model-predictive control (MPC) structure for a baker's yeast drying process. Mathematical model consists of two partial nonlinear differential equations that are obtained from heat and mass balances inside dried granules. The drying curves that are obtained from granule-based model were used as training data for neural network (NN) models. The target is to predict the moisture content and product activity, which are very important parameters in drying process, for different horizon values. Genetic-based search algorithm determines the optimal drying profile by solving optimization problem in MPC. As a result of the performance evaluation of the proposed control structure, which is compared with the model based on nonlinear partial differential equation (PDE) and with feedforward neural network (FFN) models, it is particularly satisfactory for the drying process of a baker's yeast.