A sensor region in a single-mode optical fiber loop was created and utilized in order to study the coating effect on sensor durability and system sensitivity by the Fiber Loop Ringdown Spectroscopy (FLRDS) technique. The sensor system was simply designed without any additional optical components. The bending loss theory in the single-mode fiber (SMF) was taken into account in data calculation. After stretching was performed on 10.0 cm long coated and noncoated sensorheads from the mid-points, the strain detection limits were determined as 5.3345 με and 6.7497 με with bare and coated sensorheads, respectively. The purpose of this study is to analyze the effect of N,N-Diethyl-p-phenylenediamine (NDPD) coating of the sensorhead on the sensor durability and sensitivity. The baseline stability of the system was obtained as 1.18% by considering a hundred consecutive data. Regarding to obtained results, the difference between calculated total optical losses of FLRDS systems with noncoated and NDPD coated sensorheads shows that coating sensorhead enhanced the sensor durability and the system sensitivity. An FLRDS system with high sensitivity, simple design and easy setup offers real-time measurement with continuous monitoring and provides advantages on durability by modification the sensorhead such as NDPD coating. Due to its attractive features such as low cost, simplicity, easy setup, high sensitivity, increased durability and continuous monitoring, an FLRDS system has a wide range of application areas in structural health monitoring, transportation, early detection, biomedical, chemical trace elements, rail and asphalt applications for continuous monitoring in a real-time merit.

Ultrasonic sensing may become a useful technique for distance measurement and object detection when optical visibility is not available. However, the research on detecting multiple target objects and locating their coordinates is limited. This makes it a valuable topic. Reflection signal data obtained from a single ultrasonic sensor may be just enough for the measurements of distance and reflection strength. On the other hand, if extracted properly, a scanned set of signal data by the same sensor holds a significant amount of information about the surrounding geometries. Evaluating this dataset from a single sensor scanning can be a perfect application for convolutional neural networks (CNNs). This study proposes an imaging technique based on a scanned dataset obtained by a single low-cost ultrasonic sensor. So that images are suitable for desired outputs in a CNN, a 3D printer is converted to an ultrasonic image scanner and automated to perform as a data acquisition system for the desired datasets. A deep learning model demonstrated by this work extracts object features using convolutional neural networks (CNNs) and performs coordinate estimation using regression layers. With the proposed solution, by training a reasonable amount of obtained data, 90% accuracy was achieved in the classification and position estimation of multiple objects with the CNN algorithm as a result of converting the signals obtained from ultrasonic sensors into images.

Lagrangian description of electrical circuit model of a reluctance type accelerator system is introduced. The effectiveness of the general purpose magnetostatic finite element analysis (FEA) tools on the introduced model is demonstrated. Electrical equivalent circuit model of the system lays out many properties of the system in terms of voltage, current, power or energy distributions of electrical components, and also electrical equivalent of the mechanical components, in an easily perceivable form. These type of actuators are simple mechanisms when they are in steady state. However, when they are in transient, their dynamics may become very complex. The aim of the study is to show how this approach allows many researchers without the full electromagnetic background, to model and understand the dynamics of the reluctance type linear or rotary motor or actuators whether they are saturated or not. To exhibit the validity of the proposed model and solution of dynamic behaviors with FEA, it is verified on a basic capacitor discharge type driver circuit including the electromagnetic accelerator coil and projectile.

A novel swing-up algorithm for an inverted pendulum under a restricted cart track length is introduced. The algorithm achieves the swing-up task by means of a control law that uses a finite input set. The algorithm neither knows nor uses values of cart and pendulum masses, or pendulum length. In the swing-up process, it self-learns a weight that is used in normalizing the energy injected to the pendulum. This weight makes it possible for the algorithm to determine the number of sampling periods needed to reach the upright equilibrium state. Also, by predicting the multi-step ahead cart position, it gives rise to a control law that keeps the cart within the track and makes the pendulum reach the neighbourhood of the upright equilibrium state in a smooth manner. The validity and robustness of the swing-up algorithm are verified through laboratory experiments.

With the development of technology, target and object recognition issues are becoming increasingly important day by day. In this article, studies on ultrasonic signal processing and object recognition techniques in the literature were examined. An integrated system design that includes ultrasonic signal data obtained from different objects has been designed to contribute to the literature. With this system design, a new method is proposed that includes signal pre-processing, feature extraction and classification processes. In addition, different machine learning and artificial neural networks algorithms are used to organize and classify the data obtained from ultrasonic signals, to obtain high-performance classification rates and to achieve comprehensive object recognition. As a result of the study carried out in this context, the data set obtained from different types of objects at different distances and angles was arranged and passed through various pre-processing and statistical feature extraction methods. In conclusion, a high-performance classification rate of 97.8% using the random forest algorithm and 99.10% using the artificial neural networks algorithm was obtained during the classification phase.

A novel algorithm, called the edge determination algorithm, for exact computation of the frequency response of a linear interval system is proposed. The algorithm formulates candidate curves for the frequency response boundaries as cubic Bezier curves. The edge determination algorithm operates on the cubic Bezier control points of these curves to obtain those, or their parts, that are on the frequency response boundaries. It presents the frequency response boundaries as an array whose entries are the cubic Bezier control points of the curves on the boundaries. Examples for two different cases are presented to illustrate the mechanics and validity of the algorithm.