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Article Citation - WoS: 1Citation - Scopus: 1Neural Network Based Estimation of Resonant Frequency of an Equilateral Triangular Microstrip Patch Antenna(Univ Osijek, Tech Fac, 2013) Kapusuz, Kamil Yavuz; Tora, Hakan; Can, Sultan; Airframe and Powerplant Maintenance; Department of Electrical & Electronics EngineeringThis study proposes an artificial neural network (ANN) model in order to approximate the resonant frequencies of equilateral triangular patch antennas. The neural network structure applied here is trained and tested for both single-layer and double-layer antennas. It is shown upon experiment that the resonant frequencies obtained from the neural network are both more accurate than the calculated frequencies by formula and satisfactorily close to the measured frequencies. Results appear to be promising as per the available literature. This paper also may offer more efficient approach to developing antennas of such nature. While the total absolute error of 7 MHz and the average error of 0,09 % are achieved for single-layer antenna, the total absolute and average errors are 49 MHz and 0,07 % for the double-layered antenna, respectively.Conference Object Citation - WoS: 8Hand Gesture Classification Using Inertial Based Sensors Via a Neural Network(Ieee, 2017) Akan, Erhan; Tora, Hakan; Uslu, BaranIn this study, a mobile phone equipped with four types of sensors namely, accelerometer, gyroscope, magnetometer and orientation, is used for gesture classification. Without feature selection, the raw data from the sensor outputs are processed and fed into a Multi-Layer Perceptron classifier for recognition. The user independent, single user dependent and multiple user dependent cases are all examined. Accuracy values of 91.66% for single user dependent case, 87.48% for multiple user dependent case and 60% for the user independent case are obtained. In addition, performance of each sensor is assessed separately and the highest performance is achieved with the orientation sensor.Conference Object Citation - Scopus: 11Hand Gesture Classification Using Inertial Based Sensors Via a Neural Network(Institute of Electrical and Electronics Engineers Inc., 2017) Akan,E.; Tora,H.; Uslu,B.In this study, a mobile phone equipped with four types of sensors namely, accelerometer, gyroscope, magnetometer and orientation, is used for gesture classification. Without feature selection, the raw data from the sensor outputs are processed and fed into a Multi-Layer Perceptron classifier for recognition. The user independent, single user dependent and multiple user dependent cases are all examined. Accuracy values of 91.66% for single user dependent case, 87.48% for multiple user dependent case and 60% for the user independent case are obtained. In addition, performance of each sensor is assessed separately and the highest performance is achieved with the orientation sensor. © 2017 IEEE.
