RSS-based wireless LAN indoor localization and tracking using deep architectures

Wireless Local Area Network (WLAN) positioning is a challenging task indoors due to environmental constraints and the unpredictable behavior of signal propagation, even at a fixed location. The aim of this work is to develop deep learning-based approaches for indoor localization and tracking by utilizing Received Signal Strength (RSS). The study proposes Multi-Layer Perceptron (MLP), One and Two Dimensional Convolutional Neural Networks (1D CNN and 2D CNN), and Long Short Term Memory (LSTM) deep networks architectures for WLAN indoor positioning based on the data obtained by actual RSS measurements from an existing WLAN infrastructure in a mobile user scenario. The results, using different types of deep architectures including MLP, CNNs, and LSTMs with existing WLAN algorithms, are presented. The Root Mean Square Error (RMSE) is used as the assessment criterion. The proposed LSTM Model 2 achieved a dynamic positioning RMSE error of 1.73 m, which outperforms probabilistic WLAN algorithms such as Memoryless Positioning (RMSE: 10.35 m) and Nonparametric Information (NI) filter with variable acceleration (RMSE: 5.2 m) under the same experiment environment.ECSEL Joint Undertaking ; European Union's H2020 Framework Programme (H2020/2014-2020) Grant ; National Authority TUBITA

Adaptation of California Measure of Mental Motivation –CM3

Education without doubt, plays a vital role for individuals to gain the essential personal traits of the 21st century, also known as “knowledge age”. One of the most important skills among these fundamental qualities which the individuals should be equipped with is critical thinking. California Measure of Mental Motivation-CM3 was developed by Giancarlo, Blohm & Urdan (2004) to assess secondary school students’ critical thinking dispositions which is one of the characteristic components of critical thinking. Detecting secondary school students’ dispositions toward critical thinking is desired to be one of the main foci in many Turkish educational studies; however, it seems there is no Turkish psychological measurement instrument, assessing this feature among these age groups. Adapting this scale into Turkish culture will support the future studies on critical thinking by supplying a new instrument to the psychological measurement literature. The adaptation process has revealed that CM3 retains a four-factor structure in Turkish culture, similarly to the original form, and this structure has been confirmed by confirmatory factor analysis. In terms of the internal consistency, the Cronbach’s alpha reliability values have appeared to be between the reference values based in the literature. Furthermore, the test-retest reliability analysis has exposed considerably high level and significant correlation

Experiences of Turkish teachers working abroad

*Özdemir, Hasan Fehmi ( Aksaray, Yazar ) *Orakçı, Şenol ( Aksaray, Yazar )Turkish teachers seek to work abroad for a variety of reasons with a mixed degree of success. This study examined the common essence of the experiences of Turkish teachers, through 25 interviews with teachers commissioned to teach Turkish and Turkish culture to Turkish students in Germany and France. Interviews were conducted using semi-structured interview questions and audio recordings. The analyses used an appropriate content analysis process for a qualitative phenomenological approach. Krippendorff’s alpha coefficient showed high interrater reliability with a value of.83, and to ensure the credibility the codings have been submitted to internal and external checks. The direct quotations of the participants’ discourses were shared in the text. The results of the study showed that there was a particular driving force that directed teachers to teach abroad. This driving force embodied itself in a sense of curiosity and pride for being chosen to work abroad

Relationships between cognitive flexibility, perceived quality of faculty life, learning approaches, and academic achievement

Orakçı, Şenol ( Aksaray, Yazar )This study aims to explore the relationships between cognitive flexibility (CF), perceived quality of faculty life (PQFL), learning approaches (LA) and academic achievement (AA). This correlational comparison study was conducted with 1573 undergraduates at Ankara University. The data collection tools were "Cognitive Flexibility Scale (CFS)", "Quality of Faculty Life Scale (QFLS)" and "Approaches to Learning Questionnaire (ALQ)". Grade point average (GPA) was used as a measurement of AA. The three subscales of QFLS; satisfaction from faculty (SF), faculty members (SFM), and school climate and student relationships (SSCSR) were found positively correlated with deep approach to learning (DAL), CF and AA, and negatively correlated with surface approach to learning (SAL). DAL was also found positively correlated with CF and AA, but negatively correlated with SAL. CF and AA were positively correlated with all variables, except SAL. Although CF showed a positive correlation with AA, it assumed a negative explanatory role for AA when it was included in the model as a mediating variable. The regression estimates in the path analysis model revealed that DAL, SAL and SF were positive explanatory variables for AA, whereas SSCSR was a negative explanatory variable for AA and SAL was a negative explanatory variable for CF

Low-complexity deep learning-based beamforming in MISO systems

This study proposes a low-complexity deep learning-based beamforming neural network (BFNN) for massive multiple-input single-output (MISO) systems. We adopt an unsupervised learning-based convolutional neural network (CNN) model. The network is trained to obtain an analog phase shifters (PSs)-based beamforming vector of a given user by maximizing the system spectral efficiency (SE) while maintaining the transmitted power constraint. The channel state information (CSI) for millimeter wave (mmWave) channel and signal-to-noise-ratio (SNR) are used as inputs to the network. We also proposed a novel input feeding arrangement to the network and assessed its performance by using different input data representations. Simulation results show that the CNN-BFNN has the lowest complexity compared to a fully connected neural network (FCNN) and the existing conventional algorithms. Furthermore, the CNN model with fast Fourier transform (FFT) input provides the highest SE performance among all other input data representations