Neural network based estimation of resonant frequency of an equilateral triangular microstrip patch antenna

U ovom se radu predlaže model umjetne neuronske mreže za procjenu rezonantnih frekvencija antena s istostraničnom trokutastom osnovom. Neuronska mreža primijenjena ovdje uvježbana je i testirana za jednoslojne kao i dvoslojne antene. Eksperiment je pokazao da su rezonantne frekvencije dobivene neuronskom mrežom za obadvije antene točnije od frekvencija izračunatih formulom i da su zadovoljavajuće blizu izmjerenim frekvencijama. Rezultati su obećavajući u odnosu na raspoloživu literaturu. Ovaj rad može također ponuditi učinkovitiji pristup za razvoj takvih antena. Dok je ukupna apsolutna greška od 7 MHz i prosječna greška od 0,09 % dobivena za jednoslojnu antenu; za dvoslojnu antenu je ukupna apsolutna greška 49 MHz, a prosječna greška 0,07 %.This 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

Design and implementation of an isolated-word Turkish speech recognition system with data augmentation

Bu çalışmada toplamda doksan iki adet sesli komuttan oluşan bir yalıtık sözcüklü Türkçe konuşma tanıma sistemi tasarlanmış ve gerçekleştirilmiştir. Sistem, destek vektör makinesi (SVM) tabanlı olup, eğitimde kullanılan veri kümesi kaydedilen konuşmaların yapay olarak çeşitlendirilip artırılmasıyla elde edilmiştir. Farklı yapay veri oranlarının tanıma başarımı üzerindeki etkisi incelenmiştir. Akustik öznitelik olarak, mel frekansı kepstral katsayıları (MFCC) kullanılmıştır. Ayrıca, ses aktivitesi tespitinin ve MFCC katsayılarının tanıma başarımına etkileri de irdelenmiştir. Sonuçta doksan iki yalıtık komut için ortalama %92.6’lık doğrulukla çalışan bir konuşma tanıma sistemi geliştirilmiştir.In this study, an isolated-word Turkish speech recognition system comprising of ninety-two voiced commands has been designed and implemented. The system is support vector machine (SVM) based and the data set used in training has been obtained by augmenting the original recordings artificially. The effect of different augmented data amounts on recognition performance has been examined. As acoustic features, mel frequency cepstral coefficients (MFCC) were used. Moreover, the effects of voice activity detection and MFCCs on recognition performance have also been investigated. In the end, 92.6% recognition accuracy on average has been obtained for ninety-two isolated commands

Risk Assessment of Sea Level Rise for Karasu Coastal Area, Turkey

Sea Level Rise (SLR) due to global warming is becoming a more pressing issue for coastal zones. This paper presents an overall analysis to assess the risk of a low-lying coastal area in Karasu, Turkey. For SLR scenarios of 1 m, 2 m, and 3 m by 2100, inundation levels were visualized using Digital Elevation Model (DEM). The eight-side rule is applied as an algorithm through Geographic Information System (GIS) using ArcMap software with high-resolution DEM data generated by eleven 1:5000 scale topographic maps. The outcomes of GIS-based inundation maps indicated 1.40%, 6.02%, and 29.27% of the total land area by 1 m, 2 m, and 3 m SLR scenarios, respectively. Risk maps have shown that water bodies, low-lying urban areas, arable land, and beach areas have a higher risk at 1 m. In a 2 m scenario, along with the risk of the 1 m scenario, forests become at risk as well. For the 3 m scenario, almost all the territorial features of the Karasu coast are found to be inundated. The effect of SLR scenarios based on population and Gross Domestic Product (GDP) is also analyzed. It is found that the 2 and 3 m scenarios lead to a much higher risk compared to the 1 m scenario. The combined hazard–vulnerability data shows that estuarine areas on the west and east of the Karasu region have a medium vulnerability. These results provide primary assessment data for the Karasu region for the decision-makers to enhance land use policies and coastal management plans

IMPLEMENTATION OF TURKISH TEXT-TO-SPEECH SYNTHESIS ON A VOICE SYNTHESIZER CARD WITH PROSODIC FEATURES

This study is on hardware implementation of the Turkish text-to-speech (TTS) synthesis with a voice synthesizer card. Here, a fully functional TTS system, capable of synthesizing every Turkish text, including abbreviations, numbers, etc. is designed and implemented. The system is additionally enriched by applying some prosodic attributes for more intelligible and natural speech production. A set of rules required for proper pronunciation and stress patterns are precisely defined in a lexicon utilized for synthesizing Turkish speech. Performance of the developed system is assessed by the Mean Opinion Score (MOS) test. An average score of 3.8 out of 5 is achieved. It indicates that the proposed synthesizer can be successfully integrated to many practical Turkish TTS applications