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Simulation of Parkinsonian Basal nuclei with network motifs

Çalım, Ali | Özer, Mahmut | Uzuntarla, Muhammet

Proceedings | 2017 | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

Nowadays, neurodegenerative diseases which affect human life quite negatively with motor, cognitive and psychiatric disorders are becoming widespread. One of the most common neurodegenerative disorder is Parkinson's disease. Recent electrophysiological experiments have shown that Basal Ganglia, a special region in the midbrain, is related to Parkinsonism. Beta frequency oscillations, which are important symptoms of Parkinson's disease, emerge intensively in Globus Pallidus and Subtalamus nuclei. In this study, anatomical connections of Globus Pallidus and Subtalamus are constructed computationally, and the cellular properties that g . . .ive rise to emergence of beta oscillations are investigated. © 2017 IEEE Daha fazlası Daha az

Ultrasonic signal delay determination for distance estimation in underwater space [Sualti Uzaylarda Mesafe Kestirimi İçin Ultrasonik İşaret Gecikmesinin Belirlenmesi]

Onur, Tuğba Özge | Hacıoğlu, Rıfat

Proceedings | 2017 | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

Reverberation and multipath propagation afîect the accuracy of time of flight (TOF) estimation in confined underwater medium. In this paper, time delay has been estimated by obtaining the transmitted signal and first echo signal from measured signal acquised by using a wideband transducer with 5 MHz central frequency. Cross-correlation (CC) and generalized cross-correlation (GCC) methods have been applied to the obtained signals and their performances have been compared. The results show that GCC method has better performance for time delay estimation in confined underwater medium where multipath propagation and reverberation are se . . .vere. © 2017 IEEE Daha fazlası Daha az

Effect of linear and non-linear measurements of heart rate variability in prediction of PAF attack

Narin, Ali | Özer, Mahmut | İşler, Yalçın

Proceedings | 2017 | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

Paroxysmal Atrial Fibrillation (PAF) is a very common rhythm disorder that causes rapid and irregular impulses in the heart. In this study, it is aimed to determine whether patients can be warned before PAF events. 30-minute HRV data used in this study. Each piece of data was divided into 10 pieces of 5-minute parts. Time domain measurements from linear measurements of HRV and Poincare measurements from nonlinear measurements of HRV were used for each segment. Detecting performances were measured for each segment using k-nearest neighbor classifier. Particularly linear measurements have been shown to achieve up to 82% success in pre . . .dicting PAF attack and was observed that PAF attack could be detected 12,5 minutes earlier. © 2017 IEEE Daha fazlası Daha az

Classification of power quality disturbances with S-transform and artificial neural networks method [S-Dönüşümü ve Yapay Sinir Aglari Yöntemi ile Gç Kalitesi Bozulmalarinin Siniflandirilmasi]

Karasu S. | Sarac Z.

Conference Object | 2017 | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

In this study, classification of 11 different Power Quality (PQ) disturbances with Artificial Neural Networks (ANN) has been done by using the attributes obtained with S-Transform. It was aimed to achieve accurate and high classification performance in noisy environment by using the least number of attributes representing PQ disturbances. The most suitable ones from the attributes were selected by Sequential Forward Selection (SFS) method. The performance of models with different hidden layer neuron numbers tested at different noise levels (40 dB, 30 dB and 20 dB) by using the selected attributes. In this study, it was found that fo . . .r the most appropriate number of attributes and optimal model parameters, performance in noisy environment (20 dB) and overall performance were 99.0%. © 2017 IEEE Daha fazlası Daha az

The fast optimization of digital holography system [Sayisal Holografi Sisteminin Hizli Optimizasyonu]

Kaya G.U. | Sarac Z.

Conference Object | 2017 | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

In digital holography applications, the position of the object, which is in the system, substantially affects the quality and clarity of three dimensional reconstructed image obtained from hologram. Therefore, obtaining a good three dimensional image by locating the image to be reconstructed in the correct position in the system, can sometimes take a long time. The usage of artificial intelligence algorithms instead of capturing the best images by recording the image several times and reduction of this time would be wise. For the first time in this study by using the classification method approach used in artificial neural networks, . . . which is an artificial intelligence methods, the best image is obtained with optimization via software, not on the digital holographic system. The three important features that affected the system, while the holographic setup is constructing, are examined for 142 different situations and the results of these situations are classified into 5 different classes. After classification by using artificial neural networks, the random values are taken for the test process of the trained system and the performance of the system is determined. © 2017 IEEE Daha fazlası Daha az

Modeling of three-axis gimbal system on unmanned air vehicle (UAV) under external disturbances [Insansiz Hava Araci Üzerinde Bulunan 3 Eksenli Yalpa Sisteminin Diş Bozucu Altinda Modellenmesi]

Altan, Aytaç | Hacıoğlu, Rıfat

Conference Object | 2017 | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

This study focuses on the modelling of 3 axis gimbal system with the RRR joint structure on the Unmanned Aerial Vehicle (UAV), which is autonomously moving for the target tracking, based on experimental input (motor velocities) and output (end effector position) data. The fact that UAVs move in a certain direction and that the camera on the end effector of the gimbal system on it is adhere to the correct target attracts many researchers. The transfer function of the 3 axis gimbal system is obtained by linearly structured OE-Output Error model using experimentally obtained data under different external disturbance effects. Model degr . . .ee is determined and data set based verification is applied. Also, the performance is compared by examining the effect of external disturbance in the transfer function obtained. © 2017 IEEE Daha fazlası Daha az

Prediction of wind speed with non-linear autoregressive (NAR) neural networks [Rüzgâr Hizinin Dogrusal Olmayan Otoregresif Sinir Aglari ile Tahmini]

Karasu, Seçkin | Altan, Aytaç | Saraç, Zehra | Hacıoğlu, Rıfat

Proceedings | 2017 | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

In this study, the wind speed prediction model is created that gives a minimum error for different hidden layer neuron numbers and delay step numbers. Using the one-minute time series, the prediction of the next wind speed is performed with the NAR neural network model. The predicted values of wind speed obtained are compared with predicted values of wind speed obtained with filter methods. For different window functions and lengths, wind speed prediction is made using filters with different weight coefficients. For the number of hidden layer neurons is 14 and the number of delay steps is 10, MAE, MSE and RMSE values are calculated . . .as 0.0315, 0.0019, 0.0445, respectively, with NAR neural network. It is seen that the proposed method for the wind speed dataset has a higher prediction performance than thefilter methods. © 2017 IEEE Daha fazlası Daha az

Cell counting and recognition of immunohistochemically dyed seminiferous tubules with feed-forward neural network

Aydemir, Zübeyr | Erkaymaz, Okan | Ferah, Meryem Akpolat

Proceedings | 2017 | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

In this study, the features of the seminiferous tubule sections were extracted and the presence of the cells and cell stain types detected with the help of the feed forward artificial neural network. By looking at the section view with a small window, 78 features were extracted from the pixels seen by the window and used as an input to the artificial neural network. Artificial neural network outputs are decides presence of the cell and the staining of the cell. The results obtained with the artificial neural network were determined by using the connected component labeling method. The results obtained with the help of the user and t . . .he results obtained with the artificial neural network were compared. It has been shown that the proposed ANN model performs cell counting process comparable to the literature (%76 accuracy). © 2017 IEEE Daha fazlası Daha az

Effects of synaptic time delay on vibrational resonance in neuronal networks

Ağaoğlu, Şükrüye Nihal | Özer, Mahmut | Çalım, Ali | Uzuntarla, Muhammet

Proceedings | 2017 | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

In the phenomenon of vibrational resonance, the excitable system is under the influence of two periodic forces: a low-frequency (signal), a high-frequency (carrier). In this study, the effects of synaptic time delay on the vibrational resonance were investigated in two coupled FitzHugh-Nagumo neurons with electrical or chemical coupling. It is seen that, for both types of coupling by appropriate choice of synaptic time delay can be had a curative effect to transmission between two neurons at certain values of synaptic conductivity. © 2017 IEEE.

Land use and cover classification of Sentinel-IA SAR imagery: A case study of Istanbul [Sentinel-1A SAR Görüntüsü ile Arazi Örtüsü ve Kullanimi Siniflandirmasi: Istanbul Örnegi]

Ustuner M. | Sanli F.B. | Bilgin G. | Abdikan S.

Conference Object | 2017 | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

In this study, Sentinel-1A SAR imagery for land use/cover classification and its impacts on classification algorithms were addressed. Sentinel-1A imagery has dual polarization (VV and VH) and freely available from ESA. Istanbul was selected as the study region. After the pre-processing steps including the applying the precise orbit file, calibration, multilooking, speckle filtering and terrain correction, the imagery was classified as the following step. Three classification algorithms (SVM, RF and K-NN) were implemented and the impacts of additional bands (VV-VH, VV+VH etc.) were investigated. Results demonstrated that highest clas . . .sification accuracy of this study was obtained by SVM classification with the original bands (VV and VH) of Sentinel-1A imagery. Moreover, it was concluded that additional bands had different impacts on each classifier within accuracy. © 2017 IEEE Daha fazlası Daha az

Effects of autaptic coupling stregth and ion channel blockage on firing regularity in a Hodgkin-Huxley neuron

Uzun, Rukiye | Özer, Mahmut

Proceedings | 2017 | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

We investigate how firing dynamics of neuron changes with the autpse's coupling strength for different ratio of the blockage of potassium and sodium ion channels embedded in membranes affects, with using a biophysically more realistic Hodgkin-Huxley neuron model. In the study, we assume that neuron has an excitatory chemical autapse. It is found that the neuron dynamics does not change for too small coupling strength whether it increases after a certain coupling strength' value in case of different ion channel blockage case. Besides, it is determined that potassium ion channel blockage has a healing effect whereas sodium ion channel . . . blockage has a destructive effect on the neuron dynamics increasing with the autaptic coupling strength. © 2017 IEEE Daha fazlası Daha az

Detection of knee abnormality from surface EMG signals by artificial neural networks

Erkaymaz, Okan | Senyer, İrem | Uzun, Rukiye

Proceedings | 2017 | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

Using surface EMG signals is a non-invasive measurement method obtained as a result of muscle activity. In this study, surface EMG data have been used for classification, taken from healthy individuals or individuals with knee abnormalities in gait position. For this purpose, first feature extraction was realized by discrete wavelet transform from the data. Then, extracted features were classified by artificial neural network approach that is widely used in the literature. In classification process, artificial neural networks were trained by using simple cross-validation algorithm. During training the optimal network topology was de . . .termined. The highest classification performance of proposed model was obtained in rate fiction 80%-20% and 70%-30% of data set. Our results revealed that proposed artificial neural network model is able to detect knee abnormality from surface EMG signals. © 2017 IEEE Daha fazlası Daha az

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