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Koleksiyon [14]
Tam Metin [2]
Yayın Türü [2]
Yazar [18]
Konu Başlıkları [19]
Yayıncı [3]
Yayın Dili [2]
Dergi Adı [4]
Determination of fruit health status and yield with unmanned aerial vehicle

Kulu, Nükhet | Başkaya, Merve | Keleş, Aysel | Altan, Aytaç | Hacıoğlu, Rıfat


In this study, it is aimed to determine the number of reference fruits and health status (sturdy, rotten, mottled, non-spotted) by using real-time image or recorded video taken from the autonomous Unmanned Aerial Vehicle (UAV) camera in orchards. In the determinations made by using image processing techniques, sturdy-rotten and mottled-speckless distinction are made for oranges and apricots, respectively. These distinction and determination processes are carried out using highly trained classifiers. Three types of multi-trained classifiers performance have been compared and a highly trained classifier which has high performance has . . .been preferred for object detection. The accuracy of the Haar, local binary pattern (LBP), and histogram of oriented gradients (HOG) classifiers are compared in Python using the open source computer vision library. It has been shown experimentally that Haar classifier achieves high performance in determining real-time reference fruit health status and yield. Daha fazlası Daha az

Early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability

Narin, Ali | İşler, Yalçın | Özer, Mahmut | Perc, Matjaž

Article | 2018 | Physica A: Statistical Mechanics and its Applications509 , pp.56 - 65

Atrial fibrillation (AF) is the most common arrhythmia type and its early stage is paroxysmal atrial fibrillation (PAF). PAF affects negatively the quality of life by causing dyspnea, chest pain, feeling of excessive fatigue, and dizziness. In this study, our aim is to predict the onset of paroxysmal atrial fibrillation (PAF) events so that patients can take precautions to prevent PAF events. We use an open data from Physionet, Atrial Fibrillation Prediction Database. We construct our approach based on the heart rate variability (HRV) analysis. Short-term HRV analysis requires 5-minute data so that each dataset was divided into 5-mi . . .nute data segments. HRV features for each segment are calculated from time-domain measures and frequency-domain measures using power spectral density estimations of fast Fourier transform, Lomb–Scargle, and wavelet transform methods. Different combinations of these HRV features are selected by Genetic Algorithm and then applied to k-nearest neighbors classification algorithm. We compute the classifier performances by the 10-fold cross-validation method. The proposed approach results in 92% sensitivity, 88% specificity and 90% accuracy in the 2.5–7.5 min time interval priors to PAF event. The proposed method results in better classification performance than the similar studies in literature. Comparing the existing studies, we propose that our approach provide better tool to predict PAF events. © 2018 Elsevier B.V Daha fazlası Daha az

Effect of channel noise and dynamic synapse structure on latency dynamics in neural system

Çalım, Ali | Özer, Mahmut | Uzuntarla, Muhammet

Proceedings | 2018 | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 , pp.1 - 4

Experimental and theoretical studies in recent years suggest that the first spike latency is an effective information carrier and contains more neural information than other spikes. Noise Delayed Decay (NDD) phenomenon emerges when the first spike latency of the neuron exposed to the periodic driving is maximum at a certain noise intensity interval. In this study, the latency dynamics of a single Hodgkin-Huxley neuron is investigated under periodic driving, background activity through dynamic synapses, and channel noise. The system response with first spike latency is investigated as a function of the presynaptic firing rate, the pa . . .rameter with an appropriate biophysical reality to control the level of activity in the nervous system. First, NDD behavior is investigated under suprathreshold stimulation in the presence of synapses at different levels of depression and channel noise. It is then desired to observe the NDD phenomenon under subthreshold stimulation with the same strategy. Our results have shown that the background activity occurring in the presence of dynamic synapses and the channel noise are significant system dynamics in observing the NDD behavior. © 2018 IEEE Daha fazlası Daha az

Classification of refractive disorders from electrooculogram (EOG) signals by using data mining techniques

Kaya, Ceren | Erkaymaz, Okan | Ayar, Orhan | Özer, Mahmut

Proceedings | 2018 | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 , pp.1 - 4

Refractive disorders are common health problems in the community and they are the most important cause of visual impairment. In this study, it was aimed to classify the individuals who have hypermetropia and myopia refractive disorders or not. For this, horizontal and vertical Electrooculogram (EOG) signal data from the right and left eyes of the individuals were used. The performance of the data was investigated by using Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF) and REP Tree (RT) data mining methods. According to the obtained results, REP Tree method has shown the most successful classification performance to d . . .etect hypermetropia and myopia refractive disorders from Electrooculogram (EOG) signals. © 2018 IEEE Daha fazlası Daha az

Computer-assisted diagnosis of vertebral column diseases by adaptive neuro-fuzzy inference system

Uzun, Rukiye | İşler, Yalçın | Erkaymaz, Okan | Kocadayı, Yasemin

Proceedings | 2018 | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 , pp.1 - 4

In this study, a clustering algorithm based on adaptive neural fuzzy inference system (ANFIS) was used for computer-assisted diagnosis of the vertebral column disorder from machine learning databases of UCI (University of California Irvine). Features of pelvic incidence, pelvic tilt and lumbar lordosis angle given in this dataset was applied to the inputs of the algorithm. The performance of algorithm was evaluated using mean square error and regression coefficient criteria to discriminate patients with vertebral column disease from healthy subjects. As a result, the classification performance of 90.82% was obtained by using the gen . . .erated model of ANFIS. © 2018 IEEE Daha fazlası Daha az

Subthreshold signal detection in heterogeneous neural networks

Çalım, Ali | Özer, Mahmut | Uzuntarla, Muhammet

Proceedings | 2018 | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 , pp.1 - 4

In this study, effects of the heterogeneity in neuronal networks and subthreshold signal features on subthreshold signal detection in the nervous system is investigated. As most of studies in the literature investigate the subject by considering neuron populations as homogenous systems, in this study, the populations are considered as heterogeneous in terms of neuronal excitability. The information processing performance of the neuron populations is systematically studied by using mathematical equations for modeling the dynamics of the neurons, which are basic units of the system. As a result of the simulations performed, it is seen . . . that the sub-threshold signal frequency and the heterogeneity in the excitability are important system parameters for optimizing the information encoding performance. It is shown that the population encoding performance is maximized depending on the subthreshold signal frequency at different optimum levels of heterogeneity in the population. © 2018 IEEE Daha fazlası Daha az

Effect of feature selection by genetic algorithm on early prediction performance of PAF attack

Narin, Ali | İşler, Yalçın | Özer, Mahmut

Proceedings | 2018 | Proceedings - 2018 Innovations in Intelligent Systems and Applications Conference, ASYU 2018 , pp.1 - 4

The heart is very important to pump in a healthy way, but any disease that can occur in the heart has vital preventive measures. One of the most important of these diseases is Atrial Fibrillation (AF). This disease is a disturbance caused by excitations that occur outside of the sinoatrial node that occurs in the atrium of the heart. Paroxysmal Atrial Fibrillation (PAF) is the first stage of AF. Early prediction of this disease prevents the disease from passing to the other heavier stages. In this study, it was aimed to develop a warning system that warns PAF patients before an attack begins. Starting from the PAF, 99 pieces of data . . . consisting of 10 parts in 5 minutes were used. Time domain measurements and poincare plot measurements were obtained over the data. the features that best distinguish the classes have been determined by choosing a feature with a genetic algorithm. As a result, PAF can be predicted up to 7.5 minutes before the attack occurs using the selected features. © 2018 IEEE Daha fazlası Daha az

Vibrational resonance in a Hodgkin-Huxley neuron under electromagnetic induction

Baysal, Veli | Yılmaz, Ergin

Proceedings | 2018 | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 , pp.1 - 4

In this paper, effects of electromagnetic induction on vibrational resonance phenomenon in a Hodgkin-Huxley neuron are investigated. By stimulating Hodgkin-Huxley neuron with both high-frequency signal and low-frequency weak signal, its weak signal detection capacity have been investigated under electromagnetic induction effect. Obtained results show that electromagnetic induction causes decreasing of the amplitude of vibrational resonance effect emerging depending on the amplitude of high frequency signal. Also, vibrational resonance phenomenon occurs at smaller amplitudes of high frequency signal in Hodgkin-Huxley neuron which is . . .under electromagnetic induction effect. Finally, it is found that the best detection of the weak signal in a Hodgkin-Huxley neuron under electromagnetic induction effect is realized under an optimal electromagnetic current intensity. © 2018 IEEE Daha fazlası Daha az

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