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Articles 1 - 6 of 6
Full-Text Articles in Physical Sciences and Mathematics
A New Spectral Estimation-Based Feature Extraction Method For Vehicle Classification In Distributed Sensor Networks, Erdem Köse, Ali̇ Köksal Hocaoğlu
A New Spectral Estimation-Based Feature Extraction Method For Vehicle Classification In Distributed Sensor Networks, Erdem Köse, Ali̇ Köksal Hocaoğlu
Turkish Journal of Electrical Engineering and Computer Sciences
Ground vehicle detection and classification with distributed sensor networks is of growing interest for border security. Different sensing modalities including electro-optical, seismic, and acoustic were evaluated individually and in combination to develop a more efficient system. Despite previous works that mostly studied frequency-domain features and acoustic sensors, in this work we analyzed the classification performance for both frequency and time-domain features and seismic and acoustic modalities. Despite their infrequent use, we show that when fused with frequency-domain features, time-domain features improve the classification performance and reduce the false positive rate, especially for seismic signals. We investigated the performance of seismic …
Plant Disease And Pest Detection Using Deep Learning-Based Features, Muammer Türkoğlu, Davut Hanbay
Plant Disease And Pest Detection Using Deep Learning-Based Features, Muammer Türkoğlu, Davut Hanbay
Turkish Journal of Electrical Engineering and Computer Sciences
The timely and accurate diagnosis of plant diseases plays an important role in preventing the loss of productivity and loss or reduced quantity of agricultural products. In order to solve such problems, methods based on machine learning can be used. In recent years, deep learning, which is especially widely used in image processing, offers many new applications related to precision agriculture. In this study, we evaluated the performance results using different approaches of nine powerful architectures of deep neural networks for plant disease detection. Transfer learning and deep feature extraction methods are used, which adapt these deep learning models to …
Classification Of The Likelihood Of Colon Cancer With Machine Learning Techniques Using Ftir Signals Obtained From Plasma, Suat Toraman, Mustafa Gi̇rgi̇n, Bi̇lal Üstündağ, İbrahi̇m Türkoğlu
Classification Of The Likelihood Of Colon Cancer With Machine Learning Techniques Using Ftir Signals Obtained From Plasma, Suat Toraman, Mustafa Gi̇rgi̇n, Bi̇lal Üstündağ, İbrahi̇m Türkoğlu
Turkish Journal of Electrical Engineering and Computer Sciences
Colon cancer is one of the major causes of human mortality worldwide and the same can be said for Turkey. Various methods are used for the determination of cancer. One of these methods is Fourier transform infrared (FTIR) spectroscopy, which has the ability to reveal biochemical changes. The most common features used to distinguish patients with cancer and healthy subjects are peak densities, peak height ratios, and peak area ratios. The greatest challenge of studies conducted to distinguish cancer patients from healthy subjects using FTIR signals is that the signals of cancer patients and healthy subjects are similar. In the …
Biometric Person Authentication Framework Using Polynomial Curve Fitting-Based Ecg Feature Extraction, Şahi̇n Işik, Kemal Özkan, Semi̇h Ergi̇n
Biometric Person Authentication Framework Using Polynomial Curve Fitting-Based Ecg Feature Extraction, Şahi̇n Işik, Kemal Özkan, Semi̇h Ergi̇n
Turkish Journal of Electrical Engineering and Computer Sciences
The applications of modern biometric techniques for person identification systems rapidly increase for meeting the rising security demands. The distinctive physiological characteristics are more correctly measurable and trustworthy since previous measurements are not appropriately made for physiological properties. While a variety of strategies have been enabled for identification, the electrocardiogram (ECG)-based approaches are popular and reliable techniques in the senses of measurability, singularity, and universal awareness of heartbeat signals. This paper presents a new ECG-based feature extraction method for person identification using a huge amount of ECG recordings. First of all, 1800 heartbeats for each of the 36 subjects have …
Local Directional-Structural Pattern For Person-Independent Facial Expression Recognition, Farkhod Makhmudkhujaev, Md Tauhid Bin Iqbal, Byungyong Ryu, Oksam Chae
Local Directional-Structural Pattern For Person-Independent Facial Expression Recognition, Farkhod Makhmudkhujaev, Md Tauhid Bin Iqbal, Byungyong Ryu, Oksam Chae
Turkish Journal of Electrical Engineering and Computer Sciences
Existing popular descriptors for facial expression recognition often suffer from inconsistent feature description, experiencing poor accuracies. We present a new local descriptor, local directional-structural pattern (LDSP), in this work to address this issue. Unlike the existing local descriptors using only the texture or edge information to represent the local structure of a pixel, the proposed LDSP utilizes the positional relationship of the top edge responses of the target pixel to extract more detailed structural information of the local texture. We further exploit such information to characterize expression-affiliated crucial textures while discarding the random noisy patterns. Moreover, we introduce a globally …
Scale-Invariant Mfccs For Speech/Speaker Recognition, Zekeri̇ya Tüfekci̇, Gökay Di̇şken
Scale-Invariant Mfccs For Speech/Speaker Recognition, Zekeri̇ya Tüfekci̇, Gökay Di̇şken
Turkish Journal of Electrical Engineering and Computer Sciences
The feature extraction process is a fundamental part of speech processing. Mel frequency cepstral coefficients (MFCCs) are the most commonly used feature types in the speech/speaker recognition literature. However, the MFCC framework may face numerical issues or dynamic range problems, which decreases their performance. A practical solution to these problems is adding a constant to filter-bank magnitudes before log compression, thus violating the scale-invariant property. In this work, a magnitude normalization and a multiplication constant are introduced to make the MFCCs scale-invariant and to avoid dynamic range expansion of nonspeech frames. Speaker verification experiments are conducted to show the effectiveness …