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Full-Text Articles in Physical Sciences and Mathematics
Evaluation Of Mother Wavelets On Steady-State Visually-Evoked Potentials Fortriple-Command Brain-Computer Interfaces, Ebru Sayilgan, Yilmaz Kemal Yüce, Yalçin İşler
Evaluation Of Mother Wavelets On Steady-State Visually-Evoked Potentials Fortriple-Command Brain-Computer Interfaces, Ebru Sayilgan, Yilmaz Kemal Yüce, Yalçin İşler
Turkish Journal of Electrical Engineering and Computer Sciences
Wavelet transform (WT) is an important tool to analyze the time-frequency structure of a signal. The WT relies on a prototype signal that is called the mother wavelet. However, there is no single universal wavelet that fits all signals. Thus, the selection of mother wavelet function might be challenging to represent the signal to achieve the optimum performance. There are some studies to determine the optimal mother wavelet for other biomedical signals; however, there exists no evaluation for steady-state visually-evoked potentials (SSVEP) signals that becomes very popular among signals manipulated for brain-computer interfaces (BCIs) recently. This study aims to explore, …
Learning Multiview Deep Features From Skeletal Sign Language Videos Forrecognition, Ashraf Ali Shaik, Venkata Durga Prasad Mareedu, Venkata Vijaya Kishore Polurie
Learning Multiview Deep Features From Skeletal Sign Language Videos Forrecognition, Ashraf Ali Shaik, Venkata Durga Prasad Mareedu, Venkata Vijaya Kishore Polurie
Turkish Journal of Electrical Engineering and Computer Sciences
The most challenging objective in machine translation of sign language has been the machine?s inability tolearn interoccluding finger movements during an action process. This work addresses the problem of teaching a deeplearning model to recognize differently oriented skeletal data. The multi-view 2D skeletal sign language video data isobtained using 3D motion-captured system. A total of 9 signer views were used for training the proposed network andthe 6 for testing and validation. In order to obtain multi-view deep features for recognition, we proposed an end-to-endtrainable multistream convolutional neural network (CNN) with late feature fusion. The fused multiview features arethen inputted to …
Learning Prototypes For Multiple Instance Learning, Özgür Emre Si̇vri̇kaya, Mert Yüksekgönül, Mustafa Gökçe Baydoğan
Learning Prototypes For Multiple Instance Learning, Özgür Emre Si̇vri̇kaya, Mert Yüksekgönül, Mustafa Gökçe Baydoğan
Turkish Journal of Electrical Engineering and Computer Sciences
Multiple instance learning (MIL) is a weakly supervised learning method that works on the labeled bag of instances data. A prototypical network is a popular embedding approach in MIL. They overcome the common problems that other MIL approaches may have to deal with including dimensionality, loss of instance-level information, and complexity. They demonstrate competitive performance in classification. This work proposes a simple model that provides a permutation invariant prototype generator from a given MIL data set. We aim to find out prototypes in the feature space to map the collection of instances (i.e. bags) to a distance feature space and …
A Fast And Memory-Efficient Two-Pass Connected-Component Labeling Algorithm For Binary Images, Bilal Bataineh
A Fast And Memory-Efficient Two-Pass Connected-Component Labeling Algorithm For Binary Images, Bilal Bataineh
Turkish Journal of Electrical Engineering and Computer Sciences
Connected-component labeling is an important process in image analysis and pattern recognition. It aims to deduct the connected components by giving a unique label value for each individual component. Many algorithms have been proposed, but they still face several problems such as slow execution time, falling in the pipeline, requiring a huge amount of memory with high resolution, being noisy, and giving irregular images. In this work, a fast and memory-efficient connected-component labeling algorithm for binary images is proposed. The proposed algorithm is based on a new run-base tracing method with a new resolving process to find the final equivalent …
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 …
An Efficient Algorithm To Decompose A Compound Rectilinear Shape Into Simplerectilinear Shapes, Imran Sharif, Debasis Chaudhuri, Naveen Kushwaha, Ashok Samal, Brij Mohan Singh
An Efficient Algorithm To Decompose A Compound Rectilinear Shape Into Simplerectilinear Shapes, Imran Sharif, Debasis Chaudhuri, Naveen Kushwaha, Ashok Samal, Brij Mohan Singh
Turkish Journal of Electrical Engineering and Computer Sciences
Detection of a compound object is a critical problem in target recognition. For example, buildings form an important class of shapes whose recognition is important in many remote sensing based applications. Due to the coarse resolution of imaging sensors, adjacent buildings in the scenes appear as a single compound shape object. These compound objects can be represented as the union of a set of disjoint rectilinear shaped objects. Separating the individual buildings from the resulting compound objects in a segmented image is often difficult but important nevertheless. In this paper we propose a new and efficient technique to decompose a …
Study On The Recognition Method Of Airport Perimeter Intrusion Incidents Based On Laser Detection Technology, Huazhu Wu, Zengcai Wang, Changyou Wang
Study On The Recognition Method Of Airport Perimeter Intrusion Incidents Based On Laser Detection Technology, Huazhu Wu, Zengcai Wang, Changyou Wang
Turkish Journal of Electrical Engineering and Computer Sciences
Currently, detection technology is very important for airport perimeter security. When the perimeter is invaded or destroyed, the perimeter security alarm system can promptly alert personnel. In this paper, based on analysis and comparison of several detection technologies commonly used in airport perimeter security and according to the characteristics of airport perimeters and laser detection, an airport perimeter security alarm system based on laser detection is proposed. It analyzes factors that affect the performance of a laser alarm system, divides intrusions into six categories, estimates the different alarm thresholds by testing, and judges the intrusion category according to the number …
A Reduced Probabilistic Neural Network For The Classification Of Large Databases, Abdelhadi Lotfi, Abdelkader Benyettou
A Reduced Probabilistic Neural Network For The Classification Of Large Databases, Abdelhadi Lotfi, Abdelkader Benyettou
Turkish Journal of Electrical Engineering and Computer Sciences
The probabilistic neural network (PNN) is a special type of radial basis neural network used mainly for classification problems. Due to the size of the network after training, this type of network is usually used for problems with a small-sized training dataset. In this paper, a new training algorithm is presented for use with large training databases. Application to the handwritten digit database shows that the reduced PNN performs better than the standard PNN for all of the studied cases with a big gain in size and processing speed. This new type of neural network can be used easily for …
Human Identification Using Gait, Murat Eki̇nci̇
Human Identification Using Gait, Murat Eki̇nci̇
Turkish Journal of Electrical Engineering and Computer Sciences
Gait refers to the style of walking of an individual. This paper presents a view-invariant approach for human identification at a distance, using gait recognition. Recognition of a person from their gait is a biometric of increasing interest. Based on principal component analysis (PCA), this paper describes a simple, but efficient approach to gait recognition. Binarized silhouettes of a motion object are represented by 1-D signals, which are the basic image features called distance vectors. The distance vectors are differences between the bounding box and silhouette, and are extracted using 4 projections of the silhouette. Based on normalized correlation of …
An Ann Based Approach To Improve The Distance Relaying Algorithm, Hassan Khorashadi Zadeh, Zuyi Li
An Ann Based Approach To Improve The Distance Relaying Algorithm, Hassan Khorashadi Zadeh, Zuyi Li
Turkish Journal of Electrical Engineering and Computer Sciences
This paper presents an artificial neural network- (ANN) based approach to improve the performance of the distance relaying algorithm. The proposed distance relay uses magnitudes of voltages and currents as input signals to find fault locations. In this approach, an ANN has been included in the protection algorithm as an extension of the existing methods, which improves the reliability of the protection operation. The design procedure of the proposed relay is presented in detail. Simulation studies are performed and the influence of changing system parameters, such as fault resistance and source impedance, is studied. Performance studies show that the proposed …