Browsing by Subject "Fourier"

Now showing 1 - 6 of 6

Open Access
Heart sound segmentation using signal processing methods
(2015) Şahin, Devrim
Heart murmurs are pathological heart sounds that originate from blood flowing with abnormal turbulence due to physiological defects of the heart, and are the prime indicator of many heart-related diseases. Murmurs can be diagnosed via auscultation; that is, by listening with a stethoscope. However, manual detection and classification of murmur requires clinical expertise and is highly prone to misclassification. Although automated classification algorithms exist for this purpose; they heavily depend on feature extraction from ‘segmented’ heart sound waveforms. Segmentation in this context refers to detecting and splitting cardiac cycles. However, heart sound signal is not a stationary signal; and typically has a low signal-to-noise ratio, which makes it very difficult to segment using no external information but the signal itself. Most of the commercial systems require an external electrocardiography (ECG) signal to determine S1 and S2 peaks, but ECG is not as widely available as stethoscopes. Although algorithms that provide segmentation using sound alone exist, a proper comparison between these algorithms on a common dataset is missing. We propose several modifications to many of these algorithms, as well as an evaluation method that allows a unified comparison of all these approaches. We have tested each combination of algorithms on a real data set [1], which also provides manual annotations as ground truth. We also propose an ensemble of several methods, and a heuristic for which algorithm’s output to use. Whereas tested algorithms report up to 62% accuracy, our ensemble method reports a 75% success rate. Finally, we created a tool named UpBeat to enable manual segmentation of heart sounds, and construction of a ground truth dataset. UpBeat is a starting medium for auscultation segmentation, time-domain based feature extraction and evaluation; which has automatic segmentation capabilities, as well as a minimalistic drag-and-drop interface which allows manual annotation of S1 and S2 peaks.
Open Access
Holograms deep inside Silicon
(Optical Society of America, 2016) Makey, Ghaith; Tokel, Onur; Turnalı, Ahmet; Pavlov, Ihor; Elahi, Parviz; Yavuz, Ozg ¨ un; İlday, F. Ömer
Through the Nonlinear Laser Lithography method, we demonstrate the first computer generated holograms fabricated deep inside Silicon. Fourier and Fresnel holograms are fabricated buried inside Si wafers, and a generation algorithm is developed for hologram fabrication. © OSA 2016.
Open Access
A hybrid approach for line segmentation in handwritten documents
(2012) Adıgüzel, Hande; Şahin, Emre; Duygulu, Pınar
This paper presents an approach for text line segmentation which combines connected component based and projection based information to take advantage of aspects of both methods. The proposed system finds baselines of each connected component. Lines are detected by grouping baselines of connected components belonging to each line by projection information. Components are assigned to lines according to different distance metrics with respect to their size. This study is one of the rare studies that apply line segmentation to Ottoman documents. Further, it proposes a new method, Fourier curve fitting, to detect the peaks in a projection profile. The algorithm is demonstrated on different printed and handwritten Ottoman datasets. Results show that the method manages to segment lines both from printed and handwritten documents under different writing conditions at least with 92% accuracy.
Open Access
Image feature extraction using 2D mel-cepstrum
(IEEE, 2010) Çakır, Serdar; Çetin, A. Enis
In this paper, a feature extraction method based on two-dimensional (2D) mel-cepstrum is introduced. Feature matrices resulting from the 2D mel-cepstrum, Fourier LDA approach and original image matrices are individually applied to the Common Matrix Approach (CMA) based face recognition system. For each of these feature extraction methods, recognition rates are obtained in the AR face database, ORL database and Yale database. Experimental results indicate that recognition rates obtained by the 2D mel-cepstrum method is superior to the recognition rates obtained using Fourier LDA approach and raw image matrices. This indicates that 2D mel-cepstral analysis can be used in image feature extraction problems. © 2010 IEEE.
Open Access
Kesirli fourier dönüşümünün zaman bölgesinde sonlu farklar yöntemine uygulanması
(IEEE, 2010-04) Sayın, I.; Arıkan F.; Arıkan, Orhan
Bilgisayarların hız ve belleklerinin gelişmesi ile birlikte elektromanyetik problemlerin çözümünde saysal yöntemler sıkça kullanılmaya başlanmış ve bu konuda çok sayda araştırma yapılmıştır. Saysal Elektromanyetik yöntemleri genel olarak zaman ve frekans tabanlı yöntemler olarak sınıflandırılabilir. Zaman tabanlı yöntemler geçici tepkilerin ve geniş bantlı problemlerin incelenmesinde kullanışlı olurken, frekans tabanlı yöntemler durağan hal tepkilerin ve dar bantlı problemlerin incelenmesinde en iyi çözümü vermektedir. Her iki yaklaşımın da avantajlarını ön plana çıkarabilecek bir yöntem geliştirilebileceği düşünülmektedir. Uzayda ve/veya zamanda Kesirli Fourier Dönüşümü uygulanarak bazı durumlarda hesaplama karmaşıklığı azaltılabilir. Kesirli Fourier Dönüşümü, sürekli Fourier Dönüşümünün genelleştirilmiş halidir. Son yıllarda bu konu üzerinde çeşitli çalışmalar yapılmakta ve uygulama alanları genişlemektedir. Genel olarak, sinyal işleme ve gürültü süzme gibi alanlarda kullanılmaktadır. Bu çalmada Kesirli Fourier Dönüşümü, ilk kez Maxwell denklemlerine zaman bölgesinde uygulanmış ve elde edilen diferansiyel denklemler sonlu farklar yaklaşımı ile ayrık hale getirilmiştir. Elde edilen ayrık sonlu fark denklemlerinin çözümü için öneriler sunulmuştur.
Open Access
Mel-cepstral methods for image feature extraction
(IEEE, 2010) Çakır, Serdar; Çetin, A. Enis
A feature extraction method based on two-dimensional (2D) mel-cepstrum is introduced. The concept of one-dimensional (1D) mel-cepstrum which is widely used in speech recognition is extended to 2D in this article. Feature matrices resulting from the 2D mel-cepstrum, Fourier LDA, 2D PCA and original image matrices are converted to feature vectors and individually applied to a Support Vector Machine (SVM) classification engine for comparison. The AR face database, ORL database, Yale database and FRGC version 2 database are used in experimental studies, which indicate that recognition rates obtained by the 2D mel-cepstrum method is superior to the recognition rates obtained using Fourier LDA, 2D PCA and ordinary image matrix based face recognition. This indicates that 2D mel-cepstral analysis can be used in image feature extraction problems. © 2010 IEEE.