Yazar "Kavsaoglu, Ahmet Resit" seçeneğine göre listele

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    An optimum algorithm for adaptive filtering on acoustic echo cancellation using TMS320C6713 DSP
    (Academic Press Inc Elsevier Science, 2010) Ozbay, Yueksel; Kavsaoglu, Ahmet Resit
    In this Study, it is aimed to enhance the intelligibility of speech by canceling out the echo noise. For this purpose, the data transfer software, which is necessary for real time processing of voice signals, and the adaptive filtering algorithm software for the application of acoustic echo cancellation have been developed. An algorithm has been proposed for the determination of optimum adaptation rate (mu) for the least-mean-square (LMS) adaptation algorithm that is used in the adaptive filter. The effectiveness of our optimum mu value determination algorithm was demonstrated on a single direction voice conference application with one speaker. In this study, we used a DSP card (TMS320C6713), a Laptop computer, an amplifier, a loudspeaker and two microphones in two applications. In the first application, two microphones were placed close to the loudspeaker, while in the other application, one microphone was placed close to loudspeaker and speech trial was implemented in the far-end microphone. Output of the adaptive filter was observed for mu values of 0, 0.1, 100 and optimum (a value between 0.01 and 100). The best results in the adaptive filter were obtained from optimum mu value. (C) 2009 Elsevier Inc. All rights reserved.
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    Improvement algorithm application with image processing interface software for the improvement of mini gel electrophoresis images
    (Gazi Univ, Fac Engineering Architecture, 2022) Kavsaoglu, Ahmet Resit; Ozkara, Kerim
    It is aimed to carry out an experimental study by creating an algorithm function that enables the improvement of gel electrophoresis band images with the help of the Python programming language-based, mini gel electrophoresis system image processing interface. With this system, the analysis of gel images under UV (Ultraviolet) light was made with the designed interface software. The images were transferred to the interface software via the camera and filters were applied to highlight the lanes and bands. The two-term power function, which estimates BP (Base Pair) numbers with the most accurate result, was used. The software can be used on Raspberry Pi 3B+ by being integrated into the mini gel electrophoresis system via Python programming language with OpenCV library. In this study, gel images obtained from the mini gel electrophoresis system controlled by the embedded system were used to estimate the BP numbers of the bands in the images. By importing the gel images from the camera or from the file in the system, the interface software algorithm enabled the estimation of BP numbers in the manual measurements and reducing the average error rates to the range of 0.55% - 0.86%. In the interface software, which enables the calculation of lanes and BP numbers in gel images with the lowest error rate, two-term power function has been applied to the image processing algorithm instead of the two-term exponential function, based on the principle that exponential functions can also be defined as power functions. It has been revealed that the BP values calculated with this two-term power function work in accordance with its purpose with the value of R-2=0.9999533, which shows the closeness of the actual BP values.
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    Mini Gel Electrophoresis System Based on Embedded System
    (Ieee, 2018) Mersinkaya, Ismail; Kavsaoglu, Ahmet Resit
    The current technologies are utilizable for applying the electrophoresis through agarose or polyacrylamide gels. This standart method is used to seperate, identify and purify nucleic acids, and to get faster as well as more efficiently results by identifying the bands including the basepairs. In this study, Mini Gel Electrophoresis System Based Embedded System (MESBES) is developed to increase the sensibility and accuracy of electrophoresis system by using laboratory environment, and so this system achives to wipe-out the errors based on humans as well as laboratory environment. Additionally, this system also provides faster, more accure and easily accomplishment resulting of the analyses done in laboratory environments by the software of embedded system, microprocessor, human interface, remote access and software development features. MESBES has an upgradeable structure with the typical characteristics of embedded systems such as low power consumption, and updatable. During the process of electrophoresis, getting instantaneous data with user interface, taking band images by applying the UV to gel, at the end of the process transfering the band images to the electronic media automatically can be provided.
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    Real Time Heart Rate Detection Using Non-Contact Photoplethysmography Signals
    (Ieee, 2014) Kavsaoglu, Ahmet Resit; Polat, Kemal; Bozkurt, Mehmet Recep
    Heart is contracted rhythmically so as to drive nutrients and oxygen necessary for life through our organs with blood arteries. The frequency for the rhythmic contraction of heart just as a pump is called heart rate (HR). Heart rate variation (HRV) is a measure of a fluctuation of time interval between heart beats. HRV is calculated considering electrodiagram (ECG) signals, arterial blood pressure signals or photoplethysmography (PPG) signals-derived time series of in-between heart beats. HRV is used as a significant indicator for the detection of healthiness and sickness state. Such pathological cases as high blood pressure, heart failure, and septic shock can be diagnosed using HRV. Therefore, accurate and rapid detection of HR is essential to correct diagnosis. In this study, real-time heart rate detection was derived from contactless PPG signals. PPG calling for contact with skin becomes useless in case of tissue scars or burns. In such cases, the use of contactless PPG is superior. Contactless PPG consists of a light source and a camera that senses reflection or transmittance of the light source. Camera images obtained were processed through an interface prepared in the MATLAB (TM) GUI setting, and real-time heart rate detection was carried out.