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Öğe An Application on Digitalization in Liquid Lens Glasses Design(Ieee, 2021) Kavsaoglu, Ahmet Resit; Buqunadah, Areej MohammedMany eyeglass users have to change their lenses due to the continual decline in vision. Sometimes it can be uncomfortable for some people to use multiple eyeglasses of varying degrees to perform different activities. Therefore, researchers in the field of optics are developing solutions to solve this problem. This work aims to develop an adjustable eyeglass which are able to changes refraction degrees of their lenses. This type of eyeglasses helps patients with myopia and similar vision issues. In this study, a medical eyeglass was designed based on controlling the distance between two lenses by injecting a liquid silicone oil for changing the degree of the lenses. The amount of liquid between the lenses is controlled by an android application. Several tests and experiments have been performed to ensure the effectiveness and readiness of the eyeglass. The tests were performed with the help of a lensometer, and acceptable results are obtained as accurate. In the scientific literature, it is common to use a manual adjustment mechanism for the injection of a specified amount of a fluid. In this work, a different injection mechanism that automatically controlled by a mobile phone was used. By this way, the eyeglasses adjustment process became easier for individuals who wear eyeglasses. Personalized values in settings can be stored in the memory of the mobile application to make easy to change between different configurations.Öğe C# Interface Design for Real-Time Signal Recording Oriented of Bionic Hand Control with Leap Motion and EMG Devices(Ieee, 2020) Kavsaoglu, Ahmet Resit; Bilece, Burak; Altiyaprak, Besimcan; Boyukcolak, FurkanThere are people who have a lost limb or have no innate limb. In this study, it is aimed to create a data processing environment to improve the working performance of the prostheses to be developed for people with hand loss. Basically, Leap Motion and EMG devices were used. Simultaneous recording of data obtained with EMG and Leap Motion is provided using Arduino microcontroller and C # Interface design. In addition, a bionic hand control is provided from finger movements obtained with Leap Motion.Öğe Computer-aided interface design for real-time pupil motion detection and an application for physically disabled persons(2021) Kavsaoglu, Ahmet Resit; Mersınkaya, İsmail; Yıldız, Ömer Faruk; Güdek, HasanIn this study, a human-computer interface was created in C# so that individuals with physical mobility disabilities such as ALS can express their wishes. In this system created, pupil movements were analyzed and the patient's wishes were expressed both visually and audibly. In the system created for the tracking of the pupil, the face of the patient, which was detected by the camera, was detected autonomously by the system. An adaptive IR LED light source has been designed to illuminate the eye area of the user. Pupil motion detection was performed with the developed image processing algorithms. According to the movements of the detected pupil, commands were created on the user interface to express the wishes of the patient by using the location information of the patient. An application study was carried out by creating the prototype of the controlled patient bed with a 3D printer. At the end of this study, pupil motion detection was carried out using a camera without any contact with the user. With the algorithm created for pupil motion detection, it is ensured that the patient can express his wishes without the need for any movement other than eye movement. With this study, a uniquely developed algorithm that can be used in pupil tracking systems of individuals with physical movement disabilities such as ALS has been acquired.Öğe A Data Acquisiton System with sEMG Signal and Camera Images for Finger Classification with Machine Learning Algorithms(Eos Assoc, 2024) Mersinkaya, Ismail; Kavsaoglu, Ahmet ResitAdvances in robotics and biomedical engineering have expanded the possibilities of Human-Computer Interaction (HCI) in the last few years. The identification of hand movements is the accurate and real-time signal acquisition of hand movements through the use of image-based systems and surface electromyography sensors. This study uses multithreading to record motion signals from the forearm muscles in conjunction with a surface electromyography (sEMG) sensor and a camera image. The finger movement information labels were tabulated and analyzed along with the simultaneous acquisition of surface electromyography signals and these gestures through the camera. After the acquisition, signal processing techniques were applied to the sEMG signal markered from the camera. Therefore, once the interface is established, data sets suitable for machine learning can be generated.Öğe 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, KerimIt 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.Öğe Indoor Path Finding and Simulation for Smart Wheelchairs(Ieee, 2021) Sahin, Halil Ibrahim; Kavsaoglu, Ahmet ResitThe new systems obtained by adding computer support to battery powered wheelchairs are called smart wheelchairs. Today, there are various map providers and navigation systems that a mobile vehicle can use to get from one location to another. However, these map providers do not support indoor spaces and satellite-aided global positioning systems cannot provide solutions in indoor environments. In this study, by using the pre-mapping of interior spaces and the use of appropriate path finding algorithms, individuals can reach their desired location in an environment that they do not know exactly, via smart wheelchairs. The designed interface software provides the path was determined with the jump point search, one of the shortest path finding algorithms, and the system was simulated with the simulation software.Öğe An innovative P300 speller brain-computer interface design: Easy screen(Elsevier Sci Ltd, 2022) Aygun, Abdullah Bilal; Kavsaoglu, Ahmet ResitBackground: P300 spellers are brain-computer interfaces (BCIs) that display desired characters, once at a time, on a screen through the detection of P300 event-related potentials (ERPs) generated in response to flashing visual stimuli using classification methods to determine desired outcomes. Individual words can also be displayed, rather than the letter-by-letter display typical of P300 spellers. Method: An innovative interface using a 7 x 7 letters visual stimulus matrix was designed, as the Easy Screen P300 Speller. In addition to alphabetic characters, 20 shortcut elements (E1-E20) can be used to display words directly on the screen. After first one or more letters of a desired word are determined, 20 words are formed in the word list. If the selection of a shortcut element is detected, the word corresponding to that element is displayed on the screen. Result and discussion: An innovative P300 speller BCI was tested for 19 men and 11 women. Offline, online, and word typing studies were performed using the designed interface. During online analysis, on average, each subject focused on 28.37 of 30 characters. Each subject was asked to display 10 words pre-selected according to the subjects' wishes using the Easy Screen P300 Speller. The same word could be displayed in an average of 1.31 min using the Easy Screen P300 Speller compared with 4.53 min using a conventional P300 speller. This paper uncovers upper results in terms of character detection accuracy and Output Characters per Minute (OCM) value across word-typing interfaces than state-of-the-art.Öğe An innovative peak detection algorithm for photoplethysmography signals: an adaptive segmentation method(Tubitak Scientific & Technological Research Council Turkey, 2016) Kavsaoglu, Ahmet Resit; Polat, Kemal; Bozkurt, Mehmet RecepThe purpose of this paper is twofold. The first purpose is to detect M-peaks from raw photoplethysmography (PPG) signals with no preprocessing method applied to the signals. The second purpose is to estimate heart rate variability (HRV) by finding the peaks in the PPG signal. HRV is a measure of the fluctuation of the time interval between heartbeats and is calculated based on time series between strokes derived from electrocardiogram (ECG), arterial pressure (AP), or PPG signals, separately. PPG is a method widely used to measure blood volume of tissue on the basis of blood volume change in every heartbeat. In the estimation of the HRV signal from the PPG signal, HRV is calculated by measuring the time intervals between the peak values in the PPG signal. In the present paper, a novel peak detection algorithm was developed for PPG signals. Finding peak values correctly from PPG signals, the HRV signal can be estimated. This peak detection algorithm has been called an adaptive segmentation method (ASM). In this method, the PPG signals are first separated into segments with sample sizes and then the peak points in these signals are detected by comparing with maximum points in these segments. To evaluate the estimated pulse rate and HRV signals from PPG, Poincare plots and time domain features including minimum, maximum, mean, mode, standard deviation, variance, skewness, and kurtosis values were used. Our experimental results demonstrated that ASM could be even used both in the estimation of HRV signals and to detect the peaks from raw and noisy PPG signals without a pre-processing method.Öğe An Innovative Respiratory Rate Detection System Using Adaptive Filter with Speech Boundaries Detection Algorithm in Audio Signal(Springer International Publishing Ag, 2022) Kavsaoglu, Ahmet Resit; Elhashmi, MohamedThe adaptive filter is a variation of the digital filtering technique. This form of filter, which does not resemble the classical filtering technique, consists of three basic elements. These elements are collector element, weighting (multiplication) element and a digital filter structure. A system, which has these elements, can make the necessary change in the filter characteristics depending on the environmental media by changing the filter coefficients. Breathing corresponds to the movement of the thorax and lungs and to volume and pressure changes that occur successive in these organs. Respiratory rate (RR) means the respiratory frequency per minute. Since the RR is used to detect and monitor the serious diseases, designing a respiratory rate detection system by means of using adaptive filtering is considered one of the important issues. In this study, a system that detects respiratory rate has been implemented. In this system, there are adaptive filtering, speech boundaries detection algorithm in the sound signal, two stethoscopes whose internal part is placed a microphone and a MATLAB GUI interface design. One of the microphones is placed to upper part of the trachea and the other is placed to upper part of the heart. The sound signals coming from the stethoscope on the heart are used as a noise source and a preprocessing is carried out by means of making free from this noise the sound signals received from the microphone placed on the trachea. After this pre-processing, a clean breathing sound signal is reached by means of making free the heart sounds from the stethoscope placed on the trachea at the adaptive filter output. Inhalation and exhalation time intervals can be determined by running the speech boundaries detection algorithm on this clean breathing sound signal. The respiratory rate is obtained by using these determined time intervals.Öğe Mini Gel Electrophoresis System Based on Embedded System(Ieee, 2018) Mersinkaya, Ismail; Kavsaoglu, Ahmet ResitThe 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.Öğe A Novel Automatic Audiometric System Design Based on Machine Learning Methods Using the Brain's Electrical Activity Signals(Mdpi, 2023) Kucukakarsu, Mustafa; Kavsaoglu, Ahmet Resit; Alenezi, Fayadh; Alhudhaif, Adi; Alwadie, Raghad; Polat, KemalThis study uses machine learning to perform the hearing test (audiometry) processes autonomously with EEG signals. Sounds with different amplitudes and wavelengths given to the person tested in standard hearing tests are assigned randomly with the interface designed with MATLAB GUI. The person stated that he heard the random size sounds he listened to with headphones but did not take action if he did not hear them. Simultaneously, EEG (electro-encephalography) signals were followed, and the waves created in the brain by the sounds that the person attended and did not hear were recorded. EEG data generated at the end of the test were pre-processed, and then feature extraction was performed. The heard and unheard information received from the MATLAB interface was combined with the EEG signals, and it was determined which sounds the person heard and which they did not hear. During the waiting period between the sounds given via the interface, no sound was given to the person. Therefore, these times are marked as not heard in EEG signals. In this study, brain signals were measured with Brain Products Vamp 16 EEG device, and then EEG raw data were created using the Brain Vision Recorder program and MATLAB. After the data set was created from the signal data produced by the heard and unheard sounds in the brain, machine learning processes were carried out with the PYTHON programming language. The raw data created with MATLAB was taken with the Python programming language, and after the pre-processing steps were completed, machine learning methods were applied to the classification algorithms. Each raw EEG data has been detected by the Count Vectorizer method. The importance of each EEG signal in all EEG data has been calculated using the TF-IDF (Term Frequency-Inverse Document Frequency) method. The obtained dataset has been classified according to whether people can hear the sound. Naive Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms have been applied in the analysis. The algorithms selected in our study were preferred because they showed superior performance in ML and succeeded in analyzing EEG signals. Selected classification algorithms also have features of being used online. Naive Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms were used. In the analysis of EEG signals, Light Gradient Strengthening Machine (LGBM) was obtained as the best method. It was determined that the most successful algorithm in prediction was the prediction of the LGBM classification algorithm, with a success rate of 84%. This study has revealed that hearing tests can also be performed using brain waves detected by an EEG device. Although a completely independent hearing test can be created, an audiologist or doctor may be needed to evaluate the results.Öğe A novel study to classify breath inhalation and breath exhalation using audio signals from heart and trachea(Elsevier Sci Ltd, 2023) Kavsaoglu, Ahmet Resit; Sehirli, EftalRespiration is a vital process for all living organisms. In the diagnosis and the detection of many health problems, patient's respiration rate, breath inhalation, and breath exhalation conditions are primarily taken into consid-eration by doctors, clinicians, and healthcare staff. In this study, an interactive application is designed to collect audio signals, present visual information about them, create a novel 21253x20 audio signal dataset for the detection of breath inhalation and breath exhalation that can be performed through nose and mouth, and classify audio signals based on machine learning (ML) models as breath inhalation and breath exhalation. Audio signals are received from both volunteers' hearts (method 1) and trachea (method 2). ML models as decision tree (DT), Naive Bayes (NB), support vector machines (SVM), k-nearest neighbor (KNN), gradient boosted trees (GBT), random forest (RF), and artificial neural network model (ANN) are used on the created dataset to classify the received audio signals from nose and mouth into two different conditions. The highest sensitivity, specificity, accuracy, and Matthews correlation coefficient (MCC) for the classification of breath inhalation and breath exhalation are respectively obtained as 91.82%, 87.20%, 89.51%, and 0.79 by method 2 based on majority voting of KNN, RF, and SVM. This paper mainly focuses on usage of audio signals and ML models as a novel approach to classify respiratory conditions based on breath inhalation and breath exhalation via an interactive application. This paper uncovers that audio signals received from method 2 are more effective and eligible to extract information than audio signals received from method 1.Öğe Python ile mini jel elektroforez kontrol yazılımı ve sistem tasarımı(2019) Kavsaoglu, Ahmet Resit; Mersınkaya, İsmailDNA, RNA ve protein molekülleri gibi yüklü makro moleküllerin bir elektrik alan içerisinde (-) ve (+) yüklü kutuplar arasında bir kutuptan diğerine doğru hareket ettirilerek ayrıştırılması yöntemine elektroforez denir. Klasik elektroforez işleminde güç kaynağı, UV (morötesi) transillüminatör ve jel görüntüleme için kullanılan cihaz ve malzemeler, süreç kontrolü işlemleri birbirinden ayrı işlemler olarak ve deneyi yapan kişiler tarafından yapılmaktadır. Gülümseme etkisi (smile effect), yayınım etkisi ve diğer etkiler analizlerdeki hassasiyeti düşürebilmekte ve sonuçlar üzerinde hatalı band görüntüsü ve band yoğunluğu hesaplamasına neden olabilmektedir. Bu çalışma, elektroforez işlemi süresince güç kaynağının gerilim dalgalanmasından ve deneyi yapan kişilerin zamanında güç kaynağını kapatmadığında bandların jel dışına taşması gibi hataları en aza indirerek analiz hassasiyetini arttırmayı; kullanılan cihazların işlem sırasına göre çalışabilmesi ve hesaplama işlemlerinin otomatik olarak bir mikroişlemci ve mikrodenetleyici arabirimleri tarafından yapılabilmesini sağlayan kontrol yazılımı, mini jel elektroforez sistem tasarımı ve uygulama çalışmalarını içermektedir. Tasarlanan kontrol yazılımı ve klasik elektroforez ile yapılan işlemler karşılaştırıldığında olumlu sonuçlar elde edildiği görülmüştür.
