Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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An Aloha Based Throughput Analysis For Cognitive Radio Applications in Tv Bands
(2014) Tuncer, A. Turgut
Since existing spectrum is limited and could not efficiently be used, new ways to solve this problem are brought forth. The unused bands currently allocated to TV broadcasting services can be opportunistically reassigned to support broadband networking services while continuing to provide broadcast TV The fragmented and unused TV channels have considerable amount of bandwidth potential and long transmission ranges. This paper considers an OFDM based wireless communications system using TV bands. To make a good use of spectral resources, two OFDM paradigms, ie., Fixed Carrier Spacing (FCS) and Fixed Carrier Number (FCN), have been investigated deeply for Aloha-Medium Access Control protocol in TV white spaces.
Analysis of Deep Neural Network Models for Acoustic Scene Classification
(2019) Basbug, Ahmet Melih; Sert, Mustafa
Acoustic Scene Classification is one of the active fields of both audio signal processing and machine learning communities. Due to the uncontrolled environment characteristics and the multiple diversity of environmental sounds, the classification of acoustic environment recordings by computer systems is a challenging task. In this study, the performance of deep learning algorithms on acoustic scene classification problem which includes continuous information in sound events are analyzed. For this purpose, the success of the AlexNet and the VGGish based 4- and 8-layered convolutional neural networks utilizing long-short-term memory recurrent neural network (LSTM-RNN) and Gated Recurrent Unit Recurrent Neural Network (GRU-RNN) architectures have been analyzed for this classification task. In this direction, we adapt the LSTM-RNN and the GRU-RNN models with the 4- and 8-layared CNN architectures for the classification. Our experimental results show that 4-layered CNN with GRU structure improve the accuracy.
Analysis of Heart Diseases from ECG Signal
(2014) Kantar, Tugce; Koseoglu, Ovul; Erdamar, Aykut; https://orcid.org/0000-0001-8588-480X; AAA-6844-2019
At the present time, the main method used in the diagnosis of heart disease is the electrocardiography (ECG). The purpose of this study is design a decision support algorithm which automatically detect the normal sinus rhythm or other pathologies. The improved algorithm will provide support to the doctor can be also used for educational purposes. Within the scope of this study, with the design of the rule based algorithm which automatically detect a normal sinus rhythm and non normal sinus rhythm, in total it can detect eight pathologies. In maincode there are thirteen functions that are used for diagnose eight different ECG pathology automatically. Higher success is being anticipated in future for the prediction power of the developed method with continuing research on the matter.
Analysis of the effect of the number of criteria and alternatives on the ranking results in applications of the multi criteria decision making approaches in machining center selection problems
(2020) Ic, Yusuf Tansel; Yurdakul, Mustafa
Multi criteria machining center selection models are widely used in the literature. In the applications of multi-criteria decision making models, machining center selection criteria are directly taken from catalogues. It is known that to have a ranking model sensitive to the weights of the selection criteria, it is especially important to limit the number of selection criteria to 7 +/- 2. A similar proposal can be put forward for the number of machining centers. In this study, whether or not reducing the number of criteria and alternative machining centers make the ranking results more sensitive to the changes in the criteria weights is studied using Spearman's rank correlation test. The study results show that the ranking results become more sensitive with a reduced number of criteria and alternative machining centers.
Analysis of True Random Number Generation Method Utilizing FM Radio Signals
(2015) Tanyer, Suleyman Gokhun; Inam, Sitki Cagdas; Atalay, Kumru Didem; 0000-0001-9506-2391; 0000-0003-0820-9186; I-5023-2013
Random number generator (RNG)'s utilize some source of entropy. Various RNGs; pseudo, quasi and true RNGs provide observed samples for given distributions which are necessary in statistical signal processing and simulations applications. True random number generators generally utilize the last insignificant bit of observed samples obtained from a source of entropy. In this work, samples obtained from an FM broadcasting are examined. Least significant bit is used to generate 16 bits observed samples. Randomness and goodness-of-fit test results are compared with the raw samples. It is shown that it is possible to generate true random numbers of sufficient merits using FM signals even when the band is occupied with broadcasting signals.
Anterior Kolon Kırığının İlioingunial Yaklaşım Tedavi Tekniği İle Medial Stoppa Tedavi Tekniğinin Biyomekanik Dayanımı Açısından Karşılaştırılması
(2014) Göker, Cihad
Anterior kolon kırığı tedavisinde ilioinguinal yaklaşım altın standard olarak kullanılmaktadır. Medial stoppa tekniği ise pelvise ve asetabulumun medyal duvarına ulaşarak asetabulum kırıklarının tedavisinde kullanılan yeni bir cerrahi yaklaşımdır. Medial stoppa tekniğinin ilioinguinal yaklaşıma göre bazı avantajları vardır. İlioinguinal yaklaşımda açılan inguinal bağın üzerindeki pencerelerin stoppa yaklaşımında açılmaması, inguinal bağın ve kanalın, lateral kütanöz femoral ve femoral sinirlerin korunması, eksternal iliyak damar hasarının daha az oranda gözlenmesi bunlardan bazılarıdır. Medial stoppa tekniğinin avantajları olsa da yeni bir cerrahi yöntem olduğu için literatürde çalışmaları azdır. Bu çalışmada, anterior kolon kırığı tedavisinde kullanılan bu iki yöntemin biyomekanik dayanım açısından karşılaştırılması yapılmıştır. Deneyde on beş adet sawbone pelvis kullanılmıştır. Bunlardan beş tanesine her hangi bir işlem yapılmadan kuvvet uygulanmış ve sawbone’ların kuvvet karşısındaki cevabı kaydedilmiştir. On tanesinde ise pelvisin her iki kolonunda da kırık çizgisi oluşturulmuş ve her iki kolonda oluşturulan bu iki kırık her iki yöntem ile tedavi edilmiştir. Daha sonra her iki cerrahi yöntemin mekanik olarak rijitliğini karşılaştırmak için implant uygulanmış numunelere aksiyel eksende yük uygulanmıştır. Sonuç olarak; Medial stoppa tekniğinin cerrahi açıdan avantajları olsa da biyomekanik dayanım açısından ilioinguinal yaklaşım ile tedavi edilen kolon medial stoppa tekniği ile tedavi edilen kolona göre daha rijit çıkmıştır.
Author Recognition from Lyrics
(2015) Kirmaci, Basar; Ogul, Hasan
Music information retrieval has been an important task due to the wide use of internet and related technologies for entertainment. In previous studies, the problem has been considered using the meta-data or melodic content. The use of lyrics in this context is not that common. There is not study either for Turkish songs in this respect. In this study, we discuss the predictability of the author using the text data in a Turkish lyric. To this end, we propose a system that can predict the author using the features extracted from text content. The performance of the system is evaluated on a large data set collected from writers with different music styles.
Automatic Brain Tissue Segmentation on TOF MRA Image
(2020) Ozen, Sinasi Kutay; Aksahin, Mehmet Feyzi
For the segmentation of brain vessels from MRA images, brain tissue is used in the head, eye, skull, etc. must be separated from the structures. For this reason, studies are carried out for the segmentation of brain tissue. In this study, the method that automatically segregates brain tissue from magnetic resonance angiography images taken with time of flight (TOF) technique is presented. The method in the study consists of live steps. First of all, the tip contrast values in the image are filtered by anisotropic diffusion filtering method. Parameters of anisotropic diffusion method are determined automatically by the natural image quality evaluator method. Sudden density transitions arc detected by applying LoG edge detection filter on the filtered image. It is made ready for image analysis by applying etching on the image with density transitions. According to the conditions determined in image analysis, brain tissue is obtained separated from other head structures. As a result of this study, an easy-to-apply, fast-delivering, high-accuracy automatic algorithm has been introduced.
Automatic Classification of Respiratory Sounds During Sleep
(2018) Kilic, Erkin; Erdamar, Aykut
Sounds like snoring, coughing, sneezing, whistling, which have different acoustic properties, can emerge involuntarily during the sleep. These sounds may affect negatively the sleep quality of the other people in the same environment, just as it may affect directly the sleep quality. To increase the sleep quality, these sounds should be recorded and evaluated by a sleep expert. This is an expertise required process that can be time-consuming and subjective results. In this study, it has been aimed that developing a computer-aided diagnosing algorithm which will classify the sounds emerging during the sleep automatically with high accuracy by analyzing the records in a fast and effective way to help the sleep expert to diagnose. The mathematical features have been obtained in frequency and time domains by applying continuous wavelet transform for the different type of sounds. Support vector machine used as a classifier. 390 and 449 segments were used for training and testing respectively. As a result of the study, six different parameters which are exhalation, simple snoring, high frequency duplex snoring, low frequency duplex snoring, triplex snoring and coughing were classified with 96.44% accuracy rate.
Automatic Detection of Sleep Spindles With Quadratic Discriminant Analysis
(2018) Kokerer, Sila Turku; Celik, Elif Oyku; Kantar, Tugce; Erdamar, Aykut
Sleep, is in the event of temporary loss of consciousness. Sleep and wakefulness causes some different kind of potential changes in brain. Transient waveforms observed in sleep electroencephalography are structures with specific amplitude and frequency characteristics that can occur in some stages of sleep. The main objective of this study is to develop a method to detect sleep spindle, which is one of these structures, with high-accuracy. Sleep spindles that require the expertise to determine visually, is a process that can be time-consuming and subjective results. In this study, electroencephalography records, scored by expert sleep physicians, were analyzed by different methods. Two features have been determined that express the presence of sleep spindle. Sleep spindles were detected by these features and quadratic discriminant analysis. As a result, the performance of the algorithm was evaluated and sensitivity, specificity and accuracy were determined as 95.74%, 98.08% and 97.76%, respectively.
Automatic Glacoma Detection Using Whale Optimization and Support Vector Machines
(2022) Ozen, Sinasi Kutay; Aksahin, Mehmet Feyzi
Glaucoma is among the most common causes of permanent blindness in humans. The mass screening will aid in early diagnosis in a large population, as the initial symptoms are not obvious. This type of mass screening requires an automated diagnostic technique. Our proposed automation extracts feature by obtaining disk-to-cup ratio by applying histogram equalization, median filter, otsu thresholding, and whale optimization algorithm, respectively, on the optic disc region obtained by preprocessing. In addition, the optic disc circumference, optic disc area, optic cup circumference, and optic cup area values obtained from the optic disc region are given to the support vector machine model together with the cup-disc ratio, and glaucoma detection is made automatically. The proposed system has been validated on a real ophthalmological images of both normal and glaucoma cases. The results show the effectiveness of the proposed method when compared with other existing systems.
Automatic Vascular Segmentation on Angio Images
(2017) Akshain, Mehmet Feyzi; Ozen, S. Kutay; Eren, Neyyir Tuncay
Cardiovascular disease is one of today's major health problems. These diseases are the result of constriction or blockage of coronary vessels feeding heart. Diagnosis of Cardiovascular contraction is determined visually by physicians with angio imaging method. Visually defined vascular diseases may give subjective results. Vessel constrictions in angiograms are automatically determined by vascular segmentation to help physicians diagnose cardiovascular stenosis and to minimize subjective results. In present study, adaptive thresholding method and frangi filter were applied to the pre-processed angiogram images. After this application, the noise cleaning method on the determined cardiovascular image was performed and the vein structure was detected with high accuracy rate and low calculation time.
Calibration Transfer Between E-Noses
(2014) Guney, Selda; Fernandez, Luis; Marco, Santiago; https://orcid.org/0000-0002-0573-1326; AAC-7404-2020
Electronic nose is an instrument which is composed of gas sensor array and pattern recognition unit. It is generally used for classifying, identifying or quantifying the odors or volatile organic components for these commonly used devices, calibration transfer is an important issue because of differences in each instrument, sensor drift, changes in environmental conditions or background changes. Calibration transfer is a transfer of model between different instruments which have different conditions. In this study, calibration transfer is applied to the e-noses which have different temperature conditions. Also the results of the direct standardization, piecewise direct standardization and orthogonal signal correction which are different calibration methods were compared. The results of the piecewise direct standardization method are more successful than the other methods for the dataset which is used in this study.
Can Sleep Apnea be Detected by Heart Sounds?
(2017) Yildiz, Metin; Tabak, Zeynep; Yetkin, Sinan
Objective: It has previously been shown that there are morphological changes in hearth sounds during respiration and holding breath. In this study, for the first time in the literature, it was investigated whether sleep apnea could be detected automatically from heart sounds by teaching various classifiers of time and frequency plane parameters which are thought to be able to characterize the morphological changes seen in heart sounds during apnea. Materials and Methods: For this purpose, heart sounds were recorded simultaneously with full polysomnography records from 17 people. Classification studies were performed by assigning feature vectors obtained from heart sounds to K nearest neighbors and support vector machines. Results: The best result with K nearest neighbor classifier was 48% accuracy, 100% selectivity level. With support vector machines classifier, 82% accuracy and 42% selectivity values were reached. Conclusion: According to these values, it is concluded that the parameters of the heart sound used in this study do not make it possible to diagnose the sleep apnea from the heart sounds.
City Hospitals Model in Biomedical Calibration Service
(2015) Kocak, Onur; Budak, Erdem I.; Beytar, Faruk; Ozgode, Busra; Coruh, Baris; Kocoglu, Arif; Erogul, Osman
Clinical engineering comprise of management of medical technology, medical device maintenance, repair, and calibration which they are bought according to capacity of health institution. In this study, entirely using principles have been given about medical device maintenance, repair, and calibration. A model has been designed which is biomedical calibration production service to new vast city hospitals. The hospitals have high bed capacity and because of that there are more different types of medical devices in their inventory. According to the model has been depicted about separating medical devices too. Besides, process planning has been materialized in biomedical calibration. A new work flow model has been suggested result of evaluating both of calibration and preventive maintenance. Moreover in this study mentioned about laboratory accreditation to international traceability need. Furthermore an offset investment model has been examined to medical device calibrators which they will have bought city hospitals. Urgent actions have detected for all consider authority to the investment model success.
Classification of Different Objects with Artificial Neural Networks Using Electronic Nose
(2015) Ozsandikcioglu, Umit; Atasoy, Ayten; Guney, Selda; 0000-0002-5397-6301; 0000-0003-1188-2902; 0000-0002-0573-1326; AAR-4368-2020; HJH-3630-2023; AAC-7404-2020
In this paper; an e-nose with low cost which consisting of 8 different gas sensors was used and with this e-nose 9 different odors ((mint, lemon, egg, rotten egg, angelica root, nail polish, naphthalene, rose water, and acetone) was classified. This 9 different odor are classified with Artificial Neural Networks and by using different activation functions, and then the successes of the classification were compared with each other. The maximum success of the testing data is obtained with 100% accuracy rate by using logsig activation function in hidden layer and tansig activation function in output layer. In conclusion; using the chemical database containing the odor of the different objects, distinct odors were shown to be classified correctly.
Classification of Heart Sound Recordings With Continuous Wavelet Transform Based Algorithm
(2018) Karaca, Busra Kubra; Oltu, Burcu; Kantar, Tugce; Kilic, Erkin; Aksahin, Mehmet Feyzi; Erdamar, Aykut
Cardiovascular diseases are the major cause of death in the world. Early diagnosis of heart diseases provide an effective treatment. Heart diseases can be diagnosed using data obtained from heart sounds. Heart sounds are listened by a physician with auscultation method and the disease diagnosis can vary depending on the physician's experience and hearing ability. For this reason, automatic detection of anomalies in heart sounds can give more objective results. In this study, features were obtained by processing phonocardiogram signals taken from Physionet database. The heart sounds are classified as normal and abnormal using these features and the k - nearest neighbor method. As a result, sensitivity, specificity and accuracy were determined as 100%, 96.1% and 98.2%, respectively.
Classification of Obstructive Sleep Apnea using Multimodal and Sigma-based Feature Representation
(2019) Memis, Gokhan; Sert, Mustafa
Obstructive sleep apnea (OSA) is a sleep disorder characterized by a decrease in blood oxygen saturation and waking up after a long time. Diagnosis can be made by following a full night with a polysomnogram device, so there is a need for computer-based methods for the diagnosis of OSA. In this study, a method based on feature selection is proposed for OSA classification using oxygen saturation and electrocardiogram signals. Standard deviation (sigma) based features have been created to increase accuracy and reduce computational complexity. To evaluate the effectiveness, comparisons were made with selected machine learning algorithms. The achievements of the obtained features were compared with Naive Bayes (NB), k-nearest neighborhood (kNN) and Support Vector Machine (SVM) classifiers. The tests performed on the PhysioNet dataset consisting of real clinical samples show that the use of sigma-based features result an average performance increase of 1.98% in all test scenarios.
Classification of Patients with Heart Failure
(2014) Bayrak, Tuncay; Ogul, Hasan; https://orcid.org/0000-0001-6826-4350; U-4603-2019
Echocardiography is imaging of anatomy and physiology of heart with high frequency sound waves by using ultrasonic transducers. The signals obtained by using this method are defined as echocardiogram. In this way, the function of heart can be investigated and any abnormal case is determined according to many parameters. In this study, the classification was realized, according to 7 of features obtained from echocardiogram signals belong to 74 of patient in Machine Learning Repository (UCI) database. Naive Bayes was determined as the best classification method for this dataset and 63% sensitivity, 84% specificity, and an accuracy value of 77% has been reached. In conclusion, this study presents an investigation of determination of which features are significant in death based on heart failure.
Classification of Sleep Apnea by Photoplethysmography Signal
(2018) Aksahin, Mehmet Feyzi; Karaca, Busra Kubra; Oltu, Burcu
Sleep apnea is a very common respiratory disorder in the community that includes a range from upper airway obstruction to respiratory abnormalities and the absence of a breathing effort, which can lower people's standard of living and even cause death. Therefore, the sleep apnea needs to be diagnosed in a practical way and with high accuracy. The diagnosis of apnea is made by recording the physiological parameters of the patient with polysomnography (PSG) method and by examination of these parameters by specialist physicians, but it is a tedious and time consuming process. In order to simplify the apnea diagnosis process, phospletismography (PPG) signals are used instead of PSG records. PPG signals are suitable for diagnosis of apnea because they reflect changes in respiration. In the proposed study, a decision support system was developed to automatically diagnose apnea and to make apnea diagnosis easier and more objective using PPG signals. In the decision support system, the peaks of the PPG signal were determined and the heart rate variability (HRV) vector was generated depending on the time difference between these peaks. The mean and standard deviation values of the generated vector are determined as features for each epoch. The presence of the apnea at each epoch is classified using "Subspace K Nearest Neighbor (Subspace KNN)" and specified features. The "Subspace KNN" classifier was trained with 85% accuracy and then system was tested. As a result, sensitivity, accuracy and specificity rates were calculated as 91%, 95% and 90% respectively.