The aim of this study was to determine the thermophysiological comfort behavior of fabrics based on copper wire that can be used for electro-textile applications. For this purpose, hybrid folding yarns were produced by twisting cotton/polyester yarn with copper wire. These electrically conductive hybrid yarns were then used to produce upholstery fabrics with different weave types as plain, 2/1 twill and sateen weave in three different density levels as tight, medium and loose. Thermophysiological comfort properties such as air permeability, thermal and water vapor properties of the hybrid fabrics were measured. In addition, the heat transfer properties of the fabrics were investigated with thermal camera videos, and porosity values were determined from microscope images. In this way, the main thermophysiological comfort properties of the basic electro-textile structures were revealed. According to the results obtained, it was found that the use of conductive wire in the fabric structure did not negatively affect the thermophysiological comfort properties of the fabrics, and fabric density was a determining parameter in relation to the thermophysiological comfort properties of the fabrics. The obtained results of this study may be used to improve the design of electro-textile structures taking into account the thermophysiological comfort.

In this study, classification of Normal and Extra systolic heart sounds (HS) have been carried out using in PASCAL Heart Sounds (HS) data base. The extrasystole is the HS that is produced by performing an extra beat in each heart cycle, unlike the heartbeat normal cycle. It can be felt by people as palpitations. Occurrence of these sounds in certain age groups may be the indication of tachycardia. In this study, firstly HS have been normalized at first. Then an elliptic filter has used for noise reduction. HS features have been obtained using Mel-Frequency Cepstrum Coefficients. These features have classified using Artificial Neural Network. In this study, 45 extra systoles heart sounds have used. 30 of them have been used as training data for classification while remaining 15 ones have been used for the test. Certainty, sensitivity, accuracy values have been calculated using confusion matrix. Classification success has been calculated as 90%.

Smart textile products developed by evaluating the human body’s data through sensors have become widespread in recent years. The majority of these products include textile-based information and communication technologies that integrate electronic components into clothing. People use seats, chairs, armchairs, etc., to sit constantly in vehicles, work or at home. The use of these items varies according to the requirements and purposes. In this study, an electro-textile-based upholstery fabric design was carried out to be used in sitting furniture. Electronic components containing capacitive sensors were placed in the designed fabric structure to make it usable in different areas where upholstery fabrics are used. In addition, the sensor connection circuit was developed to receive data from the fabric surface. The data taken from the fabric surface were made meaningful using the calibration and normalization algorithm. The 3D pressing map of the reaction on the fabric surface for different sitting positions was drawn. Electromagnetic field and vibration tests were carried out to examine the response behaviour in different environments where the fabric can be used. According to the findings, it was observed that the pressing areas formed on the surface were displayed in a significant way over the 3D pressing map, and the system was not affected by the electromagnetic field and vibrations. Besides, the fabric was applied on the various surfaces to test calibration and threshold algorithm. Obtained results and circumstances showed that the designed calibration and threshold algorithm were successful to obtained significant results. As a result of the study, upholstery electro fabric with a response time of 0.01 seconds in data collection was developed, which can be used in different environments such as home, workplace and vehicle. It can be used over furniture and in wet and dry conditions and is not affected by the electromagnetic field and vibration in the environment.

The aim of this chapter is to classify normal and extra systole heart sounds using artificial intelligence methods. Initially, both heart sounds have been passed from Butterworth, Chebyshev, Elliptic digital filter in specific frequency values to remove noise. Afterwards, features of heart sounds have been obtained for classification. For this process, wavelet transform and Mel-frequency cepstral coefficients (MFCC) methods have been applied. Training and test data have been created for classifier by taking means and standard deviation of gained feature. Support vector machine (SVM) and artificial neural network (ANN) methods have been used for classification of these heart sounds. Using wavelet and MFCC features, classification success of SVM has been obtained as 93.33% and 100%, respectively. Using wavelet and MFCC features, classification success of ANN has been obtained as 83.33% and 90%, respectively.

It is possible to detect visual surface defects with software in industrial tile production and increase productivity by automating the quality control process. In this process, low error rate and low cost are important indicators. In order to eliminate this negativity and the effect of the human factor, error detection software has been developed in an artificial intelligence-based industrial artificial vision environment. Spots, scratches, cracks, pore defects, which are the most common surface defects, are classified according to 6 different geometric and wavelet transform attributes. Firstly, an industrial artificial vision environment was created. In this environment, a total of 150 tile images, equal numbers from each class, were obtained on the real-time production line. The resulting images were converted into binary images by preprocessing and filtering. For classification, the support vector machines method, which performs high in two-class classifications, is used with the one versus all approach. In classifications made using RBF kernel function using wavelet features as classification performance, a higher success was achieved in all defect classes than geometric features. Real-time application software for all these processes has been developed with the Python language on Ubuntu operating system.