Onur Toker

Rotation Angle Estimation Algorithms for Textures and Their Real-Time Implementation on the FU-SmartCam

In this paper, we study rotation angle estimation algorithms for textures and discuss their real-time implementation on the FU-SmartCam. Rotation angle estimation for textures is closely related to the weft-straightening problem encountered in the textile industry. Earlier weft-straightening machines were based on simpler sensors and hardware, but with the increased complexity of fabric types, and demand for faster and more accurate machines, the whole industry started to switch to real-time machine vision based systems. For example, one of the leading companies in this area, Erhardt+Leimer, started to implement realtime machine vision systems based on the tattile smart camera. In this work, we both study rotation angle estimation algorithms for textures, and their real-time implementation on the locally developed Fatih University smart camera (FU-SmartCam). We also compare the FU-SmartCam with the tattile smart camera used in the Erhardt+Leimer system, both in terms of hardware and ease of prototyping.

Design of intelligent fiber optic statistical mode sensors using novel features and artificial neural networks

In this paper, novel statistical features namely first moment and second moment were proposed in the analysis of fiber optic statistical mode sensors to design consistent and sensitive sensors. These features used first time in literature. Experiments were conducted to measure force using statistical mode sensors and analyses were implemented using proposed statistical features. Good results were concluded and it was seen that these statistical features can be used in the design of statistical mode sensors. After that, Artificial Neural Networks (ANNs) with sensor fusion, intelligent sensor architecture was proposed to predict the force values measured by statistical mode sensors. It was seen that using the statistical features with sensor fusion provides better prediction of force values. Multilayer Perceptron (MLP) with different algorithms were used in ANN model. All of them can predict the force values with considerable errors. Statistical mode sensors can be calibrated and fault tolerance of the sensor can be decreased, hence more reliable sensors can be designed.

Polar format statistical image processing based fiber optic pressure sensors

This paper presents detailed study on the development of a fiber optic sensor system to design a pressure sensor with different sensor configurations. The sensor used in the experiments is based on modal power distribution (MPD) technique. MPD technique is spatial modulation of the modal power in multimode fibers. Stress measurements and CCD camera based techniques were investigated in this research. Differently from earlier MPD works, all of the data gathered from CCD camera are used instead of using some part of the data, the ring shaped pictures taken from the CCD camera converted to polar coordinates, and so stripe shaped pictures are obtained. Four different features are calculated from these converted pictures. R component of the center of mass in the polar form is the first feature. It is calculated because it was expected to decrease monotonically with respect to increasing applied pressure. Second and third features are ring thickness in polar form with taking brightness of each pixel into account and ring thickness in polar form without taking brightness of each pixel into account. These features are calculated to analyze the effect of each pixel’s brightness. It was expected for these two features that there will not be a big margin between them. Fourth feature is the ratio between third feature and first feature. A MATLAB code is written to correlate these features and applied force to the sensor. Various experiments conducted to analyze this correlation. Pictures are taken from CCD camera with 1 kg steps and from the written MATLAB code, graphics of each feature versus the applied force are generated. Experimental results showed that, the sensitivity of the proposed sensor is much higher than sensors that uses only some part of the collected data in earlier MPD studies. Furthermore, results are almost exactly the same that what was expected for the four proposed features. Results also showed that converting pictures to the polar form increases the sensitivity and reliability.

A novel periodic macrobending hetero-core fiber optic sensor embedded in textiles

This paper presents the design of a novel periodic macrobending hetero-core fiber optic sensor embedded in textiles. In order to demonstrate the superiority of the proposed sensor, experiments were done on a macrobending sensor constructed from 62.5-50-62.5 hetero-core fiber and a macrobending sensor constructed from 62.5/125 μm multi-mode fiber having different loops. Experimental results show that the sensitivity of the proposed macrobending sensor constructed using hetero-core optical fiber is much higher than the sensor constructed from plain multi-mode optical fiber. It is also shown that, the sensitivity of the sensor increases as the number of loops is increased. In addition, the forward and reverse loading effects of the designed sensor exhibit a very close behavior indicating the reversibility of the fiber optic sensor.

Intelligent fiber optic statistical mode sensors using novel features and artificial neural networks

In this paper, intelligent statistical mode sensors are proposed and analyzed. Several statistical features are used in design of intelligent sensor systems. Force measurement experiments are conducted and experimental data is analyzed using newly proposed statistical features. After that, Artificial Neural Networks (ANNs) with sensor data fusion, which is an intelligent sensor architecture, was proposed to estimate the force values. Multilayer perceptron (MLP) with different algorithms are used in the ANN model, and all of them can predict the force values with acceptable error levels. Using sensor fusion with ANNs, statistical mode sensors can be calibrated and fault tolerance of the sensor can be decreased, hence more reliable intelligent sensors can be designed.

Advanced image processing and artificial intelligence based approaches to fiber optic statistical mode sensor design

In this paper, we consider advanced image processing and artificial intelligence based techniques for design of fiber optic statistical mode sensors. Output from an optical fiber exhibits a speckle pattern when projected on a flat surface, and intensity, size, and location of these speckles do change with external effects like pressure, temperature, vibration, etc. Statistical mode sensors reported in the literature use global image differencing or global correlation like approaches for sensor design. Namely, lots of localized information in the image is not taken into account, instead global difference and/or correlation are used for sensor construction. We propose the use of localized information with image processing techniques; generate more features, and analysis of these via artificial intelligence based methods. In this way, we can capture more information from the speckle pattern distribution, and hence design a better sensor.

Extraction of blood vessel centerlines on retinal images

Vessel extraction has important role to determined eyes vessels changing that are occured by systemetic disease. In this study;included the extraction of vessels centerlines. Since blood vessels often present poor contrast and narrow vessels width, the multiscale matched filter used with thresholding method used to extraction of blood vessels. Thinning algorithm applied to multiscale matched filter with thresholding response for extraction of vessels centerline, and extracted centerlines points have been shown on the original image to determination of visually accuracy. This study has been applied on 20 color fundus images were taken and 16 in the successful results.

In vitro pulsatile flow waveform simulation and comparison using AC pump and proportional pneumatic valve

Patients, with renal failure, can be taken to the hemodialysis after fistula or graft surgery. In hemodialysis, blood is pumped with the help of the artificial kidney machine and the waste product from the blood is filtered and blood is returned to the patients after cleaning process. AV graft implantation is the creation of a polytetrafluoroethylene (PTFE) bridge a bridge between the arteries and veins. In this study simulation of the pulsatile flow signal in the AV graft and vein connection is aimed experimentally. Suitable for this purpose AC centrifugal pump, pump driver card, pneumatic valve, data acquisition card, flow meters and other equipments are used for close loop control mechanism in laboratory. Pulsatile waveforms obtained by AC centrifugal pump and pneumatic valve performance are compared to each other in close loop control. The results showed that AV graft signal can be modeled by AC centrifugal pump and pneumatic valve successfully. Current study could be a guide and motivation to the establishment of the different experimental systems in future.

Kharitonov theorem with degree drop: the complex case

In this paper, we study the complex version of the Kharitonov theorem without the constant-degree assumption. It is proved that, for a complex-interval polynomial with degree drop, robust Hurwitz stability is equivalent to the Hurwitz stability of the eight Kharitonov polynomials. Furthermore, it is also shown that for a robustly stable complex-interval polynomial, degree drop cannot exceed one and degree drop can only occur at one of the corners of the rectangular region in the complex plane corresponding to the leading coefficient of the uncertain polynomial.

A novel periodic macrobending hetero-core fiber optic sensor embedded in textile for respiratory movements' analysis

This paper presents the design of a novel periodic macrobending hetero-core fiber optic sensor embedded in textile for respiratory movements’ analysis. We report on several different designs based on textiles which have different loop periodicity and configuration of optical fiber types. In all experiments, the changes of textile elongation are measured during breathing movements. In order to demonstrate the superiority of the proposed sensor, experiments were done on a macrobending sensor constructed from 62.5-50-62.5 hetero-core fiber and a macrobending sensor constructed from 62.5/125 μm multi-mode fiber having different loops. Experimental results show that the sensitivity of the proposed macrobending sensor constructed using hetero-core optical fiber is much higher than the sensor constructed from plain multi-mode optical fiber. It is also shown that, the sensitivity of the sensor increases as the number of loops is increased. On the other hand, several experiments were performed for periodic macrobending sensors having different bending radius by changing the lengths of loops amplitude and period. We demonstrate that the sensors tested on different patients’ morphology can successfully sense respiratory movements.