Browsing by Author "Dülek, B."

Now showing 1 - 4 of 4

Open Access
Computer vision based analysis of potato chips-A tool for rapid detection of acrylamide level
(Wiley - VCH Verlag GmbH & Co. KGaA, 2006) Gökmen, V.; Senyuva, H. Z.; Dülek, B.; Çetin, E.
In this study, analysis of digital color images of fried potato chips were combined with parallel LCMS based analysis of acrylamide in order to develop a rapid tool for the estimation of acrylamide during processing. Pixels of the fried potato image were classified into three sets based on their Euclidian distances to the representative mean values of typical bright yellow, yellowish brown, and dark brown regions using a semiautomatic segmentation algorithm. The featuring parameter extracted from the segmented image was NA2 value which was defined as the number of pixels in Set-2 divided by the total number of pixels of the entire fried potato image. Using training images of potato chips, it was shown that there was a strong linear correlation (r = 0.989) between acrylamide level and NA2 value. Images of a number of test samples were analyzed to predict their acrylamide level by means of this correlation data. The results confirmed that computer vision system described here provided explicit and meaningful description from the viewpoint of inspection and evaluation purpose for potato chips. Assuming a provisional threshold limit of 1000 ng/g for acrylamide, test samples could be successfully inspected with only one failure out of 60 potato chips.
Open Access
Computer vision-based image analysis for the estimation of acrylamide concentrations of potato chips and french fries
(Elsevier BV, 2007) Gökmen, V.; Şenyuva, H. Z.; Dülek, B.; Çetin, A. Enis
In this study, digital colour images of fried potato chips and french fries were analyzed to estimate acrylamide levels based on the correlation with analyses using liquid chromatography-mass spectrometry. In fried potato images, bright yellow (Region 1), yellowish brown (Region 2) and darker brown (Region 3) regions were clearly visible, having different kinds of image pixels with characteristic mean values of red, green and blue components. Pixels of the fried potato image were classified into three sets (Set 1, Set 2 and Set 3) by means of semi-automatic and automatic segmentation. There was a strong correlation between acrylamide concentration and NA2 value, which is defined as the number of pixels in Set 2 divided by the total number of pixels of the entire fried potato image. To verify the applicability of this approach, a linear regression equation was used to estimate the acrylamide concentrations of a number of commercial potato chips and home-made french fries. Mean differences between the measured and predicted acrylamide concentrations were found to be +4 ± 14% and 14 ± 24% for commercial potato chips and home-made french fries, respectively
Open Access
Convexity properties of detection probability for noncoherent detection of a modulated sinusoidal carrier
(Institute of Electrical and Electronics Engineers, 2018) Öztürk, Cüneyd; Dülek, B.; Gezici, Sinan
In this correspondence paper, the problem of noncoherent detection of a sinusoidal carrier is considered in the presence of Gaussian noise. The convexity properties of the detection probability are characterized with respect to the signal-To-noise ratio (SNR). It is proved that the detection probability is a strictly concave function of SNR when the false alarm probability α satisfies α > e-2, and it is first a strictly convex function and then a strictly concave function of SNR for α < e-2. In addition, optimal power allocation strategies are derived under average and peak power constraints. It is shown that on-off signaling can be optimal for α < e-2 depending on the power constraints, whereas transmission at a constant power level that is equal to the average power limit is optimal in all other cases.
Open Access
Sensor selection and design for binary hypothesis testing in the presence of a cost constraint
(IEEE, 2020) Oymak, Berkay; Dülek, B.; Gezici, Sinan
We consider a sensor selection problem for binary hypothesis testing with cost-constrained measurements. Random outputs related to a parameter vector of interest are assumed to be generated by a linear system corrupted with Gaussian noise. The aim is to decide on the state of the parameter vector based on a set of measurements collected by a limited number of sensors. The cost of each sensor measurement is determined by the number of amplitude levels that can reliably be distinguished. By imposing constraints on the total cost, and the maximum number of sensors that can be employed, a sensor selection problem is formulated in order to maximize the detection performance for binary hypothesis testing. By characterizing the form of the solution corresponding to a relaxed version of the optimization problem, a computationally efficient algorithm with near optimal performance is proposed. In addition to the case of fixed sensor measurement costs, we also consider the case where they are subject to design. In particular, the problem of allocating the total cost budget to a limited number of sensors is addressed by designing the measurement accuracy (i.e., the noise variance) of each sensor to be employed in the detection procedure. The optimal solution is obtained in closed form. Numerical examples are presented to corroborate the proposed methods.