A procedure for creating efficient reflectance spectra from CIE tristimulus colour values is described using a modified linear model. By fixing certain criteria based on colour difference values, the proposed technique preliminarily selects a series of suitable samples from a main data set containing the reflectance values of a large number of different coloured samples, based on the colour specifications of a given sample. In this way, a series of different databases containing the reflectance values of confirmed samples relating to the particular samples are formed. Then, a well‐known principal components linear model is applied using three basis functions. This operation yields higher cumulative variances in comparison with the original database, having the same number of basis vectors. The performance of the proposed method is evaluated using a different collection of samples and the resulting spectra show considerable improvements in terms of root mean square error as well as colour difference values under different illuminants.

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K/S and reflectance graphs are essential tools in characterizing the dyeing behavior. In textile coloration, estimating the dye concentration is poor using the Kubelka-Munk model due to its low scalability and deviation of the reflectance function from linearity after low dye concentrations, particularly in the wavelengths where the gradient of K/S against dye concentration is noticeable. This paper focused on extending the validity of the Kubelka-Munk function, which originates from the linearity of reflectance function against higher dye concentration. A data set of dyed polyester specimens with three disperse dyes in a dye concentration range was prepared. At the present work, K/S was analyzed by describing the scalability property, and the suitable wavelengths in the visible spectrum where K/S benefits from minor deviation from linearity were also discussed. It was observed that the K/S function is not always scalable and deviates in λ max after a specific dye concentration for K/S > 17. Accordingly, the wavelengths other than λ max were found that could be as important as λ max. For the K/S values > 25, no practical region was achieved.

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Wireless sensor networks (WSNs) have several important applications, both in research and domestic use. Generally, their main role is to collect and transmit data from an ROI (region of interest) to a base station for processing and analysis. Therefore, it is vital to ensure maximum coverage of the chosen area and communication between the nodes forming the network. A major problem in network design is the deployment of sensors with the aim to ensure both maximum coverage and connectivity between sensor node. The maximum coverage problem addressed here focuses on calculating the area covered by the deployed sensor nodes. Thus, we seek to cover any type of area (regular or irregular shape) with a predefined number of homogeneous sensors using a genetic algorithm to find the best placement to ensure maximum network coverage under the constraint of connectivity between the sensors. Therefore, this paper tackles the dual problem of maximum coverage and connectivity between sensor nodes. We define the maximum coverage and connectivity problems and then propose a mathematical model and a complex objective function. The results show that the algorithm, called GAFACM (Genetic Algorithm For Area Coverage Maximization), covers all forms of the area for a given number of sensors and finds the best positions to maximize coverage within the area of interest while guaranteeing the connectivity between the sensors.

Endoscopic color imaging technology has been a great improvement to assist clinicians in making better decisions since the initial introduction. In this study, a novel combined method, including quadratic objective functions for the dichromatic model by Krebs et al. and Wyszecki`s spectral decomposition theory and the well-known principal component analysis technique is employed. New algorithm method working for color space converting of a conventional endoscopic color image, as a target image, with a Narrow Band Image (NBI), as a source image. The images of the target and the source are captured under known illuminant/sensor/filters combinations, and matrix Q of the decomposition theory is computed for such combinations. The intrinsic images which are extracted from the Krebs technique are multiplied by the matrix Q to obtain their corresponding fundamental stimuli. Subsequently, the principal component analysis technique was applied to the obtained fundamental stimuli in order to prepare the eigenvectors of the target and the source. Finally, the first three eigenvectors of each matrix were then considered as the converting mapping matrix. The results precisely seem that the color gamut of the converted target image gets closer to the NBI image color gamut.