The purpose of this study is to propose a fully sustainable dyeing process for nylon 6. In order to achieve this goal, Rhubarb flower parts were used to produce a brown hue on nylon 6 fabric. The effects of dyeing parameters such as dyeing time, temperature, dyebath pH, M:L, salt addition, dispersing agent, and dye concentration on color strength were investigated. Using 100%owf dye in an acidic medium at boil and the material to liquor ratio of 1:30 for 75 min was determined to be the optimal condition for dyeing nylon 6 with rhubarb flower. In order to achieve acceptable color fastness, four natural mordants were applied, including walnut husks, pistachio hulls, pine cones, and green coffee. Colorimetric measurements revealed that mordanting did not affect the hue of the color compared to the non-mordant sample. In addition, diverse natural mordants produced the same color (i.e., brown) with varying color strengths, of which 10%owf walnut husk generated the strongest color. Bio-mordanted samples were also found to have excellent color fastness, thereby providing an effective substitute for metal mordants.
Manipulating existing camouflage patterns is a challenging issue in the process of camouflage pattern design. In this article, we present an effective approach based on the neural style transfer method to generate a hybrid camouflage pattern by manipulating two given camouflage patterns. Using a convolutional network trained on image recognition, content and style are represented by the correlations between feature maps in several layers of the network. In this regard, we utilized different commonly used camouflage patterns as content and style images. Then, by performing the style transfer algorithm on selected camouflage patterns, a new hybrid camouflage pattern was generated. The hybrid pattern inherits the appearance features of the content and the style images. Also, the colors of the hybrid pattern were controlled by carefully selecting the input colors. It was concluded that the proposed method is useful for adding or deleting some features of an existing camouflage pattern. In fact, it is a professional tool for pixelating, depixelating, blurring, and gradual coloring of camouflage patterns. Furthermore, we demonstrate the effectiveness of hybrid camouflage patterns using visual assessment. The results of the subjective assessment show that the proposed method is efficient for generating successful camouflage patterns.
The aim of this study is to describe a robust unified framework for segmentation of the phonocardiogram (PCG) signal sounds based on the false-alarm probability (FAP) bounded segmentation of a properly calculated detection measure. To this end, first the original PCG signal is appropriately pre-processed and then, a fixed sample size sliding window is moved on the pre-processed signal. In each slid, the area under the excerpted segment is multiplied by its curve-length to generate the Area Curve Length (ACL) metric to be used as the segmentation decision statistic (DS). Afterwards, histogram parameters of the nonlinearly enhanced DS metric are used for regulation of the α-level Neyman-Pearson classifier for FAP-bounded delineation of the PCG events. The proposed method was applied to all 85 records of Nursing Student Heart Sounds database (NSHSDB) including stenosis, insufficiency, regurgitation, gallop, septal defect, split sound, rumble, murmur, clicks, friction rub and snap disorders with different sampling frequencies. Also, the method was applied to the records obtained from an electronic stethoscope board designed for fulfillment of this study in the presence of high-level power-line noise and external disturbing sounds and as the results, no false positive (FP) or false negative (FN) errors were detected. High noise robustness, acceptable detection-segmentation accuracy of PCG events in various cardiac system conditions, and having no parameters dependency to the acquisition sampling frequency can be mentioned as the principal virtues and abilities of the proposed ACL-based PCG events detection-segmentation algorithm.
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