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This dataset collects samples of different kinds of defective and normal chenille yarn images for the same batch of chenille yarn made of polyester material, aiming to facilitate the task of recognizing and classifying chenille yarn defects in computer vision and machine learning algorithms. This dataset consists of a total of 2500 images of 5 major chenille yarn defects and 2500 normal chenille yarn images, totaling 5000 images. It is captured by an industrial camera in the state of chenille yarn movement.
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The data set are images taken from the Particle Image Velocimetry (PIV) method and the Planar Laser-Induced Fluorescence (PLIF) method. These methods set out the macro-scale experimental techniques that can enable fluid dynamic knowledge to inform molecular communication performance and design. Fluid dynamic experiments can capture latent features that allow the receiver to detect coherent signal structures and infer transmit parameters for optimal decoding.
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This data set is regarding the paper submitted to the IEEE Transactions on Molecular, Biological, and Multi-Scale Communications. The title of the paper is 'Molecular Signal Tracking and Detection Methods in Fluid Dynamic Channels' with the ID of TMBMC-TPS-19-0014.R2. The data are images taken from the particle image velocimetry (PIV) method and the Planar Laser-Induced fluorescence (PLIF) method. The images are being used to describe these two experimental methods for the molecular communication community.
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The color fractal images with independent RGB color components were generated using the midpoint displacement alogrithm, applied independenlty on each RGB color component. This data set contains 9 images of varying complexity, expressed as the color fractal dimension, as a function of the Hurst coefficient that was varied from 0.1 to 0.9 in steps of 0.1. Each fractal object was independently rendered as a color image. The data set is intented to be used as a reference data set for color texture complexity analysis when considering fractal dimension estimation.
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Accurate segmentation of test line and control line for colloidal gold immunochromatographic strip (GICS) images with image processing algorithms is essential to quantitative analysis of GICS. As common methods for GICS image segmentation, fuzzy c-means (FCM) algorithm and cellular neural network (CNN) algorithm both require presetting initial conditions (specifying initial parameters or training models) and take long running time, due to high calculation cost.
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As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed.
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