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The deficiency of the mapping between fashion color (FoCo) value and linguistic color expression causes the difficulty of machine-based fashion understanding tasks that are heavily associated with color matching. The purpose of this paper is to propose the FoCo system and construct it with four steps, in order to bridge this gap.

The color distribution in HSB color space is analyzed to estimate the rough number of color categories. Similar color values are grouped to obtain the initial HSB value range for each color category. The intra-category color differences are calculated to determine their final HSB value ranges and Pantone color is used for fine-tuning.

With practical applications in mind, the FoCo system is designed as a hierarchical structure with three layers.

The FoCo system is designed as a hierarchical structure with three layers: color units for color matching-related tasks, color categories for style analysis tasks and color tones for color recognition tasks. Extensive experiments demonstrate the effectiveness of the FoCo system.

Efficient and high-accuracy intelligent color and material sorting systems are the main bottlenecks restricting the recycling of waste textiles. The mixing of waste textiles with different colors will make the reconstructed raw material of textile fiber useless or with low quality. In this study, some challenges about the automatic color sorting for waste textile recycling are discussed. A computer vision-based color sorting system for waste textile recycling is introduced, which can classify the required colors well and meet the efficiency requirements of an automatic recycling line.

There are four aspects, (1) two cameras with different exposure times and white-balance parameters are involved for establishing the computer vision system. (2) Two standard color databases with two cameras are constructed. (3) A statistical model to determine the colors of textile samples is presented in which uniform sampling of pixels and mid-tone enhancing techniques are exploited. (4) The experiments with a number of waste textile samples from a factory in Hong Kong are conducted to illustrate the efficiency of the developed system.

The experiments with a number of waste textile samples from a factory in Hong Kong are reported. The total classification accuracy performs good. The research methods and results reported in this study can provide an important reference for improving the intelligent level of color sorting for waste textile recycling.

It is the first time to introduce computer vision technology to a color sorting system for recycling waste textiles, especially in a real recycling factory in Hong Kong. The research methods and results reported in this study also deliver guidance for designing a computer vision-based color sorting system for other industrial scenarios.

This review provides an overview of recent advances in electrochemical sensors for analyte detection in saliva, highlighting their potential applications in diagnostics and health care. The purpose of this paper is to summarize the current state of the field, identify challenges and limitations and discuss future prospects for the development of saliva-based electrochemical sensors.

The paper reviews relevant literature and research articles to examine the latest developments in electrochemical sensing technologies for saliva analysis. It explores the use of various electrode materials, including carbon nanomaterial, metal nanoparticles and conducting polymers, as well as the integration of microfluidics, lab-on-a-chip (LOC) devices and wearable/implantable technologies. The design and fabrication methodologies used in these sensors are discussed, along with sample preparation techniques and biorecognition elements for enhancing sensor performance.

Electrochemical sensors for salivary analyte detection have demonstrated excellent potential for noninvasive, rapid and cost-effective diagnostics. Recent advancements have resulted in improved sensor selectivity, stability, sensitivity and compatibility with complex saliva samples. Integration with microfluidics and LOC technologies has shown promise in enhancing sensor efficiency and accuracy. In addition, wearable and implantable sensors enable continuous, real-time monitoring of salivary analytes, opening new avenues for personalized health care and disease management.

This review presents an up-to-date overview of electrochemical sensors for analyte detection in saliva, offering insights into their design, fabrication and performance. It highlights the originality and value of integrating electrochemical sensing with microfluidics, wearable/implantable technologies and point-of-care testing platforms. The review also identifies challenges and limitations, such as interference from other saliva components and the need for improved stability and reproducibility. Future prospects include the development of novel microfluidic devices, advanced materials and user-friendly diagnostic devices to unlock the full potential of saliva-based electrochemical sensing in clinical practice.