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Within the domain of architectural urban informatization, the automated precision recognition of two-dimensional paper schematics emerges as a pivotal technical challenge. Recognition methods traditionally employed frequently encounter limitations due to the fluctuating quality of architectural drawings and the bounds of current image processing methodologies, inhibiting the realization of high accuracy. The research delineates an innovative framework that synthesizes refined semantic segmentation algorithms with image processing techniques and precise coordinate identification methods, with the objective of enhancing the accuracy and operational efficiency in the identification of architectural elements. A meticulously curated data set, featuring 13 principal categories of building and structural components, facilitated the comprehensive training and assessment of two disparate deep learning models. The empirical findings reveal that these algorithms attained mean intersection over union (MIoU) values of 96.44% and 98.01% on the evaluation data set, marking a substantial enhancement in performance relative to traditional approaches. In conjunction, the framework’s integration of the Hough Transform with SQL Server technology has significantly reduced the coordinate detection error rates for linear and circular elements to below 0.1% and 0.15%, respectively. This investigation not only accomplishes the efficacious transition from analog two-dimensional paper drawings to their digital counterparts, but also assures the precise identification and localization of essential architectural components within the digital image coordinate framework. These developments are of considerable importance in furthering the digital transition within the construction industry and establish a robust foundation for the forthcoming extension of data collections and the refinement of algorithmic efficacy.

Within the domain of architectural urban informatization, the automated precision recognition of two-dimensional paper schematics emerges as a pivotal technical challenge. Recognition methods traditionally employed frequently encounter limitations due to the fluctuating quality of architectural drawings and the bounds of current image processing methodologies, inhibiting the realization of high accuracy. The research delineates an innovative framework that synthesizes refined semantic segmentation algorithms with image processing techniques and precise coordinate identification methods, with the objective of enhancing the accuracy and operational efficiency in the identification of architectural elements. A meticulously curated data set, featuring 13 principal categories of building and structural components, facilitated the comprehensive training and assessment of two disparate deep learning models. The empirical findings reveal that these algorithms attained mean intersection over union (MIoU) values of 96.44% and 98.01% on the evaluation data set, marking a substantial enhancement in performance relative to traditional approaches. In conjunction, the framework's integration of the Hough Transform with SQL Server technology has significantly reduced the coordinate detection error rates for linear and circular elements to below 0.1% and 0.15%, respectively. This investigation not only accomplishes the efficacious transition from analog two-dimensional paper drawings to their digital counterparts, but also assures the precise identification and localization of essential architectural components within the digital image coordinate framework. These developments are of considerable importance in furthering the digital transition within the construction industry and establish a robust foundation for the forthcoming extension of data collections and the refinement of algorithmic efficacy. [ABSTRACT FROM AUTHOR]

Total bilirubin values have been used as a potential marker to pre-screen and diagnose various liver-based diseases such as jaundice, bile obstruction, liver cancer, etc. A device known as KromaHealth Kit, composed of paper and an acrylic backbone, is developed to quantify total bilirubin in human serum using image processing and machine learning technology. The biochemical assays are deposited on absorbent paper pads that act as reaction zones when serum is added. A dedicated smartphone app captures images of the colorimetric changes on the pad and converts them into quantitative values of bilirubin. The range of bilirubin concentration that can be quantified using the device ranges from 0.5 mg/dl to 7.0 mg/dl. The precision, limit of detection, interference analysis, linearity, stability, and comparison with a predicate are studied in this paper in accordance with clinical and laboratory standards institute. The results indicate that the KromaHealth Kit can be used as an inexpensive alternative to conventional bilirubin testing in clinical settings. With its level of precision, ease-of-use, long shelf-life, and short turnaround time, it will prove to be invaluable in limited-resource settings.

Paper based lateral flow systems are widely used biosensor platforms to detect biomolecules in a liquid sample. Proteins, bacteria, oligonucleotides, and nanoparticles were investigated in the literature. In this work we designed a magnetic platform including dual magnets and tested the flow of micron size immunomagnetic particles alone and when loaded with cells on two different types of papers. The prewetting conditions of the paper and the applied external magnetic field are the two dominant factors affecting the particle and cell transport in paper. The images recorded with a cell phone, or with a bright field optical microscope were analyzed to measure the flow of particles and cells. The effect of prewetting conditions and magnetic force were measured, and it was shown that in the worst case, minimum 90% of the introduced cells reached to the edge of the paper. The paper based magnetophoretic lateral flow systems can be used for cell assays. [ABSTRACT FROM AUTHOR]

Various techniques were combined to optimize an optical inspection system designed to automatically inspect defects in manufactured paper bowls. A self-assembled system was utilized to capture images of defects on the bowls. The system employed an image sensor with a multi-pixel array that combined a complementary metal-oxide semiconductor and a photo detector. A combined ring light served as the light source, while an infrared (IR) LED matrix panel was used to provide constant IR light to highlight the outer edges of the objects being inspected. The techniques employed in this study to enhance defect inspections on produced paper bowls included Gaussian filtering, Sobel operators, binarization, and connected components. Captured images were processed using these technologies. Once the non-contact inspection system’s machine vision method was completed, defects on the produced paper bowls were inspected using the system developed in this study. Three inspection methods were used in this study: internal inspection, external inspection, and bottom inspection. All three methods were able to inspect surface features of produced paper bowls, including dirt, burrs, holes, and uneven thickness. The results of our study showed that the average time required for machine vision inspections of each paper bowl was significantly less than the time required for manual inspection. Therefore, the investigated machine vision system is an efficient method for inspecting defects in fabricated paper bowls.

Microfluidic paper-based analytical devices are emerging as promising options for on-the-spot detection of chemical contaminants in water. The coupling of these devices with digital imaging technology has attracted immense interest in developing portable sensor applications. However, the potential of digital techniques still remains to be fully explored. In this work, we integrate digital imaging with microfluidic paper-based analytical devices to develop a portable colorimetric assay for iron. The experimental conditions are optimised using image analysis and the effects of imaging equipment and colorimetric analysis methods on the readout of devices are studied using numerical, graphical and statistical techniques. The experimental results are obtained for the estimation of iron contamination in water samples using a colorimetric assay based on iron (III)-thiocyanate reaction. The digital image acquisition approach using smartphone as imaging equipment is adopted for on-the-spot colorimetric data collection. Image processing techniques are employed to estimate the iron concentration in water from digital images. Greyscale colour intensity obtained from a digital image is identified as a readout of the analytical device and employed for quantitative assessment. The limit of detection for iron is estimated to be 0.26 ppm. The microfluidic approach offers good recovery and repeatability with relative standard deviation of 1.4%. The maximum percentage difference for intensities between the image analysis methods is 4.94% and that between the imaging equipment is 9.96%. The gradients of regression lines for pairwise equipment and method outputs with zero intercept range from 0.9931 to 1.0162 with high values of coefficients of determination. The correlation coefficient values for various paired comparisons (>0.99) suggest strong agreement. Statistical test results suggest no significant differences between methods and between imaging equipment (significance level 0.05). The findings suggest the efficacy of digital imaging and colorimetric analysis for paper-based analytical devices. [ABSTRACT FROM AUTHOR]

Unprinting of paper can reduce multiple life-cycle stages in the recycling of paper to yield significant environmental impact. Laser-unprinting has been demonstrated for uncoated paper but causes significant damage to coated paper. This work explores a scalable optical (non-laser) process for unprinting coated paper. Printed coated paper is exposed to pulsed broad-spectrum Intense Pulsed Light (IPL) from a xenon lamp and the toner is then removed by dabbing gently with an ethanol wipe. While black toner is easily unprinted, unprinting of colored prints (red, blue, green) is best realized by incorporating an initial overprint of black toner. An unprinting throughput on the order of mm2/s is achieved. Three distinct regimes of unprinting are identified based on the extent of toner removal and damage of the paper. The optical properties of the unprinted paper, characterized via UV–Visible spectrophotometry, are correlated to these regimes to understand the potential for in-situ optical process monitoring. Scanning Electron Micrography and Fourier-Transform Infrared Spectroscopy are performed to understand the underlying mechanisms that govern the occurrence of the different unprinting regimes. Further, the potential impact of the developed approach on recycling of paper is discussed in the context of the capabilities of current optical unprinting approaches and the potential elimination of life-cycle stages in conventional paper recycling. • Intense Pulsed Light can scalably unprint toner from coated paper with minimal chemical usage. • Our overprinting approach enables colored prints to be removed as well. • Unprinting throughput of mm2/s is achieved. • Three distinct unprinting regimes exist, depending on process parameters and toner types. • Thermal weakening of toner-coating bonds and cracking are the primary governing mechanisms. [ABSTRACT FROM AUTHOR]

In this paper, the paper cups were used as the research objects, and the machine vision detection technology was combined with different image processing techniques to investigate a non-contact optical automatic detection system to identify the defects of the manufactured paper cups. The combined ring light was used as the light source, an infrared (IR) LED matrix panel was used to provide the IR light to constantly highlight the outer edges of the detected objects, and a multi-grid pixel array was used as the image sensor. The image processing techniques, including the Gaussian filter, Sobel operator, Binarization process, and connected component, were used to enhance the inspection and recognition of the defects existing in the produced paper cups. There were three different detection processes for paper cups, which were divided into internal, external, and bottom image acquisition processes. The present study demonstrated that all the detection processes could clearly detect the surface defect features of the manufactured paper cups, such as dirt, burrs, holes, and uneven thickness. Our study also revealed that the average time for the investigated Automatic Optical Detection to detect the defects on the paper cups was only 0.3 s. [ABSTRACT FROM AUTHOR]

Various techniques were combined to optimize an optical inspection system designed to automatically inspect defects in manufactured paper bowls. A self-assembled system was utilized to capture images of defects on the bowls. The system employed an image sensor with a multi-pixel array that combined a complementary metal-oxide semiconductor and a photo detector. A combined ring light served as the light source, while an infrared (IR) LED matrix panel was used to provide constant IR light to highlight the outer edges of the objects being inspected. The techniques employed in this study to enhance defect inspections on produced paper bowls included Gaussian filtering, Sobel operators, binarization, and connected components. Captured images were processed using these technologies. Once the non-contact inspection system's machine vision method was completed, defects on the produced paper bowls were inspected using the system developed in this study. Three inspection methods were used in this study: internal inspection, external inspection, and bottom inspection. All three methods were able to inspect surface features of produced paper bowls, including dirt, burrs, holes, and uneven thickness. The results of our study showed that the average time required for machine vision inspections of each paper bowl was significantly less than the time required for manual inspection. Therefore, the investigated machine vision system is an efficient method for inspecting defects in fabricated paper bowls. [ABSTRACT FROM AUTHOR]

This paper focuses on how to reduce cheating and minimize errors while automatically grading paper-based multiple-choice questions (MCQ) by making the whole process relatively fast, less expensive, more credible, and fairer especially when the number of examinees and number of questions are large. Credibility is obtained when techniques and best practices are introduced in the design process of MCQ. Fairness is obtained by personalizing evaluation through permutation of answers and questions. The distance introduced in personalization has led to the modification of the traditional automatic grading process where an application mapping the test number with its responses in the grading software is loaded automatically at each start of the grading process. On the extracted header fields, 2DFFT is applied as well as the reduction of computed coefficients to obtain the corresponding final local characteristic in the representation. The minimization of image processing errors is then obtained by training a support vector machine (SVM) for handwriting optical character recognition (OCR) using the Mixed National Institute of Standards and Technology (MNIST) dataset with 99.5% accuracy. The tests are carried out in several subjects at Fotso Victor University Institute of Technology (UIT) in Bandjoun and the ColTech of the University of Bamenda and teachers as well as students after investigation have confirmed that our method reduces cheating and improves the error rate during grading with fewer complaints. [ABSTRACT FROM AUTHOR]

During the folding process substrates are exposed to high-localized stresses, which in the case of coated papers and boards, can lead to decreased aesthetic features or complete loss of functionality. Production efficiency of the folding process could be improved by an automated, computer vision-based inspection system. For such a task, different existing computer-aided fold-crack evaluation approaches were analyzed. A detailed research was conducted to propose an image processing based fold cracking assessment via finding optimal sample preparation and digitization techniques and developing an algorithm for the digital image analysis and feature extraction. The analysis of the applicability of different sample preparation and digitization parameters, as well as the proposed digital image feature, was done by correlation evaluation, one-way analysis of variance (ANOVA) and corresponding post hoc tests. The results indicated that the developed algorithm fulfils the set requirements and the proposed feature of digitized samples faithfully describes the analyzed fold-cracks. [ABSTRACT FROM AUTHOR]

There are over 180 different currencies on the globe. Each currency is distinct in terms of scale, paper, colors, patterns, text, etc. It is tough to keep track of all of the different currencies. Often, determining whether a currency is genuine or counterfeit is challenging. Many methods investigate currency recognition. In this review, the following techniques are widely used in image processing for currency recognition tools, including acquisition, image enhancement, segmentation, and object detection and recognition. This review shows the ability of image processing techniques, including pre-processing, edge detection, feature extraction, and thresholding, to recognize paper currency have been used. Thus, the outcome of this review indicates that for currency recognition the techniques that used for feature extraction are (LBP, SIFT, SURF and ROI) and choosing the best technique depends on the application. [ABSTRACT FROM AUTHOR]

Introduction: The electrocardiogram (ECG) plays an important role in the diagnosis of heart diseases. However, most patterns of diseases are based on old datasets and stepwise algorithms that provide limited accuracy. Improving diagnostic accuracy of the ECG can be done by applying machine learning algorithms. This requires taking existing scanned or printed ECGs of old cohorts and transforming the ECG signal to the raw digital (time (milliseconds), voltage (millivolts)) form. Objectives: We present a MATLAB-based tool and algorithm that converts a printed or scanned format of the ECG into a digitized ECG signal. Methods: 30 ECG scanned curves are utilized in our study. An image processing method is first implemented for detecting the ECG regions of interest and extracting the ECG signals. It is followed by serial steps that digitize and validate the results. Results: The validation demonstrates very high correlation values of several standard ECG parameters: PR interval 0.984 +/-0.021 (p-value

Measuring the colorimetric signals produced by the biospecific accumulation of colorimetric probes and recording the results is a key feature for next-generation paper-based rapid tests. Manual processing of these tests is time-consuming and prone to a loss of accuracy when interpreting faint and patchy signals. Proprietary, closed-source readers and software companies offering automated smartphone-based assay readings have both been criticized for interoperability issues. Here, we introduce a minimal reader prototype composed of open-source hardware and open-source software that has the benefits of automatic assay quantification while avoiding the interoperability issues associated with closed-source readers. An image-processing algorithm was developed to automate the selection of an optimal region of interest and measure the average pixel intensity. When used to quantify signals produced by lateral flow immunoassays for detecting antibodies against SARS-CoV-2, results obtained with the proposed algorithm were comparable to those obtained with a manual method but with the advantage of improving the precision and accuracy when quantifying small spots or faint and patchy signals.

In the production of paper cups in industrial factories, it is tried to print high quality cups with less waste loss with the help of sensors and heating resistances mounted on the paper cup machine. In this study, a system that detects faulty products based on image processing and removes it by controlling the machine with servo motors, asynchronous motors and programmable logic controller (PLC) is designed. For fault product detection, classification has been performed using real-time Haarcascade algorithm and You Only Look Once (YOLO) algorithm which is a deep learning methods, and real-time object detection has been carried out using the OpenCv library. With this study, an effective faulty product detection and removing hardware system was realized by adapting artificial intelligence algorithms to a machine used in industry. Based on the results, a whole system can be applied to systems that involve removing a faulty product from a band in any production, packaging etc. facility is proposed. A hardware consisting of servo motors, asynchronous motors and PLC was designed to separate faulty cups from the existing paper cup production machine in this study. Then, a data set composed of 1068 images was created with images taken from the camera for faulty and faultless paper cups. Using this dataset, the effect of different deep learning methods on performance in the real-time system has been examined and successful results have been obtained. The optimal outcome was achieved, yielding a real-time application accuracy rate of 90.8% through the utilization of the Yolov5x architecture. [ABSTRACT FROM AUTHOR]

Thermal paper is widely used as a print medium for different applications but it constitutes a tricky substrate for fingermark visualization. An earlier work (J Forensic Sci 2015;60:1034) reported how to visualize fingermarks on untreated thermal paper by illuminating the item with a UV‐A light source. In the present paper, the potential of the near infrared (NIR) luminescence has been tested on thermal paper compared to the mentioned method. A controlled study was carried out utilizing eccrine enriched fingermarks. The promising outcomes obtained were further confirmed by performing a pseudo‐operational trial. Data clearly showed that the use of the NIR filter gave better results. Finally, preliminary tests suggested a different mechanism of reaction induced by fingermarks with respect to the one behind the thermal printing. Thus, NIR luminescence represents a refinement to the suite of optical examination processes, including the potential to increase the number of marks recovered in a noncontact, nondestructive way. [ABSTRACT FROM AUTHOR]

[Display omitted] • A simple paper microPAD for alkaline phosphatase detection. • Detection is based on the In-situ formation of gold nanoparticles. • AuNP formation is aided by the AA formed from the dephosphorylation of AAP by ALP. • Wide range of interference-free ALP detection upto 400 U/L. Affordable alkaline phosphatase enzyme detection methods are essential for diagnosing hepatic diseases. In this work, an in-situ formation of gold nanoparticles was utilized to develop a colorimetric method to detect alkaline phosphatase. Alkaline phosphatase dephosphorylates the substrate 2-phospho-L-ascorbic acid and produces ascorbic acid. Ascorbic acid, thus formed, is a good reducing agent that reduces Au3+ ions to gold nanoparticles. Alkaline phosphatase concentrations from 20 U/L to 400 U/L were spectrophotometrically detected using the surface plasmon resonance properties of the in-situ formed gold nanoparticles. The detection shows a sensitivity of 0.004 ± 0.0002 (a.u)/(U/L), a limit of detection of 2.8 mU/L, and a limit of quantification of 9.5 mU/L. A three-dimensional microfluidic paper test strip was fabricated to translate this detection method into a point-of-care testing device. The blue color formed on the paper was scanned, and the color intensities were analyzed using image processing. The three-dimensional microfluidic paper-based analytical devices could detect alkaline phosphatase concentration from 20 U/L to 400 U/L with an improved sensitivity of 0.0980 ± 0.0016 (a.u)/(U/L). The limit of detection and limit of quantification of the analysis on the paper strip was 0.748 U/L and 2.49 U/L, respectively. [ABSTRACT FROM AUTHOR]

Today, computer graphics and graphic image processing techniques have been widely used in daily life and industrial production. Due to the development of computers, computer graphics has brought more convenience to our daily life. In order to give full play to the value of computers, this paper takes the Hakka paper-cut art with local characteristics as the starting point, first of all its development history, artistic characteristics, compositional forms, expression techniques, cultural connotations, Hakka paper-cut patterns, and the symbolic meaning of folk customs, and then we design a visualization system for the paper-cut works of Gannan Hakka based on computer graphics. In addition, the system provides a solution for the integration of Gannan Hakka paper-cut art and Jiangxi native product packaging design and provides a reference for the theory and practice of modern native product packaging design. [ABSTRACT FROM AUTHOR]

It is critical for manufacturers to identify quality issues in production and prevent defective products being delivered to customers. We investigate the use of deep neural networks to perform automatic quality inspections based on image processing to eliminate the current manual inspection. A deep neural network was implemented in a real-world industrial case study, and its ability to detect quality problems was evaluated and analyzed. The results show that the network has an accuracy of 94.5%, which is considered good in comparison to the 70–80% accuracy of a trained human inspector. [ABSTRACT FROM AUTHOR]

As we all know day by day the technology is getting better and better, the production of counterfeit currency has been rapidly increasing. The counterfeit currency problem is faced by almost all countries. Since the real economy is affected, it has affected the economy of the country. Even when the drastic step of demonetization was taken in 2016 to overcome counterfeit currency, this problem did not end. The only one solution for this problem for a common man is to detect the fake currency, by using the fake currency detector machine. These machines are used in banks and large scale business, but for small scale businesses or for a common man these machines are not affordable. There are lot of researches taking place on this matter by using deep learning, image processing and machine learning techniques. This paper gives the complete methodology of fake note detector machine, which is affordable even for a common man. By implementing the applications of image processing techniques we can find out whether the currency notes are fake or not. Image processing technique consists of a number of operations that can be performed on an image, some of which include image segmentation, edge detection, gray scale conversion, preprocessing etc. The proposed system will detect the counterfeit currency of new denominations by distinguishing each denomination based on its size and depending on the features of each currency the comparison takes place. Based on the features matched, it detects whether the currency is counterfeit or not. The system have advantages like simplicity, reliability and cost effective. Which is affordable by a common man since the common man is the one who will be effected most, when the counterfeit currency are circulated in the market because he has to pay the real value of that currency. [ABSTRACT FROM AUTHOR]

Abstract: This article explores the application of image processing techniques in recyclable waste paper sorting. In recycling, waste papers are segregated into various grades as they are subjected to different recycling processes. Highly sorted paper streams facilitate high quality end products and save processing chemicals and energy. From 1932 to 2009, different mechanical and optical paper sorting methods have been developed to fill the paper sorting demand. Still, in many countries including Malaysia, waste papers are sorted into different grades using a manual sorting system. Because of inadequate throughput and some major drawbacks of mechanical paper sorting systems, the popularity of optical paper sorting systems has increased. Automated paper sorting systems offer significant advantages over human inspection in terms of worker fatigue, throughput, speed, and accuracy. This research attempts to develop a smart vision sensing system that is able to separate the different grades of paper using first-order features. To construct a template database, a statistical approach with intra-class and inter-class variation techniques are applied to the feature selection process. Finally, the K-nearest neighbor (KNN) algorithm is applied for paper object grade identification. The remarkable achievement obtained with the method is the accurate identification and dynamic sorting of all grades of papers using simple image processing techniques. [Copyright &y& Elsevier]

[Display omitted] • A novel analytical technique for colorimetric glucose determination on µPADs. • Non-invasive sweat glucose detection independent of sample volume. • Seamless operation: no extra gadgets nor smartphone camera is required. • Computationally inexpensive readout on portable Arm platforms demonstrated. The colorimetric method is widely exploited for glucose measurement on microfluidic paper-based and microfluidic thread/paper-based analytical devices, μPADs and μTPADs, respectively. However, two significant challenges still hinder the real-world applications: the variation of the generated color based on sample volume variation, and the consistent dependency on an imaging camera - usually a smartphone camera - for color readout. The latter also suffers from multiple limitations, such as the variable ambient light conditions and imaging variation among different smartphone brands and models. This manuscript suggests a new device demonstrating a novel viewpoint to the colorimetric method to address both aforementioned challenges. The presented device, Volume-Independent Autonomous box (VIA box), continuously monitors the transient color of the µPAD upon the introduction of the sample till color stabilization, in contrast to similar studies where merely the final stabilized color is read. The interpretation of the transient color profile with respect to time results in the measurement of the glucose level independent of sample volume. In addition, the VIA box monitors µPAD's color using an ordinary RGB sensor instead of an imaging camera in similar studies. Since merely RGB values are recorded in any measurement instance, the computational cost is extremely low compared to the relevant literature where image processing techniques are used. VIA box exploits a simple low-end microcontroller to continuously monitor the transient color, interpret the color profile, and show the glucose level. The samples used in this manuscript are made of artificial sweat samples, including the known glucose range in real sweat samples. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the average droplet size and range of droplet size, which affects agricultural pesticide quality using image analysis techniques. In the study droplet diameters was measured by using 26 x 76 mm water sensitive paper produced by Ciba-Geigy with a little sprayer. A Nikon D300s camera body and a 105-mm macro lens were used for capturing images. Image J software was used for image evaluations, and Microsoft Excel software was used for evaluation of numerical data. At the end of the study, number median diameter (NMD) was calculated as 75 μm, diameter surface (ds) was calculated as 31 μm, volume median diameter (VMD) was calculated as 300 μm, Sauter diameter (dv/s) was calculated as 37 μm, and VMD/NMD was found 4.00 from evaluations. [ABSTRACT FROM AUTHOR]

We report a paper-based chemosensor array device (PCSAD) for the quantitative detection of oxyanions including the herbicide glyphosate (GlyP) in aqueous media. The mechanism of the oxyanion detection relies on a coordination-binding-based sensor array. In this study, the competitive coordination binding among Zn2+, four catechol dyes, and seven oxyanions caused noticeable colour changes. The colour changes were employed for qualitative and quantitative analyses using an in-house automated image-processing algorithm with pattern recognition for digital images. A linear discrimination analysis discerned similarly structured oxyanions with 100% accuracy. The regression analysis allowed the accurate quantification of GlyP in the herbicide products with a limit of detection of 16 mg/L, which is lower than the health advisory value for children (20 mg/L) stipulated by the environmental protection agency (EPA). PCSAD is a powerful sensor device for the on-site quantification of aqueous anions for environmental assessment. [ABSTRACT FROM AUTHOR]

In the production of facial oil blotting papers, a certain number of papers must be counted before packaging. Currently, the papers are counted by hand, and this is hard work. Also, there are risks of adhesion of dust and wrinkles. In order to solve these problems, we propose a vision‐based approach. After the papers, which are arranged by shifting, are captured, the proposed image processing steps detect the boundaries of the papers. By counting the detected boundaries, the number of papers can be counted. Since the parameters for the proposed image processing are optimized by genetic algorithm, prior setting by a user is not necessary. For the experiments, an image dataset was constructed with six types of facial oil blotting papers. The proposed method achieved higher F‐measure than in other related works. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Microfluidic paper-based analytical devices (µPADs) have attracted much attention over the past decade because they offer clinicians the ability to deliver point-of-care testing and onsite analysis. Many of the advantages of µPADs, however, are limited to work in a laboratory setting due to the difficulties of processing data when using electronic devices in the field. This review focuses on the use of µPADs that have the potential to work without batteries or with only small and portable devices such as smartphones, timers, or miniaturized detectors. The µPADs that can be operated without batteries are, in general, those that allow the visual judgment of analyte concentrations via readouts that are measured in time, distance, count, or text. Conversely, a smartphone works as a camera to permit the capture and processing of an image that digitizes the color intensity produced by the reaction of an analyte with a colorimetric reagent. Miniaturized detectors for electrochemical, fluorometric, chemiluminescence, and electrochemiluminescence methods are also discussed, although some of them require the use of a laptop computer for operation and data processing. [ABSTRACT FROM AUTHOR]

Perchlorate, a hazardous pollutant, is mainly found in untreated wastewater from urban and industrial sites and unregulated surface and groundwater sources. Effective monitoring of perchlorate in water is essential to mitigate its potential harmful effects. Microfluidic systems are evolving as promising technologies for detecting chemical contaminants in water due to their ability to enable rapid analysis with minimal consumption of reagents and samples. The integration of paper-based microfluidic devices with digital imaging has garnered enormous attention from the perspective of developing portable analytical techniques. Nevertheless, there is a need for further exploration to fully realize the potential of these systems. This study aimed to develop and evaluate the performance of a microfluidic paper-based device for measuring perchlorate levels in water samples. Smartphone-based digital imaging was integrated with microfluidic paper-based analytical device to establish a reliable colorimetric method for detecting perchlorate contamination. The results demonstrated successful quantitative estimation of perchlorate levels in water samples using a colorimetric assay based on the methylene blue-perchlorate reaction. Real-time, on-site colorimetric data were collected using a digital smartphone, and image processing methods were used to detect the occurrence of perchlorate in water samples from digital images. The developed approach yielded a broad linear response ranging from 4 to 12 µg/L (R2 = 0.97) for perchlorate detection, with a limit of detection of 3.41 µg/L and a limit of quantification of 10.34 µg/L. The findings underscore the effectiveness of colorimetric analysis and digital imaging for paper-based analytical devices. The limitations of this method include the capability to detect only a single analyte and the requirement for additional steps in image processing to obtain analytical results. Future developments should focus on designing devices for simultaneous detection of multiple contaminants and exploring automated methods of image analysis. [ABSTRACT FROM AUTHOR]

Eco-design has become very important in last few years. According to the environmental suggestions not to waste materials, energy etc., and use of recycle materials we were study paper recycling process in relation to used printed typefaces, their sizes and used different leading. For prints conventional and model offset inks with greater environmental benefits were used for printing. Characteristics of the recycled fibers followed by image analysis and some spectrophotometric methods are discussed. The results of these studies except for the scientific contribution should serve as a reference in design, especially for environmentally friendly graphic products. [ABSTRACT FROM AUTHOR]

Several miniaturized viscometers, or microviscometers, have been developed exploiting numerous rapid prototyping techniques. Among them, paper microstrips, famously known as microfluidic paper-based analytical devices ($\mu $ PADs), have become popular due to their cost-efficacy, simple fabrication, fast response, and easily disposable. Many fabrication methods are existing to develop paper microstrips. Herein, an alternative fabrication method is proposed where fused deposition modeling (FDM)-based 3-D printer (3DP) has been employed using polycaprolactone (PCL) filament. F, image processing has been utilized to measure viscosity in such microfluidic domain. Viscosity was calculated by measuring the time taken by the fluid to cover a fixed length between two spots in the microchannel based on the programed and color-coded regions-of-interest. The image processing program was developed considering the change in the gray scale in the virtual region of interests (ROIs) in the microchannel during the fluid flow in the paper microstrips. A 3-D printed handheld platform, containing raspberry pi with on-board camera and display, was developed to execute the image processing and automate the entire work flow. In the proposed device, the accuracy was measured to be >92%. [ABSTRACT FROM AUTHOR]

Image denoising-removal of additive white Gaussian noise from an image-is one of the oldest and most studied problems in image processing. Extensive work over several decades has led to thousands of papers on this subject, and to many well-performing algorithms for this task. Indeed, 10 years ago, these achievements led some researchers to suspect that "Denoising is Dead, in the sense that all that can be achieved in this domain has already been obtained. However, this turned out to be far from the truth, with the penetration of deep learning (DL) into the realm of image processing. The era of DL brought a revolution to image denoising, both by taking the lead in today's ability for noise suppression in images, and by broadening the scope of denoising problems being treated. Our paper starts by describing this evolution, highlighting in particular the tension and synergy that exist between classical approaches and modern artificial intelligence (AI) alternatives in design of image denoisers. The recent transitions in the field of image denoising go far beyond the ability to design better denoisers. In the second part of this paper we focus on recently discovered abilities and prospects of image denoisers. We expose the possibility of using image denoisers for service of other problems, such as regularizing general inverse problems and serving as the prime engine in diffusion-based image synthesis. We also unveil the (strange?) idea that denoising and other inverse problems might not have a unique solution, as common algorithms would have us believe. Instead, we describe constructive ways to produce randomized and diverse high perceptual quality results for inverse problems, all fueled by the progress that DL brought to image denoising. This is a survey paper, and its prime goal is to provide a broad view of the history of the field of image denoising and closely related topics in image processing. Our aim is to give a better context to recent discoveries, and to the influence of the AI revolution in our domain. [ABSTRACT FROM AUTHOR]

Paper-based microfluidics and sensors have attracted great attention. Although a large number of paper-based devices have been developed, surprisingly there are only a few studies investigating paper actuators. To fulfill the requirements for the integration of both sensors and actuators into paper, this work presents an unprecedented platform which utilizes ferromagnetic particles for actuation and graphite for motion monitoring. The use of the integrated mechanical sensing element eliminates the reliance on image processing for motion detection and also allows real-time measurements of the dynamic response in paper-based actuators. The proposed platform can also be quickly fabricated using a simple process, indicating its potential for controllable paper-based lab on chip. [ABSTRACT FROM AUTHOR]

There is a high demand for facile on-site mercury ion quantification methods in water due to mercury's serious toxicity. Graphene quantum dot (GQD), fluorescent under ultra-violet (UV) light, can be easily synthesized and used as a mercury ion detection probe because the brightness of GQD's fluorescence decreases with the increase of the number of mercury ions. We developed a facile mercury (II) ion quantification method using GQD coated filter paper disks in a 3D-printed well array. Changes of GQD's fluorescent brightness dependent on mercury ion concentration were measured via optical microscopy image processing. We also observed the dependency of mercury ion detection sensitivity on the amount of GQD and type of GQDs. Our approach may be easily adapted for hand-held mobile mercury ion quantification devices in the future due to its simplicity and small sample size. • Concentrations of mercury (II) ions were optically quantified using graphene quantum dot coated filter paper disks. • The GQDs could be evenly physisorbed by making even contacts between filter paper disks and a well array's bottom surface. • Due to this work's simplicity, small sample volume, cost-effectiveness, it may be developed as mobile devices. [ABSTRACT FROM AUTHOR]

The glossiness of paper and painted objects is one of the important factors in the evaluation of their manufactured quality. Although the gloss of specimens is judged visually in quality control, specular glossiness measuring instruments now offer a replacement for visual evaluation. However, in evaluation of glossiness in a group consisting of specimens of different colors, the specular glossiness measuring instrument has certain deficiencies, as a result of which the measured glossiness values are not proportional to human evaluations of glossiness (psychological glossiness); however, the instrument is suitable to a group consisting of specimens of similar color. Thus, research intended to obtain suitable measurements of physical glossiness for colored specimens was undertaken. First, an attempt was made to approximate the gloss appearance perceived by the human eye by using a model of the intensity distribution of the light reflected from a specimen surface. Utilizing the obtained gloss appearance, a new physical glossiness is defined and is provisionally named “specular diffusion ratio glossiness” (GSD): The new glossiness is shown to be nearly proportional to the psychological glossiness by the use of the glossiness measuring apparatus developed in this study. In the latter half of this paper, the application of the new glossiness index GSD to currently used specular glossiness measuring instruments is proposed, and its utility is confirmed by using various colored papers. Finally, problems for future research are identified. © 1997 Scripta Technica, Inc. Syst Comp Jpn. 28 (1): 95–112, 1997 [ABSTRACT FROM AUTHOR]

Paper currency recognition (PCR) is an important area of pattern recognition. A system for the recognition of paper currency is one kind of intelligent system which is a very important need of the current automation systems in the modern world of today. It has various potential applications including electronic banking, currency monitoring systems, money exchange machines, etc. This paper proposes an automatic paper currency recognition system for paper currency. A method of recognizing paper currencies has been introduced. This is based on interesting features and correlation between images. It uses Radial Basis Function Network for classification. The method uses the case of Saudi Arabian paper currency as a model. The method is quite reasonable in terms of accuracy. The system deals with 110 images, 10 of which are tilted with an angle less than 15o. The rest of the currency images consist of mixed including noisy and normal images 50 each. It uses fourth series (1984–2007) of currency issued by Saudi Arabian Monetary Agency (SAMA) as a model currency under consideration. The system produces accuracy of recognition as 95.37%, 91.65%, and 87.5%, for the Normal Non-Tilted Images, Noisy Non-Tilted Images, and Tilted Images respectively. The overall Average Recognition Rate for the data of 110 images is computed as 91.51%. The proposed algorithm is fully automatic and requires no human intervention. The proposed technique produces quite satisfactory results in terms of recognition and efficiency. [ABSTRACT FROM AUTHOR]