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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.

Microfluidic technology, also known as lab-on-a-chip, enables the fabrication of low-cost, user-friendly, and portable detection devices. Microfluidic chips can be utilized for detecting biological and chemical analytes in various liquid samples, including water or biofluids such as urine, blood, and sweat. The specific and quantitative detection of ions has garnered increased attention in recent years due to their potential harm to environmental and human health. Inorganic ions are special chemicals that hold positive or negative charges with relatively small molecular weights. Among the various types of microfluidic platforms, paper-based systems are favored as simple analytical tools that rely on the generation of hydrophilic–hydrophobic contrast on filter paper. In this study, a paper-based microfluidic device was developed as an analytical tool for quantifying several ions, such as iron (Fe3+). The reaction spot was created by simply melting a wax crayon to form hydrophobic barriers that define hydrophilic zones. After spotting Fe3+ samples and potassium thiocyanate (KSCN) as a detection reagent on the reaction zone, an immediate and obvious color change was observed with different ion concentrations ranging between 50 and 500 ppm. While the naked-eye detection of color change was easy at high concentrations, quantifying ion concentrations in samples required the use of a smartphone camera. The captured images were then analyzed using ImageJ software (Java 1.8.0-internal (32-bit)). The developed paper-based microfluidic device exhibited good performance in quantifying Fe3+ ions in samples. Indeed, this simple platform is easy to store and transport, and allows the transportation of aqueous solutions without the need for external pumping or a power supply.

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.

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]

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.

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]

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.

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]

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]

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]

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]

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]

In this paper, a non-supervised technique for on-line paper characterisation is presented. The method uses self-organising maps (SOM) and texture analysis for clustering different kinds of paper according to their properties. A light-through technique is used to get pictures of paper. Then, effective texture features are extracted from greyscale images and the dimensionality of the feature data is reduced with SOM allowing visual analysis of measurements. The method makes it possible to implicitly extract important information about paper formation. The approach provides excellent results. A classification error below 1% was achieved for four quality classes when local binary pattern (LBP) texture features were used. The improvement to the previously used texture features in paper inspection is huge: the classification error was reduced by over 40 times. In addition to the excellent classification accuracy, the method also offers a self-intuitive user interface and a synthetic view of the inspected data. [ABSTRACT FROM AUTHOR]

Development of paper-based microfluidic devices that perform colorimetric measurements requires quantitative image analysis. Because the design geometries of paper-based microfluidic devices are not standardized, conventional methods for performing batch measurements of regularly spaced areas of signal intensity, such as those for well plates, cannot be used to quantify signal from most of these devices. To streamline the device development process, we have developed an open-source program called ColorScan that can automatically recognize and measure signal-containing zones from images of devices, regardless of output zone geometry or spatial arrangement. This program, which measures color intensity with the same accuracy as standard manual approaches, can rapidly process scanned device images, simultaneously measure identified output zones, and effectively manage measurement results to eliminate requirements for time-consuming and user-dependent image processing procedures. [ABSTRACT FROM AUTHOR]