2D Barcode Research Articles

Multiplexed Random Access Approach to DNA Microspheres for High‐Capacity Data Storage

AbstractWith the rapid growth of modern information data, DNA has emerged as a novel medium for data storage due to its durability, replicability, sustainability, and high density. This study focuses on the compatibility, high capacity, and random access of DNA data storage systems. A calcium phosphate (CaP) mineralized fluorescent SiO2 DNA (FSD@CaP) microspheres is developed by encapsulating fluorescence‐labeled encoding DNA files onto the surface of the SiO2 microspheres that are intrinsically sized and fluorescent as addressed two‐dimensional (2D) barcodes. The various sizes of SiO2 and various fluorescence‐labeled encoding DNA files forming 2D barcodes of FSD@CaP. The 2D barcodes enhance the system's compatibility while allowing multiplexed random access to DNA files. The high‐capacity DNA storage permits effective single access to the DNA file using a minimal number of FSD@CaP microspheres, without impacting the matrix of the system. Ultimately, random access to arbitrary DNA files within a complex pool of distinct 2D barcode‐tagged FSD@CaP microspheres is demonstrated by utilizing a flow cytometer for multiplexed sorting. Consequently, the strategy of this storage system provides a scalable concept for compatibility and multiplexed random access to DNA data sets.

Hybrid Digital Certificate Management System with QR Code and IoT Integrated on Hyperledger Fabric Blockchain

In contemporary society, management of physical documents such as educational certificates, identity proofs, vehicle registrations, and marriage certificates is an integral part of daily life. However, in present online world, there is a pressing need for digital transformation and the management of these documents. One significant challenge associated with this transformation is the susceptibility of original documents to replication or duplication. This vulner- ability is particularly concerning in the case of educational certificates, where fraud is prevalent. Fraudulent activities in the education sector can influence the proliferation of counterfeit educational and skill certificates, posing serious risks to society. For instance, individuals holding fraudulent degrees in professions such as engineering, medicine, law, and pharmacy may lack genuine competence, thereby posing substantial societal harm. To address the issue of certificate oversight and deter forgery, various approaches have been employed. Traditional methods typically involve the use of centralized databases or web servers for certificate storage, which introduces vulnerabilities because they represent single points of failure that leads to forgery and information loss. An optimal solution lies in the adoption of a Blockchain system that leverages a decentralized database structure to enhance data storage capacity and security. Blockchain technology has demonstrated disruptive potential and in- novative capabilities across multiple sectors because of its decentralized, transparent, and secure attributes. Its impact spans various domains, including banking, supply chain management, healthcare, education, and finance. Notably, in the education sector, Blockchain technology holds promise in enhancing security, transparency, and efficiency across different educational processes. In this study, we explore existing Blockchain oriented certificate management systems, critically analyze their limitations, and propose a novel hybrid educational certificate management model. The proposed model integrates Hyperledger Fabric, IoT, and 2D Barcode to develop a robust and secure framework for managing educational certificates.

Enhancement of Two-Dimensional Barcode Restoration Based on Recurrent Feature Reasoning and Structural Fusion Attention Mechanism

In practical scenarios, such as in electronics, where barcodes on electronic component carriers often wear out, and in logistics, where package labels frequently get damaged, this type of damage makes the recognition of two-dimensional (2D) barcodes challenging. In this study, a new repair method was introduced for quick response (QR) and PDF417 codes. In addition, a structural fusion attention (SFA) mechanism with a recurrent feature reasoning network was integrated to enhance structural integrity and recognition rates. The proposed method significantly outperforms existing inpainting models in terms of accuracy and robustness, which is demonstrated by the custom dataset provided by the authors. Notably, the approach ensures near-perfect recognition rates despite extensive structural impairments. It achieves an accuracy of 98% for large-area PDF417 occlusions and maintains a recognition rate of 100% for QR codes with 75–90% structural damage. These findings highlight the exceptional ability of the proposed method to restore 2D barcodes impaired by diverse levels of structural occlusion.

Processing 2D barcode data with metaheuristic based CNN models and detection of malicious PDF files

Portable Document Format (PDF) is a file format created to create portable and printable documents across platforms. PDF files are one of the most widely used application types in computer-based systems. Thanks to the functionality that PDF files provide, they are used by many users around the world. Malware developers can exploit PDF files due to various factors. Malware can integrate embedded files, JavaScript, PDF files, etc. As a result, PDFs are susceptible to security vulnerabilities in computer-based systems. In this study, we utilised the CIC-Evasive-PDFMal2022 dataset, made accessible by the Canadian Cybersecurity Institute in 2022, that includes two categories, namely benign and malicious. In the preprocessing step, the proposed model transformed text-based PDF parameter data into the 2D PDF417 barcode. 2D Convolutional Neural Network (CNN) models (MobileNetV2, ResNet18, and ShuffleNet) are trained using the dataset generated by the preprocessing step. CNN is a type of artificial neural network used in image recognition, processing, and classification. Type/class based feature sets were then obtained by each CNN model. In the last step, the metaheuristic optimization method (Honey Badger Algorithm) was used. Thanks to this method, the best performing feature set was determined among the feature sets of the types extracted from each CNN model. It was then classified by the softmax method, and an overall accuracy of 99.73% was achieved. The proposed approach has successfully trained 1D data with 2D CNNs. In addition, with the barcode imaging technique, direct understanding of the data by the users is prevented.

QR Code Recognition Based on Image Processing

To solve the QR code recognition problem caused by ordinary camera collection, the recognition algorithm based on image processing is put forward in this paper. The whole process including image binarization, image tilt correction, image orientation, image geometric correction and image normalization allows images collected on different illumination conditions. Experiments show that the improved method can enhance the recognition speed of two-dimensional code and accuracy. QR i.e. “Quick Response” code is a 2D matrix code that is designed by keeping two points under consideration, i.e. it must store large amount of data as compared to 1D barcodes and it must be decoded at high speed using any handheld device like phones. QR code provides high data storage capacity, fast scanning, omnidirectional readability, and many other advantages including, error-correction (so that damaged code can also be read successfully) and different type of versions. Different varieties of QR code symbols like logo QR code, encrypted QR code, QR Code are also available so that user can choose among them according to their need. Now these days, a QR code is applied in different application streams related to marketing, security, academics etc. and gain popularity at a really high pace. Day by day more people are getting aware of this technology and use it accordingly. The popularity of QR code grows rapidly with the growth of smartphone users and thus the QR code is rapidly arriving at high levels of acceptance worldwide.

Improving Immunization Health Care Data Quality using Two-Dimensional Barcoding and Barcode Scanning Practices.

Manual data entry is time-consuming, inefficient, and error prone. In contrast, leveraging two-dimensional (2D) barcodes and barcode scanning tools is a rapid and effective practice for automatically entering vaccine data accurately and completely. CDC pilots documented clinical and public health impacts of 2D barcode scanning practices on data quality and completeness, time savings, workflow efficiencies, and staff experience. Data entry practices and entered records from routine and mass vaccination settings were analyzed. Data quality improvement opportunities were identified. A sample of 50 million emergency use authorization (EUA) coronavirus disease 2019 (COVID-19) vaccine records were analyzed for accuracy and completeness across three data fields: lot number, expiration date, and National Drug Code (NDC). The EUA COVID-19 vaccines lacked a 2D barcode containing these data fields, which necessitated manual data entry at administration. A CDC pilot at clinic compared scanned and manually entered data for routine vaccines across these same data fields. Analysis of 50 million manually entered EUA COVID-19 vaccine administration records indicated significant gaps in data accuracy and completeness across three data fields. Over half of the analyzed EUA vaccine NDCs (53%) and one-third of the expiration dates (35%) had missing or inaccurate data recorded. Pilot data also showed many errors when manually entered. However, when the pilot's routine vaccines were scanned (out of 71,969 records), nearly all entries were complete and accurate across all three data fields (ranging from 99.7% to 99.999% accurate). Vaccine 2D barcode scanning practices increased data accuracy and completeness (up to 99.999% accurate) across data fields assessed. When used consistently, vaccine 2D barcode scanning can resolve issues demonstrated in manually entered data. To realize these benefits, the immunization community should widely use scanning practices. To increase use, CDC developed a Vaccine 2D Barcode National Adoption Strategy and implementation resources.

Review ―The Digital Revolution for the Security of Hiding Information inside the Quick Response Code‖

The Digital Revolution has significantly transformed secure and transmitted information, with QR codes emerging as a powerful tool in this landscape. QR codes that stands for Quick Response codes are 2D barcodes that can contain a vast amount of information; they have great opportunities to encrypt information, so they provide reliable solutions in terms of data hiding and data transfer. The described confrontation of the Digital Revolution insists on enhancing and changing ways to ensure information security, and the application of QR codes is a good example of how technological advancements can offer efficient ideas for concealing and protecting information. As encryption and QR code technology evolve, their role in securing data will undoubtedly expand, offering new levels of security in our increasingly digital world.

Generating 2D Barcode for DNA Barcode Sequences.

DNA barcode sequence is a short DNA sequence representing a sample from a particular species. The commonly used DNA barcodes are at least 200bps long. This large number of characters cannot be encoded in two-dimensional codes for sample recognition and tracking. In the present study, we described a method that can be used to compress the DNA sequences and then generate the corresponding QR code. With the large numbers of software and hardware, the QR code can be used efficiently for printing, labeling, and scanning.

Durable and sustainable smart tags for identity management and condition monitoring: Case study for reusable packaging and recyclable data carriers

AbstractReusable packaging has been used as a case study for development of durable and sustainable smart tags that can provide both identity management and potentially also condition monitoring capabilities. Four different smart tag technologies have been investigated, including laser engraved 2D bar codes, printed 2D bar codes with combined temperature indicator, printed NFC (Near‐Field Communication) tags and RFID (Radio Frequency Identification) tags as labels. All technologies showed excellent durability towards repeated washing and heating when protective layers were used. SWOT analysis was also carried out to weight the advantages and disadvantages of the different technologies summarizing the choice of the technology per use and end‐of‐life requirements. Sustainability of the investigated technologies is discussed from the smart tag recyclability perspective and the challenges that can be seen related to the use of electronic functionalities and/or plastic‐based protective layers.

Multilevel Encoding Physically Unclonable Functions Based on The Multispecies Structure in Diamonds

AbstractThe multilevel encoding (MLE) scheme is an effective method for improving the anticounterfeiting encryption capabilities of physically unclonable functions (PUFs). However, owing to the correlation between encoding layers, the encoding capacity (EC) is difficult to improve by orders of magnitude. Herein, four noncorrelated structures in the diamond crystal structure (carbon–carbon single bond, defect luminescence structures, spin structures, and electron energy distribution structures) are considered for MLE. First, the microdiamonds containing nitrogen‐vacancy (NV) color centers are embedded into polydimethylsiloxane (PDMS) to fabricate PUFs. Using an optical imaging system, four codable images of four noncorrelated structures are read. The noncorrelation of the four‐level encoding structure is verified by calculating the Hamming distance (0.496 ± 0.02). The results show that EC exponentially improves to 24×10 000/(100 pixels)2. Furthermore, the encoding method based on the energy level does not depend on physical structure parameters, such as the size and position of the spin structure. Thus, it is protected from structural modeling attacks, resulting in high security. Moreover, PUF labels based on PDMS flexible substrates can be employed for various flexible applications. In the proposed scheme, the information is encrypted by a four‐level two‐dimensional (2D) barcode and decoded by self‐developed PUF authentication software. The proposed scheme presents a way for developing next‐generation PUFs with super‐high EC.

Inventory Management System for a General Items Warehouse of the Textile Industry

This research is based on Inventory Management System for a General Items Warehouse of the Textile Industry. The overall inventory is managed by applying classification tools such as ABC, FSN & HML that categorize inventory based on consumption value, issuance rate and unit price respectively. Also, it helps to appropriately position the items on the desired rack and position. The optimized layout is designed that reduces the retrieval time, uplift the storage capacity, and have cross aisles that reduce the retrieval time of any item from the warehouse. The system for proper traceability & tracking of the items is also studied that is based on the 1D Barcode. This whole study improves the overall operation of the Supply Chain.

Reading Multilevel 2-D Barcodes Using a Machine Learning Approach

This paper addresses the reading problem of multi-level 2D barcodes over a Print-and-Capture (PC) channel. The prior reading schemes have different limitations to hinder their applications, e.g., suffering from quantization error, being sensitive to the predetermined decision boundaries, and being sensitive to the selection of initial parameters. In this paper, we introduce a machine learning approach to address the above limitations using a new ensemble clustering algorithm. Based on the new ensemble clustering algorithm, we propose two reading schemes of a multi-level 2D barcode. Specifically, the first proposed scheme is named the Ensemble Clustering (EC) reading scheme. In the EC reading scheme, we introduce a weighted ensemble mechanism to assign different weights to different base clustering results. Then, we propose the second scheme, named the Enhanced Ensemble Clustering (EEC) reading scheme, to further improve the reading performance with the help of the reference symbols. We implement our approach and conduct extensive performance comparisons through an actual excremental platform under various multi-level 2D barcodes and various capturing devices. From experimental results, we observe that both proposed reading schemes have better performance than the prior reading schemes. Moreover, the EEC reading scheme has better performance than the EC reading scheme, and their performance gap becomes more apparent as the distortion of a PC channel increases.

Implementation of 2D barcode medication labels and smart pumps in pediatric acute care: Lessons learned.

In pediatric intensive care, prescription, administration, and interpretation of drug doses are weight-dependent. The use of standardized concentrations simplifies the preparation of drugs and increases safety. For safe administration as well as easy interpretation of intravenous drug dosing regimens with standardized concentrations, the display of weight-related dose rates on the infusion device is of pivotal significance. We report on challenges in the implementation of a new information technology-supported medication workflow. The workflow was introduced on eight beds in the pediatric heart surgery intensive care unit as well as in the pediatric anesthesia at the University of Bonn Medical Center. The proposed workflow utilizes medication labels generated from prescription data from the electronic health record. The generated labels include a 2D barcode to transfer data to the infusion devices. Methods Clinical and technical processes were agilely developed. The reliability of the system under real-life conditions was monitored. User satisfaction and potential for improvement were assessed. In addition, a structured survey among the nursing staff was carried out. The questionnaire addressed usability as well as the end-users' perception of effects on patient safety. The workflow has been applied 44111 times during the pilot phase. A total of 114 known failures in the technical infrastructure were observed. The survey showed good ratings for usability and safety (median "school grade" 2 or B for patient safety, intelligibility, patient identification and handling). The medical management of the involved acute care facilities rated the process as clearly beneficial regarding patient safety, suggesting a rollout to all pediatric intensive care areas. A medical information technology-supported medication workflow can increase user satisfaction and patient safety as perceived by the clinical end-users in pediatric acute care. The successful implementation benefits from an interdisciplinary team, active investigation of possible associated risks, and technical redundancy.

Conversion of 1D Data Used in Detection of Malware Types to 2D Barcode Types and Analysis with Deep Networks

Kötü amaçlı yazılımlar bilgisayar tabanlı sistemlere zarar vermek, önemli bilgileri elde etmek veya değiştirmek amaçlı hazırlanmış yazılımlardır. Bu tür yazılımlar insanların etkileşim içerisinde olduğu ağ ortamlarını hedef alırlar. Bu ağ ortamlarında kullanılan akıllı cihazlar günümüzde hayatımızın vazgeçilmez parçalarından biri olmuştur. Akıllı cihazların güvenliğini sağlayabilmek, zararlı yazılımların tespitini gerçekleştirebilmek için son zamanlarda yapay zekâ tabanlı birçok çalışma gerçekleşmiştir. Bu çalışmanın veri kümesi gizlenmiş kötü amaçlı yazılım türlerini içerisinde barındıran metin tabanlı içeriklerden oluşmaktadır. Önerilen yaklaşım, önişlem adımından ve derin öğrenme modelinden oluşmaktadır. Önişlem adımında metin tabanlı veriler, 2-boyutlu barkod türlerine dönüştürülerek iki yeni veri kümesi elde edilmiştir. Bir sonraki adımda veri kümeleri tasarlanmış derin ağ modeli tarafından eğitilerek özellik setleri çıkartılmıştır. Son adımda özellik setleri birleştirilerek sınıflandırma süreci Softmax yöntemi kullanılarak gerçekleşmiştir. Deneysel analizler önerilen yaklaşımın genel performansı artırdığı görülmüştür ve sınıflandırma sürecinde genel doğruluk başarısı %100 olarak elde edilmiştir.

System for determining the job status of individual laborers in a large-scale greenhouse

To obtain the work status of individual laborers by drawing the work progress map and determining the work speeds of individual laborers in a large-scale greenhouse, we developed a simple system comprising smartphone and web-based server applications, using smartphones, two-dimensional (2D) barcodes (called QR codes), a cloud server, and a personal computer. Using the smartphone applications, the laborers read the QR codes on their smartphones when they started or ended the designated work. The timestamps obtained when reading the QR codes were sent to the cloud server. The work progress map was drawn and the work speeds of individual laborers were estimated by the web-based server application in the cloud server based on the length of the gutter and the time difference between the start and end of the designated work. In the field trial, we placed QR codes at the end of the gutters (556 gutters) in a model greenhouse where bell pepper plants were grown in an area of 3 ha. The work progress map and work speeds of individual laborers were obtained rapidly and remotely in the greenhouse based on the collected data. Because the system has a simple structure, its installation is relatively easy, indicating lower cost than that of the commercially available systems.

OAcode: Overall Aesthetic 2D Barcode on Screen

Nowadays, two-dimensional (2D) barcodes have been widely used in various domains. And a series of aesthetic 2D barcode schemes have been proposed to improve the visual quality and readability of 2D barcodes for better integration with marketing materials. Yet we believe that the existing aesthetic 2D barcode schemes are partially aesthetic because they only beautify the data area but retain the position detection patterns with the blackwhite appearance of traditional 2D barcode schemes. Thus, in this paper, we propose the first overall aesthetic 2D barcode scheme, called OAcode, in which the position detection pattern is canceled. Its detection process is based on the pre-designed symmetrical data area of OAcode, whose symmetry could be used as the calibration signal to restore the perspective transformation in the barcode scanning process. Moreover, an enhanced demodulation method is proposed to resist the lens distortion common in the camera-shooting process. The experimental results illustrate that when 5$\times$5 cm OAcode is captured with a resolution of 720$\times$1280 pixels, at the screen-camera distance of 10 cm and the angle less or equal to 25{\deg}, OAcode has 100% detection rate and 99.5% demodulation accuracy. For 10$\times$10 cm OAcode, it could be extracted by consumer-grade mobile phones at a distance of 90 cm with around 90% accuracy.

1D Barcode Detection: Novel Benchmark Datasets and Comprehensive Comparison of Deep Convolutional Neural Network Approaches.

Recent advancement in Deep Learning-based Convolutional Neural Networks (D-CNNs) has led research to improve the efficiency and performance of barcode recognition in Supply Chain Management (SCM). D-CNNs required real-world images embedded with ground truth data, which is often not readily available in the case of SCM barcode recognition. This study introduces two invented barcode datasets: InventBar and ParcelBar. The datasets contain labeled barcode images with 527 consumer goods and 844 post boxes in the indoor environment. To explore the influential capability of the datasets that affect recognition process, five existing D-CNN algorithms were applied and compared over a set of recently available barcode datasets. To confirm the model's performance and accuracy, runtime and Mean Average Precision (mAP) were examined based on different IoU thresholds and image transformation settings. The results show that YOLO v5 works best for the ParcelBar in terms of speed and accuracy. The situation is different for the InventBar since Faster R-CNN could allow the model to learn faster with a small drop in accuracy. It is proven that the proposed datasets can be practically utilized for the mainstream D-CNN frameworks. Both are available for developing barcode recognition models and positively affect comparative studies.

Construction of an SNP fingerprinting database and population genetic analysis of 329 cauliflower cultivars

Cauliflower is one of the most important vegetable crops grown worldwide. However, the lack of genetic diversity information and efficient molecular markers hinders efforts to improve cauliflower. This study aims to construct DNA fingerprints for 329 cauliflower cultivars based on SNP markers and the KASP system. After rigorous filtering, a total of 1662 candidate SNPs were obtained from nearly 17.9 million SNP loci. The mean values of PIC, MAF, heterozygosity and gene diversity of these SNPs were 0.389, 0.419, 0.075, and 0.506, respectively. We developed a program for in silico simulations on 153 core germplasm samples to generate ideal SNP marker sets from the candidates. Finally, 41 highly polymorphic KASP markers were selected and applied to identify 329 cauliflower cultivars, mainly collected from the public market. Furthermore, based on the KASP genotyping data, we performed phylogenetic analysis and population structure analysis of the 329 cultivars. As a result, these cultivars could be classified into three major clusters, and the classification patterns were significantly related to their curd solidity and geographical origin. Finally, fingerprints of the 329 cultivars and 2D barcodes with the genetic information of each sample were generated. The fingerprinting database developed in this study provides a practical tool for identifying the authenticity and purity of cauliflower seeds and valuable genetic information about the current cauliflower cultivars.

急性膵炎診療ガイドライン作成に取り掛かった30年の経緯と今回の第5版（JPN Guidelines 2021）での工夫：やさしい解説を取り入れ参考資料をQRコードで示した．

急性膵炎診療ガイドライン作成に取り掛かった30年の経緯と今回の第5版（JPN Guidelines 2021）での工夫：やさしい解説を取り入れ参考資料をQRコードで示した．

Establishment of low-cost laboratory automation processes using AutoIt and 4-axis robots.

In most small laboratories, many processes are not yet automated because existing laboratory automation solutions are usually expensive and inflexible to use. Examples of this are autosamplers that are only compatible with one specific laboratory instrument or larger liquid handling stations that are expensive and usually self-contained. A flexible and inexpensive way to automate laboratory processes would be to automate existing laboratory equipment with the help of suitable robotic arms. In this study, we investigate the feasibility of such a strategy based on a low-cost 4-axis robot and freely available software. We used the scripting language AutoIt that automates any Windows-based instrument control software. Using these tools, we automated three fundamentally different laboratory processes: a pipetting process, a use as an autosampler for an atomic absorption spectroscopy instrument, and a more complex process involving the inoculation of bacterial cultures. We also integrated a conventional webcam for 2D barcode recognition. Compared to a trained professional who performed all experiments manually, all setups showed no significant differences in accuracy and precision. In summary, the tested system consisting of a 4-axis robot and freely available software is suitable for flexible automation and has potential for even more complex laboratory processes. Limitations such as a lack of collaboration and speed will be addressed in follow-up studies. The system thus represents a well-suited flexible laboratory automation system for both research and teaching purposes.