2D Barcode Research Articles

Leveraging Barcodes in Customer Communications: Enhancing Efficiency and Implementation Strategies

Barcodes have become an essential tool in many industries; they are vital in efficiency, accuracy, and automation. As demands for barcodes have risen in the retail, logistics, healthcare, and manufacturing arenas, businesses have been forced to embrace them as they strive to acquire operational efficiencies and improve customer service. Barcodes are the speed and reliability in tracking products, managing inventory, and processing transactions, thus decreasing manual data input and human errors. They allow firms to catch data automatically, make workflows more efficient, and share real-time information. This document investigates the necessity of barcodes in modern manufacturing's spectrum, evaluating their diverse usages, benefits, and the technology in barcode systems. Also, it lays out some of these best practices that are key to the realization of barcode solutions and that are used to optimize operational flows, improve the accuracy of the data gathered, and integrate different systems and platforms. Through the proper use of barcodes, many businesses can take on a competitive edge. They will be able to cut costs and ensure their service to their clients is top-notch. Keywords Barcode, Product Tracking, 2D Barcodes, 1D Barcodes, QR Codes, Customer communication management (CCM)

Development of a Semiquantitative Barcode Readout Approach for Paper-Based Analytical Devices (PADs) for Enzymatic H2O2 and Glucose Detection.

The integration of barcode technology with smartphones on paper-based analytical devices (PADs) presents a promising approach to bridging manual detection with digital interpretation and data storage. However, previous studies of 1D barcode approaches have been limited to providing only a "yes/no" response for analyte detection. Herein, a method of using barcode readout for semiquantitative signal detection on PADs has been achieved through the integration of barcode technology with a distance-based measurement concept on PADs. To demonstrate the feasibility of this concept, a PAD fabrication strategy incorporating barcodes was explored, using the enzymatic reaction between horseradish peroxidase (HRP), 3,3'-diaminobenzidine (DAB), and H2O2 as a model system. The enzyme-catalyzed polymerization of DAB to polyDAB in the presence of hydrogen peroxide results in the appearance of color observable by the naked eye inside a paperfluidic channel, with the color-changed length depending on the H2O2 concentration. At the same time, the barcode pattern displayed as a result of this distance-based color evolution overlaid with a paper-based barcode layer can be read using a smartphone application. Parameters affecting the signal readout performance were studied. The developed device can be used to detect H2O2 concentrations in the range of 0.25 to 10 mM within 90 min with 79.6% of barcode signals correctly readable. Additionally, results from different smartphone models showed a consistent reading performance (78.4-79.6%). Finally, the quantification of glucose levels in artificial urine samples was demonstrated. This developed PAD signaling strategy offers end-users more simplicity and can be used as a standalone device or in conjunction with other digital devices.

A multi-channel CRISPR-based method for rapid, sensitive detection of four diseases of Brassica rapa in the field.

Pathogens significantly restrict the production of Brassica rapa (B. rapa L. ssp. Pekinensis), with climate change and evolving planting patterns exacerbating disease prevalence. Multichannel rapid diagnostic methods in the field can facilitate the early detection and control of diseases in B. rapa. Here, we established a multichannel lateral flow biosensor (LFB) combined with a CRISPR/Cas12a cleavage assay for the simultaneous detection of four B. rapa diseases. Key innovations of this study include: (1) High specificity and sensitivity, down to pathogen concentrations of 1.5pg/μl-due to the optimization of crRNA secondary structure: the more stable the crRNA, the higher its detection sensitivity. (2) Optimized visual detection parameters. We identified ideal concentration ratios for the visual fluorescence detection system: 50nM Cas12a, 50nM crRNA, and 500nM ssDNA fluorescent probe. Furthermore, the optimal concentrations of components on the LFB detection system were 3μl SA-GNPs, 500nM ssDNA test strip probe, 0.5mg/ml biotin-BSA as the test line, and 1mg/ml anti-FITC as the control line. (3) Field-Ready Cas-AIRPA Platform. We developed the on-site Cas-AIRPA platform for the simultaneous detection of B. rapa pathogens by combining rapid nucleic acid extraction and a four-channel lateral flow biosensor (4-LFB), which quickly provides disease-related information through a specific 2D barcode. Analysis of B. rapa samples in the field confirmed the suitability of the Cas-AIRPA platform for rapid (~25min) and simultaneous on-site detection of four diseases of B. rapa. This platform can also be adapted to detect other plant diseases in the field.

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

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

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

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

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.