Data Matrix Code Research Articles

A Data Matrix Code Recognition Method Based on L-Shaped Dashed Edge Localization Using Central Prior.

The recognition of data matrix (DM) codes plays a crucial role in industrial production. Significant progress has been made with existing methods. However, for low-quality images with protrusions and interruptions on the L-shaped solid edge (finder pattern) and the dashed edge (timing pattern) of DM codes in industrial production environments, the recognition accuracy rate of existing methods sharply declines due to a lack of consideration for these interference issues. Therefore, ensuring recognition accuracy in the presence of these interference issues is a highly challenging task. To address such interference issues, unlike most existing methods focused on locating the L-shaped solid edge for DM code recognition, we in this paper propose a novel DM code recognition method based on locating the L-shaped dashed edge by incorporating the prior information of the center of the DM code. Specifically, we first use a deep learning-based object detection method to obtain the center of the DM code. Next, to enhance the accuracy of L-shaped dashed edge localization, we design a two-level screening strategy that combines the general constraints and central constraints. The central constraints fully exploit the prior information of the center of the DM code. Finally, we employ libdmtx to decode the content from the precise position image of the DM code. The image is generated by using the L-shaped dashed edge. Experimental results on various types of DM code datasets demonstrate that the proposed method outperforms the compared methods in terms of recognition accuracy rate and time consumption, thus holding significant practical value in an industrial production environment.

Influence of laser marking parameters on data matrix code quality on polybutylene terephthalate/glass fiber composite surface using microscopy and spectroscopy techniques

<abstract> <p>Laser marking on polymer composite surfaces can be difficult to read and cause readability problems for electronic decoding equipment on production lines due to poor interaction between the laser and the fibers used to reinforce these materials. This problem can be solved with the right choice of marking parameters, resulting in savings for companies by avoiding production problems such as rejection, scrap and customer complaints. The present work uses the polybutylene terephthalate/glass fiber (PBT/GF) composite used in the manufacture of instrument panels for motorcycles. The tests were carried out with different laser marking parameters using a neodymium:yttrium-aluminum-garnet (Nd:YAG) laser. Subsequently, the laser-marked data matrix codes (DMC) were analyzed using a microscope verifier to evaluate the quality according to the ISO/IEC 29158:2020 standard. A detailed analysis of these surfaces was also carried out to observe some physical and chemical changes using scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS). The optical analysis showed that lower radiation power and pulse frequency and higher marking speed corresponded to weaker laser marking and therefore poorer DMC code quality, which was confirmed by the SEM. EDS showed that the laser marking process did not cause the chemical changes on the sample surface.</p> </abstract>

Using Different Types of Artificial Neural Networks to Classify 2D Matrix Codes and Their Rotations-A Comparative Study.

Artificial neural networks can solve various tasks in computer vision, such as image classification, object detection, and general recognition. Our comparative study deals with four types of artificial neural networks-multilayer perceptrons, probabilistic neural networks, radial basis function neural networks, and convolutional neural networks-and investigates their ability to classify 2D matrix codes (Data Matrix codes, QR codes, and Aztec codes) as well as their rotation. The paper presents the basic building blocks of these artificial neural networks and their architecture and compares the classification accuracy of 2D matrix codes under different configurations of these neural networks. A dataset of 3000 synthetic code samples was used to train and test the neural networks. When the neural networks were trained on the full dataset, the convolutional neural network showed its superiority, followed by the RBF neural network and the multilayer perceptron.

DEVICE FOR AUTOMATED CONTROL OF EXPIRATION DATES OF FOODSTUFFS, STORED IN REFRIGERATOR

One of the main problems of storing food (especially perishable) in refrigerators is compliance with their expiration dates. To solve it, it is necessary to constantly monitor and promptly notify food consumers about the approaching expiration of these deadlines. At present, this problem is solved mainly manually by checking the expiration date information printed on food packaging visually or using a hand-held scanner. At the same time, time is spent on checking, there is a high probability of untimely consumption of the food product or even its spoilage, which leads to certain financial losses. Today, given the development of Internet of Things technologies, it is possible to simply and cheaply automate the process of monitoring the expiration dates of food products stored in refrigerators and having a corresponding label on the packaging. Since 2021, Russia has introduced mandatory labeling of dairy products using the 2D Data Matrix code, which allows you to obtain data on expiration dates from the Chestny Znak state labeling and tracking system. Currently, the industry is already producing smart household refrigerators that can be connected to the Internet to transmit various information to the user. However, the common disadvantages of the known designs of smart refrigerators are their high cost, design complexity and the impossibility of working with 2D Data Matrix code labels applied to food packaging. A solution is proposed in the form of a stand-alone wireless smart device that allows you to simply and economically implement automatic notification of users about the approaching expiration date of food stored in the refrigerator, thereby eliminating the possibility of untimely consumption and spoilage. The proposed device does not require structural changes to the equipment of the refrigerator, has a low cost and ease of installation and use, and allows you to turn an ordinary refrigerator into a smart one.

Quantifying the Displacement of Data Matrix Code Modules: A Comparative Study of Different Approximation Approaches for Predictive Maintenance of Drop-on-Demand Printing Systems.

Drop-on-demand printing using colloidal or pigmented inks is prone to the clogging of printing nozzles, which can lead to positional deviations and inconsistently printed patterns (e.g., data matrix codes, DMCs). However, if such deviations are detected early, they can be useful for determining the state of the print head and planning maintenance operations prior to reaching a printing state where the printed DMCs are unreadable. To realize this predictive maintenance approach, it is necessary to accurately quantify the positional deviation of individually printed dots from the actual target position. Here, we present a comparison of different methods based on affinity transformations and clustering algorithms for calculating the target position from the printed positions and, subsequently, the deviation of both for complete DMCs. Hence, our method focuses on the evaluation of the print quality, not on the decoding of DMCs. We compare our results to a state-of-the-art decoding algorithm, adopted to return the target grid positions, and find that we can determine the occurring deviations with significantly higher accuracy, especially when the printed DMCs are of low quality. The results enable the development of decision systems for predictive maintenance and subsequently the optimization of printing systems.

Construct stochastic processes model to solve graphic and math problems

The use of probabilistic reasoning has been used to a variety of applications, such as image identification, computer diagnostics, stock price prediction, movie recommendation, and cyber intrusion detection. However, the range of probabilistic programming was limited until recently (partly due to the low computing capacity), and the bulk of inference techniques needed to be developed manually for each task. However, in 2015, using a 50-line probabilistic computer vision program, 3D representations of human faces were produced from 2D images of those faces.Use a computer to model various complex stochastic phenomena. All projects are programmed in java language. Find the actual number of 1s in a binary number. Formulate and analyze mathematical models to real life phenomena like Data Matrix and QR code. Those code mentioned above can provide a link to the real-life website or some certain message needed to be shown. using some random numbers to model uncertainty. To model some fractals. The first fractal maybe not self-avoiding. But the second one will never meet the same path for twice. To apply the concept stochastic processes. More java features and some random fractals and regression. Multiple factor analysis.

Femtosecond laser marking depth of field 3D code

In order to reduce the market in a large number of counterfeit imitation products, product traceability anti-counterfeiting by more and more attention. Therefore, it is imperative to study product traceability and anti-counterfeiting marking technology. However, for products within a limited size, the data capacity of traditional Data Matrix (DM) code is insufficient to meet the storage requirements, and it is easy to clone and copy, which limits the development of laser marking technology. In order to improve the replication difficulty and information storage capacity of the Data Matrix code, the depth-of-field (DOF) 3D code is devised by adding Z-axis depth information to the original Data Matrix code, which can accommodate a larger information storage capacity and improve the reproduction difficulty of DM code, thus meeting the current commercial demand. In this paper, cemented carbide is taken as the research object, and DOF 3D code is marked on its surface by femtosecond laser, and the optimal process parameters are determined. At the same time, the recognition and information reading of DOF 3D code is studied, and the correctness and reliability of its information acquisition are tested.

Reading Direct-Part Marking Data Matrix Code in the Context of Polymer-Based Additive Manufacturing.

A novel approach to detect and decode direct-part-marked, low-contrast data matrix codes on polymer-based selective laser sintering manufactured parts, which is able to work on lightweight devices, is presented. Direct-part marking is a concept for labeling parts directly, which can be carried out during the additive manufacturing's design process. Because of low contrast in polymer-based selective laser sintering manufactured parts, it is a challenging task to detect and read codes on unicolored parts. To achieve this, at first, codes are located using a deep-learning-based approach. Afterwards, the calculated regions of interest are passed into an image encoding network in order to compute readable standard data matrix codes. To enhance the training process, rendered images, improved with a generative adversarial network, are used. This process fulfills the traceability task in assembly line production and is suitable for running on mobile devices such as smartphones or cheap sensors placed in the assembly line. The results show that codes can be localized with 97.38% mean average precision, and a readability of 89.36% is achieved.

Data Matrix Based Low Cost Autonomous Detection of Medicine Packages

Counterfeit medicine is still a crucial problem for healthcare systems, having a huge impact in worldwide health and economy. Medicine packages can be traced from the moment of their production until they are delivered to the costumers through the use of Data Matrix codes, unique identifiers that can validate their authenticity. Currently, many practitioners at hospital pharmacies have to manually scan such codes one by one, a very repetitive and burdensome task. In this paper, a system which can simultaneously scan multiple Data Matrix codes and autonomously introduce them into an authentication database is proposed for the Hospital Pharmacy of the Centro Hospitalar de Vila Nova de Gaia/Espinho, E.P.E. Relevant features are its low cost and its seamless integration in their infrastructure. The results of the experiments were encouraging, and with upgrades such as real-time feedback of the code’s validation and increased robustness of the hardware system, it is expected that the system can be used as a real support to the pharmacists.

En route to automated maintenance of industrial printing systems: digital quantification of print-quality factors based on induced printing failure

Abstract. Tracking and tracing are a key technology for production process optimization and subsequent cost reduction. However, several industrial environments (e.g. high temperatures in metal processing) are challenging for most part-marking and identification approaches. A method for printing individual part markings on metal components (e.g. data matrix codes (DMCs) or similar identifiers) with high temperatures and chemical resistance has been developed based on drop-on-demand (DOD) print technology and special ink dispersions with submicrometer-sized ceramic and glass particles. Both ink and printer are required to work highly reliably without nozzle clogging or other failures to prevent interruptions of the production process in which the printing technology is used. This is especially challenging for the pigmented inks applied here. To perform long-term tests with different ink formulations and to assess print quality over time, we set up a test bench for inkjet printing systems. We present a novel approach for monitoring the printhead's state as well as the print-quality degradation. This method does not require measuring and monitoring, e.g. electrical components or drop flight, as it is done in the state of the art and instead uses only the printed result. By digitally quantifying selected quality factors within the printed result and evaluating their progression over time, several non-stationary measurands were identified. Some of these measurands show a monotonic trend and, hence, can be used to measure print-quality degradation. These results are a promising basis for automated printing system maintenance.

A novel tracking system for the iron foundry field based on deep convolutional neural networks

In modern manufacturing the ability of retracing produced components is crucial for quality management and process optimization. Tracking is essential, especially for analyzing the influence of the production parameters on the final quality of the castings. In the iron foundry industry, common marking methods, such as a datamatrix code, cannot be used due to harsh environmental conditions and the rough surface of the cast parts. This work presents a new coding and reading system that guarantees unique marking in the casting process.The coding is built up over several beveled pins and is read out using an optical 2D handheld scanner. With a deep convolutional neural network approach of object detection and classification, a stable image processing algorithm is presented. With a first prototype a reading accuracy of 99.86% for each pin was achieved with an average scanning time of 0.43 s. The presented code is compatible with existing foundry processes, while the handheld scanner is intuitive and reliable. This allows immediate benefits for process optimization.

Data Extraction Method for Industrial Data Matrix Codes Based on Local Adjacent Modules Structure

A 2D barcode is a reliable way to provide lifetime traceability of parts that are exposed to harsh environments. However, there are considerable challenges in adopting mobile cameras to read symbols directly marked on metal surfaces. Images captured by mobile cameras are usually of low quality with poor contrast due to the reflective surface of 2D barcode symbols. To deal with this problem, a novel laser-marked Data Matrix symbols reading method based on deep learning is proposed for mobile phone captured images. Utilizing the barcode module features, we train different convolutional neural network (CNN) models to learn the colors of two adjacent modules of a Data Matrix symbol. Depending on whether the colors of the two adjacent modules are the same or not, an edge image is transformed from a square grid, which is the same size as the barcode. A correction method based on the KM algorithm is used to get a corrected edge image, which helps to reconstruct the final barcode image. Experiments are carried out on our database, and the results show that the proposed algorithm outperforms in high accuracy of barcode recognition.

A Nested Texture Inspired Novel Image Pattern Based Optical Camera Communication

In Optical Camera Communication, pattern recognition is a vital component of display-based communication. However, as the distance between the source and camera increases, the visibility of the pattern reduces. Decoding each pattern requires extensive computational resources. This paper proposes a nested texture-inspired novel binary hierarchical image pattern classification-based modulation for optical camera communication to provide efficient pattern classification and achieve longer communication distance. Custom 8x8 patterns are generated at the transmitter based on input data using nested outer and inner patterns. On the receiver side, multi-threaded pattern matching is performed simultaneously for inner and outer patterns for faster decoding. A simulator is built to test the performance of linear search and two variants of binary search for pattern matching. A hardware prototype and Matlab app are designed to perform experiments to test the performance of the proposed technique in a real-world environment. Experiments were conducted to select optimal camera parameter values for the best Signal to Noise Ratio(SNR) and to analyze the impact of pattern intensity profile on decoding at various distances. The proposed pattern communication technique showcased 48.6%, and 42.5% improvement in Bit Error Ratio(BER) compared to Data Matrix and QR Code Pattern-based communication techniques, respectively.

Comparative Study of Data Matrix Codes Localization and Recognition Methods.

We provide a comprehensive and in-depth overview of the various approaches applicable to the recognition of Data Matrix codes in arbitrary images. All presented methods use the typical “L” shaped Finder Pattern to locate the Data Matrix code in the image. Well-known image processing techniques such as edge detection, adaptive thresholding, or connected component labeling are used to identify the Finder Pattern. The recognition rate of the compared methods was tested on a set of images with Data Matrix codes, which is published together with the article. The experimental results show that methods based on adaptive thresholding achieved a better recognition rate than methods based on edge detection.

Ultrafast femtosecond laser micro-marking of single-crystal natural diamond by two-lens focusing system

The inscription of unique logo and security marking on diamonds and gemstones is in high demand by worldwide manufacturers and businesses for anti-counterfeiting purposes and traceability. Short pulsed lasers enable marking of transparent materials, challenge remains to produce digital security micro-features on thin facets of the natural diamond in a non-intrusive manner. We propose the design and demonstration of a novel two-lens focusing system to inscribe diamond at microscale and high throughput by an ultrafast laser scanning process. A threshold laser fluence of ∼2.5 J/cm2 and a scan speed of 2 mm/s realized writing of high-contrast data matrix code and serial number without inducing defects and cracking in the diamond. Characterization revealed smooth ablation depth profile with distinct laser-induced periodic surface structures (LIPSS) in the laser inscribed regions determined through scanning electron microscopy (SEM) and 3D optical microscopy. Raman spectroscopy revealed diamond cubic structure dominating with mixing of graphitic structure in the laser markings at various scanning speeds. Comparing with the 0.05 NA f-theta lens, the two-lens focusing system offered 7x improvement in the marking resolution (3 μm in line-width) in addition to its simplicity and add-on flexibility to industrial laser marking systems.

Recognition of Data Matrix Codes in Images and their Applications in Production Processes

Abstract Data Matrix codes can be a significant factor in increasing productivity and efficiency in production processes. An important point in deploying Data Matrix codes is their recognition and decoding. In this paper is presented a computationally efficient algorithm for locating Data Matrix codes in the images. Image areas that may contain the Data Matrix code are to be identified firstly. To identify these areas, the thresholding, connected components labelling and examining outer bounding-box of the continuous regions is used. Subsequently, to determine the boundaries of the Data Matrix code more precisely, we work with the difference of adjacent projections around the Finder Pattern. The dimensions of the Data Matrix code are determined by analyzing the local extremes around the Timing Pattern. We verified the proposed method on a testing set of synthetic and real scene images and compared it with the results of other open-source and commercial solutions. The proposed method has achieved better results than competitive commercial solutions.

Using New digital anti-counterfeiting Technologies to Protect the Egyptian Pharmaceuticals Packages against counterfeiting

Medicine counterfeiting is an urgent problem, it is a growing issue that cannot be tolerated and one that cannot be ignored. The World Health Organization (WHO) estimates that counterfeit medicine accounts for 10% of the market worldwide, and more than 30% in some countries. As these counterfeit medicine increasing, patients will be the ones to pay the price. According to a 2017 report by PricewaterhouseCoopers, the counterfeit medicine market earns between $163 billion to $217 billion per year, making it one of the most profitable types of counterfeited goods. Counterfeiters increasingly use advanced means to produce fake medicines. So, the pharmaceutical industry must continuously update innovative technological solutions to ensure the protection and traceability of products, to identify fake products, and to secure the supply and distribution chain. The packaging is a necessary component that helps in making the medicine safe until it is used. Counterfeit medicine is an important issue for Sanofi, which is doing best efforts to fight counterfeit of its medicines and has developed several programs to promote access to safe medicines. Nevertheless, PLAVIX® Clopidogrel, one of its important and effective drugs that used in Egypt to reduce the chances of blood clots forming, which packed in a package secured with many overt and covert features to increase its protection from counterfeiting, like Serial Number, Sanofi Security Label (SASL), Data Matrix Code, Linear Barcode and Scratched Code, it can still be counterfeited. So the research hypothesis is that finding an effective, comprehensive solution like using new digital technologies, such as using RFID tags, NFC tags, ATT–Titanium Security, Fiber Tags, and others, will greatly increase the level of the medicine security and consequently achieve the research aim which is protecting The Egyptian pharmaceuticals packages against counterfeiting.

DID Code: A Bridge Connecting the Materials Genome Engineering Database with Inheritable Integrated Intelligent Manufacturing

Abstract A data identifier (DID) is an essential tag or label in all kinds of databases—particularly those related to integrated computational materials engineering (ICME), inheritable integrated intelligent manufacturing (I3M), and the Industrial Internet of Things. With the guidance and quick acceleration of the development of advanced materials, as envisioned by official documents worldwide, more investigations are required to construct relative numerical standards for material informatics. This work proposes a universal DID format consisting of a set of build chains, which aligns with the classical form of identifier in both international and national standards, such as ISO/IEC 29168-1:2000, GB/T 27766–2011, GA/T 543.2–2011, GM/T 0006–2012, GJB 7365–2011, SL 325–2014, SL 607–2018, WS 363.2–2011, and QX/T 39–2005. Each build chain is made up of capital letters and numbers, with no symbols. Moreover, the total length of each build chain is not restricted, which follows the formation of the Universal Coded Character Set in the international standard of ISO/IEC 10646. Based on these rules, the proposed DID is flexible and convenient for extending and sharing in and between various cloud-based platforms. Accordingly, classical two-dimensional (2D) codes, including the Hanxin Code, Lots Perception Matrix (LP) Code, Quick Response (QR) code, Grid Matrix (GM) code, and Data Matrix (DM) Code, can be constructed and precisely recognized and/or decoded by either smart phones or specific machines. By utilizing these 2D codes as the fingerprints of a set of data linked with its cloud-based platforms, progress and updates in the composition–processing–structure–property–performance workflow process can be tracked spontaneously, paving a path to accelerate the discovery and manufacture of advanced materials and enhance research productivity, performance, and collaboration.

Identification Feature Design for Homogenization Product Based on Characteristic Difference

Aiming at the serious homogenization problems between the products with the same model and batches, and the difficulty identification due to wear, pollution and other factors which led small characteristic differences between cutters failure to identify, an identification feature design method of homogenization product to prevent tracking failure was presented. The feature enhanced direct parts marking (DPM) was analyzed, Data Matrix (DM) codes feature design based on marking process and affine transformation was proposed. Feature differences between individuals were enhanced from the source, DM code texture feature design based on Gray-level Co-occurrence (GLCM) was proposed and DM code texture library was established in product manufacturing process. By using the method of Hamming distance and Euclidean distance, the similarity between the target characteristics and DM code marking feature, manufacturing process texture library was matched when product identification was failed to read. The experimental results showed that the proposed method could identify the product failure recovery or remedy better.

Pilot Implementation of Falsified Medicines Directive in Hospital Pharmacy to Develop Best Practices for Medicine Decommissioning Process.

Background: The introduction of a medicines verification and decommissioning system into the hospital pharmacy may result in an increased workload for pharmacy staff. The pilot implementation allows us to understand all the implications of the process, optimize process workflows, and estimate the time and cost of implementation. Methods: All the packages received at the hospital pharmacy had a 2D data matrix codes and were scanned. We analyzed the time needed to unpack a variety of products, scan them, and receive the notification. Results: In total, 144 packages were scanned at an average time of 3.05 s, with most (86.9%) under 4 s. Manual decommissioning using handheld scanners was less efficient than the automated solution tested and resulted in an additional 0.4 full-time equivalent hours per million packages per year. The pattern and total time of manual scanning depended not only on the quantity but also the size of the package and type of packing. Conclusions: This evaluation of scanning performance allows optimizing the process at operational, technical, and resource levels for medicine verification and decommissioning.