Code Recognition Research Articles

QR code recognition based on HOG and multiclass SVM classifier

QR code recognition based on HOG and multiclass SVM classifier

South African Literature and the “Strange Seductiveness” of the Fantastic

In postcolonial literature, magic realism and science fiction are two subgenres that have worked diligently to contest realism as a Western novelistic tradition. In the South African context, the fantastic initiates a process of psychic liberation from old (white) world-narrative domination and its cognitive codes. It recapitulates problems of historical consciousness in (post)apartheid cultures and interrogates inherited notions of imperial history. This article reads two “fantastic” texts that belong to a similar postcolonial culture―South Africa―and strives to explain the ways in which these texts recapitulate, in both their narrative discourse and their thematic content, the “real” social and historical context in which (post)apartheid South African culture exists and thrives. Zakes Mda’s Ways of Dying and Lauren Beukes’s Zoo City use magic realism and science fiction respectively to re-view and debunk inherited literary modes of colonial discourse and to work towards more authentic yet challenging codes of recognition. By so doing, they offer positive and liberating responses to emerging cultural forms.

A smartphone-based centrifugal mHealth platform implementing hollow daisy-shaped quick response chip for hematocrit measurement

Due to the ever-increasing challenge of emerging and reemerging infections on global health, the development of POCT tools has been propelled. However, conventional point-of-care testing methods suffer from several limitations, including cumbersome operation, long detection times, and low accuracy, which hamper their widespread application. Compared to traditional disease diagnostic equipment, mobile health platforms offer several advantages, including portability, ease of operation, and automated analysis of detection results through recognition algorithms. Consequently, they hold great promise for the future. Here, we developed a smartphone-based centrifugal mHealth platform implementing daisy-shaped quick response chip for hematocrit measurement. The centrifugal microfluidic chip is combined with a smartphone through a back-clip-on mobile phone adapter whose control circuit is designed with low power consumption to enable the platform to operate without requiring a high-power source that is inconvenient to carry, thereby achieving the goal of portability. Concurrently, we designed a quick response chip featuring a unique hollow daisy structure that is in line with the properties of hematocrit detection. The distinctive configuration of the chip enables adequate centrifugal force to be supplied for hematocrit detection. Additionally, our customized quick response code recognition algorithm is able to recognize this chip, facilitating non-experts in performing hematocrit intelligent recognition with their smartphones.

Streamlining Attendance Management in Education: A Web-Based System Combining Facial Recognition and QR Code Technology

Attendance tracking has long posed challenges in educational institutions due to the inefficiency and error-prone nature of traditional paper-based methods. In response, many institutions have embraced web technologies and automated attendance systems, incorporating biometrics, QR codes, barcodes, and RFID-based technologies. However, the applicability of these systems may vary across different educational settings. This paper introduces a web-based student attendance management system that combines facial recognition technology and QR codes to address the challenges associated with manual attendance tracking in a university college. The system leverages a centralized database for streamlined monitoring and auditing of attendance records, offering the flexibility to choose between face recognition and QR code attendance marking options. User acceptance tests were conducted to evaluate the system's effectiveness, and the results indicate that the proposed system greatly improves attendance tracking transparency and demonstrates high usability based on positive user ratings. Additionally, the preference for face recognition over QR code scanning was evident. Incorporating these technologies into the automated attendance system represents a substantial advancement in educational technology, offering an accurate and efficient way of recording attendance.

Evaluation of inpatient hyponatremia: if formal recognition (coding) of hyponatremia makes a difference in management and outcomes

Evaluation of inpatient hyponatremia: if formal recognition (coding) of hyponatremia makes a difference in management and outcomes

Lightweight container code recognition based on multi-reuse feature fusion and multi-branch structure merger

Lightweight container code recognition based on multi-reuse feature fusion and multi-branch structure merger

Channel Pruning-Based YOLOv7 Deep Learning Algorithm for Identifying Trolley Codes

The identification of trolley codes poses a challenge in engineering, as there are often situations where the accuracy requirements for their detection cannot be met. YOLOv7, being the state-of-the-art target detection method, demonstrates significant efficacy in addressing the challenge of trolley coding recognition. Due to the substantial dimensions of the model and the presence of numerous redundant parameters, the deployment of small terminals in practical applications is constrained. This paper presents a real-time approach for identifying trolley codes using a YOLOv7 deep learning algorithm that incorporates channel pruning. Initially, a YOLOv7 model is constructed, followed by the application of a channel pruning algorithm to streamline its complexity. Subsequently, the model undergoes fine-tuning to optimize its performance in terms of both speed and accuracy. The experimental findings demonstrated that the proposed model exhibited a reduction of 32.92% in the number of parameters compared to the pre-pruned model. Additionally, it was observed that the proposed model was 24.82 MB smaller in size. Despite these reductions, the mean average precision (mAP) of the proposed model was only 0.03% lower, reaching an impressive value of 99.24%. We conducted a comparative analysis of the proposed method against five deep learning algorithms, namely YOLOv5x, YOLOv4, YOLOv5m, YOLOv5s, and YOLOv5n, in order to assess its effectiveness. In contrast, the proposed method considers the speed of detection while simultaneously ensuring a high mean average precision (mAP) value in the detection of trolley codes. The obtained results provide confirmation that the suggested approach is viable for the real-time detection of trolley codes.

South African Literature and the “Strange Seductiveness” of the Fantastic

Abstract In Postcolonial literature, magic realism and science fiction are two sub-genres that have worked diligently to contest realism as a western novelistic tradition. In the South African context, the fantastic initiates a process of psychic liberation from old (White) world narrative domination and its cognitive codes. It recapitulates problems of historical consciousness in (post)apartheid cultures and interrogates inherited notions of imperial history. This paper reads two “fantastic” texts that belong to a similar post-colonial culture—South Africa—and strives to explain the ways in which these texts recapitulate, in both their narrative discourse and their thematic content, the “real” social and historical context in which (post)apartheid South African culture existed and thrived. Zakes Mda’s Ways of Dying and Lauren Beukes’ Zoo City use magic realism and science fiction respectively to re-view and debunk inherited literary modes of colonial discourse and to work towards more authentic yet challenging “codes of recognition”. By so doing, they offer positive and liberating responses to new emerging cultural forms.

Beyond a PPR-RNA recognition code: Many aspects matter for the multi-targeting properties of RNA editing factor PPR56.

The mitochondrial C-to-U RNA editing factor PPR56 of the moss Physcomitrium patens is an RNA-binding pentatricopeptide repeat protein equipped with a terminal DYW-type cytidine deaminase domain. Transferred into Escherichia coli, PPR56 works faithfully on its two native RNA editing targets, nad3eU230SL and nad4eU272SL, and also converts cytidines into uridines at over 100 off-targets in the bacterial transcriptome. Accordingly, PPR56 is attractive for detailed mechanistic studies in the heterologous bacterial setup, allowing for scoring differential RNA editing activities of many target and protein variants in reasonable time. Here, we report (i) on the effects of numerous individual and combined PPR56 protein and target modifications, (ii) on the spectrum of off-target C-to-U editing in the bacterial background transcriptome for PPR56 and two variants engineered for target re-direction and (iii) on combinations of targets in tandem or separately at the 5'- and 3'-ends of large mRNAs. The latter experimentation finds enhancement of RNA editing at weak targets in many cases, including cox3eU290SF as a new candidate mitogenome target. We conclude that C-to-U RNA editing can be much enhanced by transcript features also outside the region ultimately targeted by PPRs of a plant editing factor, possibly facilitated by its enrichment or scanning along transcripts.

Interactive projection information generation and feedback for reading behaviour

Interactive reading is a new reading behavior in the age of intelligence. The dual-track development of new media and interactive technology has led to changes in both reading behavior and reading experience. This paper has discussed the information generation and information feedback of interactive reading, and analysed the important applications of projection technology, gesture recognition and QR code technology in interactive reading behavior from various projection interaction technologies. Through the development process of the interactive projection of the atlas series named “City Flowers of Beijing”, we aim to demonstrate the design of interactive and sustainable reading forms, deliver the multi-dimensional, multi-level and diversified reading information, create a reading experience that is both physiologically and psychologically satisfactory to the audience in the age of intelligence, and provide a new direction for the development of human-computer reading behavior in the new media age.

Towards Diagnostic Aided Systems in Coronary Artery Disease Detection: A Comprehensive Multiview Survey of the State of the Art

Introduction. Coronary artery disease (CAD) is one of the main causes of death all over the world. One way to reduce the mortality rate from CAD is to predict its risk and take effective interventions. The use of machine learning- (ML-) based methods is an effective method for predicting CAD-induced death, which is why many studies in this field have been conducted in recent years. Thus, this study aimed to review published studies on artificial intelligence classification algorithms in CAD detection and diagnosis. Methods. This study systematically reviewed the most cutting-edge techniques for analyzing clinical and paraclinical data to quickly diagnose CAD. We searched PubMed, Scopus, and Web of Science databases using a combination of related keywords. A data extraction form was used to collect data after selecting the articles based on inclusion and exclusion criteria. The content analysis method was used to analyze the data, and based on the study’s objectives, the results are presented in tables and figures. Results. Our search in three prevalent databases resulted in 15689 studies, of which 54 were included to be reviewed for data analysis. Most studies used laboratory and demographic data classification and have shown desirable results. In general, three ML methods (traditional ML, DL/NN, and ensemble) were used. Among the algorithms used, random forest (RF), linear regression (LR), neural networks (NNs), support vector machine (SVM), and K-nearest networks (KNNs) have the most applications in the field of code recognition. Conclusion. The findings of this study show that these models based on different ML methods were successful despite the lack of a benchmark for comparing and analyzing ML features, methods, and algorithms in CAD diagnosis. Many of these models performed better in their analyses of CAD features as a result of a closer look. In the near future, clinical specialists can use ML-based models as a powerful tool for diagnosing CAD more quickly and precisely by looking at its design’s technical facets. Among its incredible outcomes are decreased diagnostic errors, diagnostic time, and needless invasive diagnostic tests, all of which typically result in decreases in diagnostic expenses for healthcare systems.

Machine Vision-based Integrated Inspection System for Beverage Bottles with Multiple Defects

Currently, in the beverage filling production line, the corresponding detection device is usually installed after each production process to detect whether the beverage is defective. This method of installing testing devices in different working positions can classify and eliminate defects of defective products, but it needs to manufacture multiple sets of different types of defective testing machines, which has problems such as high resource consumption and waste of installation space. In order to solve these problems, an expandable multi-functional integrated inspection system has been designed in this paper, which can synchronize defects detection in different processes. The system includes a multitasking detection network based on multi-class decoders. By jointly training the detection network with the collected data set, the results of beverage bottle label segmentation, region of interest (ROI) detection, and code recognition can be obtained simultaneously, thus the system can effectively realize the synchronous detection of different defects such as label and spray code missing, inversion and so on. The system also provides an expandable multitasking interface, which can be extended according to the other actual requirements of the production line. Experimental results show that the system achieves 96% detection accuracy, 18000 bottles/h detection speed and good robustness.

Blind Recognition of Reed-Solomon Codes Based on Galois Field Fourier Transform and Reliability Verification

The Galois field Fourier transform (GFFT) method has been widely applied in the blind recognition of Reed-Solomon (RS) codes in recent years. However, as the code length increases, the computational complexity increases rapidly. Also, blind recognition in a poor channel environment remains challenging. To solve the above challenges, a blind recognition algorithm is proposed based on GFFT and reliability verification (RV). Low-complexity GFFT (LC-GFFT) is proposed to calculate the shortened spectrums, reducing the computational complexity and improving the correct codeword utilization rate (CUR). Next, the criteria for parameter estimation (PE) are given. The RV module is designed to verify the correctness of the estimated parameters. The simulations demonstrate that our algorithm can recognize RS(15, 11, 2, 19) and RS(31, 15, 8, 37) with over 95% correct rate when SNR is above 0.8dB and 2.5dB, achieving at least 7dB of signal-to-noise (SNR) defined gain over the existing algorithms. Moreover, the recognition time of our method is reduced by 74.8% compared to that of the GFFT-based method for RS(31, 15, 8, 37) when <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> = 1 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> .

Recognition of Convolutional Codes

Recognition of Convolutional Codes

On the minimal elements of the genetic code and their semiotic functions (degeneracy, complementarity, wobbling)

We address semiotic features of genetic coding, primarily the mechanisms for distinguishing between triplets. It implies that the minimal elements that allow codon recognition and amino acid coding should be identified. Half a century ago, linguist Roman Jakobson and microbiologist François Jacob revealed functional similarities between nucleotides in the genetic code and phonemes in natural language. Developing this analogy, we introduce the concept of a semiotic nucleotide. Unlike “material” nucleotides, its characteristics are limited to the function of differentiation within the processing of genetic information.We demonstrate that, similarly to phonemes, nucleotides are also non-elementary entities and can be represented as a set of two differential features: a) the number of rings and b) the number of hydrogen bonds. This makes it possible to convert semiotic nucleotides into double-byte units of digital information. Proceeding from this assumption, we suggest a new vision of such phenomena as the heterogeneity of the genetic code in terms of coding types, to reveal the code-distinguishing potential of positions within triplets, and represent wobbling as a specific reading regime. All these phenomena relate to the peculiarity of the triplet's third position, where complete or partial neutralization of distinguishing features is possible.

Flexible and low-voltage ITO synaptic transistors for biotic tactile sensing

Artificial intelligence technology has fueled the requirement for flexible hardware. Although flexible electronic devices have become promising candidates in recent years, they inevitably face problems like performance degradation caused by deformation. In this paper, we report a stable performance and flexible indium tin oxide synaptic transistor with an ultralow back-sweep subthreshold swing of 28.52 mV/dec, which is ascribed to the positive charge trapping/de-trapping effect introduced by a naturally oxidized Al2O3 layer. Even after bending 1 × 103 times, the flexible artificial synapse shows stable electrical performance without evident attenuation. Furthermore, the synaptic transistor exhibits good compatibility with an external thin-film pressure sensor, and their combination empowers the device to realize tactile sensing, which can achieve the function of Braille code recognition. Evidently, the reported flexible synaptic transistor demonstrates its potential for artificial perception processing.

Can the machine smell? The problem of the universal olfactory dictionary

In technology research, much has been achieved in the field of visual and auditory analysis as compared to the perception of smells. This article discusses the latest advances in the field of electronic (E-nose) used to recognize the olfactory code. All studies can be conditionally divided according to the goals into: 1. Research aimed at the practical application of electronic systems in areas such as food industry, medicine, the environment, etc. 2. Research aimed at cognitive processes and psycho-emotional reactions of a person during olfactory experience, as well as the ability of a machine to predetermine human reactions to various smells. In this case, an important role is played by language, which acts as a representation of olfactory sensations. The methods of conceptual and semantic analysis are becoming more and more in demand in studies devoted to machine recognition of the olfactory code.

Design and Implementation for BIC Code Recognition System of Containers using OCR and CRAFT in Smart Logistics

The BIC (Bureau International des Containers et du Transport Intermodal) Code is the identification code for ocean shipping containers and is crucial for logistics, transportation, and security. Accurate recognition of container BIC Code is essential for efficient import and export processes, authorities' ability to intercept illegal goods and safe transportation. Nevertheless, the current practice of employees recognizing and manually entering container BIC codes is inefficient and prone to error. Although automated recognition efforts have been made, challenges remain due to the aging of containers, manufacturing differences between companies, and the mixing of letters and numbers in the 11-digit combination. In this paper, we propose the design and implementation of a BIC Code recognition system using an open source-based OCR engine, deep learning object detection algorithm, and text detector model. In the logistics industry, various attempts are being made to seamlessly link the data required at each stage of transportation between these systems. If we can secure the stability and consistency of BIC Code recognition that can be used in the field through our research, it will contribute to overcoming the instability caused by false positives.

Distinct localization of chiral proofreaders resolves organellar translation conflict in plants

Plants have two endosymbiotic organelles originated from two bacterial ancestors. The transition from an independent bacterium to a successful organelle would have required extensive rewiring of biochemical networks for its integration with archaeal host. Here, using Arabidopsis as a model system, we show that plant D-aminoacyl-tRNA deacylase 1 (DTD1), of bacterial origin, is detrimental to organellar protein synthesis owing to its changed tRNA recognition code. Plants survive this conflict by spatially restricting the conflicted DTD1 to the cytosol. In addition, plants have targeted archaeal DTD2 to both the organelles as it is compatible with their translation machinery due to its strict D-chiral specificity and lack of tRNA determinants. Intriguingly, plants have confined bacterial-derived DTD1 to work in archaeal-derived cytosolic compartment whereas archaeal DTD2 is targeted to bacterial-derived organelles. Overall, the study provides a remarkable example of the criticality of optimization of biochemical networks for survival and evolution of plant mitochondria and chloroplast.

Neuronal contact predicts connectivity in the C. elegans brain

Axons must project to particular brain regions, contact adjacent neurons, and choose appropriate synaptic targets to form a nervous system. Multiple mechanisms have been proposed to explain synaptic partnership choice. In a "lock-and-key" mechanism, first proposed by Sperry's chemoaffinity model,1 a neuron selectively chooses a synaptic partner among several different, adjacent target cells, based on a specific molecular recognition code.2 Alternatively, Peters' rule posits that neurons indiscriminately form connections with other neuron types in their proximity; hence, neighborhood choice, determined by initial neuronal process outgrowth and position, is the main predictor of connectivity.3,4 However, whether Peters' rule plays an important role in synaptic wiring remains unresolved.5 To assess the nanoscale relationship between neuronal adjacency and connectivity, we evaluate the expansive set of C.elegans connectomes. We findthat synaptic specificity can be accurately modeled as a process mediated by a neurite adjacency threshold and brain strata, offering strong support for Peters' rule as an organizational principle of C.elegans brain wiring.