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Mechanical engineering drawings (MEDs) accompany a product lifecycle from conceptional design to final production. The digitisation of MEDs has become increasingly important due to demands for data authenticity, intellectual property protection, efficient data storage and communication, and compliance with data integrity and security regulations. Unlike CAD-based engineering design software, legacy MEDs are often manually drawn or contain manually labeled specifications on blueprints. A notable gap exists in the automated process pipeline of modern Computer-Aided Tolerance (CAT) software, particularly in integrating Geometrical Tolerance Specification Callouts (GTSC) on MEDs. This study proposes an integrated model based on digital image processing and deep learning, which combines character (symbol, text and number) localization, segmentation, and recognition to intelligently identify and read GTSCs on MEDs. The focus of this work is on image filtering, GTSC block localization and tilt correction, multiple lines and character segmentation, and semantic recognition. Experiment results demonstrate that this innovative technique effectively automates the labor-intensive process of reading and registering GTSC with a precision performance that meets industry benchmarks. [ABSTRACT FROM AUTHOR]

Generation of hidden attractor with complicated phase portrait in chaotic system with no equilibrium has presented a new research focus in the past decade. However, the existing approaches usually follow the rule that you reap what you sow, i.e., taking an no-equilibrium chaotic system as the seed. In this paper, a novel approach to the generation of no-equilibrium multi-fold hidden attractors is presented. The offset boosting and the operation of complex number are applied to several cases, including the seed chaotic systems with no equilibrium, with single unstable node, or with single stable node-focus. In specific, the offset boosting shifts the single non-zero equilibrium to the origin, then the operation of complex number rules out the equilibrium in the folded system and generates multi-fold trajectories. The highlight is that the proposed approach is not limited to the no-equilibrium seed system, but also applicable for different types of single-equilibrium seed systems, even the seed system with a single line of equilibrium. The detailed numerical simulations and experiments confirmed the feasibility of the proposed approach. Finally, an image encryption algorithm and its practical microcontroller-based implementation are presented to support the potential application. • Systematic approach for folding trajectory of chaotic system. • New multi-fold chaotic hidden attractors with no equilibrium. • New chaos-based encryption algorithm for image security. • Dynamic memory scheme for microcontroller implementation. [ABSTRACT FROM AUTHOR]

The need for efficient and reliable image processing techniques has never been greater in medical imaging. The proposed work analyzes and designs parallel VLSI (Very Large-Scale Integration) architectures that meet the stringent demands of high-throughput medical image processing. The remainder of the paper is organized as follows, beginning with a discussion of the computational challenges that arise organically in medical imaging and why it is crucial to surmount these obstacles. The section on the proposed work describes the architecture, parallelization strategies, and processing pipeline, providing context for the hardware and Software underlying the system. The evaluation results below demonstrate significant enhancements in processing speed and image quality. Due to their real-time capabilities and high efficiency, parallel VLSI architectures have the potential to revolutionize medical image processing, resulting in more accurate diagnoses and more effective treatments. Research into optimizing these structures and discovering new applications is anticipated to result in further advances in medical imaging. [ABSTRACT FROM AUTHOR]

The present study investigated experimentally the dynamic interactions between surface waves and submerged vertical tensioned barriers with full and partial penetrations. The range of tension for the barrier was set within the flexible membrane regime in the experiments which measurements have not been reported in the literature so far. In addition, an extensive Internet of Things (IoT) system with five GoPro cameras was developed for the measurements to quantify both the surface wave transformation as well as dynamic response of the barrier. The cameras were synchronized through the IoT system to cover the entire wave flume, and the recorded videos were converted to spatial and temporal data using image processing techniques. The experimental results were found to agree with the analytical predictions based on the linear wave theory reasonably well. In particular, the measured reduction in the tensioning effect on the wave transmission and reflection with decreased barrier length was in close argument with the predictions. Similar good agreement was also observed for the dynamic response of the tensioned barrier during the wave interaction. However, additional energy loss was noted in the experiments possibly due to energy dissipation at the boundary ends of the experimental barrier and wave-induced flow separation with partial penetration which are not considered in the analytical analysis. [ABSTRACT FROM AUTHOR]

This paper proposes a Generalized Multilevel B-spline Approximation (GMBA) method, which addresses scattered data interpolation problems in image processing. Mathematically, the GMBA provides a better solution for the B-spline control lattice by superimposing identical level B-splines compared with traditional Multilevel B-spline Approximation (MBA). Specifically, the GMBA allows the spacing of next control lattice to be subdivided arbitrarily or remained unchanged, which is determined by a predefined spacing set or the current error level. These improvements bring higher approximation accuracy and more flexibility for algorithm design to avoid over-fitting. In this paper, basic GMBA algorithm and its refined algorithm are compiled for image processing. Finally, six relevant cases are involved to test the GMBA, including surface approximation, image enlargement, image completion, and Salt-and-Pepper (SAP) noise removal. The experimental results show that the GMBA has better performance than the MBA in surface approximation and image processing, performs comparatively fast with the best performance on more than half of the standard test images compared with traditional algorithms, and has partially better performance even than deep learning algorithms. The GMBA can effectively recover meaningful details in images contaminated with even extremely high SAP noise level (up to 99%). • We give a generalized multilevel B-spline approximation (GMBA) method. • GMBA allows the control spacing to be subdivided arbitrarily or remained unchanged. • GMBA offers higher approximation accuracy and greater flexibility than traditional MBA. • GMBA provides superior performance and less run time than many of the state-of-the-art methods for SAP noise removal. • GMBA can recovery meaningful detail at noise levels as high as 99% for SAP noise removal. [ABSTRACT FROM AUTHOR]

Fireworks play a vital role in festive celebrations and entertainment. These fireworks industries are administered by the Petroleum and Explosives Safety Organisation (PESO), Nagpur, Government of India. Even though PESO prescribed the standard composition for various fireworks products, concerns persist regarding the potential deviation from these standards, particularly in the pursuit of increased noise levels to attract consumers. This change in the burning and reactive properties of chemicals could potentially lead to accidents and environmental pollution. The current work investigated the combustion characteristics of two unknown market flash powder samples and a PESO standard sample. The samples were openly ignited in a controlled atmosphere, and video recordings of the resulting chemical flames were analyzed to determine fire area, flame intensity, and motion magnitude. The residues were also collected for EDAX and SEM analysis. Fire pixels from video frames are segmented to create a comprehensive database detailing flame area and motion magnitude under different circumstances. From the flame area profile, motion magnitude and SEM/EDAX analysis, it is found that PESO standard sample combustion is better than the unknown flash powder combustion. • Burning flame of fireworks chemicals captured by CCD. • Flame area and volume had been found by Digital Image Processing. • Residue analysis was carried out. • Through flame analysis and residue analysis, fuel properties had been explored. [ABSTRACT FROM AUTHOR]

In this paper, image processing techniques are employed to investigate the growth and detachment characteristics of initial bubbles in vertical glass tubes. The study explores the effects of different continuous phases (water and Therminol®66), nozzle diameters, air flow rates, and continuous phase liquid levels on the detachment diameter and shape of bubbles. Furthermore, based on the detachment characteristics obtained under various experimental conditions, a novel predictive model for bubble detachment diameter is established in both water and Therminol®66. The results show that the bubble detachment diameter in the continuous phase therminol® 66 is larger than the bubble detachment diameter in water, and that the shape of the bubbles in therminol® 66 is close to spherical, with an ellipsoidal shape in water. With the increase in nozzle diameter and airflow, the bubble detachment diameter increases, and the shape is stretched in the vertical direction. With the increase in the continuous phase liquid level, the bubble detachment diameter decreases, and the shape change is not significant. In addition, the accuracy of the bubble detachment diameter prediction model was validated through experimental data. Compared with the experimental values, the average absolute errors were 5.04% and 5.11% in water and Therminol®66, respectively. This study aims to investigate the characteristics of initial bubble growth and detachment, providing valuable references for nozzle design. [Display omitted] • The growth characteristics of initial bubbles under different working fluids are studied • The shape and diameter of the bubble detachment process are studied under different operating conditions. • A new bubble detachment diameter prediction model is established [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) allows to produce parts layer by layer from an STL file. It is then possible through this technology to obtain customized geometries and complex shapes at a lower cost. However, these shapes pose problems of defect control, microstructure, residual stresses, and deformations in parts. This study aims to develop an efficient method allowing defect detection while printing pieces using Fused Deposition Modelling (FDM). The monitoring system contains a camera acquisition system for automatic image capture of filament layers deposited on the print bed. Various monitoring techniques have been simulated to achieve an optimal defect correction solution. Material excess and deficiency are detectable in the layer of actual printed parts. Defects are controlled and compared to original part obtained from Computer Aided Design (CAD). An app-designer application was created in this regard. It displays the image reference generated from the G-code, the layer image captured by the camera, and returns the error percentage in the printed layers. The developed method of surface calculation has shown its efficiency in detecting the lack and excess of material, which has an accuracy of 1.07%. This method allows users to stop and monitor printing to save cost, material, and time. [ABSTRACT FROM AUTHOR]

• Understanding plastic litter in cities is crucial to preventing marine pollution. • Citizen science is suitable for long-term, extensive data collection. • Despite human error, ∼30 % of data could be used to visualize and quantify litter. • Litter in cities was studied using Pirika and deep learning-based image processing. • Trained models exhibited high precision and recall in classifying litter types. Cities are a major source of litter pollution. Determination of the abundance and composition of plastic litter in cities is imperative for effective pollution management, environmental protection, and sustainable urban development. Therefore, here, a multidisciplinary approach to quantify and classify the abundance of litter in urban environments is proposed. In the present study, litter data collection was integrated via the Pirika smartphone application and conducted image analysis based on deep learning. Pirika was launched in May 2018 and, to date, has collected approximately one million images. Visual classification revealed that the most common types of litter were cans, plastic bags, plastic bottles, cigarette butts, cigarette boxes, and sanitary masks, in that order. The top six categories accounted for approximately 80 % of the total, whereas the top three categories accounted for more than 60 % of the total imaged litter. A deep-learning image processing algorithm was developed to automatically identify the top six litter categories. Both precision and recall derived from the model were higher than 75 %, enabling proper litter categorization. The quantity of litter derived from automated image processing was also plotted on a map using location data acquired concurrently with the images by the smartphone application. Conclusively, this study demonstrates that citizen science supported by smartphone applications and deep learning-based image processing can enable the visualization, quantification, and characterization of street litter in cities. [ABSTRACT FROM AUTHOR]