Automated Acquisition System Research Articles

Automatic System for Acquisition and Analysis of Microscopic Digital Images Containing Activated Sludge

Automatic System for Acquisition and Analysis of Microscopic Digital Images Containing Activated Sludge

Machine vision system for automatic defect detection of ultrasound probes

Industry 4.0 conceptualizes the automation of processes through the introduction of technologies such as artificial intelligence and advanced robotics, resulting in a significant production improvement. Detecting defects in the production process, predicting mechanical malfunctions in the assembly line, and identifying defects of the final product are just a few examples of applications of these technologies. In this context, this work focuses on the detection of ultrasound probes’ surface defects, with a focus on Esaote S.p.A.’s production line probes. To date, this control is performed manually and therefore biased by many factors such as surface morphology, color, size of the defect, and by lighting conditions (which can cause reflections preventing detection). To overcome these shortfalls, this work proposes a fully automatic machine vision system for surface acquisition of ultrasound probes coupled with an automated defect detection system that leverage artificial intelligence. The paper addresses two crucial steps: (i) the development of the acquisition system (i.e., selection of the acquisition device, analysis of the illumination system, and design of the camera handling system); (ii) the analysis of neural network models for defect detection and classification by comparing three possible solutions (i.e., MMSD-Net, ResNet, EfficientNet). The results suggest that the developed system has the potential to be used as a defect detection tool in the production line (full image acquisition cycle takes ~ 200 s), with the best detection accuracy obtained with the EfficientNet model being 98.63% and a classification accuracy of 81.90%.

Nutrient Stress Symptom Detection in Cucumber Seedlings Using Segmented Regression and a Mask Region-Based Convolutional Neural Network Model

The health monitoring of vegetable and fruit plants, especially during the critical seedling growth stage, is essential to protect them from various environmental stresses and prevent yield loss. Different environmental stresses may cause similar symptoms, making visual inspection alone unreliable and potentially leading to an incorrect diagnosis and delayed corrective actions. This study aimed to address these challenges by proposing a segmented regression model and a Mask R-CNN model for detecting the initiation time and symptoms of nutrient stress in cucumber seedlings within a controlled environment. Nutrient stress was induced by applying two different treatments: an indicative nutrient deficiency with an electrical conductivity (EC) of 0 dSm−1, and excess nutrients with a high-concentration nutrient solution and an EC of 6 dSm−1. Images of the seedlings were collected using an automatic image acquisition system two weeks after germination. The early initiation of nutrient stress was detected using a segmented regression analysis, which analyzed morphological and textural features extracted from the images. For the Mask R-CNN model, 800 seedling images were annotated based on the segmented regression analysis results. Nutrient-stressed seedlings were identified from the initiation day to 4.2 days after treatment application. The Mask R-CNN model, implemented using ResNet-101 for feature extraction, leveraged transfer learning to train the network with a smaller dataset, thereby reducing the processing time. This study identifies the top projected canopy area (TPCA), energy, entropy, and homogeneity as prospective indicators of nutritional deficits in cucumber seedlings. The results from the Mask R-CNN model are promising, with the best-fit image achieving an F1 score of 93.4%, a precision of 93%, and a recall of 94%. These findings demonstrate the effectiveness of the integrated statistical and machine learning (ML) methods for the early and accurate diagnosis of nutrient stress. The use of segmented regression for initial detection, followed by the Mask R-CNN for precise identification, emphasizes the potential of this approach to enhance agricultural practices. By facilitating the early detection and accurate diagnosis of nutrient stress, this approach allows for quicker and more precise treatments, which improve crop health and productivity. Future research could expand this methodology to other crop types and field conditions to enhance image processing techniques, and researchers may also integrate real-time monitoring systems.

Embedded Wireless Sensor for In Situ Concrete Internal Relative Humidity Monitoring.

The moisture content within the concrete pore network significantly influences the mechanical, thermal, and durability characteristics of concrete structures. This paper introduces a novel fully embedded wireless temperature and relative humidity sensor connected to an automatic acquisition system designed for continuous concrete monitoring. Relative humidity measurements from this new sensor are compared with those obtained by a commercial system based on the borehole method at different depths (2.5 and 4.0 cm) and exposure conditions (oven drying and humid chamber). The results allow for proving that both systems provide consistent internal relative humidity measurements aligned with the exposure conditions and highlight the capability of fully embedded wireless sensors as a practical and reliable alternative to the conventional borehole method. Additionally, the continuous monitoring of the wireless cast-in sensor exhibits reliability during unintended temperature fluctuations, emphasizing the effectiveness of permanently installed sensors in promptly detecting unintended curing variations in real time. The continuous real-time information provided combined with the practicality of these sensors might assist construction managers to improve the quality control of the concrete curing process and shrinkage behavior, and ensure the integrity of concrete surface finishing.

Automatic Acquisition System for Mine Pressure Monitoring in Coal Mine Working-Face Footage

The existing mine pressure monitoring system has realized the online continuous monitoring of the working-face stent resistance, roadway roof offcuts, and anchor rod/rope working resistance. However, the mine pressure monitoring information of the working face currently includes only the stent resistance and the monitoring time, and there is no information on the working-face advance. The mine pressure data cannot be precisely analyzed due to a lack of measurement point locations. Mine pressure data analysis combined with the working-face feed information is the basis for safe and efficient mining and for improving the intelligence level of the comprehensive mining face. According to the special electromagnetic environment of the underground, this system adopts UWB (ultra-wide-band) technology and the SDS-TWR (symmetric double-sided two-way ranging) ranging method, with the UWB positioning base station as the core and installs positioning tags at the end supports of the working face to collect information. The data are uploaded to the host computer via Ethernet for coordinate solving, automatically collecting the working-face footage data and providing positional information for mine pressure monitoring. The application results show that the system operates normally and can collect real-time information of working-face footage and monitor mine pressure data, and meet the requirements of coal mine positioning accuracy, positioning error is less than 30 cm, the application effect is good.

An Automatic Radial Pulse Signal Acquisition System Based on Visual and Tactile Pulse-Finding Algorithm

An Automatic Radial Pulse Signal Acquisition System Based on Visual and Tactile Pulse-Finding Algorithm

Rapid and Automatic Image Acquisition System for Structural Surface Defects of High-Speed Rail Tunnels

Continuous high-speed (>80 km/h) tunnel-image detection introduces new challenges to defect image acquisition, including machine recognition of entry and exit from a tunnel, storage of massive image data streams, image distortion due to surface photography, and encoder errors. In this study, an automatic image acquisition system is designed, and several critical technologies are proposed for high-speed rail tunnels. The system realizes quick tunnel identification and automatic camera control and uses the proposed line-scan software-matching method to obtain accurate images. Before storage, the real-time modification of the distorted images is implemented using an error-correction algorithm. An alternative mapping storage algorithm is proposed to improve the efficiency and stability of long-term storage. The test results show that the proposed method effectively reduces photographic errors. The lateral pixel-error rate of the corrected image is 1.60%, which is 10 times lower than that of the pre-correction, and the error rate of the longitudinal image is controlled within 10% when the system is moving at a variable speed and within 1% when it is moving at a constant speed. Furthermore, experiments have proven that the alternate mapping storage algorithm improves the storage efficiency of RAID by 50% and ensures data integrity; storage is accomplished by 8 HDDs at a camera throughput of 1.52 GB/s. This study will contribute to improvements in the speed and accuracy of tunnel defect-image detection.

Anti-interference damage localization in composite overwrapped pressure vessels using machine learning and ultrasonic guided waves

This paper describes a structural health monitoring (SHM) technique for composite overwrapped pressure vessels (COPVs), which utilizes machine learning (ML) algorithms to achieve anti-interference damage localization based on ultrasonic guided waves. Firstly, this study developed a multi-channel guided wave signal automatic acquisition system and established a comprehensive dataset comprising multi-region damage/health signals from composite pressure vessels. Subsequently, based on the interaction between guided wave features and damage, a feature extraction-ML based anti-interference damage localization scheme was proposed. This scheme achieved robust damage localization through steps such as data cleaning, feature extraction, feature selection, and ML-based damage localization. Furthermore, this study introduced the guided wave-temporal convolutional network (GW-TCN) model based on deep learning method. The GW-TCN model integrates the temporal correlation and the multi-channel physical properties of guided waves, while leveraging the advantages of deep learning's adaptive feature extraction. It effectively mitigates the vibration and sensor failure interferences that mimic real operational conditions, enabling precise damage localization. Finally, through validation on multiple data samples, it was demonstrated that the model exhibits good generalization ability. The research findings will contribute to the advancement of composite pressure vessel SHM technology, paving the way for improved safety and reliability in industrial applications.

Efficient unsupervised learning of biological images with compressed deep features

Machine learning has significantly impacted the analysis of biological images and is now an important part of many biological data analysis pipelines. A variety of biological and biomedical domain-related tasks is gaining benefit from image analysis and pattern recognition tools developed currently. Applications include diagnostic histopathology, environmental monitoring, synthetic biology, genomics, and proteomics. Particularly in the last decade, several deep learning and advanced computer vision methods such as convolutional neural networks (CNNs), typically trained in a supervised fashion, have started to be largely employed in biological image classification. Moreover, the advancement of automatic acquisition systems has been generating a massive amount of biological data, which requires to be analyzed by domain experts. However, the cost of manual annotation of such data has become a bottleneck, impairing the application of supervised machine learning algorithms. Biological images generally have an intrinsic high variability, whose identity is sometimes hard to assign and strongly dependent on the annotator’s expertise. In this context, a limited number of annotation-free (i.e., unsupervised) learning solutions have been proposed, typically based on hand-crafted features, specifically tailored for a certain biological domain. Nonetheless, a successful unsupervised learning approach must be accurate, and sufficiently robust to deal with different biological domains. This paper aims at providing a viable solution to these issues, proposing an unsupervised learning algorithm based on compressed deep features for image classification. We exploit features extracted from ImageNet pre-trained transformers and CNNs, further compressed with a customized β-Variational AutoEncoder (β-VAE), that we call reconstruction VAE (R-VAE). We test our algorithm on biological images coming from diverse domains characterized by high variability in shape and texture information and acquired with widely differing imaging platforms. Considered image datasets range from multi-cellular organisms (plankton, coral) to sub-cellular organelles (budding yeast vacuoles, human cells’ nuclei, etc.). Our results show that the compressed deep features extracted from different pre-trained vision models establish new unsupervised learning state-of-the-art performances for the investigated datasets.

In-domain versus out-of-domain transfer learning in plankton image classification

Plankton microorganisms play a huge role in the aquatic food web. Recently, it has been proposed to use plankton as a biosensor, since they can react to even minimal perturbations of the aquatic environment with specific physiological changes, which may lead to alterations in morphology and behavior. Nowadays, the development of high-resolution in-situ automatic acquisition systems allows the research community to obtain a large amount of plankton image data. Fundamental examples are the ZooScan and Woods Hole Oceanographic Institution (WHOI) datasets, comprising up to millions of plankton images. However, obtaining unbiased annotations is expensive both in terms of time and resources, and in-situ acquired datasets generally suffer from severe imbalance, with only a few images available for several species. Transfer learning is a popular solution to these challenges, with ImageNet1K being the most-used source dataset for pre-training. On the other hand, datasets like the ZooScan and the WHOI may represent a valuable opportunity to compare out-of-domain and large-scale plankton in-domain source datasets, in terms of performance for the task at hand.In this paper, we design three transfer learning pipelines for plankton image classification, with the aim of comparing in-domain and out-of-domain transfer learning on three popular benchmark plankton datasets. The general framework consists in fine-tuning a pre-trained model on a plankton target dataset. In the first pipeline, the model is pre-trained from scratch on a large-scale plankton dataset, in the second, it is pre-trained on large-scale natural image datasets (ImageNet1K or ImageNet22K), while in the third, a two-stage fine-tuning is implemented (ImageNet rightarrow large-scale plankton dataset rightarrow target plankton dataset). Our results show that an out-of-domain ImageNet22K pre-training outperforms the plankton in-domain ones, with an average boost in test accuracy of around 6%. In the next part of this work, we adopt three ImageNet22k pre-trained Vision Transformers and one ConvNeXt, obtaining results on par (or slightly superior) with the state-of-the-art, corresponding to the usage of CNN models ensembles, with a single model. Finally, we design and test an ensemble of our Vision Transformers and the ConvNeXt, outperforming the state-of-the-art existing works on plankton image classification on the three target datasets. To support scientific community contribution and further research, our implemented code is open-source and available at https://github.com/Malga-Vision/plankton_transfer.

Real-time perception of rock–machine interaction information in TBM tunnelling using muck image analysis

Tunnel boring machine (TBM) is frequently used in tunnel construction due to its advantages in safety, high efficiency, environmental friendliness. However, the tunnelling efficiency of a TBM heavily depends on the rock–machine interaction conditions. To realize the real-time perception of rock–machine interaction information during the TBM tunnelling, an automatic acquisition and analysis system for rock muck is developed. Firstly, a framework for using the system to perceive the rock mass and TBM conditions was constructed. Then the method to quantitatively evaluate the geometric properties of rock muck is elaborated. Finally, the system was applied to a TBM-excavated tunnel to verify the functions of rock muck in the real-time perception of rock–machine interaction information. The results show that the geometric properties of rock muck can indicate both the rock mass conditions and the TBM performance. In rock mass condition perception: Rock blocks trend to be large in size and elongated in shape as the uniaxial compressive strength (UCS) of the surrounding rock decrease. The blocks produced from a relatively intact rock mass trend to be small in size and round in shape. Larger or smaller blocks with flat planes are frequently produced when TBM drives in the jointed rock masses. In TBM performance perception: The TBM utilization can be estimated through the ratio of the number of images with muck to the total number of captured images. A high value of the field penetration index (FPI) corresponds to blocks with large sizes and elongated shapes (or small sizes and round shapes). Elongated blocks are produced when TBM drives at a low FPI. These results are significant for the safe and efficient TBM tunnelling.

An Investigation on Conductive Intracardiac Communication Dynamic Channel Gain During the Cardiac Cycle for Leadless Pacemakers

In galvanic coupling conductive intracardiac communication(GCCIC) of the leadless pacemakers, the electrical signal transmitted directly through the myocardium and blood is inevitably affected by the cardiac cycle. Established studies focused more on the effect of the myocardium. However, our preliminary in-vitro experiments suggested that blood volume variations also significantly impacted signal transmission. In this article, we analyzed the blood volume variations during the cardiac cycle and designed an in-vitro experimental platform containing a simulated heart beating system and an automatic channel characteristic acquisition system, which controlled two peristaltic pumps to realize the periodic blood volume variations and the continuous acquisition of channel gain. Through the in-vitro porcine heart experiment, the effect of frequency and blood volume variations during the cardiac cycle on two channel gains was analyzed. Considering the impact of high-frequency signal leakage, the channel gain variations of the low frequency are the main concern. The results showed that the channel gain was positively correlated with frequency; it changed periodically with blood volume variations in the cardiac cycle, and the trends were different due to the different signal paths of the two channels. For the Right Ventricle-Right Atrium channel, the gain varied from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-67$</tex-math></inline-formula> dB to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-53$</tex-math></inline-formula> dB and is inversely correlated with blood volume. The gain fluctuation range was smaller for the Right Ventricle-Left Ventricle channel, about 2 dB. This study shows that the gain of intracardiac communication channels, especially the RV-RA channel, is influenced by blood volume variations during the cardiac cycle.

Application of Internet of Things to Agriculture-The LQ-FieldPheno Platform: A High-Throughput Platform for Obtaining Crop Phenotypes in Field.

The lack of efficient crop phenotypic measurement methods has become a bottleneck in the field of breeding and precision cultivation. However, high-throughput and accurate phenotypic measurement could accelerate the breeding and improve the existing cultivation management technology. In view of this, this paper introduces a high-throughput crop phenotype measurement platform named the LQ-FieldPheno, which was developed by China National Agricultural Information Engineering Technology Research Centre. The proposed platform represents a mobile phenotypic high-throughput automatic acquisition system based on a field track platform, which introduces the Internet of Things (IoT) into agricultural breeding. The proposed platform uses the crop phenotype multisensor central imaging unit as a core and integrates different types of equipment, including an automatic control system, upward field track, intelligent navigation vehicle, and environmental sensors. Furthermore, it combines an RGB camera, a 6-band multispectral camera, a thermal infrared camera, a 3-dimensional laser radar, and a deep camera. Special software is developed to control motions and sensors and to design run lines. Using wireless sensor networks and mobile communication wireless networks of IoT, the proposed system can obtain phenotypic information about plants in their growth period with a high-throughput, automatic, and high time sequence. Moreover, the LQ-FieldPheno has the characteristics of multiple data acquisition, vital timeliness, remarkable expansibility, high-cost performance, and flexible customization. The LQ-FieldPheno has been operated in the 2020 maize growing season, and the collected point cloud data are used to estimate the maize plant height. Compared with the traditional crop phenotypic measurement technology, the LQ-FieldPheno has the advantage of continuously and synchronously obtaining multisource phenotypic data at different growth stages and extracting different plant parameters. The proposed platform could contribute to the research of crop phenotype, remote sensing, agronomy, and related disciplines.

Chemical selection for the calibration of general-purpose electronic noses based on Silhouette coefficients

Sensor drift is often application-dependent and results in a reduction in the overall long-term performance of electronic noses. Even with drift compensation it is remains challenging to transfer these models to other application scenarios. In order to remedy this deficiency, different/generic chemicals are needed to provide a general-purpose calibration approach that can be applied to a wide range of electronic noses. In this article, we investigated a method to identify these chemicals based on four criteria (universality, safety, sensibility, and differentiation). This concept was tested on an in-house electronic nose comprising 37 gas sensors and four environmental sensors combined with an automatic gas acquisition system. The 14 different volatile compounds were tested over four months. The silhouette coefficient was used to evaluate the extent of the sensor drift. Six different chemicals (acetone, alcohol, ethyl acetate, tetrahydrofuran, acetaldehyde, and n-hexane) were finally selected as the most appropriate to calibrate our electronic nose (E-nose). We believe our research may motivate the design of a reasonable chemical selection method for the calibration of general-purpose E-noses.

End-to-End Plankton Database Collection System

Monitoring living plankton status has greatly developed owing to machine vision technology. One of these developments is the automatic video acquisition system, which can capture high-resolution information and record considerable details of a scene. However, existing optical sensors cannot usually obtain a whole image, in which all details of plankton are fully clear, due to the influence of many factors, such as depth-of-field limitation of lenses, movement of plankton, and large difference in plankton scale. The captured video also needs to be filtered to eliminate the redundancy before using for the sparsity of the sample. This procedure is time-consuming and costly. Therefore, in this article, we develop an end-to-end plankton database collection system that can directly generate complete, clear plankton images from video. First, the regions of interest of the plankton are extracted. Then, the same plankton which appears in successive frames is identified to reconstruct the clearest morphological and structure features through their detailed information. Experimental results indicate that the proposed system can effectively compress the original data. The proposed fusion method also outperforms the state-of-the-art methods, especially for images with anisotropic blur. Furthermore, this system can monitor the abundance and distribution of marine plankton with the help of an embedded computing platform.

Construction of Automatic Acquisition System for Multichannel Electronic Communication Signals Based on Sorting Optimization Algorithm

In both the military and the civilian world, multichannel electronic communication signals are widely used. The goal of signal processing in contemporary communication systems is to capture the signal. The traditional multichannel electronic communication signal automatic capture system has a limited range of performance, which slows down the rate of capture. This study used the sorting algorithm to build an automatic acquisition system for multichannel electronic communication signals in order to address this issue. The system processes the communication signal using the quick sorting method in the sorting optimization algorithm, achieving the goal of more precisely obtaining the carrier frequency and pseudo-code phase, and resolving the issue of slow system acquisition caused by the lengthy pseudo-code period. According to the experimental findings, the improved system’s acquisition rate is 6.89% higher than the conventional system’s, ensuring efficiency and reducing processing time in real-world communication applications.

Feasibility of an automatic ultrasonographic image acquisition system associated with an artificial intelligence algorithm for real-time monitoring of cardiac motion during cardiac radio-ablation

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): This publication is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 954783. Background Stereotactic arrhythmia radio-ablation (STAR) is a promising strategy for the non-invasive treatment of ventricular arrhythmias (VA). The management of the cardio-respiratory motion of the target and the reduction of the uncertainties related to patients’ positioning are two of the main challenges that STAR has to overcome. A prototype of a system was developed that can automatically acquire and interpret echocardiographic images using an artificial intelligence (AI) algorithm to calculate cardiac displacement in real-time. Purpose To evaluate the feasibility of this automatic acquisition system in patients with a history of VA. Methods We conducted a single center, feasibility study enrolling consecutive patients with a history of VA. Echocardiographic images were automatically acquired from the parasternal and apical views with a dedicated probe. The system was designed to hold the probe fixed to the chest in the supine position during both free-breathing and short expiratory breath-hold sequences, to simulate STAR treatment. The primary endpoint was the percentage of patients reaching a score ≥2 in a multi-parametric assessment evaluating the quality of automatically acquired images in terms of: A - allowing a proper identification of the cardiac cycle phase using the AI algorithm, B - allowing a correct measurement of the heart displacement by the AI algorithm, C - ability to visually distinguish typical cardiac structures, and D - stability of the image throughout the respiration cycle. Moreover, we investigated the potential impact of clinical and demographic characteristics on achieving the primary outcome. Results From May to September 2021, we enrolled 24 patients (63±14 years, 21% females). All of them had a history of VA and 21 (88%) had an ICD. Eight patients (33%) had coronary artery disease, 12 (50%) had non-ischemic cardiomyopathy, and 3 had idiopathic VA. Parasternal as well as apical images were obtained from all patients except from one, in whom parasternal view could not be collected due to the patient’s inability to maintain the supine position. The primary outcome was achieved in 23 patients (96%) for the apical view, in 20 patients (87%) for the parasternal view, and in all patients in at least one of the two views. The images quality was maximal (i.e. score=4) in at least one of the two windows in 19 patients (79%). Atrial fibrillation arrhythmia was the only clinical characteristics associated with a poor score outcome in both imaging windows (apical p=0,022, parasternal p=0,014). Conclusion An automatic ultrasonographic image acquisition system associated with an AI algorithm is feasible for real-time monitoring of cardiac motion in patients with a history of VA. The possibility of real-time, non-invasive monitoring of cardiac position would lead to a significant improvement in the quality and safety of STAR treatment, particularly in case of treatment with heavy particles such as protons and carbon ions.

Overcoming the error of optical power measurement caused by the curvature radius.

In traditional focimeter measurements, the lens cannot completely coincide with the diaphragm owing to the change of radius, resulting in an increase in the power measurement error with an increase in the lens power. We proposed a method, using the SVM machine learning algorithm, to restore the measurement of the focimeter, using a lens power data set obtained from lens features, obtained through an automatic acquisition system. Total up to 83 groups of single focus lenses with refractive indices of 1.56 and 1.60, ranging from -10 m-1 to + 8 m-1 every 0.25 m-1 and -10.5 m-1 to -15 m-1 every 0.5 m-1, were used for lens image acquisition. The experimental results show that the ten-fold average F1 score of the classification under this method is 100%. The test lens power can be accurately identified and the measurement error can be overcome.

Feasibility of an Automatic Ultrasonographic Image Acquisition System Associated With an Artificial Intelligence Algorithm for Real-Time Monitoring of Cardiac Motion During Cardiac Radio-Ablation.

BackgroundThe management of the cardio-respiratory motion of the target and the reduction of the uncertainties related to patient's positioning are two of the main challenges that stereotactic arrhythmia radio-ablation (STAR) has to overcome. A prototype of a system was developed that can automatically acquire and interpret echocardiographic images using an artificial intelligence (AI) algorithm to calculate cardiac displacement in real-time.MethodsWe conducted a single center study enrolling consecutive patients with a history of ventricular arrhythmias (VA) in order to evaluate the feasibility of this automatic acquisition system. Echocardiographic images were automatically acquired from the parasternal and apical views with a dedicated probe. The system was designed to hold the probe fixed to the chest in the supine position during both free-breathing and short expiratory breath-hold sequences, to simulate STAR treatment. The primary endpoint was the percentage of patients reaching a score ≥2 in a multi-parametric assessment evaluating the quality of automatically acquired images. Moreover, we investigated the potential impact of clinical and demographic characteristics on achieving the primary endpoint.ResultsWe enrolled 24 patients (63 ± 14 years, 21% females). All of them had a history of VA and 21 (88%) had an ICD. Eight patients (33%) had coronary artery disease, 12 (50%) had non-ischemic cardiomyopathy, and 3 had idiopathic VA. Parasternal, as well as apical images were obtained from all patients except from one, in whom parasternal view could not be collected due to the patient's inability to maintain the supine position. The primary endpoint was achieved in 23 patients (96%) for the apical view, in 20 patients (87%) for the parasternal view, and in all patients in at least one of the two views. The images' quality was maximal (i.e., score = 4) in at least one of the two windows in 19 patients (79%). Atrial fibrillation arrhythmia was the only clinical characteristics associated with a poor score outcome in both imaging windows (apical p = 0.022, parasternal p = 0.014).ConclusionsThese results provide the proof-of-concept for the feasibility of an automatic ultrasonographic image acquisition system associated with an AI algorithm for real-time monitoring of cardiac motion in patients with a history of VA.

Novel method for coke optical texture measurement and application on coke derived from different ranks of coking coal

AbstractThe optical texture of coke is undoubtedly one of the important factors that determined many properties of coke, but it still mostly depends on the skills and experience of manual analysis. In this paper, a novel automatic measurement method is proposed for coke optical texture. this method through two analyzer angle images(10° and 170°) under polarizing microscope to recognize isochromatic regions which occurs color changed between blue and red by image processing, then characterize the dimensional sizes of isochromatic regions and classify them into different anisotropic texture type. Two fitted ellipse methods are used to characterize the regions' dimensional sizes by the range of shape factorS, followed by classifying different types of anisotropic texture according to the long and short axes of fitted ellipse. An automatic image acquisition and analysis system is established based on the above method. Finally, eight coke samples derived from different ranks of coking coal were selected to test the proposed method. The result shows that it can effectively recognize different types of anisotropic texture and provide composition of different anisotropic texture type for each coke. Moreover, the anisotropic degree of coke shows a significant positive correlation with parent coal rank and a negative correlation with coke reactivity index (CRI).