Automatic Acquisition Research Articles

Three-Dimensional Point Cloud Task-Specific Uncertainty Assessment Based on ISO 15530-3 and ISO 15530-4 Technical Specifications and Model-Based Definition Strategy

Data-driven manufacturing in Industry 4.0 demands digital metrology not only to drive the in-process quality assurance of manufactured products but also to supply reliable data to constantly adjust the manufacturing process parameters for zero-defect manufacturing processes. Better quality, improved productivity, and increased flexibility of manufacturing processes are obtained by combining intelligent production systems and advanced information technologies where in-process metrology plays a significant role. While traditional coordinate measurement machines offer strengths in performance, accuracy, and precision, they are not the most appropriate in-process measurement solutions when fast, non-contact and fully automated metrology is needed. In this way, non-contact optical 3D metrology tackles these limitations and offers some additional key advantages to deploying fully integrated 3D metrology capability to collect reliable data for their use in intelligent decision-making. However, the full adoption of 3D optical metrology in the manufacturing process depends on the establishment of metrological traceability. Thus, this article presents a practical approach to the task-specific uncertainty assessment realisation of a dense point cloud data type of measurement. Finally, it introduces an experimental exercise in which data-driven 3D point cloud automatic data acquisition and evaluation are performed through a model-based definition measurement strategy.

Intelligent monitoring and assessment on early-age hydration and setting of cement mortar through an EMI-integrated neural network

In this study, the authors presented a smart sensing method for monitoring and evaluating the early-age hydration and hardening process of cement mortar through the integration of the EMI technique with deep learning (DL) models. The main research works and contributions of this research are given as follows: The development of the penetration resistance was acquired by measuring the Vicat needle penetration depth. Meanwhile, the EMI spectra within two scanning frequency ranges, 1000 kHz and 3000 kHz, were continuously captured. The key innovation of this research is that a DL-based framework named EMI-integrated neural network (EMI-IntNet) was proposed, which could facilitate the achievement of intelligent monitoring and assessment. The EMI-IntNet, which combines a one-dimensional convolutional neural network (1D-CNN), a long short-term memory neural network (LSTM) and an attention mechanism, can accurately predict the dynamic penetration impedance from raw EMI data without any pre-processing or subjective judgment. Two datasets collected in two separate vibration modes (d31 and d33) were used to validate the effectiveness of the proposed method. The results show that the EMI-IntNet can quantitatively evaluate the dynamic penetration impedance of cement mortar with an error of less than 2 % and a coefficient of determination (R2) value over 0.99 for both datasets. The EMI-IntNet’s forecasting accuracy, noise immunity and adaptability were thoroughly investigated, which outperform that of the machine learning-based approaches, 1D-CNN and 1D-CNN-LSTM. In conclusion, the proposed method enables automatic data acquisition, feature extraction and identification of EMI signals, contributing to the intelligent monitoring and accurate assessment of the mechanical impedance change throughout the early-age hydration and setting of cement mortar.

Establishing Plankton Imagery Dataflows Towards International Biodiversity Data Aggregators

Over the last decade, plankton research has experienced extensive developments in automatic image acquisition for identifying and quantifying plankton species. This information is useful for the reporting of plankton occurrences and ecological data. Imagery instruments can vary in the way they sample (benchtop or in situ imagers) and the particle’s size range they target (see Lombard et al. (2019) for an extensive comparison of instruments and specifications). However, due to the wide variety of instruments and their (automatic) output data and formats, it is challenging to integrate datasets that originate from different sources. For this reason, we developed recommendations for plankton imagery data management, which can promote the ability to make these datasets as FAIR (Findable, Accessible, Interoperable and Reusable principles), as possible. The workflow presented here could inspire other Biodiversity Information Standards TDWG communities working with (automated) imagery data (e.g., camera traps) such as the Audubon Core and Machine Observations Interest Group. The recommended data format follows the OBIS-ENV-DATA format (De Pooter et al. 2017), a Darwin Core-based approach to standardise biodiversity data (Wieczorek et al. 2012) used in EurOBIS, the European node of the Ocean Biodiversity Information System (OBIS) and EMODnet Biology, the European Marine Biodiversity Data Network. However, this format does not include sufficient information for imagery data, therefore we propose the use of additional Darwin Core terms. For example, by including the terms identifiedBy, identificationVerificationStatus and identificationReferences in the Occurrence table, more clarity is reported regarding the uncertainty of the classification made by an algorithm. Thus, data providers can publish manually validated datasets or datasets produced by fully automated plankton identification workflows; and users can choose to use validated or not validated data. See in Suppl. material 1 a practical example on how to report an imagery dataset following the best practices. Moreover, the OBIS-ENV-DATA format allows the ingestion of additional information thanks to the use of the Darwin Core (DwC) Extended Measurement Or Facts or eMoF extension in the DwC Event core. The eMoF stores biotic, abiotic and sampling measurements and facts that are related to the Event and Occurrence table. An important aspect of this extension is that it includes standardised terms and controlled vocabularies, such as the British Oceanographic Data Centre (BODC) vocabularies, to standardise parameters that are not covered by DwC. The advantages of these is to unambiguously report information and to include those measurements that cannot be reported in the Event and Occurrence table (e.g., reporting abundance or biomass of plankton), and that are crucial to investigate ecosystem functioning questions. As a consequence, biodiversity data aggregators can extend their scope beyond species occurrence data. Fig. 1 summarises a typical dataflow that goes from imagery data acquisition to publication in several steps: Images are cropped and classified with software. This can be done in EcoTaxa, a web application that allows users to taxonomically classify images of individual organisms. Data is formatted in OBIS-ENV-DATA format. This format can be exported from EcoTaxa through its API. Data is submitted to EurOBIS via the IPT (Integrated Publishing Toolkit). Data is quality controlled by the BioCheck tool. Data in EurOBIS can flow to EMODnet Biology, OBIS and GBIF (Global Biodiversity Information Facility). Images are cropped and classified with software. This can be done in EcoTaxa, a web application that allows users to taxonomically classify images of individual organisms. Data is formatted in OBIS-ENV-DATA format. This format can be exported from EcoTaxa through its API. Data is submitted to EurOBIS via the IPT (Integrated Publishing Toolkit). Data is quality controlled by the BioCheck tool. Data in EurOBIS can flow to EMODnet Biology, OBIS and GBIF (Global Biodiversity Information Facility). Plankton imagery instrument operators now have the possibility to format their data following the best practices and recommendations for plankton imagery data management (Martin-Cabrera et al. 2022). After a dataset is formatted following these guidelines, it can be submitted to the international biodiversity data aggregators, EurOBIS, EMODnet Biology and GBIF. Additionally a (semi) automated dataflow is presented where data providers can classify images in EcoTaxa and export the data in the required formats using an API before submission to EurOBIS. The next steps are to disseminate these best practices, encouraging plankton imagery data generators to implement these workflows to share their data easily, enriching these data portals and encouraging cross collaborations to create data products covering broader geographic scales and plankton species.

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.

Abnormality classification and localization using dual-branch whole-region-based CNN model with histopathological images

The task of classification and localization with detecting abnormalities in medical images is considered very challenging. Computer-aided systems have been widely employed to address this issue, and the proliferation of deep learning network architectures is proof of the outstanding performance reported in the literature. However, localizing abnormalities in regions of images that can support the confidence of classification continues to attract research interest. The difficulty of using digital histopathology images for this task is another drawback, which needs high-level deep learning models to address the situation. Successful pathology localization automation will support automatic acquisition planning and post-imaging analysis. In this paper, we address issues related to the combination of classification with image localization and detection through a dual branch deep learning framework that uses two different configurations of convolutional neural networks (CNN) architectures. Whole-image based CNN (WCNN) and region-based CNN (RCNN) architectures are systematically combined to classify and localize abnormalities in samples. A multi-class classification and localization of abnormalities are achieved using the method with no annotation-dependent images. In addition, seamless confidence and explanation mechanism is provided so that outcomes from WCNN and RCNN are mapped together for further analysis. Using images from both BACH and BreakHis databases, an exhaustive set of experiments was carried out to validate the performance of the proposed method in achieving classification and localization simultaneously. Obtained results showed that the system achieved a classification accuracy of 97.08%, a localization accuracy of 94%, and an area under the curve (AUC) of 0.10 for classification. Further findings from this study revealed that a multi-neural network approach could provide a suitable method for addressing the combinatorial problem of classification and localization anomalies in digital medical images. Lastly, the study's outcome offers means for automating the annotation of histopathology images and the support for human pathologists in locating abnormalities.

Revealing Unforeseen Diagnostic Image Features With Deep Learning by Detecting Cardiovascular Diseases From Apical 4-Chamber Ultrasounds.

BackgroundWith the increase of highly portable, wireless, and low‐cost ultrasound devices and automatic ultrasound acquisition techniques, an automated interpretation method requiring only a limited set of views as input could make preliminary cardiovascular disease diagnoses more accessible. In this study, we developed a deep learning method for automated detection of impaired left ventricular (LV) function and aortic valve (AV) regurgitation from apical 4‐chamber ultrasound cineloops and investigated which anatomical structures or temporal frames provided the most relevant information for the deep learning model to enable disease classification.Methods and ResultsApical 4‐chamber ultrasounds were extracted from 3554 echocardiograms of patients with impaired LV function (n=928), AV regurgitation (n=738), or no significant abnormalities (n=1888). Two convolutional neural networks were trained separately to classify the respective disease cases against normal cases. The overall classification accuracy of the impaired LV function detection model was 86%, and that of the AV regurgitation detection model was 83%. Feature importance analyses demonstrated that the LV myocardium and mitral valve were important for detecting impaired LV function, whereas the tip of the mitral valve anterior leaflet, during opening, was considered important for detecting AV regurgitation.ConclusionsThe proposed method demonstrated the feasibility of a 3‐dimensional convolutional neural network approach in detection of impaired LV function and AV regurgitation using apical 4‐chamber ultrasound cineloops. The current study shows that deep learning methods can exploit large training data to detect diseases in a different way than conventionally agreed on methods, and potentially reveal unforeseen diagnostic image features.

Modulation of Multichannel Electronic Communication Signal Parameters Based on a Nonlinear Phase Principle

In order to solve the difficulty of the optical fiber communication system and network as the infrastructure of the global communication network, which is faced with the requirement of large capacity and high speed, the author proposes a method to modulate the parameters of multichannel electronic communication signals based on the nonlinear phase principle. The method includes multichannel electronic communication signal search, multichannel electronic communication signal parameter modulation, and multichannel electronic communication signal automatic acquisition algorithm realization. Experimental results show that under the conditions that the sampling frequency is set to f = 8.145 MHz, the length of the C/A code period is set to 1 ms, and the 1 ms data packet contains 8145 sampling points, under the same experimental conditions, the capture probability of this experiment reaches 100% and the traditional algorithm has a capture probability of up to 70%. Modulation of multichannel electronic communication signal parameters based on nonlinear phase principle has a positive guiding role in building a high-speed optical fiber communication system and network with long distance, large capacity, high elasticity, high reliability, and low delay.

METransE: Manifold-like mechanism enhanced embedding for reasoning over knowledge graphs

The knowledge graph (KG) has attracted much concern due to its positive effect on AI-related applications. While even for those large-scale KGs widely used, they are still far from being complete and comprehensive. This prompts reasoning over KGs to be one of the most basic and attention-grabbing tasks. However, most existing reasoning methods only focus on the semantics explicitly indicated by triples, but ignore the other valuable information hidden in the KGs that can assist in reasoning. In addition, such one-sided focus on triples also makes the embedding space completely induced by triples independently, rather than reproducing the semantics in KGs fully. All these constrain the reasoning performance. Motivated by these, we propose a more reasonable and better-performing reasoning framework that can discovery and utilize the reasoning-related experience in the KG to guide reasoning. To cooperate with such reasoning mechanism and make the embedding space more interpretable, a type-aware embedding model, METransE, is proposed. It introduces type information through our manifold-like mechanism, which considers restraining excessive aggregation of similar entities to avoid mutual confusion in reasoning, and is more suitable for the needs of KG reasoning. Furthermore, different from the previous work that highly relies on explicit types, we also take the automatic acquisition of types into account to ensure the universality of our model. Extensive experimental results demonstrate the ideal reasoning performance of our model and the type-related interpretability of the embedding space.

Automatic and Accurate Acquisition of Stem-Related Phenotypes of Mature Soybean Based on Deep Learning and Directed Search Algorithms.

The stem-related phenotype of mature stage soybean is important in soybean material selection. How to improve on traditional manual methods and obtain the stem-related phenotype of soybean more quickly and accurately is a problem faced by producers. With the development of smart agriculture, many scientists have explored soybean phenotypes and proposed new acquisition methods, but soybean mature stem-related phenotype studies are relatively scarce. In this study, we used a deep learning method within the convolutional neural network to detect mature soybean stem nodes and identified soybean structural features through a novel directed search algorithm. We subsequently obtained the pitch number, internodal length, branch number, branching angle, plant type spatial conformation, plant height, main stem length, and new phenotype-stem curvature. After 300 epochs, we compared the recognition results of various detection algorithms to select the best. Among them, YOLOX had a maximum average accuracy (mAP) of 94.36% for soybean stem nodes and scale markers. Through comparison of the phenotypic information extracted by the directed search algorithm with the manual measurement results, we obtained the Pearson correlation coefficients, R, of plant height, pitch number, internodal length, main stem length, stem curvature, and branching angle, which were 0.9904, 0.9853, 0.9861, 0.9925, 0.9084, and 0.9391, respectively. These results show that our algorithm can be used for robust measurements and counting of soybean phenotype information, which can reduce labor intensity, improve efficiency, and accelerate soybean breeding.

Collaborative Measurement System of Dual Mobile Robots That Integrates Visual Tracking and 3D Measurement

The manufacturing accuracy of large-scale complex components determines the performance and quality of aircraft, ships, high-speed rail, and other equipment. High-precision 3D measurement plays a crucial role in ensuring manufacturing accuracy. At present, the existing measurement methods rely heavily on manual labor, which cannot satisfy the requirements of industry quality and efficiency. This paper introduces an integrated mobile robotic measurement system for the accurate and automatic 3D measurement of large-scale components with complex curved surfaces. The system consists of the mobile optical scanning measurement device, visual tracking device, and software platform, which can realize comprehensive and accurate data acquisition and stitching of large-scale complex components. The combination of visual tracking and 3D measurement based on the coordinated motion of the dual robot achieved the automatic data acquisition of large-scale complex components without sticking coded targets. Additionally, this paper also introduces a DeepMerge algorithm that combines local and global features of the point cloud, which effectively corrects the initial stitching error of the visual tracking system. The validity of the measurement system and method was shown by the measurement and stitching experiments on the surface of the vehicle nose, ensuring the accurate measurement of the robot’s wide range of motion.

Open-source low-cost design of a buoy for remote water quality monitoring in fish farming.

In this paper we present the design of an open-source and low-cost buoy prototype for remote monitoring of water quality variables in fish farming. The designed battery-powered system periodically measures temperature, pH and dissolved oxygen, transmitting the information locally through a low-power wide-area network protocol to a gateway connected to a cloud service for data storage and visualization. We provide a novel buoy design that can be easily constructed with off-the-shelf materials, delivering a stable anchored float for the IoT device and the probes immersed in the water pond. The prototype was tested at an operating fish farm, showing promising results for a low-cost remote monitoring tool that enables automatic data acquisition and storage in fish farming scenarios. All the elements of this design, including hardware and software designs, are freely available under permissive licenses as an open-source project.

The Sepsis Bundle Effect: An Evaluation of Culture Results and Utilization in Pediatric Appendicitis.

Sepsis prevention pathways, which often include blood and urine cultures, are common in children's hospitals. Fever and tachycardia, signs often seen in patients with appendicitis, frequently trigger these pathways. We hypothesized that cultures were frequently obtained in children with appendicitis. We conducted a single-center retrospective cohort study evaluating children with image-confirmed appendicitis from 4/1/2019 to 10/1/2020, coinciding with the initiation of sepsis prevention pathways. Factors associated with culture acquisition, as well as culture results, treatment, and outcomes were evaluated. Six hundred and fifty eight children presented with acute appendicitis during the 1.5-year period, with a median age of 10.67 years (interquartile range (IQR) 8.17-14.08). Cultures were obtained in 22.9%, including blood culture (BCx) in 8.1% and urine culture (UCx) in 17.9%. Culture acquisition decreased by 17.6% after sepsis protocol initiation. Blood culture acquisition correlated with fever (P = .003) and younger age (P = .03), whereas the attainment of BCx and UCx was associated with female sex (P = .04, P < .0001), complicated appendicitis (P = .0001, P = .03), and unknown diagnosis (P < .0001, P < .0001). There were five positive UCx (4.24%); however, all remained asymptomatic despite a short antibiotic duration dictated by institutional appendicitis protocol. The one positive BCx (1.89%) was suspected contamination and not treated. The findings of this cohort suggest a low incidence of positive culture as well as lack of impact on clinical management in image-proven appendicitis and the initiation of a sepsis bundle without automatic culture acquisition may result in decreased culture attainment.

Variation of Aerosol Optical Depth Measured by Sun Photometer at a Rural Site near Beijing during the 2017–2019 Period

In recent years, the Beijing–Tianjin–Hebei region has become one of the worst areas for haze pollution in China. Sun photometers are widely used for aerosol optical property monitoring due to the advantages of fully automatic acquisition, simple maintenance, standardization of data processing, and low uncertainty. Research sites are mostly concentrated in cities, while the long-term analysis of aerosol optical depth (AOD) for the pollution transmission channel in rural Beijing is still lacking. Here, we obtained an AOD monitoring dataset from August 2017 to March 2019 using the ground-based CE-318 sun photometer at the Gucheng meteorological observation site in southwest Beijing. These sun photometer AOD data were used for the ground-based validation of MODIS (Moderate Resolution Imaging Spectroradiometer) and AHI (Advanced Himawari Imager) AOD data. It was found that MODIS and AHI can reflect AOD variation trends by sun photometer on daily, monthly, and seasonal scales. The original AOD measurements of the sun photometer show good correlations with satellite observations by MODIS (R = 0.97), and AHI (R = 0.89), respectively, corresponding to their different optimal spatial and temporal windows for matching with collocated satellite ground pixels. However, MODIS is less stable for aerosols of different concentrations and particle sizes. Most of the linear regression intercepts between the satellite and the photometer are less than 0.1, indicating that the errors due to surface reflectance in the inversion are small, and the slope is least biased (AHI: slope = 0.91, MODIS: slope = 0.18) in the noon period (11 a.m.–2 p.m.) and most biased in summer (AHI: slope = 0.77, MODIS: slope = 1.31), probably due to errors in the aerosol model. The daily and seasonal variation trends between CE-318 AOD measurements in the Gucheng site and fine particulate observations from the national air quality site nearby were also compared and investigated. In addition, a typical haze–dust complex pollution event in North China was analyzed and the changes in AOD during the pollution event were quantified. In processing, we use sun photometer and satellite AOD data in combination with meteorological and PM data. Overall, this paper has implications for the study of AOD evolution patterns at different time scales, the association between PM2.5 concentrations and AOD changes, and pollution monitoring.

VoCSK: Verb-oriented commonsense knowledge mining with taxonomy-guided induction

Commonsense knowledge acquisition is one of the fundamental issues in realizing human-level AI. However, commonsense knowledge is difficult to obtain because it is a human consensus and rarely explicitly appears in texts or other data. In this paper, we focus on the automatic acquisition of a typical kind of implicit verb-oriented commonsense knowledge (e.g., “person eats food”), which is the concept-level knowledge of verb phrases. For this purpose, we propose a taxonomy-guided induction method to mine verb-oriented commonsense knowledge from verb phrases with the help of a probabilistic taxonomy. First, we design an entropy-based triplet filter to cope with noisy verb phrases. Then, we propose a joint model based on the minimum description length principle and a neural language model to generate verb-oriented commonsense knowledge. Besides, we introduce two strategies to accelerate the computation, including the simulated annealing-based approximate solution and the verb phrase clustering method. Finally, we conduct extensive experiments to prove that our solution is more effective than competitors in mining verb-oriented commonsense knowledge. We construct a commonsense knowledge base called VoCSK, containing 259 verbs and 18,406 verb-oriented commonsense knowledge. To verify the usefulness of VoCSK, we utilize the knowledge in this KB to improve the model performance on two downstream applications.

IoT Smart Agriculture and Agricultural Product Income Insurance Participant Behavior Based on Fuzzy Neural Network

With the continuous development of modern science, more and more attention is paid to the application of science and technology for agricultural production. First of all, this article analyzes the development trend of modern agriculture, and designs the system through an overall plan of four parts: automatic acquisition, terminal control, network transmission, and cloud platform and terminal application. Taking automatic irrigation system and temperature automatic control system as examples, design an automatic control algorithm based on fuzzy neural network, and obtain the best control strategy through fuzzy inference and neural network training. Then, this paper studies the reliability of “price setting” in agricultural income insurance. This article takes the main crops of a certain province as an example to verify and analyze the risks of farmers' income levels from the viewpoint of corn yield risks and price risks, and advocate the necessity and feasibility of agricultural income insurance. Finally, from the perspective of different participants, this article analyzes the impact of insurance premium subsidies on the enthusiasm of agricultural producers to participate in insurance, the impact of insurance companies on the improvement of insurance operation efficiency in the planting industry, and the stability of the government's promotion of agricultural production. It also influences to design a set of IoT automatic control system for intelligent agriculture. The system realizes the acquisition and remote control of agricultural data by establishing an Internet of Things cloud platform, and uses automatic control algorithms based on fuzzy neural networks to realize automatic water-saving irrigation, automatic temperature control, and other functions. Based on this research, the main actions of joining agricultural income insurance and the participation model based on the perspective of tripartite participants.

Performance evaluation of PV powered solar tunnel dryer integrated with a mobile alert system for shrimp drying

The study was aimed at evaluating the drying characteristics, modelling of drying behaviour, and performance of PV powered walk-in-type semi-cylindrical forced convection solar tunnel dryer integrated with a mobile alert system using shrimps (Metapenaeus dobsoni). The dryer was fabricated using UV stabilized polythene film, CPVC pipes, and GI frames and coupled with a PV panel. A mobile alert system consisting of an Arduino UNO, load sensor, and GSM module was evaluated as an automatic data acquisition and information support system for the dryer users. Fresh shrimp samples were dried to the moisture content of 10.54% (w.b) from 79.22% (w.b) in 6 h. The graph of drying rate against the time exhibited that shrimp drying took place under the falling-rate drying period. Mathematical modelling of the drying process revealed that the Page model could accurately predict the drying behaviour of shrimp. The maximum drying efficiency of 20.22% was attained for drying shrimp in the solar tunnel dryer under forced convection. PV panel obtained an average electrical efficiency of 11.64% and supported the load requirement of 0.636 kWh/day for drying. The mobile alert system was effectively tested as an automatic data acquisition and information support system for dryer users.

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.

MR-double-zero – Proof-of-concept for a framework to autonomously discover MRI contrasts

PurposeA framework for supervised design of MR sequences for any given target contrast is proposed, based on fully automatic acquisition and reconstruction of MR data on a real MR scanner. The proposed method does not require any modeling of MR physics and thus allows even unknown contrast mechanisms to be addressed. MethodsA derivative-free optimization algorithm is set up to repeatedly update and execute a parametrized sequence on the MR scanner to acquire data. In each iteration, the acquired data are mapped to a given target contrast by linear regression. ResultsIt is shown that with the proposed framework it is possible to find an MR sequence that yields a predefined target contrast. In the present case, as a proof-of principle, a sequence mapping absolute creatine concentration, which cannot be extracted from T1 or T2-weighted scans directly, is discovered. The sequence was designed in a comparatively short time and with no human interaction. ConclusionsNew MR contrasts for mapping a given target can be discovered by derivative-free optimization of parametrized sequences that are directly executed on a real MRI scanner. This is demonstrated by ‘re-discovery’ of a chemical exchange weighted sequence. The proposed method is considered to be a paradigm shift towards autonomous, model-free and target-driven sequence design.

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.

Performance of electrical energy monitoring data acquisition system for plant-based microbial fuel cell

Plant microbial fuel cell (Plant-MFC) is an emerging technology that uses the metabolic activity of electrochemically active bacteria (EABs) to continue the production of bioelectricity. Since its invention and to date, great efforts have been made for its application both in real-time and large-scale. However, the construction of platforms or systems for automatic voltage monitoring has been insufficiently studied. Therefore, this study aimed to develop an automatic real-time voltage data acquisition system, which was coupled with an ATMEGA2560 connected to a personal computer. Before the system operation started it was calibrated to obtain accurate data. During this experiment, the power generation performance of two types of reactors i.e. (i) Plant-MFC and (ii) control microbial fuel cell (C-MFC), was evaluated for 15 days. The Plant-MFC was planted with an herbaceous perennial plant (Stevia rebaudiana), electrode system was placed close to the plant roots at the depth of 20 cm. The results of the study have indicated that the Plant-MFC, was more effective and achieved higher bioelectricity generation than C-MFC. The maximum voltage reached with Plant-MFC was 850 mV (0.85 V), whereas C-MFC achieved a maximum voltage of 762 mV (0.772 V). Furthermore, the same reactor demonstrated a maximum power generation of 66 mW m¯2 on 10 min of polarization, while a power density with C-MFC was equal to 13.64 mW m¯2. S.rebaudiana showed a great alternative for power generation. In addition, the monitoring acquisition system was suitable for obtaining data in real-time. However, more studies are recommended to enhance this type of system.