Efficient Data Acquisition Research Articles

Practical Method for Achieving Single-Photon Femtosecond Time-Resolved Spectroscopy: Transient Stimulated Emission.

Recent advances in single-photon femtosecond spectroscopy have highlighted the power of entangled photons in probing the properties of materials, previously inaccessible through semiclassical spectroscopic approaches. In this study, we theoretically propose a new single-photon-based femtosecond time-resolved spectroscopy technique termed single-photon transient stimulated emission (SP-TSE). SP-TSE not only enables the selective investigation of singly excited superposition states but also harnesses the quantum mechanical nature of photons for the efficient data acquisition of transient responses, thereby supporting the feasibility of experimental realization of SP-TSE. The key aspect of SP-TSE is the selective detection of two-photon states produced through stimulated emission using coincidence counting techniques. Our theoretical framework, supported by numerical simulations, demonstrates the efficacy in capturing the pure decoherence dynamics of a model molecular cavity, highlighting its potential to reveal quantum mechanical properties that are difficult to observe with semiclassical femtosecond time-resolved experiments.

Mapping-based design method for high-quality integral projection system

A general method for designing an integral projection system is proposed, including optical design and digital preprocessing based on the mapping within the projection system. The per-pixel mapping between the sub-images and the integral projection image is generated by incorporating an integral projection imaging model as well as the ray data of all sub-channels. By tracing rays for sparsely sampled field points of the central sub-channel and constructing the mapping between the central sub-channel and other sub-channels, the efficient acquisition of ray data for all sub-channels is achieved. The sub-image preprocessing pipeline is presented to effectively address issues such as overlapping misalignment, optical aberrations, inhomogeneous illumination, and their collective contribution. An integral projection optical system with a field of view (FOV) of 80°, an F-number of 2, and uniform image performance is given as a design example. The ray tracing simulation results and quantitative analysis demonstrate that the proposed system yields distortion-free, uniformly illuminated, and high-quality integral projection images.

Real-time charged track reconstruction for CLAS12

This paper presents the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use machine learning algorithms to reconstruct tracks, including their momentum and direction, with high accuracy from raw hits of the CLAS12 drift chambers. The reconstruction is performed in real-time, with the rate of data acquisition, and allows for the identification of event topologies in real-time. This approach revolutionizes the Nuclear Physics experiments' data processing, allowing us to identify and categorize the experimental data on the fly, and will lead to a significant reduction in experiment data processing. It can also be used in streaming readout applications leading to more efficient data acquisition and post-processing.

Research on Deformation Monitoring of Roadway Surrounding Rock Based on Mobile 3D Laser Scanning Technology

Aiming at the problems of low data acquisition efficiency, unintuitive data form, low relative accuracy, and inability to continuously monitor the whole cross-section in traditional roadway rock deformation monitoring methods, a roadway rock deformation monitoring method based on mobile 3D laser scanning technology is proposed. Firstly, the mobile 3D laser scanning technology is used to obtain the real 3D point cloud data of the roadway surrounding rock; secondly, the multi-scale dimensional features are used to classify the 3D point cloud data, filter the invalid point cloud data, and form the accurate point cloud data of the roadway surrounding rock surface; then the 3D laser scanning is carried out on a regular basis for the deformation-prone areas of the roadway, and the Iterative Closest Point Algorithm (ICP algorithm) is used to align the overlapping part of the point cloud to achieve the point cloud splicing and point cloud stitching of the two scans. The point cloud splicing and point cloud overlapping of the two scans are achieved; finally, the multi-scale model-to-model point cloud comparison algorithm (M3C2 algorithm) is used to compare the point cloud data of the two periods of the roadway, and the appropriate parameters are selected to indicate the displacement change of the surface of the roadway rock with the size of the color value, so as to realize the monitoring of the deformation of the roadway rock in the three-dimensional full cross-section of the roadway. The method is applied to the deformation monitoring of the surrounding rock in the return roadway of 21407 working face in a coal mine, and the results show that: the mobile laser scanning technology can be used to obtain the complete point cloud data of the roadway, and the deformation area of the surrounding rock is successfully identified after the classification of the scanned point clouds in different time periods, the alignment of the point clouds, and the comparison of the point clouds, and the deformation condition of the surrounding rock of the roadway is visually reflected in the three-dimensional full-section deformation condition of the surrounding rock of the roadway. This method is suitable for monitoring the overall de-formation of the surrounding rock of the roadway under complex environmental conditions, and the de-formation of the surrounding rock is identified by different color areas.

From Lima to Canton and Beyond: Mobile and Digital Research Infrastructure for Closing the Gap Between Resource-rich and Resource-poor Organisations

ABSTRACT Access to research infrastructure for heritage science is the best way of narrowing the collections knowledge gap between resource-rich and resource-poor cultural institutions. Here we demonstrate a novel workflow that streamlines the use of a mobile laboratory for data collection (MOLAB) followed by a digital platform for data processing, analysis and interpretation (DIGILAB). The data collection and processing are taken care of by heritage science specialists, while the data analysis and interpretation can be performed collaboratively with conservators and curators. MOLAB with specialist personnel and mobile scientific instruments can sometimes be aided by in-house fixed laboratory (FIXLAB) equipment and personnel from resource-rich institutions to assist the resource-poor institutions if their collection can be hosted by the resource-rich institutions for analysis. The aim is to widen participation in the adoption of heritage science and material analysis in the understanding and conservation of collections, while ensuring high-quality, efficient and reliable data acquisition, processing, analysis and interpretation. The approach taken in this project combines access to various research infrastructure platforms to achieve extra benefit through adding further expert-assisted data analysis and interpretation capabilities, enabled by a new digital platform for data processing and visualisation.

Improved Re-Parameterized Convolution for Wildlife Detection in Neighboring Regions of Southwest China.

To autonomously detect wildlife images captured by camera traps on a platform with limited resources and address challenges such as filtering out photos without optimal objects, as well as classifying and localizing species in photos with objects, we introduce a specialized wildlife object detector tailored for camera traps. This detector is developed using a dataset acquired by the Saola Working Group (SWG) through camera traps deployed in Vietnam and Laos. Utilizing the YOLOv6-N object detection algorithm as its foundation, the detector is enhanced by a tailored optimizer for improved model performance. We deliberately introduce asymmetric convolutional branches to enhance the feature characterization capability of the Backbone network. Additionally, we streamline the Neck and use CIoU loss to improve detection performance. For quantitative deployment, we refine the RepOptimizer to train a pure VGG-style network. Experimental results demonstrate that our proposed method empowers the model to achieve an 88.3% detection accuracy on the wildlife dataset in this paper. This accuracy is 3.1% higher than YOLOv6-N, and surpasses YOLOv7-T and YOLOv8-N by 5.5% and 2.8%, respectively. The model consistently maintains its detection performance even after quantization to the INT8 precision, achieving an inference speed of only 6.15 ms for a single image on the NVIDIA Jetson Xavier NX device. The improvements we introduce excel in tasks related to wildlife image recognition and object localization captured by camera traps, providing practical solutions to enhance wildlife monitoring and facilitate efficient data acquisition. Our current work represents a significant stride toward a fully automated animal observation system in real-time in-field applications.

SCREENES: Enhancing non-uniform sampling reconstruction for symmetrical NMR spectroscopy

BackgroundSymmetrical NMR spectroscopy, such as Total Correlation Spectroscopy (TOCSY) and other homonuclear spectroscopy, displays symmetry in chemical shift but are generally not symmetrical in terms of intensity, which constitutes a pivotal branch of multidimensional NMR spectroscopy and offers a robust tool for elucidating the structures and dynamics of complex samples, particularly in the context of biological macromolecules. Non-Uniform Sampling (NUS) stands as a critical technique for accelerating multidimensional NMR experiments. However, symmetrical NMR spectroscopy inherently presents dynamic peak intensities, where cross peaks tend to be substantially weaker compared to diagonal peaks. Recovering these weaker cross peaks from NUS data poses a significant challenge, often resulting in compromised data quality. ResultsWe enhance the reconstruction quality of NUS symmetrical NMR spectroscopy based on the assumption that the asymmetry in intensity is mild. Regarding the sampling schedule, we employ the symmetrical sampling structure integrated with Poisson sampling schedule to enhance the efficiency of data acquisition. In term of the reconstruction algorithm, we propose the new method by incorporating hard and soft symmetrical constraints into our recently developed L1-norm-based Compressed Sensing (CS) method known as Sparse Complex-valued REconstruction Enabled by Newton method (SCREEN). Additionally, we propose a two-step reconstruction strategy that separately addresses diagonal and cross peaks. In this two-step strategy, cross peaks are effectively reconstructed by excluding the stronger diagonal peaks. Extensive experimental results validate the effectiveness of our proposed methodology. SignificanceThis method enhances the overall quality of the reconstructed NUS symmetrical NMR spectra, especially in terms of cross peaks, thereby enriching the interpretation of spectral information. Furthermore, it boosts the robustness towards regularization parameters, facilitating a user-friendly experience.

A Portable Tool for Spectral Analysis of Plant Leaves That Incorporates a Multichannel Detector to Enable Faster Data Capture

In this study, a novel system was designed to enhance the efficiency of data acquisition in a portable and compact instrument dedicated to the spectral analysis of various surfaces, including plant leaves, and materials requiring characterization within the 410 to 915 nm range. The proposed system incorporates two nine-band detectors positioned on the top and bottom of the target surface, each equipped with a digitally controllable LED. The detectors are capable of measuring both reflection and transmission properties, depending on the LED configuration. Specifically, when the upper LED is activated, the lower detector operates without its LED, enabling the precise measurement of light transmitted through the sample. The process is reversed in subsequent iterations, facilitating an accurate assessment of reflection and transmission for each side of the target surface. For reliability, the error estimation utilizes a color checker, followed by a multi-layer perceptron (MLP) implementation integrated into the microcontroller unit (MCU) using TinyML technology for real-time refined data acquisition. The system is constructed with 3D-printed components and cost-effective electronics. It also supports USB or Bluetooth communication for data transmission. This innovative detector marks a significant advancement in spectral analysis, particularly for plant research, offering the potential for disease detection and nutritional deficiency assessment.

Methodology of Spatial Data Acquisition and Development of High-Definition Map for Autonomous Vehicles – Case Study from Wrocław, Poland

Autonomous drive systems are a dynamically developed sector of the automotive industry. The key problem in such technological solutions is to provide a reliable navigation system, which is typically based on high-definition (HD) maps supporting the identification of the position of a maneuvering vehicle. HD maps should include possibly up-to-date and detailed information on traffic lanes and on the traffic rules and regulations on such lanes. An effective development of an HD map should be based on the geodetic measurement methods, which ensure efficient and accurate acquisition of spatial data. This article presents the results of an experiment consisting in the manipulation of data obtained with the use of the mobile laser scanning method and further in employing this data in the development of an HD map in an open-source environment. The applied measurement technology and the processing method allowed data of high resolution (frequently above 1000 points per m2) and of high accuracy (3D accuracy down to less than 5 cm). The obtained data were processed in the Vector Map Builder environment (which is accessible from the level of an internet browser) and the final product - HD map was created in the Lanelet2 open-source environment. The above-described experiments allowed two main conclusions. Most importantly, they demonstrate the importance of planning and performing in-field mobile laser scanning measurements. They also point to the important role of the human analyst who needs to manually vectorize the key elements of road infrastructure and to define traffic rules.

Interface Control and Status Monitoring of Electronic Information Equipment based on Nonlinear Data Encryption

An advanced electronic information equipment interface control and status monitoring system is proposed to ensure the fairness, objectivity, and security of information while identifying responsibility for traffic accidents. Through an in-depth analysis of the system's security requirements and the current landscape of information security technology, a robust security strategy is developed for each crucial system stage. A PC-based platform is developed for efficient data acquisition, secure processing, reliable transmission, and fortified storage, focusing on implementing nonlinear data encryption methods. Performance evaluation of the system involved rigorous testing using files ranging from 3MB to 10MB. The results of the proposed system revealed a significant improvement in the system's overall speed and efficiency, showcasing an average performance enhancement of one quarter compared to the original platform. The proposed system demonstrated an impressive 15% to 30% increase in processing speed, establishing its capability to ensure data integrity protection during information transmission, facilitate accurate identification of data recording equipment post-accident, and safeguard the security of stored data. The developed electronic information equipment interface control and status monitoring system effectively addresses critical challenges associated with ensuring data integrity and security in traffic accident investigations.

LoRaWAN-based hybrid internet of wearable things system implementation for smart healthcare

This study introduces the design and development of an Internet of Wearable Things-based Hybrid Healthcare Monitoring System (IoWT-HHMS) for smart medical applications. The system incorporates smart wearable sensing units for real-time, remote monitoring of vital health parameters such as Blood Pressure (BP), Heart Rate (HR), and Body Temperature (BT). A key innovation is the development of a hybrid wireless network communication mechanism within the IoWT-HHMS, utilising the FiPy microcontroller. This mechanism supports both short- and long-range connectivity and integrates an algorithm for efficient data acquisition and updating to the IoT platform. The IoWT-HHMS has undergone extensive testing and validation across various scenarios, including sensor functionality, performance of Wi-Fi and LoRaWAN networks, hybrid network connectivity, and accuracy assessment using the Datacake dashboard. The tests evaluated crucial aspects such as communication reliability, power consumption, and latency. The results demonstrate the system's high stability and accuracy in reading health parameters. Comparisons with reference devices reveal impressive accuracy levels for Systolic BP (SBP), Diastolic BP (DBP), HR, and BT, recording 96.37 %, 95.17 %, 97 %, and 98.57 % accuracy, respectively. Both Wi-Fi and LoRaWAN networks proved reliable in indoor and outdoor settings, maintaining data transmission over distances up to 1.5 km without data loss. In conclusion, the developed IoWT-HHMS shows great promise for efficient and effective real-time remote monitoring of patients' health conditions using an innovative hybrid wireless network communication mechanism.

High-throughput Phenotyping of Maize Roots Using Digital Image Analysis

Recent research on maize root architecture has made significant progress, but further research is needed to optimize methods for efficient and accurate acquisition of root architecture data. This study aimed to assess the effectiveness of digital imaging for root phenotyping of Zea mays L. Field experiments were carried out at two locations in the province of Antioquia, Colombia, in 2019 and 2020 to analyze root architecture variables of 12 genotypes of maize. Two methodologies were used: manual phenotyping and digital image analysis. Pearson’s correlation coefficients among variables were estimated. Principal Component Analysis (PCA) was used to summarize and uncover clustering patterns in the multivariate data set. The results indicated correlations between diameter (r = 0.94) and manually measured root diameter. The manually measured right and left root angles correlated with image-derived root angle at r = 0.92 and 0.88, respectively, and root length at r = 0.62. The PCA highlighted that the digital method explained the highest proportion of variation in root areas and diameters, while the manual method dominated in root angle variables. These results corroborate a feasible method to optimize root architecture phenotyping for research questions. This protocol can be adopted under the automatic analysis with REST software for acquiring images of variables associated with roots’ angle, length, and diameter.

A Comparative Study of Multi-Rotor Unmanned Aerial Vehicles (UAVs) with Spectral Sensors for Real-Time Turbidity Monitoring in the Coastal Environment

Complex coastal environments pose unique logistical challenges when deploying unmanned aerial vehicles (UAVs) for real-time image acquisition during monitoring operations of marine water quality. One of the key challenges is the difficulty in synchronizing the images acquired by UAV spectral sensors and ground-truth in situ water quality measurements for calibration, due to a typical time delay between these two modes of data acquisition. This study investigates the logistics for the concurrent deployment of the UAV-borne spectral sensors and a sampling vessel for water quality measurements and the effects on the turbidity predictions due to the time delay between these two operations. The results show that minimizing the time delay can significantly enhance the efficiency of data acquisition and consequently improve the calibration process. In particular, the outcomes highlight notable improvements in the model’s predictive accuracy for turbidity distribution derived from UAV-borne spectral images. Furthermore, a comparative analysis based on a pilot study is conducted between two multirotor UAV configurations: the DJI M600 Pro with a hyperspectral camera and the DJI M300 RTK with a multispectral camera. The performance evaluation includes the deployment complexity, image processing productivity, and sensitivity to environmental noises. The DJI M300 RTK, equipped with a multispectral camera, is found to offer higher cost-effectiveness, faster setup times, and better endurance while yielding good image quality at the same time. It is therefore a more compelling choice for widespread industry adoption. Overall, the results from this study contribute to advancement in the deployment of UAVs for marine water quality monitoring.

Spectral Data Processing for Field-Scale Soil Organic Carbon Monitoring

Carbon sequestration in soils under agricultural use can contribute to climate change mitigation. Spatial–temporal soil organic carbon (SOC) monitoring requires more efficient data acquisition. This study aims to evaluate the potential of spectral on-the-go proximal measurements to serve these needs. The study was conducted as a long-term field experiment. SOC values ranged between 14 and 25 g kg−1 due to different fertilization treatments. Partial least squares regression models were built based on the spectral laboratory and field data collected with two spectrometers (site-specific and on-the-go). Correction of the field data based on the laboratory data was done by testing linear transformation, piecewise direct standardization, and external parameter orthogonalization (EPO). Different preprocessing methods were applied to extract the best possible information content from the sensor signal. The models were then thoroughly interpreted concerning spectral wavelength importance using regression coefficients and variable importance in projection scores. The detailed wavelength importance analysis disclosed the challenge of using soil spectroscopy for SOC monitoring. The use of different spectrometers under varying soil conditions revealed shifts in wavelength importance. Still, our findings on the use of on-the-go spectroscopy for spatial–temporal SOC monitoring are promising.

A Parallel Sequential SBAS Processing Framework Based on Hadoop Distributed Computing

With the rapid development of microwave remote sensing and SAR satellite systems, the use of InSAR techniques has been greatly encouraged due to the abundance of SAR data with unprecedented temporal and spatial coverage. Small Baseline Subset (SBAS) is a promising time-series InSAR method for applications involving deformation monitoring of the Earth’s crust, and the sequential SBAS method is an extension of SBAS that allows long-term and large-scale surface displacements to be obtained with continuously auto-updating measurement results. As the Chinese LuTan-1 SAR system has begun acquiring massive SAR image data, the need for an efficient and lightweight InSAR processing platform has become urgent in various research fields. However, traditional sequential algorithms are incapable of meeting the huge challenges of low efficiency and frequent human interaction in large-scale InSAR data processing. Therefore, this study proposes a distributed parallel sequential SBAS (P2SBAS) processing chain based on Hadoop by effectively parallelizing and improving the current sequential SBAS method. P2SBAS mainly consists of two components: (1) a distributed SAR data storage platform based on HDFS, which supports efficient inter-node data transfer and continuous online data acquisition, and (2) several parallel InSAR processing algorithms based on the MapReduce model, including image registration, filtering, phase unwrapping, sequential SBAS processing, and so on. By leveraging the capabilities associated with the distributed nature of the Hadoop platform, these algorithms are able to efficiently utilize the segmentation strategy and perform careful boundary processing. These parallelized InSAR algorithm modules can achieve their goals on different nodes in the Hadoop distributed environment, thereby maximizing computing resources and improving the overall performance while comprehensively considering performance and precision. In addition, P2SBAS provides better computing and storage capabilities for small- and medium-sized teams compared to popular InSAR processing approaches based on cloud computing or supercomputing platforms, and it can be easily deployed on clusters thanks to the integration of various existing computing components. Finally, to demonstrate and evaluate the efficiency and accuracy of P2SBAS, we conducted comparative experiments on a set of 32 TerraSAR images of Beijing, China. The results demonstrate that P2SBAS can fully utilize various computing nodes to improve InSAR processing and can be applied well in large-scale LuTan-1 InSAR applications in the future.

Using only the red-edge bands is sufficient to detect tree stress: A case study on the early detection of PWD using hyperspectral drone images

Pine wilt disease (PWD) is a destructive disease of pine trees caused by the pine wood nematode and early identification is crucial for disease control. Hyperspectral drone imagery has the potential to achieve early detection, although specific methods have not been sufficiently explored, including the spectral characteristics of early infections and the most efficient identification methods. This study aimed to examine the spectral responses to early infection, quantify the separability of healthy and early infections, and compare the accuracy and efficiency of different methods including single bands, vegetation indices (VIs) and 1st derivative reflectance and indices. We collected hyperspectral drone data in southeast China and used linear discriminant analysis (LDA) to determine the separability of healthy trees and trees at an early stage of infection. We also used bands with the same wavelengths as Sentienl-2 images (denoted S2 bands) to propose a standard for band selection.We found that it was possible to separate healthy trees and those at an early stage of infection around 0.71–0.78 using different methods. Among individual bands, the red-edge bands had the highest separability of 0.74. Using standard vegetation indices resulted in separability between 0.67 and 0.71; in addition, we proposed three new indices that achieved separability between 0.73 and 0.75. The 1st derivative reflectance at 714 nm had the highest separability of 0.78 in this study, while using the 1st derivative reflectance indices was slightly less accurate. The classification accuracy was also slightly lower when using Random Forest (RF) with all bands, sensitive bands, and S2 bands.We conclude that, of the methods tested, the red-edge bands are most sensitive to early infection, and using the 1st derivative reflectance at 714 nm or using the 1st derivative reflectance at the red-edge and blue-edge inflection point (REIP and BEIP) was sufficient for early identification. Acquiring and using additional wavelengths hardly improved the classification. Using wavelengths similar to those in the Sentienl-2 images achieved similar results, and thus can be used as a standard for dimensionality reduction of hyperspectral data pertaining to forest disease. The newly proposed VIs and 1st derivative reflectances at the yellow-edge inflection point (YEIP) and BEIP delivered better performance than the other tested indices, and could be alternatives for early identification. This study proposes simple and practical methods for early identification of wilt and provides insights for efficient data acquisition and data reduction.

Efficient data acquisition with wireless powered sensor networks for smart healthcare in hospitals

Efficient data acquisition with wireless powered sensor networks for smart healthcare in hospitals

Efficient Data Acquisition with Wireless Powered Sensor Networks for Smart Healthcare in Hospitals

Efficient Data Acquisition with Wireless Powered Sensor Networks for Smart Healthcare in Hospitals

Efficient Data Acquisition and Reconstruction in Ultrasound Imaging

Efficient Data Acquisition and Reconstruction in Ultrasound Imaging

Architecture of intercomponent interaction of a microprocessor-powered sustainable relay protection system based on open standards IoT

Nowadays, the problem of the coordination of relay protection systems during faults becomes widespread, as the trip of the circuit breaker must be fast. One of the solutions is the application of the Internet of Things. The object of this research is a relay protection system architecture, which uses elements of the Internet of Things and is based on open standards. The research aims to address the efficiency of using the Internet of Things in the relay protection systems, as well as the relevance of the proposed architecture. This architecture unifies processes encompassing relay protection, data collection, analysis, real-time communication protocols, and secure data transmission techniques. Apart from the increased quality of relay protection, it provides a well-organized framework for the precise and efficient acquisition, transmission, and analysis of data. As a result of the research, it was derived that the Internet of Things can be used in relay protection applications along with the proposed architecture if certain conditions are met.