Automatic Acquisition Research Articles

Automatic plant phenotyping analysis of Melon (Cucumis melo L.) germplasm resources using deep learning methods and computer vision

Cucumis melo L., commonly known as melon, is a crucial horticultural crop. The selection and breeding of superior melon germplasm resources play a pivotal role in enhancing its marketability. However, current methods for melon appearance phenotypic analysis rely primarily on expert judgment and intricate manual measurements, which are not only inefficient but also costly. Therefore, to expedite the breeding process of melon, we analyzed the images of 117 melon varieties from two annual years utilizing artificial intelligence (AI) technology. By integrating the semantic segmentation model Dual Attention Network (DANet), the object detection model RTMDet, the keypoint detection model RTMPose, and the Mobile-Friendly Segment Anything Model (MobileSAM), a deep learning algorithm framework was constructed, capable of efficiently and accurately segmenting melon fruit and pedicel. On this basis, a series of feature extraction algorithms were designed, successfully obtaining 11 phenotypic traits of melon. Linear fitting verification results of selected traits demonstrated a high correlation between the algorithm-predicted values and manually measured true values, thereby validating the feasibility and accuracy of the algorithm. Moreover, cluster analysis using all traits revealed a high consistency between the classification results and genotypes. Finally, a user-friendly software was developed to achieve rapid and automatic acquisition of melon phenotypes, providing an efficient and robust tool for melon breeding, as well as facilitating in-depth research into the correlation between melon genotypes and phenotypes.

A Calculating Method for the Height of Multi-Type Buildings Based on 3D Point Cloud

Building height is a critical variable in urban studies, and the automated acquisition of the precise building height is essential for intelligent construction, safety, and the sustainable development of cities. The building height is often approximated by the building’s highest point. However, the calculation method of the building height of the various roof types differs according to building codes, making it challenging to accurately calculate the height of buildings with complex roof structures or multiple upper appendages. Consequently, this paper utilizes point clouds to propose an automated method for calculating building heights conforming to design codes. The model considers roof types and allows for fast, automated, and highly accurate building height estimation. First, roofs are extracted from the point cloud by combining normal vector density clustering with a region-growing algorithm. Second, combined with variational Bayes, a Gaussian mixture model is employed to segment the roof surfaces. Finally, roofs are classified based on slope characteristics, achieving the automatic acquisition of building heights for various roof types over large areas. Experiments were conducted on Vaihingen and STPLS3D datasets. In the Vaihingen area, the maximum error, root-mean-square-error (RMSE), and mean absolute error (MAE) of the measured heights are 1.92 cm, 1.18 cm, and 1.13 cm, respectively. In the STPLS3D area, these values are 1.79 cm, 0.82 cm, and 0.68 cm, respectively. The results demonstrate that the proposed method is reliable and effective, which offers valuable data for the development, construction, and planning of three-dimensional (3D) cities.

Heat Capacity of Fish Oil at High Temperatures and Pressures

This paper reports new results from studies of the isobaric heat capacities of OMEGA-3 “950” fish oil provided by SOLGAR INC (USA) at high temperatures and high pressures. The measurements were carried out on a differential scanning calorimeter (ITs-400) with an automatic data acquisition system in the temperature range from 298.15 to 473.15 K and at pressures from 0.098 to 39.2 MPa. The coverage factor for the 95% confidence level for a two-way coverage interval is assumed to be 2. The expanded measurement uncertainty of heat capacity is estimated to be 2.4%, pressure is estimated to be 0.05% and temperature is estimated to be 15 mK. Experimental data on the fish oil isobaric heat capacity as a function of temperature and pressure are approximated by the correlation equation proposed in the work. At atmospheric pressure, the deviations between those calculated by the correlation equation and the current measured data on isobaric heat capacity are within the range of average absolute deviations (AAD) = 0.22% (standard deviation St. Dev = 0.28% and maximum deviation Max. Dev = 0.39%). Additionally, a comparison was made of the state parameters obtained during the experiment and the literature data at the studied parameters.

A model predictive control algorithm for double-hormone artificial pancreas based on optimal three-branch decision switching rules

At present, double-hormone artificial pancreas is a hotspot in the research of regulating blood glucose level; however, there are few studies on switching mechanism of the subsystems for double-hormone artificial pancreas, and most of the existing studies rely on the prior knowledge of application fields. A model predictive control algorithm for double-hormone artificial pancreas based on optimal three-branch decision switching rules is proposed to solve this problem. The blood glucose controllers of insulin subsystems, zero infusion subsystems, and glucagon subsystems are constructed using model predictive control algorithms. Then, using the three-branch decision theory, switching rules between the three subsystems are designed, and the optimal switching threshold value can be solved by the mixed fruit fly algorithm. The algorithm can realize automatic acquisition of the threshold in the three-branch decision switching rules and gets rid of the dependence on the prior knowledge in the field of diabetes research. By the end, the UVa simulation platform is used to conduct experiments on 33 virtual patients and the simulation results verify that the proposed algorithm can control blood glucose effectively within the normal range and can achieve satisfactory performabce in error of tracking set point, safety, operation efficiency, group control quality, and system stability.

Study on non-destructive visualization identification of concrete internal cracks based on SH array ultrasound

Ultrasonic nondestructive testing (NDT) technology was one of the most frequently used and fastest-developing NDT techniques in detection field. This method had high detection sensitivity, more accurate defect localization, and was harmless to the human body. With the development of ultrasonic testing technology, modern ultrasonic imaging technology generally had automatic data acquisition and processing functions, which allowed for intuitive and clear recognition of internal defects by the human eyes. The overall purpose of this study was to visualize the cracks in concrete by synthetic aperture focusing technique (SAFT) and full-waveform inversion (FWI) algorithms on the basis of ultrasonic nondestructive testing technology. At present, there have been studies on the use of ultrasonic instruments to detect the internal defects of concrete. However, the accuracy and authenticity of this method have not been compared in previous studies. Therefore, in this experiment, four concrete specimens with different buried depths, angles, shapes, and thicknesses of cracks were designed. Based on SH ultrasonic wave NDT technology, two types of operations were performed on the collected ultrasonic wave data: one was the application of SAFT method, and the other was the first application of FWI method for detecting internal cracks in concrete. The study found that both algorithms were most accurate in determining the burial depth of cracks, with error results not exceeding 10 %. They also showed good recognition effects for the angle, shape, and thickness of the buried cracks. This proved that both identification methods had the capability to recognize internal cracks. The Introview software bundled with the ultrasonic instrument could identify cracks using SAFT directly and quickly. When FWI was used with MATLAB computation, the results were more precise and intuitive. The overall purpose of this study was to detect cracks in concrete by SAFT and FWI algorithms on the basis of ultrasonic nondestructive testing technology. Combining the two crack identification techniques could be of great significance for ensuring the long-term safety and stability of concrete structures, providing strong support for structural maintenance and reinforcement.

Research on automatic data acquisition scheme of tool profile wear based on B/S structure

Research on automatic data acquisition scheme of tool profile wear based on B/S structure

A novel method for tomato stem diameter measurement based on improved YOLOv8-seg and RGB-D data

The automatic acquisition of crop information can promote the rapid development of precision agriculture. Tomatoes, as a representative greenhouse crop, can demonstrate their growth status and overall health could be exhibited by measuring the diameters of their main stems. Automated measurement of the diameters of the trunk stems can not only reduce labor costs, but also enhance efficiency, thereby facilitating the improvement of tomato cultivation and planting management. Therefore, this study proposes a novel method based on an instance segmentation algorithm combined with RGB-D data to measure the diameter of a tomato trunk. First, we utilize the improved YOLOv8-seg to acquire the masks and bounding boxes of the buds and stems. Namely, we replace the SPPF module with Soft-SPPF to enhance the model’s capability to extract multi-scale features. Additionally, we improve the neck layer using cross-stage and weighted feature fusion to enhance the feature fusion effect. Then, we design a method to calculate the diameter of the tomato trunk by utilizing the instance segmentation results and depth information. Specifically, we first obtain the measurement point and the ROI (Region of Interest) to be measured by identifying the intersection between the bounding box of the bud and the straight line fitted by the stem mask in an image. We then filter out irrelevant depth information using the mask within the ROI, optimize the coordinate values of the measurement point, and calculate the main stem diameter. The results demonstrate that the measurements obtained in this study have a RMSE (Root Mean Square Error) of 1.5 mm and a MAPE (Mean Absolute Percentage Error) of 12.37 % compared with the manual measurements. Compared with the method based on object detection, the direct acquisition of contour information for the target instances by the instance segmentation algorithm reduces the algorithmic complexity of the subsequent processing. The proposed method can offer precise and reliable information on the diameter of the main stem of a tomato in real-world scenarios. It can be extended to other types of crops in greenhouses.

Development of an Internet-based Product-related Child Injury Textual Data Platform (IPCITDP) in China.

Internet-based media stories provide valuable information for emerging risks of product-related child injury prevention and control, but critical methodological challenges and high costs of data acquisition and processing restrict practical use by stakeholders. We constructed a data platform through literature reviews and multi-round research group discussions. Developed components included standard search strategies, filtering criteria, textual document classification, information extraction standards and a keyword dictionary. We used ten thousand manually labelled media stories to validate the textual document classification model, which was established using the Bidirectional Encoder Representation from Transformers (BERT). Multiple information extraction methods based on natural language processing algorithms were adopted to extract data for 29 structured variables from media stories. They were evaluated through manual validation of 1000 media stories about product-related child injury. We mapped the geographic distribution of media sources and media-reported product-related child injury events. We developed an internet-based product-related child injury textual data platform, IPCITDP, consisting of four layers - automatic data search and acquisition, data processing, data storage, and data application - concerning product-related child injury online media stories in China. Each layer occurred daily. External validation demonstrated high performance for the BERT classification model we established (accuracy = 0.9703) and the combined information extraction strategies (accuracy >0.70 for 25 variables). As of 31 December 2023, IPCITDP collected 35 275 eligible product-related child injury reports from 13 261 news media websites or social media platform accounts which were geographically located across all 31 mainland Chinese provinces and covered over 97% of prefecture-level cities. The injury cases in IPCITDP were typically reported several months or years earlier than official announcements about the product-related child injury risks. Our data platform added data concerning 15 supplementary variables that the national product-related injury surveillance system lacks. Two examples demonstrate the value of IPCITDP in supplementing existing data and providing early epidemiological detection of emerging signals concerning product-related child injury: magnetic beads and electric self-balancing scooters. Our data platform provides injury data that can support early detection of new product-related child injury characteristics and supplement existing data sources to reduce the burden of product-related injury among Chinese children.

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.

Ant-observer: A new approach for automatic acquisition and autonomous analyses of individual species abundance and interactions

Traditional methods, such as direct observations or manually photographing bait stations, are limited in both data collection efficiency and accuracy. Consequently, they fail to capture the dynamic changes and complex relationships between ant species. We developed a novel system as a potential solution that combines processed consumer-grade camera equipment and software that acquires data needed for in situ experiments. This software features a user-friendly graphics user interface (GUI) for data collection and post-hoc analysis from videos captured by the camera equipment. Researchers can use this system to (1) count species abundance per frame; (2) calculate species speed; (3) detect ant interactions; and (4) count the abundance of different subcastes within species on baits. To demonstrate the complete workflow, we applied this system on ant videos collected in Hong Kong. Practical guides (supplement materials) detailing suggestions for camera equipment and software operations in both field and laboratory studies are provided. We present the results of ten in situ experiments of ants (each lasting 1–1.5 h) conducted in Hong Kong though processed consumer-grade camera equipment, with videos analyzed using the newly developed software. The analysis captured a range of variables related to ant foraging behavior, including resource discovery time, nestmate recruitment rate, and interspecific interactions. Our results confirm that the developed software provides an integrated and efficient approach for data collection, extraction, and analysis in in situ experiments, enhancing the capabilities of insect ecology research.

Comparative analysis of parametric B-spline and Hermite cubic spline based methods for accurate ECG signal modeling

Analyzing Electrocardiogram (ECG) signals is imperative for diagnosing cardiovascular diseases. However, evaluating ECG analysis techniques faces challenges due to noise and artifacts in actual signals. Machine learning for automatic diagnosis encounters data acquisition hurdles due to medical data privacy constraints. Addressing these issues, ECG modeling assumes a crucial role in biomedical and parametric spline-based methods have garnered significant attention for their ability to accurately represent the complex temporal dynamics of ECG signals. This study conducts a comparative analysis of two parametric spline-based methods—B-spline and Hermite cubic spline—for ECG modeling, aiming to identify the most effective approach for accurate and reliable ECG representation. The Hermite cubic spline serves as one of the most effective interpolation methods, while B-spline is an approximation method. The comparative analysis includes both qualitative and quantitative evaluations. Qualitative assessment involves visually inspecting the generated spline-based models, comparing their resemblance to the original ECG signals, and employing power spectrum analysis. Quantitative analysis incorporates metrics such as root mean square error (RMSE), Percentage Root Mean Square Difference (PRD) and cross correlation, offering a more objective measure of the model's performance. Preliminary results indicate promising capabilities for both spline-based methods in representing ECG signals. However, the analysis unveils specific strengths and weaknesses for each method. The B-spline method offers greater flexibility and smoothness, while the cubic spline method demonstrates superior waveform capturing abilities with the preservation of control points, a critical aspect in the medical field. Presented research provides valuable insights for researchers and practitioners in selecting the most appropriate method for their specific ECG modeling requirements. Adjustments to control points and parameterization enable the generation of diverse ECG waveforms, enhancing the versatility of this modeling technique. This approach has the potential to extend its utility to other medical signals, presenting a promising avenue for advancing biomedical research.

Machine learning-based 3D scan coverage prediction for smart-control applications

Automatic control of a workpiece being manufactured is a requirement to ensure in-line correction and thus move towards a more intelligent manufacturing system. There is therefore a need to develop control strategies which are capable of taking precise account of real working conditions and enabling first-time-right control. As part of such a smart-control strategy, this paper introduces a machine learning-based approach capable of accurately predicting a priori the 3D coverage of a part according to a scan configuration given as input, i.e. predicting before scanning it which areas of the part will be acquired for real. This corresponds to a paradigm shift, where coverage estimation no longer relies on theoretical visibility criteria, but on rules learned from a large amount of data acquired in real-life conditions. The proposed 3D Scan Coverage Prediction Network (3DSCP-Net) is based on a 3D feature encoding and decoding module, which is capable of taking into account the specifics of the scan configuration whose impact on the 3D coverage is to be predicted. To take account of real working conditions, features are extracted at various levels, including geometric ones, but also features characterising the way structured-light projection behaves. The method is thus able to incorporate inter-reflection and overexposure issues into the prediction process. The database used for the training was built using an ad-hoc platform specially designed to enable the automatic acquisition and labelling of numerous point clouds from a wide variety of scan configurations. Experiments on several parts show that the method can efficiently predict the scan coverage, and that it outperforms conventional approaches based on purely theoretical visibility criteria.

Intelligent Identification of Surrounding Rock Grades Based on a Self-Developed Rock Drilling Test System

The classification of surrounding rock is crucial for formulating safe tunnel construction plans and support measures. However, the complex geological environment of tunnels presents a challenge in obtaining accurate drilling parameters for rock mass classification. This paper presents the development of a rock drilling testing system, which includes a propulsion speed acquisition system, oil pressure acquisition system, air pressure acquisition system, and an automatic data acquisition system. This system enables real-time, high-precision automatic collection and storage of parameters such as propulsion speed, with data collected twice per second for each parameter. Leveraging the Qingdao Metro Line 6 as a case study, we conducted rock mass drilling and constructed a rock mass classification database. By employing kernel density estimation and Pearson correlation analysis, we quantified the correlation between rock mass classification and the drilling parameters. The results indicated that relying on a single drilling parameter is insufficient for accurately determining rock mass classification. Both impact pressure and rotational pressure showed the strongest correlation with rock mass classification, each with a correlation coefficient below −0.8 (indicating a strong negative correlation). Outlier values of drilling parameters were excluded using the interval method. Based on the remaining data, we established an intelligent rock mass classification model using the random forest algorithm. This model demonstrated good accuracy and generalization performance, with an average accuracy exceeding 0.9. The proposed rock drilling testing system, combined with the intelligent rock mass classification model, forms an integrated system for the intelligent identification of rock mass grades. This system has significant implications for the intelligent and safe construction of drill-and-blast tunnels.

Pollen identification of three notorious illicit drug plants and its potential applications in forensic practice.

Opium poppy, coca and cannabis are raw materials for three notorious illicit drugs. For a long time, drug lords have been growing and smuggling these drugs in a variety of ways and channels and are continually finding new ways of trafficking their wares, which has led to the increasing difficulty of global drug enforcement. In the present paper, we propose an innovative pollen identification system for these important drug plants, which provides a tool for screening and detection of the drugs to aid in drug enforcement. By utilizing the characteristics of these fine particles, their abundant production, and high resistance to decay, we believe this tool could be applied in the following scenarios: detecting and dynamically monitoring drug cultivation activities; determining whether a suspect has been to fields of drug plants and determining whether the site has ever been planted with a drug plant and/or was involved in drug production. In the future, combined with microscope automatic image acquisition technology and intelligent image recognition technology, this pollen identification system is expected to be used to screen three notorious illicit drug plants, thus enhancing the efficiency of drug related crime investigations.

Flexible circuit-based spatially aware modular optical brain imaging system for high-density measurements in natural settings.

Functional near-infrared spectroscopy (fNIRS) presents an opportunity to study human brains in everyday activities and environments. However, achieving robust measurements under such dynamic conditions remains a significant challenge. The modular optical brain imaging (MOBI) system is designed to enhance optode-to-scalp coupling and provide a real-time probe three-dimensional (3D) shape estimation to improve the use of fNIRS in everyday conditions. The MOBI system utilizes a bendable and lightweight modular circuit-board design to enhance probe conformity to head surfaces and comfort for long-term wearability. Combined with automatic module connection recognition, the built-in orientation sensors on each module can be used to estimate optode 3D positions in real time to enable advanced tomographic data analysis and motion tracking. Optical characterization of the MOBI detector reports a noise equivalence power of 8.9 and at 735 and 850nm, respectively, with a dynamic range of 88dB. The 3D optode shape acquisition yields an average error of 4.2mm across 25 optodes in a phantom test compared with positions acquired from a digitizer. Results for initial in vivo validations, including a cuff occlusion and a finger-tapping test, are also provided. To the best of our knowledge, the MOBI system is the first modular fNIRS system featuring fully flexible circuit boards. The self-organizing module sensor network and automatic 3D optode position acquisition, combined with lightweight modules ( ) and ergonomic designs, would greatly aid emerging explorations of brain function in naturalistic settings.

Investigating the influence of temperature on salt solubility in water: a STEM approach with pre-university chemistry students

Abstract In a society heavily influenced by technological advancements, developing scientific and technological literacy among young people is essential. Along these lines, this research describes a STEM activity developed to promote the teaching of chemistry content related to the solubility of potassium nitrate in water. It also facilitated the mastery of technological skills such as programming Arduino microcontrollers and using Microsoft Excel as tools for automatic data acquisition and analysis. Eighty pre-university Chemistry students participated in this research. This pedagogical approach was divided into three main stages: (1) preliminary research on components used in the experimental apparatus; (2) introduction to the assembly of electrical circuits and Arduino programming; (3) experimental investigation of the dependence of potassium nitrate solubility on temperature. The activity allowed the students to successfully achieve the proposed chemistry learning objectives while mobilizing other scientific and technological knowledge and skills. Despite the students’ limited prior knowledge of programming and electronics, as well as their limited proficiency in data analysis software, the integration of programming in the Chemistry class proved to be a differentiating factor with a highly positive impact, particularly in terms of motivation and interest among most students.

The general patterns of water flow in loess slope system and implications for slope geological security

Loess regions face significant challenges in quantifying hydrological processes and assessing geological environmental risks due to the prevalent development of preferential pathways and the limitations of existing monitoring technologies. To advance this knowledge, this study presents an improved electrical resistivity tomography (ERT) device, specifically designed for loess moisture observations. By refining the testing principle, power supply mode, and data collection method within the existing ERT framework, the new device offers unmanned operation, automatic data acquisition, remote transmission, and cost efficiency. It effectively tracks water movement and groundwater level fluctuations across various hydrological conditions, supporting long-term online monitoring of hydrological processes of loess slopes. Through the analysis of monitoring data and classification of 12 observed preferential flow types, water movement in loess systems can be generalized into four general patterns: uniform infiltration, preferential infiltration, inflowing diffusion, and lateral flow. This generalized scheme provides a simplified modeling approach for other researchers to quantify slope hydrodynamics and to assess geological safety risks involving preferential flow. Based on these insights and field investigations, a conceptual framework is proposed to elucidate the seepage-structure synergistic initiating mechanism of loess landslides. This framework suggests that water entry and movement patterns within the slope depend on the slope geological structure related to preferential pathways and the prevailing hydrological scenarios. Landslide occurs as the result of the progressive failure and reciprocal evolution between the slope hydrological environments and geological structure, which may also pose potential eco-hydrological risks. The outcome advances the development of slope hydrological monitoring technology and enhances the understanding of water movement laws and the associated geological environmental risks in loess slope systems, which is of vital importance to the early warning methods of loess landslides that account for preferential flow and for theoretical modeling of preferential flow in related disciplines.

Leveraging Descriptor Learning and Functional Map-based Shape Matching for Automatic Landmark Acquisition.

Geometric morphometrics is widely employed across the biological sciences for the quantification of morphological traits. However, the scalability of these methods to large datasets is hampered by the requisite placement of landmarks, which can be laborious and time consuming if done manually. Additionally, the selected landmarks embody a particular hypothesis regarding the critical geometry pertinent to the biological inquiry at hand. Modifying this hypothesis lacks flexibility, necessitating the acquisition of an entirely new set of landmarks on the entire dataset to reflect any theoretical adjustments. In our research, we investigate the precision and accuracy of landmarks derived from the comprehensive set of functional correspondences acquired through the functional map framework of geometry processing. We use a deep functional map network to learn shape descriptors that effectively yield functional map-based and point-to-point correspondences between the specimens in our dataset. We then interrogate these maps to identify corresponding landmarks given manually placed landmarks from the entire dataset. We assess our method by automating the landmarking process on a dataset comprising mandibles from various rodent species, comparing its efficacy against MALPACA, a cutting-edge technique for automatic landmark placement. Compared to MALPACA, our model is notably faster and maintains competitive accuracy. The Root Mean Square Error (RMSE) analysis reveals that while MALPACA generally exhibits the lowest RMSE, our models perform comparably, especially with smaller training datasets, suggesting strong generalizability. Visual evaluations confirm the precision of our landmark placements, with deviations remaining within an acceptable range. These findings underscore the potential of unsupervised learning models in anatomical landmark placement, providing a viable and efficient alternative to traditional methods.

Sensorweb Systems for Global High-Resolution Monitoring of Environmental Phenomena

Many science phenomena are difficult to observe because they occur infrequently and without warning. Over the course of the last 20 years, the concept of a “sensorweb,” or a network of interdependent instruments that autonomously coordinate to observe phenomena of interest, has been developed and implemented. In one use case, sensorwebs use in situ and low- to moderate-resolution sensors for event detection and pointable sensors with high spatial resolution for subsequent observation of targets, thus accomplishing efficient high-resolution monitoring. These sensorwebs have previously been implemented to study flood extent and volcanic lava flow. In the flood monitoring case, mostly remote sensors have been used for low-resolution flood detection, while more in-situ sensors have been used for volcanic activity detection in the volcano monitoring application. To improve these sensorwebs, we integrate commercial satellite constellations for high-resolution observations, resulting in higher spatial and temporal resolutions than the individual assets used in previous work. We also spatially extend sensorwebs to the global scale, produce a high volume of alerts, and automatically generate useful high-resolution data products. These resulting data products include surface water extent products for our flood sensorweb and volcanic lava classifiers for our volcano sensorweb. Our sensorwebs represent the state of the art in automatic acquisition and processing of data for environmental phenomenon monitoring. We demonstrate the capabilities of these improved sensorwebs in our new flood sensorweb and volcano sensorweb systems and describe ongoing efforts to utilize additional in-situ and spaced-based sensors and generate additional data products.

Regularized automatic frequency response function acquisition of a milling robot operating in a high-dimensional workspace

Regularized automatic frequency response function acquisition of a milling robot operating in a high-dimensional workspace