Pixel Error Research Articles

Nose Tracking Assistive Robot Control for the People with Motor Dysfunctions

<h3>Research Objectives</h3> To Implement Human Computer Interface (HCI) with digital image processing for a new approach to control assistive robots. <h3>Design</h3> Experimental Study. <h3>Setting</h3> In UWM BioRobotics Lab. <h3>Participants</h3> Healthy participants. <h3>Interventions</h3> Assistive robot control for daily living activities(Eating, Picking/Placing Objects), Nose tracking cursor control, User interface. <h3>Main Outcome Measures</h3> #Control the computer cursor with nose movements. #App Interface is developed to control the robotic arm. #Control the interface using nose and teeth (showing teeth triggered the click events) to control the assistive robotic arm. <h3>Results</h3> Result contains the demonstration of the control of a robotic arm with nose tracking control through an user interface. External webcam was used to get the real-time video for tracking the nose for cursor movement. We conducted experiments on healthy subjects. The feedbacks are recorded in Tables. We compared the results with other two techniques which are eye tracking and camera mouse for cursor movement and clicking events in terms of error pixels and other factors. <h3>Conclusions</h3> The nose tracking cursor control is mainly for the disabled people who are struggling with or unable to do their basic day to day tasks for their motor dysfunctions. This method will help them to access and control over a computer as well as a robotic assistive arm to ease their lives. <h3>Author(s) Disclosures</h3> No Conflict

Homography-based robust pose compensation and fusion imaging for augmented reality based endoscopic navigation system

BackgroundAugmented reality (AR) based fusion imaging in endoscopic surgeries rely on the quality of image-to-patient registration and camera calibration, and these two offline steps are usually performed independently to get the target transformation separately. The optimal solution can be obtained under independent conditions but may not be globally optimal. All residual errors will be accumulated and eventually lead to inaccurate AR fusion. MethodsAfter a careful analysis of the principle of AR imaging, a robust online calibration framework was proposed for an endoscopic camera to enable accurate AR fusion. A 2D checkerboard-based homography estimation algorithm was proposed to estimate the local pose of the endoscopic camera, and the least square method was used to calculate the compensation matrix in combination with the optical tracking system. ResultsIn comparison with conventional methods, the proposed compensation method improved the performance of AR fusion, which reduced physical error by up to 82%, reduced pixel error by up to 83%, and improved target coverage by up to 6%. Experimental results of simulating mechanical noise revealed that the proposed compensation method effectively corrected the fusion errors caused by the rotation of the endoscopic tube without recalibrating the camera. Furthermore, the simulation results revealed the robustness of the proposed compensation method to noises. ConclusionsOverall, the experiment results proved the effectiveness of the proposed compensation method and online calibration framework, and revealed a considerable potential in clinical practice.

A Novel Method for Approximating Object Location Error in Bounding Box Detection Algorithms Using a Monocular Camera

Many autonomous vehicles and advanced driver-assistance systems are equipped with front-facing cameras that detect and track objects using deep-learning-based algorithms. However, the localization capability of monocular cameras is often overlooked. In this paper, a novel method for estimating the pixel-wise error in a detected object's location versus its ground truth is proposed. As the object moves away from the camera, the pixel errors are shown to be normally distributed with unique spreads along the image's vertical axis (y-pixel). The pixel error appears to be smaller as objects get farther away, while at the same distance range, objects have similar error distribution across the camera's horizontal view. The horizontal axis (x-pixel) error appears to be smaller while the distance moves further away. However, the x-pixel location along a constant y-pixel row has no impact on the error distribution. The estimated x and y-pixel error distributions can in turn be used to form a spatial error distribution for finding the location of a detected object within a certain confidence interval. The spatial errors are then projected onto the world coordinate system using a camera transformation matrix to give a more realistic sense of what this error means. The results show that location estimation using monocular cameras generates an elliptical error distribution around the object with a larger error in the y-pixel direction compared to the x-pixel direction. This error distribution can be important to fuse information from multiple range-detecting sensors as well as multi-vehicle and multi-object tracking. The uncertainty characterization for position measurement, as demonstrated in this paper is an essential element of tracking and, is sensor and algorithm dependent.

MedicalGuard: U-Net Model Robust against Adversarially Perturbed Images

Deep neural networks perform well for image recognition, speech recognition, and pattern analysis. This type of neural network has also been used in the medical field, where it has displayed good performance in predicting or classifying patient diagnoses. An example is the U-Net model, which has demonstrated good performance in data segmentation, an important technology in the field of medical imaging. However, deep neural networks are vulnerable to adversarial examples. Adversarial examples are samples created by adding a small amount of noise to an original data sample in such a way that to human perception they appear to be normal data but they will be incorrectly classified by the classification model. Adversarial examples pose a significant threat in the medical field, as they can cause models to misidentify or misclassify patient diagnoses. In this paper, I propose an advanced adversarial training method to defend against such adversarial examples. An advantage of the proposed method is that it creates a wide variety of adversarial examples for use in training, which are generated by the fast gradient sign method (FGSM) for a range of epsilon values. A U-Net model trained on these diverse adversarial examples will be more robust to unknown adversarial examples. Experiments were conducted using the ISBI 2012 dataset, with TensorFlow as the machine learning library. According to the experimental results, the proposed method builds a model that demonstrates segmentation robustness against adversarial examples by reducing the pixel error between the original labels and the adversarial examples to an average of 1.45.

Experimental Validation of Non-Marker Simple Image Displacement Measurements for Railway Bridges

Simple bridge displacement measurement using a video camera is effective in realizing the efficient management of numerous railway structures via condition-based maintenance. Although non-marker image measurement is significantly influenced by the measuring environment, its practical applicability considering the displacement measurement accuracy of non-marker images and the influence of various environments is not completely understood. In this study, the accuracy of non-marker image displacement measurement and the influence of illuminance are confirmed using a model bridge, and the accuracy and applicable range are discussed. Moreover, field tests on two bridges—a steel and a concrete bridge—on low-speed and high-speed railways confirm the accuracy and practical application of non-marker image measurement in a real environment. The displacement was observed to be measured with an accuracy of ~1/30 pixel (error of ~0.4 mm at 20 m position) in the daytime with sufficient brightness. Moreover, the settings for subset positions and post-processing methods to ensure accuracy in non-marker image measurement on concrete bridges with low surface contrast are discussed.

Regional Photometric Modeling of Asteroid (101955) Bennu

Abstract We present a regional photometric analysis of asteroid (101955) Bennu, using image data from the MapCam color imager of the OSIRIS-REx Camera Suite (OCAMS). This analysis follows the previously reported global photometric analysis of Bennu, which found that Bennu’s roughness was difficult to photometrically model owing to unresolved surface variation. Here we find that, even with a high-resolution shape model (20 cm per facet) and automatic image registration (&lt;1 pixel error), Bennu remains a challenging surface to photometrically model: neither a suite of empirical photometric models nor the physically motivated Hapke model were able to eliminate the scatter in the data due to pixel-scale variations. Nonetheless, the models improved on the global analysis by identifying regional variations in Bennu’s photometric response. A linear empirical model, when compared with independent measures of surface roughness and albedo, revealed correlations between those characteristics and phase slope. A regional Hapke analysis showed the same structure in its single-scattering albedo and asymmetry factors; although the Hapke parameters were loosely constrained, complicating interpretation of their spatial variation, the regional variation in relative parameter sensitivity also correlated with shallower phase slope, higher albedo, and less macroscopic roughness.

Cone beam CT based validation of neural network generated synthetic CTs for radiotherapy in the head region.

In the past years, many different neural network-based conversion techniques for synthesizing computed tomographys (sCTs) from MR images have been published. While the model's performance can be checked during the training against the test set, test datasets can never represent the whole population. Conversion errors can still occur for special cases, for example, for unusual anatomical situations. Therefore, the performance of sCT conversion needs to be verified on a patient specific level, especially in the absence of a planning CT (pCT). In this study, the capability of cone-beam CTs (CBCTs) for the validation of sCTs generated by a neural network was investigated. 41 patients with tumors in the head region were selected. 20 of them were used for model training and 10 for validation. Different implementations of CycleGAN (with/without identity and feature loss) were used to generate sCTs. The pixel (MAE, RMSE, PSNR) and geometric error (DICE, Sensitivity, Specificity) values were reported to identify the best model. VMAT plans were created for the remaining 11 patients on the pCTs. These plans were re-calculated on sCTs and CBCTs. An automatic density overriding method ( ) and a population-based dose calculation method ( ) were employed for CBCT-based dose calculation. The dose distributions were analysed using 3D global gamma analysis, applying a threshold of 10% with respect to the prescribed dose. Differences in DVH metrics for the PTV and the organs-at-risk were compared among the dose distributions based on pCTs, sCTs, and CBCTs. The best model was the CycleGAN without identity and feature matching loss. Including the identity loss led to a metric decrease of 10% for DICE and a metric increase of 20-60HU for MAE. Using the 2%/2mm gamma criterion and pCT as reference, the mean gamma pass rates were 99.0 0.4% for sCTs. Mean gamma pass rate values comparing pCT and CBCT were 99.0 0.8% and 99.1 0.8% for the and , respectively. The mean gamma pass rates comparing sCT and CBCT resulted in 98.4 1.6% and 99.2 0.6% for and , respectively. The differences between the gamma-pass-rates of the sCT and two CBCT-based methods were not significant. The majority of deviations of the investigated DVH metrices between sCTs and CBCTs were within 2%. The dosimetric results demonstrate good agreement between sCT, CBCT, and pCT based calculations. A properly applied CBCT conversion method can serve as a tool for quality assurance procedures in an MR only radiotherapy workflow for head patients. Dosimetric deviations of DVH metrics between sCT and CBCTs of larger than 2% should be followed up. A systematic shift of approximately 1% should be taken into account when using the approach in an MR only workflow.

Evaluation of permissible pixel positioning errors for displaying computer-generated holograms in projection photolithography

The authors carried out the estimation of the permissible positioning errors when displaying reflective phase holograms intended for application in holographic photolithography on solid media using electron-beam lithography devices. The work deals with the projection of holographic photolithography based on computer generated Fresnel holograms. The synthesis of holograms involved mathematical modeling of the physical processes of hologram recording and reconstruction using the following parameters: the characteristic size of the binary object is 20 × 20 nm or 80 × 80 nm, the wavelength of the radiation is 13.5 nm, the pixel size of the hologram is 20 × 20 nm, the distance between the planes of the object and the hologram is from 20.4 to 31.6 microns, the angle of incidence of the reference wave is 14º42′. For each of the three objects used in the modeling (namely, “Angles”, “Line grid target” and “Enlarged angles”), four computer-generated holograms were synthesized with different values for standard deviation of the pixel positioning error. The simulation of these errors was carried out by violating the equidistance of the points (pixels) on the hologram aperture. The holograms distorted in this way were subjected to the standard procedure of numerical reconstruction in virtual space. Comparison of the quality of the images obtained at different values of the positioning errors of the hologram pixels made it possible to evaluate their influence on the quality of the reconstructed image. It has been shown that the criterion used for estimating the permissible value of the positioning error in analog holography cannot be applied to synthesized holograms, because of the peculiar properties of interference fringes in discrete holograms. The results demonstrated a significant dependence of the permissible (in terms of image quality) pixel positioning errors on the object presentation method. The analysis revealed the impossibility of applying a single tolerance for pixel positioning errors to all possible synthesis conditions of computer-generated holograms and hence indicates the necessity of including a feature for estimating permissible hologram positioning errors into the software package for the synthesis and reconstruction of holograms. Based on the analysis of the technological parameters of modern electron-beam lithography devices, the authors confirmed the possibility of their use for manufacturing computer-generated holograms in modern high-resolution photolithography. Modeling the permissible positioning errors of computer-generated holograms by the proposed method allows evaluating the practical possibility of producing holograms with the required structure and high quality of the reconstructed image with a specific electron beam lithography device.

A Universal Automatic Bottom Tracking Method of Side Scan Sonar Data Based on Semantic Segmentation

Determining the altitude of side-scan sonar (SSS) above the seabed is critical to correct the geometric distortions in the sonar images. Usually, a technology named bottom tracking is applied to estimate the distance between the sonar and the seafloor. However, the traditional methods for bottom tracking often require pre-defined thresholds and complex optimization processes, which make it difficult to achieve ideal results in complex underwater environments without manual intervention. In this paper, a universal automatic bottom tracking method is proposed based on semantic segmentation. First, the waterfall images generated from SSS backscatter sequences are labeled as water column (WC) and seabed parts, then split into specific patches to build the training dataset. Second, a symmetrical information synthesis module (SISM) is designed and added to DeepLabv3+, which not only weakens the strong echoes in the WC area, but also gives the network the capability of considering the symmetry characteristic of bottom lines, and most importantly, the independent module can be easily combined with any other neural networks. Then, the integrated network is trained with the established dataset. Third, a coarse-to-fine segmentation strategy with the well-trained model is proposed to segment the SSS waterfall images quickly and accurately. Besides, a fast bottom line search algorithm is proposed to further reduce the time consumption of bottom tracking. Finally, the proposed method is validated by the data measured with several commonly used SSSs in various underwater environments. The results show that the proposed method can achieve the bottom tracking accuracy of 1.1 pixels of mean error and 1.26 pixels of standard deviation at the speed of 2128 ping/s, and is robust to interference factors.

Real-Time Multi-Guidewire Endpoint Localization in Fluoroscopy Images.

The real-time localization of the guidewire endpoints is a stepping stone to computer-assisted percutaneous coronary intervention (PCI). However, methods for multi-guidewire endpoint localization in fluoroscopy images are still scarce. In this paper, we introduce a framework for real-time multi-guidewire endpoint localization in fluoroscopy images. The framework consists of two stages, first detecting all guidewire instances in the fluoroscopy image, and then locating the endpoints of each single guidewire instance. In the first stage, a YOLOv3 detector is used for guidewire detection, and a post-processing algorithm is proposed to refine the guidewire detection results. In the second stage, a Segmentation Attention-hourglass (SA-hourglass) network is proposed to predict the endpoint locations of each single guidewire instance. The SA-hourglass network can be generalized to the keypoint localization of other surgical instruments. In our experiments, the SA-hourglass network is applied not only on a guidewire dataset but also on a retinal microsurgery dataset, reaching the mean pixel error (MPE) of 2.20 pixels on the guidewire dataset and the MPE of 5.30 pixels on the retinal microsurgery dataset, both achieving the state-of-the-art localization results. Besides, the inference rate of our framework is at least 20FPS, which meets the real-time requirement of fluoroscopy images (6-12FPS).

UAV-Assisted Wide Area Multi-Camera Space Alignment Based on Spatiotemporal Feature Map

In this paper, we investigate the problem of aligning multiple deployed camera into one united coordinate system for cross-camera information sharing and intercommunication. However, the difficulty is greatly increased when faced with large-scale scene under chaotic camera deployment. To address this problem, we propose a UAV-assisted wide area multi-camera space alignment approach based on spatiotemporal feature map. It employs the great global perception of Unmanned Aerial Vehicles (UAVs) to meet the challenge from wide-range environment. Concretely, we first present a novel spatiotemporal feature map construction approach to represent the input aerial and ground monitoring data. In this way, the motion consistency across view is well mined to overcome the great perspective gap between the UAV and ground cameras. To obtain the corresponding relationship between their pixels, we propose a cross-view spatiotemporal matching strategy. Through solving relative relationship with the above air-to-ground point correspondences, all ground cameras can be aligned into one surveillance space. The proposed approach was evaluated in both simulation and real environments qualitatively and quantitatively. Extensive experimental results demonstrate that our system can successfully align all ground cameras with very small pixel error. Additionally, the comparisons with other works on different test situations also verify its superior performance.

A novel inequality-constrained weighted linear mixture model for endmember variability

Spectral unmixing has trigged considerable attention to both multispectral and hyperspectral data processing. Among different kinds of spectral unmixing models, the linear mixture model (LMM) has been widely applied with its simplicity in light scattering mechanisms and flexibility in different scenarios. Recently, a variety of optimized LMMs have been developed to enhance the decomposing ability. However, two major challenges still exist: endmember variability and global optimum solution with the inequality constraint. In this study, aiming to avoid the influence of endmember variability and to extend the application of LMM, a novel inequality-constrained weighted linear mixture model (IWLMM) is proposed. Based on the errors-in-variables (EIV) model and the theory of weighted total least squares (WTLS), the issue of endmember variability and stochastic errors of both endmembers and mixed pixels are eliminated. With the non-negativity constraint, a brief optimal algorithm to solve the IWLMM is generated as well. In order to indicate the advantages of proposed IWLMM, comparative experiments with the conventional fully constrained least squares (FCLS), three currently proposed LMM-based models (perturbed LMM (PLMM), extended LMM (ELMM) and augmented LMM (ALMM)) and three physics-based nonlinear mixture models (bilinear-Fan model (BFM), generalized bilinear model (GBM) and polynomial post-nonlinear model (PPNM)) on four synthetic and real scenarios with hyperspectral and multispectral datasets were performed. The experimental results suggest the IWLMM outperforms the other seven linear and nonlinear unmixing models after adding the correction for endmember variability by modeling the perturbation and scaling factors of spectral features simultaneously. It has an impressive ability for decomposing and reconstructing pixels.

Application of Deep Learning for Speckle Removal in GOCI Chlorophyll-a Concentration Images (2012–2017)

The detection and removal of erroneous pixels is a critical pre-processing step in producing chlorophyll-a (chl-a) concentration values to adequately understand the bio-physical oceanic process using optical satellite data. Geostationary Ocean Color Imager (GOCI) chl-a images revealed that numerous speckle noises with enormously high and low values were randomly scattered throughout the seas around the Korean Peninsula as well as in the Northwest Pacific. Most of the previous methods used to remove abnormal chl-a concentrations have focused on inhomogeneity in spatial features, which still frequently produce problematic values. Herein, a scheme was developed to detect and eliminate chl-a speckles as well as erroneous pixels near the boundary of clouds; for the purpose, a deep neural network (DNN) algorithm was applied to a large-sized GOCI database from the 6-year period of 2012–2017. The input data of the proposed DNN model were composed of the GOCI level-2 remote-sensing reflectance of each band, chl-a concentration image, median filtered, and monthly climatology chl-a image. The quality of the individual images as well as the monthly composites of chl-a data was improved remarkably after the DNN speckle-removal procedure. The quantitative analyses showed that the DNN algorithm achieved high classification accuracy with regard to the detection of error pixels with both very high and very low chl-a values, and better performance compared to the general arithmetic algorithms of the median filter and threshold scheme. This implies that the implemented method can be useful for investigating not only the short-term variations based on hourly chl-a data but also long-term variabilities with composite products of the GOCI chl-a concentration over the span of a decade.

Electrocardiogram transmission over OFDM system

Abstract Remote patient monitoring is an important area of research. Electrocardiogram (ECG) is a vital health parameter which can be remotely transmitted to monitor the patient’s health. In this field, there are many research directions which include ECG security, patient data hiding in ECG, ECG classification, ECG transmission and reception. In this paper, an effective methodology for ECG transmission over OFDM wireless communication system has been presented. Issues related to transmission of ECG as image have also been discussed. Before ECG is transmitted over OFDM analog ECG signal is to be converted into series of bits for which sampling and quantization has to be performed. The quantization error arises due to number of chosen samples and quantization levels. In this work, it is shown that the quantization error can be brought down to zero using symbolic aggregate approximation (SAX) for data representation. Performance of the ECG transmission using different methodologies has been compared using peak signal-to-noise ratio (PSNR) Structural Similarity Index Metric (SSIM), Bit Error Rate (BER) and Pixel Error Rate (PER).

A study of edge preserving filters in image matching

This article presents a study on edge preserving filters in image matching which comprises a development of stereo matching algorithm using two edge preserving filters. Fundamentally, the framework is reconstructed by several sequential processes. The output of these processes is a disparity map or depth map. The corresponding points between two images require accurate matching to make accurate depth map estimation. Thus, the propose work in this article utilizes sum of squared differences (SSD) with dual edge preserving filters. These filters are used due to edge preserved properties and to increase the accuracy. The median filter (MF) and bilateral filter (BF) will be utilized. The SSD produces preliminary results with low noise and the edge preserving filters reduce noise on the low texture regions with edge preserving properties. Based on the experimental analysis using the standard benchmarking evaluation system from the Middlebury, the disparity map produced is 6.65% for all error pixels. It shows an accurate edge preserved properties on the disparity maps. To make the proposed work more reliable with current available methods, the quantitative measurement has been made to compare with other existing methods and it displays the proposed work in this article perform much better.

Geolocation Error Estimation and Correction on Long-Term MWRI Data

Due to the limitation of the satellite attitude measurement accuracy and the system servo control error of the payload scanning mechanism, an optimal use of Micro-Wave Radiation Imager (MWRI) observations requires high geolocation accuracy. In the operational system, the MWRI geolocation accuracy reaches 1 pixel, and there still exists room for improvement. In this article, we improve upon the coastline inflection point method (CIM) and propose to assign the accurate correspondence by employing a nonrigid point set registration method. First, the method identifies a set of latent variables to recognize outliers and then applies nonparametric geometric constraints to the correspondence as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> distribution. Second, the maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> (MAP) estimation is applied by the expectation–maximization (EM) algorithm to obtain correct inliers. The comparison with other methods demonstrates that the proposed method can provide more accurate estimation of geolocation bias. In addition, the pixel error and changes in spacecraft attitude with the long-term geolocation data in FY-3C MWRI before and after correction were analyzed during the period from April 1 to August 30, 2018. The results have shown that the geolocation errors are reduced from [0.50, 0.60] pixels to [0.20, 0.33] pixels in the along- and cross-track directions after the attitude correction. In addition, the reduction of the standard deviation shows that the geolocation quality of MWRI is improved.

Fully convolutional network-based registration for augmented assembly systems

Image-based registration methods have been widely used in augmented assembly systems. However, many challenges remain to be solved, such as low robustness and poor timeliness during registration. This paper presents a deep learning approach for registration. To reduce the workload of data collection, an automatic picture generation method is offered for deep learning algorithm, and a dataset is built for detecting the keypoints of assembly objects. We propose a fully convolutional network (FCN) model for detecting keypoints from a single RGB image. The FCN model, which consists of 13 layers, can accurately detect the location of keypoints with a low computational resource consumption. The detected keypoints are used to solve the camera position and orientation for augmented assembly registration. The experiments demonstrate that our method is robust against different rotation angles of the assembly object and against background interference. The detection accuracy is high under different camera motion blurs; to be specific, the pixel error in different directions of 640 × 480 images was only 0.9 pixels. The FCN-based registration approach was shown to be fast during augmented assembly experiments, achieving up to 30 FPS.

Improvements in high-temporal resolution active fire detection and FRP retrieval over the Americas using GOES-16 ABI with the geostationary Fire Thermal Anomaly (FTA) algorithm

Geostationary imaging sensors offer unique high temporal resolution capabilities with which to characterise the fast-changing dynamics of landscape fires. The new R-Series of Geostationary Operational Environmental Satellite (GOES-R) are the most advanced geostationary weather satellites currently operating, and each carry the new Advanced Baseline Imager (ABI) which greatly improves on the spatial, temporal and radiometric characteristics of the previous GOES Imager. Here we develop and evaluate the first landscape fire radiative power (FRP) observation system for the first of the GOES-R series (GOES-16), comparing its outputs to those generated near simultaneously from the forerunner GOES-13 Imager and from the far higher spatial resolution, but far less frequently imaging, Moderate Spatial Resolution Imaging Spectroradiometer (MODIS). Across the Americas, when examining near-simultaneous data from August to September 2017, the geostationary Fire Thermal Anomaly (FTA) algorithm originally developed for use with Meteosat’s Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) enables GOES-16 ABI to detect substantially more (6%) active fire (AF) pixels than GOES-13 Imager, due mainly to the former sensors‘ smaller pixel area. ABI detects even greater numbers of AF pixels due to its higher temporal resolution, but here we are only comparing results from near-simultaneous imagery. The majority of fires having an FRP substantially less than ~30 ​MW remain undetected even by ABI, and since these are the most common fire type ABI still shows a high error of omission (68%) compared to MODIS centre of swath data (±30° scan angle). ABI AF pixel errors of commission are far lower than errors of omission however at ~12% when using MODIS as a benchmark. This appears far more favorable than results found when using the Fire Detection and Characterization (FDC) algorithm, an adaptation of the Wildfire Automated Biomass Burning Algorithm (WF_ABBA) algorithm long-applied to GOES, which when applied to ABI data was recently assessed to have an 88% commission error. When ABI and MODIS both successfully detect a fire at the same time there is strong agreement and low bias in terms of their retrieved FRP. Overall therefore we find that the FTA approach to generating AF data from GOES-16 ABI sensor appears well performing across the Americas, and is compatible with the data from this system joining that from Meteosat, Meteosat over India Ocean and Himawari-8 to become part of the global geostationary active fire observation system now reaching maturity. The AF detection and FRP data provided from GOES-16 operating as GOES-East, and also now GOES-17 operating from the GOES-West position, are now available in near real-time for use worldwide.

A Gradient-Based Recurrent Neural Network for Visual Servoing of Robot Manipulators with Acceleration Command

Recent decades have witnessed the rapid evolution of robotic applications and their expansion into a variety of spheres with remarkable achievements. This article researches a crucial technique of robot manipulators referred to as visual servoing, which relies on the visual feedback to respond to the external information. In this regard, the visual servoing issue is tactfully transformed into a quadratic programming problem with equality and inequality constraints. Differing from the traditional methods, a gradient-based recurrent neural network (GRNN) for solving the visual servoing issue is newly proposed in this article in the light of the gradient descent method. Then, the stability proof is presented in theory with the pixel error convergent exponentially to zero. Specifically speaking, the proposed method is able to impel the manipulator to approach the desired static point while maintaining physical constraints considered. After that, the feasibility and superiority of the proposed GRNN are verified by simulative experiments. Significantly, the proposed visual servo method can be leveraged to medical robots and rehabilitation robots to further assist doctors in treating patients remotely.

Adjusting fictitious domain parameters for fairly priced image-based modeling: Application to the regularization of Digital Image Correlation

The integration of numerical simulation and experimental measurements in cellular materials at the sub-cellular scale is a real challenge. On the experimental side, the almost absence of texture makes displacement fields measurement tricky. On the simulation side, it requires the construction of reliable and specimen-specific geometric and mechanical models from digital images. For this purpose, high order based fictitious domain approaches have proven to be an efficient alternative to boundary conforming finite elements for the analysis of geometrically complex objects. A number of discretization parameters needs to be set by the user by making a trade-off between accuracy and computational cost. In addition to numerical errors (interpolation, integration etc.), there are additional geometric and model errors due to the pixelation of the image (e.g., quantization, sampling, noise). In the literature, discretization parameters are often analyzed without taking pixelation into account, which can lead to over-calculations. In this paper, these parameters are adjusted to obtain (a) the best possible accuracy (bounded by pixelation errors) while (b) ensuring minimal complexity (concept of fair price). In order to analyze the different sources of error, various two-dimensional synthetic experiments are generated by mimicking the image acquisition process from high-resolution numerical simulations considered as a reference. The approach leads to a modeling that outperforms conventional approaches both in terms of accuracy and complexity. Eventually, it is shown that the presented image-based models provide a unique opportunity to assist digital volume correlation and allow the measurement of relevant local kinematics within cellular materials.