Absolute Map Research Articles

High dynamic range mapping for the evaluation of parallel transmit arrays.

Demonstration of a high dynamic-range and high SNR method for acquiring absolute maps from a combination of gradient echo and actual-flip-angle measurements that is especially useful during the construction of parallel-transmit arrays. Low flip angle gradient echo images, acquired when transmitting with each channel individually, are used to compute relative maps. Instead of computing these in a conventional manner, the equivalence of the problem to the ESPIRiT parallel image reconstruction method is used to compute maps with a higher SNR. Absolute maps are generated by calibration against a single actual flip-angle acquisition when transmitting on all channels simultaneously. Depending on the number of receiver channels and the location of the receive elements with respect to the subject being investigated, moderate to high gains in the SNR of the acquired maps can be achieved. The proposed method is especially suited for the acquisition of maps during the construction of transceiver arrays. Compared to the original method, maps with higher SNR can be computed without the need for additional measurements, and maps can also be generated using previously acquired data. Furthermore, easy adoption and fast estimation of receiver channels is possible because of existing highly optimized open-source implementations of ESPIRiT, such as in the BART toolbox.

Monocular Absolute Depth Estimation from Motion for Small Unmanned Aerial Vehicles by Geometry-Based Scale Recovery.

In recent years, there has been extensive research and application of unsupervised monocular depth estimation methods for intelligent vehicles. However, a major limitation of most existing approaches is their inability to predict absolute depth values in physical units, as they generally suffer from the scale problem. Furthermore, most research efforts have focused on ground vehicles, neglecting the potential application of these methods to unmanned aerial vehicles (UAVs). To address these gaps, this paper proposes a novel absolute depth estimation method specifically designed for flight scenes using a monocular vision sensor, in which a geometry-based scale recovery algorithm serves as a post-processing stage of relative depth estimation results with scale consistency. By exploiting the feature correspondence between successive images and using the pose data provided by equipped navigation sensors, the scale factor between relative and absolute scales is calculated according to a multi-view geometry model, and then absolute depth maps are generated by pixel-wise multiplication of relative depth maps with the scale factor. As a result, the unsupervised monocular depth estimation technology is extended from relative depth estimation in semi-structured scenes to absolute depth estimation in unstructured scenes. Experiments on the publicly available Mid-Air dataset and customized data demonstrate the effectiveness of our method in different cases and settings, as well as its robustness to navigation sensor noise. The proposed method only requires UAVs to be equipped with monocular camera and common navigation sensors, and the obtained absolute depth information can be directly used for downstream tasks, which is significant for this kind of vehicle that has rarely been explored in previous depth estimation studies.

Mapping the strain-stiffening behavior of the lung and lung cancer at microscale resolution using the crystal ribcage.

Lung diseases such as cancer substantially alter the mechanical properties of the organ with direct impact on the development, progression, diagnosis, and treatment response of diseases. Despite significant interest in the lung's material properties, measuring the stiffness of intact lungs at sub-alveolar resolution has not been possible. Recently, we developed the crystal ribcage to image functioning lungs at optical resolution while controlling physiological parameters such as air pressure. Here, we introduce a data-driven, multiscale network model that takes images of the lung at different distending pressures, acquired via the crystal ribcage, and produces corresponding absolute stiffness maps. Following validation, we report absolute stiffness maps of the functioning lung at microscale resolution in health and disease. For representative images of a healthy lung and a lung with primary cancer, we find that while the lung exhibits significant stiffness heterogeneity at the microscale, primary tumors introduce even greater heterogeneity into the lung's microenvironment. Additionally, we observe that while the healthy alveoli exhibit strain-stiffening of ∼1.75 times, the tumor's stiffness increases by a factor of six across the range of measured transpulmonary pressures. While the tumor stiffness is 1.4 times the lung stiffness at a transpulmonary pressure of three cmH2O, the tumor's mean stiffness is nearly five times greater than that of the surrounding tissue at a transpulmonary pressure of 18 cmH2O. Finally, we report that the variance in both strain and stiffness increases with transpulmonary pressure in both the healthy and cancerous lungs. Our new method allows quantitative assessment of disease-induced stiffness changes in the alveoli with implications for mechanotransduction.

A Foggy Weather Simulation Algorithm for Traffic Image Synthesis Based on Monocular Depth Estimation.

This study addresses the ongoing challenge for learning-based methods to achieve accurate object detection in foggy conditions. In response to the scarcity of foggy traffic image datasets, we propose a foggy weather simulation algorithm based on monocular depth estimation. The algorithm involves a multi-step process: a self-supervised monocular depth estimation network generates a relative depth map and then applies dense geometric constraints for scale recovery to derive an absolute depth map. Subsequently, the visibility of the simulated image is defined to generate a transmittance map. The dark channel map is then used to distinguish sky regions and estimate atmospheric light values. Finally, the atmospheric scattering model is used to generate fog simulation images under specified visibility conditions. Experimental results show that more than 90% of fog images have AuthESI values of less than 2, which indicates that their non-structural similarity (NSS) characteristics are very close to those of natural fog. The proposed fog simulation method is able to convert clear images in natural environments, providing a solution to the problem of lack of foggy image datasets and incomplete visibility data.

Stitching interferometry for long X-ray mirrors with lateral multi-shift-based absolute calibration.

To eliminate the reference mirror (REF) error of the Fizeau interferometer for measuring X-ray mirrors, the reference calibration method of lateral multi-shift measurements at the hundreds-micrometer pixel level is presented. Because of the high aspect ratio of long X-ray mirrors, by shifting the surface under test (SUT) along the tangential direction with integer multiple pixels, we extend the calibration method by using the difference between multiple shifted measurements to build an augmented multi-matrix for extracting the two-dimensional (2D) absolute surface. The method can be applied to arbitrary measurement regions of the test optics, and the measurement for both the benchmark sub-aperture and calibration of the REF is accomplished in a single measuring process. Furthermore, by adjusting the shift to the millimeter scale, reference-subtracted sub-apertures can be stitched to obtain the absolute 2D map of the X-ray mirror. Experimental results show that all the 2D discrepancies reach sub-nanometer repeatability, and comparison results between the long trace profiler (LTP) and the proposed method have been performed. Therefore, the results demonstrated that the proposed method meets the requirements of X-ray mirror measurements.

Spatially explicit estimation of freshwater fish stock biomass with limited data: A case study of an endangered endemic fish on the Tibetan Plateau, China

Accurate evaluation of fish stock biomass is essential for effective conservation management and targeted species enhancement efforts. However, this remains challenging owing to limited data availability. Therefore, we present an integrated modeling framework combining catch per unit effort with ensemble species distribution modeling called CPUESDM, which explicitly assesses and validates the spatial distribution of stock biomass for freshwater fish species with limited data, applied to Herzensteinia microcephalus. The core algorithm incorporates the Leslie regression model, ensemble species distribution modeling, and exploratory spatial interpolation techniques. We found that H. microcephalus biomass in the Yangtze River source area yielded an initial estimate of 113.52 tons. Our validation results demonstrate high accuracy with a Cohen's kappa coefficient of 0.78 and root mean square error of 0.05. Furthermore, our spatially-explicit, global, absolute biomass density map effectively identified areas with high and low concentrations of biomass distribution centers. Additionally, this study offers access to the source code, example raw data, and a step-by-step instruction manual for other researchers using field data to explore the application of this model. Our findings can help inform for future conservation efforts around fish stock biomass estimation, especially for endangered species.

Whole-liver flip-angle shimming at 7 T using parallel-transmit kT -point pulses and Fourier phase-encoded DREAM mapping.

To obtain homogeneous signal throughout the human liver at 7 T. Flip angle (FA) shimming in 7T whole-liver imaging was performed through parallel-transmit kT -pointpulses based on subject-specific multichannel absolute maps from Fourier phase-encoded dual refocusing echo acquisition mode (PE-DREAM). The optimal number of Fourier phase-encoding steps for PE-DREAM mapping was determined for a 7T eight-channel parallel-transmission system. FA shimming experiments were performed in the liver of 7 healthy subjects with varying body mass index. In these subjects, first shimming and Fourier PE-DREAM mapping were performed. Subsequently, three small-flip-angle 3D gradient-echo scans were acquired, comparing a circularly polarized (CP) mode, a phase shim, and a kT -point pulse. Resulting homogeneity was assessed and compared with estimated FA maps and distributions. Fourier PE-DREAM with 13 phase-encoding steps resulted in a good tradeoff between accuracy and scan time. Lower coefficient of variation values (average [min-max] across subjects) of the estimated FA in the volume of interest were observed using kT -points (7.4 [6.6%-8.0%]), compared with phase shimming (18.8 [12.9%-23.4%], p < 0.001) and CP (43.2 [39.4%-47.1%], p < 0.001). kT -points delivered whole-liver images with the nominal FA and the highest degree of homogeneity. CP and phase shimming resulted in either inaccurate or imprecise FA distributions. Here, locations having suboptimal FA in the estimated FA maps corresponded to liver areas suffering from inconsistent signal intensity and T1 -weighting in the gradient-echo scans. Homogeneous whole-liver 3D gradient-echo acquisitions at 7 T can be obtained with eight-channel kT -point pulses calculated based on subject-specific multichannel absolute Fourier PE-DREAM maps.

Passive Guided Wave Tomography for Erosion Monitoring in an Air/Sand Test Loop

The detection and quantification of defects in pipes by non-destructive testing techniques plays a crucial role in several industries (petrochemical and nuclear in particular). Once a defect is detected, assessing its severity enables to schedule maintenance appropriately, hence reducing maintenance cost and rupture or leakage risks. Structural Health Monitoring (SHM) is an approach that consists in instrumenting a structure with permanent sensors to monitor its health status throughout its life. Guided elastic waves are particularly suitable for SHM applications on thin structures (such as pipes) thanks to their ability to propagate over long distances and their high sensitivity. An SHM approach called "passive guided waves tomography" has emerged in recent years to detect and quantify corrosion in pipes (or plate-like structures). This technology is based on the combination of guided elastic wave tomography algorithms and a passive method such as the so-called ambient noise cross-correlation. It allows to obtain absolute and precise maps of the thickness of an area surrounded by a distribution of sensors without emitting waves, simply by analyzing the elastic noise that exists naturally in the pipe (due to vibrations, fluid-structure interactions with a moving fluid, etc.). In this paper passive guided waves tomography is tested in representative conditions thanks to an erosion loop that circulates air and sand. By impacting the tube wall, sand grains erode the structure, creating thickness losses that we monitor with passive tomography.

Automatic Tree Height Measurement Based on Three-Dimensional Reconstruction Using Smartphone.

Tree height is a crucial structural parameter in forest inventory as it provides a basis for evaluating stock volume and growth status. In recent years, close-range photogrammetry based on smartphone has attracted attention from researchers due to its low cost and non-destructive characteristics. However, such methods have specific requirements for camera angle and distance during shooting, and pre-shooting operations such as camera calibration and placement of calibration boards are necessary, which could be inconvenient to operate in complex natural environments. We propose a tree height measurement method based on three-dimensional (3D) reconstruction. Firstly, an absolute depth map was obtained by combining ARCore and MidasNet. Secondly, Attention-UNet was improved by adding depth maps as network input to obtain tree mask. Thirdly, the color image and depth map were fused to obtain the 3D point cloud of the scene. Then, the tree point cloud was extracted using the tree mask. Finally, the tree height was measured by extracting the axis-aligned bounding box of the tree point cloud. We built the method into an Android app, demonstrating its efficiency and automation. Our approach achieves an average relative error of 3.20% within a shooting distance range of 2-17 m, meeting the accuracy requirements of forest survey.

Representing the photobiological dimension of light in northern architecture

Daylight can enhance the quality and inhabitability of architecture through a better relationship with the exterior environment, especially by its intensity, chromaticity and ability to synchronize the human circadian clock. Daylight integration in architecture remains a challenge in Nunavik (Quebec, Canada) due to its subarctic climate, photoperiod and solar geometry. The objective of this research is to implement photobiological metrics of light in architectural representations by isolating the photopic (daytime vision) and melanopic (circadian clock) portions of the electromagnetic spectrum, and to spatialize daylight and artificial light in relation to landscapes and indoor architectural spaces. An automated and low-cost capture tool based on Raspberry Pi microcomputers and Camera Modules (RPiCM) captures high dynamic range images, which accurately measure luminance to render human perception. Absolute photopic luminance maps (cd/m2) are supplemented with false colour displays of photopic/melanopic contents of light regarding building surface materials. The research develops photometric captures of absolute photopic and melanopic illuminance (lux, EML). Photobiological metrics of light are integrated into a set of physical properties of lighting patterns to perform light assessments and are ultimately represented as a graphical display to help designers and researchers to evaluate architectural interior–exterior relationships through daylight qualities.

Guiding severely anisotropic twinning bands in Magnesium: An in situ investigation by full-field microscopic image correlation

A critical issue for lightweight Magnesium alloys is their propensity for strain localization at the component scale, linked to the peculiar twinning phenomenon. For the (c-axes-aligned) rolling texture, highly compact coordinated twinning bands (CTBs) form that, uniquely, have a singular plane of shear. Normally, where these bands emerge, and progress are up to stochastic factors. Here, a cross-notched sample design is nominated to guide the bands of conjugate orientations into predetermined diagonal corridors and enforce their overlap at a prefixed location. The deformation fields in the Magnesium AZ31 sample are in situ monitored with an advanced microscopic image correlation (micro-DIC) variant that has a unique combination of extreme field coverage (∼1.5 ×105 grains), intragranular resolution (∼102 data points per grain) and very high time-step resolution (0.05–0.1% nominal strain increments over the twin plateau). The latter allows investigating the emergence, advance, lateral growth, and interaction of CTBs with extreme detail over absolute and sequential micro-DIC strain maps. The sample design is successful in guiding the CTB formations into the designated corridors. Seed segments of CTBs first make a cross connection across the sample, followed by lateral expansion that proceeds until the corridor is roughly filled. This happens sequentially for the two diagonal corridors, forcing the second band to pass through the first. The band strain is reduced by about 25% at the enforced overlap compared to the characteristic intensity it exhibits outside it (around 2.3%)—a direct measurement of the dilution of the strain activity of a CTB as it crosses another CTB. The uniqueness of this application in guiding severely anisotropic bands is contrasted with the micro-DIC fields of a transversely isotropic sample of the same material that is analogously put under a twin-dominated deformation.

Long-range photon-efficient 3D imaging without range ambiguity.

Single-photon light detection and ranging (LiDAR) has broad applications ranging from remote sensing to target recognition. In most cases, however, the repetition period of the pulsed laser limits the maximum distance that can be unambiguously determined. The relative distances are normally obtained using a depth map. Here, we propose and demonstrate a photon-efficient three-dimensional (3D) imaging framework which permits the operation of high laser pulse repetition rates for long-range depth imaging without range ambiguity. Our approach uses only one laser period per pixel and borrows the information from neighboring pixels to reconstruct the absolute depth map of the scene. We demonstrate the absolute depth map recovery at ranges between 2.2 km and 13.8 km using ∼1.41 signal photons per pixel. We also show the capability to image the absolute distances of moving targets in real time.

Spontaneous cortical dynamics from the first years to the golden years

In the largest and most expansive lifespan magnetoencephalography (MEG) study to date (n = 434, 6 to 84 y), we provide critical data on the normative trajectory of resting-state spontaneous activity and its temporal dynamics. We perform cutting-edge analyses to examine age and sex effects on whole-brain, spatially-resolved relative and absolute power maps, and find significant age effects in all spectral bands in both types of maps. Specifically, lower frequencies showed a negative correlation with age, while higher frequencies positively correlated with age. These correlations were further probed with hierarchical regressions, which revealed significant nonlinear trajectories in key brain regions. Sex effects were found in absolute but not relative power maps, highlighting key differences between outcome indices that are generally used interchangeably. Our rigorous and innovative approach provides multispectral maps indicating the unique trajectory of spontaneous neural activity across the lifespan, and illuminates key methodological considerations with the widely used relative/absolute power maps of spontaneous cortical dynamics.

Inclusion of seismic attributes in reservoir ensemble coverage analysis

There is a lack of quality-control (QC) methods to ensure measured seismic data are within the span of modeled seismic data in the context of ensemble-based seismic history matching of reservoir models. The dimensionality of seismic data makes it difficult to visualize the data and further compare them to the large number of ensembles in an efficient manner. Two attributes called coverage and importance are introduced to incorporate the key elements of reviewing an ensemble. The coverage attribute delineates where the set of models replicates the measured data, and the importance attribute identifies where it is important to fit the data above the noise threshold. The two attributes are then combined to highlight in which spatial area our reservoir model ensemble appropriately models the data and where a significant discrepancy exists between our ensemble of models and the measured data. The attributes are closely connected to noise, as coverage always must be analyzed in terms of the noise level. Although noise may not be explicitly corrected for, the methodology corrects the attributes for the noise assessed. The method is applied on two data examples from field seismic data: a 4D absolute difference amplitude map and a 4D relative impedance difference cube. The first example shows how changing the oil-water contact of the ensemble can improve the coverage without any history matching, and the second shows how it is more difficult to get a good coverage using 3D seismic attributes rather than using 2D maps of seismic data. The proposed QC attributes provide tools to better manage coverage of seismic data in the ensemble.

Vector bundles on flag varieties

AbstractWe study vector bundles on flag varieties over an algebraically closed field k. In the first part, we suppose to be the Grassmannian parameterizing linear subspaces of dimension d in , where k is an algebraically closed field of characteristic . Let E be a uniform vector bundle over G of rank . We show that E is either a direct sum of line bundles or a twist of the pullback of the universal subbundle or its dual by a series of absolute Frobenius maps. In the second part, splitting properties of vector bundles on general flag varieties in characteristic zero are considered. We prove a structure theorem for bundles over flag varieties which are uniform with respect to the ith component of the manifold of lines in . Furthermore, we generalize the Grauert–Mlich–Barth theorem to flag varieties. As a corollary, we show that any strongly uniform i‐semistable bundle over the complete flag variety splits as a direct sum of special line bundles.

Brain-Inspired Navigation Model Based on the Distribution of Polarized Sky-Light

This paper proposes a brain-inspired navigation model based on absolute heading for the autonomous navigation of unmanned platforms. The proposed model combined the sand ant’s strategy of acquiring absolute heading from the sky environment and the brain-inspired navigation system, which is closer to the navigation mechanism of migratory animals. Firstly, a brain-inspired grid cell network model and an absolute heading-based head-direction cell network model were constructed based on the continuous attractor network (CAN). Then, an absolute heading-based environmental vision template was constructed using the line scan intensity distribution curve, and the path integration error was corrected using the environmental vision template. Finally, a topological cognitive node was constructed according to the grid cell, the head direction cell, the environmental visual template, the absolute heading information, and the position information. Numerous topological nodes formed the absolute heading-based topological map. The model is a topological navigation method not limited to strict geometric space scale, and its position and absolute heading are decoupled. The experimental results showed that the proposed model is superior to the other methods in terms of the accuracy of visual template recognition, as well as the accuracy and topology consistency of the constructed environment topology map.

Visual Attention-Based Self-Supervised Absolute Depth Estimation Using Geometric Priors in Autonomous Driving

Although existing monocular depth estimation methods have made great progress, predicting an accurate absolute depth map from a single image is still challenging due to the limited modeling capacity of networks and the scale ambiguity issue. In this paper, we introduce a fully Visual Attention-based Depth (VADepth) network, where spatial attention and channel attention are applied to all stages. By continuously extracting the dependencies of features along the spatial and channel dimensions over a long distance, VADepth network can effectively preserve important details and suppress interfering features to better perceive the scene structure for more accurate depth estimates. In addition, we utilize geometric priors to form scale constraints for scale-aware model training. Specifically, we construct a novel scale-aware loss using the distance between the camera and a plane fitted by the ground points corresponding to the pixels of the rectangular area in the bottom middle of the image. Experimental results on the KITTI dataset show that this architecture achieves the state-of-the-art performance and our method can directly output absolute depth without post-processing. Moreover, our experiments on the SeasonDepth dataset also demonstrate the robustness of our model to multiple unseen environments.

Probabilistic volcanic hazard assessment at an active but under-monitored volcano: Ceboruco, Mexico

A probabilistic volcanic hazard assessment (PVHA) for Ceboruco volcano (Mexico) is reported using PyBetVH, an e-tool based on the Bayesian Event Tree (BET) methodology. Like many volcanoes, Ceboruco is under-monitored. Despite several eruptions in the late Holocene and efforts by several university and government groups to create and sustain a monitoring network, this active volcano is monitored intermittently rather than continuously by dedicated groups. With no consistent monitoring data available, we look at the geology and the eruptive history to inform prior models used in the PVHA. We estimate the probability of a magmatic eruption within the next time window (1 year) of ~ 0.002. We show how the BET creates higher probabilities in the absence of monitoring data, which if available would better inform the prior distribution. That is, there is a cost in terms of higher probabilities and higher uncertainties for having not yet developed a sustained volcano monitoring network. Next, three scenarios are developed for magmatic eruptions: i) small magnitude (effusive/explosive), ii) medium magnitude (Vulcanian/sub-Plinian) and iii) large magnitude (Plinian). These scenarios are inferred from the Holocene history of the volcano, with their related hazardous phenomena: ballistics, tephra fallout, pyroclastic density currents, lahars and lava flows. We present absolute probability maps (unconditional in terms of eruption size and vent location) for a magmatic eruption at Ceboruco volcano. With PyBetVH we estimate and visualize the uncertainties associated with each probability map. Our intent is that probability maps and uncertainties will be useful to local authorities who need to understand the hazard when considering the development of long-term urban and land-use planning and short-term crisis management strategies, and to the scientific community in their efforts to sustain monitoring of this active volcano.

Deteksi Batimetri Perairan Dangkal di Pulau Menjangan, Provinsi Bali Menggunakan Citra Landsat

Remote sensing-based research in Indonesia using satellite imagery frequently faces the challenge of cloud coverage due to the tropical country. One spatial data that can be extracted from satellite imagery is bathymetry. However, cloud-covered water bathymetric extraction still needs to be examined. This study aims to understand the ability of Landsat 7 ETM+ acquired on 29 July 2013, and Landsat 8, acquired on 24 July 2020, as the representative of non-cloudy image compared to Landsat 8, acquired on 9 August 2020, as the cloudy image. Stumpf algorithm was applied, including a statistical approach of linear regression analysis with in-situ data measurement from Single Beam Echo-Sounder (SBES) to derive the absolute bathymetric map with several classes of depth ranging from 0 – 2 m up to 10 m. To assess the accuracy, RMSE and confusion matrix was used. The result shows that Landsat 7 ETM+ yields the highest R2 with 0,52, while the lowest total RMSE (8,167 m) and highest overall accuracy of about 69% from the confusion matrix was achieved by the cloudy image of Landsat 8. Nevertheless, the highest absolute depth value yield by Landsat 8 non-cloudy image with 16,1 m. This research confirms that the highest R2 value does not always produce the best model, but it is still promised to be used. Furthermore, the quality of the imagery based on its percentage of cloud coverage is affecting the resulted model.

Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms

Surface ocean dynamics play a key role in the Earth system, contributing to regulate its climate and affecting the marine ecosystem functioning. Dynamical processes occur and interact in the upper ocean at multiple scales, down to, or even less than, few kilometres. These scales are not adequately resolved by present observing systems, and, in the last decades, global monitoring of surface currents has been based on the application of geostrophic balance to absolute dynamic topography maps obtained through the statistical interpolation of along-track satellite altimeter data. Due to the cross-track distance and repetitiveness of satellite acquisitions, the effective resolution of interpolated data is limited to several tens of kilometres. At the kilometre scale, sea surface temperature pattern evolution is dominated by advection, providing indirect information on upper ocean currents. Computer vision techniques are perfect candidates to infer this dynamical information from the combination of altimeter data, surface temperature images and observing-system geometry. Here, we exploit one class of image processing techniques, super-resolution, to develop an original neural-network architecture specifically designed to improve absolute dynamic topography reconstruction. Our model is first trained on synthetic observations built from a numerical general-circulation model and then tested on real satellite products. Provided concurrent clear-sky thermal observations are available, it proves able to compensate for altimeter sampling/interpolation limitations by learning from primitive equation data. The algorithm can be adapted to learn directly from future surface topography, and eventual surface currents, high-resolution satellite observations.