High-Resolution Object Deformation Reconstruction with Active Range Camera

Space-borne Differential Interferometric Synthetic Aperture Radar (DInSAR) has been extensively used in the last two decades to measure ground surface deformation, providing key information for the characterization and understanding of many natural and anthropogenic processes. However, conventional DInSAR technique measures only one component of the surface deformation (i.e. the satellite's line-of-sight (LOS)), causing the interpretation of DInSAR measurements to be challenging and potentially narrowing the understanding of the mechanisms and dynamics of the deformation processes at work. Presently available methods that estimate 3D surface deformation from DInSAR generally operate on individual interferograms and therefore do not produce 3D surface deformation time series. However, the availability of time series is essential for studying surface deformation processes, bringing information on, e.g., temporally and spatially-variable external forcing conditions and characteristics of future deformation patterns. The Multidimensional Small Baseline Subset (MSBAS) method was already able to produce 2D (east and vertical) surface deformation time series from multi-tracks and multi-sensors DInSAR data. Here we propose a novel version of the MSBAS (MSBAS-3D) method that can produce 3D (north, east, and vertical) surface deformation time series from ascending and descending DInSAR data. This new method proposes measuring the surface deformation for processes producing motion parallel to the surface, such as landslides and glacier flows, while conserving the DInSAR accuracy. The ability of MSBAS-3D to capture the full 3D deformation pattern of processes with a surface signature is illustrated for a large, slow-moving, deep-seated landslide, for which long DInSAR and dGNSS time series, as well as ground truth data, are available. Surface deformation is measured over a four-year period using 1D (LOS), 2D and 3D MSBAS methods, and the advantages and limitations of each approach are described. In this case, the novel MSBAS-3D technique produces superior results that greatly simplify interpretation of the processes at work. The MSBAS-3D software can be downloaded from http://insar.ca/.

UC Davis has fabricated an ultrashort pulse reflectometer (USPR) diagnostic instrument for electron density profile measurements on compact, short duration, magnetically-confined fusion-energy concept devices such as spheromaks and FRCs. The USPR system transmits extremely short duration (~few nsec) chirped waveforms that together span 29 to 75 GHz. These chirped waveforms illuminate and reflect from the target plasma, with each frequency component reflecting from a different density layer (higher frequencies probe deeper into the plasma before reflecting). The reflected waveforms are split into roughly 42 different frequencies; time-of-flight (TOF) measurements made at each frequency with high resolution (~25 psec measurement resolution which corresponds to ~5 mm). These TOF data may then be inverted via software to generate electron density profiles with high time resolution (~10 μsec). At the heart of the system is a field programmable gate array (FPGA) based controller which collects and processes all of the USPR data in addition to generating all of the control signals required for maximum flexibility. The FPGA controller has the software flexibility to be easily reconfigured for different plasma devices, and the entire system sufficiently compact to be easily and quickly transported between devices. A high speed impulse generator was transformed into a set of three ultrashort pulse transmitter chirps using a combination of dispersive waveguide, frequency doublers and high-pass filters. A mm-wave controller was fabricated to sequentially switch between the three chirps, directing the chirps one-by-one to three different mm-wave assemblies spanning 29-75 GHz. Each mm-wave assembly consists of a high power active multiplier chain which converts the transmitter chirp to higher frequencies, and a broadband mixer which downconverts the reflected waveform to the 2-18 GHz range of the UPSR receiver. The 16-channel receiver (shared by all 3 mm-wave assemblies) was fabricated employing custom TOF modules capable of operating at a high 1 MHz sampling rate. Laboratory testing of the full system revealed the presence of unwanted harmonics from the multiplication process, with interference observed in the downconverted reflections at selected frequency channels that could not be completely filtered out. Additional interference effects arising from internal reflections within the mm-wave assemblies were minimized using a high-speed switch which served to “gate out” much of these reflections. The USPR diagnostic was transported and installed onto the HIT-SIU plasma device, becoming operational on 11/08/2022. Although designed to span 3 distinct mm-wave bands, the HIT-SIU plasmas at this time were sufficiently low density such that only the lowest of the three bands was likely to have strong plasma reflections. The system was then set to operate on only the lowest band (assembly #1), with data collected every 1 μsec rather than 3 μsec which would have been the case when cycling through all three bands. Connected to HIT-SIU, time-varying plasma reflections were observed on 9 of 16 possible frequency channels. Close examination of the data collected revealed issues previously unobserved in laboratory testing, associated with (a) reflections from the small aperture horns required for operation within the HIT-SIU device, and (b) a dependence of the recorded TOF with the threshold voltage of a given channel. Plans were made to address each of these issues before undertaking any future campaigns.

STF-Trans: A two-stream spatiotemporal fusion transformer for very high resolution satellites images

Time-of-flight (TOF) positron emission tomography (PET) scanners offer the potential for significantly improved signal-to-noise ratio (SNR) and lesion detectability in clinical PET. However, fully 3D TOF PET image reconstruction is a challenging task due to the huge data size. One solution to this problem is to rebin TOF data into a lower dimensional format. We have recently developed Fourier rebinning methods for mapping TOF data into non-TOF formats that retain substantial SNR advantages relative to sinograms acquired without TOF information. However, mappings for rebinning into non-TOF formats are not unique and optimization of rebinning methods has not been widely investigated. In this paper we address the question of optimal rebinning in order to make full use of TOF information. We focus on FORET-3D, which approximately rebins 3D TOF data into 3D non-TOF sinogram formats without requiring a Fourier transform in the axial direction. We optimize the weighting for FORET-3D to minimize the variance, resulting in H(2)-weighted FORET-3D, which turns out to be the best linear unbiased estimator (BLUE) under reasonable approximations and furthermore the uniformly minimum variance unbiased (UMVU) estimator under Gaussian noise assumptions. This implies that any information loss due to optimal rebinning is as a result only of the approximations used in deriving the rebinning equation and developing the optimal weighting. We demonstrate using simulated and real phantom TOF data that the optimal rebinning method achieves variance reduction and contrast recovery improvement compared to nonoptimized rebinning weightings. In our preliminary study using a simplified simulation setup, the performance of the optimal rebinning method was comparable to that of fully 3D TOF MAP.

Abstract. The ability to detect convective regions and to add latent heating to drive convection is one of the most important additions to short-term forecast models such as National Oceanic and Atmospheric Administration's (NOAA's) High-Resolution Rapid Refresh (HRRR) model. Since radars are most directly related to precipitation and are available in high temporal resolution, their data are often used for both detecting convection and estimating latent heating. However, radar data are limited to land areas, largely in developed nations, and early convection is not detectable from radars until drops become large enough to produce significant echoes. Visible and infrared sensors on a geostationary satellite can provide data that are more sensitive to small droplets, but they also have shortcomings: their information is almost exclusively from the cloud top. Relatively new geostationary satellites, Geostationary Operational Environmental Satellite-16 and Satellite-17 (GOES-16 and GOES-17), along with Himawari-8, can make up for this lack of vertical information through the use of very high spatial and temporal resolutions, allowing better observation of bubbling features on convective cloud tops. This study develops two algorithms to detect convection at vertically growing clouds and mature convective clouds using 1 min GOES-16 Advanced Baseline Imager (ABI) data. Two case studies are used to explain the two methods, followed by results applied to 1 month of data over the contiguous United States. Vertically growing clouds in early stages are detected using decreases in brightness temperatures over 10 min. For mature convective clouds which no longer show much of a decrease in brightness temperature, the lumpy texture from rapid development can be observed using 1 min high spatial resolution reflectance data. The detection skills of the two methods are validated against Multi-Radar/Multi-Sensor System (MRMS), a ground-based radar product. With the contingency table, results applying both methods to 1-month data show a relatively low false alarm rate of 14.4 % but missed 54.7 % of convective clouds detected by the radar product. These convective clouds were missed largely due to less lumpy texture, which is mostly caused by optically thick cloud shields above.

Initialisation using high spatial, temporal and spectral resolution satellite observations

We have irradiated bulk single crystal piezoelectric AlN operating as an ultrasonic transducer up to a fast and thermal neutron fluence of 1.85X1018 n/cm2 and 5.8X1018 n/cm2 respectively along with a gamma dose of 26.8 MGy. Our primary interest was in the radiation effects on the piezoelectric performance of the AlN crystal. Ideally standard procedures [2] for extracting material properties from the impedance spectra would have been applied. However, this procedure was anticipated to be very error prone due the effects of radiation on passive components, such as electrodes and cabling. As a result the transducer was coupled to an aluminum cylinder and operated in pulse echo mode throughout the irradiation. In addition to the pulse echo testing impedance data was obtained. Although, material parameters such as d 33 could not be measured in-situ during irradiation with our procedure, our work is the only publication, to the author's knowledge, showing the operation of an ultrasonic transducer in a reactor core. Further, the piezoelectric coefficient was measured prior to irradiation and found to be 5.5 pC/N which is unchanged form as grown samples, and in fact higher than the measured d33 for many as grown samples. Further proof of the viability of utilizing ultrasonic transducers in a reactor core is provided by means of successful time of flight (TOF) measurements throughout the irradiation. The TOF data was used to measure the gamma heating of an aluminum cylinder. The heating due to short lived isotopes in the Al was also detected via TOF.

While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties, the temporal resolution of the data is rather low, which can be easily made worse by cloud contamination. In contrast, although Moderate Resolution Imaging Spectroradiometer (MODIS) can only achieve a spatial resolution of 250 m in its normalised difference vegetation index (NDVI) product, it has a high temporal resolution, covering the Earth up to multiple times per day. To combine the high spatial resolution and high temporal resolution of different data sources, a new method (Spatial and Temporal Adaptive Vegetation index Fusion Model [STAVFM]) for blending NDVI of different spatial and temporal resolutions to produce high spatial–temporal resolution NDVI datasets was developed based on Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). STAVFM defines a time window according to the temporal variation of crops, takes crop phenophase into consideration and improves the temporal weighting algorithm. The result showed that the new method can combine the temporal information of MODIS NDVI and spatial difference information of HJ-1 CCD NDVI to generate an NDVI dataset with both high spatial and high temporal resolution. An application of the generated NDVI dataset in crop biomass estimation was provided. An average absolute error of 17.2% was achieved. The estimated winter wheat biomass correlated well with observed biomass (R 2 of 0.876). We conclude that the new dataset will improve the application of crop biomass estimation by describing the crop biomass accumulation in detail. There is potential to apply the approach in many other studies, including crop production estimation, crop growth monitoring and agricultural ecosystem carbon cycle research, which will contribute to the implementation of Digital Earth by describing land surface processes in detail.

There is an increasing demand for satellite remote sensing data with both high spatial and temporal resolution in many applications. But it still is a challenge to simultaneously improve spatial resolution and temporal frequency due to the technical limits of current satellite observation systems. To this end, much R&D efforts have been ongoing for years and lead to some successes roughly in two aspects, one includes super resolution, pan-sharpen etc. methods which can effectively enhance the spatial resolution and generate good visual effects, but hardly preserve spectral signatures and result in inadequate analytical value, on the other hand, time interpolation is a straight forward method to increase temporal frequency, however it increase little informative contents in fact. In this paper we presented a novel method to simulate high resolution time series data by combing low resolution time series data and a very small number of high resolution data only. Our method starts with a pair of high and low resolution data set, and then a spatial registration is done by introducing LDA model to map high and low resolution pixels correspondingly. Afterwards, temporal change information is captured through a comparison of low resolution time series data, and then projected onto the high resolution data plane and assigned to each high resolution pixel according to the predefined temporal change patterns of each type of ground objects. Finally the simulated high resolution data is generated. A preliminary experiment shows that our method can simulate a high resolution data with a reasonable accuracy. The contribution of our method is to enable timely monitoring of temporal changes through analysis of time sequence of low resolution images only, and usage of costly high resolution data can be reduces as much as possible, and it presents a highly effective way to build up an economically operational monitoring solution for agriculture, forest, land use investigation, environment and etc. applications.

In this article, we analyze the effects of ambient light on Time of Flight (ToF) depth imaging for a plant’s leaf in sunlight, shadow and room conditions. ToF imaging is sensitive to ambient light and we try to find the best possible integration
\ntimes (IT) for each condition. This is important in order to optimize camera calibration. Our analysis is based on several
\nstatistical metrics estimated from the ToF data. We explain the estimation of the metrics and propose a method of predicting the deteriorating behavior of the data in each condition using camera flags. Finally, we also propose a method to improve the quality of a ToF image taken in a mixed condition having different ambient light exposures.

Insulin is released from pancreatic islets in a biphasic and pulsatile manner in response to elevated glucose levels. This highly dynamic insulin release can be studied in vitro with islet perifusion assays. Herein, a novel platform to perform glucose-stimulated insulin secretion (GSIS) assays with single islets is presented for studying the dynamics of insulin release at high temporal resolution. A standardized human islet model is developed and a microfluidic hanging-drop-based perifusion system is engineered, which facilitates rapid glucose switching, minimal sample dilution, low analyte dispersion, and short sampling intervals. Human islet microtissues feature robust and long-term glucose responsiveness and demonstrate reproducible dynamic GSIS with a prominent first phase and a sustained, pulsatile second phase. Perifusion of single islet microtissues produces a higher peak secretion rate, higher secretion during the first and second phases of insulin release, as well as more defined pulsations during the second phase in comparison to perifusion of pooled islets. The developed platform enables to study compound effects on both phases of insulin secretion as shown with two classes of insulin secretagogs. It provides a new tool for studying physiologically relevant dynamic insulin secretion at comparably low sample-to-sample variation and high temporal resolution.

To address the challenges of low accuracy and the difficulty in balancing a large field of view and long distance when tracking high-speed moving targets with a single sensor, an ROI adaptive digital zoom tracking method is proposed. In this paper, we discuss the impact of ROI on image processing and describe the design of the ROI adaptive digital zoom tracking system. Additionally, we construct an adaptive ROI update model based on normalized target information. To capture target changes effectively, we introduce the multi-scale regional measure and propose an improved particle filter algorithm, referred to as the improved multi-scale regional measure resampling particle filter (IMR-PF). This method enables high temporal resolution processing efficiency within a high-resolution large field of view, which is particularly beneficial for high-resolution videos. The IMR-PF can maintain high temporal resolution within a wide field of view with high resolution. Simulation results demonstrate that the improved target tracking method effectively improves tracking robustness to target motion changes and reduces the tracking center error by 20%, as compared to other state-of-the-art methods. The IMR-PF still maintains good performance even when confronted with various interference factors and in real-world scenario applications.

While the first time-of-flight (TOF)-positron emission tomography (PET) systems were already built in the early 1980s, limited clinical studies were acquired on these scanners. PET was still a research tool, and the available TOF-PET systems were experimental. Due to a combination of low stopping power and limited spatial resolution (caused by limited light output of the scintillators), these systems could not compete with bismuth germanate (BGO)-based PET scanners. Developments on TOF system were limited for about a decade but started again around 2000. The combination of fast photomultipliers, scintillators with high density, modern electronics, and faster computing power for image reconstruction have made it possible to introduce this principle in clinical TOF-PET systems. This paper reviews recent developments in system design, image reconstruction, corrections, and the potential in new applications for TOF-PET. After explaining the basic principles of time-of-flight, the difficulties in detector technology and electronics to obtain a good and stable timing resolution are shortly explained. The available clinical systems and prototypes under development are described in detail. The development of this type of PET scanner also requires modified image reconstruction with accurate modeling and correction methods. The additional dimension introduced by the time difference motivates a shift from sinogram- to listmode-based reconstruction. This reconstruction is however rather slow and therefore rebinning techniques specific for TOF data have been proposed. The main motivation for TOF-PET remains the large potential for image quality improvement and more accurate quantification for a given number of counts. The gain is related to the ratio of object size and spatial extent of the TOF kernel and is therefore particularly relevant for heavy patients, where image quality degrades significantly due to increased attenuation (low counts) and high scatter fractions. The original calculations for the gain were based on analytical methods. Recent publications for iterative reconstruction have shown that it is difficult to quantify TOF gain into one factor. The gain depends on the measured distribution, the location within the object, and the count rate. In a clinical situation, the gain can be used to either increase the standardized uptake value (SUV) or reduce the image acquisition time or administered dose. The localized nature of the TOF kernel makes it possible to utilize local tomography reconstruction or to separate emission from transmission data. The introduction of TOF also improves the joint estimation of transmission and emission images from emission data only. TOF is also interesting for new applications of PET-like isotopes with low branching ratio for positron fraction. The local nature also reduces the need for fine angular sampling, which makes TOF interesting for limited angle situations like breast PET and online dose imaging in proton or hadron therapy. The aim of this review is to introduce the reader in an educational way into the topic of TOF-PET and to give an overview of the benefits and new opportunities in using this additional information.

Electroencephalogram (EEG) signals exhibit nonstationary dynamics with high temporal resolution but limited spatial resolution. A critical challenge lies in identifying stable neural states during rapid emotional transitions and decoding dynamic interregional interactions. To address this, we propose a dynamic microstate temporal graph attention network (DMT-GAT) that integrates transient EEG microstates with brain functional networks. First, EEG signals are segmented into four prototypical microstates (labeled as MS1, MS2, MS3, and MS4) via global field power peak detection and K-means clustering. Emotion-related microstates (MS3/MS4) are then selected through independent t-tests based on valence and arousal ratings. Next, a brain functional network is constructed by calculating phase-locked value synchronization specifically on the time series of MS3/MS4 microstates, capturing millisecond-scale interregional dynamics during emotional shifts. Frequency-domain features are integrated into the network nodes, forming graph-structured data. Finally, a GAT with multi-head mechanisms classifies emotions by adaptively weighting node interactions. On the DEAP dataset, our method achieves average accuracies of 99.19% (valence) and 99.26% (arousal). For the SEED dataset, it maintains a robust accuracy of 95.29%. Crucially, the DMT-GAT uniquely reveals prefrontal-amygdala interactions during emotional regulation, bridging the gap between dynamic brain networks and rapid neurodynamics. This work provides a novel framework for high-resolution emotion recognition and advances understanding of neural mechanisms underlying affective transitions.

Improved estimation of rural residential coal emissions considering coal-stove combinations and combustion modes.