High Depth Resolution Research Articles

Synthesis and luminescent properties of ZnWO4: Cr3+ nanoparticles for NIR-II fluorescence imaging

Fluorescence imaging is of paramount importance in the health and medical aspect, especially for emission in second near-infrared window, where high-resolution deep-tissue imaging can be achieved. Herein, ZnWO4: Cr3+ nanoparticles (NPs) with broadband NIR-II emission were synthesized by a facile solvothermal+calcination method. It is found that Cr3+-doping have no obvious effect of ZnWO4 phase, and the particles of as-prepared NPs are of mono-dispersed cube-shape with average size of ∼100 nm. At optimal 520 nm excitation, the as-synthesized ZnWO4: Cr3+ NPs exhibit an efficient NIR emission peaking at 987 nm and EQE of 4.03% with FWHM of 212 nm. The ZnWO4: Cr3+ NPs possess excellent stability, good biocompatibility and high brightness. NIR-II imaging with high penetration depth and micron spatial resolution was achieved on a mice model. To sum up, our work indicates that ZnWO4: Cr3+ NPs has great application potential as fluorescent agent for NIR-II bio-imaging.

Deep learning enables super-resolution hydrodynamic flooding process modeling under spatiotemporally varying rainstorms

Real-time information on flooding extent, severity, and duration is necessary for effective metropolitan flood emergency management. Existing pluvial flood analysis methods are unable to simulate real-time regional flooding processes under spatiotemporally varying rainstorms. This paper presents a deep learning-enabled super-resolution hydrodynamic flood analysis method to simulate the real-time pluvial flooding process over a large area under spatiotemporally varying rainstorms. Compared with existing flood downscaling techniques, which are limited to flow depth, the proposed method produces high-resolution flow depth and velocity predictions, providing more comprehensive information for flood emergency management. The proposed method adopts a coarse-grid hydrodynamic model to generate a low-resolution flood map time series, which is subsequently converted to high-resolution flood maps by a deep learning model. The deep learning model can be trained using a limited number of assumed rainfall scenarios, which greatly reduces data preparation effort. The proposed method is applied to a complex terrain of 352 km2 in Hong Kong that covers both mountainous and urban areas. Results show that the proposed method simulates the spatiotemporal variations of flood depth and velocity with root mean square errors as low as 0.082 m and 0.088 m/s, respectively, and correlation coefficients of 0.962 and 0.921, respectively. The computation time for a 48-h rainfall event in the study area is less than 30 s, which is 2690 times faster than the direct fine-grid hydrodynamic analysis. The deep learning-enabled super-resolution hydrodynamic flood analysis method provides a promising computational tool for emergency flood risk management.

Surface topography characterization using a simple optical device and artificial neural networks

State-of-the-art methods for quantifying wear in cylinder liners of large internal combustion engines for stationary power generation require disassembly and cutting of the examined liner. This is followed by laboratory-based high-resolution microscopic surface depth measurement that quantitatively evaluates wear based on bearing load curves (also known as Abbott-Firestone curves). Such reference methods are destructive, time-consuming and costly. The goal of the research presented here is to develop nondestructive yet reliable methods for quantifying the surface topography. A novel machine learning framework is proposed that allows prediction of the bearing load curves representing the depth profiles from reflection RGB images of the liner surface. These images can be collected with a simple handheld microscope. A joint deep learning approach involving two neural network modules optimizes the prediction quality of surface roughness parameters as well. The network stack is trained using a custom-built database containing 422 perfectly aligned depth profile and reflection image pairs of liner surfaces of large gas engines. The observed success of the method suggests its great potential for on-site wear assessment of engines during service.

High-resolution Moho depth and Vp/Vs ratio distributions in northeast China from joint inversion of receiver functions and gravity data and their geological implications

High-resolution Moho depth and Vp/Vs ratio distributions in northeast China from joint inversion of receiver functions and gravity data and their geological implications

Molecular Engineering of Self-Immolative Bioresponsive MR Probes.

Real-time detection of bio-event in whole animals provides essential information for understanding biological and therapeutic processes. Magnetic resonance (MR) imaging represents a non-invasive approach to generating three-dimensional anatomic images with high spatial-temporal resolution and unlimited depth penetration. We have developed several self-immolative enzyme-activatable agents that provide excellent in vivo contrast and function as gene expression reporters. Here, we describe a vast improvement in image contrast over our previous generations of these bioresponsive agents based on a new pyridyl-carbamate Gd(III) complex. The pyridyl-carbamate-based agent has a very low MR relaxivity in the "off-state" (r1 = 1.8 mM-1 s-1 at 1.41 T). However, upon enzymatic processing, it generates a significantly higher relaxivity with a Δr1 = 106% versus Δr1 ∼ 20% reported previously. Single X-ray crystal and nuclear magnetic relaxation dispersion analyses offer mechanistic insights regarding MR signal enhancement at the molecular scale. This work demonstrates a pyridyl-carbamate-based self-immolative molecular platform for the construction of enzymatic bio-responsive MR agents, which can be adapted to a wide range of other targets for exploring stimuli-responsive materials and biomedical applications.

High-resolution passive depth estimation for broadband sources in deep water based on orthogonal matching pursuit algorithm

The propagation mode of reliable acoustic path (RAP) in deep water is widely used in the field of target detection. A receiver set at the bottom of the seabed can receive acoustic signals from RAP. The direct (D) wave and the surface-reflected (SR) wave from a broadband source form an undulating interference structure in the frequency domain. This feature is sensitive to target depth and is thus used for depth estimation. Traditionally, depth estimation using the interference pattern, the scalar intensity in the frequency domain is processed by the optimal Fourier transform to extract the line spectrum in the depth domain. Based on this method, we proposed a passive depth estimation method for underwater broadband sources in deep water based on the orthogonal matching pursuit (OMP) algorithm. The OMP-based method is applied to scalar vertical line array (VLA) and single vector sensor (SVS) data. The OMP-based method largely eliminates side lobe interference, reduces the width of the main lobe, and improves depth estimation resolution while ensuring the stability and reliability of the results. Finally, the experimental data of VLA and SVS in the South China Sea are processed to validate the proposed method.

Additional diagnostic value of CNV-seq over conventional karyotyping in prenatal diagnosis: A systematic review and meta-analysis.

To identify the additional diagnostic value of CNV-seq over conventional karyotyping on the part of chromosomal abnormalities in prenatal diagnosis. This was a systematic review conducted in accordance with PRISMA criteria. In order to clarify related research, PubMed, Web of Science databases (including Core Collection, BIOSIS Previews, MEDLINE, and so on), The Cochrane Library and Wiley Online Library were searched with the terms: "prenatal diagnosis," "CNV-seq," "karyotyping," published from January 2010 to May 2022. No language restrictions. RenMan 5.4 was used for the meta-analysis. Eight studies were included in this systemic review and meta-analysis, including 11 091 pregnant women with high-risk pregnancy factors or with structurally abnormal fetus under ultrasound. CNV-seq detected a 2% (95% CI, -0% to 4%) additional chromosomal anomalies over conventional karyotyping in the six series. A 4% (95% CI, 3%-6%) pooled mean incremental yield of pathogenic CNVs by CNV-seq over karyotyping was observed, with a 1%-16% range. CNV-seq, applied in prenatal diagnosis, may detect more chromosomal abnormalities when compared with karyotyping. With the advantages of wide coverage, high throughput, high resolution, no culture, good compatibility, and adjustable sequencing depth, CNV-seq has high application value in prenatal diagnosis.

Corrosion behavior of a compositionally complex alloy utilizing simultaneous Al, Cr, and Ti passivation

The corrosion behavior of the Al0.3Cr0.5Fe2Mn0.25Mo0.15Ni1.5Ti0.3 compositionally complex dual-phase alloy was explored in a variety of electrolytes. Comparable corrosion resistance to 316L stainless steel and Ni-Cr binaries was observed in NaCl despite lower Cr concentrations. Individual elemental fates during corrosion were identified by combinations of in-situ atomic emission spectroelectrochemistry and ex-situ X-ray photoelectron spectroscopy with high-resolution surface analysis and depth profiling. A potentiostatically grown passive film was enriched in Al(III), Cr(III), Ti(IV), and Mo(VI). Al-Ti and Cr-Ti layers were suggested but significant Al, Cr, and Ti mixing was present. Proposed benefits of Al-Cr-Ti coexistence are addressed.

High dive efficiency in shallow water

Dive studies across mammals, birds, reptiles and fish often focus on deep dives, and shallow water diving has tended to be overlooked. For air-breathers, foraging in shallow water poses challenges since the lungs generate buoyancy, and shallow divers must trade off the extent of inhalation against the negative buoyancy needed to avoid floating to the surface. Using high-resolution depth loggers, we addressed this knowledge gap around the ecology of shallow water diving at a foraging site for hawksbill turtles (Eretmochelys imbricata) where depth was typically < 3 m. Contrary to predictions, dive durations were long, particularly at night (mean dive duration per turtle: 17–61 min, n = 12 turtles, n = 2576 nocturnal dives), despite warm water temperatures (24–37 °C). Dive efficiency (% time submerged) for hawksbills was 98%, the highest recorded for any air-breathing marine vertebrate including penguins (60–78%), seals (51–91%), cetaceans (68–87%), and other sea turtle species (68–95%). Hawksbills usually dive for much longer (42–286% increase) than green and loggerhead turtles when depth and temperature are accounted for. Hawksbill turtles likely forage in very shallow water to reduce predation risk from sharks: of 423 hawksbills captured by hand, none had any evidence of shark attack, although large sharks were present in nearby deeper water. Our results challenge the prediction that shallow water dives by air-breathers will usually be short and open the way for comparative studies of the ecology of shallow water diving in a range of other taxa. Our work emphasises the likely importance of predation risk in shaping patterns of habitat utilisation.

A High-Resolution Global Moho Model from Combining Gravimetric and Seismic Data by Using Spectral Combination Methods

The high-resolution Moho depth model is required in various geophysical studies. However, the available models’ resolutions could be improved for this purpose. Large parts of the world still need to be sufficiently covered by seismic data, but existing global Moho models do not fit the present-day requirements for accuracy and resolution. The isostatic models can relatively reproduce a Moho geometry in regions where the crustal structure is in an isostatic equilibrium, but large segments of the tectonic plates are not isostatically compensated, especially along active convergent and divergent tectonic margins. Isostatic models require a relatively good knowledge of the crustal density to correct observed gravity data. To overcome the lack of seismic data and non-uniqueness of gravity inversion, seismic and gravity data should be combined to estimate Moho geometry more accurately. In this study, we investigate the performance of two techniques for combining long- and short-wavelength Moho geometry from seismic and gravity data. Our results demonstrate that both Butterworth and spectral combination techniques can be used to model the Moho geometry. The results show the RMS of Moho depth differences between our model and the reference models are between 1.7 and 4.7 km for the Butterworth filter and between 0.4 and 4.1 km for the spectral combination.

Fully Cross-Attention Transformer for Guided Depth Super-Resolution

Modern depth sensors are often characterized by low spatial resolution, which hinders their use in real-world applications. However, the depth map in many scenarios is accompanied by a corresponding high-resolution color image. In light of this, learning-based methods have been extensively used for guided super-resolution of depth maps. A guided super-resolution scheme uses a corresponding high-resolution color image to infer high-resolution depth maps from low-resolution ones. Unfortunately, these methods still have texture copying problems due to improper guidance from color images. Specifically, in most existing methods, guidance from the color image is achieved by a naive concatenation of color and depth features. In this paper, we propose a fully transformer-based network for depth map super-resolution. A cascaded transformer module extracts deep features from a low-resolution depth. It incorporates a novel cross-attention mechanism to seamlessly and continuously guide the color image into the depth upsampling process. Using a window partitioning scheme, linear complexity in image resolution can be achieved, so it can be applied to high-resolution images. The proposed method of guided depth super-resolution outperforms other state-of-the-art methods through extensive experiments.

Analysis of Liquefaction Triggering in the Nakdong-river Delta Sediments Based on Broadband Hybrid Ground Motion Simulation and Microtremor Array Method

We present a case study of liquefaction triggering analysis performed in the Nakdong-river delta region, based on the shear wave velocities obtained by the Microtremor Array Method and the ground motions generated by the broadband hybrid ground motion simulation. Assuming a M&lt;sub&gt;w&lt;/sub&gt;6.2 earthquake occuring at the Dongrae fault, the ground motions at the studied sites exceeded the design spectra provided by the Korean Design Standard and many of the studied sites exhibited low factors of safety against liquefaction triggering. We found that the Idriss and Boulanger (2008) method yields larger Cyclic Stress Ratios (CSR), compared with the site response analysis based method. Cyclic Resistance Ratios (CRR) estimated from the shear wave velocity were sensitive to small changes in the velocity, which suggests that high accuracy and depth resolution are desired when the shear wave velocity is used to estimate the CRR.

Centimeter-wave Free-space Neural Time-of-Flight Imaging

Depth sensors have emerged as a cornerstone sensor modality with diverse applications in personal hand-held devices, robotics, scientific imaging, autonomous vehicles, and more. In particular, correlation Time-of-Flight (ToF) sensors have found widespread adoption for meter-scale indoor applications such as object tracking and pose estimation. While they offer high depth resolution at competitive costs, the precision of these indirect ToF sensors is fundamentally limited by their modulation contrast, which is in turn limited by the effects of photo-conversion noise. In contrast, optical interferometric methods can leverage short illumination modulation wavelengths to achieve depth precision three orders of magnitude greater than ToF, but typically find their range is restricted to the sub-centimeter. In this work, we merge concepts from both correlation ToF design and interferometric imaging; a step towards bridging the gap between these methods. We propose a computational ToF imaging method that optically computes the GHz ToF correlation signal in free space before photo-conversion. To acquire a depth map, we scan a scene point-wise and computationally unwrap the collected correlation measurements. Specifically, we repurpose electro-optical modulators used in optical communication for ToF imaging with centimeter-wave signals, and achieve all-optical correlation at 7.15 GHz and 14.32 GHz modulation frequencies. While GHz modulation frequencies increase depth precision, these high modulation rates also pose a technical challenge. They result in dozens of wraps per meter which cannot be estimated robustly by existing phase unwrapping methods. We tackle this problem with a proposed segmentation-inspired phase unwrapping network , which exploits the correlation of adjacent GHz phase measurements to classify regions into their respective wrap counts. We validate this method in simulation and experimentally, and demonstrate precise depth sensing using centimeter wave modulation that is robust to surface texture and ambient light. Compared to existing analog demodulation methods, the proposed system outperforms all of them across all tested scenarios. CCS Concepts: • Computing methodologies ;

Validation of Recent DSM/DEM/DBMs in Test Areas in Greece Using Spirit Leveling, GNSS, Gravity and Echo Sounding Measurements

Recent Digital Surface, Elevation, and Bathymetric Models (DSM/DEM/DBM) aim to provide high-resolution and accurate height and depth information needed for a variety of surveying, geodetic, geophysical, and other applications. In this study, first we aim to validate in two test areas some of the most used models, i.e., ASTER GDEM; AW3D30 DSM; Copernicus DEM; EU-DEM; GEBCO 2020; NASADEM HGT; SRTM15+ and SRTM Global, using GNSS; spirit leveling; and gravity measurements. The validation is performed along two traverses of 14.5 and 12.0 km each in Northern and Central Greece, respectively. Since these models are based on geoid heights obtained from global geopotential models, we also investigate their influence on the validation results. Next, we carry out comparisons between GEBCO 2020, SRTM15+, and the Greek Seas DTM, with depths derived from in situ coastal measurements in six different areas in Northern Greece. From the analysis, we conclude that the heights obtained from the Copernicus DEM provide the best overall results in terms of mean value and standard deviation while also showing consistent results in the two test areas. Similarly, the Greek Seas DTM shows better consistency with the measured depths in the coastal test areas.

High depth resolution wavelength scanning interferometry with narrow scanning bandwidth via sinewaves separation

Wavelength scanning interferometry (WSI) is a promising tomographic imaging technique. However, the depth resolution is fundamentally limited by a narrow wavelength scanning bandwidth, which brings challenges to the frequency extraction for depth information. In this work, we proposed a parameter estimation (PE) paradigm via sinewaves separation, which allows high depth resolution tomographic measurement with narrow-band sources. To obtain accurate depth information, a sinewaves separation model is designed to estimate the frequency of the overlapped signals and it can achieve higher depth resolution without any prior knowledge even under limited scanning bandwidth. Moreover, the estimated frequency is used for phase retrieval and the multi-layer 3D profile measurement can be further implemented. The simulation and experimental results show that the PE method can reach the same depth resolution level as discrete Fourier transform (DFT) even reducing 60% scanning bandwidth when PSNR is higher than 35 dB.

Spatially lagged predictors from a wider area improve PM2.5 estimation at a finer temporal interval—A case study of Dallas-Fort Worth, United States

Fine particulate matter, also known as PM2.5, has many adverse impacts on human health. However, there are few ground monitoring stations measuring PM2.5. Satellite data help fill the gaps in ground measurements, but most studies focus on estimating daily PM2.5 levels. Studies examining the effects of environmental exposome need accurate PM2.5 estimates at fine temporal intervals. This work developed a Convolutional Neural Network (CNN) to estimate the PM2.5 concentration at an hourly average using high-resolution Aerosol Optical Depth (AOD) from the MODIS MAIAC algorithm and meteorological data. Satellite-acquired AOD data are instantaneous measurements, whereas stations on the ground provide an hourly average of PM2.5 concentration. The current work aimed to refine PM2.5 estimates at temporal intervals from 24-h to 1-h averages. Our premise posited the enabling effects of spatial convolution on temporal refinements in PM2.5 estimates. We trained a CNN to estimate PM2.5 corresponding to the hour of AOD acquisition in the Dallas-Fort Worth and surrounding area using 10 years of data from 2006–2015. The CNN accepts images as input. For each PM2.5 station, we strategically subset temporal MODIS images centering at the PM2.5 station. Hence, the resulting image-patch size represented the size of the area around the PM2.5 station. It thus was analogous to spatial lag in spatial statistics. We systematically increased the image-patch size from 3 × 3, 5 × 5, … , to 19 × 19 km2 and observed how increasing the spatial lag impacted PM2.5 estimation. Model performance improved with a larger spatial lag; the model with a 19 × 19 km2 image-patch as input performed best, with a correlation coefficient of 0.87 and a RMSE of 2.57 g/m3 to estimate PM2.5 at in situ stations corresponding to the hour of satellite acquisition time. To overcome the problem of a reduced number of image-patches available for training due to missing AOD, the study employed a data augmentation technique to increase the number of samples available to train the model. In addition to avoiding overfitting, data augmentation also improved model performance.

Using deep ensemble forest for high-resolution mapping of PM2.5 from MODIS MAIAC AOD in Tehran, Iran

High-resolution mapping of PM2.5 concentration over Tehran city is challenging because of the complicated behavior of numerous sources of pollution and the insufficient number of ground air quality monitoring stations. Alternatively, high-resolution satellite Aerosol Optical Depth (AOD) data can be employed for high-resolution mapping of PM2.5. For this purpose, different data-driven methods have been used in the literature. Recently, deep learning methods have demonstrated their ability to estimate PM2.5 from AOD data. However, these methods have several weaknesses in solving the problem of estimating PM2.5 from satellite AOD data. In this paper, the potential of the deep ensemble forest method for estimating the PM2.5 concentration from AOD data was evaluated. The results showed that the deep ensemble forest method with [Formula: see text] gives a higher accuracy of PM2.5 estimation than deep learning methods ([Formula: see text]) as well as classic data-driven methods such as random forest ([Formula: see text]). Additionally, the estimated values of PM2.5 using the deep ensemble forest algorithm were used along with ground data to generate a high-resolution map of PM2.5. Evaluation of produced PM2.5 map revealed the good performance of the deep ensemble forest for modeling the variation of PM2.5 in the city of Tehran.

Snow depth estimation at country-scale with high spatial and temporal resolution

Monitoring snow depth is important for applications such as hydrology, energy planning, ecology, and safety evaluation for outdoor winter activities. Most methods able to estimate snow depth for large regions can only do so in a spatial resolution of up to 1 km ground sampling distance (GSD). This limits their usage in high alpine areas, where this resolution fails to capture local snow distribution patterns caused by the pronounced topographical features. In this work we use a recurrent convolutional neural network to estimate snow depth at high spatial resolution (10 m GSD), weekly, and at large scale based on satellite data sources and elevation maps, without the need for measurement stations on the ground. The proposed method achieves unprecedented results for large-scale, high-resolution snow depth mapping. The resulting maps are evaluated over a period of three years against high-fidelity snow depth maps obtained with airborne photogrammetry. Finally, we also produce well-calibrated uncertainty estimates for every individual snow depth estimate via a probabilistic regression framework.

A 200 MHz 14 W Pulsed Optical Illuminator With Laser Driver ASIC and On-Chip DLL-Based Time Interpolator for Indirect Time-of-Flight Applications

The applications requiring three-dimensional information have been growing rapidly, including facial recognition, gesture recognition, auto-focus for 2D camera, self-driving autonomous vehicles, etc. Indirect Time-of-Flight (iToF) technique has been adopted widely for 3D depth imaging due to its high spatial resolution and excellent depth accuracy performance. In this brief, a 200 MHz pulsed optical Illuminator for iToF applications is reported. A 940 nm GaAs-based triple-junction VCSEL array is adopted and an integrated pulsed VCSEL driver ASIC in 180 nm BCD process is realized. Optical pulses with peak power up to 14 W can be transmitted by the laser illuminator with a maximum driving current of 6.5 A by the driver ASIC. The modulation frequency can be up to 200 MHz. Automatic optical power control (AOPC) is realized for stable optical power output and laser eye safety protection. An on-chip DLL-based time interpolator is proposed to eliminate the mechanical moving components used during system calibration. The measured absolute accuracy of the time interpolator is +/−50 ps with a resolution of 800 ps and tuning range of 49.6 ns. This brief effectively extends the measurement distance of the iToF system. The calibration efficiency is also improved with the proposed method.

Precision Navigation of Venous Thrombosis Guided by Viscosity-Activatable Near-Infrared Fluorescence.

Thrombus are blood clots formed by abnormal hemostasis in blood vessels and are closely associated with various diseases such as pulmonary embolism, myocardial infarction and stroke. Early diagnosis and treatment of thrombus is the key to reducing the high risk of thrombotic disease. Given that early thrombus is small in early size, free instability, wide regional distribution and fast formation, it is urgent to develop all-inclusive detection methods that combine high signal-to-noise ratio, in situ dynamic and rapid in-depth tissue imaging. Near-infrared (NIR) fluorescence imaging, with its excellent high spatiotemporal resolution and tissue penetration depth, is a powerful technique for direct visualization of thrombotic events in situ. Considering the fibrin highly expressed in the thrombus is a typical thrombotic target. Moreover, the viscosity of the thrombus is markedly higher than its surroundings. Therefore, we developed a fibrin-targeting and viscosity-activating thrombus NIR fluorescent probe (TIR-V) for high-resolution and high-sensitivity in situ lighten-up thrombus. TIR-V has the advantages of good thrombus targeting, significant "off-on" fluorescence specific response to viscosity, bright NIR fluorescence and good biocompatibility. The thrombus is clearly delineated by a high signal-to-noise ratio NIR fluorescence imaging, enabling imaging detection and precise navigation of thrombotic regions. This work demonstrates the potential of TIR-V as a bifunctional probe for definitive diagnostic imaging and direct navigation of thrombotic lesions in clinical applications.