Spatial Response Function Research Articles

ECCENTRIC: A fast and unrestrained approach for high-resolution in vivo metabolic imaging at ultra-high field MR

Abstract A novel method for fast and high-resolution metabolic imaging, called ECcentric Circle ENcoding TRajectorIes for Compressed sensing (ECCENTRIC), has been developed at 7 Tesla MRI. ECCENTRIC is a non-Cartesian spatial-spectral encoding method designed to accelerate magnetic resonance spectroscopic imaging (MRSI) with high signal-to-noise at ultra-high field. The approach provides flexible and random sampling of the Fourier space without temporal interleaving to improve spatial response function and spectral quality. ECCENTRIC enables the implementation of spatial-spectral MRSI with reduced gradient amplitudes and slew-rates, thereby mitigating electrical, mechanical, and thermal stress of the scanner hardware. Moreover, it exhibits robustness against timing imperfections and eddy-current delay. Combined with a model-based low-rank reconstruction, this approach enables simultaneous imaging of up to 14 metabolites over the whole brain at 2–3 mm isotropic resolution in 4–10 min. MRSI ECCENTRIC was performed on four healthy volunteers, yielding high-resolution spatial mappings of neurochemical profiles within the human brain. This innovative tool introduces a novel approach to neuroscience, providing new insights into the exploration of brain activity and physiology.

A novel algorithm for evaluating cement azimuthal density based on perturbation theory in horizontal well

Cement density monitoring plays a vital role in evaluating the quality of cementing projects, which is of great significance to the development of oil and gas. However, the presence of inhomogeneous cement distribution and casing eccentricity in horizontal wells often complicates the accurate evaluation of cement azimuthal density. In this regard, this paper proposes an algorithm to calculate the cement azimuthal density in horizontal wells using a multi-detector gamma-ray detection system. The spatial dynamic response functions are simulated to obtain the influence of cement density on gamma-ray counts by the perturbation theory, and the contribution of cement density in six sectors to the gamma-ray recorded by different detectors is obtained by integrating the spatial dynamic response functions. Combined with the relationship between gamma-ray counts and cement density, a multi-parameter calculation equation system is established, and the regularized Newton iteration method is employed to invert casing eccentricity and cement azimuthal density. This approach ensures the stability of the inversion process while simultaneously achieving an accuracy of 0.05 g/cm3 for the cement azimuthal density. This accuracy level is ten times higher compared to density accuracy calculated using calibration equations. Overall, this algorithm enhances the accuracy of cement azimuthal density evaluation, provides valuable technical support for the monitoring of cement azimuthal density in the oil and gas industry.

Contributed Session III: In vivo calcium imaging of macaque foveolar retinal ganglion cells reveals spatiochromatic receptive field properties.

Here, we optically record responses to spatial and chromatic stimuli using a calcium indicator in the living macaque eye to characterize the receptive field (RF) properties of retinal ganglion cells (RGCs) serving the foveal center. GCaMP6s was expressed in three female macaques. Adaptive optics ophthalmoscopy was used to image fluorescence (488nm ex, 520/35nm em) from RGCs whose RF centers were driven by cones in the central 36 arcmin of the fovea and additional RGCs driven by cones in the central 6 arcmin of the foveola. Using cone isolating and luminance flicker (1.3deg, 0.15Hz, LED 420nm, 530nm, 660nm), we derived cone weights in over 250 RGCs. Using drifting gratings (1.9deg, 6Hz, 4-50c/deg, 561nm), we derived the spatial frequency responses of 15 L vs. M cone opponent RGCs at the foveolar center. Employing computational modeling (ISETbio), we inferred the full spatial difference of gaussians center and surround structure for those 15 cells. Of the 34 foveolar RGCs, 44% exhibited L vs. M cone opponency, 15% were L+M ON, 6% were -L-M OFF, and 35% showed only L or only M responses. The spatial frequency response functions of 12/15 L vs. M opponent cells peaked at high spatial frequencies (25-40c/deg) and had a strong bandpass characteristic. Our model indicates that the responses of all 15 L vs. M opponent cells are consistent with single cone input to their RF centers and that our data are consistent with extrafoveal data when the blurring of the optics is accounted for.

Compartment-based reconstruction of 3D acquisition-weighted 31 P cardiac magnetic resonance spectroscopic imaging at 7 T: A reproducibility study.

Even at 7 T, cardiac 31 P magnetic resonance spectroscopic imaging (MRSI) is fundamentally limited by low signal-to-noise ratio (SNR), leading to long scan times and poor temporal and spatial resolutions. Compartment-based reconstruction algorithms such as magnetic resonance spectroscopy with linear algebraic modeling (SLAM) and spectral localization by imaging (SLIM) may improve SNR or reduce scan time without changes to acquisition. Here, we compare the repeatability and SNR performance of these compartment-based methods, applied to three different acquisition schemes at 7 T. Twelve healthy volunteers were scanned twice. Each scan session consisted of a 6.5-min 3D acquisition-weighted (AW) cardiac 31 P phase encode-based MRSI acquisition and two 6.5-min truncated k-space acquisitions with increased averaging (4 × 4 × 4 central k-space phase encodes and fractional SLAM [fSLAM] optimized k-space phase encodes). Spectra were reconstructed using (i) AW Fourier reconstruction; (ii) AW SLAM; (iii) AW SLIM; (iv) 4 × 4 × 4 SLAM; (v) 4 × 4 × 4 SLIM; and (vi) fSLAM acquisition-reconstruction combinations. The phosphocreatine-to-adenosine triphosphate (PCr/ATP) ratio, the PCr SNR, and spatial response functions were computed, in addition to coefficients of reproducibility and variability. Using the compartment-based reconstruction algorithms with the AW 31 P acquisition resulted in a significant increase in SNR compared with previously published Fourier-based MRSI reconstruction methods while maintaining the measured PCr/ATP ratio and improving interscan reproducibility. The alternative acquisition strategies with truncated k-space performed no better than the common AW approach. Compartment-based spectroscopy approaches provide an attractive reconstruction method for cardiac 31 P spectroscopy at 7 T, improving reproducibility and SNR without the need for a dedicated k-space sampling strategy.

TSC-1 Optical Payload Hyperspectral Imager Preliminary Design and Performance Estimation

The Thai Space Consortium aims at building capacities in space technologies and industries with the objective to develop satellites in Thailand. In this framework, the first Earth Observation satellite that will be developed by this consortium is called TSC-1. This satellite comprises a hyperspectral imager orbiting in a Sun-Synchronous Low-Earth Orbit at the altitude equal to 630 km. The optical payload is specified to provide data cubes with a Ground Sample Distance equal to 30 m, a swath equal to 30 km, a spectral resolution equal to 10 nm over the spectral domain from 400 nm to 1000 nm with a Signal-to-Noise Ratio (SNR) higher than 100. Firstly, we present the trade-off performed to select the design of the Front Telescope and the Spectrometer. Secondly, we describe the payload design and present the image quality, Modulation Transfer Function and distortion. Next, we establish the tolerance budget to estimate the performance of the optical system including manufacturing errors, assembly errors and stability of the mechanical structure. After that, we calculate the instrument’s spatial and spectral response functions and the contamination of the adjacent pixels due to the straylight. Finally, we estimate radiometric performance in both nadir pointing mode and forward motion compensation mode.

Spatial–Temporal Evolution Characteristics and Economic Effects of China’s Cultural and Tourism Industries’ Collaborative Agglomeration

In this era of industrial integration, the synergistic energy given collaborative agglomerations of the culture and tourism industries is crucial for fulfilling the potential of the underlying resources. The cultural grasp of artistic depths when fully supported can transform the cultural experiences for tourists and participants alike. In this study, the theory of spatial economics is used to analyze the spatial coupling degree of the Chinese culture and tourism industries from 2010 to 2019, based on the coupling coordination degree model. A spatial correlation test model was used to analyze the spatial–temporal evolution characteristics of industrial collaborative agglomeration, and a spatial vector autoregression model and impulse response function was used to analyze the economic effects of industrial collaborative agglomeration. The results show: (1) A coupling and coordination relationship exists between Chinese culture and the tourism industries. This collaborative bond is in the initial stage. (2) The overall spatial correlation between these industries can potentially provide significant and positive relationships among several components of the community, tourist, and cultural spectrum. The local spatial correlation of culture and tourism industries in Eastern China is ranked the highest; the central region is in the middle. The western region ranks the lowest. (3) The collaborative synergy of the cultural and tourism industries has a nonlinear economic effect on economic development, while the impacts of different industrial collaborative groups have the potential to strengthen the Chinese economy from a more technological perspective. This study provides theoretical support and recommendations for promoting the coordinated development of Chinese culture and tourism industries, which can also serve as an example for other regions seeking a stronger relationship between their culture, economic growth of the region as a whole, and the tourism industries.

The effect of geometry dependent Chamber Spatial Response Function in small field profile measurements

The effect of Chamber-Spatial Response Function (CSRF) on beam profile measurements has been studied for Standard Ionization Chamber-Radiation Field Analyzer setup. The Chamber-Spatial Response Function is assumed to be Gaussian and parameterized through the projected geometry of the chamber along the measurement axis. CSRF widths was measured by constructing chamber diameter-FWHM correlation for a set of ionization chambers. The results were analyzed through Geant4 simulations. The geometrical parameters for the chamber, used in Geant4 simulation, have been derived from the CT scan images of the chamber. An excellent agreement between measurement and simulation is obtained. Various factors effecting the CSRF, such as chamber width and Compton current are analyzed. The analysis shows that the Compton current has significant role in broadening the CSRF. Further, it is also observed that a spherical chamber geometry provides better and compact CSRF.

Using a Vegetation Index-Based Mixture Model to Estimate Fractional Vegetation Cover Products by Jointly Using Multiple Satellite Data: Method and Feasibility Analysis

Remote sensing fractional vegetation cover (FVC) requires both finer-resolution and high-frequency in climate and ecosystem research. The increasing availability of finer-resolution (≤ 30 m) remote sensing data makes this possible. However, data from different satellites have large differences in spatial resolution, spectral response function, and so on, making joint use difficult. Herein, we showed that the vegetation index (VI)-based mixture model with the appropriate VI values of pure vegetation (Vv) and bare soil (Vs) from the MODIS BRDF product via the multi-angle VI method (MultiVI) was feasible to estimate FVC with multiple satellite data. Analyses of the spatial resolution and spectral response function differences for MODIS and other satellites including Landsat 8, Chinese GF 1, and ZY 3 predicted that (1) the effect of Vv and Vs downscaling on FVC estimation uncertainty varied from satellite to satellite due to the positioning differences, and (2) after spectral normalization, the uncertainty (RMSDs) for FVC estimation decreased by ~2.6% compared with the results without spectral normalization. FVC estimation across multiple satellite data will help to improve the spatiotemporal resolution of FVC products, which is an important development for numerous biophysical applications. Herein, we proved that the VI-based mixture model with Vv and Vs from MultiVI is a strong candidate.

Dealing with spatial heterogeneity in pointwise-to-gridded- data comparisons

Abstract. Most studies on validation of satellite trace gas retrievals or atmospheric chemical transport models assume that pointwise measurements, which roughly represent the element of space, should compare well with satellite (model) pixels (grid box). This assumption implies that the field of interest must possess a high degree of spatial homogeneity within the pixels (grid box), which may not hold true for species with short atmospheric lifetimes or in the proximity of plumes. Results of this assumption often lead to a perception of a nonphysical discrepancy between data, resulting from different spatial scales, potentially making the comparisons prone to overinterpretation. Semivariogram is a mathematical expression of spatial variability in discrete data. Modeling the semivariogram behavior permits carrying out spatial optimal linear prediction of a random process field using kriging. Kriging can extract the spatial information (variance) pertaining to a specific scale, which in turn translates pointwise data to a gridded space with quantified uncertainty such that a grid-to-grid comparison can be made. Here, using both theoretical and real-world experiments, we demonstrate that this classical geostatistical approach can be well adapted to solving problems in evaluating model-predicted or satellite-derived atmospheric trace gases. This study suggests that satellite validation procedures using the present method must take kriging variance and satellite spatial response functions into account. We present the comparison of Ozone Monitoring Instrument (OMI) tropospheric NO2 columns against 11 Pandora spectrometer instrument (PSI) systems during the DISCOVER-AQ campaign over Houston. The least-squares fit to the paired data shows a low slope (OMI=0.76×PSI+1.18×1015 molecules cm−2, r2=0.66), which is indicative of varying biases in OMI. This perceived slope, induced by the problem of spatial scale, disappears in the comparison of the convolved kriged PSI and OMI (0.96×PSI+0.66×1015 molecules cm−2, r2=0.72), illustrating that OMI possibly has a constant systematic bias over the area. To avoid gross errors in comparisons made between gridded data vs. pointwise measurements, we argue that the concept of semivariogram (or spatial autocorrelation) should be taken into consideration, particularly if the field exhibits a strong degree of spatial heterogeneity at the scale of satellite and/or model footprints.

Geolocation Error Estimation Method for the Wide Swath Polarized Scanning Atmospheric Corrector Onboard HJ-2 A/B Satellites

Polarized Scanning Atmospheric Corrector (PSAC) onboard the Huanjing Jianzai (HJ)-2 A/B satellites is a cross-track scanning polarimetric remote sensor that measures the intensity and direction of light reflected by the Earth and its atmosphere by 9 full polarized spectral bands from near-ultraviolet (near-UV) to shortwave infrared (SWIR). In particular, geolocation accuracy is an important factor for polarization observations. An automatic coastline inflection method (CIM) is implemented for PSAC geolocation error estimation. Over five months of globally middle or low latitude coastline area measurements are used to obtain statistical result. The results of the comparison with the Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG) show PSAC geolocation error is smaller than 0.38 ground sample distance (GSD) or 3.25 km in 95% confidence level. In cross-track direction, the geolocation error estimation is affected by the instrument sampling characteristics like spatial response function (SRF). Thus, the correction method is proposed by establishing relationship between measurement radiance in CIM and offset proportion of PSAC GSD. The biases are obviously reduced after correction.

Optical design of an Offner coded aperture snapshot spectral imaging system based on dual-DMDs in the mid-wave infrared band.

With the advantages of flexible encoding and high frame rate, the digital micromirror devices (DMD) have been used as a binary encoding mask in the coded aperture snapshot spectral imaging (CASSI) systems. But the use of DMD will cause the image plane to tilt at a specific angle, so it is almost impossible to realize the strict matching between the optical system of CASSI and the cold stop of the infrared focal plane array in the mid-wave infrared band. In this paper, a CASSI system with two DMDs based on the Offner spectrometer is proposed to solve the above problem. The concept and working principle are described in detail. Under the premise of the matching optical parameters, the telescopic system, Offner spectroscopic system and microscopic system are designed independently. Then the integrated optimization design method is adopted, and the aberration of the microscopic system is used to offset the astigmatic aberration of the Offner spectroscopic system, and the imaging quality of the system is improved. The results of performance measurements confirm that the system has desirable spatial resolution and spectral response functions. Thus, the concept and optical design of the proposed system are verified to be effective and valuable.

3-D Large-Pitch Synthetic Transmit Aperture Imaging With a Reduced Number of Measurement Channels: A Feasibility Study.

A 3-D synthetic transmit aperture ultrasound imaging system with a fully addressed array usually leads to high hardware complexity and cost since each element in the array is individually controlled. To reduce the hardware complexity, we had presented the large-pitch synthetic transmit aperture (LPSTA) ultrasound imaging for 2-D imaging using a 1-D phased array to reduce the number of measurement channels M (the product of number of transmissions, [Formula: see text], and the number of receiving channels in each transmission, [Formula: see text]). In this article, we extend this method to a 2-D matrix array for 3-D imaging. We present both numerical simulations and experimental measurements. We combined L × L adjacent elements into transmission subapertures (SAP) and K × K adjacent elements into receive SAPs in synthetic transmit aperture (STA) imaging. In the image reconstruction, we conducted the first attempt to apply and integrate Gaussian-approximated spatial response function (G-SRF) with delay and sum (DAS) to improve the image contrast, especially for the near-field targets. The imaging performance obtained from G-SRF was also evaluated numerically and compared with the previously presented frequency-domain SRF (Freq-domain SRF). The 3-D large-pitch synthetic transmit aperture (3-D-LPSTA) with G-SRF can provide a computationally efficient solution compared with the standard 3-D-STA method. With approximately 1900-fold reduction in the number of measurement channels, 3-D-LPSTA can provide image contrast comparable with the standard 3-D-STA with a full array and significantly better than using a periodically sparse array with similar complexity. In addition to reducing the system complexity, the 3-D-LPSTA achieves 700-fold reduction in computational complexity and 523-fold reduction in data storage. Finally, we evaluated and implemented the G-SRF using phantom data, which were consistent with the simulation results showing that the G-SRF can improve the image contrast. The results demonstrate that the proposed 3-D-LPSTA shows the great potential for designing an inexpensive ultrasound system to ensure the real-time 3-D clinical ultrasound imaging using large arrays. The limits of the proposed method were also discussed.

Design of on‐farm precision experiments to estimate site‐specific crop responses

AbstractSite‐specific prescriptions require estimating response functions to controllable inputs across the field. The methodology of applying geographically weighted regression to on‐farm precision experimentation studies opens new opportunities to study site‐specific responses to inputs in farmers' fields by locally estimating the regression coefficients. However, the effect of the experiment's spatial layout, such as plot dimensions and randomization, and spatial structure of the yield response on the experiment performance are yet to be studied. Detailed information about these effects is needed to improve trial design to detect site‐specific responses. A simulation study was conducted using 14,400 fields of 37 ha and 9‐m resolution. Coefficients from a spatial variable response function were drawn from five random fields generated by unconditional Gaussian geostatistical simulations. Four levels of nitrogen were assigned to plots using 18 systematic and randomized chessboard designs with different plot sizes. Simulated yield data was obtained by combining the coefficients, treatment, and random error. The effect of spatial structure and the designs was assessed with measures of agreement between the true and estimated maps of regression coefficients. The ability to capture or approximate the true spatial pattern of the response function increased as the underlying response function's spatial structure increases. Overall differences in performance between design were observed across the spatial structure tested, mostly related to randomization and plot dimensions. In general best results were achieved by systematic designs with small or intermediate plot sizes (r = 0.54 ± 0.05, MAE = 0.005 ± 0.0005, SDR = 0.81 ± 0.06, and CP = 0.50 ± 0.04). Our methodology provides a path for testing designs under different spatial variability scenarios.

Evaluation of the Vegetation-Index-Based Dimidiate Pixel Model for Fractional Vegetation Cover Estimation

Remote sensing estimation based on the dimidiate pixel model (DPM) using vegetation indices (VIs) is a common approach for mapping fractional vegetation cover (FVC). The major drawback of DPM is that it does not consider real endmember conditions and multiple scattering between soil and vegetation. An analysis of FVC uncertainties caused by these model deficiencies is still lacking. Here, we first calculated the FVC theoretical uncertainty caused by reflectance uncertainties based on the law of prapagation of uncertainty (LPU). Then, we tested the performance of DPM using six VIs over 3-D forest scenes. We simulated both Aqua-MODIS and Landsat-OLI surface reflectance (SR) at their corresponding spatial resolutions and spectral response functions (SRFs) using a well-validated 3-D radiative transfer (RT) model which helps to separate the model and input uncertainties. We found that ratio vegetation index (RVI)- and enhanced vegetation index (EVI)-based models were most affected by sensors, followed by the normalized difference vegetation index (NDVI)-, enhanced vegetation index 2 (EVI2)-, renormalized difference vegetation index (RDVI)-, and difference vegetation index (DVI)-based models. Without considering SR uncertainties, the DVI-based model performed best (FVC absolute difference < 0.1); however, the commonly used NDVI model reached a maximum difference of 0.35. At the same time, input uncertainty increased the uncertainty of FVC retrieval. We noticed that the increase of solar zenith angle (SZA) resulted in a clear increase of retrieved FVC under the uniform distribution, which can be explained by the increased shadow proportion. Besides, model accuracy was dominated by the purity of soil (vegetation) endmember in low (high) vegetation cover area. This study provides a reference for the selection of the optimal VI for FVC retrieval based on the DPM.

The Fragmented United States of America: The Impact of Scattered Lock-Down Policies on Country-Wide Infections

Fragmented by policies, united by outcomes: This is the picture of the United States that emerges from our analysis of the spatial diffusion of COVID-19 and the scattered lock-down policies introduced by individual states to contain it. We first use spatial econometric techniques to document direct and indirect spillovers of new infections across county and state lines, as well as the impact of individual states' lock-down policies on infections in neighboring states. We find consistent statistical evidence that new cases diffuse across county lines, holding county level factors constant, and that the diffusion across counties was affected by the closure policies of adjacent states. Spatial impulse response functions reveal that the diffusion across counties is persistent for up to ten days after an increase in adjacent counties. We then develop a spatial version of the epidemiological SIR model where new infections arise from interactions between infected people in one state and susceptible people in the same or in neighboring states. We incorporate lock-down policies into our model and calibrate the model to match both the cumulative and the new infections across the 48 contiguous U.S. states and DC. Our results suggest that, had the states with the less restrictive social distancing measures tightened them by one level, the cumulative infections in other states would be about 5% smaller. In our spatial SIR model, the spatial containment policies such as border closures have a bigger impact on flattening the infection curve in the short-run than on the cumulative infections in the long-run.

Integration model of POSP measurement spatial response function

The particulate observing scanning polarimeter (POSP) measurement spatial response function (SRF) relates to the weighted contribution of each location within the measurement footprint, which is determined by the percentage of the dwell time of each location on the Earth surface to the overall sampling integration time. The SRF resulting from a combination of the equally weighted instantaneous field of view (IFOV) during integration is required for an accurate modeling. Simply using a mean value SRF assuming an equivalent weight at each sampling position instead of the actual SRF will inevitably introduce errors. Considering the data fusion between POSP and high spatial resolution sensors, a discrete integration method that takes the effect of actual weights into account is proposed in this paper. The simulation results of the integral model and the mean value model show that the larger the intensity change in the sampling area covered by the IFOV of the POSP during a single sampling, the more significant the difference between the two results. Meanwhile, the integration SRF is validated by resampling the simultaneous imaging polarization camera (SIPC) data, which is compared with POSP data acquired at the same time in an aerial experiment. The results show that the integration SRF model is more accurate to characterize the details of POSP measurement than the mean value SRF model. The proposed SRF reduces the root mean square error (RMSE) of convolved results and measurements by 5∼30% with different radiance contrast scene.

Constructing 10-m NDVI Time Series From Landsat 8 and Sentinel 2 Images Using Convolutional Neural Networks

Normalized difference vegetation index (NDVI) carries valuable information related to the photosynthetic activity of vegetation and is essential for monitoring phenological changes and ecosystem dynamics. The medium to high spatial resolution satellite images from Landsat 8 and Sentinel 2 offer opportunities to generate dense NDVI time series at 10-m resolution to improve our understanding of the land surface processes. However, synergistic use of Landsat 8 and Sentinel 2 for generating frequent and consistent NDVI data remains challenging as they have different spatial resolutions and spectral response functions. In this letter, we developed an attentional super resolution convolutional neural network (ASRCNN) for producing 10-m NDVI time series through fusion of Landsat 8 and Sentinel 2 images. We evaluated its performance in two heterogeneous areas. Quantitative assessments indicated that the developed network outperforms five commonly used fusion methods [i.e., enhanced deep convolutional spatiotemporal fusion network (EDCSTFN), super resolution convolutional neural network (SRCNN), spatial and temporal adaptive reflectance fusion model (STARFM), enhanced STARFM (ESTARFM), and flexible spatiotemporal data fusion (FSDAF)]. The influence of the method selection on the fusion accuracy is much greater than that of the fusion strategy in blending Landsat-Sentinel NDVI. Our results illustrate the advantages and potentials of the deep learning approaches on satellite data fusion.

Large-Pitch Synthetic Transmit Aperture Imaging: A Feasibility Study.

A 3-D or large-aperture 2-D synthetic transmit aperture (STA) ultrasound imaging system with a fully sampled array usually leads to high hardware complexity and cost since each element in the array is individually controlled. To reduce the hardware complexity, we propose a large-pitch method for STA (LPSTA) imaging integrated with a spatial response function (SRF) in the image reconstruction to improve image quality. To achieve this, we decreased the total number of measurement channels M (the product of the number of transmissions IT and the number of the receive channels in each transmission IR ). We combined L adjacent elements in transmission and K adjacent elements in receive into subapertures (SAPs), where L and K were coprime (no common factors) integers to suppress the grating lobes. We denoted it as an ( N/L, N /K ) system, where N is the number of transducer elements. In this article, first, we derived the beam pattern of the LPSTA using a far-field approximation. We demonstrated that the coprime selection and SRF can significantly reduce the grating lobes level (GLL) by using a beam pattern analysis. We also found that the LPSTA can have a similar beam pattern as that of a full array when the target is located along the steering direction. Second, the imaging performance of LPSTA was evaluated and validated with Field II simulations and experiments. The simulation results demonstrated that the proposed LPSTA with ( N /3, N /5) can achieve on average ~25% improvement in lateral resolution, ~24.6% and ~42.3% improvement in contrast-to-noise ratio (CNR) and contrast ratio (CR), respectively, over B-mode with a large-pitch receiver with ( N , N /5). LPSTA achieved comparable image contrast to the standard STA with the full array ( N , N ) at the cost of a reduced field of view. The experiment results were consistent with the simulation results. Finally, in addition to reducing the system (hardware) complexity, the LPSTA was more computationally efficient than the standard STA with a full array. The proposed method may help in realizing clinical applications of real-time 2-D or 3-D ultrasound imaging using large arrays.

Frequency-Domain Electrothermal Impedance Spectroscopy of an Actively Switching Power Semiconductor Converter

This paper develops impedance spectroscopy for characterizing transient heat transfer in assembled power semiconductor converter systems. The method addresses limits associated with traditional methods, for e.g., data sheet transient thermal impedance curves are step responses that only characterize the junction-to-case spatial-domain segment. The presented method utilizes an actively switching semiconductor as a controlled modulator of dynamic loss (heat) injections, along with multiple temperature measurements, to form spatial electrothermal impedance frequency response function (FRF) models. Complementary investigations of numerical and analytical modeling methods yield a general, compact thermal model template, for interpreting measured FRFs. A pulsewidth modulated power converter with integrated die and heat sink temperature sensing was characterized with the spectroscopy method. Obtained ambient-referred and relative FRFs describe transient heat transfer, including fast (die-level) and slow (sink-level) modes, over frequency decades with coherence. The FRFs were parameterized and interpreted using the derived compact thermal model.

A simple self-adjusting model for correcting the blooming effects in DMSP-OLS nighttime light images

Night-time light (NTL) data from the Defense Meteorological Satellite Program (DMSP) Operation Linescan System (OLS) provide important observations of human activities; however, DMSP-OLS NTL data suffer from problems such as saturation and blooming. This research developed a self-adjusting model (SEAM) to correct blooming effects in DMSP-OLS NTL data based on a spatial response function and without using any ancillary data. By assuming that the pixels adjacent to the background contain no lights (i.e., pseudo light pixels, PLPs), the blooming effect intensity, a parameter in the SEAM model, can be estimated by pixel-based regression using PLPs and their neighboring light sources. SEAM was applied to all of China, and its performance was assessed for twelve cities with different population sizes. The results show that SEAM can largely reduce the blooming effect in the original DMSP-OLS dataset and enhance its quality. The images after blooming effect correction have higher spatial similarity with Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) images and higher spatial variability than the original DMSP-OLS data. We also found that the average effective blooming distance is approximately 3.5 km in China, which may be amplified if the city is surrounded by water surfaces, and that the blooming effect intensity is positively correlated to atmospheric quality. The effectiveness of the proposed model will improve the capacity of DMSP-OLS images for mapping the urban extent and modeling socioeconomic parameters.