Loss Of Resolution Research Articles

Advancing application of satellite remote sensing technologies for linking atmospheric and built environment to health.

The intricate interplay between human well-being and the surrounding environment underscores contemporary discourse. Within this paradigm, comprehensive environmental monitoring holds the key to unraveling the intricate connections linking population health to environmental exposures. The advent of satellite remote sensing monitoring (SRSM) has revolutionized traditional monitoring constraints, particularly limited spatial coverage and resolution. This innovation finds profound utility in quantifying land covers and air pollution data, casting new light on epidemiological and geographical investigations. This dynamic application reveals the intricate web connecting public health, environmental pollution, and the built environment. This comprehensive review navigates the evolving trajectory of SRSM technology, casting light on its role in addressing environmental and geographic health issues. The discussion hones in on how SRSM has recently magnified our understanding of the relationship between air pollutant exposure and population health. Additionally, this discourse delves into public health challenges stemming from shifts in urban morphology. Utilizing the strategic keywords "SRSM," "air pollutant health risk," and "built environment," an exhaustive search unfolded across prestigious databases including the China National Knowledge Network (CNKI), PubMed and Web of Science. The Citespace tool further unveiled interconnections among resultant articles and research trends. Synthesizing insights from a myriad of articles spanning 1988 to 2023, our findings unveil how SRMS bridges gaps in ground-based monitoring through continuous spatial observations, empowering global air quality surveillance. High-resolution SRSM advances data precision, capturing multiple built environment impact factors. Its application to epidemiological health exposure holds promise as a pioneering tool for contemporary health research. This review underscores SRSM's pivotal role in enriching geographic health studies, particularly in atmospheric pollution domains. The study illuminates how SRSM overcomes spatial resolution and data loss hurdles, enriching environmental monitoring tools and datasets. The path forward envisions the integration of cutting-edge remote sensing technologies, novel explorations of urban-public health associations, and an enriched assessment of built environment characteristics on public well-being.

Classifying marine mammals signal using cubic splines interpolation combining with triple loss variational auto-encoder

In practical applications of passive sonar principles for extracting characteristic frequencies of acoustic signals, scientists typically employ traditional time-frequency domain transformation methods such as Mel-frequency, Short time Fourier transform (STFT), and Wavelet transform (WT). However, these solutions still face limitations in resolution and information loss when transforming data collected over extended periods. In this paper, we present a study using a two-stage approach that combines pre-processing by Cubic-splines interpolation (CSI) with a probability distribution in the hidden space with Siamese triple loss network model for classifying marine mammal (MM) communication signals. The Cubic-splines interpolation technique is tested with the STFT transformation to generate STFT-CSI spectrograms, which enforce stronger relationships between characteristic frequencies, enhancing the connectivity of spectrograms and highlighting frequency-based features. Additionally, stacking spectrograms generated by three consecutive methods, Mel, STFT-CSI, and Wavelet, into a feature spectrogram optimizes the advantages of each method across different frequency bands, resulting in a more effective classification process. The proposed solution using an Siamese Neural Network-Variational Auto Encoder (SNN-VAE) model also overcomes the drawbacks of the Auto-Encoder (AE) structure, including loss of discontinuity and loss of completeness during decoding. The classification accuracy of marine mammal signals using the SNN-VAE model increases by 11% and 20% compared to using the AE model (2013), and by 6% compared to using the Resnet model (2022) on the same actual dataset NOAA from the National Oceanic and Atmospheric Administration - United State of America.

Bio-friendly long-term subcellular dynamic recording by self-supervised image enhancement microscopy

Fluorescence microscopy has become an indispensable tool for revealing the dynamic regulation of cells and organelles. However, stochastic noise inherently restricts optical interrogation quality and exacerbates observation fidelity when balancing the joint demands of high frame rate, long-term recording and low phototoxicity. Here we propose DeepSeMi, a self-supervised-learning-based denoising framework capable of increasing signal-to-noise ratio by over 12 dB across various conditions. With the introduction of newly designed eccentric blind-spot convolution filters, DeepSeMi effectively denoises images with no loss of spatiotemporal resolution. In combination with confocal microscopy, DeepSeMi allows for recording organelle interactions in four colors at high frame rates across tens of thousands of frames, monitoring migrasomes and retractosomes over a half day, and imaging ultra-phototoxicity-sensitive Dictyostelium cells over thousands of frames. Through comprehensive validations across various samples and instruments, we prove DeepSeMi to be a versatile and biocompatible tool for breaking the shot-noise limit.

Dark-Channel Soft-Constrained and Object-Perception-Enhanced Deep Dehazing Networks Used for Road Inspection Images.

Haze seriously affects the visual quality of road inspection images and contaminates the discrimination of key road objects, which thus hinders the execution of road inspection work. The basic assumptions of the classical dark-channel prior are not suitable for road images containing light-colored lane lines and vehicles, while typical deep dehazing networks lack physical model interpretability, and they focus on global dehazing effects, neglecting the preservation of object features. For this reason, this paper proposes a Dark-Channel Soft-Constrained and Object-Perception-Enhanced Deep Dehazing Network (DCSC-OPE-Net) for the information recovery of road inspection images. The network is divided into two modules: a dark-channel soft-constrained dehazing module and a near-view object-perception-enhanced module. Unlike the traditional dark-channel algorithms that impose strong constraints on dark pixels, a dark-channel soft-constrained loss function is constructed to ensure that the features of light-colored vehicles and lane lines are effectively maintained. To avoid resolution loss due to patch-based dark-channel processing for image dehazing, a resolution enhancement module is used to strengthen the contrast of the dehazed image. To autonomously perceive and enhance key road features to support road inspection, edge enhancement loss combined with a transmission map is embedded into the network to autonomously discover near-view objects and enhance their key features. The experiments utilize public datasets and real road inspection datasets to validate the performance of the proposed DCSC-OPE-Net compared with typical networks using dehazing evaluation metrics and road object recognition metrics. The experimental results demonstrate that the proposed DCSC-OPE-Net can obtain the best dehazing performance, with an NIQE score of 4.5 and a BRISQUE score of 18.67, and obtain the best road object recognition results (i.e., 83.67%) among the comparison methods.

Convection compensation in 3D iDOSY-HMBC 1H–13C-correlation experiments

3D iDOSY-HMBC (3D incorporated Diffusion Ordered SpectroscopY-Heteronuclear Multiple Bond Correlation) pulse sequences were modified to incorporate convection compensation element. No additional delays were required, and convection compensation was directly constructed within the existing delay periods in 3D iDOSY-HMBC pulse sequence. Convection compensation was achieved by pulsed field gradient double echo, thus avoiding the intensity loss normally related to stimulated echo methods. The incorporated convection compensation element improves the usability of 3D iDOSY-HMBC. In case of elevated temperatures, thermal convection leads to loss of resolution in DOSY-dimension or even to severe decrease of signal intensity, thus making convection uncompensated 3D iDOSY-HMBC infeasible. Aforementioned problems can be circumvented via utilization of the presented convection compensated 3D iDOSY-HMBC-versions.

Dipole and quadrupole resonance excitation in linear quadrupoles.

In the development of commercial quadrupole mass spectrometers, there is an interest in improving the performance characteristics such as transmission, resolution, and mass range. In particular, parametric and dipolar resonance excitation of trapping ions are used for linear quadrupole mass filters. Theoretical methods and numerical simulation of ion trajectories were applied for study of ion-optical properties. The review is devoted to description of different excitation methods to improve QMF performance and consists of three parts. The first part presents the results of a linear ion trap simulation for various operating conditions and excitation methods. The second part considers the effects of dipole excitation (DE) on the performance of the quadrupole mass filter. The last part analyzes the formation of stability islands by different methods of quadrupole excitation. To date conditions of mass separation in quadrupole mass filters with sin wave supply were described for stability islands of the first and third stability regions formed by quadrupole and DE. By complicating the electronics such methods allow to overcome the destructive influence of electric field distortions and obtain a resolving power and ion transmission efficiency comparable with commercial devices. At quadrupole resonance excitation by a two-frequency signal, it is possible to reduce the length of electrodes three times without losses in resolution and transmission, which reduces the cost of rod set production with micrometer accuracy. Dipole resonance excitation allows controlling the shape of the mass peak by changing amplitude and phase of the auxiliary AC signal. The main factors affecting the resolving power of a linear ion trap are described theoretically. The numerical modeling results are confirmed by experiment.

Adeno-associated viral capsid stability on anion exchange chromatography column and its impact on empty and full capsid separation

Recombinant adeno-associated viral vector (rAAV) mediated gene therapy is gaining traction in treating genetic disorders. Current rAAV production systems yield a mixture of capsids largely devoid of the transgene (empty capsid) compared with the desired therapeutic product (full capsid). Anion exchange chromatography (AEX) is an attractive method for separating empty and full AAV capsids because of its scalability. Resin types and buffer composition are key considerations for AEX and must support capsid stability to be suitable for downstream processing. We examined the impact of binding durations (0-8 h) using various binding ionic strengths (15-75mM), pH (7.5-9.0), resin chemistry (POROS XQ, POROS HQ, POROS I, and BIA QA monolith), and proprietary Q resins with different ligand densities for effects on capsid stability. Empty capsids were altered upon extended binding, leading to retention time shifts and loss of resolution between empty and full capsids. Viral capsid protein analysis reveals that full capsids have more viral capsid protein 3 (VP3) proteins than empty capsids. Analytical hydrophilic liquid chromatography showed that empty capsid retention time shift is accompanied by changes to the empty capsid's native VP3 protein. Among the potential stabilizing additives considered, magnesium chloride was the most effective at reducing negative impacts caused by extended binding.

Single cell in vivo optogenetic stimulation by two-photon excitation fluorescence transfer

Optogenetic manipulation with single-cell resolution can be achieved by two-photon excitation. However, this frequently requires relatively high laser powers. Here, we developed a novel strategy that can improve the efficiency of current two-photon stimulation technologies by positioning fluorescent proteins or small fluorescent molecules with high two-photon cross-sections in the vicinity of opsins. This generates a highly localized source of endogenous single-photon illumination that can be tailored to match the optimal opsin absorbance. Through neuronal and vascular stimulation in the live mouse brain, we demonstrate the utility of this technique to achieve efficient opsin stimulation, without loss of cellular resolution. We also provide a theoretical framework for understanding the potential advantages and constrains of this methodology, with directions for future improvements. Altogether, this fluorescence transfer illumination method opens new possibilities for experiments difficult to implement in the live brain such as all-opticalneural interrogation and control of regional cerebral blood flow.

Understanding the impact of along-transect resolution on acoustic surveys

Resolution can be defined as the minimum distance between two consecutive sampling points taken by an instrument. In acoustic surveys, the main parameter determining the resolution of sampling along a transect is the distance between successive echosounder transmissions or “pings”. An increase in either the time interval between pings or the speed of the vessel increases the inter-ping distance, hence decreasing the effective sampling resolution. This study investigated whether a loss in along-transect resolution affects the mean backscattered acoustic energy, leading to uncertainty and/or bias in abundance estimates. To this end, a real acoustic survey was echo-integrated, followed by the application of a systematic resampling scheme to simulate a decrease in pinging resolution. For each transect, the mean backscattered acoustic energy calculated at each resolution was compared with that at the original resolution. Transects were characterised according to their heterogeneity and spatial autocorrelation to investigate their effect on the relationship between abundance error and sampling resolution. Uncertainty was seen to increase with decreasing resolution, with higher heterogeneity and lower spatial autocorrelation accelerating the rise in imprecision. Although the mean bias across replicates was zero, the asymmetry of the bias distributions increased with decreasing resolution, leading to an increasing probability and magnitude of underestimation (https://aztigps.shinyapps.io/PingRateStudio/).

HPG-GAN: High-Quality Prior-Guided Blind Face Restoration Generative Adversarial Network

To address the problems of low resolution, compression artifacts, complex noise, and color loss in image restoration, we propose a High-Quality Prior-Guided Blind Face Restoration Generative Adversarial Network (HPG-GAN). This mainly consists of Coarse Restoration Sub-Network (CR-Net) and Fine Restoration Sub-Network (FR-Net). HPG-GAN extracts high-quality structural and textural priors and facial feature priors from coarse restoration images to reconstruct clear and high-quality facial images. FR-Net includes the Facial Feature Enhancement Module (FFEM) and the Asymmetric Feature Fusion Module (AFFM). FFEM enhances facial feature information using high-definition facial feature priors obtained from ArcFace. AFFM fuses and selects asymmetric high-quality structural and textural information from ResNet34 to recover overall structural and textural information. The comparative evaluations on synthetic and real-world datasets demonstrate superior performance and visual restoration effects compared to state-of-the-art methods. The ablation experiments validate the importance of each module. HPG-GAN is an effective and robust blind face deblurring and restoration network. The experimental results demonstrate the effectiveness of the proposed network, which achieves better visual quality against state-of-the-art methods.

Modeling of surface damage at the Si/SiO2-interface of irradiated MOS-capacitors

Surface damage caused by ionizing radiation in SiO2 passivated silicon particle detectors consists mainly of the accumulation of a positively charged layer along with trapped-oxide-charge and interface traps inside the oxide and close to the Si/SiO2-interface. High density positive interface net charge can be detrimental to the operation of a multi-channel n-on-p sensor since the inversion layer generated under the Si/SiO2-interface can cause loss of position resolution by creating a conduction channel between the electrodes. In the investigation of the radiation-induced accumulation of oxide charge and interface traps, a capacitance-voltage characterization study of n/γ- and γ-irradiated Metal-Oxide-Semiconductor (MOS) capacitors showed that close agreement between measurement and simulation were possible when oxide charge density was complemented by both acceptor- and donor-type deep interface traps with densities comparable to the oxide charges. Corresponding inter-strip resistance simulations of a n-on-p sensor with the tuned oxide charge density and interface traps show close agreement with experimental results. The beneficial impact of radiation-induced accumulation of deep interface traps on inter-electrode isolation may be considered in the optimization of the processing parameters of isolation implants on n-on-p sensors for the extreme radiation environments.

From Algorithm to Module: Adaptive and Energy-Efficient Quantization Method for Edge Artificial Intelligence in IoT Society

Next-generation industrial edge artificial intelligence (AI) applications will undoubtedly emerge on energy-efficient, highly-integrated platforms incorporating various sensors, processors, and functional modules. Effectively bridging sensor data captured from a microprocessor or central processing unit with dedicated processing modules is still being studied in academia and industry. Therefore, embedding the data quantization module onto edge AI chips, connecting sensors, processors, and functional modules, is critical to achieving adaptive transformation of diverse data representation formats. This paper proposes a novel adaptive and low-power quantization technique and systematically validates its effectiveness from algorithm to hardware module for industrial IoT applications, covering precise navigation for autonomous vehicles and accurate classification utilizing deep neural networks (DNNs). The proposed quantization method merges an adaptive conversion function from floating-point to fixed-point binaries and an adaptive radix-point determination function, ensuring adequate resolution and minimal error loss of the fixed-point inputs to the edge AI modules. The experimental results demonstrate that the quantization error in the proposed quantization technique contributes negligible errors to the navigation solutions of the strapdown inertial navigation system and the DNNs' top-1 and top-5 classification accuracy (on the order of 10 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{-8}$</tex-math></inline-formula> and 10 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{-7}$</tex-math></inline-formula> ). Moreover, a quantization-on-multiplier (QoM) hardware module is designed, synthesized, and routed in accordance with the proposed quantization technique. The simulation results indicate that the QoM's power consumption and area are 0.1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$mW$</tex-math></inline-formula> and 649.552 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu m^{2}$</tex-math></inline-formula> , accounting for 5% and 14.15% of its total energy consumption and area, respectively. With our proposed on-chip quantization technique, the time required to quantize the parameters of DNNs is up to 1142 times shorter than with the existing benchmark off-chip quantization approaches.

Overcoming Resolution Loss in Laser Phase Plate Cryo-Electron Microscopy.

Overcoming Resolution Loss in Laser Phase Plate Cryo-Electron Microscopy.

3D electrode configurations for spectral induced polarization surveys of landfills

There is growing interest in the use of spectral induced polarization (SIP) surveys to characterize the near-surface environment. Few attempts have been made to perform field SIP surveys in a 3D configuration; when done, they are typically conducted using a series of parallel 2D electrode lines with collinear measurements. However, such measurements are limited in the resolution between the lines which is critical in the case of heterogeneous subsurface conditions, such as in landfills. To overcome this, we investigate here the enhanced resolution in SIP measurements through true 3D measurements, i.e., the resolving capabilities of different electrode configurations distributed across measuring planes. First, we investigate, through a synthetic study, the difference between results from using 2D parallel collinear electrode arrays and true 3D configurations. Second, we collected SIP data (in the frequency range between 1 and 240 Hz) using 2D and 3D configurations in two landfills to evaluate the application of our results in real field conditions. Both the synthetic and the field experiments demonstrate that measurements of parallel 2D collinear arrays result in the creation of artifacts and the loss of resolution in the 3D structure, especially of polarizable features. In contrast, the 3D configurations are able to resolve polarizable anomalies in synthetic and field measurements, resulting in a better delineation of the geometry of waste units. Our results also demonstrate that 3D configurations are better suited to recover the frequency-dependence of the electrical properties; thus, permitting an improved interpretation of waste composition and the quantification of waste volume.

The VA-CAL Test Quantifies Improvement of Visual Acuity in Achromatopsia by Means of Short-Wave Cutoff Filter Glasses in Daily Living Conditions.

To quantify visual performance of patients with achromatopsia at various contrast and luminance combinations typical for daily living conditions, in comparison to controls, and to measure beneficial effects of short-wavelength cutoff filter glasses used by patients with achromatopsia to reduce glare sensation. Best-corrected visual acuity (BCVA) was tested with Landolt rings using an automated device (VA-CAL test). The visual acuity space was assessed for each participant with and without filter glasses (transmission >550 nm) at 46 contrast-luminance combinations (18%-95%; 0-10,000cd/m2). The BCVA differences between both conditions were calculated for each combination as absolute values and relative to individual standard BCVA. Fourteen achromats (mean ± SD: 37.9 ± 17.6years) and 14 normally sighted controls (mean ± SD: 25.2 ± 2.8years) were included in the study. Without filter glasses, achromats' BCVA was best at 30cd/m2 (mean ± SEM: 0.76 ± 0.046 logarithm of the minimum angle of resolution [logMAR], contrast = 89%) and worst at 10,000cd/m2 (mean ± SEM: 1.41 ± 0.08 logMAR, contrast = 18%), a deterioration up to 0.6 logMAR due to increased luminance and decreased contrast. Filter glasses improved achromats' BCVA for almost all luminances by about 0.2 logMAR but lowered controls' BCVA by about 0.1 logMAR. The VA-CAL test provides numerical proof that short-wavelength cutoff filter glasses can help patients with achromatopsia in everyday life, avoiding the common situation of severe visual impairment at certain daily object contrasts and ambient luminances. The VA-CAL test discovers losses of spatial resolution in the visual acuity space not seen in standardized BCVA assessment. Filter glasses improve the patients' daily visual performance, rendering them a strongly recommended visual aid in achromatopsia.

Review of Underwater Image Enhancement Using CNN and U-net

Due to environmental scattering, images taken in the underwater environment still exhibit color distortion, loss of resolution, and decreased contrast. The suggested study presents a method for enhancing the aesthetic appeal of underwater photography. Yet, because of unwanted staining, decreased contrast, and detail loss brought on by light scattering and absorption, photos that are directly taken in the marine environment are still highly damaged, drastically limiting the amount of information that can be extracted from the image. Thus, acquiring precise and clear photographs is a crucial requirement for aiding scientists in their understanding of the underwater environment. The CNN algorithms are employed in many different applications, including Identification of the object, tracking, recognition, and navigation. Separating the water component from the other component is the most important step here. To get rid of water from a color image, we're suggesting a practical technique.

Molecular Latent Space Simulators for Distributed and Multimolecular Trajectories.

All atom molecular dynamics (MD) simulations offer a powerful tool for molecular modeling, but the short time steps required for numerical stability of the integrator place many interesting molecular events out of reach of unbiased simulations. The popular and powerful Markov state modeling (MSM) approach can extend these time scales by stitching together multiple short discontinuous trajectories into a single long-time kinetic model but necessitates a configurational coarse-graining of the phase space that entails a loss of spatial and temporal resolution and an exponential increase in complexity for multimolecular systems. Latent space simulators (LSS) present an alternative formalism that employs a dynamical, as opposed to configurational, coarse graining comprising three back-to-back learning problems to (i) identify the molecular system's slowest dynamical processes, (ii) propagate the microscopic system dynamics within this slow subspace, and (iii) generatively reconstruct the trajectory of the system within the molecular phase space. A trained LSS model can generate temporally and spatially continuous synthetic molecular trajectories at orders of magnitude lower cost than MD to improve sampling of rare transition events and metastable states to reduce statistical uncertainties in thermodynamic and kinetic observables. In this work, we extend the LSS formalism to short discontinuous training trajectories generated by distributed computing and to multimolecular systems without incurring exponential scaling in computational cost. First, we develop a distributed LSS model over thousands of short simulations of a 264-residue proteolysis-targeting chimera (PROTAC) complex to generate ultralong continuous trajectories that identify metastable states and collective variables to inform PROTAC therapeutic design and optimization. Second, we develop a multimolecular LSS architecture to generate physically realistic ultralong trajectories of DNA oligomers that can undergo both duplex hybridization and hairpin folding. These trajectories retain thermodynamic and kinetic characteristics of the training data while providing increased precision of folding populations and time scales across simulation temperature and ion concentration.

Chemical Transformations Can Occur during DMS Separations: Lessons Learned from Beer's Bittering Compounds.

While developing a DMS-based separation method for beer's bittering compounds, we observed that the argentinated forms of humulone tautomers (i.e., [Hum + Ag]+) were partially resolvable in a N2 environment seeded with 1.5 mol % of isopropyl alcohol (IPA). Attempting to improve the separation by introducing resolving gas unexpectedly caused the peaks for the cis-keto and trans-keto tautomers of [Hum + Ag]+ to coalesce. To understand why resolution loss occurred, we first confirmed that each of the tautomeric forms (i.e., dienol, cis-keto, and trans-keto) responsible for the three peaks in the [Hum + Ag]+ ionogram were assigned to the correct species by employing collision-induced dissociation, UV photodissociation spectroscopy, and hydrogen-deuterium exchange (HDX). The observation of HDX indicated that proton transfer was stimulated by dynamic clustering processes between IPA and [Hum + Ag]+ during DMS transit. Because IPA accretion preferentially occurs at Ag+, which can form pseudocovalent bonds with a suitable electron donor, solvent clustering also facilitated the formation of exceptionally stable microsolvated ions. The exceptional stability of these microsolvated configurations disproportionately impacted the compensation voltage (CV) required to elute each tautomer when the temperature within the DMS cell was varied. The disparity in CV response caused the peaks for the cis- and trans-keto species to merge when a temperature gradient was induced by the resolving gas. Moreover, simulations showed that microsolvation with IPA mediates dienol to trans-keto tautomerization during DMS transit, which, to the best of our knowledge, is the first observation of keto/enol tautomerization occurring within an ion-mobility device.

High-performance transient SBS-based microwave measurement using high-chirp-rate modulation and advanced algorithms.

The transient stimulated Brillouin scattering (SBS) effect, enabled by optical chirp chain (OCC) technology, has already been proposed and demonstrated for microwave frequency identification with high temporal resolution. Through increasing the OCC chirp rate, the instantaneous bandwidth can be effectively extended without loss of the temporal resolution. However, the higher chirp rate results in more asymmetric transient Brillouin spectra, which worsens the demodulation accuracy when using the traditional fitting method. In this Letter, advanced algorithms, including image processing and artificial neural network, are employed to improve the measurement accuracy and demodulation efficiency. A microwave frequency measurement scheme is implemented with 4 GHz instantaneous bandwidth and 100 ns temporal resolution. Through the proposed algorithms, the demodulation accuracy of transient Brillouin spectra under 50 MHz/ns high chirp rate is improved from 9.85 MHz to 1.17 MHz. Moreover, owing to the matrix computations of the proposed algorithm, the time consumption is reduced by two orders of magnitude compared with the fitting method. The proposed method allows a high-performance OCC transient SBS-based microwave measurement, which provides new possibilities to realize real-time microwave tracking for diverse application fields.

How Fast is Your Detector? The Effect of Temporal Response on Image Quality.

With increasing interest in high-speed imaging, there should be an increased interest in the response times of our scanning transmission electron microscope detectors. Previous works have highlighted and contrasted the performance of various detectors for quantitative compositional or structural studies, but here, we shift the focus to detector temporal response, and the effect this has on captured images. The rise and decay times of eight detectors' single-electron response are reported, as well as measurements of their flatness, roundness, smoothness, and ellipticity. We develop and apply a methodology for incorporating the temporal detector response into simulations, showing that a loss of resolution is apparent in both the images and their Fourier transforms. We conclude that the solid-state detector outperforms the photomultiplier tube-based detectors in all areas bar a slightly less elliptical central hole and is likely the best detector to use for the majority of applications. However, using the tools introduced here, we encourage users to effectively evaluate which detector is most suitable for their experimental needs.