Acquisition Scheme Research Articles

The New Method for Ground Insulation Monitoring of the Secondary Circuit in Substations

When two-or-more-point grounding faults occur in the secondary circuit of a substation, they will lead to large data measurement errors in the protection, measurement, and control devices, which can easily cause protection malfunction. At present, there is a lack of an online panoramic monitoring system for the secondary circuits of substations, and it is difficult to accurately identify the faults in these secondary circuits. In order to solve this problem, this paper proposes a cloud–edge collaboration framework for the power Internet of Things (IoT), which can accurately identify faults by monitoring the secondary circuit of power grid substations with panoramic online status, reducing maintenance costs and improving overall operational efficiency. Firstly, the collection requirements of current transformer (CT) and potential transformer (PT) fault characteristics in the secondary circuit of the substation are analyzed. The platform architecture of the secondary loop panoramic monitoring system of the cloud-side cooperative substation is proposed, along with the algorithm and data wireless acquisition scheme of the edge computing, cloud computing, and cloud-side system. Furthermore, the edge computing platform and the current and voltage wireless sensors are developed. The maximum error in the accuracy test of the voltage sensor is 1.9180%, while the maximum error in the accuracy test of the current sensor is 1.3406%.

Application Research of Power Measurement Data Exchange Based on Uninterruptible Power Supply

With the maturity of the smart meter embedded system integration mode, the power consumption information can be fed back to the management center by the intelligent terminal through the power Internet of Things. The smart terminal adopts the narrowband data communication mode. However, the narrowband channel interval is hindered by interference signals, resulting in serious data packet loss. Therefore, a stable and efficient acquisition scheme is needed to ensure its smooth interaction. Based on the application of smart meter data communication, we analyze the power consumption optimization and carrier transmission optimization of the power Internet of Things and propose a data analysis method based on self-learning and external characteristics, which is used to calculate the offline execution strategy of smart meters when they encounter factors such as power outage or intrusion. A production task-driving rule is designed. The external characteristics of deep learning data of the power Internet of Things are collected. The accuracy of data classification is judged according to the parallel Internet of Things of a convolutional neural network and a long-term and short-term memory deep neural network. When dealing with high-dimensional original data, the data network is graded to reduce the work of feature selection and the difficulty of training. Moreover, we improve the autonomous execution ability of an embedded terminal device in an offline state. We also strengthen the ability of abnormal data detection and judgment learning to meet the requirements of efficient and stable operation of the power grid. Simulation results verify that compared with the current mainstream schemes, the performance of the proposed scheme is improved by more than 17%. Besides, the fault tolerance rate is enhanced by 29%, and the power consumption after optimization is reduced by 26.3% and 42.8%. These effects highlight the role of data processing in embedded systems. It solves the problem of abnormal data and energy loss affecting the accuracy of error estimation of electric energy meters, opens up a new way for data acquisition and processing of large-scale smart meters, and further improves the service level of information management of power systems.

Pure Shift NMR in Continuous Flow.

Flow NMR is an expanding analytical approach with applications that include in-line analysis for process control and optimisation, and real-time reaction monitoring. The samples monitored by flow NMR are typically mixtures that yield complex 1D1H spectra. "Pure shift" NMR is a powerful approach to simplifying1H NMR spectra, but its standard implementation is not compatible with continuous flow because of interference between sample motion and the position-dependent spin manipulations that are required in pure shift NMR. Here we show that pure shift NMR spectra can be successfully collected for continuously flowing samples, thanks to an adapted acquisition scheme, robust solvent suppression, and a velocity-compensation strategy. The resulting method is used to collect ultrahigh resolution reaction monitoring data. Pure shift NMR spectra are expected to benefit many applications of flow NMR.

A 3D fast MR elastography sequence with interleaved multislab acquisition and Hadamard encoding.

To enhance the functional capability of MRI, this study aims to develop a novel MR elastography (MRE) sequence that achieves rapid acquisition without distortion artifacts. A displacement-encoded stimulated echo (DENSE) with multiphase acquisition scheme was used to capture wave images. A center-out golden-angle stack-of-stars sampling pattern was introduced for improved SNR and data incoherence. A combination of Hadamard encoding and interleaved multislab acquisition schemes was used to increase the acquisition efficiency of MRE data with multiple directions and phase offsets. A generalized parallel-imaging and compressed-sensing method was further applied to accelerate the acquisition process. The imaging results of the proposed sequence were compared with those from six gradient echo (GRE)/EPI/DENSE-based MRE sequences via phantom and brain acquisitions. The proposed sequence achieved a 6-fold acceleration compared with GRE MRE. With the application of a conventional parallel-imaging and compressed-sensing algorithm, the scanning speed was further accelerated by 8-fold, matching the speed of EPI-based MRE. Phantom tests revealed small variances in stiffness measurements across the seven sequences (< 9.23%). The proposed sequence exhibited a higher contrast-to-noise ratio (1.38) than the two EPI-based sequences (0.61/0.76) and similar to GRE-based sequences (1.34/1.22/1.58). Brain imaging validated the effectiveness of the proposed sequence in accurate stiffness estimation and distortion artifact avoidance. A rapid DENSE-based MRE sequence with interleaved multislab acquisition and Hadamard encoding was developed at a speed matching EPI-based sequences, without compromising SNR or introducing distortion artifacts.

Method for co-registration of high-resolution specimen PET-CT with histopathology to improve insight into radiotracer distributions

BackgroundAs the spatial resolution of positron emission tomography (PET) scanners improves, understanding of radiotracer distributions in tissues at high resolutions is important. Hence, we propose a method for co-registration of high-resolution ex vivo specimen PET images, combined with computed tomography (CT) images, and the corresponding specimen histopathology.MethodsWe applied our co-registration method to breast cancer (BCa) specimens of patients who were preoperatively injected with 0.8 MBq/kg [18\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{18}$$\\end{document}F]fluorodeoxyglucose ([18F]FDG). The method has two components. First, we used an image acquisition scheme that minimises and tracks tissue deformation: (1) We acquired sub-millimetre (micro)-PET-CT images of ±2 mm-thick lamellas of the fresh specimens, enclosed in tissue cassettes. (2) We acquired micro-CT images of the same lamellas after formalin fixation to visualise tissue deformation. (3) We obtained 1 hematoxylin and eosin (H&E) stained histopathology section per lamella of which we captured a digital whole slide image (WSI). Second, we developed an automatic co-registration algorithm to improve the alignment between the micro-PET-CT images and WSIs, guided by the micro-CT of the fixated lamellas. To estimate the spatial co-registration error, we calculated the distance between corresponding microcalcifications in the micro-CTs and WSIs. The co-registered images allowed to study standardised uptake values (SUVs) of different breast tissues, as identified on the WSIs by a pathologist.ResultsWe imaged 22 BCa specimens, 13 cases of invasive carcinoma of no special type (NST), 6 of invasive lobular carcinoma (ILC), and 3 of ductal carcinoma in situ (DCIS). While the cassette framework minimised tissue deformation, the best alignment between the micro-PET-CT images and WSIs was achieved after deformable co-registration. We found an overall average co-registration error of 0.74 ± 0.17 mm between the micro-PET images and WSIs. (Pre)malignant tissue (including NST, ILC, and DCIS) generally showed higher SUVs than healthy tissue (including healthy glandular, connective, and adipose tissue). As expected, inflamed tissue and skin also showed high uptake.ConclusionsWe developed a method to co-register micro-PET-CT images of surgical specimens and WSIs with an accuracy comparable to the spatial resolution of the micro-PET images. While currently, we only applied this method to BCa specimens, we believe this method is applicable to a wide range of specimens and radiotracers, providing insight into distributions of (new) radiotracers in human malignancies at a sub-millimetre resolution.

Hyperspectral acquisition with ScanImage at the single pixel level: application to time domain coherent Raman imaging

We present a comprehensive strategy and its practical implementation using the commercial ScanImage software platform to perform hyperspectral point scanning microscopy when a fast time-dependent signal varies at each pixel level. In the proposed acquisition scheme, the scan along the X-axis is slowed down while the data acquisition is maintained at a high pace to enable the rapid acquisition of the time-dependent signal at each pixel level. The ScanImage generated raw 2D images have a very asymmetric aspect ratio between X and Y, the X axis encoding both for space and time acquisition. The results are X-axis macro-pixel where the associated time-dependent signal is sampled to provide hyperspectral information. We exemplified the proposed hyperspectral scheme in the context of time-domain coherent Raman imaging, where a pump pulse impulsively excites molecular vibrations that are subsequently probed by a time-delayed probe pulse. In this case, the time-dependent signal is a fast acousto-optics delay line that can scan a delay of 4.5ps in 25μs at each pixel level. With this acquisition scheme, we demonstrate ultra-fast hyperspectral vibrational imaging in the low frequency range [10cm−1, 150 cm−1] over a 500 μm field of view (64 x 64 pixels) in 130ms (∼ 7.5 frames/s). The proposed acquisition scheme can be readily extended to other applications requiring the acquisition of a fast-evolving signal at each pixel level.

Advancing Holistic Human Capital Development: A Perspective on the Presidential Amnesty Programme

The Presidential Amnesty Programme (PAP) was a policy thrust to tackle the intractable agitations and youth restiveness in the Niger Delta region due to utter neglect of the development of the region despite the enormous contributions to the national treasury from its rich-oil deposits and gas resources. Accordingly, this study uncovers human capital development in the Niger Delta states from the lens of the PAP. The study applied a quantitative research strategy with a structured questionnaire. A sample of 574 respondents was drawn through a multi-stage sampling design from 13,254 chiefs, community leaders, youths and the staff of the amnesty office, which served as the target population of the study. From the primary and secondary data collected and analysed, the findings demonstrated that the Presidential Amnesty Programme had a positive significant effect on vocational skills acquisition, there was a positive relationship between PAP and university educational advancement, and there was a positive relationship between budgetary provision and human capital development among others. We concluded that the PAP has contributed to the human capital development of youths in the region. We recommended inter alia that the amnesty programme of the federal government of Nigeria should be committed to exposing ex-militants to vocational skill acquisition scheme to ensure self-reliance, and also should improve on the university educational advancement for ex-militants to acquire professional certification in the Niger Delta States.

Evaluating T1-weighted MRI techniques for fetal gastrointestinal diagnostics: A comparative study

PurposeIn clinical practice, fetal gastrointestinal magnetic resonance imaging (MRI) encounters significant challenges. T1-weighted images are particularly susceptible to the effects of fetal and maternal movements compared to other weighted images, complicating the acquisition of satisfactory results. This study aimed to compare three fast 3D-T1 weighted gradient echo (GRE) sequences—free-breathing stack-of-stars VIBE (STAR-VIBE), breath-hold VIBE (BH-VIBE), and free-breathing multi-average VIBE (MA-VIBE)—for fetal gastrointestinal MRI in fetuses with both normal and abnormal gastrointestinal tracts between 21 and 36 weeks of gestation. MethodsThis study enrolled 67 pregnant women who underwent fetal abdominal MRI at our hospital between October 2022 and October 2023, during their gestational period of 21–36 weeks. Among these participants, 22 were suspected of having fetal gastrointestinal anomalies based on ultrasound findings, while the remaining 45 were considered to have normal fetal gastrointestinal development. All subjects underwent True fast imaging with steady-state precession sequence scanning along with three T1-weighted imaging techniques on a Siemens 1.5-T Aera scanner: STAR-VIBE, BH-VIBE, and MA-VIBE. Two radiologists evaluated image quality, intestinal clarity, and lesion conspicuity using a five-point scale where higher scores indicated superior performance for each technique; they were blinded to the acquisition schemes used. Interobserver variability assessments were also conducted. ResultsThe free-breathing MA-VIBE sequence demonstrated significantly better performance than both STAR-VIBE and BH-VIBE in terms of fetal gastrointestinal MRI quality (3.81 ± 0.40 vs. 3.35 ± 0.70 vs. 2.90 ± 0.64; p < .05). The STAR-VIBE and BH-VIBE sequences exhibited moderate consistency (kappa = 0.586 and kappa = 0.527 respectively; P < .05), whereas the MA-VIBE sequence showed higher consistency (kappa = 0.712; P < .05). ConclusionThe free-breathing MA-VIBE sequence provided superior visualization for assessing fetal intestinal conditions compared to other methods employed in this study. On a 1.5 T MRI device, T1-weighted images based on the free-breathing MA-VIBE sequence can effectively overcome motion artifacts and compensate for the reduced signal-to-noise ratio caused by the application of acceleration techniques, thus significantly improving the quality of T1-weighted images.

Cardiac diffusion-weighted and tensor imaging: a Society for Cardiovascular Magnetic Resonance (SCMR) special interest group consensus statement

Thanks to recent developments in Cardiovascular magnetic resonance (CMR), cardiac diffusion-weighted magnetic resonance is fast emerging in a range of clinical applications. Cardiac diffusion-weighted imaging (cDWI) and diffusion tensor imaging (cDTI) now enable investigators and clinicians to assess and quantify the 3D microstructure of the heart. Free-contrast DWI is uniquely sensitized to the presence and displacement of water molecules within the myocardial tissue, including the intra-cellular, extra-cellular and intra-vascular spaces. CMR can determine changes in microstructure by quantifying: a) mean diffusivity (MD) –measuring the magnitude of diffusion; b) fractional anisotropy (FA) – specifying the directionality of diffusion; c) helix angle (HA) and transverse angle (TA) –indicating the orientation of the cardiomyocytes; d) E2A and E2A mobility – measuring the alignment and systolic-diastolic mobility of the sheetlets, respectively.This document provides recommendations for both clinical and research cDWI and cDTI, based on published evidence when available and expert consensus when not. It introduces the cardiac microstructure focusing on the cardiomyocytes and their role in cardiac physiology and pathophysiology. It highlights methods, observations and recommendations in terminology, acquisition schemes, post-processing pipelines, data analysis and interpretation of the different biomarkers. Despite the ongoing challenges discussed in the document and the need for ongoing technical improvements, it is clear that cDTI is indeed feasible, can be accurately and reproducibly performed and, most importantly, can provide unique insights into myocardial pathophysiology.

Research on automatic data acquisition scheme of tool profile wear based on B/S structure

Research on automatic data acquisition scheme of tool profile wear based on B/S structure

Modelling and Validating the Nonthermal Plasma Parameters for Producing Liquid Hydrocarbon from Solid Polyolefin Wastes

This study solved a set of equations to verify the dynamic optimal conditions of nonthermal plasma (NTP)-chemical conversion of solid polyolefin wastes into liquid petroleum hydrocarbons. Furthermore, a novel optimisation model was validated with non-linear experimental conditions to assess the quantitative relationship between the process variables responsible for the degradation rate of wastes. The central composite design (CCD) experimental design was developed based on the Response Surface Model (RSM) technique. These techniques significantly improved the model predictions because of the more-detailed electrochemical description. Experiments were conducted in an in-house-designed and -developed NTP system with advanced data acquisition schemes. Both experimental and the numerical findings exhibited a good agreement, and the results indicated that the electrical factors of NTP could significantly affect the conversion yield (Yconv%) of solid polyolefin-derived wastes to liquid hydrocarbons. Additionally, the model investigation indicated that factors such as power discharge (x1), voltage intensity (x2), and reaction retention time (RTT) (x3) significantly influenced the conversion yield. After optimisation, a maximum conversion percentage (Yconv%) of ≈93% was achieved. The findings indicated that this recommended framework could be effectively employed for scaling the plasma synergistic pyrolysis technique for generating the maximal Yconv% of plastic wastes to yield an oil. Thereafter, the analysis of variance (ANOVA) technique was applied to examine the accuracy of the developed structure in order to upgrade this laboratory-scale processes to an industrial-scale process with &gt;95% effectiveness. The calorific value of the produced oil was seen to be from 43,570.5 J/g to 46,025.5 J/g due to changes of the arrangements of the process factors, which specified that the liquid hydrocarbons showed similar characteristics like commercial diesel in this respect.

Extracting Scalar Couplings From Complex 1H NMR Spectra Using a Simple 2D J-Resolved Sequence.

Measurement of scalar couplings between protons is a very challenging task because of complex multiplet patterns and severe overlapping of these multiplets in congested 1D spectra. Numerous 2D J-resolved sequences now exist that utilize either the Zangger-Sterk or PSYCHE or z-filter elements along with selective refocusing and pure-shift schemes to generate high-resolution phase-sensitive spectra with simple doublets in dimension. Herein, we present a 2D J-resolved sequence that employs a simple element consisting of hard pulses and inter-pulse delays to generate phase-sensitive spectra. This simple element in combination with selective refocusing eliminates all the undesired components including the intense axial peaks, thus provides clean 2D J-resolved spectra with signals of only two targeted protons with simple doublets in dimension and full multiplets of target protons in dimension. This high selectivity thus obviates the need for extra filtering elements and pure-shift acquisition schemes that are integrated into existing sequences to facilitate coupling measurements in overcrowded signals. It is therefore anticipated that this sequence, with the ease of implementation and ability to extract coupling values from highly congested spectra, should turn out an important tool for structural and conformational analyses in chemical and biological studies.

A Cost-Effective and Easy-to-Fabricate Conductive Velcro Dry Electrode for Durable and High-Performance Biopotential Acquisition.

Compared with the traditional gel electrode, the dry electrode is being taken more seriously in bioelectrical recording because of its easy preparation, long-lasting ability, and reusability. However, the commonly used dry AgCl electrodes and silver cloth electrodes are generally hard to record through hair due to their flat contact surface. Claw electrodes can contact skin through hair on the head and body, but the internal claw structure is relatively hard and causes discomfort after being worn for a few hours. Here, we report a conductive Velcro electrode (CVE) with an elastic hook hair structure, which can collect biopotential through body hair. The elastic hooks greatly reduce discomfort after long-time wearing and can even be worn all day. The CVE electrode is fabricated by one-step immersion in conductive silver paste based on the cost-effective commercial Velcro, forming a uniform and durable conductive coating on a cluster of hook microstructures. The electrode shows excellent properties, including low impedance (15.88 kΩ @ 10 Hz), high signal-to-noise ratio (16.0 dB), strong water resistance, and mechanical resistance. After washing in laundry detergent, the impedance of CVE is still 16% lower than the commercial AgCl electrodes. To verify the mechanical strength and recovery capability, we conducted cyclic compression experiments. The results show that the displacement change of the electrode hook hair after 50 compression cycles was still less than 1%. This electrode provides a universal acquisition scheme, including effective acquisition of different parts of the body with or without hair. Finally, the gesture recognition from electromyography (EMG) by the CVE electrode was applied with accuracy above 90%. The CVE proposed in this study has great potential and promise in various human-machine interface (HMI) applications that employ surface biopotential signals on the body or head with hair.

Investigating the missing-wedge problem in small-angle X-ray scattering tensor tomography across real and reciprocal space.

Small-angle-scattering tensor tomography is a technique for studying anisotropic nanostructures of millimetre-sized samples in a volume-resolved manner. It requires the acquisition of data through repeated tomographic rotations about an axis which is subjected to a series of tilts. The tilt that can be achieved with a typical setup is geometrically constrained, which leads to limits in the set of directions from which the different parts of the reciprocal space map can be probed. Here, we characterize the impact of this limitation on reconstructions in terms of the missing wedge problem of tomography, by treating the problem of tensor tomography as the reconstruction of a three-dimensional field of functions on the unit sphere, represented by a grid of Gaussian radial basis functions. We then devise an acquisition scheme to obtain complete data by remounting the sample, which we apply to a sample of human trabecular bone. Performing tensor tomographic reconstructions of limited data sets as well as the complete data set, we further investigate and validate the missing wedge problem by investigating reconstruction errors due to data incompleteness across both real and reciprocal space. Finally, we carry out an analysis of orientations and derived scalar quantities, to quantify the impact of this missing wedge problem on a typical tensor tomographic analysis. We conclude that the effects of data incompleteness are consistent with the predicted impact of the missing wedge problem, and that the impact on tensor tomographic analysis is appreciable but limited, especially if precautions are taken. In particular, there is only limited impact on the means and relative anisotropies of the reconstructed reciprocal space maps.

Phenotypic-Based Maturity Detection and Oil Content Prediction in Xiangling Walnuts

The maturity grading of walnuts during harvesting relies on experience. In this paper, walnut images in a natural environment were collected to construct a dataset, and deep learning algorithms were utilized to combine walnut internal physical and chemical indicators to carry out research on walnut maturity detection methods and further research on walnut oil content prediction by combining walnut images with walnut oil content indicators. The main contents of this paper include collecting walnut images in a natural environment, constructing datasets, and using deep learning algorithms combined with internal physical and chemical indexes of walnuts to study walnut maturity detection and oil content prediction methods. First, two walnut image acquisition schemes were designed, and a total of 9504 images were collected from 23 August to 21 September 2021. The dataset was expanded to 18,504 images through data preprocessing and image enhancement. A self-supervised Gaussian attention network (GATCluster) walnut ripeness detection method based on image clustering is proposed to develop ripeness criteria through unsupervised clustering, and the accuracy of the criteria is verified by analysis of variance (ANOVA). The maturity detection accuracy of the test set of 1500 images is 88.33%. Secondly, a walnut oil content prediction method based on improved ResNet34 is proposed. The feature extraction capability is improved by introducing the Squeeze-and-Excitation Networks (SENet) channel attention mechanism and the convolutional self-attention module. The prediction results on 50 images show that the root mean square error, average absolute percentage error, and regression coefficient are 2.96, 0.103, and 0.8822, respectively. The experiments show that the method performs well in predicting the oil content of walnuts at different maturity levels.

Functional connectivity across the human subcortical auditory system using an autoregressive matrix-Gaussian copula graphical model approach with partial correlations.

The auditory system comprises multiple subcortical brain structures that process and refine incoming acoustic signals along the primary auditory pathway. Due to technical limitations of imaging small structures deep inside the brain, most of our knowledge of the subcortical auditory system is based on research in animal models using invasive methodologies. Advances in ultrahigh-field functional magnetic resonance imaging (fMRI) acquisition have enabled novel noninvasive investigations of the human auditory subcortex, including fundamental features of auditory representation such as tonotopy and periodotopy. However, functional connectivity across subcortical networks is still underexplored in humans, with ongoing development of related methods. Traditionally, functional connectivity is estimated from fMRI data with full correlation matrices. However, partial correlations reveal the relationship between two regions after removing the effects of all other regions, reflecting more direct connectivity. Partial correlation analysis is particularly promising in the ascending auditory system, where sensory information is passed in an obligatory manner, from nucleus to nucleus up the primary auditory pathway, providing redundant but also increasingly abstract representations of auditory stimuli. While most existing methods for learning conditional dependency structures based on partial correlations assume independently and identically Gaussian distributed data, fMRI data exhibit significant deviations from Gaussianity as well as high-temporal autocorrelation. In this paper, we developed an autoregressive matrix-Gaussian copula graphical model (ARMGCGM) approach to estimate the partial correlations and thereby infer the functional connectivity patterns within the auditory system while appropriately accounting for autocorrelations between successive fMRI scans. Our results show strong positive partial correlations between successive structures in the primary auditory pathway on each side (left and right), including between auditory midbrain and thalamus, and between primary and associative auditory cortex. These results are highly stable when splitting the data in halves according to the acquisition schemes and computing partial correlations separately for each half of the data, as well as across cross-validation folds. In contrast, full correlation-based analysis identified a rich network of interconnectivity that was not specific to adjacent nodes along the pathway. Overall, our results demonstrate that unique functional connectivity patterns along the auditory pathway are recoverable using novel connectivity approaches and that our connectivity methods are reliable across multiple acquisitions.

Ground-based simulation of laser link acquisition for inter-satellite laser interferometry

Laser link acquisition and pointing technique is one of the essential techniques for the inter-satellite laser interferometry for space-based gravitational waves detection and next-generation Earth gravity measurement missions. The first step of building up inter-satellite laser link is using an acquisition camera to capture the inter-satellite laser beam signals within a pre-scanning uncertain cone. Subsequently, high-precision angle measurement technology, namely differential wavefront sensing, is used to achieve a high pointing precision required. Due to the distance constraint of a ground-based simulation experiment, it is difficult to verify directly the feasibility of an inter-satellite laser link acquisition and pointing control scheme. By means of controlling the optical properties of the received laser beam, the long-distance beam propagation is simulated with two optical benches of an inter-satellite interferometer, and the process of a laser link acquisition experiment has been demonstrated. The experimental results show that the inter-satellite laser beams could establish the dual-way locking successfully. The fluctuation of the laser beam pointing direction (in-loop) can be suppressed to about 5 µrad in atmospheric environment. The results verify the feasibility of the laser link acquisition scheme. The experimental setup can be extended to conduct experiments with various parameters, providing technical support for further testing of the inter-satellite laser link acquisition and pointing control methods.

AI-Powered 3D Image Reconstruction for Analyzing Lithium Ion Battery Microstructures at the Nanoscale

A clean energy future will rely heavily on lithium-ion batteries to shift away from carbon-based energy sources. These devices are closed off from the outside world and operate under highly non-ambient conditions (e.g., liquid electrolytes, air-free environments). Their performance is largely driven by the 3D microstructures of the internal components and their evolutions. For example, breached layers in batteries due to lithium dendrite growth can lead to catastrophic failures and safety concerns. As such, imaging the internal microstructures and processes is important for observing and measuring device construction details and understanding how defects are distributed and change performance during operation. In particular, 3D imaging allows for analysis of features and parameters that can only be fully understood in 3D, like porosity and tortuosity, and can also provide realistic microstructural input for 3D modelling and simulation approaches.Within this scope, X-ray microscopy provides a unique method to image electrochemical devices because of the non-destructive nature of the technique. Modern x-ray microscopes enable 3D imaging even at the nanoscale, bringing capabilities once reserved for the synchrotron into the laboratory. Advances in tomographic image reconstruction have allowed researchers to increasingly maximize the impact of x-ray microscopy data through higher image quality and enabling faster data acquisition schemes. Recently, artificial intelligence (AI) has been incorporated into these algorithms, dramatically increasing the performance and capability of the technique. Here, we apply multiple novel reconstruction technologies that leverage AI to dramatically improve the performance of laboratory-based nanoscale x-ray microscopes, showing examples of improvements in imaging lithium-ion battery electrode materials.We show how AI-powered tomographic reconstruction can reveal new levels of detail in lithium-ion battery materials and enable up to 10 times faster data collection across the micrometer and nanometer length-scales. These capabilities shift the paradigm in volume analysis for electrochemical devices and give researchers unprecedented insight into the detailed microstructures that drive device performance, enabling new levels of understanding and helping to power the drive to a clean energy future.

Fast bias-corrected conductivity mapping using stimulated echoes.

To demonstrate the potential of a double angle stimulated echo (DA-STE) method for fast and accurate "full" homogeneous Helmholtz-based electrical properties tomography using a simultaneous magnitude and transceive phase measurement. The combination of a spin and stimulated echo can be used to yield an estimate of both magnitude and transceive phase and thus provides the means for "full" EPT reconstruction. An interleaved 2D acquisition scheme is used for rapid acquisition. The method was validated in a saline phantom and compared to a double angle method based on two single gradient echo acquisitions (GRE-DAM). The method was evaluated in the brain of a healthy volunteer. The magnitude obtained with DA-STE showed excellent agreement with the GRE-DAM method. Conductivity values based on the "full" EPT reconstruction also agreed well with the expectations in the saline phantom. In the brain, the method delivered conductivity values close to literature values. The method allows the use of the "full" Helmholtz-based EPT reconstruction without the need for additional measurements. As a result, quantitative conductivity values are improved compared to phase-based EPT reconstructions. DA-STE is a fast complex- mapping technique that could render EPT clinically relevant at 3T.

Simultaneous whole-liver water and mapping with isotropic resolution during free-breathing.

To develop and validate a data acquisition scheme combined with a motion-resolved reconstruction and dictionary-matching-based parameter estimation to enable free-breathing isotropic resolution self-navigated whole-liver simultaneous water-specific ( ) and ( ) mapping for the characterization of diffuse and oncological liver diseases. The proposed data acquisition consists of a magnetization preparation pulse and a two-echo gradient echo readout with a radial stack-of-stars trajectory, repeated with different preparations to achieve different and contrasts in a fixed acquisition time of 6 min. Regularized reconstruction was performed using self-navigation to account for motion during the free-breathing acquisition, followed by water-fat separation. Bloch simulations of the sequence were applied to optimize the sequence timing for insensitivity at 3 T, to correct for relaxation-induced blurring, and to map and using a dictionary. The proposed method was validated on a water-fat phantom with varying relaxation properties and in 10 volunteers against imaging and spectroscopy reference values. The performance and robustness of the proposed method were evaluated in five patients with abdominal pathologies. Simulations demonstrate good insensitivity of the proposed method in measuring and values. The proposed method produces co-registered and maps with a good agreement with reference methods (phantom: ; ). The proposed and mapping exhibits good repeatability and can be robustly performed in patients with pathologies. The proposed method allows whole-liver and quantification with high accuracy at isotropic resolution in a fixed acquisition time during free-breathing.