Limited Spatial Resolution Research Articles

A Multi-Hyperspectral Image Collaborative Mapping Model Based on Adaptive Learning for Fine Classification

Hyperspectral (HS) data, encompassing hundreds of spectral channels for the same area, offer a wealth of spectral information and are increasingly utilized across various fields. However, their limitations in spatial resolution and imaging width pose challenges for precise recognition and fine classification in large scenes. Conversely, multispectral (MS) data excel in providing spatial details for vast landscapes but lack spectral precision. In this article, we proposed an adaptive learning-based mapping model, including an image fusion module, spectral super-resolution network, and adaptive learning network. Spectral super-resolution networks learn the mapping between multispectral and hyperspectral images based on the attention mechanism. The image fusion module leverages spatial and spectral consistency in training data, providing pseudo labels for spectral super-resolution training. And the adaptive learning network incorporates spectral response priors via unsupervised learning, adjusting the output of the super-resolution network to preserve spectral information in reconstructed data. Through the experiment, the model eliminates the need for the manual setting of image prior information and complex parameter selection, and can adjust the network structure and parameters dynamically, eventually enhancing the reconstructed image quality, and enabling the fine classification of large-scale scenes with high spatial resolution. Compared with the recent dictionary learning and deep learning spectral super-resolution methods, our approach exhibits superior performance in terms of both image similarity and classification accuracy.

Spatial downscaling analysis of GPM IMERG precipitation dataset based on multiscale geographically weighted regression model: a case study of the Inner Mongolia Reach of the Yellow River basin

The Inner Mongolia Reach of the Yellow River Basin is characterized by a relative scarcity of meteorological stations. While satellite precipitation products can complement observations from meteorological stations, their limited spatial resolution restricts their efficacy in regional studies. This study utilizes the GPM IMERG precipitation dataset, considering various factors that influence the spatial distribution of precipitation, such as the Normalized Difference Vegetation Index (NDVI), elevation, slope, aspect, and topographical relief, to construct a multiscale geographically weighted regression (MGWR) model. A spatial downscaling method for the GPM IMERG precipitation dataset is proposed, and its reliability is validated through an accuracy assessment. Moreover, the scale differences in the impact of different factors on the spatial pattern of precipitation in the Inner Mongolia Reach of the Yellow River Basin are scrutinized. The results indicate that: 1) The downscaled GPM IMERG precipitation data (1 km × 1 km) exhibit enhanced accuracy compared to the pre-downscaled data (approximately 11 km × 11 km). The correlation coefficient, Bias, and RMSE of the annual precipitation data after downscaling of GPM IMERG are 0.865, 6.05%, and 68.50 mm/year, respectively. For the monthly downscaled precipitation data, the correlation coefficient, Bias, and RMSE are 0.895, 6.09%, and 16.25 mm/month, respectively. The downscaled GPM IMERG precipitation dataset exhibit high accuracy on both annual and monthly temporal scales. 2) Different factors demonstrate localized effects on precipitation in both dry and wet years. Elevation is the dominant factor influencing the spatial heterogeneity of annual precipitation. The findings from this study can provide technical support for hydrological modeling, drought monitoring, and water resource management in data-scarce areas of the Inner Mongolia Reach of the Yellow River Basin.

A Spectrotemporal EEG Mapping Approach for Decoding Imagined Marathi Language Phonemes

Individuals facing verbal communication impairments resulting from brain disorders like paralysis or autism encounter significant challenges when unable to articulate speech. This research proposes the design and development of a wearable system capable of decoding imagined speech using electroencephalogram (EEG) signals obtained during the mental process of speech generation. The system’s main objective is to offer an alternative communication method for individuals who can hear and think but face challenges in articulating their thoughts verbally. The design suggested includes user-friendliness, wearability, and comfort for seamless integration into daily life. A minimal number of electrodes are strategically placed on the scalp to minimize invasiveness. Achieving precise localization of the cortical areas responsible for generating the EEG patterns during imagined speech is vital for accurate decoding. Literature studies are utilized to determine the cortical positions associated with speech processing. Due to the inherent limitations in EEG spatial resolution, meticulous experiments are conducted to map the scalp positions onto their corresponding cortical counterparts. Specifically, we focus on identifying the scalp location over the superior temporal gyrus (T3) using the internationally recognized 10-20 electrode placement system by employing a circular periphery movement with a 2 cm distance increment. Our research involves nine subjects spanning various age groups, with the youngest being 23 and the oldest 65. Each participant undergoes ten iterations, during which they imagine six Marathi syllables. Our work contributes to the development of wearable assistive technology, enabling mute individuals to communicate effectively by translating their imagined speech into actionable commands. This innovation ultimately enhances their social participation and overall well-being.

Current Landscape of the Robotic Tactile Array Sensors and Algorithm‐Based Performance Enhancement

The automated robots utilizing the traditional rigid tactile sensors have achieved significant success in effective task execution. Advancing into the era of service robots and humanoids that should perform sophisticated works in daily life, recent research trends in robotic tactile sensors have been shifting from the conventional rigid sensors to flexible/stretchable sensors. Over the past decade, the flexible/stretchable tactile sensors with human skin‐like mechanical compliance and stimulation perceptions have seen considerable advances. However, there are many substantial remaining challenges to produce practically useful flexible/stretchable tactile sensors such as the signal hysteresis originating from sensor material and the limited spatial resolution resulting from excessive addressing lines and signal interferences. Incorporation of learning‐based algorithms and data analysis techniques have been introduced recently and made remarkable successes in improving the challenges. Because most robotic tactile sensors are fabricated using the electronic sensing mechanisms or platforms, this article presents a concise overview on recent flexible/stretchable tactile sensors. This article introduces a brief discussion on the sensor performance parameters and sensing mechanisms, and the algorithmic approaches. In addition, it addresses existing challenges associated with current tactile array sensors and algorithms, then discusses prospects, and proposes a research direction for immediate practical uses in robots.

Investigating the use of 3-component-2-dimensional particle image velocimetry fields as inflow boundary condition for the direct numerical simulation of turbulent channel flow

Direct numerical simulation (DNS) of turbulent wall-bounded flows requires long streamwise computational domains to establish the correct spatial evolution of large-scale structures with high fidelity. In contrast, experimental measurements can relatively easily capture large-scale structures but struggle to resolve the dissipative flow scales with high fidelity. One methodology to overcome the shortcomings of each approach is by incorporating experimental velocity field measurements into DNS as an inflow boundary condition. This hybrid approach combines the strengths of DNS and experimental measurements, allowing for a reduction in the streamwise computational domain and accelerated development of large-scale structures in turbulent wall-bounded flows. To this end, this paper reports the results of an investigation to establish the impact of limited spatial resolution and limited near-wall experimental inflow data on the DNS of a wall-bounded turbulent shear flow. Specifically, this study investigates the spatial extent required for the DNS of a turbulent channel flow to recover the turbulent velocity fluctuations and energy when experimental inflow data is typically unable to capture fluctuations down to the viscous sub-layer or the smallest viscous scales (i.e. the Kolmogorov scale or their surrogate viscous scale in wall-bounded turbulent shear slows) is used as the inflow to a DNS. A time-resolved numerically generated experimental field is constructed from a periodic channel flow DNS (PCH-DNS) at Reτ=\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Re_{\ au } =$$\\end{document} 550 and 2300, which is subsequently used as the inflow velocity field for an inflow–outflow boundary conditions DNS. The time-resolved experimental inflow field is generated by appropriately filtering the small scales from the PCH-DNS velocity by integrating over a spatial domain that is representative of a particle image velocimetry interrogation window. This study shows that the recovery of small scales requires a longer domain as the spatial resolution at the inflow decreases with all flow scales recovered and their correct scale-dependent energy is re-established once the flow has developed for 3 channel heights.

Evaluation of four computed tomography reconstruction algorithms using a coronary artery phantom.

Despite advancements in coronary computed tomography angiography (CTA), challenges in positive predictive value and specificity remain due to limited spatial resolution. The purpose of this experimental study was to investigate the effect of 2nd generation deep learning-based reconstruction (DLR) on the quantitative and qualitative image quality in coronary CTA. A vessel model with stepwise non-calcified plaque was scanned using 320-detector CT. Image reconstruction was performed using four techniques: hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), DLR, and 2nd generation DLR. The luminal peak CT number, contrast-to-noise ratio (CNR), and edge rise slope (ERS) were quantitatively evaluated via profile curve analysis. Two observers qualitatively graded the graininess, lumen sharpness, and overall lumen visibility on the basis of the degree of confidence for the stenosis severity using a five-point scale. The image noise with HIR, MBIR, DLR, and 2nd generation DLR was 23.0, 21.0, 16.9, and 9.5 HU, respectively. The corresponding CNR (25% stenosis) was 15.5, 15.9, 22.1, and 38.3, respectively. The corresponding ERS (25% stenosis) was 203.2, 198.6, 228.9, and 262.4 HU/mm, respectively. Among the four reconstruction methods, the 2nd generation DLR achieved the significantly highest CNR and ERS values. The score of 2nd generation DLR in all evaluation points (graininess, sharpness, and overall lumen visibility) was higher than those of the other methods (overall vessel visibility score, 2.6±0.5, 3.8±0.6, 3.7±0.5, and 4.6±0.5 with HIR, MBIR, DLR, and 2nd generation DLR, respectively). 2nd generation DLR provided better CNR and ERS in coronary CTA than HIR, MBIR, and previous-generation DLR, leading to the highest subjective image quality in the assessment of vessel stenosis.

THE FEASIBILITY OF FUSING SATELLITE IMAGERIES FOR HIGH-FREQUENCY SEA LEVEL MONITORING

Enhancing the capacity to monitor swift environmental shifts at finer scales requires satellite image that offers high spatial and temporal resolution. However, no individual satellite can offer images meeting both criteria simultaneously. To tackle this challenge, spatial temporal fusion algorithms have been developed to derive fine-scale and time-series images. Conversely, effective monitoring of water levels is crucial for preventing natural disaster, such as flood and tsunami mitigation. Yet, monitoring these natural changes regularly poses challenges for remote sensing satellites, given their limitations in either spatial or temporal resolution. For instance, the spatial resolution of 30 meters of Landsat 8 provides imagery with a but lacks the temporal resolution needed to capture dynamic events. On the contrary, the Himawari 8 has the capability to monitor the entire hemisphere every 10 minutes. However, its inadequate resolution affects the precision of sea water change mapping. This research seeks to utilize Landsat OLI and Himawari-8 images jointly for tracking sea level variation patterns. Our approach involves calculating a water index from both Landsat and Himawari images, then using an image fusion algorithm to merge these indices. Next, we identify water coverage by applying a specific threshold on the water index. The comparison of water percentages with reference water height observations has delivered encouraging outcomes.

Regional analysis to delineate intrasample heterogeneity with RegionalST.

Spatial transcriptomics has greatly contributed to our understanding of spatial and intra-sample heterogeneity, which could be crucial for deciphering the molecular basis of human diseases. Intra-tumor heterogeneity, e.g. may be associated with cancer treatment responses. However, the lack of computational tools for exploiting cross-regional information and the limited spatial resolution of current technologies present major obstacles to elucidating tissue heterogeneity. To address these challenges, we introduce RegionalST, an efficient computational method that enables users to quantify cell type mixture and interactions, identify sub-regions of interest, and perform cross-region cell type-specific differential analysis for the first time. Our simulations and real data applications demonstrate that RegionalST is an efficient tool for visualizing and analyzing diverse spatial transcriptomics data, thereby enabling accurate and flexible exploration of tissue heterogeneity. Overall, RegionalST provides a one-stop destination for researchers seeking to delve deeper into the intricacies of spatial transcriptomics data. The implementation of our method is available as an open-source R/Bioconductor package with a user-friendly manual available at https://bioconductor.org/packages/release/bioc/html/RegionalST.html.

Alpha-180 spin-echo-based line-scanning method for high-resolution laminar-specific fMRI in animals

Abstract Laminar-specific functional magnetic resonance imaging (fMRI) has been widely used to study circuit-specific neuronal activity by mapping spatiotemporal fMRI response patterns across cortical layers. Hemodynamic responses reflect indirect neuronal activity given the limitation of spatial and temporal resolution. Previously, a gradient-echo-based line-scanning fMRI (GELINE) method was proposed with high temporal (50 ms) and spatial (50 µm) resolution to better characterize the fMRI onset time across cortical layers by employing two saturation RF pulses. However, the imperfect RF saturation performance led to poor boundary definition of the reduced region of interest (ROI) and aliasing problems outside of the ROI. Here, we propose an α (alpha)-180 spin-echo-based line-scanning fMRI (SELINE) method in animals to resolve this issue by employing a refocusing 180˚ RF pulse perpendicular to the excitation slice (without any saturation RF pulse) and also achieve high spatiotemporal resolution. In contrast to GELINE signals which peaked at the superficial layer, we detected varied peaks of laminar-specific BOLD signals across deeper cortical layers using the SELINE method, indicating the well-defined exclusion of the large draining-vein effect using the spin-echo sequence. Furthermore, we applied the SELINE method with a 200 ms repetition time (TR) to sample the fast hemodynamic changes across cortical layers with a less draining vein effect. In summary, this SELINE method provides a novel acquisition scheme to identify microvascular-sensitive laminar-specific BOLD responses across cortical depth.

High-speed impulsive stimulated Brillouin microscopy

Brillouin microscopy, which maps the elastic modulus from the frequency shift of scattered light, has evolved to a faster speed for the investigation of rapid biomechanical changes. Impulsive stimulated Brillouin scattering (ISBS) spectroscopy has the potential to speed up measurement through the resonant amplification interaction from pulsed excitation and time-domain continuous detection. However, significant progress has not been achieved due to the limitation in signal-to-noise ratio (SNR) and the corresponding need for excessive averaging to maintain high spectral precision. Moreover, the limited spatial resolution also hinders its application in mechanical imaging. Here, by scrutinizing the SNR model, we design a high-speed ISBS microscope through multi-parameter optimization including phase, reference power, and acquisition time. Leveraging this, with the further assistance of the Matrix Pencil method for data processing, three-dimensional mechanical images are mapped under multiple contrast mechanisms for a millimeter-scale polydimethylsiloxane pattern immersed in methanol, enabling the identification of these two transparent materials without any contact or labeling. Our experimental results demonstrate the capability to maintain high spectral precision and resolution at a sub-millisecond integration time for one pixel. With a two-order improvement in the speed and a tenfold improvement in the spatial resolution over the state-of-the-art systems, this method makes it possible for ISBS microscopes to sensitively investigate rapid mechanical changes in time and space.

Advancing Hyperspectral Image Analysis with CTNet: An Approach with the Fusion of Spatial and Spectral Features.

Hyperspectral image classification remains challenging despite its potential due to the high dimensionality of the data and its limited spatial resolution. To address the limited data samples and less spatial resolution issues, this research paper presents a two-scale module-based CTNet (convolutional transformer network) for the enhancement of spatial and spectral features. In the first module, a virtual RGB image is created from the HSI dataset to improve the spatial features using a pre-trained ResNeXt model trained on natural images, whereas in the second module, PCA (principal component analysis) is applied to reduce the dimensions of the HSI data. After that, spectral features are improved using an EAVT (enhanced attention-based vision transformer). The EAVT contained a multiscale enhanced attention mechanism to capture the long-range correlation of the spectral features. Furthermore, a joint module with the fusion of spatial and spectral features is designed to generate an enhanced feature vector. Through comprehensive experiments, we demonstrate the performance and superiority of the proposed approach over state-of-the-art methods. We obtained AA (average accuracy) values of 97.87%, 97.46%, 98.25%, and 84.46% on the PU, PUC, SV, and Houston13 datasets, respectively.

Key learnings from concordant systematic biopsies in prostate-specific membrane antigen positron emission tomography/computed tomography-guided prostate biopsies: Enhancing targeting accuracy.

Prostate cancer (PCa) diagnosis and staging have evolved with the advent of 68Ga-Prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA-PET/CT). This study investigates the role of complementary systematic biopsies (SB) during PSMA-PET/CT-guided targeted prostate biopsies (PET-TB) for PCa detection, grading, and distribution. We address the uncertainty surrounding the necessity of SB in conjunction with PET-TB. We analyzed PCa grading and distribution in 30 men who underwent PET-TB and SB because of contraindication to magnetic resonance imaging or high clinical suspicion of PCa. Tumor distribution was assessed in relation to the PET-highlighted lesions. Standardized reporting schemes, encompassing SUVmax, PRIMARY score, and miTNM classification, were evaluated. 80% of patients were diagnosed with PCa, with 70% classified as clinically significant (csPCa). SB detected more csPCa cases than PET-TB, but the differences were not statistically significant. Discordant results were observed in 25% of cases, where SB outperformed PET-TB. Spatial analysis revealed that tumor-bearing cores from SB were often located in close proximity to the PET-highlighted region. Reporting schemes showed potential for csPCa detection with significantly increased SUVmax in csPCA patients. Subsequent follow-up data underscored the importance of SB in precise PCa grading and staging. While PET-TB can simplify prostate biopsy and reduce invasiveness by core number, SB cannot be omitted yet due to potential PET-TB targeting errors. Factors such as limited spatial resolution and fusion inaccuracies contribute to the need for SB. Standardization in reporting schemes currently cannot compensate for targeting errors highlighting the need for refinement.

Attention to audiovisual speech shapes neural processing through feedback-feedforward loops between different nodes of the speech network.

Selective attention-related top-down modulation plays a significant role in separating relevant speech from irrelevant background speech when vocal attributes separating concurrent speakers are small and continuously evolving. Electrophysiological studies have shown that such top-down modulation enhances neural tracking of attended speech. Yet, the specific cortical regions involved remain unclear due to the limited spatial resolution of most electrophysiological techniques. To overcome such limitations, we collected both electroencephalography (EEG) (high temporal resolution) and functional magnetic resonance imaging (fMRI) (high spatial resolution), while human participants selectively attended to speakers in audiovisual scenes containing overlapping cocktail party speech. To utilise the advantages of the respective techniques, we analysed neural tracking of speech using the EEG data and performed representational dissimilarity-based EEG-fMRI fusion. We observed that attention enhanced neural tracking and modulated EEG correlates throughout the latencies studied. Further, attention-related enhancement of neural tracking fluctuated in predictable temporal profiles. We discuss how such temporal dynamics could arise from a combination of interactions between attention and prediction as well as plastic properties of the auditory cortex. EEG-fMRI fusion revealed attention-related iterative feedforward-feedback loops between hierarchically organised nodes of the ventral auditory object related processing stream. Our findings support models where attention facilitates dynamic neural changes in the auditory cortex, ultimately aiding discrimination of relevant sounds from irrelevant ones while conserving neural resources.

Spatial resolution effects on the solar open flux estimates

Context. Spectropolarimetric observations used to infer the solar magnetic fields are obtained with a limited spatial resolution. The effects of this limited resolution on the inference of the open flux over the observed region have not been extensively studied. Aims. We aim to characterize the biases that arise in the inference of the mean flux density by performing an end-to-end study that involves the generation of synthetic data, its interpretation (inversion), and a comparison of the results with the original model. Methods. We synthesized polarized spectra of the two magnetically sensitive lines of neutral iron around 630 nm from a state-of-the-art numerical simulation of the solar photosphere. We then performed data degradation to simulate the effect of the telescope with a limited angular resolution and interpreted (inverted) the data using a Milne-Eddington spectropolarimetric inversion code. We then studied the dependence of the inferred parameters on the telescope resolution. Results. The results show a significant decrease in the mean magnetic flux density – related to the open flux observed at the disk center – with decreasing telescope resolution. The original net magnetic field flux is fully resolved by a 1m telescope, but a 20 cm aperture telescope yields a 30% smaller value. Even in the fully resolved case, the result is still biased due to the corrugation of the photospheric surface. Conclusions. Even the spatially averaged quantities, such as the open magnetic flux in the observed region, are underestimated when the magnetic structures are unresolved. The reason for this is the presence of nonlinearities in the magnetic field inference process. This effect might have implications for the modeling of large-scale solar magnetic fields; for example, those corresponding to the coronal holes, or the polar magnetic fields, which are relevant to our understanding of the solar cycle.

Optimizing intracardiac flow assessment with high frequency ultrasound in a mouse model

Cardiomyopathy, a disorder affecting the heart muscle, is associated with severe symptoms, such as heart failure, arrhythmia, or cardiac arrest. Echocardiography is the primary clinical tool for myocardial assessment, which provides diagnostic and prognostic insights. However, current clinical parameters, such as myocardial strain, often detect the condition only after symptoms become pronounced, showing the need for early-stage assessment. Based on preliminary studies, intracardiac flow patterns have the potential to detect abnormal myocardial changes earlier than the current clinical parameters. Mouse models are commonly imaged with ultrasound in the cardiovascular disease research but their use for flow pattern evaluation has been hindered by spatial and temporal resolution limitations. Here, we address this limitation by conducting data acquisition using a Verasonics scanner with a range of high-frequency probes (16 to 42 MHz), transmit cycles, and transmit angles. A singular value decomposition (SVD) filter and frequency de-aliasing were also investigated to optimize flow quantification. Our findings show that a higher transmission frequency, while providing superior spatial resolution, may not yield the best flow quantification due to increased Doppler aliasing. Furthermore, SVD filtering and Doppler de-aliasing should be used with caution because improper implementation can adversely impact the quantification results.

Revealing Multiple Nested Molecular Outflows with Rotating Signatures in HH270mms1-A with ALMA

We present molecular line observations of the protostellar outflow associated with HH270mms1 in the Orion B molecular cloud with ALMA. The 12CO (J = 3−2) emissions show that the outflow velocity structure consists of four distinct components of low (≲10 km s−1), intermediate (∼10–25 km s−1) and high (≳40 km s−1) velocities in addition to the entrained gas velocity (∼25–40 km s−1). The high- and intermediate-velocity flows have well-collimated structures surrounded by the low-velocity flow. The chain of knots is embedded in the high-velocity flow or jet, which is the evidence of episodic mass ejections induced by time-variable mass accretion. We could detect the velocity gradients perpendicular to the outflow axis in both the low- and intermediate-velocity flows. We confirmed the rotation of the envelope and disk in the 13CO and C17O emission and found that their velocity gradients are the same as those of the outflow. Thus, we concluded that the velocity gradients in the low- and intermediate-velocity flows are due to the outflow rotation. Using observational outflow properties, we estimated the outflow launching radii to be 67.1–77.1 au for the low-velocity flow and 13.3–20.8 au for the intermediate-velocity flow. Although we could not detect the rotation in the jets due to the limited spatial resolution, we estimated the jet launching radii to be (2.36–3.14) × 10−2 au using the observed velocity of each knot. Thus, the jet is driven from the inner disk region. We could identify the launching radii of distinct velocity components within a single outflow with all the prototypical characteristics expected from recent theoretical works.

Line-based iterative geometric calibration method for a tomosynthesis system.

A next generation tomosynthesis (NGT) system, capable of two-dimensional source motion, detector motion in the perpendicular direction, and magnification tomosynthesis, was constructed to investigate different acquisition geometries. Existing position-based geometric calibration methods proved ineffective when applied to the NGTgeometries. A line-based iterative calibration method is developed to perform accurate geometric calibration for the NGTsystem. The proposed method calculates the system geometry through virtual line segments created by pairs of fiducials within a calibration phantom, by minimizing the error between the line equationscomputed from the true and estimated fiducial projection pairs. It further attempts to correct the 3D fiducial locations based on the initial geometric calibration. The method's performance was assessed via simulation and experimental setups with four distinct NGT geometries: X, T, XZ, and TZ. The X geometry resembles a conventional DBT acquisition along the chest wall. The T geometry forms a "T"-shaped source path in mediolateral (ML) and posteroanterior (PA) directions. A descending detector motion is added to both X and T geometries to form the XZ and TZ geometries, respectively. Simulation studies were conducted to assess the robustness of the method to geometric perturbations and inaccuracies in fiducial locations. Experimental studies were performed to assess the impact of phantom magnification and the performance of the proposed method for various geometries, compared to the traditional position-based method. Star patterns were evaluated for both qualitative and quantitative analyses; the Fourier spectral distortions (FSDs) graphs and the contrast transfer function (CTF) were extracted. The limit of spatial resolution (LSR) was measured at 5% modulation of theCTF. The proposed method presented is highly robust to geometric perturbation and fiducial inaccuracies. After the line-based iterative method, the mean distance between the true and estimated fiducial projections was [X, T, XZ, TZ]: [0.01, 0.01, 0.02, 0.01] mm. The impact of phantom magnification was observed; a contact-mode acquisition of a calibration phantom successfully provided an accurate geometry for 1.85× magnification images of a star pattern, with the X geometry. The FSD graphs for the contact-mode T geometry acquisition presented evidence of super-resolution, with the LSR of [0°-quadrant: 8.57, 90°-quadrant: 8.47] lp/mm. Finally, a contact-mode XZ geometry acquisition and a 1.50× magnification TZ geometry acquisition were reconstructed with three calibration methods-position-based, line-based, and iterative line-based. As more advanced methods are applied, the CTF becomes more isotropic, the FSD graphs demonstrate less spectral leakage as super-resolution is achieved, and the degree of blurring artifacts reducessignificantly. This study introduces a robust calibration method tailored to the unique requirements of advanced tomosynthesis systems. By employing virtual line segments and iterative techniques, we ensure accurate geometric calibration while mitigating the limitations posed by the complex acquisition geometries of the NGT system. Our method's ability to handle various NGT configurations and its tolerance to fiducial misalignment make it a superior choice compared to traditional calibrationtechniques.

Restoration of the Tully–Fisher Relation by Statistical Rectification

I employ the Lucy rectification algorithm to recover the inclination-corrected distribution of local disk galaxies in the plane of absolute magnitude (M i ) and H i velocity width (W 20). By considering the inclination angle as a random variable with a known probability distribution, the novel approach eliminates one major source of uncertainty in studies of the Tully–Fisher relation: inclination angle estimation from axial ratio. Leveraging the statistical strength derived from the entire sample of 28,264 H i-selected disk galaxies at z < 0.06 from the Arecibo Legacy Fast ALFA survey, I show that the restored distribution follows a sharp correlation that is approximately a power law between −16 > M i > −22: Mi=M0−2.5β[log(W20/250km/s)] , with M 0 = −19.77± 0.04 and β = 4.39 ± 0.06. At the brighter end (M i < −22), the slope of the correlation decreases to β ≈ 3.3, confirming previous results. Because the method accounts for measurement errors, the intrinsic dispersion of the correlation is directly measured: σ(logW20)≈0.06 dex between −17 > M i > −23, while σ(M i ) decreases from ∼0.8 in slow rotators to ∼0.4 in fast rotators. The statistical rectification method holds significant potential, especially in the studies of intermediate-to-high-redshift samples, where limited spatial resolution hinders precise measurements of inclination angles.

Identifying galaxy cluster mergers with deep neural networks using idealized Compton-y and X-ray maps

ABSTRACT We present a novel approach to identify galaxy clusters that are undergoing a merger using a deep learning approach. This paper uses massive galaxy clusters spanning 0 ≤ z ≤ 2 from The Three Hundred project, a suite of hydrodynamic resimulations of 324 large galaxy clusters. Mock, idealized Compton-y and X-ray maps were constructed for the sample, capturing them out to a radius of 2R200. The idealized nature of these maps mean they do not consider observational effects such as foreground or background astrophysical objects, any spatial resolution limits or restriction on X-ray energy bands. Half of the maps belong to a merging population as defined by a mass increase ΔM/M ≥ 0.75, and the other half serves as a controlled, relaxed population. We employ a convolutional neural network architecture and train the model to classify clusters into one of the groups. A best-performing model was able to correctly distinguish between the two populations with a balanced accuracy (BA) and recall of 0.77, ROC-AUC of 0.85, PR-AUC of 0.55, and F1 score of 0.53. Using a multichannel model relative to a single-channel model, we obtain a 3 per cent improvement in BA score, and a 6 per cent improvement in F1 score. We use a saliency interpretation approach to discern the regions most important to each classification decision. By analysing radially binned saliency values we find a preference to utilize regions out to larger distances for mergers with respect to non-mergers, greater than ∼1.2R200 and ∼0.7R200 for SZ and X-ray, respectively.

Efficient Blind Hyperspectral Unmixing Framework Based on CUR Decomposition (CUR-HU)

Hyperspectral imaging captures detailed spectral data for remote sensing. However, due to the limited spatial resolution of hyperspectral sensors, each pixel of a hyperspectral image (HSI) may contain information from multiple materials. Although the hyperspectral unmixing (HU) process involves estimating endmembers, identifying pure spectral components, and estimating pixel abundances, existing algorithms mostly focus on just one or two tasks. Blind source separation (BSS) based on nonnegative matrix factorization (NMF) algorithms identify endmembers and their abundances at each pixel of HSI simultaneously. Although they perform well, the factorization results are unstable, require high computational costs, and are difficult to interpret from the original HSI. CUR matrix decomposition selects specific columns and rows from a dataset to represent it as a product of three small submatrices, resulting in interpretable low-rank factorization. In this paper, we propose a new blind HU framework based on CUR factorization called CUR-HU that performs the entire HU process by exploiting the low-rank structure of given HSIs. CUR-HU incorporates several techniques to perform the HU process with a performance comparable to state-of-the-art methods but with higher computational efficiency. We adopt a deterministic sampling method to select the most informative pixels and spectrum components in HSIs. We use an incremental QR decomposition method to reduce computation complexity and estimate the number of endmembers. Various experiments on synthetic and real HSIs are conducted to evaluate the performance of CUR-HU. CUR-HU performs comparably to state-of-the-art methods for estimating the number of endmembers and abundance maps, but it outperforms other methods for estimating the endmembers and the computational efficiency. It has a 9.4 to 249.5 times speedup over different methods for different real HSIs.