Pixel Size Research Articles

Seaweed Growth Monitoring with a Low-Cost Vision-Based System.

In this paper, we introduce a method for automated seaweed growth monitoring by combining a low-cost RGB and stereo vision camera. While current vision-based seaweed growth monitoring techniques focus on laboratory measurements or above-ground seaweed, we investigate the feasibility of the underwater imaging of a vertical seaweed farm. We use deep learning-based image segmentation (DeeplabV3+) to determine the size of the seaweed in pixels from recorded RGB images. We convert this pixel size to meters squared by using the distance information from the stereo camera. We demonstrate the performance of our monitoring system using measurements in a seaweed farm in the River Scheldt estuary (in The Netherlands). Notwithstanding the poor visibility of the seaweed in the images, we are able to segment the seaweed with an intersection of the union (IoU) of 0.9, and we reach a repeatability of 6% and a precision of the seaweed size of 18%.

Accurate magnification determination for cryoEM using gold

Determining the correct magnified pixel size of single-particle cryoEM micrographs is necessary to maximize resolution and enable accurate model building. Here we describe a simple and rapid procedure for determining the absolute magnification in an electron cryomicroscope to a precision of <0.5%. We show how to use the atomic lattice spacings of crystals of thin and readily available test specimens, such as gold, as an absolute reference to determine magnification for both room temperature and cryogenic imaging. We compare this method to other commonly used methods, and show that it provides comparable accuracy in spite of its simplicity. This magnification calibration method provides a definitive reference quantity for data analysis and processing, simplifies the combination of multiple datasets from different microscopes and detectors, and improves the accuracy with which the contrast transfer function of the microscope can be determined. We also provide an open source program, magCalEM, which can be used to accurately estimate the magnified pixel size of a cryoEM dataset ex post facto.

Comparative Assessment of Different Image Velocimetry Techniques for Measuring River Velocities Using Unmanned Aerial Vehicle Imagery

The accurate measurement of river velocity is essential due to its multifaceted significance. In response to this demand, remote measurement techniques have emerged, including large-scale particle image velocimetry (LSPIV), which can be implemented through cameras or unmanned aerial vehicles (UAVs). This study conducted water surface velocity measurements in the Xihu River, situated in Miaoli County, Taiwan. These measurements were subjected to analysis using five distinct algorithms (PIVlab, Fudaa-LSPIV, OpenPIV, KLT-IV, and STIV) and were compared with surface velocity radar (SVR) results. In the quest for identifying the optimal parameter configuration, it was found that an IA size of 32 pixels × 32 pixels, an image acquisition frequency of 12 frames per second (fps), and a pixel size of 20.5 mm/pixel consistently yielded the lowest values for mean error (ME) and root mean squared error (RMSE) in the performance of Fudaa-LSPIV. Among these algorithms, Fudaa-LSPIV consistently demonstrated the lowest mean error (ME) and root mean squared error (RMSE) values. Additionally, it exhibited the highest coefficient of determination (R2 = 0.8053). Subsequent investigations employing Fudaa-LSPIV delved into the impact of various water surface velocity calculation parameters. These experiments revealed that alterations in the size of the interrogation area (IA), image acquisition frequency, and pixel size significantly influenced water surface velocity. This parameter set was subsequently employed in an experiment exploring the incorporation of artificial particles in image velocimetry analysis. The results indicated that the introduction of artificial particles had a discernible impact on the calculation of surface water velocity. Inclusion of these artificial particles enhanced the capability of Fudaa-LSPIV to detect patterns on the water surface.

TMIC-49. AUTOMATIC SEGMENTATION OF DISTINCT GLIOBLASTOMA HALLMARKS ON DIGITIZED H&amp;E SLIDES VIA DEEP LEARNING: CURATION OF A LARGE MULTI-INSTITUTIONAL COHORT

Abstract PURPOSE Glioblastoma (GBM) is a heterogeneous brain tumor. Standard treatment constitutes surgery, chemo-radiation, and chemotherapy. After surgery, the resected tissue is analyzed under a microscope by neuropathologists to identify distinct histological hallmarks. However, visual analysis of GBM pathological features on Hematoxylin and Eosin (H&amp;E)-stained slides is subjective, time-consuming, and more importantly, requires wide expertise. We present a hierarchical deep learning-strategy to automatically segment distinct GBM hallmarks that first distinguishes necrosis (NE) from cellular tumor (CT), and subsequently, segments regions of microvascular proliferation (MV) and pseudo-palisading cell patterns (PC) within the CT region, on digitized H&amp;E slides. METHODS We employed n=541 publicly available studies including IvyGap (n=41), and TCGA (n=500). For the IvyGap cohort, expert-driven segmentations of CT, NE, MV and PC were available. We randomly employed n=21 slides from 4 patients for training, n=5 slides from 2 cases for validation, and n=2 slides from 2 cases for testing. We employed an EfficientNet model using ~600 patches of 224×224 pixels of NE and CT sampled for every slide. For semantic segmentation of MV and PC, we separately trained two EfficientNet-UNet models trained on ~200 squared-patches of 1792 and 896 pixels size, respectively. RESULTS Our hierarchical deep learning model on the IvyGap cohort achieved an accuracy of 96%, with an area under the curve (AUC) of 98% for distinguishing necrosis from non-necrotic (i.e. CT) regions. For segmenting PC and MV within the non-necrotic regions, our DL models achieve a dice score of 0.69 and 0.77, respectively. Additionally, the segmentation results for each of the pathological attributes were also generated and vetted for the TCGA cohort. CONCLUSION We developed a hierarchical model for automatic segmentation of NE, CT, and MP and PC in H&amp;E-stained Glioblastoma images which could reduce cost, time, and effort in annotating these pathological phenotypes, for quantifying the GBM micro-environment.

Spatial structure of in situ reflectance in coastal and inland waters: implications for satellite validation

Validation of satellite-derived aquatic reflectance involves relating meter-scale in situ observations to satellite pixels with typical spatial resolution ∼ 10–100 m within a temporal “match-up window” of an overpass. Due to sub-pixel variation these discrepancies in measurement scale are a source of uncertainty in the validation result. Additionally, validation protocols and statistics do not normally account for spatial autocorrelation when pairing in situ data from moving platforms with satellite pixels. Here, using high-frequency autonomous mobile radiometers deployed on ships, we characterize the spatial structure of in situRrs in inland and coastal waters (Lake Balaton, Western English Channel, Tagus Estuary). Using variogram analysis, we partition Rrs variability into spatial and intrinsic (non-spatial) components. We then demonstrate the capacity of mobile radiometers to spatially sample in situRrs within a temporal window broadly representative of satellite validation and provide spatial statistics to aid satellite validation practice. At a length scale typical of a medium resolution sensor (300 m) between 5% and 35% (median values across spectral bands and deployments) of the variation in in situRrs was due to spatial separation. This result illustrates the extent to which mobile radiometers can reduce validation uncertainty due to spatial discrepancy via sub-pixel sampling. The length scale at which in situRrs became spatially decorrelated ranged from ∼ 100–1,000 m. This information serves as a guideline for selection of spatially independent in situRrs when matching with a satellite image, emphasizing the need for either downsampling or using modified statistics when selecting data to validate high resolution sensors (sub 100 m pixel size).

Cryo-X-ray phase contrast imaging enables whole biopsy imaging prior to multi-omic analysis

Abstract Background Analysis of myocardial biopsies using conventional two-dimensional (2D) histological techniques is limited in the detection of important three-dimensional (3D) structural features, such as disarray, due to sectioning and artefacts from dehydration or chemical processing. More advanced techniques such as light-sheet microscopy, micro-computed tomography or synchrotron radiation-based X-ray phase contrast imaging can provide non-destructive, 3D histological information at high resolution for formalin-fixed and/or paraffin-embedded (FFPE) biopsies. However, formalin fixation induces crosslinking of proteins which can impact quality and their subsequent analysis, for example, fragmentation of nucleic acids. A method for imaging 3D structure of myocardial biopsies in near-native state is needed which can then be followed by downstream multi-omic applications. Purpose To develop a technique called cryogenic X-ray phase contrast imaging (Cryo-X-PCI) to image snap-frozen myocardial biopsies at controlled low temperatures, followed by multi-omic analysis starting with analysis of DNA and RNA. Methods Myocardial biopsies (n=46) were obtained from C57BL/6 mice and snap-frozen in liquid nitrogen fresh or after 4% paraformaldehyde (PFA);10% formalin fixation. Pilot human aortic stenosis biopsies were also included (n=3, fresh). X-PCI was performed at -70°C using a cryojet, 20 keV beam energy and 0.65 µm detector pixel size (Figure 1). Datasets were reconstructed using Gridrec algorithm and Paganin phase retrieval. Morphologic analysis (myocardial disarray index, fractional anisotropy, helical angle, and intrusion angle) of biopsies was performed using a structure tensor-based approach via in-house MATLAB script. After imaging, DNA and RNA were extracted from murine biopsies followed by assessment of quantity and quality. Results Increased intercellular space between aggregates of myocytes was observed in all samples. This is consistent with reported findings in skeletal muscle using cryogenic conditions. Nonetheless, morphologic analysis of the biopsies proved possible (Figure 2). Optimal recovery of DNA and RNA was obtained from fresh frozen samples as compared to 4% PFA-fixed or 10% formalin-fixed samples. Conclusions This study demonstrates Cryo-X-PCI as a potential tool for analysing myocardial biopsies of heart muscle disease including morphologic analyses. Levels of DNA and RNA were not significantly affected indicating that downstream genomics, proteomics and transcriptomics analyses should be feasible.Setup for Cryo-X-PCI of frozen biopsiesMorphologic analysis of a biopsy

Synchrotron-based X-ray phase contrast imaging of transmural cardiac tissue in patients treated for advanced heart failure

Abstract Background Cardiac imaging is essential for identifying structural changes in advanced heart failure (HF) and understanding underlying pathophysiology. Synchrotron radiation-based X-ray phase contrast imaging (X-PCI) is a non-destructive imaging modality that can provide high resolution three-dimensional (3D) visualisation of cardiac tissue on the microstructural level. Thus far, such analyses including the quantification of myocyte aggregates ranging from the epicardium to the endocardium, have been confined to animal models and human fetal tissue. We aimed to explore the feasibility of using X-PCI to quantify and compare structural organisation and orientation of myocyte aggregates in the myocardium across different advanced HF aetiologies. Methods and design Four adult patients were included - two receiving a left ventricular assist device (LVAD), and two undergoing heart transplantation (HTx). Aetiology of advanced HF in both the LVAD and HTx group was ischaemic heart disease and dilated cardiomyopathy (DCM), respectively. Transmural tissue samples were obtained by left ventricular apical coring (LVAD) and from the explanted hearts (HTx). The tissue specimens were imaged by X-PCI at the TOMCAT beamline using an effective pixel size of 5.8 µm. Orientation of aggregates of cardiomyocytes was assessed using a structure tensor-based method and morphological parameters were derived - the helical angle (HA), or the angle between the circumferential left ventricular plane and myocyte aggregates, and fractional anisotropy (FA), the degree of anisotropy or disorganisation of the local myocardium. Results The general patient characteristics are shown in Table 1. X-PCI enabled 3D visualisation of transmural myocardial tissue samples showing differences in organisation of myocardial structure in different HF aetiologies (Figure 1). The epicardial adipose layer was thicker in DCM, paired with a thinner epicardial myocyte layer (yellow arrows). Furthermore, the epi-to-endocardial HA transition was clearly visualized in ICM (double arrows), whereas there was clear disruption of the HA gradient in DCM samples – a finding further quantified via FA (green arrows). Conclusion X-PCI allows non-destructive advanced imaging of ex-vivo tissue. By harnessing the power of digital 3D imaging on a cellular level, it provides a novel insight to differential information on tissue organisation in different aetiologies of advanced heart failure.Table 1Figure 1

Fast wavefront sensing method based on diffraction basis vectors for tightly focused optical systems.

Recently, phase retrieval techniques have garnered significant attention with their exceptional flexibility. However, their application is limited in optical systems with high numerical aperture due to the disregarded polarization properties of the beam. In this paper, a fast wavefront sensing method for tightly focused systems is proposed. Firstly, a vector diffraction model based on the chirp-Z transform is established to analytically describe the focal spot using the modal coefficients of polynomials and diffraction basis vectors, which accommodating any pixel size and resolution, thereby enabling to break through sampling constraints and remove lateral errors. Additionally, a modified Newton-gradient second-order algorithm is introduced to simultaneously optimize wavefront in multiple polarization directions, without the need for diffraction operators during iterations. Both numerical simulations and error analysis confirm the efficacy and precision of the proposed wavefront sensing method.

Optimal sampling rate for 3D single molecule localization.

Resolution of single molecule localization microscopy (SMLM) depends on the localization accuracy, which can be improved by utilizing engineered point spread functions (PSF) with delicate shapes. However, the intrinsic pixelation effect of the detector sensor will deteriorate PSFs under different sampling rates. The influence of the pixelation effect to the achieved 3D localization accuracy for different PSF shapes under different signal to background ratio (SBR) and pixel dependent readout noise has not been investigated in detail so far. In this work, we proposed a framework to characterize the 3D localization accuracy of pixelated PSF at different sampling rates. Four different PSFs (astigmatic PSF, double helix (DH) PSF, Tetrapod PSF and 4Pi PSF) were evaluated and the pixel size with optimal 3D localization performance were derived. This work provides a theoretical guide for the optimal design of sampling rate for 3D super resolution imaging.

Ptychographic lens-less birefringence microscopy using a mask-modulated polarization image sensor

Birefringence, an inherent characteristic of optically anisotropic materials, is widely utilized in various imaging applications ranging from material characterizations to clinical diagnosis. Polarized light microscopy enables high-resolution, high-contrast imaging of optically anisotropic specimens, but it is associated with mechanical rotations of polarizer/analyzer and relatively complex optical designs. Here, we present a form of lens-less polarization-sensitive microscopy capable of complex and birefringence imaging of transparent objects without an optical lens and any moving parts. Our method exploits an optical mask-modulated polarization image sensor and single-input-state LED illumination design to obtain complex and birefringence images of the object via ptychographic phase retrieval. Using a camera with a pixel size of 3.45 μm, the method achieves birefringence imaging with a half-pitch resolution of 2.46 μm over a 59.74 mm2 field-of-view, which corresponds to a space-bandwidth product of 9.9 megapixels. We demonstrate the high-resolution, large-area, phase and birefringence imaging capability of our method by presenting the phase and birefringence images of various anisotropic objects, including a monosodium urate crystal, and excised mouse eye and heart tissues.

Abstract 13256: Efficient and Precise Phenotyping of Murine Fetal Hearts by Means of Synchrotron-Based Phase-Contrast Micro-Computed Tomography

Mouse models are routinely utilized to investigate the role of genes in congenital heart disease. Cardiovascular phenotyping is, however, quite challenging because of the small size of fetal mouse hearts. The most used method is histological assessment, which typically provides only two-dimensional information and thus might overlook important structural defects. Three-dimensional (3D) methods with sufficient resolution are either time-consuming, require specific sample preparation and/or selective staining, or cause destruction of the sample. Here, synchrotron radiation-based phase-contrast micro-computed tomography (SRμCT) was evaluated for fast and precise phenotyping of unstained, intact murine fetal hearts in a non-destructive way. Embryonic day (E) 18.5 hearts from control mice and mice that lack the secreted protease ADAMTS6 were formalin-fixed, paraffin-embedded and then scanned with SRμCT at the TOMCAT beamline (Swiss Light Source) with a 1.63 μm effective pixel size. Following SRμCT, the hearts were sectioned and processed for standard hematoxylin and eosin staining (H&amp;E), and for specific immunofluorescence staining. SRμCT enabled evaluation of the hearts with a high level of detail. A full E18.5 mouse heart was scanned in less than 3 minutes, followed by virtual sectioning along arbitrary slicing planes and 3D rendering for morphological assessment. The resolution proved sufficient to trace minute features like the coronary arteries. In the Adamts6 knockouts, malformations such as a ventricular septal defect and double outlet right ventricle could be readily diagnosed. We also explored the potential of merging stained sections with SRμCT. In summary, SRμCT has the potential to become a powerful tool for fast phenotyping of mutant mouse models. The combination of 3D imaging with subsequent sectioning and methods like histology, immunohistochemistry and in situ hybridization will uncover pathophysiological mechanisms in 3D space.

Evaluation of Electron Tomography Capabilities for Shale Imaging.

Despite the advantageous resolution of electron tomography (ET), reconstruction of three-dimensional (3D) images from multiple two-dimensional (2D) projections presents several challenges, including small signal-to-noise ratios, and a limited projection range. This study evaluates the capabilities of ET for thin sections of shale, a complex nanoporous medium. A numerical phantom with 1.24 nm pixel size is constructed based on the tomographic reconstruction of a Barnett shale. A dataset of 2D projection images is numerically generated from the 3D phantom and studied over a range of conditions. First, common reconstruction techniques are used to reconstruct the shale structure. The reconstruction uncertainty is quantified by comparing overall values of storage and transport metrics, as well as the misclassification of pore voxels compared to the phantom. We then select the most robust reconstruction technique and we vary the acquisition conditions to quantify the effect of artifacts. We find a strong agreement for large pores over the different acquisition workflows, while a wider variability exists for nanometer-scale features. The limited projection range and reconstruction are identified as the main experimental bottlenecks, thereby suggesting that sample thinning, advanced holders, and advanced reconstruction algorithms offer opportunities for improvement.

Real-time MRI lungs images revealing using Hybrid feedforward Deep Neural Network and Convolutional Neural Network

This research focused on Real-time MRI lung images that were revealed using three grade processes by manipulating nanophotonics components, mapping by deep learning, machine learning, and pattern recognition. This research is Solving Magnetic resonance imaging of interstitial lung diseases with Hybrid feedforward Deep Neural Network (ffDNN) and Convolutional Neural Network (CNN) architecture. The feedforward deep neural network (ffDNN) and Convolutional Neural Network (CNN) techniques are used to Solving Magnetic resonance imaging of interstitial lung diseases on the nanophotonics components, deep learning, and machine learning Platform. The Proposed semiconductor monolithic integration approach employed for bio-Magnetic resonance imaging characterization using photonic crystal “Symptomatic Image Revealing” details of the resonant monolithic. The proposed machine-learning-based approach revealed characterizing multi-parameter design space of nanophotonic components using Nano-optic imagers. The Pattern Recognition for MRI was performed for lower dimensionality. Finally, the Hybrid feedforward Deep Neural Network (ffDNN) and Convolutional Neural Network (CNN) architecture for calculating the height and size of scatterers using the inverse design of the meta-optical structure. The temporal resolution assessment of image data pixel size 280x360 hyperspectral imaging temporal resolution is 25, and magnetic resonance imaging temporal resolution is 50. The Image distribution shows that phase shift and transmission are 2.78 degrees and at 95%. The result for the inverse design using CNN returns the efficient inverse design of test data that can be designed according to the required pressure distribution. Wavelength 1000 nanometer to 1600 machine learning method absorbance 40% and ffDNN absorbance 33%.

Universal Behavior of the Image Resolution for Different Scanning Trajectories

This study examines the characteristics of various scanning trajectories or patterns under the influence of scanning parameters in order to develop a theory to define their corresponding image resolutions. The lack of an accurate estimation of pixel size for a specified set of scanning parameters and their connection is a key challenge with existing scanning methods. Thus, this research aimed to propose a novel approach to estimate the pixel size of different scanning techniques. The findings showed that there is a link between pixel size and a frequency ratio NP, which is the ratio of two waveform frequencies that regulates the density of the scanning pattern. A theory has been developed in this study to explain the relationship between scanning parameters and scanning density or pixel size, which was not previously considered. This unique theory permitted the a priori estimate of the image resolution using a particular set of scanning parameters, including the scan time, frequencies, frequency ratio, and their amplitudes. This paper presents a novel and systematic approach for estimating the pixel size of various scanning trajectories, offering the user additional flexibility in adjusting the scanning time or frequency to achieve the desired resolution. Our findings also reveal that in order to achieve a high-quality image with high signal-to-noise and low error, the scanning trajectory must be able to generate a fairly uniform or regular pattern with a small pixel size.

Optimizing Drone-Based Surface Models for Prescribed Fire Monitoring

Prescribed burning and pyric herbivory play pivotal roles in mitigating wildfire risks, underscoring the imperative of consistent biomass monitoring for assessing fuel load reductions. Drone-derived surface models promise uninterrupted biomass surveillance but require complex photogrammetric processing. In a Mediterranean mountain shrubland burning experiment, we refined a Structure from Motion (SfM) and Multi-View Stereopsis (MVS) workflow to diminish biases in 3D modeling and RGB drone imagery-based surface reconstructions. Given the multitude of SfM-MVS processing alternatives, stringent quality oversight becomes paramount. We executed the following steps: (i) calculated Root Mean Square Error (RMSE) between Global Navigation Satellite System (GNSS) checkpoints to assess SfM sparse cloud optimization during georeferencing; (ii) evaluated elevation accuracy by comparing the Mean Absolute Error (MAE) of six surface and thirty terrain clouds against GNSS readings and known box dimensions; and (iii) complemented a dense cloud quality assessment with density metrics. Balancing overall accuracy and density, we selected surface and terrain cloud versions for high-resolution (2 cm pixel size) and accurate (DSM, MAE = 57 mm; DTM, MAE = 48 mm) Digital Elevation Model (DEM) generation. These DEMs, along with exceptional height and volume models (height, MAE = 12 mm; volume, MAE = 909.20 cm3) segmented by reference box true surface area, substantially contribute to burn impact assessment and vegetation monitoring in fire management systems.

CNR performance of semiconductor materials for X-ray imaging of breast calcifications

We present the results of a GAMOS/GEANT4 computer simulation of a standard X-ray mammography system, which consists of a Tungsten 28 kVp polychromatic X-ray source with a 50 μm Rh filter, a mammography phantom with Al2O3 spherical specks of different diameters, and a generic pixel detector (55 μm × 55 μm pixel size) with different types of semiconductor sensors. The number of photons simulated is calibrated to produce similar entrance surface dose (ESD) as the one used by a standard clinical mammography screening. Estimates of Contrast to Noise Ratio (CNR) as a function of ESD, sensor thickness and microcalcification diameter are presented for four different sensor materials: Silicon (Si), Cadmium Telluride (CdTe), Gallium Arsenide (GaAs) and Perovskite (MAPbI3). For the X-ray energy spectrum and pixel size considered, and an ESD dose of 4 mGy, our study shows that, with the exception of Si, these sensors, as thin as 200 μm, are able to resolve (with at least 3 standard deviations above background) Al2O3 spherical specks up to a minimum diameter of 180 μm, having statistically compatible CNR performance. The increase in substrate thickness has a substantial improvement in the CNR values provided by the Si sensor, while for the other cases the enhancement of CNR is marginal and consistent with statistical uncertainties with the thinnest case considered.

Evaluation of planar silicon pixel sensors with the RD53A readout chip for the Phase-2 Upgrade of the CMS Inner Tracker

The Large Hadron Collider at CERN will undergo an upgrade in order to increase its luminosity to 7.5 × 1034 cm-2s-1. The increased luminosity during this High-Luminosity running phase, starting around 2029, means a higher rate of proton-proton interactions, hence a larger ionizing dose and particle fluence for the detectors. The current tracking system of the CMS experiment will be fully replaced in order to cope with the new operating conditions. Prototype planar pixel sensors for the CMS Inner Tracker with square 50 μm × 50 μm and rectangular 100 μm × 25 μm pixels read out by the RD53A chip were characterized in the lab and at the DESY-II testbeam facility in order to identify designs that meet the requirements of CMS during the High-Luminosity running phase. A spatial resolution of approximately 3.4 μm (2 μm) is obtained using the modules with 50 μm × 50 μm (100 μm × 25 μm) pixels at the optimal angle of incidence before irradiation. After irradiation to a 1 MeV neutron equivalent fluence of Φeq = 5.3 × 1015 cm-2, a resolution of 9.4 μm is achieved at a bias voltage of 800 V using a module with 50 μm × 50 μm pixel size. All modules retain a hit efficiency in excess of 99% after irradiation to fluences up to 2.1 × 1016 cm-2. Further studies of the electrical properties of the modules, especially crosstalk, are also presented in this paper.

Calibration procedures and data correction of ePix100 detectors at the European XFEL

The European XFEL is a research facility that delivers extremely bright and short coherent X-ray pulses of tunable energy at MHz repetition rate, providing unprecedented capabilities to conduct scientific research across multiple domains. Among the suite of deployed detectors, several ePix100 modules, belonging to the family of ePix detectors developed at SLAC, are used. These charge-integrating hybrid pixel detectors offer single-photon resolution for energies above 2 keV and a dynamic range of 100 photons at 8 keV. Their low noise, small pixel size, compact dimensions, maneuverability and vacuum compatibility make them an attractive choice for some of the hard X-ray instruments at the European XFEL for imaging, spectroscopy, and scattering experiments.The European XFEL is committed to providing users with completely corrected detector data. To achieve this goal, periodic calibration procedures are conducted to generate calibration constants that allow the conversion of raw detector output into physically meaningful information through a series of successive data correction steps. In this work, an overview of the ePix100 calibration procedures and correction algorithms will be provided, with a focus on particularly relevant processes for this detector, such as common mode noise and charge sharing correction.

Single-Shot, Pixel-Encoded Strip Patterns for High-Resolution 3D Measurement

In this research, we combined two distinct, structured light methods, the single-shot pseudo-random sequence-based approach and the time-multiplexing stripe indexing method. As a result, the measurement resolution of the single-shot, spatially encoded, pseudo-random sequence-based method improved significantly. Since the time-multiplexed stripe-indexed-based techniques have a higher measurement resolution, we used varying stripes to enhance the measurement resolution of the pseudo-random sequence-based approaches. We suggested a multi-resolution 3D measurement system that consisted of horizontal and vertical stripes with pixel sizes ranging from 8 × 8 to 16 × 16. We used robust pseudo-random sequences (M-arrays) to controllably distribute various stripes in a pattern. Since single-shape primitive characters only contribute one feature point to the projection pattern, we used multiple stripes instead of single-shape primitive symbols. However, numerous stripes will contribute multiple feature points. The single character-based design transforms into an increased featured size pattern when several stripes are employed. Hence, the projection pattern contains a much higher number of feature points. So, we obtained a high-resolution measurement. Each stripe in the captured image is located using adaptive grid adjustment and stripe indexing techniques. The triangulation principle is used to measure 3D.

Design and Development of Handwritten Digit Recognition System Using Machine Learning

In any system ,the wide range of writing styles, handwritten digit recognition has proven to be a challenging task.The wide variety of writing styles has made it challenging to read handwritten numerals.. Numerous research have demonstrated the superior performance of neural networks in the classification of data. The major goal of this work is to compare various existing classification models in order to give effective and trustworthy methods for handwritten numeral recognition. As training sets, a total of 45,000 images with a pixel size of 2828 were employed. The original image was compared to the training sets and images. We also found that the classifier's accuracy increases with the amount of training data. The study's conclusion shows that K-NN maintains an accuracy of 96.7% as opposed to 96.8% while processing data at a rate nearly ten times faster than a neural network.