Multiple Precision Research Articles

A formalism to assess the accuracy of nuclear-structure weak interaction effects in precision β-decay studies

Multiple high precision β-decay measurements are being carried out these days on various nuclei, in search of beyond the Standard Model (SM) signatures. These measurements necessitate accurate SM theoretical predictions to be compared with. Motivated by the experimental surge, we present a formalism for such a calculation of β-decay observables, with controlled accuracy, based on a perturbative analysis of the theoretical observables related to the phenomena, including high order nuclear recoil and shape corrections. The accuracy of the corrections is analyzed by identifying a hierarchy of small parameters, related to the low-momentum transfer characterizing β-decays. Furthermore, we show that the sub-percent uncertainties, targeted by ongoing and planned experiments, entail an accuracy of the order of 10% for the solution of the nuclear many-body problem, which is well within the reach of modern nuclear theory for light to medium mass nuclei.

Toward Accurate and Fast Summation

We introduce a new accurate summation algorithm based on the error-free summation into floating-point buckets. Our algorithm exploits ideas from Zhu and Hayes’ OnlineExactSum , but it uses a significantly smaller number of accumulators and has a better instruction-level parallelism. In the default setting, our implementation aaaSum returns a faithfully rounded floating-point approximation of the true sum. We also discuss possible modifications for the computation of reproducible, correctly rounded, and multiple precision floating-point approximations. The computational overhead for any of these modifications is kept comparably small. Numerical tests demonstrate that aaaSum performs well for very small to large problem sizes, independent of the condition number of the problem. We compare our algorithm with other accurate and high-precision summation approaches.

Object detection and multi‐object tracking based on optimized deep convolutional neural network and unscented Kalman filtering

SummaryThe tracking of multiple objects has gained immense interest in several applications that ranges from video surveillance and robotics. The goal of tracking multiple objects is to discover correspondences and perform matching among various objects in frames. However, occlusion is a major issue in several classical techniques, which fail to track multiple moving objects. Here, the extraction of frames is done to obtain information about an action. Thereafter, the segmentation of objects is done using the entropy weighting K‐means (EWKM) algorithm to generate optimum segments from the frame. After that, the detection of the object is done using a deep convolutional neural network (DCNN) to obtain objects from segments. Proposed Jaya‐political optimizer (Jaya‐PO) is used to train DCNN, which is devised by collaborating political optimizer (PO) and Jaya optimization algorithm. After detecting the object, the tracking of multiple objects is done using the proposed Henry gas solubility optimized unscented Kalman filtering (HGSO‐based UKF), wherein the UKF is modified with HGSO. The proposed Jaya‐PO‐based DCNN and HGSO‐based UKF outperformed other methods with high accuracy of 92%, high multiple object tracking precision (MOTP) of 87.6%, high sensitivity of 91.7%, and high specificity of 92%.

Multiple object tracking in farmland based on fusion point cloud data

Object detection and tracking in a farmland environment is greatly important to unmanned agricultural machinery navigation technology. Based on the existing methods of fusion point cloud acquisition and object detection, the multiple object tracking method was further researched. Constant velocity model was selected as the motion model, and state estimation was achieved using Kalman filter. Data association between the detectors and trackers was realized through an improved GNN method based on the cost of category conversion. A tracking state machine and two threshold mechanisms were proposed to avoid “combined explosion” disaster. Fusion point cloud data were used for motion judgment to achieve the real motion attributes and speed of the objects based on ground reference. As the results, the average static drift of the tracking method was less than 6.5 cm, the discrimination accuracy of static objects exceeded 75%. Besides, the multiple object tracking precision was less than 35 cm, and the multiple object tracking accuracy exceeded 85%. Therefore, the proposed multiple object tracking method could satisfy the requirements in the farmland environment.

Nucleic Acid-conjugated Carbohydrate Nanobiosensors: A Multimodal Tool for Disease Diagnosis.

Nucleic acid-based carbohydrate sensors (NAbCSs) constitute a strategy involving nucleic acids as recognition elements for the development of a unique, stable, sensitive, mono- or multimodal detection system in the field of nanomedicine, gas sensing, and gene therapy. Thus, this advanced platform for next-generation investigation compromises cost-effective, wearable, and noninvasive sensing devices as diagnostics in healthcare. This review article highlights the importance of NAbCSs and explores the novel applications of sensors fabricated via the conjugation of nucleic acids and carbohydrates. Additionally, advances in smart portable devices, like smartphones, printers, and digital multimeters, are summarized, followed by the challenges involved in the development of futuristic sensing tools. A novel platform has been unfolded for the detection of different chemical toxins (like aflatoxin B1, ochratoxin A) and biomarkers (like miRNA in cancer) present in biosamples, food and biowarfare agents. The potential applications of biosensing in the areas of miniaturization, reusability, rapid, point-of-care or portable for home analysis techniques, cost-effective, eco-friendly, high throughput and personalized sensors for qualitative analysis of target analyte/s in bio-fluids and food have been explored. NAbCSs provide real-time monitoring of biosamples qualitatively and semi-quantitatively (luminometer, fluorimeter, etc.) in the absence of trained personnel. Explorations of NAbCSs encompass advantages in remote resource-limited access areas with simultaneous monitoring via smart devices for multiple analytes with greater precision, sensitivity, and selectivity.

A Moment and Torque Sensor With a Birfield-Type Structure, Using a Mechanically Self-Decoupled Method

A moment and torque sensor with a birfield-type structure using a mechanically self-decoupled method was proposed, analyzed, fabricated, and tested. The self-decoupling operation was achieved by arranging the moment and torque transmission along different routes by designing a birfield-type mechanical structure. Two pieces of magnetostrictive material 2826MB were pasted on the sensitive parts along the moment and torque transmitting route for detection purposes. Then, the moment and torque were measured on the sensitive parts and were found not to affect each other. The sensor can realize noncontact and dynamic detection based on the Villari effect. Experiments were conducted to test the performance of the sensor. Experimental results showed that the sensor had a good self-decoupling performance, with an error less than 1.10%. The sensor had an excellent linear performance. The sensor can also work dynamically with an error less than 3.54%. Based on the uncertainty analysis, the total uncertainty of the sensor is 0.015, which ensures the stability in multiple precision measurements. Furthermore, the sensor displayed advantages of working passively, wirelessly. All these advantages enable the sensor potential applications in cutting tools, sealed gearboxes, etc.

Research on Multi-Ship Target Detection and Tracking Method Based on Camera in Complex Scenes

Aiming at the problem that multi-ship target detection and tracking based on cameras is difficult to meet the accuracy and speed requirements at the same time in some complex scenes, an improved YOLOv4 algorithm is proposed, which simplified the network of the feature extraction layer to obtain more shallow feature information and avoid the disappearance of small ship target features, and uses the residual network to replace the continuous convolution operation to solve the problems of network degradation and gradient disappearance. In addition, a nonlinear target tracking model based on the UKF method is constructed to solve the problem of low real-time performance and low precision in multi-ship target tracking. Multi-ship target detection and tracking experiments were carried out in many scenes with large differences in ship sizes, strong background interference, tilted images, backlight, insufficient illumination, and rain. Experimental results show that the average precision of the detection algorithm of this paper is 0.945, and the processing speed is about 34.5 frame per second, where the real-time performance is much better than other algorithms while maintaining high precision. Furthermore, the multiple object tracking accuracy (MOTA) and the multiple object tracking precision (MOTP) of this paper algorithm are 76.4 and 80.6, respectively, which are both better than other algorithms. The method proposed in this paper can realize the ship target detection and tracking well, with less missing detection and false detection, and also has good accuracy and real-time performance. The experimental results provide a valuable theoretical reference for the further practical application of the method.

Exploiting Wireless Technology for Energy-Efficient Accelerators With Multiple Dataflows and Precision

As model size and the number of layers increase, Deep Neural Networks (DNNs) demand enormous computational power and throughput to meet exceedingly high prediction accuracy’s of today’s machine learning (ML) applications. Spatial hardware accelerators have been proposed that optimize the dataflow and exploit sparsity to provide a significant decrease in power consumption. As spatial architectures are traditionally designed with metallic interconnects, significant power is expended for data movement for different dataflows. In this paper, we exploit extended wireless technology to design a power-efficient and high-throughput DNN accelerator, e-WiNN, that can be configured for all representative dataflows and arithmetic precisions. We leverage novel circuit design by utilizing Dadda-algorithm based Multiply-and-Accumulate (MAC) circuits for 4-bit, 8-bit and 16-bit inputs to reduce area, power and delay constraints in 14 nm predictive technology. Our novel wireless transmitter integrates on- off keying (OOK) modulator with power amplifier that results in significant energy savings. To reduce the area overhead, we cluster wireless transceivers into groups of four such that both weights and input features can be effectively multicast to reduce the data movement. The energy efficient transceiver circuit is implemented in state-of-the-art BSIM 32 nm FinFET technology model and our link budget considers required RF power for different frequencies and inter-PE distance at three different antenna directivities including isotropic. Our detailed RTL modeling and cycle-accurate simulation results show that e-WiNN achieves 36.3% latency reduction and 76.1% energy saving when compared to state-of-art wire interconnected accelerators; 70.3% area reduction and 41.6% energy saving at the cost of 11% latency increase when compared to prior wireless accelerators on various neural networks (AlexNet, VGG16, and ResNet-9/50).

The interplay of local chemistry and plasticity in controlling microstructure formation during laser powder bed fusion of metals

Additive manufacturing (AM) of metallic components promises many advantages over conventional manufacturing processes through high design flexibility across multiple length scales and precision coupled with an astonishing combination of mechanical properties. Characterizing the relationship between microstructure and mechanical properties remains one of the major challenges for this novel technology. A natural precursor is identifying the influence of the processing path on the developing microstructure. We combine experimental studies of single-track laser powder bed fusion (LPBF) scans of AISI 316L stainless steel, finite element analyses, and large-scale three-dimensional discrete dislocation dynamics simulations to provide a unique understanding of the underlying mechanisms leading to the formation of heterogeneous defect structures in additively manufactured metals. Our results show that the interruption of dislocation slip at solidification cell walls is responsible for the formation of cellular dislocation structures, highlighting the significance of solute segregation for plastic deformation of additively manufactured components. This work provides a mechanistic perspective on heterogeneous microstructure formation and opens the potential for a reliable prediction of the resulting mechanical properties of additively manufactured parts.

Simple Multiple Precision Algorithms for Exponential Functions [Tips & Tricks

Exponential functions are essential in many areas of science and engineering. Fast and efficient computing of such functions in multiple floating-point formats is a complex task for all classes of processing units—general purpose CPUs, digital signal processors, GPUs, intelligent processing units, and tensor processors (TPU)—developed recently for neural computing and deep machine learning.

Abstract 76: Comprehensive genomic profiling as an approach to guide therapeutic planning in pediatric patients with high-risk solid tumors

Abstract Despite the great achievements in treating pediatric cancer patients in the last several decades, approximately one fifth of patients remains uncurable using standard therapeutic modalities and require search for innovative therapeutic approaches. Advances in sequencing techniques and bioinformatic data processing enabled identification of wide spectrum of molecular alterations including single nucleotide variants, copy number aberrations, fusion genes or changes in expression and methylation patterns, which could serve as therapeutic targets. Translation of comprehensive molecular profiling into clinical practice is still limited, however, multiple precision oncology initiatives have already explored feasibility of this approach. From September 2016 to December 2020, a total of 160 patients with high-risk solid tumors that were treated or consulted at Department of Pediatric Oncology of University Hospital Brno were subjected to molecular analysis of tumor tissue using whole-exome sequencing, targeted RNA sequencing, whole-transcriptome profiling and array-CGH. In 18 patients, 2 or more biopsies were analyzed due to relapse or progression of the disease. In the cohort, CNS tumors were the most prevalent (41%), followed by sarcomas (33%) and neuroblastoma (9%). All patients were presented at multidisciplinary molecular tumor board, where treatment recommendations were discussed. In 37% of patients (n = 59), therapeutic targets were identified. Most commonly identified targets included BRAF (n = 9), FGFR1 (n = 7), NF1 (n = 6), NRAS (n = 5) and PIK3CA (n = 4), making RAS/MAPK signaling most frequently altered pathway in the subgroup. Single nucleotide variants or small indels accounted for 65% of actionable findings, followed by fusion genes (12%), copy number aberrations (9%), CD274 expression (7%, confirmed by IHC staining for PD-L1 protein), and high tumor mutational burden (7%). Clinically relevant fusions were found in 25% of patients and 20% of identified fusions were targetable. 8 patients were eligible for immunotherapy based on either PD-L1 expression, or high tumor mutational burden (>10 mut/Mb). Using molecular-based approach in treating high-risk patients represents a promising strategy and helps to understand the complexity of pediatric malignancies though examining tumor biology at multiple levels. Implementing the concept of precision oncology into clinical practice could not only be beneficial in the context of chances for improved survival of high-risk patients, but might also be convenient for other patients, whose successful treatment comes at cost of various secondary complications due to intensive chemotherapy/radiotherapy approaches. Supported by Ministry of Health of the Czech Republic, grant nr. NV19-03-00562, NV19-03-00501, NV19-03-00559 and NU20-03-00240. All rights reserved. Citation Format: Petra Pokorna, Hana Palova, Tina Catela Ivkovic, Sona Adamcova, Michal Kyr, Vojtech Bystry, Robin Jugas, Karolina Trachtova, Dagmar Al Tukmachi, Tomas Merta, Jaroslav Juracek, Jiri Sana, Sona Mejstrikova, Marta Jezova, Peter Mudry, Zdenek Pavelka, Jaroslav Sterba, Ondrej Slaby. Comprehensive genomic profiling as an approach to guide therapeutic planning in pediatric patients with high-risk solid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 76.

Multiple Traffic Target Tracking with Spatial-Temporal Affinity Network.

Traffic target tracking is a core task in intelligent transportation system because it is useful for scene understanding and vehicle autonomous driving. Most state-of-the-art (SOTA) multiple object tracking (MOT) methods adopt a two-step procedure: object detection followed by data association. The object detection has made great progress with the development of deep learning. However, the data association still heavily depends on hand crafted constraints, such as appearance, shape, and motion, which need to be elaborately trained for a special object. In this study, a spatial-temporal encoder-decoder affinity network is proposed for multiple traffic targets tracking, aiming to utilize the power of deep learning to learn a robust spatial-temporal affinity feature of the detections and tracklets for data association. The proposed spatial-temporal affinity network contains a two-stage transformer encoder module to encode the features of the detections and the tracked targets at the image level and the tracklet level, aiming to capture the spatial correlation and temporal history information. Then, a spatial transformer decoder module is designed to compute the association affinity, where the results from the two-stage transformer encoder module are fed back to fully capture and encode the spatial and temporal information from the detections and the tracklets of the tracked targets. Thus, efficient affinity computation can be applied to perform data association in online tracking. To validate the effectiveness of the proposed method, three popular multiple traffic target tracking datasets, KITTI, UA-DETRAC, and VisDrone, are used for evaluation. On the KITTI dataset, the proposed method is compared with 15 SOTA methods and achieves 86.9% multiple object tracking accuracy (MOTA) and 85.71% multiple object tracking precision (MOTP). On the UA-DETRAC dataset, 12 SOTA methods are used to compare with the proposed method, and the proposed method achieves 20.82% MOTA and 35.65% MOTP, respectively. On the VisDrone dataset, the proposed method is compared with 10 SOTA trackers and achieves 40.5% MOTA and 74.1% MOTP, respectively. All those experimental results show that the proposed method is competitive to the state-of-the-art methods by obtaining superior tracking performance.

Integration of Single-Port Memory (ISPM) for Multiprecision Computation in Systolic-Array-Based Accelerators

On-chip memory is one of the core components of deep learning accelerators. In general, the area used by the on-chip memory accounts for around 30% of the total chip area. With the increasing complexity of deep learning algorithms, it will become a challenge for the accelerators to integrate much larger on-chip memory responding to algorithm needs, whereas the on-chip memory for multiprecision computation is required by the different precision (such as FP32, FP16) computations in training and inference. To solve it, this paper explores the use of single-port memory (SPM) in systolic-array-based deep learning accelerators. We propose transformation methods for multiple precision computation scenarios, respectively, to avoid the conflict of simultaneous read and write requests on the SPM. Then, we prove that the two methods are feasible and can be implemented on hardware without affecting the computation efficiency of the accelerator. Experimental results show that both methods have about 30% and 25% improvement in terms of area cost when accelerator integrates SPM without affecting the throughput of the accelerator, while the hardware cost is almost negligible.

A Close-in Puffy Neptune with Hidden Friends: The Enigma of TOI 620

We present the validation of a transiting low-density exoplanet orbiting the M2.5 dwarf TOI 620 discovered by the NASA Transiting Exoplanet Survey Satellite (TESS) mission. We utilize photometric data from both TESS and ground-based follow-up observations to validate the ephemerides of the 5.09 day transiting signal and vet false-positive scenarios. High-contrast imaging data are used to resolve the stellar host and exclude stellar companions at separations ≳0.″2. We obtain follow-up spectroscopy and corresponding precise radial velocities (RVs) with multiple precision radial velocity (PRV) spectrographs to confirm the planetary nature of the transiting exoplanet. We calculate a 5σ upper limit of M P < 7.1 M ⊕ and ρ P < 0.74 g cm−3, and we identify a nontransiting 17.7 day candidate. We also find evidence for a substellar (1–20 M J ) companion with a projected separation ≲20 au from a combined analysis of Gaia, adaptive optics imaging, and RVs. With the discovery of this outer companion, we carry out a detailed exploration of the possibilities that TOI 620 b might instead be a circum-secondary planet or a pair of eclipsing binary stars orbiting the host in a hierarchical triple system. We find, under scrutiny, that we can exclude both of these scenarios from the multiwavelength transit photometry, thus validating TOI 620 b as a low-density exoplanet transiting the central star in this system. The low density of TOI 620 b makes it one of the most amenable exoplanets for atmospheric characterization, such as with the James Webb Space Telescope and Ariel, validated or confirmed by the TESS mission to date.

Numerical Regge pole analysis of resonance structures in state-to-state reactive differential cross sections

This is the third (and the last) code in a collection of three programs [Sokolovski et al. (2011), Akhmatskaya et al. (2014)] dedicated to the analysis of numerical data, obtained in an accurate simulation of an atom-diatom chemical reaction. Our purpose is to provide a detailed description of a FORTRAN code for complex angular momentum (CAM) analysis of the resonance effects in reactive angular scattering [for CAM analysis of integral reactive cross sections see [Akhmatskaya et al. (2014)]. The code evaluates the contributions of a Regge trajectory (or trajectories) to a differential cross section in a specified range of energies. The contribution is computed with the help of the methods described in [Dobbyn et al. (1999), Sokolovski and Msezane (2004), Sokolovski et al. (2007)]. Regge pole positions and residues are obtained by analytically continuing S-matrix element, calculated numerically for the physical integer values of the total angular momentum, into the complex angular momentum plane using the PADE_II program [Sokolovski et al. (2011)]. The code represents a reactive scattering amplitude as a sum of the components corresponding to a rapid “direct” exchange of the atom, and the various scenarios in which the reactants form long-lived intermediate complexes, able to complete several rotations before breaking up into products. The package has been successfully tested on the representative models, as well as the F + H2→ HF+H benchmark reaction. Several detailed examples are given in the text. Program summaryProgram title:DCS_ReggeCPC Library link to program files:https://doi.org/10.17632/gf4gm82n6m.1Licensing provisions: GPLv3Programming language:FORTRAN 90Supplementary material:PADE_II, MPFUN and QUADPACK packages, validation suites, script files, input files, readme files, Installation and User GuideNature of problem: The package extracts the positions and residues of resonance poles from numerical scattering data supplied by the user. This information is then used for the analysis of resonance structures observed in reactive differential cross sections.Solution method: The S-matrix element is analytically continued in the complex plane of either energy or angular momentum with the help of Padé approximation of type II. Resonance Regge trajectories are identified and their contributions to a differential cross section are evaluated at different angles and energies.Additional comments including restrictions and unusual features: Use of multiple precision MPFUN package.

Multiple-precision sparse matrix–vector multiplication on GPUs

Sparse matrix–vector multiplication (SpMV) appears in many application domains, and performance is the key consideration when implementing SpMV kernels. At the same time, accuracy is also important because rounding errors can drastically change the computed result. Multiple-precision arithmetic is a common approach to improve the accuracy of results. In this paper, we implement and evaluate multiple-precision SpMV kernels in the CSR, JAD, ELLPACK, and DIA matrix storage formats for graphics processing units. In the proposed implementation, the matrix is represented in double precision, while the input and output vectors are in multiple precision and internal computations are also performed in multiple precision. Our underlying floating-point arithmetic algorithms are based on the residue number system, which is attractive because of its carry-free nature and provides an arbitrary level of precision determined by the set of moduli that comprise the base of the system. In particular, we apply sets from 8 to 64 moduli, reaching precision values from 106 to 848 bits, and demonstrate how higher precision reduces the rounding errors in the SpMV kernel. We also conduct a thorough analysis of the CSR kernel in terms of roofline performance, occupancy and memory bandwidth, and evaluate the performance and memory consumption for all the proposed kernels. Numerical experiments on matrices from real-world applications show that ELLPACK offers the highest performance in many examples, while JAD is a good trade-off between performance and space requirements. Furthermore, our proposed kernels in general substantially improve the efficiency of sparse matrix–vector multiplication compared to implementations built on top of existing multiple-precision CUDA libraries. Finally, we integrate the multiple-precision SpMV into a preconditioned conjugate gradient linear solver and identify test cases where our implementation exhibits superior convergence and numerical robustness.

Vision-based method for tracking workers by integrating deep learning instance segmentation in off-site construction

Vision-based tracking of construction workers is the fundamental step for many automated applications in off-site construction. This research proposes a vision-based method for tracking workers in off-site construction by integrating deep learning instance segmentation. The proposed method consists of three main modules including instance segmentation, instance association, and instance assignment. The instance segmentation module applies the Mask R-CNN algorithm to extract masks and bounding boxes of workers from videos. Then, an association matrix is constructed at each two consecutive frames based on the mask intersection-over-union and Kalman filtering. Finally, the instance assignment module solves the association matrix to produce tracking results. In experiments, the proposed method achieved the multiple object tracking accuracy of 96.4% and multiple object tracking precision of 86.2%. The testing results indicate the developed method can successfully track multiple workers when facing the challenges of occlusions and scale variations, etc.

One polynomial approximation to produce correctly rounded results of an elementary function for multiple representations and rounding modes

Mainstream math libraries for floating point (FP) do not produce correctly rounded results for all inputs. In contrast, CR-LIBM and RLIBM provide correctly rounded implementations for a specific FP representation with one rounding mode. Using such libraries for a representation with a new rounding mode or with different precision will result in wrong results due to double rounding. This paper proposes a novel method to generate a single polynomial approximation that produces correctly rounded results for all inputs for multiple rounding modes and multiple precision configurations. To generate a correctly rounded library for n -bits, our key idea is to generate a polynomial approximation for a representation with n +2-bits using the round-to-odd mode. We prove that the resulting polynomial approximation will produce correctly rounded results for all five rounding modes in the standard and for multiple representations with k -bits such that | E | +1 &lt; k ≤ n , where | E | is the number of exponent bits in the representation. Similar to our prior work in the RLIBM project, we approximate the correctly rounded result when we generate the library with n +2-bits using the round-to-odd mode. We also generate polynomial approximations by structuring it as a linear programming problem but propose enhancements to polynomial generation to handle the round-to-odd mode. Our prototype is the first 32-bit float library that produces correctly rounded results with all rounding modes in the IEEE standard for all inputs with a single polynomial approximation. It also produces correctly rounded results for any FP configuration ranging from 10-bits to 32-bits while also being faster than mainstream libraries.

Detection and tracking of belugas, kayaks and motorized boats in drone video using deep learning

Aerial imagery surveys are commonly used in marine mammal research to determine population size, distribution and habitat use. Analysis of aerial photos involves hours of manually identifying individuals present in each image and converting raw counts into useable biological statistics. Our research proposes the use of deep learning algorithms to increase the efficiency of the marine mammal research workflow. To test the feasibility of this proposal, the existing YOLOv4 convolutional neural network model was trained to detect belugas, kayaks and motorized boats in oblique drone imagery, collected from a stationary tethered system. Automated computer-based object detection achieved the following precision and recall, respectively, for each class: beluga = 74%/72%; boat = 97%/99%; and kayak = 96%/96%. We then tested the performance of computer vision tracking of belugas and occupied watercraft in drone videos using the DeepSORT tracking algorithm, which achieved a multiple-object tracking accuracy (MOTA) ranging from 37% to 88% and multiple object tracking precision (MOTP) between 63% and 86%. Results from this research indicate that deep learning technology can detect and track features more consistently than human annotators, allowing for larger datasets to be processed within a fraction of the time while avoiding discrepancies introduced by labeling fatigue or multiple human annotators.

Phase and dark-field imaging with mesh-based structured illumination and polycapillary optics.

X-ray phase and dark-field (DF) imaging have been shown to improve the diagnostic capabilities of X-ray systems. However, these methods have found limited clinical use due to the need for multiple precision gratings with limited field of view or requirements on X-ray coherence that may not be easily translated to clinical practice. This work aims to develop a practicable X-ray phase and DF imaging system that could be translated and practiced in the clinic. This work employs a conventional source to create structured illumination with a simple wire mesh. A mesh-shifting algorithm is used to allow wider Fourier windowing to enhance resolution. Deconvolution of the source spot width and camera resolution improves accuracy. Polycapillary optics are employed to enhance coherence. The effects of incorporating optics with two different focal lengths are compared. Information apparent in enhanced absorption images, phase images, and DF images of fat embedded phantoms were compared and subjected to a limited receiver operator characteristic (ROC) study. The DF images of the moist and dry porous object (sponges) were compared. The mesh-based phase and DF imaging system constructs images with three different information types: scatter-free absorption images, differential phase images, and scatter magnitude/DF images, simultaneously from the same original image. The polycapillary optic enhances the coherence of the beam. The deblurring technique corrects the phase signal error due to geometrical blur and the limitation of the camera modulation transfer function (MTF) and removes image artifacts to improve the resolution in a single shot. The mesh-shifting method allows the use of a wider Fourier processing window, which gives even higher resolution, at the expense of an increased dose. The limited ROC study confirms the efficacy of the system over the conventional system. DF images of moist and dry porous object show the significance of the system in the imaging of lung infections. The mesh-based X-ray phase and DF imaging system is an inexpensive and easy setup in terms of alignment and data acquisition and can produce phase and DF images in a single shot with wide field of view. The system shows significant potential for use in diagnostic imaging in a clinical setting.