Post-processing Functions Research Articles

Anatomically Guided Deep Learning System for Right Internal Jugular Line (RIJL) Segmentation and Tip Localization in Chest X-Ray

The right internal jugular line (RIJL) is a type of central venous catheter (CVC) inserted into the right internal jugular vein to deliver medications and monitor vital functions in ICU patients. The placement of RIJL is routinely checked by a clinician in a chest X-ray (CXR) image to ensure its proper function and patient safety. To reduce the workload of clinicians, deep learning-based automated detection algorithms have been developed to detect CVCs in CXRs. Although RIJL is the most widely used type of CVCs, there is a paucity of investigations focused on its accurate segmentation and tip localization. In this study, we propose a deep learning system that integrates an anatomical landmark segmentation, an RIJL segmentation network, and a postprocessing function to segment the RIJL course and detect the tip with accuracy and precision. We utilized the nnU-Net framework to configure the segmentation network. The entire system was implemented on the SimpleMind Cognitive AI platform, enabling the integration of anatomical knowledge and spatial reasoning to model relationships between objects within the image. Specifically, the trachea was used as an anatomical landmark to extract a subregion in a CXR image that is most relevant to the RIJL. The subregions were used to generate cropped images, which were used to train the segmentation network. The segmentation results were recovered to original dimensions, and the most inferior point’s coordinates in each image were defined as the tip. With guidance from the anatomical landmark and customized postprocessing, the proposed method achieved improved segmentation and tip localization compared to the baseline segmentation network: the mean average symmetric surface distance (ASSD) was decreased from 2.72 to 1.41 mm, and the mean tip distance was reduced from 11.27 to 8.29 mm.

Ejector pin-related high-cycle fatigue fracture of the critical die corner during automatic multistage cold forging of an automotive wheel nut

An experimental and numerical study on the ejector pin's mechanics during automatic multistage cold forging (AMSCF) of an automobile wheel nut is conducted. The traditional, decoupled die structural analysis method (DDSM) of analyzing die structures as one of the post-processing functions is criticized, which uses the tractions exerting on the die parts predicted from the forging simulation under the rigid die assumption. To cope with the matter of the DDSM, a multibody treatment scheme (MBTS) is proposed to simulate the AMSCF process, emphasizing the ejector pin's mechanics, using an implicit elastoplastic finite element method. The experiments qualitatively validate the finite element predictions. It is shown that the asymmetric sheared material in AMSCF greatly influences the ejector pin's mechanics, which is characterized by its lateral and longitudinal displacements because of its structural flexibility. It is emphasized that the detailed understanding of the ejector pin's mechanics may not only give a helpful connection towards smart manufacturing because of its mechanical flexibility and sensitivity to the excitations and responses, but it also reveals the reason for the die's high-cycle fatigue (HCF) fracture of the critical die corner (CDC) near at the end of the ejector pin.

A Post-processing Computational Fluid Dynamics System for Turbomachinery

Computational fluid dynamics applied to the turbomachinery domain require specialized post-processing functionality. For computational fluid dynamics turbomachinery, we have developed a lightweight post-processing software. We have developed a system utilizing the Visualization Toolkit (VTK) for rendering and the Qt toolkit as the cross-platform graphical user interface platform. The system is lightweight, which implements the fundamental postprocessing functions such as iso-surface and cut plane visualization and determines the meridional plane and the constant blade/van height plane. We designed and evaluated two different methods to determine the meridional plane and the constant blade/van height plane and used VTK filters in VTK to obtain necessary scene information. Finally, we visualized these planes by using the VTK visualization pipeline.

METHOD OF HIGH-PRECISION NEURO-FUZZY SYSTEMS DEFUZZIFIERS’ AUTOMATIC CONSTRUCTION

Increasing the precision of neuro-fuzzy approximation systems is considered. Modification of defuzzifier, which allows producing multiple types of approximating functions is presented. RMSE (Root Mean Square Error), maximal error, training time and models’s size are used to evaluate the efficiency of proposed method. Post-processing functions were also considered as a tool to increase approximation precision. Common solution to this problem is a modification of model’s fuzzy sets (membership functions and their number), which significantly increases model’s size and complexity. Given approach uses search algorithms to find the configuration of defuzzification layer, which produces the best approximation precision for a given function and fuzzy sets configuration, since all possible configurations are checked. It is possible to model classic approximation methods and combine them. Possible modifications of presented search algorithms application are described. Search depth can be limited in order to reduce the size of produced model. The computing experiment, aimed at finding best approximation precision was carried out. Presented method provided lowering of RMSE without significant increasing of model’s size. Using Fourier series components in resulting expression can be used for precise periodic functions approximation or for general precision improvement. Using post-processing functions provided significant improvement in precision for multiplication approximation, which proves, that it can be used as another tool to be used to reduce errors in some specific cases.

CORTO: The Celestial Object Rendering TOol at DART Lab

The Celestial Object Rendering TOol (CORTO) offers a powerful solution for generating synthetic images of celestial bodies, catering to the needs of space mission design, algorithm development, and validation. Through rendering, noise modeling, hardware-in-the-loop testing, and post-processing functionalities, CORTO creates realistic scenarios. It offers a versatile and comprehensive solution for generating synthetic images of celestial bodies, aiding the development and validation of image processing and navigation algorithms for space missions. This work illustrates its functionalities in detail for the first time. The importance of a robust validation pipeline to test the tool’s accuracy against real mission images using metrics like normalized cross-correlation and structural similarity is also illustrated. CORTO is a valuable asset for advancing space exploration and navigation algorithm development and has already proven effective in various projects, including CubeSat design, lunar missions, and deep learning applications. While the tool currently covers a range of celestial body simulations, mainly focused on minor bodies and the Moon, future enhancements could broaden its capabilities to encompass additional planetary phenomena and environments.

Over-the-Air Computation with Quantized CSI and Discrete Power Control Levels

In this paper, an Over-the-Air Computation (AirComp) scheme for fast data aggregation is considered. Multisource data are simultaneously transmitted by single-antenna mobile devices to a single-antenna fusion center (FC) through a wireless multiple-access channel. The optimal power levels at the devices and a postprocessing scaling function at the FC are jointly derived such that mean square error of the computation is minimized. Different than the existing approaches that rely on perfect channel state information (CSI) at the FC and assume that the devices’ optimal power levels can be selected from an infinite solution set, in the present paper, it is assumed that only quantized CSI is available at the FC and that the aforementioned optimal power levels lie in a finite discrete set of solutions. To derive the optimal power levels and FC’s scaling factor, a difficult nonconvex constrained optimization problem is formulated. An efficient and robust solution to quantization errors is developed via the deep reinforcement learning framework. Numerical results verify the good performance of the proposed approach while it exhibits a significant reduction in the required feedback.

COMPARATIVE ANALYSIS OF SOFTWARE SYSTEMS FOR HYDRAULIC TURBINE FLOW SIMULATION

A comprehensive review of modern software complexes used for calculating spatial flow in hydraulic turbine flow parts was conducted. The widely used software system, Ansys, was analyzed. An overview of Ansys was provided, including its history, popularity within the CFD community, key features, and capabilities for analyzing the flow parts of hydraulic turbines. The preprocessing tools, solver parameters, post-processing functions, and visualization capabilities of Ansys were described. The advantages and limitations of using Ansys for calculating spatial flow in hydraulic turbine flow parts were analyzed. The open-source CFD software complex, OpenFOAM, was discussed. The main functions and capabilities of the OpenFOAM program were described. Information about solver libraries, meshing capabilities, advantages, and limitations for analyzing hydraulic turbines was presented, along with insights into the support from the scientific community and resources available to OpenFOAM users. SolidWorks FlowSimulation, which integrates with SolidWorks software, was examined. The unique features of SolidWorks FlowSimulation for analyzing spatial flow in hydraulic turbines were highlighted. The possibilities of CAD integration and the advantages of accurate geometric models were discussed. The capabilities of parametric analysis were explored, and the advantages and limitations of using SolidWorks FlowSimulation for calculating spatial flow in hydraulic turbine flow parts were analyzed. A comparison of the three software complexes was conducted based on their capabilities, ease of use, accuracy, computational resources required, and cost. An assessment of the advantages and disadvantages of each program was provided, along with recommendations for choosing the most suitable program based on specific use cases, objectives, and user requirements. This article serves as a valuable resource for engineers, designers, and researchers seeking insights into the available software systems for analyzing hydraulic turbine flow parts. It enables them to make informed decisions in selecting the most suitable software system based on their specific requirements, ultimately contributing to the optimization of hydraulic turbine performance and efficiency.

An optimal sensor placement scheme for wind flow and pressure field monitoring

Sensing of wind pressures on wind-sensitive structures and wind speeds in densely populated cities is crucial to ensure structure safety and monitoring urban wind environment. However, these wind pressure or wind speed sensors are often sparsely distributed due to their high cost, and consequently the collected wind pressure or speed information is limited. Under these circumstances, it is particularly important to place these limited number of sensors in the optimal locations with the capacity of extracting the most information. In this study, an optimal sensor placement scheme based on multi-resolution dynamic mode decomposition (mrDMD), a space post-processing function: signed distance function (SDF) and particle swarm optimization-random forest (PSO-RF) is proposed, called mrDMD-SDF-PSO-RF (mrDSPR). The effectiveness of the proposed scheme is verified using two case studies of reconstruction based on sparse sensors: wind flow field of a city block and wind pressure field of a single building. The result indicates that the proposed scheme significantly improves the efficiency of the sensors. Therefore, based on the proposed optimal sensor placement scheme, the locations of placing sensors can be well-designed for various wind engineering problems, which builds a solid foundation of super-resolution reconstruction of wind pressure on buildings and wind flow field in urban environment.

Post-Processed Posteriors for Banded Covariances

We consider Bayesian inference of banded covariance matrices and propose a post-processed posterior. The post-processing of the posterior consists of two steps. In the first step, posterior samples are obtained from the conjugate inverse-Wishart posterior, which does not satisfy any structural restrictions. In the second step, the posterior samples are transformed to satisfy the structural restriction through a post-processing function. The conceptually straightforward procedure of the post-processed posterior makes its computation efficient and can render interval estimators of functionals of covariance matrices. We show that it has nearly optimal minimax rates for banded covariances among all possible pairs of priors and post-processing functions. Additionally, we provide a theorem on the credible set of the post-processed posterior under the finite dimension assumption. We prove that the expected coverage probability of the 100(1−α)% highest posterior density region of the post-processed posterior is asymptotically 1−α with respect to any conventional posterior distribution. It implies that the highest posterior density region of the post-processed posterior is, on average, a credible set of conventional posterior. The advantages of the post-processed posterior are demonstrated by a simulation study and a real data analysis.

Application of Medical Image 3D Visualization Web Platform in Auxiliary Diagnosis and Preoperative Planning

Three-dimensional visualization of medical image data can enable doctors to observe images from more angles and higher dimensions. It is of great significance for doctors to assist in diagnosis and preoperative planning. Most 3D visualization systems are based on desktop applications, which are too dependent on hardware and operating system. This makes it difficult to use across platforms and maintain. Web-based systems tend to have limited capabilities. To this end, we developed a web application, which not only provides DICOM (Digital Imaging and Communications in Medicine) image browsing and annotation functions, but also provides three-dimensional post-processing functions of multiplanar reconstruction, volume rendering, lung parenchyma segmentation and brain MRI (Magnetic Resonance Imaging) analysis. In order to improve the rendering speed, we use the Marching Cube algorithm for 3D reconstruction in the background in an asynchronous way, and save the reconstructed model as glTF (GL Transmission Format). At the same time, Draco compression algorithm is used to optimize the glTF model to achieve more efficient rendering. After performance evaluation, the system reconstructed a CT (Computed Tomography) series of 242 slices and the optimized model was only 6.37mb with a rendering time of less than 2.5s. Three-dimensional visualization of the lung parenchyma clearly shows the volume, location, and shape of pulmonary nodules. The segmentation and reconstruction of different brain tissues can reveal the spatial three-dimensional structure and adjacent relationship of glioma in the brain, which has great application value in auxiliary diagnosis and preoperative planning.

PyRaysum: Software for Modeling Ray-theoretical Plane Body-wave Propagation in Dipping Anisotropic Media

This article introduces PyRaysum, a Python software for modeling ray-theoretical body-wave propagation in dipping and/or anisotropic layered media based on the popular Fortran code Raysum. We improve and expand upon Raysum in several ways: 1) we significantly reduce the overhead by avoiding I/O operations; 2) we implement automatic phase labeling to facilitate the interpretation of complex seismograms; 3) we provide the means to correct inaccuracies in the calculated amplitude of free surface reverberations. We take advantage of the modern, object-oriented Python environment to offer various classes and methods to perform receiver function calculation, filtering and plotting. PyRaysum also integrates well with NumPy and ObsPy, two standard libraries for numerical computing and seismology. PyRaysum is built in Python version 3 and requires a Fortran compiler, but otherwise runs on all platforms. The software offers a high-level, ease-of-use user interface and is equipped with complete documentation and testing as well as tutorials to reproduce published examples from the literature. Time-optimized post-processing functions allow for the straightforward and efficient incorporation of PyRaysum synthetic data into optimization or probabilistic parametric search approaches.

Dycdtools: an R Package for Assisting Calibration and Visualising Outputs of an Aquatic Ecosystem Model

The high complexity of aquatic ecosystem models (AEMs) necessitates a large number of parameters that need calibration, and visualisation of their multifaceted and multi-layered simulation results is necessary for effective communication. Here we present an R package "dycdtools" that contains calibration and post-processing tools for a widely applied aquatic ecosystem model (DYRESM-CAEDYM). The calibration assistant function within the package automatically tests a large number of combinations of parameter values and returns corresponding values for goodness-of-fit, allowing users to narrow parameter ranges or optimise parameter values. The post-processing functions enable users to visualise modelling outputs in four ways: as contours, profiles, time series, and scatterplots. The "dycdtools" package is the first open-source calibration and post-processing tool for DYRESM-CAEDYM, and can also be adjusted for other AEMs with a similar structure. This package is useful to reduce the calibration burden for users and to effectively communicate model results with a broader community.

PyRaDiSe: A Python package for DICOM-RT-based auto-segmentation pipeline construction and DICOM-RT data conversion

Background and objective: Despite fast evolution cycles in deep learning methodologies for medical imaging in radiotherapy, auto-segmentation solutions rarely run in clinics due to the lack of open-source frameworks feasible for processing DICOM RT Structure Sets. Besides this shortage, available open-source DICOM RT Structure Set converters rely exclusively on 2D reconstruction approaches leading to pixelated contours with potentially low acceptance by healthcare professionals. PyRaDiSe, an open-source, deep learning framework independent Python package, addresses these issues by providing a framework for building auto-segmentation solutions feasible to operate directly on DICOM data. In addition, PyRaDiSe provides profound DICOM RT Structure Set conversion and processing capabilities; thus, it applies also to auto-segmentation-related tasks, such as dataset construction for deep learning model training.Methods: The PyRaDiSe package follows a holistic approach and provides DICOM data handling, deep learning model inference, pre-processing, and post-processing functionalities. The DICOM data handling allows for highly automated and flexible handling of DICOM image series, DICOM RT Structure Sets, and DICOM registrations, including 2D-based and 3D-based conversion from and to DICOM RT Structure Sets. For deep learning model inference, extending given skeleton classes is straightforwardly achieved, allowing for employing any deep learning framework. Furthermore, a profound set of pre-processing and post-processing routines is included that incorporate partial invertibility for restoring spatial properties, such as image origin or orientation.Results: The PyRaDiSe package, characterized by its flexibility and automated routines, allows for fast deployment and prototyping, reducing efforts for auto-segmentation pipeline implementation. Furthermore, while deep learning model inference is independent of the deep learning framework, it can easily be integrated into famous deep learning frameworks such as PyTorch or Tensorflow. The developed package has successfully demonstrated its capabilities in a research project at our institution for organs-at-risk segmentation in brain tumor patients. Furthermore, PyRaDiSe has shown its conversion performance for dataset construction.Conclusions: The PyRaDiSe package closes the gap between data science and clinical radiotherapy by enabling deep learning segmentation models to be easily transferred into clinical research practice. PyRaDiSe is available on https://github.com/ubern-mia/pyradise and can be installed directly from the Python Package Index using pip install pyradise.

LAVA 1.0: A general-purpose python toolkit for calculation of material properties with LAMMPS and VASP

We introduce LAVA, a general-purpose python toolkit to provide user-friendly and high throughput calculations for material properties using both LAMMPS and VASP. It contains a set of classes as well as pre-processing and post-processing functions to prepare, execute and extract information from LAMMPS/VASP simulations. An overview of the program structure is provided. The current version contains modules to calculate elastic and mechanical properties such as lattice constant, cohesive energy, cold curve, elastic constants, bulk and shear modulus, volume conserving/non-conserving deformation path, vacancy/interstitial formation energy, surface energy, stacking fault energy, melting point, radial distribution function, and thermal expansion. These functionalities are demonstrated for different interatomic potentials and DFT calculations in Al. Program summaryProgram title: LAVACPC Library link to program files:https://doi.org/10.17632/6grh8x4hgt.1Licensing provisions: BSD 3-Clause LicenseProgramming language: PythonNature of problem: This program provides user-friendly and high throughput calculations for material properties via two widely-used LAMMPS and VASP code.Solution method: LAVA can prepare the input scripts, extract, calculate and even plot the material properties from molecular dynamics simulations and density functional calculations together with bash and python scripts.

The effect of the double-horizontal-shaft vibrating mixing on the performance of UHPC

In this paper, the double-horizontal-shaft vibrating mixer is taken as the research object, and the effect of the vibrating mixing on the performance of UHPC materials are contrasted and analysed in simulations and tests. Firstly, a three-dimensional model of the double-horizontal-shaft vibrating mixer is constructed, and the EDEM software is imported for simulation analysis. Then, on the premise that materials are fully mixed in the model, a set of contrastive studies between the non-vibrating mixing and vibrating mixing was designed. The post-processing function of the EDEM software is used to analyse the material movement after mixing and the rule of the material uniformity changing over time. Finally, various performances of the material are tested in field tests. Overall, for UHPC preparation, the effect of the vibrating mixing is better than that of the non-vibrating mixing.

A Practical Light Field Representation and Coding Scheme with an Emphasis on Refocusing

Light field images have drawn a great deal of interest owing to their flexible post-processing and versatile functionalities. However, the tremendous data volume has imposed a practical limit in applications, which requires a better way to represent and compress the data. In this paper, we propose a practical representation and coding scheme for light field data with a special emphasis on retaining the refocusing functionality as much as possible. Under the proposed method, light field data are represented and compressed in the form of a rendered all-in-focus image and a depth map. Before encoding, the all-in-focus image is rendered accurately based on the depth map using the proposed difference focus measure. At the decoder, a focal stack comprising multiple images having different focus levels can be reconstructed by applying a defocusing function to the compressed all-in-focus image where the defocusing function parameter depends on the selected depth level of the compressed depth map. Compared with well-known state-of-the-art methods that compress the Fourier Disparity Layer or the focal stack, the proposed coding scheme shows much smaller loss in the refocusing capability (16.2% and 17.8% less than two state-of-the-art methods) and provides PSNR improvements of 1.60㏈ to 2.38㏈ at the same compression ratio.

Quantum random number generator based on polarization switching in gain-switched VCSELs

We experimentally study a quantum random number generator based on the random excitation of the linearly polarized modes of a gain-switched vertical-cavity surface-emitting laser (VCSEL). Our device is characterized by having polarization switching under continuous wave operation. By measuring the linear polarization mode that is excited in each pulse we collect a sufficient number of bits to evaluate if a standard statistical test suite is passed. We consider linear and Von Neumann post-processing methods in order to reduce the bias with different levels of bits rejection. The post-processed bit strings pass all tests in the standard test suite for random number generators provided by the National Institute of Standards and Technology (NIST). We finally compare the results obtained with different post-processing functions, including several [n, k, d] linear BCH codes. We show that large values of n and k are the best choice to obtain simultaneously improved throughput and randomness.

Obtaining Calibrated Probabilities with Personalized Ranking Models

For personalized ranking models, the well-calibrated probability of an item being preferred by a user has great practical value. While existing work shows promising results in image classification, probability calibration has not been much explored for personalized ranking. In this paper, we aim to estimate the calibrated probability of how likely a user will prefer an item. We investigate various parametric distributions and propose two parametric calibration methods, namely Gaussian calibration and Gamma calibration. Each proposed method can be seen as a post-processing function that maps the ranking scores of pre-trained models to well-calibrated preference probabilities, without affecting the recommendation performance. We also design the unbiased empirical risk minimization framework that guides the calibration methods to learning of true preference probability from the biased user-item interaction dataset. Extensive evaluations with various personalized ranking models on real-world datasets show that both the proposed calibration methods and the unbiased empirical risk minimization significantly improve the calibration performance.

TROT: A Three-Edge Ring Oscillator Based True Random Number Generator With Time-to-Digital Conversion

This paper introduces a new true random number generator (TRNG) based on a three-edge ring oscillator. Our design uses a new technique with a time-to-digital converter to effectively acquire jitter accumulated independently by each edge. As a part of the security evaluation, we present the stochastic model of the TRNG’s digital noise source and estimate a lower bound of the min-entropy per random bit. Starting from the obtained entropy bound, we propose a procedure for selecting and implementing an area-efficient and throughput-optimal post-processing function based on the best known linear codes that will increase the output min-entropy rate to more than 0.999. The proposed TRNG exquisitely balances low design effort and resource consumption with high throughput and a high min-entropy rate, making it more suitable for randomness-demanding and resource-constrained platforms than the state-of-the-art. The complete implementation of the TRNG digital noise source and the post-processing occupies 33 slices and achieves a throughput of 12.5 Mbps on Xilinx Zynq-7000 FPGAs. The min-entropy of the generated random bits is assessed by NIST SP 800-90B entropy estimators, and the tested sequences pass the AIS-31 test suit.

Variational Quantum-Neural Hybrid Eigensolver.

The variational quantum eigensolver (VQE) is one of the most representative quantum algorithms in the noisy intermediate-scale quantum (NISQ) era, and is generally speculated to deliver one of the first quantum advantages for the ground-state simulations of some nontrivial Hamiltonians. However, short quantum coherence time and limited availability of quantum hardware resources in the NISQ hardware strongly restrain the capacity and expressiveness of VQEs. In this Letter, we introduce the variational quantum-neural hybrid eigensolver (VQNHE) in which the shallow-circuit quantum Ansatz can be further enhanced by classical post-processing with neural networks. We show that the VQNHE consistently and significantly outperforms the VQE in simulating ground-state energies of quantum spins and molecules given the same amount of quantum resources. More importantly, we demonstrate that, for arbitrary postprocessing neural functions, the VQNHE only incurs a polynomial overhead of processing time and represents the first scalable method to exponentially accelerate the VQE with nonunitary postprocessing that can be efficiently implemented in the NISQ era.