Additional Memory Requirements Research Articles

Firefighting Water Jet Trajectory Detection from Unmanned Aerial Vehicle Imagery Using Learnable Prompt Vectors.

This research presents an innovative methodology aimed at monitoring jet trajectory during the jetting process using imagery captured by unmanned aerial vehicles (UAVs). This approach seamlessly integrates UAV imagery with an offline learnable prompt vector module (OPVM) to enhance trajectory monitoring accuracy and stability. By leveraging a high-resolution camera mounted on a UAV, image enhancement is proposed to solve the problem of geometric and photometric distortion in jet trajectory images, and the Faster R-CNN network is deployed to detect objects within the images and precisely identify the jet trajectory within the video stream. Subsequently, the offline learnable prompt vector module is incorporated to further refine trajectory predictions, thereby improving monitoring accuracy and stability. In particular, the offline learnable prompt vector module not only learns the visual characteristics of jet trajectory but also incorporates their textual features, thus adopting a bimodal approach to trajectory analysis. Additionally, OPVM is trained offline, thereby minimizing additional memory and computational resource requirements. Experimental findings underscore the method's remarkable precision of 95.4% and efficiency in monitoring jet trajectory, thereby laying a solid foundation for advancements in trajectory detection and tracking. This methodology holds significant potential for application in firefighting systems and industrial processes, offering a robust framework to address dynamic trajectory monitoring challenges and augment computer vision capabilities in practical scenarios.

Subgrid-Scale Topographic Effects on Radiation for Global Weather Forecast Models

The incoming solar radiation arriving the Earth’s surface is strongly influenced by surface terrain. Conventional global weather forecast models, with grid scales of about 10 km, lack the resolution to accurately capture terrain-induced variations. We devised a new parameterization method to incorporate high-resolution subgrid-scale terrain data into grid-scale radiative flux calculations without averaging or smoothing topographic features. Utilizing a 15″ digital elevation model from the Shuttle Radar Topography Mission, we computed subgrid-scale data and transformed them onto the Korean Integrated Model’s cubed sphere grid using a Voronoi diagram to maintain geographical accuracy. The new scheme was initially evaluated through offline ideal tests and case studies. The results demonstrated that the scheme accurately captured the variations in downward shortwave flux, keeping the mean flux on a global scale nearly constant. The global mean flux difference in all skies was less than 0.01%. Statistical analyses demonstrated improved temperature and geopotential height predictions compared to reanalysis data. The anomaly correlation coefficient for East Asia at 850 hPa increased by 0.036 at 240 forecast hours. Overall, the anomaly correlation coefficient and root mean square error of geopotential height and temperature showed enhancements, particularly in the Northern Hemisphere and tropics. Importantly, the scheme introduces negligible additional memory and CPU requirements, making it suitable for both regional and global models. Only a 0.58% increase in CPU time was observed for the 10-day forecast.

Unpaired low-dose computed tomography image denoising using a progressive cyclical convolutional neural network.

Reducing the radiation dose from computed tomography (CT) can significantly reduce the radiation risk to patients. However, low-dose CT (LDCT) suffers from severe and complex noise interference that affects subsequent diagnosis and analysis. Recently, deep learning-based methods have shown superior performance in LDCT image-denoising tasks. However, most methods require many normal-dose and low-dose CT image pairs, which are difficult to obtain in clinical applications. Unsupervised methods, on the other hand, are more general. Deep learning methods based on GAN networks have been widely used for unsupervised LDCT denoising, but the additional memory requirements of the model also hinder its further clinical application. To this end, we propose a simpler multi-stage denoising framework trained using unpaired data, the progressive cyclical convolutional neural network (PCCNN), which can remove the noise from CT images in latent space. Our proposed PCCNN introduces a noise transfer model that transfers noise from LDCT to normal-dose CT (NDCT), denoised CT images generated from unpaired CT images, and noisy CT images. The denoising framework also contains a progressive module that effectively removes noise through multi-stage wavelet transforms without sacrificing high-frequency components such as edges and details. Compared with seven LDCT denoising algorithms, we perform a quantitative and qualitative evaluation of the experimental results and perform ablation experiments on each network module and loss function. On the AAPM dataset, compared with the contrasted unsupervised methods, our denoising framework has excellent denoising performance increasing the peak signal-to-noise ratio (PSNR) from 29.622 to 30.671, and the structural similarity index (SSIM) was increased from 0.8544 to 0.9199. The PCCNN denoising results were relatively optimal and statistically significant. In the qualitative result comparison, PCCNN without introducing additional blurring and artifacts, the resulting image has higher resolution and complete detail preservation, and the overall structural texture of the image is closer to NDCT. In visual assessments, PCCNN achieves a relatively balanced result in noise suppression, contrast retention, and lesion discrimination. Extensive experimental validation shows that our scheme achieves reconstruction results comparable to supervised learning methods and has performed well in image quality and medical diagnostic acceptability.

Fast Exchange with Gaussian Basis Set Using Robust Pseudospectral Method.

In this article, we present an algorithm to efficiently evaluate the exchange matrix in periodic systems when a Gaussian basis set with pseudopotentials is used. The usual algorithm for evaluating exchange matrix scales cubically with the system size because one has to perform O(N2) fast Fourier transform (FFT). Here, we introduce an algorithm that retains the cubic scaling but reduces the prefactor significantly by eliminating the need to do FFTs during each exchange build. This is accomplished by representing the products of Gaussian basis function using a linear combination of an auxiliary basis the number of which scales linearly with the size of the system. We store the potential due to these auxiliary functions in memory, which allows us to obtain the exchange matrix without the need to do FFT, albeit at the cost of additional memory requirement. Although the basic idea of using auxiliary functions is not new, our algorithm is cheaper due to a combination of three ingredients: (a) we use a robust pseudospectral method that allows us to use a relatively small number of auxiliary basis to obtain high accuracy; (b) we use occ-RI exchange, which eliminates the need to construct the full exchange matrix; and (c) we use the (interpolative separable density fitting) ISDF algorithm to construct these auxiliary basis sets that are used in the robust pseudospectral method. The resulting algorithm is accurate, and we note that the error in the final energy decreases exponentially rapidly with the number of auxiliary functions.

AdaIN-Based Tunable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising

Recently, deep learning approaches using CycleGAN have been demonstrated as a powerful unsupervised learning scheme for low-dose CT denoising. Unfortunately, one of the main limitations of the CycleGAN approach is that it requires two deep neural network generators at the training phase, although only one of them is used at the inference phase. The secondary auxiliary generator is needed to enforce the cycle-consistency, but the additional memory requirements and the increase in the number of learnable parameters are major hurdles for successful CycleGAN training. Despite the use of additional generator, CycleGAN only translates between two domains, so it is not possible to investigate the intermediate level of denoising. To address this issue, here we propose a novel tunable CycleGAN architecture using a single generator. In particular, a single generator is implemented using adaptive instance normalization (AdaIN) layers so that the baseline generator converting a low-dose CT image to a routine-dose CT image can be switched to a generator converting high-dose to low-dose by simply changing the AdaIN code. Thanks to the shared baseline network, the additional memory requirement and weight increases are minimized, and the training can be done more stably even with small training data. Furthermore, by interpolating the AdaIN codes between the two domains, we can investigate various intermediate level of denoising results. Experimental results show that the proposed method outperforms the previous CycleGAN approaches while using only about half the parameters, and provide tunable denoising features that may be potentially useful in clinical environment.

Fast brain tumour segmentation using optimized U-Net and adaptive thresholding

Brain tumour segmentation evolved as the dominant task in brain image processing. Most of the contemporary research proposals devise deep neural networks and sparse representation to address this issue. These methods inherently suffer from high computational cost and additional memory requirements. Thus, optimization of the computational cost became a challenging task for the contemporary research. This paper discusses an optimized U-Net model with post-processing for fast brain tumour segmentation. The proposed model includes two phases: training and testing. Training phase computes weights for optimized U-Net and an adaptive threshold value. In the testing phase, a trained U-Net model predicts a rough tumour segment. Adaptive thresholding grabs the final tumour with improved segmentation results. We have considered a brain tumour dataset of 3064 images with three types of brain tumours for evaluation. Our proposed model exhibits superior results than the existing models in terms of recall and dice similarity metrics. It exhibits competitive performance in accuracy and precision. Moreover, the proposed model outperforms its competitive models in training time.

Acceleration of Transient Non-Linear Electromagnetic Field Analyses Using an Automated Subspace Correction Method

In this paper, we propose a mapping operator generation method for the subspace correction to accelerate transient and/or nonlinear eddy-current analyses. In these analyses, a sequence of linear systems whose coefficient matrices are similar or identical is solved. In our method, we automatically construct the operator using the information obtained in the preceding solution steps. The operator is generated with minimum additional memory requirement through the sampling of approximate solution vectors and the use of the Rayleigh–Ritz’s method. Numerical tests using transformer and box-shield models demonstrate that the subspace correction based on the proposed method significantly reduces the number of iterations and the computational time.

Irregular surface seismic forward modeling by a body-fitted rotated–staggered-grid finite-difference method

Finite-difference (FD) methods are widely used in seismic forward modeling owing to their computational efficiency but are not readily applicable to irregular topographies. Thus, several FD methods based on the transformation to curvilinear coordinates using body-fitted grids have been proposed, e.g., stand staggered grid (SSG) with interpolation, nonstaggered grid, rotated staggered grid (RSG), and fully staggered. The FD based on the RSG is somewhat superior to others because it satisfies the spatial distribution of the wave equation without additional memory and computational requirements; furthermore, it is simpler to implement. We use the RSG FD method to transform the firstorder stress–velocity equation in the curvilinear coordinates system and introduce the highprecision adaptive, unilateral mimetic finite-difference (UMFD) method to process the freeboundary conditions of an irregular surface. The numerical results suggest that the precision of the solution is higher than that of the vacuum formalism. When the minimum wavelength is low, UMFD avoids the surface wave dispersion. We compare FD methods based on RSG, SEM, and nonstaggered grid and infer that all simulation results are consistent but the computational efficiency of the RSG FD method is higher than the rest.

Classical radiation reaction in particle-in-cell simulations

Under the presence of ultra high intensity lasers or other intense electromagnetic fields the motion of particles in the ultrarelativistic regime can be severely affected by radiation reaction. The standard particle-in-cell (PIC) algorithms do not include radiation reaction effects. Even though this is a well known mechanism, there is not yet a definite algorithm nor a standard technique to include radiation reaction in PIC codes. We have compared several models for the calculation of the radiation reaction force, with the goal of implementing an algorithm for classical radiation reaction in the Osiris framework, a state-of-the-art PIC code. The results of the different models are compared with standard analytical results, and the relevance/advantages of each model are discussed. Numerical issues relevant to PIC codes such as resolution requirements, application of radiation reaction to macro particles and computational cost are also addressed. For parameters of interest where the classical description of the electron motion is applicable, all the models considered are shown to give comparable results. The Landau and Lifshitz reduced model is chosen for implementation as one of the candidates with the minimal overhead and no additional memory requirements.

Algorithmic Differentiation of Numerical Methods: Second-order Adjoint Solvers for Parameterized Systems of Nonlinear Equations

Adjoint mode algorithmic (also know as automatic) differentiation (AD) transforms implementations of multivariate vector functions as computer programs into first-order adjoint code. Its reapplication or combinations with tangent mode AD yields higher-order adjoint code. Second derivatives play an important role in nonlinear programming. For example, second-order (Newton-type) nonlinear optimization methods promise faster convergence in the neighborhood of the minimum through taking into account second derivative information. The adjoint mode is of particular interest in large-scale gradient-based nonlinear optimization due to the independence of its computational cost on the number of free variables. Part of the objective function may be given implicitly as the solution of a system of n parameterized nonlinear equations. If the system parameters depend on the free variables of the objective, then second derivatives of the nonlinear system's solution with respect to those parameters are required. The local computational overhead as well as the additional memory requirement for the computation of second-order adjoints of the solution vector with respect to the parameters by AD depends on the number of iterations performed by the nonlinear solver. This dependence can be eliminated by taking a symbolic approach to the differentiation of the nonlinear system.

Algorithmic Differentiation of Numerical Methods

We discuss software tool support for the algorithmic differentiation (AD), also known as automatic differentiation, of numerical simulation programs that contain calls to solvers for parameterized systems of n nonlinear equations. The local computational overhead and the additional memory requirement for the computation of directional derivatives or adjoints of the solution of the nonlinear system with respect to the parameters can quickly become prohibitive for large values of n . Both are reduced drastically by analytical (and symbolic) approaches to differentiation of the underlying numerical methods. Following the discussion of the proposed terminology, we develop the algorithmic formalism building on prior work by other colleagues and present an implementation based on the AD software dco/c++. A representative case study supports the theoretically obtained computational complexity results with practical runtime measurements.

Surface point location by walking algorithm for haptic visualization of triangulated 3D models

Haptic devices are nowadays gaining popularity because of their increasing availability. These special input/output devices provide, unlike mouse or keyboard, a native 3D manipulation, especially a more precise control and a force interaction. With more accurate description of the model, haptics can achieve more realistic force feedback. Therefore, triangulated surface models are often used for an authentic interpretation of 3D models. A common task in haptic visualization using triangulated surface models is to find a triangle which is in the collision trajectory of the haptic probe. Since the render rate of the haptic visualization is relatively high (usually about 1kHz), the task becomes highly non-trivial for complex mesh models, especially for the meshes which are changing over time. The paper presents a fast and novel location algorithm able to find the triangle which is close to the haptic probe and in the direction of the probe motion vector. The algorithm has negligible additional memory requirements, since it does not need additional searching data structures and uses only the information usually available for triangulated models. Therefore, the algorithm could handle even triangular meshes changing over time. Results show that the proposed algorithm is fast enough to be used in haptic visualization of complex-shaped models with hundreds of thousands of triangles.

Interpolation‐based impulse noise removal

Interpolation-Based Impulse Noise Removal (IBINR), a fast and simple algorithm is proposed to remove fixed valued impulse noise in this study. The proposed method removes all noisy pixels from the image and determines their values using non-linear interpolation. Compared with the state-of-the-art noise removal algorithms, IBINR has the highest or comparable performance in terms of photographic image denoising power and resource efficiency: it runs in shorter amount of time and does not have any significant additional memory requirements due to the fact that costly sorting operations are avoided.

Flexible Deterministic Router and Interface Marking for IP Traceback

involves identifying the actual source of a packet across the Internet. By identifying the real source address in packets, network security system can smartly protect the victim hosts and mitigate the attacks. Packet marking is the most important method of source identification using IP traceback and there are many variations. In this paper, we propose a modification to our previous capable IP traceback scheme, Deterministic Router and Interface Marking (DRIM), to handle fragmented traffic as well. The modification introduces nominal additional bandwidth overhead, with no additional memory requirements and processing overhead on the DRIM-enabled interface and also reduces the problem of false positives. The proposed technique is compared with similar traceback techniques against various performance metrics and is found to be efficient.

Route Optimization in Network Mobility

NEtwork MObility (NEMO) controls mobility of a number of mobile nodes in a comprehensive way using one or more mobile routers. To choose a route optimization scheme, it is very important to have a quantitative comparison of the available route optimization schemes. The focus of this paper is to analyze the degree of Route Optimization (RO), deploy-ability and type of RO supported by each class in general. The comparison shows the differences among the schemes in terms of issues, such as additional header, signaling and memory requirement. We classify the schemes established on the basic method for route optimization, and equal the schemes based on protocol overhead, such as header overhead, amount of signaling, and memory requirements. Lastly the performance of the classes of different schemes has to be estimated under norms such as available bandwidth, topology of the mobile network and mobility type.

Walking algorithms for point location in TIN models

Finding which triangle in a planar or 2.5D triangle mesh contains a query point (so-called point location problem) is a frequent task in geosciences, especially when working with triangulated irregular network models. Usually, a large number of point locations has to be performed, and so there is a need for fast algorithms having minimal additional memory requirements and resistant to changes in the triangulation. So-called walking algorithms offer low complexity, easy implementation, and negligible additional memory requirements, which makes them suitable for such applications. In this article, we focus on these algorithms, summarize, and compare them with regard to their use in geosciences. Since such a summary has not been done yet, our article should serve those who are dealing with this problem in their application to decide which algorithm would be the best for their solution. Moreover, the influence of the triangulation type on the number of the visited triangles is discussed.

Efficient opacity specification based on feature visibilities in direct volume rendering

AbstractDue to 3D occlusion, the specification of proper opacities in direct volume rendering is a time‐consuming and unintuitive process. The visibility histograms introduced by Correa and Ma reflect the effect of occlusion by measuring the influence of each sample in the histogram to the rendered image. However, the visibility is defined on individual samples, while volume exploration focuses on conveying the spatial relationships between features. Moreover, the high computational cost and large memory requirement limits its application in multi‐dimensional transfer function design.In this paper, we extend visibility histograms to feature visibility, which measures the contribution of each feature in the rendered image. Compared to visibility histograms, it has two distinctive advantages for opacity specification. First, the user can directly specify the visibilities for features and the opacities are automatically generated using an optimization algorithm. Second, its calculation requires only one rendering pass with no additional memory requirement. This feature visibility based opacity specification is fast and compatible with all types of transfer function design. Furthermore, we introduce a two‐step volume exploration scheme, in which an automatic optimization is first performed to provide a clear illustration of the spatial relationship and then the user adjusts the visibilities directly to achieve the desired feature enhancement. The effectiveness of this scheme is demonstrated by experimental results on several volumetric datasets.

Elastic wave modelling in 3-D fractured media: an explicit approach

SUMMARY We present a numerical technique for modelling elastic-wave propagation in 3-D media with discrete distributions of 2-D fractures. The scheme treats the fractures as the non-welded interfaces that satisfy the linear-slip displacement-discontinuity conditions, instead of using the equivalent medium theories. We develop the scheme following a so-called 3-D grid method. The key to derive the scheme is to extend the integral forms of the elastic-momentum equations to include the linear-slip displacement-discontinuity (LS) model. The scheme is implemented under a discretization of tetrahedral grid cells. Arbitrary 2-D non-planar fractures can be explicitly, accurately represented on the numerical mesh. The scheme can flexibly accommodate arbitrary 2-D fractures with fracture behaviour from one extreme of being perfectly welded to an extreme characterized by the stress-free conditions. No stability problem arises. In comparison with the seismic response calculation in the background medium in the absence of the fractures, the additional computational cost and memory requirements for incorporating many fractures are small. We propose a parallel numerical implementation of the scheme that needs smaller data exchanges between the subdomains. We also present the comparisons with an equivalent-medium based modelling scheme in the numerical examples.

Participating media illumination using light propagation maps

Light traveling through semi-transparent media such as smoke and marble is absorbed and scattered. To achieve proper realistic visualizations of such media, illumination algorithms must account for these events. In this article, we present a new method for solving the Radiative Transport Equation , which models such evolution of light. The new method falls into the category of the Discrete Ordinates Method and inherits its generality and computational lightness. This method is known to suffer from two main shortcomings, namely false scattering and the ray effect , which we avoid in our new method. By propagating the light using low-dimensional maps of rays we detach their transport from the Eulerian grid and use fine angular discretizations. Thus, the scattering effect at each scattering generation is eliminated and the ray effect is significantly reduced at no additional memory requirements. Results demonstrate the new method's efficiency, ability to produce high-quality approximations, and its usefulness for a wide range of computer graphics applications.

Markov chain models of coupled calcium channels: Kronecker representations and iterative solution methods

Mathematical models of calcium release sites derived from Markov chain models of intracellular calcium channels exhibit collective gating reminiscent of the experimentally observed phenomenon of stochastic calcium excitability (i.e., calcium puffs and sparks). Calcium release site models are stochastic automata networks that involve many functional transitions, that is, the transition probabilities of each channel depend on the local calcium concentration and thus the state of the other channels. We present a Kronecker-structured representation for calcium release site models and perform benchmark stationary distribution calculations using both exact and approximate iterative numerical solution techniques that leverage this structure. When it is possible to obtain an exact solution, response measures such as the number of channels in a particular state converge more quickly using the iterative numerical methods than occupation measures calculated via Monte Carlo simulation. In particular, multi-level methods provide excellent convergence with modest additional memory requirements for the Kronecker representation of calcium release site models. When an exact solution is not feasible, iterative approximate methods based on the power method may be used, with performance similar to Monte Carlo estimates. This suggests approximate methods with multi-level iterative engines as a promising avenue of future research for large-scale calcium release site models.