Generating images of artistic style from input images, also known as image style transfer, has been improved in the quality of output style and the speed of image generation since deep neural networks have been applied in the field of computer vision...
Existing lightweight single-image super-resolution (SISR) methods often rely on standard dot-product attention when explicit token-to-token similarity is modeled. Although the query and key projections are learned, the similarity in the projected coo...
This work presents the “First-Order Features Adjoint Sensitivity Analysis Methodology for Fredholm-Type Neural Integral Equations” (1st-FASAM-NIE-Fredholm) and the “Second-Order Features Adjoint Sensitivity Analysis Methodology for...
This work introduces the mathematical framework of the novel “First-Order Features Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations” (1st-FASAM-NODE). The 1st-FASAM-NODE methodology produces and computes...
This work presents the general mathematical frameworks of the “First and Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Integral Equations of Volterra Type” designated as the 1st-FASAM-NIE-V and the 2nd-FASAM-NI...
This work presents the “First-Order Features Adjoint Sensitivity Analysis Methodology for Neural Integro-Differential Equations of Fredholm-Type” (1st-FASAM-NIDE-F) and the “Second-Order Features Adjoint Sensitivity Analysis Methodo...
We present a Human Artificial Intelligence Hybrid (HAIbrid) integrating framework that reweights Thyroid Imaging Reporting and Data System (TIRADS) features and the malignancy score predicted by a convolutional neural network (CNN) for nodule maligna...
This work presents the mathematical frameworks of the “First-Order Features Adjoint Sensitivity Analysis Methodology for Neural Integro-Differential Equations of Volterra-Type” (1st-FASAM-NIDE-V) and the “Second-Order Features Adjoi...
This work illustrates the application of the “First-Order Features Adjoint Sensitivity Analysis Methodology for Neural Integro-Differential Equations of Fredholm-Type” (1st-FASAM-NIDE-F) and the “Second-Order Features Adjoint Sensit...
The Second-Order Features Adjoint Sensitivity Analysis Methodology for Response-Coupled Forward/Adjoint Linear Systems (abbreviated as “2nd-FASAM-L”), presented in this work, enables the most efficient computation of exactly obtained math...
This work presents an illustrative application of the newly developed “Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations (2nd-FASAM-NODE)” methodology to determine most efficiently t...
In mechanical equipment, rolling bearings analyze and monitor their fault based on their vibration signals. Vibration signals obtained are usually weak because the machine works in a noisy background that makes it very difficult to extract its featur...
Convolutional neural networks are widely applied in hyperspectral image (HSI) classification and show excellent performance. However, there are two challenges: the first is that fine features are generally lost in the process of depth transfer; the s...
Recently, drive-by bridge structural health monitoring has gained increasing attention due to its potential to be a cost-effective way to monitor the highway infrastructure. The pre-installed sensory system on a passing vehicle is used to capture the...
In haptic recognition, pressure information is usually represented as an image, and then used for feature extraction and classification. Deep learning that processes haptic information in end-to-end manner has attracted attention. This study proposes...
(1) Background: Ultrasonography is the main method used during pregnancy to assess the fetal growth, amniotic fluid, umbilical cord and placenta. The placenta’s structure suffers dynamic modifications throughout the whole pregnancy and many of...
In the standard bag-of-visual-words (BoVW) model, the burstiness problem of features and the ignorance of high-order information often weakens the discriminative power of image representation. To tackle them, we present a novel framework, named the S...
Epilepsy is a neurological disorder that is characterized by transient and unexpected electrical disturbance of the brain. Seizure detection by electroencephalogram (EEG) is associated with the primary interest of the evaluation and auxiliary diagnos...
Hyperspectral images contain rich features in both spectral and spatial domains, which bring opportunities for accurate recognition of similar materials and promote various fine-grained remote sensing applications. Although deep learning models have...
Aiming at the problems of poor decomposition quality and the extraction effect of a weak signal with strong noise by empirical mode decomposition (EMD), a novel fault diagnosis method based on cascaded adaptive second-order tristable stochastic reson...
Lung cancer is one of the leading causes of cancer-related mortality worldwide. The diagnosis of this disease remains a challenge due to the subtle and ambiguous nature of early-stage symptoms and imaging findings. Deep learning approaches, specifica...
This study proposes a Volterra-inspired time-weighted integral projection (VTIP) for constructing low-dimensional representations of event-level temporal data with internal phase transitions and duration structure. A predefined input template and tem...
This work introduces a new methodology, which generalizes the extant second-order adjoint sensitivity analysis methodology for computing sensitivities of model responses to primary model parameters. This new methodology enables the computation, with...
Hyperspectral images can capture subtle differences in reflectance of features in hundreds of narrow bands, and its pixel-wise classification is the cornerstone of many applications requiring fine-grained classification results. Although three-dimens...
Nowadays, state-of-the-art direct visual odometry (VO) methods essentially rely on points to estimate the pose of the camera and reconstruct the environment. Direct Sparse Odometry (DSO) became the standard technique and many approaches have been dev...
The convolutional neural network (CNN) has achieved great success in the field of scene classification. Nevertheless, strong spatial information in CNN and irregular repetitive patterns in synthetic aperture radar (SAR) images make the feature descri...
Radiomics of pancreas magnetic resonance (MR) images is positioned well to play an important role in the management of diseases characterized by diffuse involvement of the pancreas. The effect of image pre-processing configurations on these images ha...
Atherosclerosis is known as the leading factor in heart disease with the highest mortality rate among the Malaysian population. Usually, the gold standard for diagnosing atherosclerosis is by using the coronary computed tomography angiography (CCTA)...
Background: Radiomic features are increasingly used in CT of NSCLC. However, their robustness with respect to segmentation variability has not yet been demonstrated. The aim of this study was to assess radiomic features agreement across three kinds o...
In order to enhance the ability to represent rock feature information and finally improve the rock identification performance of convolution neural networks (CNN), a new pooling mode was proposed in this paper. According to whether the pooling object...
This study investigates the oscillatory properties of a fourth-order delay functional differential equation. This study’s methodology is built around two key tenets. First, we propose optimized relationships between the solution and its derivat...
Frame-wise steganalysis is a crucial task in low-bit-rate speech streams that can achieve active defense. However, there is no common theory on how to extract steganalysis features for frame-wise steganalysis. Moreover, existing frame-wise steganalys...
Automatically finding correspondences between object features in images is of main interest for several applications, as object detection and tracking, identification, registration, and many derived tasks. In this paper, we address feature correspond...
Accurate classification of ground-based auroral images is essential for studying variations in auroral morphology and uncovering magnetospheric mechanisms. However, distinguishing subtle morphological differences among different categories of auroral...
A multiscale analysis and computational method based on the Second-Order Two-Scale (SOTS) approach are proposed for the elastic quadratic eigenvalue problems in the periodic composite domain. Two typical quadratic eigenvalue problems with different d...
The aeromagnetic survey is a common remote sensing tool for detecting iron deposits. The local wavenumber of a magnetic anomaly is used to interpret the edges or positions of the sources, and can involve first- or second-order local wavenumbers. In t...
Nonlinear interaction between the magnon mode and the mechanical mode in a magnomechanical system is treated analytically where the magnon mode is coherently driven by a bichromatic microwave drive field consisting of a strong pumping field and a wea...
Spatiotemporal data from urban road traffic are pivotal for intelligent transportation systems and urban planning. Nonetheless, missing data in traffic datasets is a common challenge due to equipment failures, communication issues, and monitoring lim...
This study develops an atlas-based automated framework for segmenting infants’ brains from magnetic resonance imaging (MRI). For the accurate segmentation of different structures of an infant’s brain at the isointense age (6–12 mont...
Remote sensing image scene classification, which consists of labeling remote sensing images with a set of categories based on their content, has received remarkable attention for many applications such as land use mapping. Standard approaches are bas...
In this work, we perform a phenomenological derivation of the first- and second-order relativistic hydrodynamics of dissipative fluids. To set the stage, we start with a review of the ideal relativistic hydrodynamics from energy–momentum and pa...
The reasonable design of biomimetic non-smooth surfaces is a novel and effective way to solve problems such as the poor lubricity and serious friction and wear of friction pairs of seawater axial piston pumps. Inspired by cross-scale, second-order co...
This paper presents a generalized second-order hydrodynamic traffic model. Its central piece is the expression for the relative velocity of the congestion (compression wave) propagation. We show that the well-known second-order models of Payne–Whitha...
The availability of 5-year time series of velocity and temperature data from two sonic anemometers installed at Jang Bogo Station, Antarctica, allowed a systematic investigation of the turbulence features in a stable layer affected by submeso motions...
A self-organizing joint system classical oscillator–random environment is considered within the framework of a complex probabilistic process that satisfies a Langevin-type stochastic differential equation. Various types of randomness generated...
Purpose: To assess the efficacy of radiomics features obtained by computed tomography (CT) examination as biomarkers in order to select patients with lung adenocarcinoma who would benefit from immunotherapy. Methods: Seventy-four patients (median age...
This short review outlines the main results obtained by our research group over the last 15 years in the field of porphyrins and metal porphyrins for second order nonlinear optics (NLO). This overview aims to provide a general framework of the key fa...
Background/Objectives: This study evaluates the diagnostic efficacy of the apparent diffusion coefficient (ADC) and T1-weighted contrast-enhanced (T1W + C) and T2-weighted (T2W) imaging modalities in differentiating vestibular schwannomas (VSs) and c...
Radiomics is the process of extracting useful quantitative features of high-dimensional data that allows for automated disease classification, including atherosclerotic disease. Hence, this study aimed to quantify and extract the radiomic features fr...
PID control is the longest history and the most vital basic control mode that has been widely applied in the production process. The oscillatory dynamics of the process have various features, and parameter tuning is complicated. To reduce the complex...
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