Registering UAV-based thermal and visible images is a challenging task due to differences in appearance across spectra and the lack of public benchmarks. To address this issue, we introduce UAV-TIRVis, a dataset consisting of 80 accurately and manual...
A new class of stationary electromagnetic sources radiating outward from the surface of an infinitely long cylinder is introduced via vectorial coherent mode representation. First, two particular types of such sources are discussed: with either an el...
Advancements in satellite sensor technology have enabled access to diverse remote sensing (RS) data from multiple platforms. Hyperspectral Image (HSI) data offers rich spectral detail for material identification, while LiDAR captures high-resolution...
The objective of cross-scene hyperspectral image (HSI) classification is to develop models capable of adapting to the “domain gap” that exists between different scenes, enabling accurate object classification in previously unseen scenes....
Hyperspectral image (HSI) classification is often challenged by cross-scene domain shifts and limited target annotations. Existing approaches relying on class-agnostic moment matching or confidence-based pseudo-labeling tend to blur decision boundari...
18 October 2021
Three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is a significant technique for recovering the 3D structure of proteins or other biological macromolecules from their two-dimensional (2D) noisy projection ima...
Correlations in complex systems are often obscured by nonstationarity, long-range memory, and heavy-tailed fluctuations, which limit the usefulness of traditional covariance-based analyses. To address these challenges, we construct scale- and fluctua...
Free-space detection plays a pivotal role in autonomous vehicle applications, and its state-of-the-art algorithms are typically based on semantic segmentation of road areas. Recently, hyperspectral images have proven useful supplementary information...
To ensure all-day detection performance, unmanned aerial vehicles (UAVs) usually need both visible and infrared images for dual-modality fusion object detection. However, misalignment between the RGB-IR image pairs and complexity of fusion models con...
The high dimensionality of hyperspectral data, coupled with limited labeled samples and complex scene structures, makes spatial–spectral feature learning particularly challenging. To address these limitations, we propose a dual-branch deep lear...
A micropolarizer array (MPA) that can be integrated into a scientific camera is proposed as a real-time polarimeter that is capable of extracting the polarization parameters. The MPA is based on highly aligned carbon nanotube (CNT) films inspired by...
Multivariate time series forecasting requires modeling complex and evolving spatio-temporal dependencies as well as frequency-domain patterns; however, the existing Transformer-based approaches often struggle to effectively capture dynamic inter-seri...
Hyperspectral image (HSI) and light detection and ranging (LiDAR) data offer complementary spectral and structural information; however, the integration of these high-dimensional, heterogeneous modalities poses significant challenges. We propose a Gl...
The dominant low-light object detectors face the following spectral trilemma: (1) the loss of high-frequency structural details during denoising, (2) the amplification of low-frequency illumination distortion, and (3) cross-band interference in multi...
In recent years, cross-domain few-shot learning (CDFSL) has demonstrated remarkable performance in hyperspectral image classification (HSIC), partially alleviating the distribution shift problem. However, most domain adaptation methods rely on simila...
This study presents a generalized, high-precision measurement system based on Integrated Absorption Spectroscopy (IAS) for determining gas-phase absorption cross sections of low-volatility organic compounds (LVOCs), particularly semi-volatile organic...
Alfvén waves play an important role in the stability, heating, and transport of magnetized plasmas. They are found to be ubiquitous in solar winds (SW), which mainly propagate outward from the Sun, especially in high-speed streams that origina...
This paper presents a novel cochlear implant (CI) sound coding strategy called Spectral Feature Extraction (SFE). The SFE is a novel Fast Fourier Transform (FFT)-based Continuous Interleaved Sampling (CIS) strategy that provides less-smeared spectral...
Remaining useful life (RUL) prediction of industrial equipment is critical for achieving safe operations and optimizing predictive maintenance. To tackle the limitations of poor interpretability, inaccurate predictions, and high computational cost in...
Decoding motor imagery (MI) electroencephalogram (EEG) signals in the brain–computer interface (BCI) can assist patients in accelerating motor function recovery. To realize the implementation of plug-and-play functionality for MI-BCI applicatio...
In recent years, the global commercial aerospace industry has flourished, witnessing a rapid surge in customized satellite services. Deep learning has emerged as a pivotal tool for accurately identifying wetland vegetation. However, hyperspectral rem...
Hyperspectral image (HSI) classification is pivotal for remote sensing applications, including environmental monitoring, precision agriculture, and urban land-use analysis. However, its accuracy is often limited by scarce labeled data, class imbalanc...
The swift and non-destructive classification of wood species holds crucial significance for the utilization and trade of wood resources. Portable near-infrared (NIR) spectrometers have the potential for rapid and non-destructive wood species identifi...
Spectral indices such as NDRE (Normalized Difference Red Edge Index), CCCI (Canopy Chlorophyll Content Index), and IRECI (Inverted Red Edge Chlorophyll Index), derived from the Red Edge band of MSI/Sentinel-2A (B05, B06, B07), are critical for coffee...
Enhancing target domain discriminability is a key focus in Unsupervised Domain Adaptation (UDA) for HyperSpectral Image (HSI) classification. However, existing methods overlook bringing similar cross-domain samples closer together in the feature spac...
This study is a follow-up of a full methodology for the homogenisation and harmonisation of the two Ocean and Land Colour Instrument (OLCI) payloads based on the tandem phase analysis. Sentinel-3B was manoeuvred into a tandem configuration with its o...
Accurate correspondence of complex neuroanatomical surfaces under non-rigid deformations remains a formidable challenge in computational neuroimaging, owing to inter-subject topological variability, partial occlusions, and non-isometric distortions....
Automated defect detection for printed fabric manufacturing faces critical challenges in balancing industrial-grade accuracy with real-time deployment efficiency. To address this, we propose RT-DETR-FFD, a knowledge-distilled detector optimized for p...
Acoustic monitoring presents a promising, non-invasive modality for assessing animal welfare in precision livestock farming. In poultry, vocalizations encode biologically relevant cues linked to health status, behavioral states, and environmental str...
Image registration is a spatial alignment of corresponding images of the same scene acquired from different views, sensors, and time intervals. Especially, satellite image registration is a challenging task due to the high resolution of images. In ad...
Despite notable advancements achieved on Hyperspectral (HS) pansharpening tasks through deep learning techniques, previous methods are inherently constrained by convolution or self-attention intrinsic defects, leading to limited performance. In this...
The fusion of multi-source remote sensing data has emerged as a critical technical approach to enhancing the accuracy of ground object classification. The synergistic integration of hyperspectral images and light detection and ranging data can signif...
This paper proposes a source encoding rate control and cross-layer data stream allocation scheme for uplink millimeter-wave (mmWave) multi-user massive MIMO (MU-mMIMO) orthogonal frequency division multiplexing (OFDM) hybrid beamforming video communi...
Carbon nanotubes (CNTs) thanks to their unique physical properties have been employed in several innovative applications particularly for energy storage applications. Certain technical features of carbon nanotubes, such as their remarkable specific s...
Background/Objectives: Precise identification of seizure onset zones (SOZs) and their propagation pathways is essential for effective epilepsy surgery and other interventional therapies and is typically achieved through invasive electrophysiological...
In recent years, the rapid advancement and pervasive deployment of unmanned aerial vehicle (UAV) technology have catalyzed transformative applications across the military, civilian, and scientific domains. While aerial imaging has emerged as a pivota...
Microplastics pose a growing environmental concern, necessitating accurate and scalable methods for their detection and classification. This study presents a novel deep learning framework that integrates a transformer-based architecture with domain a...
The fusion of synthetic aperture radar (SAR) and visible images offers complementary spatial and spectral information, enabling more reliable and comprehensive scene interpretation. However, SAR speckle noise and the intrinsic modality gap pose signi...
Background/Objectives: We aim to develop an image linearization process and a program capable of quantifying vertical and left–right asymmetries observed in macular scans. We then sought to verify its applicability in clinical settings. Methods...
With the resistive random access memory (ReRAM) devices based on the Al/BaTiO3 (BTO)/ITO structure fabricated at hand, by cross-analyzing the resistive memory characteristics in terms of various barium titanate (BTO) film thicknesses, it is found tha...
Infrared and visible image fusion is a very effective way to solve the degradation of sea images for unmanned surface vessels (USVs). Fused images with more clarity and information are useful for the visual system of USVs, especially in harsh marine...
This study presents a Raman-spectroscopy-based quantitative analysis technique for measuring strain in Kevlar single fibers embedded in concrete. By irradiating the fibers with a laser, the researchers established a linear relationship between Raman...
Hyperspectral image cross-scene classification (HSICC) tasks are confronted with tremendous challenges due to spectral shift phenomena across scenes and the tough work of obtaining labels. Unsupervised domain adaptation has proven its effectiveness i...
In realistic hyperspectral image (HSI) cross-scene classification tasks, it is ideal to obtain target domain samples during the training phase. Therefore, a model needs to be trained on one or more source domains (SD) and achieve robust domain genera...
The influence of the Gulf Stream on the wind wave characteristics is investigated. Wave–current interaction inside the current field can result in significant inhomogeneities of the wave field that change the wave spectrum and wave statistics....
The Normalized Difference Vegetation Index (NDVI) is widely used for monitoring vegetation status, as accurate and reliable NDVI time series are crucial for understanding the relationship between environmental conditions, vegetation health, and produ...
Amidrapid advances in intelligent medicine, decoding brain activity from electroencephalogram (EEG) signals has emerged as a critical technical frontier for brain–computer interfaces and medical AI systems. Given the inherent spatial resolution...
Background/Objectives: Chrononutrition—the temporal organization of food intake relative to circadian rhythms—has emerged as an important factor in cardiometabolic health. While meal timing is typically analyzed as an isolated variable, l...
Facial expression recognition (FER) is a key task in affective computing and human–computer interaction, aiming to decode facial muscle movements into emotional categories. Although deep learning-based FER has achieved remarkable progress, robu...
The graph neural network (GNN) has shown outstanding performance in processing unstructured data. However, the downstream task performance of GNN strongly depends on the accuracy of data graph structural features and, as a type of deep learning (DL)...
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