Background/Objectives: Working memory (WM) performance relies on the coordination of updating and inhibition functions within the central executive system. However, their interaction under varying cognitive loads, particularly across sensory modaliti...
The fusion of data from visible (RGB) and infrared (IR) sensors is essential for robust all-day and all-weather object detection. However, existing methods often suffer from modality redundancy and noise interference. To address these challenges, we...
To address modality inconsistency, insufficient intra-modal affective representation, and the limited adaptability of conventional fusion strategies in multimodal sentiment analysis, this study proposes ECA-CMF Net, an Efficient Channel Attention-enh...
Multi-source information fusion is regarded as a key approach for improving bearing fault diagnosis. However, due to the heterogeneity of multi-source information, asymmetric information contributions, and imbalanced discriminative features, negative...
Sketch re-identification (Re-ID) aims to retrieve pedestrian photographs in the gallery dataset by a query sketch image drawn by professionals, which is crucial for criminal investigations and missing person searches in the field of public security....
Multimodal depression detection aims to identify depressive states by integrating complementary affective cues from audio, visual, and textual modalities. However, existing approaches often directly fuse multimodal features, which may introduce seman...
Estimating the relative position and velocity of micro aerial vehicles (MAVs) using visual signals is a critical issue in numerous tasks. However, traditional relative motion estimation algorithms suffer severely from non-Gaussian noise interference...
Left ventricular noncompaction (LVNC) is characterized by excessive trabeculation, which may impair left ventricular function over time. While cardiac magnetic resonance imaging (CMR) is considered the gold standard for evaluating LV morphology, the...
Object detection in real-world scenarios is often challenged by adverse visual conditions, such as low illumination, strong glare, and dense fog, which severely degrade visible-spectrum features and lead to missed detections, inaccurate localization,...
Background: Prolonged sitting during lecture-based academic activities may be accompanied by sustained attentional engagement and cognitive fatigue, with potential consequences for cognitive efficiency and broader psychological functioning. Physical...
In the task of unmanned aerial vehicle UAV-based small object detection within complex aerial scenes, objects are characterized by significant scale variation, extremely low pixel occupancy, and dense distribution. These factors severely limit the fe...
Wildfires are highly destructive natural disasters posing serious threats to ecosystems. Visible–infrared fusion is an effective paradigm for robust fire detection in complex scenarios. However, existing spatial-domain fusion methods inevitably...
In all-weather Earth observation and complex unstructured environments, traditional single-modal remote sensing object detection often fails due to low illumination and strong background interference. While RGB–infrared fusion provides compleme...
Infrared and visible image fusion (IVIF) integrates the thermal saliency of infrared images (IRs) with the structural details of visible images (VIs) to produce comprehensive scene representations. Existing methods often overemphasize one modality, l...
Accurately forecasting waypoint-level tropical cyclone (TC) intensity, defined as the local wind speed at specific maritime route waypoints under TC influence, is crucial for navigation safety and voyage planning. Conventional studies mainly focus on...
Hyperspectral images (HSIs) and Light Detection and Ranging (LiDAR) data offer complementary spectral and spatial information and are extensively applied to land cover classification. Nevertheless, current fusion–classification approaches frequ...
Thermal infrared imaging could detect hidden faults in various types of high-voltage power equipment, which is of great significance for power inspections. However, there are still certain issues with thermal-imaging-based abnormal heating detection...
The scientific foundations of sport-training methodology are commonly attributed to physiological principles; however, the extent to which these principles directly inform practical training models remains unclear. This narrative review examines the...
Infrared–visible multimodal object detection has attracted increasing attention for its robustness under challenging conditions such as low illumination, occlusion, and complex backgrounds. However, existing fusion methods often suffer from coa...
Wireless Body Area Networks (WBANs) enable real-time health monitoring essential for timely clinical intervention, yet their performance is frequently hindered by sensor degradation, noise interference, and strict low-latency constraints in resource-...
Unmanned Aerial Vehicle (UAV) platforms enable flexible and cost-effective vehicle detection for intelligent transportation systems, yet small-scale vehicles in complex aerial scenes pose substantial challenges from extreme scale variations, environm...
Early detection of small-scale fires is crucial for minimizing damage and enabling rapid emergency response. While recent deep learning-based fire detection systems have achieved high accuracy, they still face three key challenges: (1) limited deploy...
The pathological mechanism of autism spectrum disorder (ASD) exhibits dual heterogeneity: abnormal local energy metabolism and brain-wide high-order topological failure. To synergistically characterize these complex signals captured by advanced neuro...
Condition monitoring of modern industrial machinery increasingly relies on data-driven deep learning methods. However, domain shifts caused by dynamic working conditions often lead to substantial accuracy degradation in unseen target domains. Single-...
Single-modal object Re-identification (Re-ID) frequently suffers from performance degradation under complex and dynamic scene conditions. Although multi-modal object Re-ID leverages complementary information across diverse modalities to alleviate thi...
30 December 2022
The acoustic environment has been pointed out as a possible distractor during student activities in the online academic modality; however, it has not been specifically studied, nor has it been studied in relation to parameters frequently used in acad...
Underground perception requires sensing modalities with different responses to dust, darkness, and interference. This study formulates a task-aware framework combining light detection and ranging (LiDAR), millimeter-wave radar, thermal imaging, and v...
While generative models have become a standard approach for addressing the semantic-to-visual gap in Generalized Zero-Shot Learning (GZSL), existing architectures often struggle with two persistent limitations: cross-modal interference during conditi...
The core of cross-modal hashing methods is to map high dimensional features into binary hash codes, which can then efficiently utilize the Hamming distance metric to enhance retrieval efficiency. Recent development emphasizes the advantages of the un...
Semantic segmentation of multi-modal remote sensing images is an important branch of remote sensing image interpretation. Multi-modal data has been proven to provide rich complementary information to deal with complex scenes. In recent years, semanti...
In visible–infrared object detection under complex environments, cross-modal fusion often suffers from spatial misalignment, semantic inconsistency, and unstable feature responses under varying illumination conditions. To address these issues,...
Multi-modal sentiment analysis (MSA) aims to accurately identify users’ emotional states by integrating textual, acoustic, and visual modalities. However, existing methods often suffer from insufficient cross-modal interaction, rigid fusion str...
In real-world fire scenarios, complex lighting conditions and smoke interference significantly challenge the accuracy and robustness of traditional fire detection systems. Fusion of complementary modalities, such as visible light (RGB) and infrared (...
Since the errors in Chinese Spell Correction (CSC) involve phonetically or morphologically confusing Chinese characters, mainstream models have made numerous attempts to fuse phonological and morphological knowledge. We observe that in erroneous sent...
Remote sensing small object detection remains a challenging task due to limited feature representation and interference from complex backgrounds. Existing methods that rely exclusively on either visible or infrared modalities often fail to achieve bo...
The paper presents theoretical analyses and experimental investigations of broadband differential interference in planar gradient waveguides made via K+-Na+ ion exchange in BK-7 glass. This technology, due to its large polarimetric dispersion, is esp...
Robust monitoring of weld pool dynamics is critical for automated arc welding; however, single-modality sensors are frequently constrained by severe optical interference and high-frequency environmental noise. To address these limitations, we propose...
To address the challenges of diminished back-EMF, high noise interference, and reduced accuracy in traditional low-speed sensorless control, this study proposes a rotor position estimation method based on structural vibration characteristics. The cou...
Behavioral health interventions for pediatric chronic pain include cognitive-behavioral (CBT), acceptance and commitment (ACT), and family-based therapies, though literature regarding multi-family therapy (MFT) is sparse. This investigation examined...
The rapid development of social media has driven the need for opinion mining and sentiment analysis based on multimodal samples. As a fine-grained task within multimodal sentiment analysis, aspect-based multimodal sentiment analysis (ABMSA) enables t...
Breast cancer is one of the most commonly occurring cancers in women globally and is the primary cause of cancer mortality in females. BC is highly heterogeneous with various phenotypic expressions. The overexpression of HER2 is responsible for 15–30...
Invasive deep brain stimulation has proven to be clinically therapeutic for patients with drug-refractory epilepsy. The aim of this study was to develop a novel transcranial electrical device as a noninvasive stimulation modality for seizure treatmen...
Damage localization based on ultrasonic guided waves (UGWs) ensures the reliability and safety of composites. Efficient and accurate damage localization requires full integration of different modality features. However, existing deep learning-based d...
Ultra-low-resolution (ULR) thermal array sensors (e.g.,
Accurate estimation of Interrupted Sampling Repeater Jamming (ISRJ) sampling duration is essential for effective radar anti-jamming. However, in complex electromagnetic environments, the simultaneous presence of suppressive and deceptive jamming, cou...
In massive multiple-input and multiple-output (mMIMO) systems, superimposed channel state information (CSI) feedback is developed to improve the occupation of uplink bandwidth resources. Nevertheless, the interference from this superimposed mode degr...
For large-scale photovoltaic plant inspection, UAV-based infrared–visible real-time detection can combine thermal abnormality information with appearance and structural cues. This is useful for improving inspection and maintenance efficiency. H...
In complex UAV aerial scenes, small object detection tasks face challenges such as extremely low pixel occupancy, strong background interference, and sparse effective features, which are further compounded by environmental factors like low illuminati...
Plants influenced the evolution of plant-dwelling stink bugs’ systems underlying communication with chemical and substrate-borne vibratory signals. Plant volatiles provides cues that increase attractiveness or interfere with the probability of...
The semantic segmentation of remote sensing images is vital for accurate surface monitoring and environmental assessment. Multimodal remote sensing images (RSIs) provide a more comprehensive dimension of information, enabling faster and more scientif...
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