Classification of hyperspectral images is a challenging task owing to the high dimensionality of the data, limited ground truth data, collinearity of the spectra and the presence of mixed pixels. Conventional classification techniques do not cope wel...
During on-orbit service, spacecraft are subjected to hypervelocity impacts (HVIs) from micrometeoroids and space debris, causing diverse damage types that challenge structural health assessment. Unimodal approaches often struggle with similar damage...
In this study, a decision-level detection framework is presented and evaluated; it integrates sensor data (e.g., temperature, humidity, gas readings) with machine learning (ML) models and computer vision-based smoke and fire detection systems, in an...
High-resolution remote sensing (HRRS) images, when used for building detection, play a key role in urban planning and other fields. Compared with the deep learning methods, the method based on morphological attribute profiles (MAPs) exhibits good per...
Because of technological advancements and their use in the medical area, many new methods and strategies have been developed to address complex real-life challenges. Breast cancer, a particular kind of tumor that arises in breast cells, is one of the...
The theory of multi-source information fusion plays a pivotal role in decision-making, especially when handling uncertain or imprecise information. Among the existing frameworks, evidence theory has proven effective for integrating diverse informatio...
The exigency of emotion recognition is pushing the envelope for meticulous strategies of discerning actual emotions through the use of superior multimodal techniques. This work presents a multimodal automatic emotion recognition (AER) framework capab...
Diabetes mellitus (DM) is one of the major diseases that cause death worldwide and lead to complications of diabetic foot ulcers (DFU). Improper and late handling of a diabetic foot patient can result in an amputation of the patient’s foot. Ear...
Autonomous vehicles (AVs) are predicted to change transportation; however, it is still difficult to maintain robust situation awareness in a variety of driving situations. To enhance AV perception, methods to integrate sensor data from the camera, ra...
Effective civil infrastructure management necessitates the utilization of timely data across the entire asset lifecycle for condition assessment and predictive maintenance. A notable gap in current predictive maintenance practices is the reliance on...
During the past decade, deep learning-based classification methods (e.g., convolutional neural networks—CNN) have demonstrated great success in a variety of vision tasks, including satellite image classification. Deep learning methods, on the o...
Electronic tongues and artificial gustation for crucial analytes in the environment, such as metal ions, are becoming increasingly important. In this contribution, we propose a multi-level fusion framework for a hybrid impedimetric and voltammetric e...
In distributed data environments, classification tasks are challenged by inconsistencies across independently maintained sources. These environments are inherently characterized by high informational uncertainty. Our framework addresses this challeng...
This paper presents the simultaneous utilization of video images and inertial signals that are captured at the same time via a video camera and a wearable inertial sensor within a fusion framework in order to achieve a more robust human action recogn...
Lung cancer is the leading cancer type that causes mortality in both men and women. Computer-aided detection (CAD) and diagnosis systems can play a very important role for helping physicians with cancer treatments. This study proposes a hierarchical...
In cold climates, ice formation on wind turbines causes power reduction produced by a wind farm. This paper introduces a framework to predict icing at the farm level based on our recently developed Temporal Convolutional Network prediction model for...
This paper aims at exploring the potentiality of the multimodal fusion of remote sensing imagery with information coming from mobility demand data in the framework of land-use mapping in urban areas. After a discussion on the function of mobility dem...
In recent years, the development of sensor technology in industry has profoundly changed the way of operation and management in manufacturing enterprises. Due to the popularization and promotion of sensors, the maintenance of machines on the producti...
Gait recognition is a reliable biometric approach that uniquely identifies individuals based on their natural walking patterns. It is widely used to recognize individuals who are challenging to camouflage and do not require a person’s cooperati...
Sensor Data Fusion (SDT) algorithms and models have been widely used in diverse applications. One of the main challenges of SDT includes how to deal with heterogeneous and complex datasets with different formats. The present work utilised both homoge...
In many land cover classification tasks, the limited precision of individual sensors hinders the accurate separation of certain classes, largely due to the complexity of the Earth’s surface morphology. To mitigate these issues, decision fusion...
Accurate and early diagnosis of Parkinson’s Disease (PD) is challenged by the diverse manifestations of motor and non-motor symptoms across different patients. Existing studies largely rely on limited datasets and biomarkers. In this extended r...
This paper addresses the fusion processing techniques for multi-sensor data perceived through the infrared sensors of military surveillance robots, and proposes their decision-theoretic coordination to effectively monitor multiple targets. To combine...
Electronic nose (E-nose) and electronic tongue (E-tongue) can mimic the sensory perception of human smell and taste, and they are widely applied in tea quality evaluation by utilizing the fingerprints of response signals representing the overall info...
Modern healthcare systems increasingly depend on wearable Internet of Medical Things (IoMT) devices for the continuous monitoring of patients’ physiological parameters. It remains challenging to differentiate between genuine physiological anoma...
Gas path fault diagnosis involves the effective utilization of condition-based sensor signals along engine gas path to accurately identify engine performance failure. The rapid development of information processing technology has led to the use of mu...
Addressing the technical challenges in detecting Low-Slow-Small Unmanned Aerial Vehicle (LSS-UAV) cluster targets, such as weak signals and complex environmental interference coupling with strong features, this paper proposes a visible-infrared multi...
Uncertain information exists in each procedure of an air combat situation assessment. To address this issue, this paper proposes an improved method to address the uncertain information fusion of air combat situation assessment in the Dempster–S...
Background: Steel box girders are widely employed in bridge engineering due to their excellent mechanical properties and construction convenience, yet their modular design still encounters bottlenecks such as knowledge reuse difficulties and informat...
Object detection in optical remote sensing images is still a challenging task because of the complexity of the images. The diversity and complexity of geospatial object appearance and the insufficient understanding of geospatial object spatial struct...
Multi-sensor data fusion technology in an important tool in building decision-making applications. Modified Dempster–Shafer (DS) evidence theory can handle conflicting sensor inputs and can be applied without any prior information. As a result,...
Agricultural Internet of Things (Agri-IoT) systems need strong anomaly detection to monitor crops effectively. However, current approaches lack accuracy and efficiency. To mitigate this problem, we proposed an advanced hierarchical ensemble framework...
The performance of Multimodal Sentiment Analysis (MSA) is commonly hindered by two major bottlenecks: the complexity and redundancy associated with supervised feature disentanglement and the coarse granularity of static fusion mechanisms. To systemat...
High-precision habitat mapping can contribute to the identification and quantification of the human footprint on the seafloor. As a representative of seafloor habitats, seabed sediment classification is crucial for marine geological research, marine...
To address the challenge in multi-view learning within practical application scenarios—such as smart grid multi-source monitoring and complex environment perception—where view quality often exhibits significant dynamic time-varying charac...
Very-high-resolution (VHR) land cover mapping in highly heterogeneous regions faces critical challenges including strong annotation dependence, significant image heterogeneity, and insufficient spectral information. To address these challenges, this...
To address the challenging problem of multi-scale inshore–offshore ship detection in synthetic aperture radar (SAR) remote sensing images, we propose a novel deep learning-based automatic ship detection method within the framework of compositio...
Introduction: Fertility is fundamental to human well-being, significantly impacting both individual lives and societal development. In particular, sperm morphology—referring to the shape, size, and structural integrity of sperm cells—is a...
Over the last decade, the digital twin (DT) concept has effectively revolutionized conventional bridge monitoring and management. Despite their overall success, current bridge DTs encounter conceptual ambiguities, hindering their inherent potential f...
The accurate identification of rice fields through remote sensing is critical for agricultural monitoring and global food security. While optical and Synthetic Aperture Radar (SAR) data offer complementary advantages for crop mapping—spectral r...
Precipitation in any form—such as rain, snow, and hail—can affect day-to-day outdoor activities. Rainfall prediction is one of the challenging tasks in weather forecasting process. Accurate rainfall prediction is now more difficult than b...
Language-guided multimodal fusion, which integrates information from both visible and infrared images, has shown strong performance in image fusion tasks. In low-light or complex environments, a single modality often fails to fully capture scene feat...
This study proposes an integrated computational framework that combines deep learning-based visual perception analysis with multi-criteria decision making to optimize indoor architectural layouts in terms of both visual coherence and sustainability....
With the development of the Internet of Battlefield Things (IoBT), soldiers have become key nodes of information collection and resource control on the battlefield. It has become a trend to develop wearable devices with diverse functions for the mili...
Unmanned surface vessels may encounter unknown surface obstacles when sailing. Accurate detection has a significant impact on the subsequent decision-making process. In order to deal with the complex water environment, this paper proposes an object d...
Clients need to visit project sites periodically during construction to visualize progress and identify deviations from expectations. However, physical site visits are time-consuming, costly, and potentially unsafe, especially for remote and overseas...
Recently, extended multi-attribute profiles (EMAPs) have attracted much attention due to its good performance while applied to remote sensing images feature extraction and classification. Since the EMAPs connect multiple attribute features without co...
In this work, a framework is proposed for decision fusion utilizing features extracted from vehicle images and their detected wheels. Siamese networks are exploited to extract key signatures from pairs of vehicle images. Our approach then examines th...
Multimodal interaction systems can provide users with natural and compelling interactive experiences. Despite the availability of various sensing devices, only some commercial multimodal applications are available. One reason may be the need for a mo...
Soiling of solar panels has a considerable impact on the performance of photo voltaic (PV) systems, emphasizing the importance of developing reliable decision support tools for solar panel cleaning. Although recent convolutional neural network (CNN)-...
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