888 Results Found

The stable and high-precision acquisition of attitude data is crucial for sustaining the long-term robustness of laser links to detect gravitational waves in space. We introduce an effective method that utilizes an adaptive weight optimization approa...

In order to avoid the loss of optimality of the optimal weighting factor in some cases and to further reduce the estimation error of an unbiased estimator, a multi-sensor adaptive weighted data fusion algorithm based on biased estimation is proposed....

To address the challenges in recognizing various types of falls, which often exhibit high similarity and are difficult to distinguish, this paper proposes a human fall classification system based on the SE-Residual Concatenate Network (SE-RCNet) with...

Naive Bayes (NB) is one of the essential algorithms in data mining. However, it is rarely used in reality because of the attribute independence assumption. Researchers have proposed many improved NB methods to alleviate this assumption. Among these m...

In infrared (IR) and visible image fusion, the significant information is extracted from each source image and integrated into a single image with comprehensive data. We observe that the salient regions in the infrared image contain targets of intere...

Recently, generative adversarial network (GAN)-based methods for hyperspectral image (HSI) classification have attracted research attention due to their ability to alleviate the challenges brought by having limited labeled samples. However, several s...

Line structured light (LSL) measurement systems can obtain high accuracy profiles, but the overall clarity relies greatly on the sampling interval of the scanning process. Photometric stereo (PS), on the other hand, is sensitive to tiny features but...

Diabetic retinopathy (DR) is the prime cause of blindness in people who suffer from diabetes. Automation of DR diagnosis could help a lot of patients avoid the risk of blindness by identifying the disease and making judgments at an early stage. The m...

Pansharpening is a typical image fusion problem, which aims to produce a high resolution multispectral (HRMS) image by integrating a high spatial resolution panchromatic (PAN) image with a low spatial resolution multispectral (MS) image. Prior arts h...

This work aims to develop a smart LED lighting system, which is remotely controlled by Android apps via handheld devices, e.g., smartphones, tablets, and so forth. The status of energy use is reflected by readings displayed on a handheld device, and...

High-quality 3D reconstruction results are very important in many application fields. However, current texture generation methods based on point sampling and fusion often produce blur. To solve this problem, we propose a new volumetric fusion strateg...

UAV localization in denial environments is a hot research topic in the field of cross-view geo-localization. The previous methods tried to find the corresponding position directly in the satellite image through the UAV image, but they lacked the cons...

This study addresses the problem localization deviation caused by cumulative wheel odometry errors in Automated Guided Vehicles (AGVs) operating in complex environments by proposing an adaptive localization method based on multi-sensor fusion. Within...

Tropical cyclones (TCs) can cause significant economic damage and loss of life in coastal areas. Therefore, TC prediction has become a crucial topic in current research. In recent years, TC track prediction has progressed considerably, and intensity...

To obtain fused images with excellent contrast, distinct target edges, and well-preserved details, we propose an adaptive image fusion network called the adjacent feature shuffle-fusion network (AFSFusion). The proposed network adopts a UNet-like arc...

The diagnosis of overflow risk has always been an important area of research in drilling operations in the field of oil and gas engineering. In the face of the limitations and lag of traditional overflow diagnosis methods, the practical application e...

Driven by dual carbon targets, the scale of wind power integration has surged dramatically. However, its strong volatility causes insufficient short-term prediction accuracy, severely constraining grid security and economic dispatch. To address three...

The vision-based robot pose estimation and mapping system has the disadvantage of low pose estimation accuracy and poor local detail mapping effects, while the modeling environment has poor features, high dynamics, weak light, and multiple shadows, a...

Change detection (CD) stands out as a pivotal yet challenging task in the interpretation of remote sensing images. Significant developments have been witnessed, particularly with the rapid advancements in deep learning techniques. Nevertheless, chall...

Labeled hyperspectral image (HSI) information is commonly difficult to acquire, so the lack of valid labeled data becomes a major puzzle for HSI classification. Semi-supervised methods can efficiently exploit unlabeled and labeled data for classifica...

This paper is concerned with the filtering problem caused by the inaccuracy variance of measurement noise in real nonlinear systems. A novel weighted fusion estimation method of multiple different variance estimators is presented to estimate the vari...

For the past decades, recognition technologies of multispectral palmprint have attracted more and more attention due to their abundant spatial and spectral characteristics compared with the single spectral case. Enlightened by this, an innovative rob...

Functional connectivity network (FCN) has become a popular tool to identify potential biomarkers for brain dysfunction, such as autism spectrum disorder (ASD). Due to its importance, researchers have proposed many methods to estimate FCNs from restin...

Desert segmentation of remote sensing images is the basis of analysis of desert area. Desert images are usually characterized by large image size, large-scale change, and irregular location distribution of surface objects. The multi-scale fusion meth...

The imaging quality of uncooled long wave infrared (IR) cameras is always limited by the stripe non-uniformity mainly caused by fixed pattern noise (FPN). In this paper, we propose an adaptive dual-frequency denoising network-based stripe non-uniform...

The drastic increase of forest fire occurrence, which in recent years has posed severe threat and damage worldwide to the natural environment and human society, necessitates smoke detection of the early forest fire. First, a semantic segmentation met...

To alleviate the communication burden of wide-area protection and enhance the fault tolerance of multi-source criteria, this paper introduces an improved wide-area backup protection method based on multi-source information fusion. Initially, the vari...

To address the issue of insufficient extraction of target features and the resulting impact on detection performance in long-range infrared aircraft target detection caused by small imaging area and weak radiation intensity starting from the idea of...

RNA has emerged as a critical drug target, and accurate prediction of its binding affinity with small molecules is essential for the design and screening of RNA-targeted therapeutics. Although current deep learning methods have achieved progress in p...

Infrared (IR) band sensors can capture digital images under challenging conditions, such as haze, smoke, and fog, while visible (VIS) band sensors seize abundant texture information. It is desired to fuse IR and VIS images to generate a more informat...

In the context of drinking water safety assurance, water quality prediction faces challenges due to temporal fluctuations, seasonal cycles, and the impacts of sudden events. To address the issue of cumulative prediction bias caused by the simplistic...

Underwater images often suffer from degradation due to scattering and absorption. With the development of artificial intelligence, fully supervised learning-based models have been widely adopted to solve this problem. However, the enhancement perform...

With the rapid development of information storage technology and the spread of the Internet, large capacity image databases that contain different contents in the images are generated. It becomes imperative to establish an automatic and efficient ima...

The precise building extraction from high-resolution remote sensing images holds significant application for urban planning, resource management, and environmental conservation. In recent years, deep neural networks (DNNs) have garnered substantial a...

Casting defects in turbine blades can significantly reduce an aero-engine’s service life and cause secondary damage to the blades when exposed to harsh environments. Therefore, casting defect detection plays a crucial role in enhancing aircraft...

Automaticidentification and prevention of leaf diseases and insect pests on fruit crops represent a key trend in the development of smart agriculture. In order to address the limitations of existing models with low identification rates of apple leaf...

Effectively leveraging multi-source unmanned aerial vehicle (UAV) observations for reliable object recognition is often compromised by environmental extremes (e.g., occlusion and low illumination) and the inherent physical discrepancies between modal...

Microseismic signals induced by mining activities often have low signal-to-noise ratios, and traditional picking methods are easily affected by noise, making accurate identification of P-wave arrivals difficult. To address this problem, this study pr...

In order to adapt to the local brightness and contrast of input image sequences, we propose a new weighted average adaptive factor well-exposure weight evaluation scheme. The exposure weights of brighter and darker pixels are determined according to...

The acquisition of vehicle driving status information is a key function of vehicle dynamics systems, and research on high-precision and high-reliability estimation of key vehicle states has significant value. To improve the state observation effect,...

Timely data quality assessment has been shown to be crucial for the development of IoT-based applications. Different IoT applications’ varying data quality requirements pose a challenge, as each application requires a unique data quality proces...

Great achievements have been made in pedestrian detection through deep learning. For detectors based on deep learning, making better use of features has become the key to their detection effect. While current pedestrian detectors have made efforts in...

In view of the issues of missed and false detections encountered in small object detection for UAV remote sensing images, and the inadequacy of existing algorithms in terms of complexity and generalization ability, we propose a small object detection...

With the enormous amount of multi-source data produced by various sensors and feature extraction approaches, multi-view clustering (MVC) has attracted developing research attention and is widely exploited in data analysis. Most of the existing multi-...

In order to improve the accuracy and generalization ability of fault diagnosis for rotating machinery bearings under complex working conditions, a new model based on multi-feature fusion and improved weighted balance distribution adaptation is propos...

The spatial representativeness of the in-situ data is an important prerequisite for ensuring the reliability and accuracy of remote sensing product retrieval and verification. Limited by the collection cost and time window, it is essential to simulta...

Automatic modulation classification (AMC) based on deep learning (DL) is gaining increasing attention in dynamic spectrum access for 5G/6G wireless communications. However, inconsistent feature parameters between the training (source) and testing (ta...

In fields such as manufacturing and aerospace, remaining useful life (RUL) prediction estimates the failure time of high-value assets like industrial equipment and aircraft engines by analyzing time series data collected from various sensors, enablin...

How to obtain internal cavity features and perform image matching is a great challenge for laparoscopic 3D reconstruction. This paper proposes a method for detecting and associating vascular features based on dual-branch weighted fusion vascular stru...

Background: Early diagnosis of depression is crucial for effective treatment and suicide prevention. Traditional methods rely on self-report questionnaires and clinical assessments, lacking objective biomarkers. Combining functional magnetic resonanc...