To address the challenges of inadequate adaptability, insufficient power, high ground clearance, and limited functionality in existing pineapple field machinery, this study proposes a height-adjustable pineapple field management platform based on pre...
To avoid degradation of navigation performance in the navigation warfare environment, the multi-radio integrated navigation system can be used, in which all available radio navigation systems are integrated to back up Global Navigation Satellite Syst...
To deal with the insufficient recognition accuracy of traditional signal modulation recognition methods, this paper proposes a new communication signal modulation recognition method with a deep neural network that integrates a multi-feature multi-cha...
Semantic segmentation of high-resolution remote sensing images is of great application value in fields like natural disaster monitoring. Current multimodal semantic segmentation methods have improved the model’s ability to recognize different g...
The existing multi-carrier composite modulation recognition methods have failed to effectively integrate inner and outer modulation characteristics, thereby limiting the potential for improving recognition performance under low signal-to-noise ratio...
Hyperspectral target detection (HTD) aims to identify pixel-level targets within complex backgrounds, but existing HTD methods often fail to fully exploit multi-scale features and integrate global–local information, leading to suboptimal detect...
Wind power prediction plays a crucial role in enhancing power grid stability and wind energy utilization efficiency. Existing prediction methods demonstrate insufficient integration of multi-variate features, such as wind speed, temperature, and humi...
Currently, multi-posture robots have complex grasping robotic manipulators with low power density, making it difficult to miniaturize and integrate. In this paper, a multi-posture grasping manipulator actuated by shape memory alloy with different fun...
Severe convective weather is marked by abrupt onset, rapid evolution, and substantial destructive potential, posing major threats to economic activities and human safety. To address this challenge, this study proposes MRKAN, a multi-parameter predict...
This paper proposes a novel point cloud multi-scale completion algorithm guided by image rotation attention mechanisms to address the challenges of severe information loss and suboptimal fusion effects in multi-modal feature extraction and integratio...
Automatic classification of arteries and veins (A/V) in fundus images has gained considerable attention from researchers due to its potential to detect vascular abnormalities and facilitate the diagnosis of some systemic diseases. However, the variab...
An AC (alternative current) power integration of distributed energies faces multi-fold challenges such as synchronization and weak grid-induced instability. In this study, a multi-terminal DC transformer is proposed for renewable energy clusters grid...
Violence detection aims to locate violent content in video frames. Improving the accuracy of violence detection is of great importance for security. However, the current methods do not make full use of the multi-modal vision and audio information, wh...
Public safety and security require an effective monitoring system to detect violence through visual, audio, and motion data. However, current methods often fail to utilize the complementary benefits of visual and auditory modalities, thereby reducing...
Deep learning methods like convolution neural networks (CNNs) and transformers are successfully applied in hyperspectral image (HSI) classification due to their ability to extract local contextual features and explore global dependencies, respectivel...
Leveraging multi-view remote sensing images in scene classification tasks significantly enhances the precision of such classifications. This approach, however, poses challenges due to the simultaneous use of multi-view images, which often leads to a...
A novel method of analog-to-information conversion—the random interval integration—is proposed and studied in this paper. This method is intended primarily for compressed sensing of aperiodic or quasiperiodic signals acquired by commonly used sensors...
Medical image segmentation plays a crucial role in medical image processing, focusing on the automated extraction of regions of interest (such as organs, lesions, etc.) from medical images. This process supports various clinical applications, includi...
This study addresses the cross-scenario challenges in pedestrian re-identification for public safety, including perspective differences, lighting variations, occlusions, and vague feature expressions. We propose a pedestrian re-identification method...
Deep learning (DL)-based change detection (CD) methods for high-resolution (HR) remote sensing images can still be improved by effective acquisition of multi-scale feature and accurate detection of the edge of change regions. We propose a new end-to-...
Building function identification plays a crucial role in providing basic data for urban planning, management, and various intelligent applications. Today, building function identification methods using Street View Images (SVIs) have made significant...
The diameter of the optic nerve sheath is an important indicator for assessing the intracranial pressure in critically ill patients. The methods for measuring the optic nerve sheath diameter are generally divided into invasive and non-invasive method...
Multi-object tracking in unmanned aerial vehicle (UAV) videos is a critical visual perception task with numerous applications. However, existing multi-object tracking methods, when directly applied to UAV scenarios, face significant challenges in mai...
With deep-learning-based object detection methods reaching industrial-level performance, underwater object detection has emerged as a significant application. However, it is often challenged by dense small instances and image blurring due to the wate...
Sea–land segmentation (SLS) is a crucial step in coastline extraction. In CNN-based approaches for coastline feature extraction, downsampling is commonly used to reduce computational demands. However, this method may unintentionally discard sma...
Integration of multi-omics data is a challenging but necessary step to advance our understanding of the biology underlying human health and disease processes. To date, investigations seeking to integrate multi-omics (e.g., microbiome and metabolome)...
Forest fire danger prediction models often face challenges due to spatial and temporal limitations, as well as a lack of universality caused by regional inconsistencies in fire danger features. To address these issues, we propose a novel algorithm, s...
Crack segmentation in images plays a pivotal role in the monitoring of structural surfaces, serving as a fundamental technique for assessing structural integrity. However, existing methods that rely solely on RGB images exhibit high sensitivity to li...
East Dongting Lake is an important habitat for migratory birds. Accurately counting the number of migratory birds is crucial to assessing the health of the wetland ecological environment. Traditional manual observation and low-precision methods make...
Vehicle-infrastructure cooperative perception enhances the perception capabilities of autonomous vehicles by facilitating the exchange of complementary information between vehicles and infrastructure. However, real-world environments often present ch...
In recent years, deep convolutional neural networks with multi-scale features have been widely used in image super-resolution reconstruction (ISR), and the quality of the generated images has been significantly improved compared with traditional meth...
We propose a double-sided layout design method with high reliability for multi-beam phased array transmitter modules based on high-temperature co-fired ceramic technology. Efficient transmission of microwave signals and low-frequency signals is reali...
Lightning is a hazardous weather phenomenon, characterized by sudden occurrences and complex local distributions. It poses significant challenges for accurate forecasting, which is crucial for public safety and economic stability. Deep learning metho...
Action recognition involves capturing temporal information from video clips where the duration varies with videos for the same action. Due to the diverse scale of temporal context, uniform size kernels utilized in convolutional neural networks (CNNs)...
Hyperspectral image (HSI) denoising is a crucial step in image preprocessing as its effectiveness has a direct impact on the accuracy of subsequent tasks such as land cover classification, target recognition, and change detection. However, existing m...
Monitoring the growth status of crop leaves is an integral part of agricultural management and involves important tasks such as leaf shape analysis and area calculation. To achieve this goal, accurate leaf segmentation is a critical step. However, th...
Graph convolutional networks (GCNs) have obtained remarkable performance in skeleton-based action recognition. However, previous approaches fail to capture the implicit correlations between joints and handle actions across varying time intervals. To...
In this paper, we focus on the problem of contextual aggregation in the semantic segmentation of aerial images. Current contextual aggregation methods only aggregate contextual information within specific regions to improve feature representation, wh...
With the simultaneous acquisition of the infrared and optical remote sensing images of the same target becoming increasingly easy, using multi-modal data for high-performance object detection has become a research focus. In remote sensing multi-modal...
Automatic polyp segmentation in colonoscopic images is crucial for the early detection and treatment of colorectal cancer. However, complex backgrounds, diverse polyp morphologies, and ambiguous boundaries make this task difficult. To address these i...
Sleep staging is a crucial indicator for assessing sleep quality, which contributes to sleep monitoring and the diagnosis of sleep disorders. Although existing sleep staging methods achieve high classification performance, two major challenges remain...
The work presents the approaches and software developed for multi-objective optimization of nuclear power structures: the modules for energy planning package MESSAGE intended for modeling purposes of developing nuclear power systems and multi-objecti...
Aiming at the low contrast of skin lesion image and inaccurate segmentation of lesion boundary, a skin lesion segmentation method based on multi-level split receptive field and attention is proposed. Firstly, the depth feature extraction module and m...
Object detection in aerial drone imagery has attracted increasing attention in Unmanned Aerial Vehicle(UAV) sensing applications. However, small objects occupying limited image regions, with large scale variations and similar background interference,...
Multi-scale object detection is critical for analyzing remote sensing images. Traditional feature pyramid networks, which are aimed at accommodating objects of varying sizes through multi-level feature extraction, face significant challenges due to t...
Maize is one of the world’s major food crops, and its yields are closely related to the sustenance of people. However, its cultivation is hampered by various diseases. Meanwhile, maize diseases are characterized by spots of varying and irregula...
High dimensional multi-omics data integration can enhance our understanding of the complex biological interactions in human diseases. However, most studies involving unsupervised integration of multi-omics data focus on linear integration methods. In...
RGB-D saliency object detection (SOD) primarily segments the most salient objects from a given scene by fusing RGB images and depth maps. Due to the inherent noise in the original depth map, fusion failures may occur, leading to performance bottlenec...
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...
Addressing the challenges in online multi-object tracking algorithms under complex scenarios, where the independence among feature extraction, object detection, and data association modules leads to both error accumulation and the difficulty of maint...
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