With the development of signal processing technology and the use of new radar systems, signal aliasing and electronic interference have occurred in space. The electromagnetic signals have become extremely complicated in their current applications in...
The accurate identification of corn leaf diseases is crucial for preventing disease spread and improving corn yield. Plant leaf images are often affected by factors such as complex backgrounds, climate, light, and sample data imbalance. To address th...
Diabetic retinopathy (DR) is a common complication of diabetes and one of the leading causes of preventable vision loss worldwide. Because the manual grading of color fundus images is slow and depends on the availability of trained specialists, autom...
Multimodal fusion approaches that combine data from dissimilar sensors can better exploit human-like reasoning and strategies for situational awareness. The performance of a six-layer convolutional neural network (CNN) and an 18-layer ResNet architec...
Image steganalysis detects hidden information in digital images by identifying statistical anomalies, serving as a forensic tool to reveal potential covert communication. The field of deep learning-based image steganography has relatively scarce effe...
This study aimed to develop and evaluate a deep learning diagnostic model integrating a convolutional autoencoder (CAE) and ResNet18 for the early and accurate diagnosis of developmental dysplasia of the hip (DDH) in children, addressing the subjecti...
Background/Objectives: Dry eye disease (DED) is assessed by combining patient-reported symptoms with ocular surface findings. We evaluated a deep learning strategy with qualitative post hoc explanation in two independent ophthalmic imaging tasks: DED...
Medical image semantic segmentation is essential in computer-aided diagnosis systems. It can separate tissues and lesions in the image and provide valuable information to radiologists and doctors. The breast ultrasound (BUS) images have advantages: n...
Personal identification is an important aspect of managing electronic health records (EHRs), ensuring secure access to patient information, and maintaining patient privacy. Traditionally, biometric, signature, username/password, photo identity, etc.,...
This study constructs a power switching device open-circuit fault diagnosis model for a three-level neutral point clamped inverter based on the multi-scale shuffled convolutional neural network (MSSCNN) and extracts and classifies the fault informati...
Accurate evaluation of the Strouhal number is important for wind-resistant design, but traditional aerodynamic assessments are computationally expensive. To support rapid schematic design, this paper proposes a deep learning framework to predict the...
Panoramic radiographs are widely used in dental practice due to their ability to provide a comprehensive view of the teeth, jaws, and surrounding anatomical structures in a single examination. However, automated interpretation remains challenging bec...
In recent years, the amount of remote sensing imagery data has increased exponentially. The ability to quickly and effectively find the required images from massive remote sensing archives is the key to the organization, management, and sharing of re...
Coal and rock recognition is one of the key technologies in mining production, but traditional methods have limitations such as single-feature representation dimension, insufficient robustness, and unbalanced performance in lightweight design under n...
Using convolutional neural networks (CNNs) to recognize forest fires in complex outdoor environments is a hot research direction in the field of intelligent forest fire recognition. Due to the storage-intensive and computing-intensive characteristics...
This paper uses visualization techniques to analyze the learning process of six machine learning classifiers for multichannel time series classification (MTSC), including five deep learning models—1D CNN, CNN-LSTM, ResNet, InceptionTime, and Tr...
Environmental sound classification (ESC) tasks are attracting more and more attention. Due to the complexity of the scene and personnel mobility, there are some difficulties in understanding and generating environmental sound models for ESC tasks. To...
Currently, most convolutional networks use standard convolution for feature extraction to pursue accuracy. However, there is potential room for improvement in terms of the number of network parameters and model speed. Therefore, this paper proposes a...
Post-harvest losses of mango (Mangifera indica L.) in developing economies are estimated at 5% to 30%, largely due to manual management practices that depend on subjective visual assessments. This paper proposes a lightweight deep learning architectu...
In recent years, barefoot footprint recognition has become increasingly important in criminal investigations. However, the high complexity and diverse features of footprint images pose challenges to traditional methods and CNNs due to their limited l...
Due to the limitations of deep learning models in processing one-dimensional signal feature extraction, and high model complexity leading to low training accuracy and large consumption of computing resources, this paper innovatively proposes a rollin...
It is of great practical significance to quickly, accurately, and effectively identify the effects of rice diseases on rice yield. This paper proposes a rice disease identification method based on an improved DenseNet network (DenseNet). This method...
Direction of arrival (DoA) estimation is a fundamental problem in modern communication systems, such as 5G/6G cellular systems, V2X, and radar. Modern DoA estimation techniques enhance signal reception, mitigate interference, enhance communication ef...
At present, ResNet and DenseNet have achieved significant performance gains in the field of finger-vein biometric recognition, which is partially attributed to the dominant design of cross-layer skip connection. In this manner, features from multiple...
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...
Aero-engine safety and prognostics and health management (PHM) rely on robust vibration-based fault diagnosis. However, many deep learning studies on rotating machinery are evaluated under random train–test splits that mix hardware instances an...
With the continuous penetration of renewable energy and power electronic equipment into modern power systems, the occurrence frequency of composite power quality disturbances has increased significantly. The accurate classification of various composi...
Enhanced Loran (eLoran) provides a terrestrial complement to Global Navigation Satellite Systems (GNSSs), but tri-state pulse position modulation (PPM) demodulation remains difficult under composite interference. This study proposes a label-independe...
Semantic communication provides a task-oriented alternative to conventional bit-level transmission. For wireless image transmission, existing systems mainly optimize image reconstruction, while downstream classification is often handled by a separate...
Automated weeding is an important research area in agrorobotics. Weeds can be removed mechanically or with the precise usage of herbicides. Deep Learning techniques achieved state of the art results in many computer vision tasks, however their deploy...
Early and precise detection of plant diseases is essential for safeguarding crop yield and ensuring sustainable agricultural practices. In this study, we propose the Modified RefineNet with Attention based Fusion (MoRefNet-AF), a Modified RefineNet a...
The mechanical reliability of high-voltage circuit breakers (HVCBs) is crucial for power-grid stability, yet traditional diagnostic methods rely heavily on manually extracted scalar features that can discard transient information. This paper presents...
Semantic segmentation technology based on deep learning has developed rapidly. It is widely used in remote sensing image recognition, but is rarely used in natural disaster scenes, especially in landslide disasters. After a landslide disaster occurs,...
Background: Limited availability of high-quality labeled biomedical image datasets presents a significant challenge for training deep learning models in medical diagnostics. This study proposes a novel image generation framework combining conditional...
The classification of the taproots of Panax notoginseng is conducive to improving the economic added value of its products. In this study, a real-time sorting robot system for Panax notoginseng taproots was developed based on the improved DeepLabv3+...
Brain cancer is one of the most dangerous cancer types in the world, and thousands of people are suffering from malignant brain tumors. Depending on the level of cancer, early diagnosis can be a lifesaver. However, thousands of scans must be studied...
The presence of water accumulation on aircraft surfaces constitutes a considerable hazard to both performance and safety, necessitating vigilant inspection and maintenance protocols. In this study, we introduce an innovative semantic segmentation mod...
Robotic visual perception and terrain recognition are critical for autonomous locomotion and adaptive control in complex environments. However, existing models often extract weak features, confuse classes, and deliver unstable recognition. Most prior...
Multi-scale context information, especially long-range dependency, has shown to be beneficial for speaker verification (SV) tasks. In this paper, we propose three methods to systematically explore long-range context SV feature extraction based on Res...
With the growing demand for intelligent monitoring in land-based aquaculture, rapid and accurate fish counting from visual data has become important for stocking density regulation, feeding management, and production decisions. To address the challen...
Plant disease symptoms combine local texture changes with patterns distributed across a leaf, while practical recognition models must remain compact. We introduce DC-FEN, a MobileNetV3-based design that models spatial-token relations and channel inte...
The per-switch condition monitoring of insulated-gate bipolar transistors (IGBTs) underpins condition-based inverter maintenance, yet existing approaches collapse the six-switch state space into one system-level label, leaving individual devices unre...
Rapid landslide mapping is important for disaster response, yet modern segmentation networks can require substantial computation and storage. This study proposes Geography-, Boundary-, and Scar-Aware Structured Pruning (GeoBASP) for compact landslide...
Transformer-based detectors model long-range context effectively, yet their representations remain dominated by RGB appearance and can become unreliable in cluttered, occluded, or crowded scenes. We present BMF-DETR, a pseudo-depth-guided detector th...
Lung cancer continues to be one of the leading causes of cancer-related deaths worldwide, as pulmonary nodules are often diagnosed at later stages. Therefore, accurate nodule classification is crucial for enabling early detection and supporting timel...
To address the issues of high computational complexity and boundary feature loss encountered when extracting farmland information from high-resolution remote sensing images, this study proposes an innovative CNN–Transformer hybrid network, MAMN...
Field-programmable gate arrays (FPGAs) with on-package high-bandwidth memory (HBM) are an attractive substrate for low-latency, precision-customizable AI inference, yet high-level synthesis (HLS) flows expose little control over how tensors are distr...
Detecting student classroom behaviors from ordinary classroom videos is complicated by weak features for small targets such as rear-row mobile phones, feature loss caused by inter-student occlusion, imbalance between frequent learning behaviors and r...
In scientific research, spectroscopy and diffraction experimental techniques are widely used and produce huge amounts of spectral data. Learning patterns from spectra is critical during these experiments. This provides immediate feedback on the actua...
(1) Background: Soccer action recognition (SAR) is essential in modern sports analytics, supporting automated performance evaluation, tactical strategy analysis, and detailed player behavior modeling. Although recent advances in deep learning and com...
of 2