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Real-Time Super Resolution Utilizing Dilation and Depthwise Separable Convolution
Publicado 2025-04-01Materias: Enlace del recursoComputer vision applications require high-quality reproductions of original images, typically demanding complex models with many trainable parameters and floating-point operations. This increases computational load and restricts deployment on resource-constrained devices. Therefore, we designed a ne...
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Explainable AI for Lightweight Network Traffic Classification Using Depthwise Separable Convolutions
Publicado 2025-01-01Materias: Enlace del recursoWith the rapid growth of internet usage and the increasing number of connected devices, there is a critical need for advanced Network Traffic Classification (NTC) solutions to ensure optimal performance and robust security. Traditional NTC methods, such as port-based analysis and deep packet inspect...
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Fault Diagnosis Method for Shearer Arm Gear Based on Improved S-Transform and Depthwise Separable Convolution
Publicado 2025-06-01“…Then, a neural network model combining standard convolution and depthwise separable convolution is constructed for fault identification. …”To address the limitations in time–frequency feature representation of shearer arm gear faults and the issues of parameter redundancy and low training efficiency in standard convolutional neural networks (CNNs), this study proposes a diagnostic method based on an improved S-transform and a Depthwise...
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DS-AdaptNet: An Efficient Retinal Vessel Segmentation Framework With Adaptive Enhancement and Depthwise Separable Convolutions
Publicado 2025-01-01“…These techniques are integrated with an Efficient Depthwise Convolutional Neural Network (ED-CNN) architecture that employs depth-separable convolutions, dramatically reducing computational complexity while maintaining high segmentation accuracy. …”Medical image segmentation plays a crucial role in diagnosis and treatment planning, yet faces persistent challenges including limited annotated data, boundary ambiguity, and high computational demands that hinder clinical deployment. This paper presents DS-AdaptNet, an efficient segmentation framew...
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Precision-Driven Semantic Segmentation of Pipe Gallery Diseases Using PipeU-NetX: A Depthwise Separable Convolution Approach
Publicado 2025-06-01Enlace del recursoAiming at the problems of high labor cost, low detection efficiency, and insufficient detection accuracy of traditional pipe gallery disease detection methods, this paper proposes a pipe gallery disease segmentation model, PipeU-NetX, based on deep learning technology. By introducing the innovative...
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Rolling Bearing Fault Diagnosis Model Based on Multi-Scale Depthwise Separable Convolutional Neural Network Integrated with Spatial Attention Mechanism
Publicado 2025-06-01Materias: Enlace del recursoIn response to the challenges posed by complex and variable operating conditions of rolling bearings and the limited availability of labeled data, both of which hinder the effective extraction of key fault features and reduce diagnostic accuracy, this study introduces a model that combines a spatial...
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Utilizing GCN-Based Deep Learning for Road Extraction from Remote Sensing Images
Publicado 2025-06-01Materias: “…depthwise separable convolution…”The technology of road extraction serves as a crucial foundation for urban intelligent renewal and green sustainable development. Its outcomes can optimize transportation network planning, reduce resource waste, and enhance urban resilience. Deep learning-based approaches have demonstrated outstandi...
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Temporal and Modality Awareness-Based Lightweight Residual Network With Attention Mechanism for Human Activity Recognition Using a Lower-Limb Exoskeleton Robot
Publicado 2025-01-01Materias: Enlace del recursoAlthough many human activity recognition (HAR) models have achieved high accuracy, their computational complexity often limits deployment in systems with constrained hardware resources, such as wearable lower-limb exoskeletons. In addition, existing models frequently overlook the complementary natur...
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An Efficient Continuous-Variable Quantum Key Distribution with Parameter Optimization Using Elitist Elk Herd Random Immigrants Optimizer and Adaptive Depthwise Separable Convolutional Neural Network
Publicado 2025-07-01“…Subsequently, a dataset for side-channel attack authentication is gathered and preprocessed, followed by feature extraction and classification using Adaptive Depthwise Separable Convolutional Neural Networks (ADS-CNNs), which enhances security against side-channel attacks. …”Quantum memory is essential for the prolonged storage and retrieval of quantum information. Nevertheless, no current studies have focused on the creation of effective quantum memory for continuous variables while accounting for the decoherence rate. This work presents an effective continuous-variabl...
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Analytic Continual Learning-Based Non-Intrusive Load Monitoring Adaptive to Diverse New Appliances
Publicado 2025-06-01Materias: Enlace del recursoNon-intrusive load monitoring (NILM) provides a cost-effective solution for smart services across numerous appliances by inferring appliance-level information from mains electrical measurements. With the rapid growth in appliance diversity, continual learning that adapts to new appliances while reta...
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Swin-GAT Fusion Dual-Stream Hybrid Network for High-Resolution Remote Sensing Road Extraction
Publicado 2025-06-01Materias: Enlace del recursoThis paper introduces a novel dual-stream collaborative architecture for remote sensing road segmentation, designed to overcome multi-scale feature conflicts, limited dynamic adaptability, and compromised topological integrity. Our network employs a parallel “local–global” encoding scheme: the local...
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Design of a Drivable Area Segmentation Network Using a Field Programmable Gate Array Based on Light Detection and Ranging
Publicado 2025-01-01Materias: Enlace del recursoWith the continuing development of autonomous driving systems, the drivable area segmentation problem has become an indispensable part of self-driving cars. The drivable area segmentation technology introduces many features to self-driving car technology, such as providing information about the surr...
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LSEVGG: An attention mechanism and lightweight-improved VGG network for remote sensing landscape image classification
Publicado 2025-08-01Materias: Enlace del recursoRemote sensing landscape image classification is essential for environmental monitoring, land management, and ecological assessment, but presents critical challenges due to complex spatial distributions and high intra-class variability inherent in landscape scenes. Traditional deep convolutional neu...
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Expression Recognition Method Based on CBAM-DSC Network
Publicado 2023-12-01“… Aiming at the problems of complex parameters and low computational performance of facial expression network model, an expression recognition method based on Convolutional Block Attention Module-Depthwise Separable Convolution network is proposed. …”Aiming at the problems of complex parameters and low computational performance of facial expression network model, an expression recognition method based on Convolutional Block Attention Module-Depthwise Separable Convolution network is proposed. The network usage depth separable convolution is c...
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Facial Expression Recognition with Multi-perspective Feature Fusion Under Deep Residual Convolution
Publicado 2023-04-01“… Aiming at the problems of inaccurate facial expression recognition and large amount of calculation under multi-perspective in real life, a facial expression recognition model MVResNet-FER is proposed, which is based on multi-perspective feature fusion under deep residual convolution.The residual block in ResNet is first improved and the conventional convolutional network is replaced with a depthwise separable network.Second, a CBAM module is added to enhance the extraction of effective features under multi-perspective and the supplementation of shallow feature information. …”Aiming at the problems of inaccurate facial expression recognition and large amount of calculation under multi-perspective in real life, a facial expression recognition model MVResNet-FER is proposed, which is based on multi-perspective feature fusion under deep residual convolution.The residual...
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Mine hoisting steel wire rope surface damage image recognition based on improved YOLOv8n
Publicado 2025-04-01Materias: Enlace del recursoTo address issues such as background interference caused by oil stains covering the surface of mine hoisting steel wire ropes, large gaps between rope strands leading to feature confusion, and the difficulty in identifying small target damages, a surface damage image recognition method for mine hois...
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Lightweight Convolutional Network for Bearing Fault Diagnosis
Publicado 2024-08-01“…The results demonstrate that compressing convolutional models using depthwise separable convolution allows for lightweight requirements while maintaining a high diagnostic accuracy (96. 20 ± 2. 81% ).…”In the field of bearing fault diagnosis, many convolutional models with excellent performance face challenges in industrial applications due to deployment cost constraints. This paper aims to develop a lightweight diagnostic method with reduced parameters. We investigate the feasibility of using de...
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Attention-based multi-scale convolution and conformer for EEG-based depression detection
Publicado 2025-07-01“…The AMPC module captures temporal features through multiscale convolutions and extracts spatial features using depthwise separable convolutions, while applying the ECA attention mechanism to weigh key channels, enhancing the model’s focus on crucial electrode channels. …”Depression is a common mental health issue, and early detection is crucial for timely intervention. This study proposes an end-to-end EEG-based depression recognition model, AMCCBDep, which combines Attention-based Multi-scale Parallel Convolution (AMPC), Conformer, and Bidirectional Gated Recurrent...
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Decom-UNet3+: A Retinal Vessel Segmentation Method Optimized With Decomposed Convolutions
Publicado 2025-01-01“…To address this issue, we propose Decom-UNet3+, a model that optimizes the encoders by employing decomposed convolutions. Specifically, the encoders replace standard convolutional layers with asymmetric convolutions and depthwise separable convolutions, reducing the number of parameters while enhancing capability for feature extraction. …”The intricate and highly branched structure of retinal blood vessels, along with the fragility of fine vessels, makes segmentation a challenging task. To address this issue, we propose Decom-UNet3+, a model that optimizes the encoders by employing decomposed convolutions. Specifically, the encoders...
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CAs-Net: A Channel-Aware Speech Network for Uyghur Speech Recognition
Publicado 2025-06-01“…The proposed model consists of two key components: (1) the Channel Rotation Module (CIM), which reconstructs each frame’s channel vector into a spatial structure and applies a rotation operation to explicitly model the local structural relationships within the channel dimension, thereby enhancing the encoder’s contextual modeling capability; and (2) the Multi-Scale Depthwise Convolution Module (MSDCM), integrated within the Transformer framework, which leverages multi-branch depthwise separable convolutions and a lightweight self-attention mechanism to jointly capture multi-scale temporal patterns, thus improving the model’s perception of compact articulation and complex rhythmic structures. …”This paper proposes a Channel-Aware Speech Network (CAs-Net) for low-resource speech recognition tasks, aiming to improve recognition performance for languages such as Uyghur under complex noisy conditions. The proposed model consists of two key components: (1) the Channel Rotation Module (CIM), whi...
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