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Efficient one-stage detection of shrimp larvae in complex aquaculture scenarios
Published 2025-06-01“…This paper proposes an efficient one-stage shrimp larvae detection method, FAMDet, specifically designed for complex scenarios in intensive aquaculture. …”
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CTDA: an accurate and efficient cherry tomato detection algorithm in complex environments
Published 2025-03-01“…To ensure accuracy and efficiency in detecting cherry tomatoes in complex environments, the study proposes a precise, realtime, and robust target detection algorithm: the CTDA model, to support robotic harvesting operations in unstructured environments.MethodsThe model, based on YOLOv8, introduces a lightweight downsampling method to restructure the backbone network, incorporating adaptive weights and receptive field spatial characteristics to ensure that low-dimensional small target features are not completely lost. …”
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Efficient-Deformable-DETR: Enhancing underwater small target detection in complex environments
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Iterative Assessment of Edge Criticality: Efficiency Enhancement or Hidden Insufficiency Detection
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Efficient Deep Learning-Based Detection Scheme for MIMO Communication Systems
Published 2025-01-01“…A flexible design can offer a convenient tradeoff between detection complexity and bit error rate (BER). Deep learning (DL) has emerged as an efficient method for solving optimization problems in different areas. …”
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Attention Lempel-Ziv complexity: an improved Lempel-Ziv complexity with high computational efficiency for bearing early fault detection
Published 2025-06-01Subjects: “…Lempel-Ziv complexity…”
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SiFSO: Fish Swarm Optimization-Based Technique for Efficient Community Detection in Complex Networks
Published 2020-01-01“…Efficient community detection in a complex network is considered an interesting issue due to its vast applications in many prevailing areas such as biology, chemistry, linguistics, social sciences, and others. …”
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Ship-DETR: A Transformer-Based Model for EfficientShip Detection in Complex Maritime Environments
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Automatic plant disease detection using computationally efficient convolutional neural network
Published 2024-12-01“…The current study opts for the later approach due to its advantages in terms of speed, efficiency, and convenience. Convolutional neural network (CNN)‐based prominent models, such as MobileNet, ResNet50, Inception, and Xception, are preferred for automatic plant disease detection due to their high performance, but they demand substantial computational resources, limiting their use to a class of large‐scale farmers. …”
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Dual‐Function Ceramic Pigments for Energy‐Efficient and Secure Autonomous Vehicles
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Optimizing drone-based pollination method by using efficient target detection and path planning for complex durian orchards
Published 2025-07-01“…This study proposes an AI-powered drone-based pollination method for complex durian orchards, integrating improved object detection and optimized path planning. …”
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MSF-GhostNet: Computationally Efficient YOLO for Detecting Drones in Low-Light Conditions
Published 2025-01-01“…This approach significantly reduces the waste of computing resources when detecting small-sized flying objects. We also integrated autoanchor and batch size mechanisms to ensure efficient model training and avoid overfitting. …”
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Application and reflections on efficient development of deep oil and gas reservoirs in Tarim Basin
Published 2024-08-01Get full text
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Energy efficiency maximization in MIMO links aided by metasurfaces with global reflection constraints
Published 2025-07-01“…Abstract This work develops two radio resource allocation algorithms to optimize the energy efficiency of a multiple-input multiple-output (MIMO) communication system where a metasurface is deployed near the transmit antenna array to create a reconfigurable holographic beamforming structure. …”
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Efficient detection and counting method for maize seedling plots
Published 2025-08-01“…To efficiently detect and count maize seedlings in complex field conditions, this study first developed a sample dataset under diverse backgrounds and lighting scenarios and introduced a data augmentation technique called “M_AUG.” …”
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