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Yunsong Jia, Qingxin Zhao, Yi Xiong, Xin Chen and Xiang Li
The issues of inadequate digital proficiency among agricultural practitioners and the suboptimal image quality captured using mobile smart devices have been addressed by providing appropriate guidance to photographers to properly position their mobile de...
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Hailiang Gong, Xi Wang and Weidong Zhuang
This study focuses on real-time detection of maize crop rows using deep learning technology to meet the needs of autonomous navigation for weed removal during the maize seedling stage. Crop row recognition is affected by natural factors such as soil expo...
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Haedam Kim, Suhyun Park, Hyemin Hong, Jieun Park and Seongmin Kim
As the size of the IoT solutions and services market proliferates, industrial fields utilizing IoT devices are also diversifying. However, the proliferation of IoT devices, often intertwined with users? personal information and privacy, has led to a cont...
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Yingying Ren, Ryan D. Restivo, Wenkai Tan, Jian Wang, Yongxin Liu, Bin Jiang, Huihui Wang and Houbing Song
As a core component of small unmanned aerial vehicles (UAVs), GPS is playing a critical role in providing localization for UAV navigation. UAVs are an important factor in the large-scale deployment of the Internet of Things (IoT) and cyber?physical syste...
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Jingying Zhang and Tengfei Bao
Crack detection is an important component of dam safety monitoring. Detection methods based on deep convolutional neural networks (DCNNs) are widely used for their high efficiency and safety. Most existing DCNNs with high accuracy are too complex for use...
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Leila Malihi and Gunther Heidemann
Efficient model deployment is a key focus in deep learning. This has led to the exploration of methods such as knowledge distillation and network pruning to compress models and increase their performance. In this study, we investigate the potential syner...
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Tianshu Zhang, Wenwen Dai, Zhiyu Chen, Sai Yang, Fan Liu and Hao Zheng
Due to their compelling performance and appealing simplicity, metric-based meta-learning approaches are gaining increasing attention for addressing the challenges of few-shot image classification. However, many similar methods employ intricate network ar...
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Yujie Lei, Xiang Chen, Yunlong Wang, Rong Tang and Baoping Zhang
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Tahir Mehmood, Alfonso E. Gerevini, Alberto Lavelli, Matteo Olivato and Ivan Serina
Single-task models (STMs) struggle to learn sophisticated representations from a finite set of annotated data. Multitask learning approaches overcome these constraints by simultaneously training various associated tasks, thereby learning generic represen...
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Shao-Ming Lee and Ja-Ling Wu
Recently, federated learning (FL) has gradually become an important research topic in machine learning and information theory. FL emphasizes that clients jointly engage in solving learning tasks. In addition to data security issues, fundamental challenge...
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