Tomato Yield Estimation Using an Improved Lightweight YOLO11n Network and an Optimized Region Tracking-Counting Method by Aichen Wang, Yuanzhi Xu, Dong Hu, Liyuan Zhang, Ao Li, Qingzhen Zhu, Jizhan Liu

“…To address these issues, this study proposed an improved lightweight YOLO11n network and an optimized region tracking-counting method, which estimates the quantity of tomatoes at different maturity stages. An improved lightweight YOLO11n network was employed for tomato detection and semantic segmentation, which was combined with the C3k2-F, Generalized Intersection over Union (GIoU), and Depthwise Separable Convolution (DSConv) modules. …”
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SDMSEAF-YOLOv8: a framework to significantly improve the detection performance of unmanned aerial vehicle images by Linxuan Li, Xiaoyu Liu, Xuan Chen, Fengjuan Yin, Bin Chen, Yufeng Wang, Fanbin Meng

“…Four detection heads are employed for tiny target detection, each responsible for different size ranges, so as to improve the accuracy and robustness of small target detection. …”
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Deep Learning-Based Ground-Penetrating Radar Inversion for Tree Roots in Heterogeneous Soil by Xibei Li, Xi Cheng, Yunjie Zhao, Binbin Xiang, Taihong Zhang

“…Additionally, a GPR simulation data set and a measured data set are built in this study, which were used to train inversion models and validate the effectiveness of GPR inversion methods.The introduced GPR inversion model is a pyramid convolutional network with vision transformer and edge inversion auxiliary task (PyViTENet), which combines pyramidal convolution and vision transformer to improve the diversity and accuracy of data feature extraction. …”
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Food security: state Financial support Measures for sustainable Development of Agriculture in Russian Regions by A. I. Borodin, I. Yu. Vygodchikova, E. I. Dzyuba, G. I. Panaedova

“…The hierarchical procedure is based on a system of mathematical filtering of data, which is fundamentally different from existing methods for analyzing hierarchies. …”
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Channel Estimation Using CNN-LSTM in RIS-NOMA Assisted 6G Network by Chi Nguyen, Tiep M. Hoang, Adnan A. Cheema

“…This paper proposes a deep learning (DL)-based channel estimation method using a convolutional long-short term memory (CNN-LSTM) model for RIS-NOMA wireless communication systems that integrate RIS and NOMA techniques. …”
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Small object detection in complex open-pit mine backgrounds based on improved YOLOv11 by ZHU Yongjun, CAI Guangqi, HAN Jin, MIAO Yanzi, MA Xiaoping, JIAO Wenhua

“…The improved YOLOv11 model introduced a Robust Feature Downsampling (RFD) module to replace the stride convolution downsampling module, effectively preserving the feature information of small objects. …”
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Fine-Grained Extraction of Coastal Aquaculture Ponds From Remote Sensing Images Using an Edge-Supervised Multi-task Neural Network by Jian Qi, Min Ji, Fengxiang Jin, Jianran Xu, Hanyu Ji, Juan Wang

“…Furthermore, transfer experiments with JL1 imagery from Jiangmen and Yantai demonstrate the strong generalization capability of the proposed method across different environments.…”
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SMILES all around: structure to SMILES conversion for transition metal complexes by Maria H. Rasmussen, Magnus Strandgaard, Julius Seumer, Laura K. Hemmingsen, Angelo Frei, David Balcells, Jan H. Jensen

“…We compare these three different ways of obtaining SMILES for a subset of the CSD (tmQMg) and find >70% agreement for all three pairs. …”
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Efficient Identification and Classification of Pear Varieties Based on Leaf Appearance with YOLOv10 Model by Niman Li, Yongqing Wu, Zhengyu Jiang, Yulu Mou, Xiaohao Ji, Hongliang Huo, Xingguang Dong

“…Images were collected at different times of the day to cover changes in natural lighting and ensure model robustness. …”
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Enhancing Satellite Image Coregistration Using Mirror Array as Artificial Point Source for Multisource Image Harmonization by Muhammad Daniel Iman bin Hussain, Vaibhav Katiyar, Masahiko Nagai, Dorj Ichikawa

“…Results show notable improvements in geolocation accuracy, with root mean square error values of 3.59, 4.05, and 4.14 m in the subpixel range for three different GRUS-1 bands when compared to the Global Reference Image. …”
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TPDTNet: Two-Phase Distillation Training for Visible-to-Infrared Unsupervised Domain Adaptive Object Detection by Siyu Wang, Xiaogang Yang, Ruitao Lu, Shuang Su, Bin Tang, Tao Zhang, Zhengjie Zhu

“…This convolutional operation is embedded following standard convolution to mitigate the loss of detailed features. …”
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Strong Medium Compression in a Spheroidal Cavitation Bubble by A.A. Aganin, T.F. Khalitova

“…The neutron number is calculated by integral convolution. It has been shown that the degree of medium compression inside nonspherical bubbles is less than that in the spherical one due to essential differences in the focusing of shock waves. …”
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Accurate geometric correction for NOAA/AVHRR data by HUANG Jing-feng, XU Hong-wei, WANG Ren-chao, JIANG Heng-xian

“…The combined RMSs were calculated for 59 NOAA/AVHRR data of Zhejiang province, The result of variance ahalysis and multiple comparisons showed that the RMSs differences of RST, 1st degree polynomial and 2ed polynomial warping methods were significant. …”
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High-Quality Damaged Building Instance Segmentation Based on Improved Mask Transfiner Using Post-Earthquake UAS Imagery: A Case Study of the Luding Ms 6.8 Earthquake in China by Kangsan Yu, Shumin Wang, Yitong Wang, Ziying Gu

“…UAS imagery has a high spatial resolution, but the resolution is inconsistent between different flight missions. These factors make it challenging for existing methods to accurately identify individual damaged buildings in UAS images from different scenes, resulting in coarse segmentation masks that are insufficient for practical application needs. …”
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Beyond Nyquist: A Comparative Analysis of 3D Deep Learning Models Enhancing MRI Resolution by Soumick Chatterjee, Alessandro Sciarra, Max Dünnwald, Anitha Bhat Talagini Ashoka, Mayura Gurjar Cheepinahalli Vasudeva, Shudarsan Saravanan, Venkatesh Thirugnana Sambandham, Pavan Tummala, Steffen Oeltze-Jafra, Oliver Speck, Andreas Nürnberger

“…In order to overcome these limitations, super-resolution MRI deep-learning-based techniques can be utilised. In this work, different state-of-the-art 3D convolution neural network models for super resolution (RRDB, SPSR, UNet, UNet-MSS and ShuffleUNet) were compared for the super-resolution task with the goal of finding the best model in terms of performance and robustness. …”
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Radio frequency based distributed system for noncooperative UAV classification and positioning by Chaozheng Xue, Tao Li, Yongzhao Li

“…After the UAV signal is detected, the time difference of arrival (TDOA) of the UAV signal arriving at the receiver is estimated by the cross-correlation method to obtain the corresponding distance difference. …”
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CSA-Net: Complex Scenarios Adaptive Network for Building Extraction for Remote Sensing Images by Dongjie Yang, Xianjun Gao, Yuanwei Yang, Minghan Jiang, Kangliang Guo, Bo Liu, Shaohua Li, Shengyan Yu

“…The HFE obtains high-level semantic information at different levels and fuses it with low-level detailed information by skipping connections to enhance the reasoning and perception ability of building structure in complex scenes. …”
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Coalmine image super-resolution reconstruction via fusing multi-dimensional feature and residual attention network by Jian CHENG, Lifei MI, Hao LI, Heping LI, Guangfu WANG, Yongzhuang MA

“…First, a multi-branch network is employed to parallelly integrate dynamic convolution and channel attention mechanisms, capturing different spatial statistical characteristics through “horizontal-channel” and “vertical-channel” interactions. …”
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HyDA-Net: A Hybrid Dense Attention Network for Remote Sensing Multi-Image Super-Resolution by Mohamed Ramzy Ibrahim, Robert Benavente, Daniel Ponsa, Felipe Lumbreras

“…Extensive experiments using real-captured satellite datasets, namely PROBA-V and MuS2, show that HyDA-Net outperforms state-of-the-art models in different spectral bands. Moreover, a cross-dataset experiment is conducted to further evaluate the robustness and generalizability of the proposed HyDA-Net.…”
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ST-YOLOv8: Small-Target Ship Detection in SAR Images Targeting Specific Marine Environments by Fei Gao, Yang Tian, Yongliang Wu, Yunxia Zhang

“…The C2f module in the backbone’s transition sections is replaced by the Conv_Online Reparameterized Convolution (C_OREPA) module, reducing convolutional complexity and improving efficiency. …”
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