An efficient spectrum sensing of mixed kernel SVM based on sampling covariance matrix by Jianyuan NIE, Jianrong BAO, Bin JIANG, Chao LIU, Fang ZHU, Jianhai HE

“…In recent years,with the blind detection algorithms were proposed,more and more blind algorithms based on sampling covariance matrix were applied to spectrum sensing.The detection threshold was an approximation,and the detection performance would be affected for this algorithms.Thus,the mixed kernel function support vector machine (SVM) efficient spectrum sensing based on sampling covariance matrix was proposed.The statistics which were maximum minimum eigenvalue (MME) and covariance absolute value (CAV) of sensing signal sampling covariance matrices were used as the feature vectors of SVM and were trained to generate a spectrum sensing classifier.The advantage of this algorithm was that it needn’t calculate the detection threshold and the extraction of features reduces size of the sample set.The genetic algorithm (GA) was used to optimize the parameters of mixed kernel function SVM algorithm.The experimental results show that the proposed method has higher detection probability than MME and CAV algorithms,and has less sensing time than SVM,which has good practicability.…”
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An Efficient and Fast Model Reduced Kernel KNN for Human Activity Recognition by Zongying Liu, Shaoxi Li, Jiangling Hao, Jingfeng Hu, Mingyang Pan

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Efficient deep learning-based tomato leaf disease detection through global and local feature fusion by Hao Sun, Rui Fu, Xuewei Wang, Yongtang Wu, Mohammed Abdulhakim Al-Absi, Zhenqi Cheng, Qian Chen, Yumei Sun

“…Abstract In the context of intelligent agriculture, tomato cultivation involves complex environments, where leaf occlusion and small disease areas significantly impede the performance of tomato leaf disease detection models. To address these challenges, this study proposes an efficient Tomato Disease Detection Network (E-TomatoDet), which enhances tomato leaf disease detection effectiveness by integrating and amplifying global and local feature perception capabilities. …”
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Improved YOLOv10 for Visually Impaired: Balancing Model Accuracy and Efficiency in the Case of Public Transportation by Rio Arifando, Shinji Eto, Tibyani Tibyani, Chikamune Wada

“…The Improved YOLOv10 advances the YOLOv10 architecture through the incorporation of CA, which enhances long-range dependency modeling and spatial awareness, and AKConv, which dynamically adjusts convolutional kernels for superior feature extraction. These enhancements aim to improve both detection accuracy and efficiency, essential for real-time applications in assistive technologies. …”
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Detect anomalous quartic gauge couplings at muon colliders with quantum kernel k-means by Shuai Zhang, Ke-Xin Chen, Ji-Chong Yang

“…Comparing the classical k-means anomaly detection algorithm with QKKM, it is indicated that the QKKM is able to archive a better cut efficiency.…”
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A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion by Qi Wang, Guanghu Xu, Donglin Jing

“…To address this issue, this paper proposes a Rotation Target Detection Network based on Multi-kernel Interaction and Hierarchical Expansion (MIHE-Net) as a systematic solution. …”
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DKA-YOLO: Enhanced Small Object Detection via Dilation Kernel Aggregation Convolution Modules by Yicheng Qiu, Feng Sha, Li Niu

“…Finally, the model’s head layer employs multi-scale convolution kernels detect to enhance feature expression diversity, generalization ability, and computational efficiency of detection. …”
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Two-dimensional materials based two-transistor-two-resistor synaptic kernel for efficient neuromorphic computing by Qian He, Hailiang Wang, Yishu Zhang, Anzhe Chen, Yu Fu, Guodong Xue, Kaihui Liu, Shiman Huang, Yang Xu, Bin Yu

“…Our findings indicate that the synaptic kernel can significantly improve detection accuracy and inference performance on the CIFAR-10 dataset.…”
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A Focal Attention-Based Large Convolutional Kernel Network for Anomaly Detection of Coated Fuel Particles by Zhaochuan Hu, Jiang Yu, Hang Zhang, Jian Liu, Ning Chen, Rong Li

“…Second, some anomalies exhibit weak morphological features, making them difficult to detect. To address these issues, this study proposes an innovative focal attention-based large convolutional kernel network detection framework comprising three core modules. …”
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Maize Kernel Broken Rate Prediction Using Machine Vision and Machine Learning Algorithms by Chenlong Fan, Wenjing Wang, Tao Cui, Ying Liu, Mengmeng Qiao

“…Rapid online detection of broken rate can effectively guide maize harvest with minimal damage to prevent kernel fungal damage. …”
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SDKU-Net: A Novel Architecture with Dynamic Kernels and Optimizer Switching for Enhanced Shadow Detection in Remote Sensing by Gilberto Alvarado-Robles, Isac Andres Espinosa-Vizcaino, Carlos Gustavo Manriquez-Padilla, Juan Jose Saucedo-Dorantes

“…To address these issues, this study proposes the Supervised Dynamic Kernel U-Net (SDKU-Net), a novel architecture designed to enhance shadow detection in complex remote sensing scenarios. …”
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MLK-TR: a Multi-branch Large Kernel TRansformer for UAV-based images by Xun Li, Yuzhen Zhao, Yang Zhao, Zhun Guo, Jianjing Gao, Baoxi Yuan

“…To address this, this paper proposes a Multi-branch Large-Kernel TRansformer network (MLK-TR) for small target detection in UAV scenarios. …”
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Energy-Efficient Deep Learning for Cloud Detection Onboard Nanosatellite by Imane Khalil, Amina Daghouri, Mohammed Alae Chanoui, Zouhair Guennoun, Adnane Addaim

“…The customized SegNet architecture, tailored with minimal kernels and layers, achieved an accuracy of 93.50%, effectively balancing performance and computational efficiency. …”
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An Efficient Group Convolution and Feature Fusion Method for Weed Detection by Chaowen Chen, Ying Zang, Jinkang Jiao, Daoqing Yan, Zhuorong Fan, Zijian Cui, Minghua Zhang

“…Currently, research on vegetable weed detection technology is relatively limited, and existing detection methods still face challenges due to complex natural conditions, resulting in low detection accuracy and efficiency. …”
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PAMFPN: Position-Aware Multi-Kernel Feature Pyramid Network with Adaptive Sparse Attention for Robust Object Detection in Remote Sensing Imagery by Xiaofei Yang, Suihua Xue, Lin Li, Sihuan Li, Yudong Fang, Xiaofeng Zhang, Xiaohui Huang

“…This work bridges the gap between efficient computation and robust feature fusion in remote sensing detection, offering a universal solution for real-world applications.…”
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An Efficient Printing Defect Detection Based on YOLOv5-DCN-LSK by Jie Liu, Zelong Cai, Kuanfang He, Chengqiang Huang, Xianxin Lin, Zhenyong Liu, Zhicong Li, Minsheng Chen

“…The method meets the requirements of high precision and high efficiency for printing defect detection.…”
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Architecture of an automated program complex based on a multiple kernel svm classifier for analyzing malicious executable files by Алан Нафієв, Андрій Родіонов

“…In conclusion, the automated program complex developed in this study demonstrates significant improvements in the accuracy and efficiency of malware detection. By integrating multiple kernel SVM classification with static and dynamic analysis, the system shows potential for real-time malware detection and analysis. …”
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Architecture of an automated program complex based on a multiple kernel svm classifier for analyzing malicious executable files by Alan Nafiiev, Andrii Rodionov

“…In conclusion, the automated program complex developed in this study demonstrates significant improvements in the accuracy and efficiency of malware detection. By integrating multiple kernel SVM classification with static and dynamic analysis, the system shows potential for real-time malware detection and analysis. …”
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LSKAFF-YOLO: Large Separable Kernel Attentional Feature Fusion Network for Transmission Tower Detection in High-Resolution Satellite Remote Sensing Images by Xiaojin Yan, Zhixuan Li, Yongjie Zhai, Ke Liu, Ke Zhang, Zhenbing Zhao

“…High-resolution satellite remote sensing technology provides an effective solution for the efficient and stable inspection of high-voltage transmission lines. …”
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An Efficient Algorithm for Small Livestock Object Detection in Unmanned Aerial Vehicle Imagery by Wenbo Chen, Dongliang Wang, Xiaowei Xie

“…To capture high-level semantic features, we introduce the Large Kernel Attentions Spatial Pyramid Pooling (LKASPP) module. …”
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