Potato spindle tuber viroid(PSTVd)disease is one of the major diseases that threatens potato production.Therefore,an advanced,rapid and sensitive detection technology is needed to detect the disease for better control...Potato spindle tuber viroid(PSTVd)disease is one of the major diseases that threatens potato production.Therefore,an advanced,rapid and sensitive detection technology is needed to detect the disease for better control.In order to establish an easier nucleic acid spot hybridization(NASH)method,some studies were tried as the followings:(1)the pre-hybridization step of nucleic acid spot hybridization(NASH)was omitted compared with ordinary way;(2)RNA extraction(phenol extraction and Ames buffer extraction)methods were compared;(3)fixed RNA by UV lamp and oven compared with UV cross-linker;(4)hybridized the RNA in shaking incubator and so on.The results showed that RNA extracted by Ames buffer was more effective than by the phenol extraction method.Besides,the result of hybridization without pre-hybridization step was better than that with 1.5 h of pre-hybridization.The more important discovery was that the shaking incubator could replace the hybridization oven and the ordinary UV lamp could replace the UV cross-linker.After a long term repeated research and testing,a new hybridization system that could rapidly detect the PSTVd by improved NASH technique merely using common instruments and equipment was established.展开更多
An image multi-scale edge detection method based on anti-symmetrical bi-orthogonal wavelet is given in theory. Convolution operation property and function as a differential operator are analyzed,which anti-symmetrical...An image multi-scale edge detection method based on anti-symmetrical bi-orthogonal wavelet is given in theory. Convolution operation property and function as a differential operator are analyzed,which anti-symmetrical bi-orthogonal wavelet transform have. An algorithm for wavelet reconstruction in which multi-scale edge can be detected is put forward. Based on it, a detection method for small target in infrared image with sea or sky background based on the anti-symmetrical bi-orthogonal wavelet and morphology is proposed. The small target detection is considered as a process in which structural background is removed, correlative background is suppressed, and noise is restrained. In this approach, the multi-scale edge is extracted by means of the anti-symmetrical bi-orthogonal wavelet decomposition. Then, module maximum chains formed by complicated background of clouds, sea wave and sea-sky-line are removed, and the image background becomes smoother. Finally, the morphology based edge detection method is used to get small target and restrain undulate background and noise. Experiment results show that the approach can suppress clutter background and detect the small target effectively.展开更多
There are inherent vulnerabilities that are not easily preventable in the mobile Ad-Hoc networks.To build a highly secure wireless Ad-Hoc network,intrusion detection and response techniques need to be deployed;The int...There are inherent vulnerabilities that are not easily preventable in the mobile Ad-Hoc networks.To build a highly secure wireless Ad-Hoc network,intrusion detection and response techniques need to be deployed;The intrusion detection and cluster-based Ad-Hoc networks has been introduced,then,an architecture for better intrusion detection based on cluster using Data Mining in wireless Ad -Hoc networks has been shown.A statistical anomaly detection approach has been used.The anomaly detection and trace analysis have been done locally in each node and possibly through cooperation with clusterhead detection in the network.展开更多
为了解决无人机航拍图片玉米植株中心检测所面临的诸多挑战,包括植株遮挡、形态多样、光照变化以及视觉混淆等问题,提升检测精度和模型的鲁棒性,开发了一种基于YOLO-TSCAS(YOLO with triplet-attention,saliencyadaptive,and centroid a...为了解决无人机航拍图片玉米植株中心检测所面临的诸多挑战,包括植株遮挡、形态多样、光照变化以及视觉混淆等问题,提升检测精度和模型的鲁棒性,开发了一种基于YOLO-TSCAS(YOLO with triplet-attention,saliencyadaptive,and centroid awareness for scenes)模型的玉米植株中心检测算法。该算法通过三重注意力模块、显著性裁剪混合数据增强方法、自适应池化技术和选择性多单元激活函数等技术手段,有效提高了检测精度和鲁棒性。采用三重注意力模块解决目标遮挡和视觉混淆问题,使模型能够更好地关注植株中心区域。采用显著性裁剪混合数据增强方法,在训练过程中引入不同的环境和光照变化,增强了模型对复杂场景的适应能力。结合自适应池化技术提高空间分辨率,有助于捕捉更精细的特征信息,提高检测的准确性。采用选择性多单元激活函数进一步增强了网络的学习能力,使模型能够更好地适应各种场景下的植株中心检测任务。实验结果表明,与现有的YOLOX算法相比,YOLO-TSCAS算法在平均准确率和平均F1值上分别提高了22.73个百分点和0.255,并且平均对数漏检率也显著降低了0.35。与其他流行的检测模型相比,在两类植株中心目标检测精度上也取得了最佳效果。展开更多
基金Supported by Doctoral Research Startup Project of Zhaotong University。
文摘Potato spindle tuber viroid(PSTVd)disease is one of the major diseases that threatens potato production.Therefore,an advanced,rapid and sensitive detection technology is needed to detect the disease for better control.In order to establish an easier nucleic acid spot hybridization(NASH)method,some studies were tried as the followings:(1)the pre-hybridization step of nucleic acid spot hybridization(NASH)was omitted compared with ordinary way;(2)RNA extraction(phenol extraction and Ames buffer extraction)methods were compared;(3)fixed RNA by UV lamp and oven compared with UV cross-linker;(4)hybridized the RNA in shaking incubator and so on.The results showed that RNA extracted by Ames buffer was more effective than by the phenol extraction method.Besides,the result of hybridization without pre-hybridization step was better than that with 1.5 h of pre-hybridization.The more important discovery was that the shaking incubator could replace the hybridization oven and the ordinary UV lamp could replace the UV cross-linker.After a long term repeated research and testing,a new hybridization system that could rapidly detect the PSTVd by improved NASH technique merely using common instruments and equipment was established.
基金Sponsored by China Postdoctoral Science Foundation (20060400400)
文摘An image multi-scale edge detection method based on anti-symmetrical bi-orthogonal wavelet is given in theory. Convolution operation property and function as a differential operator are analyzed,which anti-symmetrical bi-orthogonal wavelet transform have. An algorithm for wavelet reconstruction in which multi-scale edge can be detected is put forward. Based on it, a detection method for small target in infrared image with sea or sky background based on the anti-symmetrical bi-orthogonal wavelet and morphology is proposed. The small target detection is considered as a process in which structural background is removed, correlative background is suppressed, and noise is restrained. In this approach, the multi-scale edge is extracted by means of the anti-symmetrical bi-orthogonal wavelet decomposition. Then, module maximum chains formed by complicated background of clouds, sea wave and sea-sky-line are removed, and the image background becomes smoother. Finally, the morphology based edge detection method is used to get small target and restrain undulate background and noise. Experiment results show that the approach can suppress clutter background and detect the small target effectively.
文摘There are inherent vulnerabilities that are not easily preventable in the mobile Ad-Hoc networks.To build a highly secure wireless Ad-Hoc network,intrusion detection and response techniques need to be deployed;The intrusion detection and cluster-based Ad-Hoc networks has been introduced,then,an architecture for better intrusion detection based on cluster using Data Mining in wireless Ad -Hoc networks has been shown.A statistical anomaly detection approach has been used.The anomaly detection and trace analysis have been done locally in each node and possibly through cooperation with clusterhead detection in the network.
文摘为了解决无人机航拍图片玉米植株中心检测所面临的诸多挑战,包括植株遮挡、形态多样、光照变化以及视觉混淆等问题,提升检测精度和模型的鲁棒性,开发了一种基于YOLO-TSCAS(YOLO with triplet-attention,saliencyadaptive,and centroid awareness for scenes)模型的玉米植株中心检测算法。该算法通过三重注意力模块、显著性裁剪混合数据增强方法、自适应池化技术和选择性多单元激活函数等技术手段,有效提高了检测精度和鲁棒性。采用三重注意力模块解决目标遮挡和视觉混淆问题,使模型能够更好地关注植株中心区域。采用显著性裁剪混合数据增强方法,在训练过程中引入不同的环境和光照变化,增强了模型对复杂场景的适应能力。结合自适应池化技术提高空间分辨率,有助于捕捉更精细的特征信息,提高检测的准确性。采用选择性多单元激活函数进一步增强了网络的学习能力,使模型能够更好地适应各种场景下的植株中心检测任务。实验结果表明,与现有的YOLOX算法相比,YOLO-TSCAS算法在平均准确率和平均F1值上分别提高了22.73个百分点和0.255,并且平均对数漏检率也显著降低了0.35。与其他流行的检测模型相比,在两类植株中心目标检测精度上也取得了最佳效果。
