High frequency(HF) communication is widely spread due to some merits like easy deployment and wide communication coverage. Spectrum prediction is a promising technique to facilitate the working frequency selection and...High frequency(HF) communication is widely spread due to some merits like easy deployment and wide communication coverage. Spectrum prediction is a promising technique to facilitate the working frequency selection and enhance the function of automatic link establishment. Most of the existing spectrum prediction algorithms focus on predicting spectrum values in a slot-by-slot manner and therefore are lack of timeliness. Deep learning based spectrum prediction is developed in this paper by simultaneously predicting multi-slot ahead states of multiple spectrum points within a period of time. Specifically, we first employ supervised learning and construct samples depending on longterm and short-term HF spectrum data. Then, advanced residual units are introduced to build multiple residual network modules to respectively capture characteristics in these data with diverse time scales. Further, convolution neural network fuses the outputs of residual network modules above for temporal-spectral prediction, which is combined with residual network modules to construct the deep temporal-spectral residual network. Experiments have demonstrated that the approach proposed in this paper has a significant advantage over the benchmark schemes.展开更多
为了提高哈希技术对旋转操作的识别能力,提出了全局-局部联合特征耦合中心方向信息估计的图像哈希认证技术.首先,引入2D线性插值技术,对输入的图像进行预处理,使其对任意的缩放操作都具有固定尺寸的哈希序列;然后,将预处理图像转变为HS...为了提高哈希技术对旋转操作的识别能力,提出了全局-局部联合特征耦合中心方向信息估计的图像哈希认证技术.首先,引入2D线性插值技术,对输入的图像进行预处理,使其对任意的缩放操作都具有固定尺寸的哈希序列;然后,将预处理图像转变为HSV彩色空间,借助二维离散小波变换(Discrete Wave Transform,DWT)处理V分量,利用其低频系数形成二次图像;再引入奇异值分解(Singular Value Decomposition,SVD)处理二次图像,提取其全局特征,将其作为第一个中间哈希序列;基于Fourier机制,借助残差方法,确定图像的显著区域,获取其位置与纹理的局部特征,作为第二个中间哈希序列;随后,引入Radon变换,通过计算图像的中心方向信息,将其与2个中间哈希序列组合,形成过渡哈希数组;借助Logistic映射,定义动态引擎参数,从而设计了分段异扩散技术,对过渡哈希数组进行加密,输出最终的哈希序列;最后,通过估算原始哈希序列与待检测哈希序列的Hamming距离,将其与用户阈值进行比较,完成图像认证.实验结果显示:与当前的图像哈希技术相比,所提算法具有更高的鲁棒性与安全性,对旋转攻击能力具有更好的识别能力.展开更多
A new gravity survey was carried out in the northern part of the onshore Kribi- Campo sub-basin in Cameroon. The data were incorporated to the existing ones and then analyzed and modeled in order to elucidate the subs...A new gravity survey was carried out in the northern part of the onshore Kribi- Campo sub-basin in Cameroon. The data were incorporated to the existing ones and then analyzed and modeled in order to elucidate the subsurface structure of the area. The area is characterized in its north-western part by considerably high positive anomalies indicative of the presence of a dense intrusive body. We find, 1) from the analysis of the gravity residual anomaly map, the high positive anomalies observed are the signature of a shallow dense structure;2) from the multi-scale analysis of the maxima of the horizontal gradient, the structure is confined between depths of 0.5 km and 5 km;3) from the quantitative interpretation of residual anomalies by spectral analysis, the depth to the upper surface of the intrusive body is not uniform, the average depth of the bottom is h1 = 3.6 km and the depths to particular sections of the roof of the intrusion are h2 = 1.6 km and h3 = 0.5 km;4) and the 3D modeling gives results that are suggestive of the presence of contacts between rocks of different densities at different depths and a dense intrusive igneous body in the upper crust of the Kribi zone. From the 3D model the dense intrusive igneous block is surrounded by sedimentary formations to the south-west and metamorphic formations to the north-east. Both formations have a density of about 2.74 g/cm3. The near surface portions of this igneous block lie at a depth range of 0.5 km to 1.5 km while its lower surface has a depth range of 3.6 km to 5.2 km. The shape of the edges and the bottom of the intrusive body are suggestive of the fact that it forms part of a broader structure underlying the Kribi-Campo sub-basin with a great influence on the sedimentary cover.展开更多
基金supported in part by the National Natural Science Foundation of China (Grants No. 61501510 and No. 61631020)Natural Science Foundation of Jiangsu Province (Grant No. BK20150717)+2 种基金China Postdoctoral Science Foundation Funded Project (Grant No. 2016M590398 and No.2018T110426)Jiangsu Planned Projects for Postdoctoral Research Funds (Grant No. 1501009A)Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province (Grant No. BK20160034)
文摘High frequency(HF) communication is widely spread due to some merits like easy deployment and wide communication coverage. Spectrum prediction is a promising technique to facilitate the working frequency selection and enhance the function of automatic link establishment. Most of the existing spectrum prediction algorithms focus on predicting spectrum values in a slot-by-slot manner and therefore are lack of timeliness. Deep learning based spectrum prediction is developed in this paper by simultaneously predicting multi-slot ahead states of multiple spectrum points within a period of time. Specifically, we first employ supervised learning and construct samples depending on longterm and short-term HF spectrum data. Then, advanced residual units are introduced to build multiple residual network modules to respectively capture characteristics in these data with diverse time scales. Further, convolution neural network fuses the outputs of residual network modules above for temporal-spectral prediction, which is combined with residual network modules to construct the deep temporal-spectral residual network. Experiments have demonstrated that the approach proposed in this paper has a significant advantage over the benchmark schemes.
文摘为了提高哈希技术对旋转操作的识别能力,提出了全局-局部联合特征耦合中心方向信息估计的图像哈希认证技术.首先,引入2D线性插值技术,对输入的图像进行预处理,使其对任意的缩放操作都具有固定尺寸的哈希序列;然后,将预处理图像转变为HSV彩色空间,借助二维离散小波变换(Discrete Wave Transform,DWT)处理V分量,利用其低频系数形成二次图像;再引入奇异值分解(Singular Value Decomposition,SVD)处理二次图像,提取其全局特征,将其作为第一个中间哈希序列;基于Fourier机制,借助残差方法,确定图像的显著区域,获取其位置与纹理的局部特征,作为第二个中间哈希序列;随后,引入Radon变换,通过计算图像的中心方向信息,将其与2个中间哈希序列组合,形成过渡哈希数组;借助Logistic映射,定义动态引擎参数,从而设计了分段异扩散技术,对过渡哈希数组进行加密,输出最终的哈希序列;最后,通过估算原始哈希序列与待检测哈希序列的Hamming距离,将其与用户阈值进行比较,完成图像认证.实验结果显示:与当前的图像哈希技术相比,所提算法具有更高的鲁棒性与安全性,对旋转攻击能力具有更好的识别能力.
文摘A new gravity survey was carried out in the northern part of the onshore Kribi- Campo sub-basin in Cameroon. The data were incorporated to the existing ones and then analyzed and modeled in order to elucidate the subsurface structure of the area. The area is characterized in its north-western part by considerably high positive anomalies indicative of the presence of a dense intrusive body. We find, 1) from the analysis of the gravity residual anomaly map, the high positive anomalies observed are the signature of a shallow dense structure;2) from the multi-scale analysis of the maxima of the horizontal gradient, the structure is confined between depths of 0.5 km and 5 km;3) from the quantitative interpretation of residual anomalies by spectral analysis, the depth to the upper surface of the intrusive body is not uniform, the average depth of the bottom is h1 = 3.6 km and the depths to particular sections of the roof of the intrusion are h2 = 1.6 km and h3 = 0.5 km;4) and the 3D modeling gives results that are suggestive of the presence of contacts between rocks of different densities at different depths and a dense intrusive igneous body in the upper crust of the Kribi zone. From the 3D model the dense intrusive igneous block is surrounded by sedimentary formations to the south-west and metamorphic formations to the north-east. Both formations have a density of about 2.74 g/cm3. The near surface portions of this igneous block lie at a depth range of 0.5 km to 1.5 km while its lower surface has a depth range of 3.6 km to 5.2 km. The shape of the edges and the bottom of the intrusive body are suggestive of the fact that it forms part of a broader structure underlying the Kribi-Campo sub-basin with a great influence on the sedimentary cover.
