共查询到17条相似文献,搜索用时 46 毫秒
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随着电子技术的不断发展,各种仪表对高频模拟信号处理能力的要求越来越高.针对目前高速率数模转换芯片的需求与实际的芯片制造工艺及其成本之间的矛盾,本文介绍了随机等效采样的原理,并利用FPGA的内部资源和自下而上的设计方法,实现了实时采样速率50M、等效采样速率1.6G的模块.该模块包括采样触发模块、采样时钟产生模块、短时间测量模块、采样数据暂存模块,并给出了通用兼容的接口.该设计的各个子模块以及顶层模块通过仿真验证,整个模块能够集成到各种信号采集系统中,具有一定的参考价值. 相似文献
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并行交替技术可以极大地提高ADC系统的采样率,但是采样时间偏差造成的周期性非均匀采样严重地降低了系统的性能,消除非均匀采样的影响,对并行交替式ADC系统发展具有积极的推动作用.本文采用频谱分析方法,研究了非理想测试信号对时间偏差估计的影响,提出了一种新的时间偏差估计算法,通过计算机仿真数据验证了算法的有效性.研究了一种基于系统输出序列频谱的滤波重构算法,通过原型电路的实际数据,证明该算法可以有效地重构非均匀采样信号,为该算法的应用提供了理论基础. 相似文献
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本文基于对自适应采样时频分布的推导,提出了一种时频滤波重构自适应采样的方法;从理论上证明了时频滤波重构方法的可行性,也通过实例验证了该重构方法可实现自适应采样的重构,并且,定性分析了时间窗对重构误差的影响;最后,以信噪比作为指标,重点分析了窗函数宽度、种类的影响,评估了影响时频滤波重构自适应采样精度的各因素。 相似文献
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暂态和短时电能质量扰动信号压缩采样与重构方法 总被引:1,自引:0,他引:1
为解决传统的电能质量信号采集压缩方法所面临的采样率高、采样资源浪费及硬件实现成本高的问题,根据压缩传感理论首次提出了暂态和短时电能质量扰动信号的压缩采样与重构方法。该方法将电能质量信号由一维信号变换为二维信号,并根据图像可稀疏表示的原理,使用比Nyquist采样数据少60%以上的随机投影采样值重构原始信号,实现了对暂态和短时电能质量测量数据的压缩采样、采样数据空间稀疏基的选取和基于全变分最小化共轭梯度法的信号重构。针对几类常见单一扰动和含有多重扰动的校准源实测信号进行了算法的仿真分析和实验验证。结果表明,所提出的方法在采样率低于Nyquist采样率73%时,单一扰动的重构信号信噪比除暂态脉冲信号外均大于35dB,多重扰动的重构信号信噪比大于22d,满足电能质量分析的要求。 相似文献
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一种并行交替采样中时基非均匀信号自适应重构方法 总被引:2,自引:0,他引:2
并行采集系统中,通道间时基延迟的不一致性严重降低了系统性能。通过对系统时基误差分量的分析,提出了一种基于自适应控制的非均匀信号重构方法。该方法不需要额外增加校准信号,能在误差估计的同时自动完成信号重构,实时性高;无需重构滤波器,降低了系统设计难度及成本。实验结果表明,经过约250次自适应迭代后,该重构算法能有效估计通道时基误差,具有迭代次数少、运算量小、能动态跟踪时基延迟变化的特点;重构后系统信噪比由原来的33dB提高到48dB,有效位数提高近2.5bit,系统性能得到了大幅提高。 相似文献
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With increasing amounts of hyperspectral images (HSI) and the limitations of the memory requirements, compressive techniques for hyperspectral images have attracted extensive research efforts in recent years. The main difficulty of applying compressed sampling (CS) theory to compression and reconstruction of hyperspectral images lies in using the spatial correlation and spectral correlation of hyperspectral images. In this paper, a reconstruction algorithm of hyperspectral images taking advantage of two‐dimensional compressed sampling (2DCS) and two‐dimensional total variation (2DTV) incorporating spectral prediction (SP) is investigated. In the sampling process, the hyperspectral images are divided into reference bands and common bands, and all bands are sampled using 2DCS independently. In the reconstruction process, the reference bands are reconstructed by 2DTV first. In order to improve the reconstruction quality of common bands, spectral prediction utilizing the spectral correlation between reference bands and common bands is conducted. Then the spectral compensation is computed by using a combination of the prediction value and the initial approximation for the common bands. The residual between the compensation value and the original value is obtained to revise the approximation for the common bands. The algorithm is implemented in an iterative manner to enhance the performance. Experimental results on popular hyperspectral datasets reveal that the proposed algorithm exploiting spectral prediction outperforms the algorithm 2DCS‐2DTV, which does not use spectral correlation, as well as the state‐of‐the‐art algorithms in terms of peak signal‐to‐noise ratio (PSNR). In particular, when the sampling rate of the reference bands is higher than that of the common bands, the proposed algorithm would improve the reconstruction quality dramatically. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. 相似文献
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基于隐马尔科夫树模型的小波域压缩采样信号重构方法 总被引:4,自引:0,他引:4
压缩传感理论利用信号的稀疏性,对其非自适应线性投影进行压缩采样,通过最优化问题准确重构原始信号。传统重构算法仅利用了信号的稀疏性,而未对转换后的信号结构进行分析。提出了一种基于4状态的隐马尔科夫树模型的小波域压缩采样信号的重构方法,相对2状态的隐马尔科夫树模型,该模型能够获取相邻尺度小波系数的更多相关特性,通过仿真结果表明,该算法具有更高的重构精度。 相似文献
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高分辨率合成孔径雷达(SAR)数据量大,导致稀疏重建过程计算量大。复数近似信息传递(CAMP)是一种收敛速度快的稀疏重建算法,经常被用于稀疏信号重建。为了解决计算量大的问题,提出了一种基于CAMP的并行算法,在计算统一设备架构(CUDA)上对CAMP算法中Chirp Scaling算子和排序算法进行优化。在Chirp Scaling算子中,主要对矩阵转置、FFT和IFFT进行并行优化,并引入并行版本的双调排序。最后,利用串行的CAMP算法和并行的CAMP算法分别重构点目标图像。实验结果表明,在正确重建的前提下,并行的CAMP算法的比串行CAMP算法快29.55倍。 相似文献
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讨论了周期信号取样的时域频域特性、非同步取样引起恋和频谱泄漏误差。针对周期信号 软件实现同步的取样方法,给出了相对非同步度和最大相对非同步度的定量计算公式,并对它们的关系特性进行了描述。在周期信号的数字化测量中,为取样参数的选取提供理论依据。 相似文献
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Yin Zhongke Wang Jianying Pierre Vandergheynst 《Frontiers of Electrical and Electronic Engineering in China》2007,2(4):432-434
It is very slow at present to reconstruct an image from its sparse decomposition results. To overcome this one of the main
drawbacks in image sparse decomposition, the property of the energy distribution of atoms is studied in this paper. Based
on the property that energy of most atoms is highly concentrated, an algorithm is proposed to fast reconstruct an image from
atoms’ parameters by limiting atom reconstruction calculating within the atom energy concentrating area. Moreover, methods
for fast calculating atom energy and normalization are also put forward. The fast algorithm presented in this paper improves
the speed of the image reconstructing by approximately 32 times without degrading the reconstructed image quality.
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Translated from Journal of University of Electronic Science and Technology of China, 2006, 35(4): 447–449 [译自: 电子科技大学学报] 相似文献