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the sampling theorem
Examples with "the sampling theorem" and their translation in Chinees
In the field of digital signal processing, the sampling theorem is a fundamental bridge between continuous-time signals (often called "analog signals") and discrete-time signals (often called "digital signals").
在数字信号处理领域，采样定理是连续信号（通常称作"模拟信号"）与离散信号（通常称作"数字信号"）之间的一个基本桥梁。
Around 2004, Emmanuel Candès, Justin Romberg, Terence Tao, and David Donoho proved that given knowledge about a signal's sparsity, the signal may be reconstructed with even fewer samples than the sampling theorem requires.
在2004年左右，Emmanuel Candès、陶哲轩和David Donoho证明了在已知信号稀疏性的情况下，可能凭借较采样定理所规定更少的采样数重建原信号，这一理论也是压缩感知的基石。
In 1999, the Eduard Rhein Foundation awarded Kotelnikov their Basic Research Award "for the first theoretically exact formulation of the sampling theorem".
1999年時愛德華萊茵基金會（英语：Eduard Rhein Foundation）給予科捷利尼科夫基礎研究獎，原因是第一位提出理論正確的採様定理。
T. Whitaker develops the sampling theorem that forms the basis of today's PCM and TCM technologies.
惠特克 开发抽样定理，成为今天的PCM和TCM技术的基础理论。
For bandpass signal under digital quadrature sampling, there is spectrum shifting. Although the sampling frequency satisfies the sampling theorem, it may cause spectrum aliasing.
在数字正交采样中，带通信号存在频谱迁移问题，虽然满足带通信号的采样定理，仍可能产生频谱混叠。
Under certain theoretical conditions, described by the sampling theorem, the original continuous function can be recovered perfectly from the DTFT and thus from the original discrete samples.
在采样定理所描述的一定理论条件下，可以由DTFT完全恢复出原来的连续函数，因此也能从原来的离散样本恢复。
Andere resultaten
Traditional information theory and digital signal processing techniques are based on the Nyquist sampling theorem, and image signals in order to avoid loss of information in the sampling, the sampling rate is greater than twice the required signal bandwidth.
传统的信息理论和数字信号处理技术都是基于奈奎斯特（Nyquist） 采样定理：即为了避免信号和图像在采样时信息的丢失，要求采样速率大于信号带宽的两倍。
However, this faithful reproduction is only possible if the sampling rate is higher than twice the highest frequency of the signal. This is essentially what is embodied in the Shannon-Nyquist sampling theorem.
然而，仅当采样率比信号频率的两倍还高的情况下才可能达到对原始信号的忠实还原，这一规律在采样定理 有所体现。
The Nyquist-Shannon sampling theorem states that a signal can be exactly reconstructed from its samples if the sampling frequency is greater than twice the highest frequency of the signal.
信号处理中的基础定理Nyquist-Shannon采样定理 说被采样信号不能被清晰地表示出频率超过采样频率一半的组成信号。
The technical reasoning behind the rate being chosen is as follows. Human hearing and signal processing The Nyquist-Shannon sampling theorem says the sampling frequency must be greater than twice the maximum frequency one wishes to reproduce.
采样定理 表明采样频率必须大于被采样信号带宽 的两倍，另外一种等同的说法是奈奎斯特频率 必须大于被采样信号的带宽。
The Nyquist-Shannon sampling theorem states that a signal can be exactly reconstructed from its samples if the sampling frequency is greater than twice the highest frequency of the signal.
从理论上说，即使奈奎斯特频率恰好大于信号带宽（但不可相等 ） ， 也足以通过信号的采样重建原信号。
Per the Nyquist-Shannon sampling theorem, the sampling frequency (8 kHz) must be at least twice the highest component of the voice frequency via appropriate filtering prior to sampling at discrete times (4 kHz) for effective reconstruction of the voice signal.
根据奈奎斯特-香农采样定理，采样频率（8kHz）必须至少是在离散时间（4kHz）采样之前进行适当滤波的语音频率最高部分的两倍，从而有效重建语音信号。
The Nyquist-Shannon sampling theorem states that perfect reconstruction of a signal is possible when the sampling frequency is greater than twice the maximum frequency of the signal being sampled, or equivalently, when the Nyquist frequency (half the sample rate) exceeds the highest frequency of the signal being sampled. If lower sampling rates are used, the original signal's information may not be completely recoverable from the sampled signal. For example, if a signal has an upper band limit of 100 Hz, a sampling frequency greater than 200 Hz will avoid aliasing and would theoretically allow perfect reconstruction.
采样定理 表明采样频率必须大于被采样信号带宽 的两倍，另外一种等同的说法是奈奎斯特频率 必须大于被采样信号的带宽。 如果信号的带宽是 100Hz，那么为了避免混叠 现象采样频率必须大于 200Hz。 换句话说就是采样频率必须至少是信号中最大频率分量频率的两倍，否则就不能从信号采样中恢复原始信号。
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Synoniemen voor the sampling theorem in het Engels
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