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    • 6. 发明申请
    • Method and an apparatus for adaptively learning a sparse impulse response of a continuous channel
    • 用于自适应地学习连续信道的稀疏脉冲响应的方法和装置
    • US20110096811A1
    • 2011-04-28
    • US12912524
    • 2010-10-26
    • Martin VetterliYue LuMartin McCormick
    • Martin VetterliYue LuMartin McCormick
    • H04B1/00
    • H04L25/0212H04L25/025H04L25/03038H04L25/03987H04L2025/03617H04L2025/03636Y10T428/115
    • A method for adaptively learning a sparse impulse response (100) of a continuous channel to which an input signal (x (t)) is applied and which delivers an output signal (y(t)), comprising the following steps: low-pass filtering the input signal and the output signal and obtain a filtered input signal (xF(t)) and a filtered output signal (yF(t)) sampling the filtered input signal and the filtered output signal with a sampling rate below the Nyquist rate and obtaining a sampled input signal (xS(t)) and a sampled output signal (yS(t)) retrieving from the sampled input signal (xS(t)) and the sampled output signal (yS(t)) an estimate (400) of the sparse impulse response (100) of the continuous channel. This method can be applied in CDMA channels, in acoustic room context, in ultra-wideband ranging and line echo cancellation problems, in transmission systems for optical fibres, in body scan devices, to name a few.
    • 一种用于自适应地学习施加输入信号(x(t))的连续信道的稀疏脉冲响应(100)并且传送输出信号(y(t))的方法,包括以下步骤:低通 对输入信号和输出信号进行滤波,并获得滤波后的输入信号(xF(t))和滤波后的输出信号(yF(t)),以低于奈奎斯特速率的采样速率对经过滤波的输入信号和滤波后的输出信号进行采样; 从采样输入信号(xS(t))和采样输出信号(yS(t))获取采样输入信号(xS(t))和采样输出信号(yS(t))估计值(400) 的连续信道的稀疏脉冲响应(100)。 这种方法可以应用于CDMA信道,在声学室上下文中,在超宽带测距和线路回波消除问题中,在用于光纤的传输系统中,在身体扫描设备中。
    • 9. 发明申请
    • SPARSE SAMPLING OF SIGNAL INNOVATIONS
    • US20090191814A1
    • 2009-07-30
    • US12139117
    • 2008-06-13
    • Thierry BluMartin VetterliLionel Coulot
    • Thierry BluMartin VetterliLionel Coulot
    • H04B1/00
    • H04L25/03006G06F17/10H03M1/0626H03M1/127H04B1/71637H04L27/2647
    • Signals, including signals from outside of the subspace of bandlimited signals associated with the Shannon theorem, are acquired while still providing an acceptable reconstruction. In some aspects a denoising process is used in conjunction with sparse sampling techniques. For example, a denoising process utilizing a Cadzow algorithm may be used to reduce the amount of noise associated with sampled information. In some aspects the denoising process may be iterative such that the denoising process is repeated until the samples are denoised to a sufficient degree. In some aspects, the denoising process converts a set of received samples into another set corresponding to a signal with a Finite Rate of Innovation (FRI), or to an approximation of such a signal. The disclosure relates in some aspects to combination of a denoising process with annihilating filter methods to retrieve information from a noisy, sparse sampled signal. The disclosure relates in some aspects to determining a sampling kernel to be used to sample the signal based on noise associated with the signal. The disclosure relates in some aspects to determining the number of samples to obtain from a signal over a period of time based on noise associated with the signal. The disclosure relates in some aspects to determining the finite number of innovations of a received signal.