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首页> 外文会议>Conference on enabling photonic technologies for aerospace applications >Photonic Analog-to-Digital Conversion Based on Oversampling Techniques
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Photonic Analog-to-Digital Conversion Based on Oversampling Techniques

机译：基于过采样技术的光子模数转换

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A novel photonic approach to analog-to-digital (A/D) conversion based on temporal and spatial oversampling techniques in conjunction with a smart pixel hardware implementation of a neural algorithm is described. In this approach, the input signal is first sampled at a rate higher than that required by the Nyquist criterion and then presented spatially as the input to the 2-D error diffusion neural network consisting of M x N pixels. The neural network processes the input oversampled analog image and produces an M x N pixel binary output image which is an optimum representation of the input analog singla. Upon convergence, the neural network minimizes an energy function representing the frequency-weighted squared error between the input analog image and the output halftoned image. Decimation and low-pass filtering techniques, common to oversampling A/D conversion, each pixel constitutes a simple oversampling modulator thereby producing a distributed A/D architecture. Spectral noise shaping across the array diffuses quantization error thereby improving the signal-to-noise ratio (SNR) performance. Here, each quantizer within the netowrk is embedded in a fully-connected, distributed mesh fedback loop which spectrally shapes the overall quantization noise significantly reducing the effects of component mismatch typically associated with parallel or channelized A/D aproaches. The 2-D neural array provides higher aggesgate bit rates which can extend the useful bandwidth of oversampling converters.
机译：基于结合时间和空间过采样技术的智能像素硬件实现神经算法来模拟 - 数字（A / d）转换的新型光子方法进行说明。在这种方法中，输入信号首先在一个速率比由奈奎斯特准则需要更高的采样，然后在空间上表示为输入到由M的2-d的误差扩散的神经网络×N个像素。该神经网络处理输入过采样的模拟图像，并产生M×N个像素，其是将输入的模拟singla的最佳表示的二进制输出图像。在收敛时，神经网络最小化表示输入模拟图像和输出半噪声图像之间的频率加权平方误差的能量函数。抽取和低通滤波技术，常见的过采样A / d转换，每一像素构成一个简单的过采样调制器，从而产生一个分布式A / d架构。跨越阵列扩散量化误差从而提高了信噪比（SNR）性能频谱噪声整形。在此，元网络内的每个量化器被嵌入在完全连接，分布式网状fedback环路频谱形状的整体量化噪声显著还原成分失配通常与平行或信道化A / d aproaches相关联的影响。 2-d神经阵列提供较高aggesgate比特率可以延伸过采样转换器的有用带宽。

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