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Analysis of Lifting and B-Spline DWT Implementations for Implantable Neuroprosthetics

机译:植入式神经假体的举升和B样条DWT实现分析

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摘要

The large amount of data generated by neuroprosthetic devices requires a high communication bandwidth for extra-cranial transmission, critically limiting the number and utility of wireless implantable applications. Discrete wavelet transform (DWT) can provide exceptionally efficient data compression for neural records. Two energy efficient hardware implementations for one dimensional, multi-level, multi-channel DWT have been compared to identify the optimal approach for real time processing within an implanted device. This paper defines area-power minimized hardware implementation of the lifting and B-spline DWT schemes and analyzes their performance tradeoffs for implantable neuroprosthetics. The lifting scheme is shown to be increasingly superior for a larger number of input channels.
机译:神经修复设备生成的大量数据需要用于颅外传输的高通信带宽,从而严重限制了无线植入应用的数量和实用性。离散小波变换(DWT)可以为神经记录提供异常有效的数据压缩。一维,多级,多通道DWT的两种高能效硬件实现方式已进行比较,以确定用于植入设备内实时处理的最佳方法。本文定义了提升和B样条DWT方案的面积功耗最小的硬件实现方式,并分析了它们在植入式神经假体中的性能折衷。对于大量输入通道,提升方案显示出越来越优越的性能。

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