Adaptive Learning-Based Compressive Sampling for Low-power Wireless Implants
Adaptive Learning-Based Compressive Sampling for Low-power Wireless Implants
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DOI：
10.1109/tcsi.2018.2853983
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发表时间：
2018-11-01
影响因子：
5.1
通讯作者：
Cevher, Volkan
中科院分区：
文献类型：
--
作者：
Aprile, Cosimo;Ture, Kerim;Cevher, Volkan
如今，可植入系统被用于将人脑与外部设备连接起来，以了解并潜在地治疗神经系统疾病。最主要的设计约束是系统的面积和功率。在本文中，我们实现并结合了先进的压缩采样算法来降低无线遥测的功耗要求。此外，我们采用可变压缩，根据实际信号需要动态修改器件性能。本文提出了一种用于无线植入式设备的面积高效自适应系统，该系统动态地降低了功率要求，压缩率从8倍降至64倍，具有高重建性能，并在人体数据集上进行了定性演示。设计并测试了两种不同版本的编码器，一种是带自适应压缩的，另一种是不带自适应压缩的，分别需要230 × 235 μ m和200 × 190 μ m的面积，而在0.8 V下仅消耗0.47 μ W。该系统由一个4线圈电感链路供电，测量功率传输效率为36%，而外部和内部线圈之间的距离为10毫米。无线数据通信由OOK调制的窄带和IR-UWB发射机建立，分别消耗124.2 pJ/bit和45.2 pJ/pulse。
Implantable systems are nowadays being used to interface the human brain with external devices, in order to understand and potentially treat neurological disorders. The most predominant design constraints are the system's area and power. In this paper, we implement and combine advanced compressive sampling algorithms to reduce the power requirements of wireless telemetry. Moreover, we apply variable compression, to dynamically modify the device performance, based on the actual signal need. This paper presents an area-efficient adaptive system for wireless implantable devices, which dynamically reduces the power requirements yielding compression rates from 8x to 64x, with a high reconstruction performance, as qualitatively demonstrated on a human data set. Two different versions of the encoder have been designed and tested, one with and the second without the adaptive compression, requiring an area of 230 x 235 mu m and 200 x 190 mu m, respectively, while consuming only 0.47 mu W at 0.8 V. The system is powered by a 4-coil inductive link with measured power transmission efficiency of 36%, while the distance between the external and internal coils is 10 mm. Wireless data communication is established by an OOK modulated narrowband and an IR-UWB transmitter, while consuming 124.2 pJ/bit and 45.2 pJ/pulse, respectively.