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A novel synthesis and growth method achieving vertically aligned zinc oxide (ZnO) nanowires on a silicon dioxide (SiO(2)) coated silicon (Si) substrate is demonstrated. The growth direction of the ZnO nanowires is determined by the crystal structure of the ZnO seed layer, which is formed by the oxidation of a DC-sputtered Zn film. The [002] crystal direction of the seed layer is dominant under optimized thickness of the Zn film and thermal treatment. Vertically aligned ZnO nanowires on SiO(2) coated Si substrate are realized from the appropriately thick oxidized Zn seed layer by a vapor-solid growth mechanism by catalyst-free thermal chemical vapor deposition (CVD). These experimental results raise the possibility of using the nanowires as functional blocks for high-density integration systems and/or photonic applications.

The importance of an embedded wearable device with automatic detection and alarming cannot be overstated, given that 15–30% of patients with atrial fibrillation are reported to be asymptomatic. These asymptomatic patients do not seek medical care, hence traditional diagnostic tools including Holter are not effective for the further prevention of associated stroke or heart failure. This is likely to be more so in the era of COVID-19, in which patients become more reluctant on hospitalization and checkups. However, little literature is available on this important topic. For this reason, this study developed efficient deep learning with model compression, which is designed to use ECG data and classify arrhythmia in an embedded wearable device. ECG-signal data came from Korea University Anam Hospital in Seoul, Korea, with 28,308 unique patients (15,412 normal and 12,896 arrhythmia). Resnets and Mobilenets with model compression (TensorFlow Lite) were applied and compared for the diagnosis of arrhythmia in an embedded wearable device. The weight size of the compressed model registered a remarkable decrease from 743 MB to 76 KB (1/10000), whereas its performance was almost the same as its original counterpart. Resnet and Mobilenet were similar in terms of accuracy, i.e., Resnet-50 Hz (97.3) vs. Mo-bilenet-50 Hz (97.2), Resnet-100 Hz (98.2) vs. Mobilenet-100 Hz (97.9). Here, 50 Hz/100 Hz denotes the down-sampling rate. However, Resnets took more flash memory and longer inference time than did Mobilenets. In conclusion, Mobilenet would be a more efficient model than Resnet to classify arrhythmia in an embedded wearable device.

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