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An eddy-current angular displacement sensor (ECS) interface intended for high precision industrial applications is presented in this paper. According to the proposed systematic model of ECS interface, offset, gain error and non-linearity are the main directions for optimizing the circuit design. Considering non-linearity, capacitors are utilized to convert voltage into current instead of transconductance (Gm) blocks. Chopper stabilization technique is introduced to eliminate noise and offset. The transceiver is designed in 0.18-µm CMOS technology. Post-simulation results show that the ECS realizes angle error

One of the major issues in implementation of hash functions algorithm is how to decrease the critical path delay and required area effectively. How to expand flexibility, generality and integrity are also important issues as well. As more information are stored and processed on the general-purpose microcontroller in the edge of internet of things (IoT), more efficient data encryption must be ensured. Before encryption of large data sets, it is preferred that the encryption is adapt to asynchronous operation for different volume and arrival data rate. In this paper, an asynchronous SHA-256 hardware accelerator with 32 computation clock cycles for each block is proposed and is at liberty to buffer intermediate hash values, which provides high throughput, robust transmission, adjustable computation frequency and is also easily integrated to microcontroller. The proposed design was implemented in SMIC 40nm technology and performed simulation and verification with Synopsys VCS. The results of post-synthesis simulation show the throughput up to 3.6Gbps.

Nowadays, cardiovascular disease is still one of the primary diseases that limit life expectation of humans. To address this challenge, this work reports an Internet of Medical Things (IoMT)-based cardiovascular healthcare system with cross-layer optimization from sensing patch to cloud platform. A wearable ECG patch with a custom System-on-Chip (SoC) features a miniaturized footprint, low power consumption, and embedded signal processing capability. The patch also integrates wireless connectivity with mobile devices and cloud platform for optimizing the complete system. On the big picture, a “wearable patch-mobile-cloud” hybrid computing framework is proposed with cross-layer optimization for performance-power trade-off in embedded-computing. The measurement results demonstrate that the on-patch compression ratio of the raw ECG signal can reach 12.07 yielding a percentage root mean square variation of 2.29%. In the test with the MIT-BIH database, the average improvement of signal to noise ratio and mean square error are 12.63 dB and 94.47%, respectively. The average accuracy of disease prediction operation executed in cloud platform is 97%.

A wireless-powered implantable sensor tag for neural recording and brain stimulation (NRS) based on standard Near Field Communication (NFC) protocol is presented. A NRS system is thus established with this dedicated but flexible sensor tag and a Proximity Coupling Device (PCD), which can be replaced with an NFC smart phone. The flexible printed sensor tag consists of a customized NRS System on Chip (SoC) and a customized NFC Proximity Card (PICC) SoC. Neural signals are captured by the sensor tag and transmitted to the PCD wirelessly. The measured signal-to-noise-and-distortion-rate (SNDR) was 35 dB in vitro.