This paper presents a system on chip (SoC) designed for high-density transcranial electrical stimulation (HD-TES) with real-time bio-impedance sensing. Multiple chips can work collaboratively to generate arbitrary waveforms for HD-TES including temporal interference (TI) stimulation. This design implements a combined digital calibration and analog switch mechanism for common-mode voltage holding (CMVH), ensuring long-time safety of HD-TES. The SoC can perform impedance measurement (IM) through transcranial alternating current stimulation (tACs) allowing to monitor the biological impedance variations during tACs. To ensure the safety of current stimulation, switched-capacitor overcurrent protection (OCP) circuit is integrated. Measured results show that the SoC can apply arbitrary stimulation currents within the voltage range of ±13V. The amplitude range of the current is from - 2.5mA to 2.5mA with a 10 μA minimum step. The common-mode voltage of the electrode remains stable near 0V when operating in HD-TES mode. Impedance measurement error is less than 5% within the extensive range of 0.5kΩ to 500kΩ.

DAC mismatch is a significant error in NS-SAR ADC. It introduces an essentially nonlinear behavior and causes harmonic distortion of the signal. In this paper, we propose an improved foreground digital calibration method. This method is combined with noise shaping technology, improves calibration accuracy and eliminates the impact of error accumulation on high-bit weights. Thus, it elegantly solves the harmonic distortion caused by the capacitor mismatch. Behavioral simulation of the improved foreground digital calibration method is demonstrated in a 12-bit prototype NS-SAR ADC. As a result, the NS-SAR ADC performance ENOB achieves 17.2-bit at 32 × OSR. Compared with conventional foreground calibration method, the SNDR increases from 81.4 dB to 106 dB and the SFDR increases from 86.2 dB to 117.4 dB. A 200-point Monte Carlo simulation demonstrates the robustness of the proposed calibration method.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is emerging as a promising technology by achieving full-space coverage and further improving system performance. However, most existing works adopted an independent phase-shift model, which is high-cost and may be difficult to achieve in realistic wideband systems. Consequently, a coupled phase-shift STAR-RIS enhanced downlink multi-user multiple-input single-output orthogonal frequency division multiplexing system is investigated for both unicast and broadcast communications in this paper. We aim to maximize the average sum-rate (ASR) for all subcarriers by jointly optimizing the precoding matrices and the reflecting and transmitting coefficients (RTCs). Specifically, a block coordinate descent algorithm is proposed to iteratively design each block of a multiblock problem reformulated by the original one. The precoding matrices are optimized by the Lagrangian multiplier method for low computational complexity. For the RTCs, an element-based alternating optimization algorithm is proposed to optimize the coupled phase-shift and amplitude coefficients. Simulation results validate the effectiveness of the proposed algorithm by comparing the ASR with that of other benchmarks. Moreover, its performance closely approaches the upper bound under various practical user proportion scenarios on both sides of the STAR-RIS.

IoT-empowered wearable multimodal monitoring system (IEMS), an IEMS for neurocritical care is developed to perform simultaneous monitoring of 8-channel electroencephalogram (EEG), 2-channel regional cerebral oxygen saturation (rSO2) based on near-infrared spectrum (NIRS), body surface temperature, electrocardiogram (ECG), photoplethysmography (PPG), and bioimpedance (Bio-Z). The IoT platform and wireless devices enable the patients’ signals available for remote diagnosis. Besides, analysis functions and artificial intelligence (AI) algorithms could be embedded in both the bedside platform and the cloud server to support physicians with clinical decisions. In the multimodal neural monitoring device, the following designs are adopted to face the neurological intensive care unit (NICU) application. Active electrodes and preamplifying free topology provide better signal quality and a larger dynamic range (DR). Dedicated low-power designs ensure the device lasts 10 h of operation. A nonwoven headset improves long-term wearability, which is also quick and easy to install. In the cardiovascular patch, the disposable electrode patch based on elastic materials ensures tight and comfortable contact with skin. Besides, the reusable wireless sensing module is tiny (20 mm$\times16$mm$\times9$mm) but could measure ECG, PPG, and Bio-Z simultaneously. Electrical tests and human subject (healthy volunteers and NICU patients) studies were conducted to examine the performance. The EEG channels show 130.75-dB DR and 0.84-$\mu {}\text{V}_{\mathrm{ RMS}}$input-referred noise, which also yields signals with high quality during human EEG monitoring. The NIRS channels exhibit good linearity and are able to operate under severe ambient interference. The temperature sensors show ±0.2 °C accuracy. Moreover, the system complies with mandatory standards for medical equipment. Overall, an IEMS can meet the requirements for NICU applications and could provide better comfort during long-term wearing.

With the widespread connectivity of everyday devices realized by the advent of the Internet of Things (IoT), communication between users of different devices has become increasingly close. In practical scenarios, obstacles present between the transceiver may cause a deterioration in the quality of the received signals. Therefore, the reconfigurable intelligent surface (RIS) is employed to create virtual Line-of-Sight (LoS) channels in an IoT network. Specifically, this article aims at maximizing the sum-rate of the RIS-assisted multiuser multiple-input–single-output (MU-MISO) communication systems by jointly optimizing the phase shift matrix of the RIS and transmit beamforming. To solve the formulated nonconvex problem, a piecewise-deep reinforcement learning (DRL) algorithm is proposed in this article. Unlike the existing alternative optimization (AO) algorithms, the proposed algorithm avoids falling into the local optimal by using an exploration mechanism. Moreover, piecewise-DRL can reduce the action dimension, allowing the algorithm to obtain faster convergence. Simultaneously, this algorithm also ensures that the parameters of the two-part networks are updated to generate a larger system sum-rate by unsupervised joint optimization. Simulations in various circumstances reveal that the proposed approach is more robust and presents better stability and faster convergence than previous state-of-the-art algorithms while obtaining competitive performance.