In this paper, we proposed two kinds of novel XOR gate circuits by using two memristors and four CMOS transistors. The architecture with two positive terminal of memristor connecting realizes AND and XOR logic simultaneously, while the other architecture with the negative terminal of memristor connecting achieves OR and XOR logic simultaneously. Furthermore, these two kinds of hybrid memristor-CMOS XOR logics are further used to build two kinds of single-bit full adders, and the adder circuits are verified by SPICE simulation. Our proposed full adder circuits have a smaller area and lower energy consumption compared to the CMOS technology.

This paper presents an adjustable CMOS voltage reference that achieves a low temperature coefficient (TC) through a novel curvature-compensation technique. The design employs thin/thick-gate NMOS and PMOS pairs to generate complementary voltages (ΔV_GS and ΔV_SG) with opposing second-order curvature. Dynamically scaling ΔV_GS via a programmable k-coefficient and summing with ΔV_SG enables output adjustability and low TC. Implemented in 180 nm CMOS, post-layout simulations show 0.5-0.95 V output range with average TC of 5 ppm/°C (best) to 15 ppm/°C (worst) from -40°C to 125°C. The circuit consumes 456.5 nA at 27°C and achieves -51 dB PSRR (@100 Hz).

This paper introduces a power management circuit (PMC) designed for energy-harvesting (EH) based IoT devices. The proposed PMC combines the benefits of single-capacitor-based power storage and a unit with adjustable charging and discharging thresholds. The single-capacitor-based design significantly reduces the area of power storage and its switch transistors compared to conventional multiple-capacitor-based designs. The threshold adjustable unit minimizes the energy wasted in power transmission and provides energy for the useful process in the EH-based IoT device, accelerating the processing. The proposed circuit is implemented using the TSMC 65 nm process. Post-layout simulation results in HSPICE show that the power management circuit occupies only 1.01× of the area of the conventional smallest circuit while reducing the average processing time of the PMC-managed IoT load by 18.8%.

This paper presents a Robust Adaptive Extended Kalman Filter (RAEKF) method to improve the disturbance rejection control of permanent magnet synchronous motors (PMSMs). PMSM equations are first established. Next, an improved extended Kalman filter is employed to estimate the states of the PMSM, accompanied by adaptive adjustment of the measurement noise covariance matrix. Finally, apply the optimized exponential reaching law in the sliding mode speed controller to improve the response speed. The efficacy of the proposed method is validated through experiments conducted on a 100 W PMSM.

There is a contradiction between the number of communication channels and the number of sensors for wearable sensing systems. Increasing the number of sensors improves monitoring accuracy and function but makes communication difficult because of the increased channel necessity. In this paper, we developed a multi-sensor wearable system with only one single transmission channel and applied it to running gesture monitoring. By combining the k-nearest neighbor (kNN) machine learning method for signal analysis, we achieved classification of different running gestures. The implementation of a single signal transmission channel is based on hardware-level amplitude modulation, which is realized by designing the flexible piezoelectric Polyvinylidene fluoride (PVDF) sensors into various sizes. The different sizes enable amplitude modulation of the sensed signals. The modulated signals from different sensors are merged into a single-channel signal and transmitted to a personal computer via a wireless transmission circuit powered by a piezoelectric energy source inside the system. By utilizing the kNN algorithm, we successfully classified signals with different characteristics. Ultimately, two distinct running gestures were successfully detected and differentiated. This design presents an effective method to reduce signal transmission pathways and energy consumption, while also demonstrating that artificial intelligence algorithms can efficiently analyze data to extract useful information. This design method may provide broad inspiration for the development of wearable devices and holds significant promise for sport sensing.

The peak-to-peak current in dual-active-bridge (DAB) converters significantly impacts conversion efficiency. Under conventional single-phase-shift (SPS) modulation, efficiency degrades at high voltage conversion ratios. This paper proposes an asymmetric duty cycle-based triple-parameter modulation (TPADM) strategy by introducing asymmetric phase-shift angles to expand modulation flexibility. First, the operating principle of TPADM is analyzed, identifying four distinct modes based on switching sequences. Second, steady-state characteristics (inductor current, power transfer) are derived via time-domain analysis for all modes. Third, a piecewise optimization method using Karush-Kuhn-Tucker (KKT) conditions minimizes peak-to-peak current, maximizing efficiency. Finally, experimental results validate the superiority of TPADM over SPS in reducing current stress and improving efficiency across wide voltage ranges.