A Sub-Nanosecond Gate Bias-Switching Circuit for GaN RF Power Amplifiers

In this letter, we present a design of a fast gate-switching power amplifier (GSPA) aimed at reducing its power consumption. This GSPA features a dedicated fast gate-switching circuit that commutates the gallium nitride (GaN) transistor between a nominal gate bias voltage (GSPA ON) and a strong negative voltage (GSPA OFF), thereby generating two discrete output power levels in an RF-pulsewidth modulation (PWM) fashion. A fast gate-switching circuit, including a commercial digital voltage isolator, is designed to switch between two gate bias voltages. The gate stability resistor and transmission line (TL) are carefully placed and designed to reduce the GSPA parasitic bias line and enable fast switching. Measured results provided a rise and fall time of 750 and 950 ps, respectively, and achieved RF pulsewidths as narrow as 5.88 ns, thus corresponding to a 170-MHz bandwidth

Recent results on proportional fair scheduling for mmWave-based industrial wireless networks

Millimeter wave (mmWave) communication has recently attracted significant attention from both industrial and academic communities. The large bandwidth availability as well as low interference nature of mmWave spectrum is particularly attractive for industrial communication. However, inherent challenges such as coverage and blockage of mmWave communication cause highly fluctuated channel quality. This paper explores wireless medium access control (MAC) schedulers for mmWave-based industrial wireless applications. Our objective is to design a high-performance and enhanced fairness MAC scheduling algorithm that responds rapidly to channel variations. The key contribution of our work is a method to modify the standard proportional fair (SPF) scheduler. It introduces more flexibility and dynamic properties. Compared to the SPF, our enhanced proportional fair (EPF) scheduler not only improves the priority for users in poor channel conditions but also accelerates the reaction time in fluctuated channel conditions. By providing higher fairness for all users and enhancing system robustness, it particularly adapts to the scatter-rich industrial mmWave communication environment. Through extensive performance evaluation based on the widely accepted network simulator (ns-3), we show that the new scheduler achieves better performance in terms of delivering ultra-low latency and reliable services over mmWave-based industrial communication.Comment: Accepted for publication in IEEE VTC Fall 202

RuNi/MMO Catalysts Derived from a NiAl-NO₃-LDH Precursor for CO Selective Methanation in H₂-Rich Gases

CO selective methanation (CO-SMET) is a promising method for deep CO removal from H2-rich gases. In this study, a series of RuNi/MMO catalysts are prepared using the support MMO-N derived from NiAl-NO3-LDHs, which was prepared from NiAl-CO3-LDHs via an acid–alcohol ion-exchange reaction. The prepared catalysts were characterized by XRD, SEM, TEM, XPS, H2-TPR, CO-TPD, CO2-TPD, NH3-TPD, and TG. The RuNi/MMO-N catalyst demonstrated excellent CO-SMET performance, successfully reducing the CO to less than 10 ppm with a selectivity greater than 50% in a reaction temperature window ranging from 180 °C to 260 °C. Compared with similar catalysts derived from NiAl-CO3-LDHs, the exceptional CO-SMET capability of the RuNi/MMO-N catalyst is suggested to be associated with a more effective hydrogen spillover, a larger number of electron-rich Ni sites, and a higher density of acid sites on the surface of RuNi/MMO-N, which are conducive to CO adsorption and the inhibition of CO2 methanation

A High-Performance Transfer Learning-Based Model for Microwave Structure Behavior Prediction

Microwave structure behavior prediction enables the estimation of circuit response over a frequency range, playing a crucial role in the design of radio frequency (RF) structures. Deep neural network (DNN) approaches have demonstrated their capability to simulate microwave structure behaviors. Nonetheless, the quality and utility of the model are constrained by the availability of data and computational capabilities. These inherent disadvantages hinder the extensive application of DNN in microwave structure behavior prediction. Transfer learning has recently been produced as a method offering improved accuracy and speed for predicting microwave circuit behavior. This paper proposes a novel transfer learning-based model to expedite the prediction process for a sequence of frequency samples. Through experimental validation, it is illustrated that the proposed methodology outperforms the conventional DNN techniques for microwave structure behavior prediction by effectively reducing the required data and shortening the training time. The proposed model also facilitates the fine-tuning of hyperparameters and reduces the simulator computing load.</p

Dual-comb optomechanical spectroscopy

Abstract Optical cavities are essential for enhancing the sensitivity of molecular absorption spectroscopy, which finds widespread high-sensitivity gas sensing applications. However, the use of high-finesse cavities confines the wavelength range of operation and prevents broader applications. Here, we take a different approach to ultrasensitive molecular spectroscopy, namely dual-comb optomechanical spectroscopy (DCOS), by integrating the high-resolution multiplexing capabilities of dual-comb spectroscopy with cavity optomechanics through photoacoustic coupling. By exciting the molecules photoacoustically with dual-frequency combs and sensing the molecular-vibration-induced ultrasound waves with a cavity-coupled mechanical resonator, we measure high-resolution broadband ( > 2 THz) overtone spectra for acetylene gas and obtain a normalized noise equivalent absorption coefficient of 1.71 × 10−11 cm−1·W·Hz−1/2 with 30 GHz simultaneous spectral bandwidth. Importantly, the optomechanical resonator allows broadband dual-comb excitation. Our approach not only enriches the practical applications of the emerging cavity optomechanics technology but also offers intriguing possibilities for multi-species trace gas detection

IEEE Comms Mag LoRa datasets

The data in this repository is associated with the IEEE Communications Magazine article by Manish Nair et al, ‘IoT Device Authentication Using Self-Organizing Feature Map Data Sets