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In spin Hall nano-oscillators (SHNOs), pure spin currents drive local regions of magnetic films and nanostructures into auto-oscillating precession. If such regions are placed in close proximity to each other they can interact and may mutually synchronize. Here, we demonstrate robust mutual synchronization of two-dimensional SHNO arrays ranging from 2 × 2 to 8 × 8 nano-constrictions, observed both electrically and using micro-Brillouin light scattering microscopy. On short time scales, where the auto-oscillation linewidth Δf is governed by white noise, the signal quality factor, Q=f∕Δf, increases linearly with the number of mutually synchronized nano-constrictions (N), reaching 170,000 in the largest arrays. We also show that SHNO arrays exposed to two independently tuned microwave frequencies exhibit the same synchronization maps as can be used for neuromorphic vowel recognition. Our demonstrations may hence enable the use of SHNO arrays in two-dimensional oscillator networks for high-quality microwave signal generation and ultra-fast neuromorphic computing.Synchronization of oscillators can be used to carry out cognitive tasks. Large two-dimensional arrays of synchronized spin Hall nano-oscillators have now been demonstrated, and may in future enable neuromorphic computing on the nanoscale.

With the increasing pursuit of intelligent systems, the integration of human components into functional systems provides a promising route to the ultimate human-compatible intelligent systems. In this work, we explored the integration of the human hand as the powerless and multiplexed infrared (IR) light source in different functional systems. With the spontaneous IR radiation, the human hand provides a different option as an IR light source. Compared to engineered IR light sources, the human hand brings sustainability with no need of external power and also additional level of controllability to the functional systems. Besides the whole hand, each finger of the hand can also independently provide IR radiation, and the IR radiation from each finger can be selectively diffracted by specific gratings, which helps the hand serve as a multiplexed IR light source. Considering these advantages, we show that the human hand can be integrated into various engineered functional systems. The integration of hand in an encryption/decryption system enables both unclonable and multilevel information encryption/decryption. We also demonstrate the use of the hand in complex signal generation systems and its potential application in sign language recognition, which shows a simplified recognition process with a high level of accuracy and robustness. The use of the human hand as the IR light source provides an alternative sustainable solution that will not only reduce the power used but also help move forward the effort in the integration of human components into functional systems to increase the level of intelligence and achieve ultimate control of these systems.

Several studies have formulated methods for monitoring welding quality based on acoustic emission (AE) signals. However, most of these studies have focused only on developing a data-driven model to predict welding quality. The relationships between weld pool behavior and AE signal features, as well as the features of AE signals propagating through two-layer metal sheets, have not been investigated carefully. Because the variation of material/laser beam interaction and weld pool condition during welding can easily alter AE signal features, it is still challenging to deliver a reliable quality monitoring system to the production line. Therefore, single-layer spot welding experiments were conducted under different laser power levels in this study to examine the fundamental mechanism generating various AE signal features in different welding stages. Moreover, experiments were performed to investigate the differences in AE signal propagation through a two-layer steel sheet and a two-layer steel plate. Potential mechanisms of AE signal generation were determined for different welding stages based on the fundamental theory of AE generation, the analysis of generated AE signals, sound signals corresponding to the generated AE signals, and photographs captured using a high-speed camera. The results show that, in the first two welding stages, the frequencies of the AE signals concentrated in different ranges and the AE signals generated at the beginning of heating were unaffected by the laser power. In the third welding stage, high gas pressure accumulated inside the produced keyhole, which resulted in the random generation of bubbles. These bubbles subsequently collapsed, which resulted in the generation of a burst AE signal. Experimental results on the propagation of AE signals through two-layer steel sheets and two-layer steel plates indicated that the generated AE signals collected at different locations were affected by the contact condition between layers and signal propagation paths. This result provides crucial information when developing a reliable welding quality monitoring system with an AE sensor on the base plate in laser spot welding.

We establish a preliminary model of neural signal generation and transmission based on our previous research, and use this model to study signal transmission on unmyelinated nerves. In our model, the characteristics of neural signals are studied both on a long-time and a short time scale. On the long-time scale, the model is consistent with the circuit model. On the short time scale, the neural system exhibits a THz and infrared electromagnetic oscillation but the energy envelope curve of the rapidly oscillating signal varies slowly. In addition, the numerical method is used to solve the equations of neural signal generation and transmission, and the effects of the temperature on signal transmission are studied. It is found that overly high and overly low temperatures are not conducive to the transmission of neural signals.

Physiological and kinematic signals from humans are often used for monitoring health. Several processes of interest (e.g., cardiac and respiratory processes, and locomotion) demonstrate periodicity. Training models for inference on these signals (e.g., detection of anomalies, and extraction of biomarkers) require large amounts of data to capture their variability, which are not readily available. This hinders the performance of complex inference models. In this work, we introduce a methodology for improving inference on such signals by incorporating phase-based interpretability and other inference tasks into a multi-task framework applied to a generative model. For this purpose, we utilize phase information as a regularization term and as an input to the model and introduce an interpretable unit in a neural network, which imposes an interpretable structure on the model. This imposition helps us in the smooth generation of periodic signals that can aid in data augmentation tasks. We demonstrate the impact of our framework on improving the overall inference performance on ECG signals and inertial signals from gait locomotion.

Applying deep learning techniques to solve radar signal processing problems such as phase-enhanced direction of arrival (DOA) estimation involves the acquisition of large-scale data, which are usually small and difficult to obtain. Hence, we develop a steering vector embedding complex-valued generative adversarial network (CVGAN) for radar data generation; this is an excellent technique for increasing the size of the dataset we can collect. First, a novel CVGAN model is proposed to mine real radar data features and generate fake data with similar distribution characteristics through game theory. To improve the interpretability of the data generated by CVGAN, a steering vector embedding technique is proposed, in which fake radar data with the desired DOA are generated by embedding the corresponding steering vector. The proposed CVGAN model can effectively reduce the dependence of the model on the amount of data we need to collect. The simulation and experimental results show that the proposed CVGAN model can fully explore the data features contained in a small number of samples and augment the datasets, and the phase enhancement learning model combined with the CVGAN has higher estimation accuracy and generalizability for DOA estimation.

The dendritic cell algorithm (DCA) is a classification algorithm based on the biological antigen presentation process. Its classification efficiently depends on a data preprocessing procedure, where feature selection and signal categorization are the main work for generating input signals. Several methods have been employed (e.g., correlation coefficient and rough set theory). Those studies preferred to measure the importance of features by evaluating their relevance to the class. Generally, they determined a mapping relationship between important features and signal categories of DCA based on expert knowledge. Typically, those studies ignore the effect of unimportant features, and the mapping relationship determined by expertise may not produce an optimal classification result. Thus, a hybrid model, GA-DCA, is proposed for feature selection and signal categorization based on the genetic algorithm (GA). This study transforms feature selection and signal categorization into a grouping task (i.e., divides features into different signal groups). This study introduces a permutation-based expression with “Group\" symbols to represent a potential feature grouping scheme. Correspondingly, adaptive operators are proposed to expand each possible scheme on the path from the initial feature grouping to the best feature grouping. GA-DCA searches the optimal feature subset and automatically assigns them to the most suitable signal groups without expertise. This study verifies the proposed approach by employing the UCI Machine Learning Repository and Keel-dataset Repository, and significant performance improvement is achieved.

With the increasing communication frequencies in 6G networks, high-speed terahertz (THz) modulation signal generation has become a critical research area. This study first proposes an on-chip high-speed THz modulation signal generation system based on lithium niobate (LN), which integrates a pair of racetrack resonator-integrated Mach–Zehnder modulators (RMZMs) with a chirped periodically poled lithium niobate (CPPLN) waveguide. The on-chip system combines near-infrared electro-optic modulation and cascaded difference-frequency generation (CDFG) for high-speed THz modulation signal generation. At 300 K, utilizing two input optical waves at frequencies of 193.55 THz and 193.14 THz, this on-chip system enables high-speed THz modulation signal generation at 0.41 THz, with a 1 Gbit/s modulation rate and a 0.25 V modulation voltage. During the simulation, when the intensity of the input optical waves is 1000 MW/cm2, the generated 0.41 THz signal reaches a peak intensity of 21.24 MW/cm2. Furthermore, based on theoretical analysis and subsequent simulation, the on-chip system is shown to support a maximum modulation signal generation rate of 7.75 Gbit/s. These results demonstrate the potential of the proposed on-chip system as a compact and efficient solution for high-speed THz modulation signal generation.

The aim of this study is to develop a research platform for rapid prototyping of the power electronics converters for solar photovoltaic (PV) system applications. This study describes the field-programmable gate array (FPGA)-based hardware-in-the-loop (HIL) simulation of voltage source inverter (VSI) used for PV system power conversion. The PV system and inverter models are realised in simulation as part of the HIL to test the real-time functionality of the FPGA controller. The generation of switching control signals for the VSI and its interface with the PV system is developed through the Xilinx System Generator (XSG) domain. The XSG automatically generates the VHSIC hardware description language (VHDL) code using hardware description language co-simulation for generation of gating signal for modulation of the VSI. To validate the proposed approach, the sinusoidal pulse-width modulation using bipolar and unipolar switching schemes and current control method have been tested for the PV supported VSI. The proposed approach of the rapid prototype model has been designed and implemented in the laboratory through XSG and MATLAB/SIMULINK interface. Performance comparison between the software simulation and real-time HIL simulation has been demonstrated.

A Pockels cell driver (PCD) can be viewed as a high-voltage pulse width generator for controlling the bi-refringence effect of electro-optical crystals. The main features of a PCD include a high repetition rate, fast on and off switching, variable pulse duration, and a true square pulse shape. The most commonly used PCD has a narrow pulse width tuning range, typically within a few microseconds. In this paper, we propose a PCD based on a novel pulse width modulation (PWM) signal-generation strategy that can continuously adjust its pulse width with a minimum step size of 10 ns and no restriction on the maximum width. Therefore, it is easily compatible with both “On-type” and “Off-type” applications of the electro-optic crystal quarter-wave voltage. The experimental results show that the rising and falling times of the proposed PCD are approximately 7.3 ns and 7.8 ns, respectively, with a maximum repetition rate of 1 MHz and a maximum voltage of approximately 2.0 kV. Finally, the functionality of the PCD is demonstrated in a home-built slab laser.