Reservoir computing is a potential neuromorphic paradigm for promoting future disruptive applications in the era of the Internet of Things, owing to its well-known low training cost and compatibility with hardware. It has been successfully implemented by injecting an input signal into a spatially extended reservoir of nonlinear nodes or a temporally extended reservoir of a delayed feedback system to perform temporal information processing. Here we propose a novel nondelay-based reservoir computer using only a single micromechanical resonator with hybrid nonlinear dynamics that removes the usually required delayed feedback loop. The hybrid nonlinear dynamics of the resonator comprise a transient nonlinear response, and a Duffing nonlinear response is first used for reservoir computing. Due to the richness of this nonlinearity, the usually required delayed feedback loop can be omitted. To further simplify and improve the efficiency of reservoir computing, a self-masking process is utilized in our novel reservoir computer. Specifically, we numerically and experimentally demonstrate its excellent performance, and our system achieves a high recognition accuracy of 93% on a handwritten digit recognition benchmark and a normalized mean square error of 0.051 in a nonlinear autoregressive moving average task, which reveals its memory capacity. Furthermore, it also achieves 97.17 ± 1% accuracy on an actual human motion gesture classification task constructed from a six-axis IMU sensor. These remarkable results verify the feasibility of our system and open up a new pathway for the hardware implementation of reservoir computing.

UWB and IMU fusion positioning methods have been widely concerned for its high accuracy and robustness in GNSS-denied environment. However, most of the existing methods are stay under the Markov assumption and ignore the credible estimation of the yaw angle. In this paper, we propose a novel tightly-coupled IMU and multiple UWB tags fusion framework based on a graph optimization model that is able to provide high precision positioning and attitude determination service, which provides a new technical approach for the practical application of UWB and IMU fusion. A relative rotation and motion state initialization algorithm is designed and presented, which solves the problems that IMU installation direction is difficult to measure and the initial state is not stationary in practical application. Simulation experiments shows the impact of UWB tags distribution distance and IMU noise parameters on system performance, which provides design reference for practical applications. Finally, the real-site experiment proves the positioning RMSE of 4 cm based on our fusion solution and these simulation evidences, which confirms the great performance of our fusion solution.

The relative pose estimation is one of the most fundamental components for multi-robot systems, while it still remains an open and challenging research topic in infrastructure-free environment. In this letter, we target improving the accuracy and robustness of relative pose estimation for ground robotic teams, and propose to fuse range and odometry measurements to estimate the relative pose using sliding window optimization. In the system, multiple UWB tags for ranging are equipped on each robot, and visual inertial odometry is applied for estimating the ego-motion pose for each robot. Aiming for simple and effective relative pose initialization, the triangulation uncertainty for multi-tag robots is analyzed, and an initialization method is designed. To cope with the complex environments such as continuous NLOS condition, a NLOS detection and range measurements filtering method is presented. We have conducted series of experiments to demonstrate the performance of the proposed approach.

Resonant accelerometers are promising because of their wide dynamic range and long-term stability. With quasi-digital frequency output, the outputs of resonant accelerometers are less vulnerable to the noise from circuits and ambience. Differential structure is usually adopted in a resonant accelerometer to achieve higher sensitivity to acceleration and to reduce common noise at the same time. Ideally, a resonant accelerometer is only sensitive to external acceleration. However, temperature has a great impact on resonant accelerometers, causing unexcepted frequency drift. In order to cancel out the frequency drift caused by temperature change, an improved temperature compensation method for differential vibrating accelerometers without additional temperature sensors is presented in this paper. Experiment results demonstrate that the temperature sensitivity of the prototype sensor is reduced from 43.16 ppm/°C to 0.83 ppm/°C within the temperature range of −10 °C to 70 °C using the proposed method.

Reservoir computing (RC) is a bio-inspired neural network structure which can be implemented in hardware with ease. It has been applied across various fields such as memristors, and electrochemical reactions, among which the micro-electro-mechanical systems (MEMS) is supposed to be the closest to sensing and computing integration. While previous MEMS RCs have demonstrated their potential as reservoirs, the amplitude modulation mode was found to be inadequate for computing directly upon sensing. To achieve this objective, this paper introduces a novel MEMS reservoir computing system based on stiffness modulation, where natural signals directly influence the system stiffness as input. Under this innovative concept, information can be processed locally without the need for advanced data collection and pre-processing. We present an integrated RC system characterized by small volume and low power consumption, eliminating complicated setups in traditional MEMS RC for data discretization and transduction. Both simulation and experiment were conducted on our accelerometer. We performed nonlinearity tuning for the resonator and optimized the post-processing algorithm by introducing a digital mask operator. Consequently, our MEMS RC is capable of both classification and forecasting, surpassing the capabilities of our previous non-delay-based architecture. Our method successfully processed word classification, with a 99.8% accuracy, and chaos forecasting, with a 0.0305 normalized mean square error (NMSE), demonstrating its adaptability for multi-scene data processing. This work is essential as it presents a novel MEMS RC with stiffness modulation, offering a simplified, efficient approach to integrate sensing and computing. Our approach has initiated edge computing, enabling emergent applications in MEMS for local computations.

Reservoir computing (RC) is an attractive paradigm of a recurrent neural network (RNN) architecture, owning to the ease of training and existing neuromorphic implementation. Its simulated performance matches other digital algorithms on a series of benchmarking tasks, such as prediction tasks and classification tasks. In this article, we propose a novel RC structure based on the coupled MEMS resonators with the enhanced dynamic richness to optimize the performance of the RC system both on the system level and data set level. Moreover, we first put forward that the dynamic richness of RC comprises linear dynamic richness and nonlinear dynamic richness, which can be enhanced by adding delayed feedbacks and nonlinear nodes, respectively. In order to set forth this point, we compare three typical RC structures, a single-nonlinearity RC structure with single-feedback, a single-nonlinearity RC structure with double-feedbacks, and the couple-nonlinearity RC structure with double-feedbacks. Specifically, four different tasks are enumerated to verify the performance of the three RC structures, and the results show the enhanced dynamic richness by adding delayed feedbacks and nonlinear nodes. These results prove that coupled MEMS resonators offer an interesting platform to implement a complex computing paradigm leveraging their rich dynamical features.