Design, Fabrication and Characterization of Disk Resonator Gyroscope with Vibration and Shock Resistance.

This paper presents a comprehensive optimization of an outer frame anchor disk resonator gyroscope (DRG) with enhanced resistance to vibration and shock, achieved by increasing the resonant frequency of the tub and translation modes. Furthermore, the wineglass mode retains a high quality factor, enhancing sensitivity and reducing the angle random walk (ARW). The performance of the proposed DRG is analyzed using dynamic equations, and its structural parameters are optimized through finite element analysis (FEA).

High-Precision Positioning and Rotation Angle Estimation for a Target Pallet Based on BeiDou Navigation Satellite System and Vision.

In outdoor unmanned forklift unloading scenarios, pallet detection and localization face challenges posed by uncontrollable lighting conditions. Furthermore, the stacking and close arrangement of pallets also increase the difficulty of positioning a target pallet. To solve these problems, a method for high-precision positioning and rotation angle estimation for a target pallet using the BeiDou Navigation Satellite System (BDS) and vision is proposed.

Unsupervised domain adaptive building semantic segmentation network by edge-enhanced contrastive learning.

Unsupervised domain adaptation (UDA) is a weakly supervised learning technique that classifies images in the target domain when the source domain has labeled samples, and the target domain has unlabeled samples. Due to the complexity of imaging conditions and the content of remote sensing images, the use of UDA to accurately extract artificial features such as buildings from high-spatial-resolution (HSR) imagery is still challenging. In this study, we propose a new UDA method for building extraction, the contrastive domain adaptation network (CDANet), by utilizing adversarial learning and contrastive learning techniques.

MEMS reservoir computing system with stiffness modulation for multi-scene data processing at the edge.

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.

High Precision Positioning and Rotation Angle Estimation of a Flatbed Truck Based on BDS and Vision.

Centimeter-level localization and precise rotation angle estimation for flatbed trucks pose significant challenges in unmanned forklift automated loading scenarios. To address this issue, the study proposed a method for high-precision positioning and rotation angle estimation of flatbed trucks using the BeiDou Navigation Satellite System (BDS) and vision technology. First, an unmanned forklift equipped with a Time-of-Flight (ToF) camera and a dual-antenna mobile receiver for BDS positioning collected depth images and localization data near the front and rear endpoints of the flatbed.

A decouple-decomposition noise analysis model for closed-loop mode-localized tilt sensors.

The development of mode-localized sensors based on amplitude output metrics has attracted increasing attention in recent years due to the potential of such sensors for high sensitivity and resolution. Mode-localization phenomena leverage the interaction between multiple coupled resonant modes to achieve enhanced performance, providing a promising solution to overcome the limitations of traditional sensing technologies. Amplitude noise plays a key role in determining the resolution of mode-localized sensors, as the output metric is derived from the measured AR (amplitude ratio) within the weakly coupled resonator system.

Analysis of the Thermally Induced Packaging Effects on the Frequency Drift of Micro-Electromechanical System Resonant Accelerometer.

Due to the working principle of MEMS resonant accelerometers, their thermally induced frequency drift is an inevitable practical issue for their extensive application. This paper is focused on reducing the thermally induced packaging effects on the frequency drift. A leadless ceramic chip carrier package with a stress-buffering layer was proposed for a MEMS resonant accelerometer, and the influences of packaging structure parameters on the frequency drift were investigated through finite element simulations and verified experimentally.

A Scale Factor Calibration Method for MEMS Resonant Accelerometers Based on Virtual Accelerations.

This paper presents a scale factor calibration method based on virtual accelerations generated by electrostatic force. This method uses a series of voltage signals to simulate the inertial forces caused by the acceleration input, rather than frequent and laborious calibrations with high-precision instruments. The error transfer model of this method is systematically analyzed, and the geometrical parameters of this novel micromachined resonant accelerometer (MRA) are optimized.

Enhanced Terahertz Fingerprint Sensing Mechanism Study of Tiny Molecules Based on Tunable Spoof Surface Plasmon Polaritons on Composite Periodic Groove Structures.

Highly sensitive detection of enhanced terahertz (THz) fingerprint absorption spectrum of trace-amount tiny molecules is essential for biosensing. THz surface plasmon resonance (SPR) sensors based on Otto prism-coupled attenuated total reflection (OPC-ATR) configuration have been recognized as a promising technology in biomedical detection applications. However, THz-SPR sensors based on the traditional OPC-ATR configuration have long been associated with low sensitivity, poor tunability, low refractive index resolution, large sample consumption, and lack of fingerprint analysis.

Design and Simulation Study of an Optical Mode-Localized MEMS Accelerometer.

In this paper, we demonstrate a novel photonic integrated accelerometer based on the optical mode localization sensing mechanism, which is designed on an SOI wafer with a device layer thickness of 220 nm. High sensitivity and large measurement range can be achieved by integrating coupled ring resonators with a suspended directional coupler on a proof mass. With the help of FEA simulation and numerical analysis, the proposed optical mode-localized sensor presents a sensitivity of 10/g (modal power ratio/acceleration) and an inertial displacement of from -8 to 10 microns corresponding to a range from -23.

Utilizing the Intrinsic Mode of Weakly Coupled Resonators for Temperature Compensation.

Accelerometers based on outputting amplitude ratios in weakly coupled resonators (WCRs) are attractive because their parametric sensitivity is higher by two or three orders of magnitudes than those based on outputting frequency. However, the impact of temperature on the coupler is a key factor in accelerometer applications. This paper proposed a novel mode-localized WCR accelerometer with a temperature compensation mechanism, with sensitive elements incorporating a double-ended tuning fork (DETF) resonator, clamped-clamped (CC) resonator, and a micro-lever coupler.

Periodic Heterogeneous Surface with Superhydrophilic/Superhydrophobic Stripes Processed by a Picosecond Laser.

Heterogeneous surface with superhydrophilic/superhydrophobic stripes (HS-s/sS) has great practical significance, which can be used in fuel cell water management, condensation heat transfer enhancement, underwater drag reduction. Herein, a fast and simple method for uniform HS-s/sS on several mesh materials, including copper, stainless steel, and nickel, is achieved by using picosecond (ps) laser line-by-line scanning. Note that the scanning period between the lines is kept constant during processing, the HS-s/sS is formed by self-organized, while the similar structure cannot be processed on solid metal surfaces using the same parameters.

Modeling and Parameter Sensitivity Improvement in ΔE-Effect Magnetic Sensor Based on Mode Localization Effect.

A mode-localized ΔE-effect magnetic sensor model is established theoretically and numerically. Based on the designed weakly coupled resonators with multi-layer film structure, it is investigated how the ΔE-effect of the magnetostrictive film under the external magnetic field causes the stiffness perturbation of the coupled resonators to induce the mode localization effect. Using the amplitude ratio (AR) as the output in the mode-localized ΔE-effect magnetic sensor can improve the relative sensitivity by three orders of magnitude compared with the traditional frequency output, which has been verified by simulations based on the finite element method (FEM).

A Novel Self-Temperature Compensation Method for Mode-Localized Accelerometers.

Mode-localized sensing paradigms applied to accelerometers have recently become popular research subjects. However, the output of mode-localized accelerometers is influenced by environment temperature due to the difference in the thermal properties of the coupling resonators and the temperature dependence of coupling stiffness. To improve the performance of mode-localized accelerometers against temperature, we proposed an in situ self-temperature compensation method by utilizing the resonant frequency besides of amplitude ratios, which can be implied online.

Enhancing the Recognition Task Performance of MEMS Resonator-Based Reservoir Computing System via Nonlinearity Tuning.

Reservoir computing (RC) is a potential neuromorphic paradigm for physically realizing artificial intelligence systems in the Internet of Things society, owing to its well-known low training cost and compatibility with nonlinear devices. Micro-electro-mechanical system (MEMS) resonators exhibiting rich nonlinear dynamics and fading behaviors are promising candidates for high-performance hardware RC. Previously, we presented a non-delay-based RC using one single micromechanical resonator with hybrid nonlinear dynamics.

Novel nondelay-based reservoir computing with a single micromechanical nonlinear resonator for high-efficiency information processing.

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.