In this study, a preparation method for the high-temperature pressure sensor based on the piezoresistive effect of p-type SiC is presented. The varistor with a positive trapezoidal shape was designed and etched innovatively to improve the contact stability between the metal and SiC varistor. Additionally, the excellent ohmic contact was formed by annealing at 950 °C between Ni/Al/Ni/Au and p-type SiC with a doping concentration of 10¹⁸cm⁻³. The aging sensor was tested for varistors in the air of 25 °C–600 °C. The resistance value of the varistors initially decreased and then increased with the increase of temperature and reached the minimum at ~450 °C. It could be calculated that the varistors at ~100 °C exhibited the maximum temperature coefficient of resistance (TCR) of ~−0.35%/°C. The above results indicated that the sensor had a stable electrical connection in the air environment of ≤600 °C. Finally, the encapsulated sensor was subjected to pressure/depressure tests at room temperature. The test results revealed that the sensor output sensitivity was approximately 1.09 mV/V/bar, which is better than other SiC pressure sensors. This study has a great significance for the test of mechanical parameters under the extreme environment of 600 °C.

This research paper presents a comprehensive study of the simultaneous localization and mapping (SLAM) algorithm for robot localization and navigation in unknown environments. The SLAM algorithm is a widely used approach for building a map of an environment and estimating the robot’s position within it, which is especially useful in dynamic and unstructured environments. The paper discusses various SLAM techniques, including the Kalman filter (KF) and GraphSLAM algorithms, and their use in probabilistic estimation of the robot’s position and orientation. The paper also explores different path-planning techniques that can be used with the map created by the SLAM algorithm to generate collision-free paths for the robot to navigate toward its goal. The paper also discusses recent advances in deep learning-based SLAM algorithms and their applications in indoor navigation with ORB and RGB-D cameras. The research concludes that SLAM-based robot localization and navigation algorithms are a promising approach for robots navigating in unstructured environments and present various opportunities for future research.

The ammonia sensing properties of single-layer graphene synthesized by chemical vapor deposition (CVD) were studied. The Au interdigitated electrode (IDE) was prepared by microelectromechanical systems (MEMS) technology, and then, the single-layer graphene was transferred to the IDE by wet transfer technology. Raman spectroscopy was used to monitor the quality of graphene films transferred to SiO₂/Si substrates. Moreover, the theory of graphene’s adsorption of gases is explained. The results show that gas sensing characteristics such as response/recovery time and response are related to the target gas, gas concentration, test temperature, and so on. In the stability test, the difference between the maximum resistance and the minimum resistance of the device is 1 ohm without ammonia, the change is less than 1% of its initial resistance, and the repeatability is up to 98.58%. Therefore, the sensor prepared with high quality single-layer graphene has good repeatability and stability for ammonia detection.

A complex signal processing technique is usually required to process the data in most sensor design structures, and integration into real applications is also challenging. This work presents a dynamic rotational sensor using polymethyl methacrylate (PMMA) fiber for robot movement assessment. The sensor design structure is based on the coupling of light intensity, in which two PMMA fibers are twisted together. Both fibers are bent after twisting and attached on the linear translation stage, which is further attached to the robot. The variation in bending radius causes the bending loss, and that loss is coupled in the second fiber. The change in the macro-bend radius corresponds to the rotation of the robot. Experimental results indicate that the sensor can operate in full rotational cycle (i.e., 0°–360°) as well as for clock and anti-clockwise rotation. Moreover, different rotational speeds (2°/s, 3°/s, 5°/s, and 10°/s) were carried out. The hysteresis loss of the sensor was about 0.77% and the sensitivity was 8.69 nW/°. The presented dynamic rotational sensor is cost-effective and easily integrated into the robot structure to analyze the robot’s circular motion.

In this work, the piezoresistive properties of heavily doped p-type 4H-SiC at room temperature were investigated innovatively. It was verified by a field emission scanning electron microscope (FESEM), X-ray diffraction (XRD), and laser Raman spectroscopy (LRS) that the crystal quality of the epitaxial layer was good. The doping concentration and thickness of the epitaxial layer were measured by secondary ion mass spectrometry (SIMS) to be ~1.12 × 10¹⁹ cm⁻³ and ~1.1 µm, respectively. The 4H-SiC cantilever beam along $[1\stackrel{-}{1}00]$ crystal orientation was fabricated, and the fixed end of the cantilever beam was integrated with longitudinal and transverse p-type 4H-SiC piezoresistors. A good ohmic contact was formed between Ni/Ti/Al/Au and a p-type 4H-SiC piezoresistor under nitrogen environment annealing at 1050 °C for 5 min. The free end of the cantilever beam was forced to cause strain on the p-type 4H-SiC piezoresistor, and then the resistances were measured by a high precision multimeter. The experimental results illustrated that longitudinal and transverse gauge factors (GFs) of the p-type 4H-SiC piezoresistors were 26.7 and −21.5, respectively, within the strain range of 0–336µε. In order to further verify the electro-mechanical coupling effect of p-type 4H-SiC, the piezoresistors on the beam were connected to the Wheatstone full-bridge circuit and the output changes were observed under cyclic loading of 0–0.5 N. The measuring results revealed that the transducer based on the 4H-SiC piezoresistive effect exhibited good linearity and hysteresis, which confirmed that p-type 4H-SiC has the potential for pressure or acceleration sensing applications.

It is an urgent to monitor the dynamic industrial process in real-time, fast and accurately, and provide useful information for visualization. Various modalities include abundant information such as conflict, redundancy and complementarity. It is reasonable to combine different information from multiple modalities to make a more accurate assessment for image reconstruction. In this paper, a convolutional attention fusion network for electrical/ultrasound dual-modal fusion image reconstruction is proposed. Two fully connected modules are used to transform the measurement spatial information of different sensitive principles into image spatial information of the same dimension, which solves the heterogeneity problem of different modal information; in the process of feature extraction, the ill-posed of image reconstruction is improved by adding feature dimension; the spatial pyramid pooling module is used to obtain multi-scale feature information and provide more effective features for image reconstruction; according to the correlation between different modal features, the importance of different modal features to the reconstruction target is judged by attention mechanism, and different modal local features with different attention weights, which promotes the effective fusion and collaborative imaging of dual-modal information. The experimental results show that the convolution attention fusion reconstruction network improves the reconstruction accuracy and anti-noise performance compared with the dual-modal concatenation fusion reconstruction method and the single-modal reconstruction method.