Enhanced performance of high Al-content AlGaN MSM photodetectors by electrode modification using hexadecanethiol.

A metal electrode modification process for AlGaN-based metal-semiconductor-metal (MSM) photodetectors have been introduced to enhance the response of solar-blind ultraviolet (UV) light detection. The hexadecanethiol organic molecules are chemically adsorbed on the electrodes of high-Al-content Al0.6Ga0.4N MSM solar-blind UV photodetectors, which can reduce the work function of the metal electrode and change the height of the Schottky barrier. This modification process significantly increases the photocurrent and responsivity of the device compared with the referential photodetector without modification. Additionally, the adverse effects caused by the surface state and polarization of the AlGaN materials are effectively reduced, which can be beneficial for improving the electrical performances of III-nitride-based UV photodetectors.

Highly sensitive real-time detection of intracellular oxidative stress and application in mycotoxin toxicity evaluation based on living single-cell electrochemical sensors.

Single-cell electrochemical sensor is widely used in the local selective detection of single living cells because of its high spatial-temporal resolution and sensitivity, as well as its ability to obtain comprehensive cellular physiological states and processes with increased accuracy. Functionalized nanoprobes can detect the oxidative stress response of cells in single-cell electrochemical sensors. Moreover, the T-2 toxin is one of the most toxic mycotoxins and widely occurs in field crops. T-2 toxin can cause mitochondrial damage in cells and increase intracellular reactive oxygen species (ROS) in various cells. As the most representative free radical of intracellular ROS, H2O2 can effectively reflect the toxic effects of intracellular T-2 toxin. In this study, a functionalized gold nanoprobe was used to dynamically monitor the production of H2O2 in a single live human hepatoma cell HepG2 stimulated by mycotoxin T-2. The concentration of H2O2 produced by HepG2 cells stimulated by T-2 toxin at 1 ppb-1 ppm was linearly correlated, R2 = 0.99055, and LOD = 0.13807 ng mL-1. Sample spiking experiments were conducted, and the recovery rate of spiking was 81.19%-130.17%. A comparative analysis of differences in the current produced by multiple toxins, HT-29 cells, as well as single cells in cell populations, was performed. This method can be applied in real-time monitoring of mycotoxin toxicity during food processing in living cells and provides a novel idea for enhancing food quality and safety in a nanoenvironment.

Improved Pedestrian Dead Reckoning Based on a Robust Adaptive Kalman Filter for Indoor Inertial Location System.

Pedestrian dead reckoning (PDR) systems based on a microelectromechanical-inertial measurement unit (MEMS-IMU) providing advantages of full autonomy and strong anti-jamming performance are becoming a feasible choice for pedestrian indoor positioning. In order to realize the accurate positioning of pedestrians in a closed environment, an improved pedestrian dead reckoning algorithm, mainly including improved step estimation and heading estimation, is proposed in this paper. Firstly, the original signal is preprocessed using the wavelet denoising algorithm. Then, the multi-threshold method is proposed to ameliorate the step estimation algorithm. For heading estimation suffering from accumulated error and outliers, robust adaptive Kalman filter (RAKF) algorithm is proposed in this paper, and combined with complementary filter to improve positioning accuracy. Finally, an experimental platform with inertial sensors as the core is constructed. Experimental results show that positioning error is less than 2.5% of the total distance, which is ideal for accurate positioning of pedestrians in enclosed environment.

An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model.

Aimed at overcoming the problems of cumulative errors and low positioning accuracy in single Inertial Navigation Systems (INS), an Optimal Enhanced Kalman Filter (OEKF) is proposed in this paper to achieve accurate positioning of pedestrians within an enclosed environment. Firstly, the errors of the inertial sensors are analyzed, modeled, and reconstructed. Secondly, the cumulative errors in attitude and velocity are corrected using the attitude fusion filtering algorithm and Zero Velocity Update algorithm (ZUPT), respectively. Then, the OEKF algorithm is described in detail. Finally, a pedestrian indoor positioning experimental platform is established to verify the performance of the proposed positioning system. Experimental results show that the accuracy of the pedestrian indoor positioning system can reach 0.243 m, giving it a high practical value.

Integrated navigation fusion strategy of INS/UWB for indoor carrier attitude angle and position synchronous tracking.

In some GPS failure conditions, positioning for mobile target is difficult. This paper proposed a new method based on INS/UWB for attitude angle and position synchronous tracking of indoor carrier. Firstly, error model of INS/UWB integrated system is built, including error equation of INS and UWB. And combined filtering model of INS/UWB is researched. Simulation results show that the two subsystems are complementary. Secondly, integrated navigation data fusion strategy of INS/UWB based on Kalman filtering theory is proposed. Simulation results show that FAKF method is better than the conventional Kalman filtering. Finally, an indoor experiment platform is established to verify the integrated navigation theory of INS/UWB, which is geared to the needs of coal mine working environment. Static and dynamic positioning results show that the INS/UWB integrated navigation system is stable and real-time, positioning precision meets the requirements of working condition and is better than any independent subsystem.

Integrated positioning for coal mining machinery in enclosed underground mine based on SINS/WSN.

To realize dynamic positioning of the shearer, a new method based on SINS/WSN is studied in this paper. Firstly, the shearer movement model is built and running regularity of the shearer in coal mining face has been mastered. Secondly, as external calibration of SINS using GPS is infeasible in enclosed underground mine, WSN positioning strategy is proposed to eliminate accumulative error produced by SINS; then the corresponding coupling model is established. Finally, positioning performance is analyzed by simulation and experiment. Results show that attitude angle and position of the shearer can be real-timely tracked by integrated positioning strategy based on SINS/WSN, and positioning precision meet the demand of actual working condition.

2D Magnetic Manipulation of a Micro-Robot in Glycerin Using Six Pairs of Magnetic Coils.

This paper demonstrates the control system of a single magnetic micro-robot driven by combined coils. The combined coils consist of three pairs of Helmholtz coils and three pairs of Maxwell coils. The rotating magnetic field, gradient magnetic field, and combined magnetic field model of the combined coils were analyzed. To make the output magnetic field quickly converge to the reference point without steady-state error, the discrete-time optimal controller was designed based on the auto disturbance rejection technology. We have designed a closed-loop controller based on a position servo. The control system includes the position control and direction control of the micro-robot. To address problems with slow sampling frequency in visual feedback and inability to feed real-time position back to the control system, a Kalman filter algorithm was used to predict the position of the micro-robot in two-dimensional space. Simulations and experiments were carried out based on the proposed structure of combined coils and control scheme. The experimental results demonstrated the uniformity and excellent dynamic performance of the generated magnetic field.

Robust Control Strategy of Gradient Magnetic Drive for Microrobots Based on Extended State Observer.

Microrobots have great application potential in the biomedical field, to realize the precision and efficiency of microrobots in vivo is research focus in this field. Microrobots are accompanied by various disturbances in complex environment. These disturbances will affect the motion control of microrobots, resulting in the inability of the micromanipulation tasks to be completed effectively. To this end, a robust motion control method is proposed for precise path tracking of microrobots in this paper. The extended state observer (ESO) is used to estimate the total disturbances and uncertainties of the system. A path tracking controller is designed by combining sliding mode control (SMC) and disturbances compensation, which is used to eliminate the total disturbances of the system and realize the fast and accurate path tracking of microrobots. Finally, the path tracking experiments are implemented in the gradient magnetic field drive system. The experimental results show that the mean absolute error of the path tracking for microrobots in a simulated vascular structure is less than 14 µm, and the root mean square error is less than 17 µm by using the robust control method proposed in this paper. Compared with the traditional PID control method, it can better suppress external disturbances and uncertainties of the system and improve the path tracking accuracy of microrobots effectively. It shows stronger anti-interference ability and robustness.

Posture adjustment and robust microinjection of zebrafish larval heart.

Unlike cells or embryos, zebrafish have a complex physiological structure, which poses challenges to posture recognition and adjustment during microinjection. Furthermore, zebrafish surface pigments exhibit strong interference with visual servo-based injection control, thus, affecting the success of microinjection and the subsequent survival rate. To address these challenges, we developed an automated microinjection system for the zebrafish heart that has advantages of high accuracy and success rate and avoids biological sample contamination. A convolutional neural networks (CNN) deep learning model is employed to determine the body axis posture. To solve the problems of blocked needle and abnormal tip positioning induced by zebrafish surface pigment during the injection process, an adaptive robust Kalman filter is proposed to suppress the abnormal values of visual feedback. Experimental results show that the success rate of body axis recognition based on the employed deep learning model exceeds 95%, and the proposed adaptive Kalman filter effectively suppresses the visual outliers, satisfying the requirements of high-precision injection for the zebrafish heart.

Temperature field model and control strategy in gravity casting process.

Temperature control is one of the most important processes during aluminum (Al) alloy engine cylinder head product casting. An improper temperature control may result in no uniformity and microstructure defects in casting parts and give rise to high defect ratio. In this paper, a mathematical model with high nonlinearity, strong coupling, and less uncertainty is developed for the solidification process in Al alloy casting. The interfacial heat transfer coefficient is combined with the mold structure comprehensively to build the temperature-structure model, and the characteristics of the uncertainty conversion are also used in order to achieve optimal temperature control during the solidification process. The cloud model integrated with Proportion-Integral-Differential (PID) temperature control system enables evaluation of the uncertainty conversion quantitatively. By inputting the temperature error and the temperature error rate, the PID inference is output through the cloud inference engine to achieve the optimal temperature curve. The superiority of the control algorithm was verified on a customized experimental platform with the temperature control system. Compared with manual operation and traditional PID control, the result shows that the error of the cloud model control is lower than the manual operation and traditional PID control. The experimental results also suggest that the performance of our cloud model is better than that of the manual operation model and the traditional PID control model regarding to stability and controllability.