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Real-time detection of liquid level in complex environments has always been a knotty issue. In this paper, an intrinsically safe liquid-level sensor system for flammable and explosive environments is designed and implemented. The poly vinyl chloride (PVC) coaxial cable is chosen as the sensing element and the measuring mechanism is analyzed. Then, the capacitance-to-voltage conversion circuit is designed and the expected output signal is achieved by adopting parameter optimization. Furthermore, the experimental platform of the liquid-level sensor system is constructed, which involves the entire process of measuring, converting, filtering, processing, visualizing and communicating. Additionally, the system is designed with characteristics of intrinsic safety by limiting the energy of the circuit to avoid or restrain the thermal effects and sparks. Finally, the approach of the piecewise linearization is adopted in order to improve the measuring accuracy by matching the appropriate calibration points. The test results demonstrate that over the measurement range of 1.0 m, the maximum nonlinearity error is 0.8% full-scale span (FSS), the maximum repeatability error is 0.5% FSS, and the maximum hysteresis error is reduced from 0.7% FSS to 0.5% FSS by applying software compensation algorithms.

Automatic modulation classification (AMC) is one of the most critical technologies for non-cooperative communication systems. Recently, deep learning (DL) based AMC (DL-AMC) methods have attracted significant attention due to their preferable performance. However, the study of most of DL-AMC methods are concentrated in the single-input and single-output (SISO) systems, while there are only a few works on DL-based AMC methods in multiple-input and multiple-output (MIMO) systems. Therefore, we propose in this work a convolutional neural network (CNN) based zero-forcing (ZF) equalization AMC (CNN/ZF-AMC) method for MIMO systems. Simulation results demonstrate that the CNN/ZF-AMC method achieves better performance than the artificial neural network (ANN) with high order cumulants (HOC)-based AMC method under the condition of the perfect channel state information (CSI). Moreover, we also explore the impact of the imperfect CSI on the performance of the CNN/ZF-AMC method. Simulation results demonstrated that the classification performance is not only influenced by the imperfect CSI, but also associated with the number of the transmit and receive antennas.

Bone conduction devices are used in audiometric tests, hearing rehabilitation, and communication systems. The mechanical impedance of the stimulated skull location affects the performance of the bone conduction devices. In the present study, the mechanical impedances of the mastoid and condyle were measured in 100 Chinese subjects aged from 22 to 67 years. The results show that the mastoid and condyle impedances within the same subject differ significantly and the impedance differences between subjects at the same stimulation position are mainly below the resonance frequency. The mechanical impedance of the mastoid is significantly influenced by age, and not related to gender or body mass index (BMI). While the mechanical impedance of the condyle is significantly affected by BMI, followed by gender, and not related to age. There are some differences in mastoid impedance between the Chinese and Western subjects. An analogy model predicts that the difference in mechanical impedance between the mastoid and condyle leads to a significant difference in the output force of the bone conduction devices. The results can be used to develop improved condyle and mastoid stimulators for the Chinese.

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