In the process of oil exploitation, the water level of an oil well can be predicted and the position of reservoir can be estimated by measuring the water content of crude oil, with reference for the automatic production of high efficiency in the oil field. In this paper, a visual measuring method for water content of crude oil is proposed. The oil and water in crude oil samples were separated by centrifugation, distillation or electric dehydration, and a water–oil layered mixture was formed according to the unequal densities. Then the volume ratio of water and oil was analyzed by digital image processing, and the water content and oil content was able to be calculated. A new method for measuring water content of crude oil based on IGAVD (image grayscale accumulated value difference) is proposed, which overcomes the uncertainty caused by environmental illumination and improves the measurement accuracy. In order to verify the effectiveness of the algorithm, a miniaturization and low-cost system prototype was developed. The experimental results show that the average power consumption is about 165 mW and the measuring error is less than 1.0%. At the same time, the real-time and remote transmission about measurement results can be realized.

The error characteristics of a typical commercial MEMS-IMU were analyzed. Using 15 ICM-42688 sensors, a 3*5 MEMS-IMU array was designed, and a weighted data fusion method based on Allan variance for the MEMS-IMU array was proposed. This effectively reduces the random measurement errors of the MEMS-IMU, providing a data foundation for the precise measurement of motion and attitude of robots, vehicles, aircraft, and other systems under low-cost conditions. The text describes a measurement method for non-stationary motion attitudes of sports vehicles based on adaptive Kalman-Mahony. Specifically, it first uses adaptive Kalman filter on array sensor data to calculate the measurement noise in real-time and adaptively adjust the filtering gain. Then, it determines the compensation coefficient of the accelerometer to the gyroscope angular velocity based on the motion state of the vehicle, and solves the attitude through complementary filtering to obtain the motion attitude quaternion. Finally, it converts it into the roll angle, pitch angle, and yaw angle of the sports vehicle. Experimental results show that the proposed MEMS-IMU array weighted data fusion method based on Allan variance has significant advantages over traditional single MEMS-IUM methods and traditional average weighting methods in reducing sensor angle random walk and zero drift instability. The proposed adaptive Kalman-Mahony attitude measurement method also shows a significant improvement in the accuracy of non-stationary motion attitude measurement compared to traditional methods.

Because of its high resolution, low cost, small volume, low power dissipation and less conversion time consumption, the direct digital synthesizer (DDS) method has been applied more and more in the fields of frequency synthesis and signal generation. However, only a limited number of precise frequency signals can be synthesized by the traditional DDS, for the reason that its accumulator modulus is fixed, and its frequency tuning word must be integer. In this paper, a precise DDS method using compound frequency tuning word is proposed, which improves the accuracy of synthesized signals at any frequency points on the premise of guaranteeing the stability of synthesized signals. In order to verify the effectiveness of the new method, a DDS frequency synthesizer based on FPGA is designed and implemented. Taking the rubidium atomic clock PRS10 as standard frequency source, the experiments shows that the frequency stability of the synthesized signal is better than 8.0 × 10⁻¹²/s, the relative frequency error is less than 4.8 × 10⁻¹², and that the frequency accuracy is improved by three orders of magnitude compared with the traditional DDS method.

High quality sleep is a fundamental guarantee for human physical health. Sleep monitoring is crucial for understanding sleep quality and structure, but traditional sleep monitoring methods often require specialized lab environments and uncomfortable sensors. This paper proposes a non-contact sleep monitoring method based on millimeter wave radar, which accurately measures breathing and heartbeat rates during sleep by preprocessing reflected signals and using Variational Mode Extraction (VME) algorithm, and simultaneously detects snoring events. In order to improve the accuracy of snoring detection, a sliding window algorithm based on data density and amplitude is introduced, effectively filtering out low-amplitude noise and accurately identifying snoring segments. Experimental results show that this method can detect snoring events with an average error of 0.10 seconds, and the accuracy of breathing rate and heart rate detection is approximately 0.72 bpm and 2.56 bpm, respectively. This method demonstrates high real-time performance and reliability, and is expected to find broad applications in the diagnosis of clinical sleep disorders and home health monitoring.

The Long-Range Navigation-C (Loran-C) system is an internationally standardized positioning, navigation, and timing service system based on a 100-kHz carrier. The development of the system antenna has always been one of the key research objects in the development of its receiver. In this article, a broadband compact small active magnetic antenna used to receive Loran-C signal outdoors and indoors is designed, which can be divided into a magnetic antenna part and an active circuit part. In the antenna part, this article designs a new “H”-shaped magnetic antenna with a high quality factor from two aspects: material selection and simulation of antenna shape. In the active circuit part, the amplifier and filter are designed by an operational amplifier, and the frequency selection is realized by a single-tube double tuning circuit to improve the signal-to-noise ratio (SNR). Through the above designs, the effective reception of the Loran-C signal is realized outdoors and indoors. Finally, the whole Loran-C active magnetic antenna is processed and tested. It can completely receive a Loran-C signal, with a clear pulse group, and the frequency point of the signal is 99.21 kHz. The length of the antenna is 2.75 cm, and the size of the circuit board is $10\times3$ cm. The SNR of the outdoor environment is 12.04 dB and that of the indoor is 16.48 dB.

Demand for Global Navigation Satellite System (GNSS) applications in the urban environment has experienced a remarkable growth in recent years. However, the received signals are subjected to various urban channel impairments, like shadowing and multipath fading. Therefore, the decoding performance is different from that in open-sky conditions. In this paper, a two-state Land Mobile Satellite (LMS) channel based on the Markov process is used to model the urban channel properties, and then, the analysis of decoding performance in terms of frame error rate (FER) in the LMS channel is performed by evaluating the effect of three major influencing factors, specifically, coding and interleaving in the GNSS message, terminal speed, and satellite elevation angle. Extensive simulations are conducted on BDS-3 B1C B-CNAV1 message and GALILEO E5a F/NAV message. The results validate the excellent error correcting performance of the nonbinary low density parity check (NB-LDPC) code of the B-CNAV1 message and the effectiveness of interleaving in both of the messages in urban condition. Furthermore, it also shows that decoding performance improvement can be achieved with higher terminal speed and higher elevation angle in urban scenarios.