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The frequency-scanning-interferometry-based (FSI-based) absolute ranging technology is a type of ranging technology possessing a high precision and no ranging blind area, so it can be used for non-cooperative targets. However, due to a tiny movement of a target, the Doppler shift and the phase modulation are introduced into the beat signal which results in ranging accuracy decrease. In order to solve this problem, first the model of vibration effect is established, and then the beat signals of two adjacent scanning periods are processed to produce a signal that is immune to vibration. The proposed method is verified by the experiments, and the experimental results show that the effect of vibration compensation is better for the target with a lower vibration velocity and at a lower vibration frequency (lower than 6 Hz). When the target is subjected to a sinusoidal vibration with an amplitude of 10 μm at a frequency of 1 Hz, by using the proposed method the standard deviation is reduced from 775 to 12 μm. Moreover, in the natural environment, by using vibration compensation the standard deviation is reduced from 289 to 11 μm.

In wireless rechargeable sensor networks (WRSNs), there is a way to use mobile vehicles to charge node and collect data. It is a rational pattern to use two types of vehicles, one is for energy charging, and the other is for data collecting. These two types of vehicles, data collection vehicles (DCVs) and wireless charging vehicles (WCVs), are employed to achieve high efficiency in both data gathering and energy consumption. To handle the complex scheduling problem of multiple vehicles in large-scale networks, a twice-partition algorithm based on center points is proposed to divide the network into several parts. In addition, an anchor selection algorithm based on the tradeoff between neighbor amount and residual energy, named AS-NAE, is proposed to collect the zonal data. It can reduce the data transmission delay and the energy consumption for DCVs’ movement in the zonal. Besides, we design an optimization function to achieve maximum data throughput by adjusting data rate and link rate of each node. Finally, the effectiveness of proposed algorithm is validated by numerical simulation results in WRSNs.

Continued interest in the metabolites of Wedelia trilobata (L.) Hitchc, a notoriously invasive weed in South China, led to the isolation of twenty-six ent-kaurane diterpenoids, including seven new ones 1–7. Their structures and relative configuration were elucidated on the basis of extensive spectroscopic analysis, including 1D- and 2D-NMR experiments. The antimicrobial activities of all isolated diterpenoids were evaluated against a panel of bacteria and fungi.

This study aimed to develop an automated optical inspection (AOI) system that can rapidly and precisely measure the dimensions of microchannels embedded inside a transparent polymeric substrate, and can eventually be used on the production line of a factory. The AOI system is constructed based on Snell’s law. The concept holds that, when light travels through two transparent media (air and the microfluidic chip transparent material), by capturing the parallel refracted light from a light source that went through the microchannel using a camera with a telecentric lens, the image can be analyzed using formulas derived from Snell’s law to measure the dimensions of the microchannel cross-section. Through the NI LabVIEW 2018 SP1 programming interface, we programmed this system to automatically analyze the captured image and acquire all the needed data. The system then processes these data using custom-developed formulas to calculate the height and width measurements of the microchannel cross-sections and presents the results on the human–machine interface (HMI). In this study, a single and straight microchannel with a cross-sectional area of 300 μm × 300 μm and length of 44 mm was micromachined and sealed with another polymeric substrate by a solvent bonding method for experimentations. With this system, 45 cross-sectional areas along the straight microchannel were measured within 20 s, and experiment results showed that the average measured error was less than 2%.