Coalescence-induced droplet jumping on superhydrophobic surfaces have recently received significant attention owing to their potential in a variety of applications. Previous studies demonstrated that the self-jumping process is inherently inefficient, with an energy conversion efficiency η ≤ 6% and dimensionless jumping velocity V j* ≤ 0.23. To realize a quick removal of droplets, increasing effort has been devoted to breaking the jumping velocity limit and inducing droplets sweeping. In this work, we used superhydrophobic surfaces with an asymmetric V-groove to experimentally achieve an enhanced coalescence-induced jumping velocity V j* ≈ 0.61, i.e., more than 700% increase in energy conversion efficiency compared with droplets jumping on flat superhydrophobic surfaces, which is the highest efficiency reported thus far. Moreover, the enhanced jumping direction shows a deviation as high as 60° from the substrate normal. The induced in-plane motion is conducive to remove a considerable number of droplets along the sweeping path and significantly increase the speed of droplet removal. Numerical simulation indicated that the jumping enhancement is a joint effect resulting from the impact of the liquid bridge on the corner of the V-groove and the suppression of droplet expansion by the sidewall of the V-groove. The transient variation of the droplet velocity and the driving force of the coalescing droplets on a surface with and without the asymmetric V-groove were revealed and discussed. Furthermore, effects of groove angle, droplet pair positions, and size mismatches on the jumping velocity and direction have been studied. The novel mechanism of simultaneously increasing the coalescence-induced droplet jumping velocity and changing the jumping direction can be further studied to enhance the efficiency of various applications.

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A two-stage blind adaptive anti-jamming algorithm for global positioning systems (GPS) is proposed in this study. The new algorithm cancels interferences by projecting the array received signals on the orthogonal complement space of interferences. Then the algorithm utilises the CLEAN method to estimate direction of arrivals of GPS signals according to the period repetitive feature of GPS coarse/acquisition (C/A) code and form the multi-beam, where each beam can point one GPS signal. Test data and simulation results show the effectiveness of the new algorithm.

Anomaly detection in big data is a key problem in the big data analytics domain. In this paper, the definitions of anomaly detection and big data were presented. Due to the sampling and storage burden and the inadequacy of privacy protection of anomaly detection based on uncompressed data, compressive sensing theory was introduced and used in the anomaly detection algorithm. The anomaly detection criterion based on wavelet packet transform and statistic process control theory was deduced. The proposed anomaly detection technique was used for through-wall human detection to demonstrate the effectiveness. The experiments for detecting humans behind a brick wall and gypsum based on ultra-wideband radar signal were carried out. The results showed that the proposed anomaly detection algorithm could effectively detect the existence of a human being through compressed signals and uncompressed data.

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Coalescence-induced droplet jumping has received considerable attention owing to its potential to enhance performance in various applications. However, the energy conversion efficiency of droplet coalescence jumping is very low and the jumping direction is uncontrollable, which vastly limits the application of droplet coalescence jumping. In this work, we used superhydrophobic surfaces with a U-groove to experimentally achieve a high dimensionless jumping velocity V j * ≈ 0.70, with an energy conversion efficiency η ≈ 43%, about a 900% increase in energy conversion efficiency compared to droplet coalescence jumping on flat superhydrophobic surfaces. Numerical simulation and experimental data indicated that a higher jumping velocity arises from the redirection of in-plane velocity vectors to out-of-plane velocity vectors, which is a joint effect resulting from the redirection of velocity vectors in the coalescence direction and the redirection of velocity vectors of the liquid bridge by limiting maximum deformation of the liquid bridge. Furthermore, the jumping direction of merged droplets could be easily controlled ranging from 17 to 90°by adjusting the opening direction of the U-groove, with a jumping velocity V j * ≥ 0.70. When the opening direction is 60°, the jumping direction shows a deviation as low as 17°from the horizontal surface with a jumping velocity V j * ≈ 0.73 and corresponding energy conversion efficiency η ≈ 46%. This work not only improves jumping velocity and energy conversion efficiency but also demonstrates the effect of the U-groove on coalescence dynamics and demonstrates a method to further control the droplet jumping direction for enhanced performance in applications.

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