This paper presents a novel 3D reactive navigation method for unmanned aerial vehicles (UAVs) in uneven terrain with unknown moving obstacles. The proposed method effectively reduces computational burden while ensuring safe and efficient UAV operations in complex environments. Through extensive simulations, the research demonstrates the efficacy of this approach. The findings lay a solid foundation for practical UAV applications in challenging 3D environments.
Rain effect in images typically is annoying for many multimedia and computer vision tasks. For removing rain effect from a single image, deep leaning techniques have been attracting considerable attentions. This paper designs a novel multi-task leaning architecture in an end-to-end manner to reduce the mapping range from input to output and boost the performance. Concretely, a decomposition net is built to split rain images into clean background and rain layers. Different from previous architectures, our model consists of, besides a component representing the desired clean image, an extra component for the rain layer. During the training phase, we further employ a composition structure to reproduce the input by the separated clean image and rain information for improving the quality of decomposition. Experimental results on both synthetic and real images are conducted to reveal the high-quality recovery by our design, and show its superiority over other state-of-the-art methods. Furthermore, our design is also applicable to other layer decomposition tasks like dust removal. More importantly, our method only requires about 50ms, significantly faster than the competitors, to process a testing image in VGA resolution on a GTX 1080 GPU, making it attractive for practical use.
Tobacco stems are an important part of the tobacco leaf, containing a lot of cellulose, result in the low utilization of tobacco leaves in cigarettes. The pre-treatment technology of tobacco stems is a key to increase the utilization of tobacco stems. In this work, steam explosion treatment was carried out on tobacco stems with different moisture contents containing 2%, 5% and 10%, respectively. The steam explosion is carried out under different pressure and pressure holding time conditions, the changes in physical structure, chemical composition and pyrolysis volatiles of tobacco stem was investigated. The results indicate that steam explosion has a negligible impact on the crystalline structure of cellulose and low-order polysaccharide crystals, while it has an obvious effect on KCl. Steam explosion increases the specific surface area by destroying the amorphous structure on tobacco stems. The contents of chemical constituents and pyrolytic volatiles indicate that there is a significant leaching effect of lignin, but no change in cellulose content. Soluble polysaccharides, the main component of hemicellulose, were converted into pyrolytic volatiles e.g., furfural and furfuryl alcohol. In conclusion, the technology of steam explosion promotes the leaching, decomposition and chemical transformation of amorphous structures on tobacco stems, thus destroying the structure of the cell walls.
This paper introduces a practical navigation approach for nonholonomic Unmanned Aerial Vehicles (UAVs) in 3D environment settings with numerous stationary and dynamic obstacles. To achieve the intended outcome, Dynamic Programming (DP) is combined with a reactive control algorithm. The DP allows the UAVs to navigate among known static barriers and obstacles. Additionally, the reactive controller uses data from the onboard sensor to avoid unforeseen obstacles. The proposed strategy is illustrated through computer simulation results. In simulations, the UAV successfully navigates around dynamic obstacles while maintaining its route to the target. These results highlight the ability of our proposed approach to ensure safe and efficient UAV navigation in complex and obstacle-laden environments.
Successful extraction of the resonant modes embedded in the transient target signature is vital to resonance-based radar target recognition. For targets in free space, although these resonant modes are theoretically aspect independent, the corresponding residues are known to be aspect dependent. When the target is located above a halfspace, the interface will electromagnetically interact with the target and alter the residue. Within the context of resonance-based target recognition, the influence from an interface to the residues, to the best of our knowledge, has not been well studied. In this paper, we study this problem through a wire target. Our results show that the residues of the mode follow a similar behavior as the dielectric properties of the halfspace vary.
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