ALBERT

Description: Materials, Vol. 11, Pages 626: Broadband Polarization Conversion Metasurface Based on Metal Cut-Wire Structure for Radar Cross Section Reduction Materials doi: 10.3390/ma11040626 Authors: Jia Ji Yang Yong Zhi Cheng Chen Chen Ge Rong Zhou Gong A class of linear polarization conversion coding metasurfaces (MSs) based on a metal cut-wire structure is proposed, which can be applied to the reduction properties of radar cross section (RCS). We firstly present a hypothesis based on the principle of planar array theory, and then verify the RCS reduction characteristics using linear polarization conversion coding MSs by simulations and experiments. The simulated results show that in the frequency range of 6–14 GHz, the linear polarization conversion ratio reaches a maximum value of 90%, which is in good agreement with the theoretical predictions. For normal incident x- and y-polarized waves, RCS reduction of designed coding MSs 01/01 and 01/10 is essentially more than 10 dB in the above-mentioned frequency range. We prepare and measure the 01/10 coding MS sample, and find that the experimental results in terms of reflectance and RCS reduction are in good agreement with the simulated ones under normal incidence. In addition, under oblique incidence, RCS reduction is suppressed as the angle of incidence increases, but still exhibits RCS reduction effects in a certain frequency range. The designed MS is expected to have valuable potential in applications for stealth field technology.

Description: IJGI, Vol. 6, Pages 300: A Hybrid Process/Thread Parallel Algorithm for Generating DEM from LiDAR Points ISPRS International Journal of Geo-Information doi: 10.3390/ijgi6100300 Authors: Yibin Ren Zhenjie Chen Ge Chen Yong Han Yanjie Wang Airborne Light Detection and Ranging (LiDAR) is widely used in digital elevation model (DEM) generation. However, the very large volume of LiDAR datasets brings a great challenge for the traditional serial algorithm. Using parallel computing to accelerate the efficiency of DEM generation from LiDAR points has been a hot topic in parallel geo-computing. Generally, most of the existing parallel algorithms running on high-performance clusters (HPC) were in process-paralleling mode, with a static scheduling strategy. The static strategy would not respond dynamically according to the computation progress, leading to load unbalancing. Additionally, because each process has independent memory space, the cost of dealing with boundary problems increases obviously with the increase in the number of processes. Actually, these two problems can have a significant influence on the efficiency of DEM generation for larger datasets, especially for those of irregular shapes. Thus, to solve these problems, we combined the advantages of process-paralleling with the advantages of thread-paralleling, forming a new idea: using process-paralleling to achieve a flexible schedule and scalable computation, using thread-paralleling inside the process to reduce boundary problems. Therefore, we proposed a hybrid process/thread parallel algorithm for generating DEM from LiDAR points. Firstly, at the process level, we designed a parallel method (PPDB) to accelerate the partitioning of LiDAR points. We also proposed a new dynamic scheduling strategy to achieve better load balancing. Secondly, at the thread level, we designed an asynchronous parallel strategy to hide the cost of LiDAR points’ reading. Lastly, we tested our algorithm with three LiDAR datasets. Experiments showed that our parallel algorithm had no influence on the accuracy of the resultant DEM. At the same time, our algorithm reduced the conversion time from 112,486 s to 2342 s when we used the largest dataset (150 GB). The PPDB was parallelizable and the new dynamic scheduling strategy achieved a better load balancing. Furthermore, the asynchronous parallel strategy reduced the impact of LiDAR points reading. When compared with the traditional process-paralleling algorithm, the hybrid process/thread parallel algorithm improved the conversion efficiency by 30%.

Description: Acute stroke is a serious cerebrovascular disease and has been the second leading cause of death worldwide. Conventional diagnostic modalities for stroke, such as CT and MRI, may not be available in emergency settings. Hence, it is imperative to develop a portable tool to diagnose stroke in a timely manner. Since there are differences in impedance spectra between normal, hemorrhagic and ischemic brain tissues, multi-frequency electrical impedance tomography (MFEIT) shows great promise in detecting stroke. Measuring the impedance spectra of healthy, hemorrhagic and ischemic brain in vivo is crucial to the success of MFEIT. To our knowledge, no research has established hemorrhagic and ischemic brain models in the same animal and comprehensively measured the in vivo impedance spectra of healthy, hemorrhagic and ischemic brain within 10 Hz–1 MHz. In this study, the intracerebral hemorrhage and ischemic models were established in rabbits, and then the impedance spectra of healthy, hemorrhagic and ischemic brain were measured in vivo and compared. The results demonstrated that the impedance spectra differed significantly between healthy and stroke-affected brain (i.e., hemorrhagic or ischemic brain). Moreover, the rate of change in brain impedance following hemorrhagic and ischemic stroke with regard to frequency was distinct. These findings further validate the feasibility of using MFEIT to detect stroke and differentiate stroke types, and provide data supporting for future research.