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.

Description: The arrival time of ocean swells is an important factor for offshore and coastal engineering and naval and recreational activities, which can also be used in evaluating the numerical wave model. Using the continuity and pattern of wave heights during the same swell event, a methodology is developed for identifying swell events and verifying swell arrival time in models from buoy data. The swell arrival time in a WAVEWATCH III hindcast database is validated with in situ measurements. The results indicate that the model has a good agreement with the observations but usually predicts an early arrival of swell, about 4 h on average. A histogram shows that about one-quarter of swell events arrive early and three-quarters late by comparison with the model. Many processes that may be responsible for the arrival time errors are discussed, but at this stage it is not possible to distinguish between them from the available data.

Description: This paper proposes a new algorithm for parallel identification of mesoscale eddies from global satellite altimetry data. By simplifying the recognition process and the sea level anomaly (SLA) contours’ search range, the method improves identification efficiency compared with the previous SSH-based method even in the single-threaded process. The global SLA map is divided into several regions. These regions are identified simultaneously with a new SSH-based method. All the eddy identification results of these regions are merged seamlessly into a global eddy map. A β-plane approximation is used to calculate the geostrophic speed in the equatorial band. Compared with the computation complexity of the previous SSH-based method, which is , the computation complexity of the new method is , where K is the number of threads and L is the number of regional SLA maps. When applying the new method to the global SLA map, the computation is ~100 times faster than the previous SSH-based method on an average computer. The new method characterizes an eddy structure by radius, amplitude, eddy core, closed SLA contour, and closed SLA contour with maximum average geostrophic speed. In situ data and another global eddy dataset are applied to validate the reliability of eddies detected by the new algorithm. Global eddy mean properties, variability, and the geographical distribution of both datasets are analyzed to demonstrate the performance of this new method and to help understand eddy activities on a global scale.

Description: A decade of newly available Argo float data for the period 2004–13 are used to investigate the three-dimensional structures of upper-ocean seasonality with emphasis on the vertical aspects of annual and semiannual cycles, yielding three main findings with oceanographic implications. First, the vertical evolution of the horizontal pattern of annual and semiannual amplitudes appears to be highly “nonlinear,” suggesting that the thermodynamic causes are depth dependent. The global ocean seasonality exhibits a vertically varying pattern in space, including midlatitude maxima in the near-surface layer due to solar forcing, zonal “strips” in the subsurface layer due to the equatorial current system, and systematic westward phase propagation in the intermediate layer due to annual Rossby waves. Second, a zone of 500 ± 300-m depths along with a 6-month periodicity are chosen as appropriate space–time “windows” for detecting eddy signatures via Argo-derived temperature amplitude and phase, respectively. It is revealed that the eddy-induced “blobby” pattern observed previously by satellite altimeter appears in the Agro result as “woodsy” bulks, which can be well illustrated in the semiannual amplitude and phase maps at window depths. Meanwhile, six eddy deserts paired in each ocean basin have also been identified. Third, the existence of a dozen vertical quasi-annual amphidromes is first reported, with cophase lines that may radiate toward the ~2000-m lower limit of Argo measurement. The well-known global meridional overturning circulation and the pseudozonal overturning currents in the equatorial Pacific, Atlantic, and Indian Oceans may possibly contribute to the observed vertical amphidromes.

Description: Seasonality is a fundamental feature of the coupled ocean–atmosphere system. The hemispherically opposite general pattern at the air–sea interface is well known, but its penetrative behavior below the sea surface is poorly understood. Over 10 years of Argo data reveal for the first time the spiral-like structure of vertical seasonality in the upper extratropical ocean: the amplitude (strength of annual sea temperature variability) decreases rapidly with depth while the phase (peaking time) rotates from August (February) at the sea surface to December (June) at the bottom of the mixed layer in the Northern (Southern) Hemisphere under a clockwise representation. It is found that, analogous to the Ekman current spiral, the oceanic seasonality is almost reversed at approximately 500-m depth with about 5% of the surface intensity left. In contrast, the seasonality of subtropical oceans below the thermocline exhibits a phase-lock pattern around May and October to the north and south of the equator, respectively. Meanwhile, a systematic westward progression of annual phase corresponding to the warmest month is observed in the equatorial regions between 10°S and 10°N. It is suggested that the seasonality spiral of extratropical oceans occurs as a result of the vertical decay of solar penetration in tandem with delayed annual maximum mixing in the context of buoyancy and turbulent induced convections, while the month of full summer appears to be “constant” May (October) in the subtropical oceans of the Northern (Southern) Hemisphere under the stratified subsurface layer due to a 9-month trapping of penetrating solar energy from May (October) to next January (June). The vertically locked westward annual phase progression in the equatorial regions is likely to be a consequence of the first baroclinic mode of β-refracted annual Rossby waves. The spiral and phase-lock behaviors of the upper oceans are of critical significance to the understanding of mixed layer and thermocline dynamics.