The main objective of this study is to investigate the factors which affect the roadside signboard cognition during driving. As for the experimental subject to this research, 10 kinds of signboards of suburban roadside shops were studied. The three visible distance of signboards in driving simulator were measured; detectable distance, confirmable distance and legible distance. Also, solid angle and visible angle were analyzed. The effect of color, size, placement of signboard and the difference of luminosity between signboard and its background were shown. The difference of luminosity can be considered when signboard is detected, confirmed and distinguished. Also, wide board placed closer to the road is effective at confirming.
The main objective of this study is to investigate the factors which affect the roadside signboard cognition during driving. As the experimental subject to this research, 10 kinds of signboards on suburban roadside shops are studied. Regarding the study objective, the visible distance of signboards in Driving Simulator were divided into three groups; detectable distance, confirmable distance and legible distance. These three distances, solid angle, and horizontal angle of boards were used as analysis data to examine how the factors of color size, and, width of boards easy the signboard cognition. The results of this study are as follows: Placement of color in high conspicuity over wide surface is effective at detecting a signboard. Placement of wide board closer to the road is effective at confirming a signboard. Placement of big letters or letters with logotype is effective at distinguishment a signboard.
A two-dimensional ordered manganese nitride superstructure is prepared by self-organization on the Cu(001). Square MnN islands of a uniform size arrange with a $(3.5\ifmmode\pm\else\textpm\fi{}0.1)$-nanometer periodicity. This structure can be reproducibly fabricated in ultrahigh vacuum chambers by means of atomic nitrogen exposure, Mn deposition, once again atomic nitrogen exposure, and subsequent annealing. Stoichiometry of the island has been determined by in situ x-ray photoelectron spectroscopy as a manganese mononitride. In-plane lattice constant of the nanoisland is $\ensuremath{\sim}8%$ larger than that of Cu(001). Deposition of a monolayer manganese upon the MnN superstructure with subsequent annealing up to $690\phantom{\rule{0.3em}{0ex}}\mathrm{K}$ gives rise to the same superstructure at the topmost layer. In this case, ``MnCu'' alloy interlayer is formed between the surface MnN and Cu substrate. The band structures of these films were studied by angle-resolved ultraviolet photoelectron spectroscopy, and the bands due to MnN are identified. The atomic model and formation mechanism of the superstructure are discussed in terms of the strain relief of the lattice in the MnN layers.
In the field of autonomous driving, object detection under point clouds is indispensable for environmental perception. In order to achieve the goal of reducing blind spots in perception, many autonomous driving schemes have added low-cost blind-filling LiDAR on the side of the vehicle. Unlike point cloud target detection based on high-performance LiDAR, the blind-filling LiDARs have low vertical angular resolution and are mounted on the side of the vehicle, resulting in easily mixed point clouds of pedestrian targets in close proximity to each other. These characteristics are harmful for target detection. Currently, many research works focus on target detection under high-density LiDAR. These methods cannot effectively deal with the high sparsity of the point clouds, and the recall and detection accuracy of crowded pedestrian targets tend to be low. To overcome these problems, we propose a real-time detection model for crowded pedestrian targets, namely RTCP. To improve computational efficiency, we utilize an attention-based point sampling method to reduce the redundancy of the point clouds, then we obtain new feature tensors by the quantization of the point cloud space and neighborhood fusion in polar coordinates. In order to make it easier for the model to focus on the center position of the target, we propose an object alignment attention module (OAA) for position alignment, and we utilize an additional branch of the targets' location occupied heatmap to guide the training of the OAA module. These methods improve the model's robustness against the occlusion of crowded pedestrian targets. Finally, we evaluate the detector on KITTI, JRDB, and our own blind-filling LiDAR dataset, and our algorithm achieved the best trade-off of detection accuracy against runtime efficiency.
Nitrogen (N)-adsorbed Cu(001)−c(2 × 2) nanopatterned surfaces are used as templates to guide the growth of low-dimensional C60 molecular nanostructures. At room temperature and during the initial stages of growth, C60 molecules preferentially adsorb on the bare Cu regions on a partially N-covered grid surface. Subsequently, a two-dimensional molecular nanomesh is formed at low (∼0.28 monatomic layer) C60 coverages. Further deposition leads to C60 growth on the c(2 × 2)−N surface until the first molecular layer is completed. For a N-saturated surface with trench structures, the <010> steps of these structures serve as initial anchoring sites for C60 growth. From there, the growth proceeds two-dimensionally until a single C60 layer is achieved due to island coalescence. In contrast, no nucleation site was observed when the <110> steps were predominant on the surface. At least up to 6 monatomic layers, the growth proceeds layer-by-layer (i.e., the overlayer morphologies are directed by the underlying substrate pattern). Four rotational domains are observed for the quasi-hexagonally close-packed C60 overlayer with a nearest-neighbor C60−C60 distance of 1.02 nm. It was found that the interaction between C60 and the c(2 × 2)−N surface is fairly weak, likely dominated by van der Waals forces, whereas the C60−Cu interface is chemisorbed. Site-specific electronic effects between these two regions can be resolved by STM even for thick films.
Parthenogenetic embryonic stem cells are considered as a promising resource for regeneration medicine and powerful tools for developmental biology. A lot of studies have revealed that embryonic stem cells have distinct microRNA expression pattern and these microRNAs play important roles in self-renewal and pluripotency of embryonic stem cells. However, few studies concern about microRNA expression pattern in parthenogenetic embryonic stem cells, especially in non-human primate—the ideal model species for human, largely due to the limited rhesus monkey parthenogenetic embryonic stem cells (rpESCs) available and lack of systematic analysis of the basics of rpESCs. Here, we derived two novel rpESCs lines and characterized their microRNA signature by Solexa deep sequencing. These two novel rpESCs shared many properties with other primate ESCs, including expression of pluripotent markers, capacity to generate derivatives representative of all three germ layers in vivo and in vitro, maintaining of euploid karyotype even after long culture. Additionally, lack of some paternally expressed imprinted genes and identity of Single-nucleotide Polymorphism (SNP) compare to their oocyte donors support their parthenogenesis origin. By characterizing their microRNA signature, we identified 91 novel microRNAs, except those are also detected in other primate ESCs. Moreover, these two novel rpESCs display a unique microRNA signature, comparing to their biparental counterpart ESCs. Then we analyzed X chromosome status in these two novel rpESCs; results suggested that one of them possesses two active X chromosomes, the other possesses only one active X chromosome liking biparental female embryonic stem cells. Taken together, our novel rpESCs provide a new alternative to existing rhesus monkey embryonic stem cells, microRNA information expands rhesus monkey microRNA data and may help understanding microRNA roles in pluripotency and parthenogenesis.
Self-assembled MnN nanoislands have been prepared on Cu(001) substrate. The nanoislands show a square shape and a well-defined size. They are regularly arrayed with a periodicity of (3.5+/-0.1) nanometer and form a two-dimensional square superstructure. The MnN island superstructure is stabilized by a short-range mechanism. A structural model has been proposed to explain the self-assembly and the high quality of the superstructure.
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