Fine resolution double edge clipping with calibration technique for built-in at-speed delay testing

Author(s):  
Chen-I Chung ◽  
Shuo-Wen Chang ◽  
Feng-Tso Chien ◽  
Ching-Hwa Cheng
2021 ◽  
Vol 13 (4) ◽  
pp. 606
Author(s):  
Tee-Ann Teo ◽  
Yu-Ju Fu

The spatiotemporal fusion technique has the advantages of generating time-series images with high-spatial and high-temporal resolution from coarse-resolution to fine-resolution images. A hybrid fusion method that integrates image blending (i.e., spatial and temporal adaptive reflectance fusion model, STARFM) and super-resolution (i.e., very deep super resolution, VDSR) techniques for the spatiotemporal fusion of 8 m Formosat-2 and 30 m Landsat-8 satellite images is proposed. Two different fusion approaches, namely Blend-then-Super-Resolution and Super-Resolution (SR)-then-Blend, were developed to improve the results of spatiotemporal fusion. The SR-then-Blend approach performs SR before image blending. The SR refines the image resampling stage on generating the same pixel-size of coarse- and fine-resolution images. The Blend-then-SR approach is aimed at refining the spatial details after image blending. Several quality indices were used to analyze the quality of the different fusion approaches. Experimental results showed that the performance of the hybrid method is slightly better than the traditional approach. Images obtained using SR-then-Blend are more similar to the real observed images compared with images acquired using Blend-then-SR. The overall mean bias of SR-then-Blend was 4% lower than Blend-then-SR, and nearly 3% improvement for overall standard deviation in SR-B. The VDSR technique reduces the systematic deviation in spectral band between Formosat-2 and Landsat-8 satellite images. The integration of STARFM and the VDSR model is useful for improving the quality of spatiotemporal fusion.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Can Yuan ◽  
Xiufen Sha ◽  
Miao Xiong ◽  
Wenjuan Zhong ◽  
Yu Wei ◽  
...  

AbstractLigusticum L., one of the largest members in Apiaceae, encompasses medicinally important plants, the taxonomic statuses of which have been proved to be difficult to resolve. In the current study, the complete chloroplast genomes of seven crucial plants of the best-known herbs in Ligusticum were presented. The seven genomes ranged from 148,275 to 148,564 bp in length with a highly conserved gene content, gene order and genomic arrangement. A shared dramatic decrease in genome size resulted from a lineage-specific inverted repeat (IR) contraction, which could potentially be a promising diagnostic character for taxonomic investigation of Ligusticum, was discovered, without affecting the synonymous rate. Although a higher variability was uncovered in hotspot divergence regions that were unevenly distributed across the chloroplast genome, a concatenated strategy for rapid species identification was proposed because separate fragments inadequately provided variation for fine resolution. Phylogenetic inference using plastid genome-scale data produced a concordant topology receiving a robust support value, which revealed that L. chuanxiong had a closer relationship with L. jeholense than L. sinense, and L. sinense cv. Fuxiong had a closer relationship to L. sinense than L. chuanxiong, for the first time. Our results not only furnish concrete evidence for clarifying Ligusticum taxonomy but also provide a solid foundation for further pharmaphylogenetic investigation.


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