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Dendrobenthamia hongkongensis is an excellent ornamental tree species, which has a wide range in germplasm resources distribution and rich varieties within species in China and has also a great development prospect of the new superior species for city landscape. So it is necessary for us to do much research for the development and utilization of this species. According to the latest existing research data of D. hongkongensis, the research achievements of D. hongkongensis in Germplasm resource distribution, bio-ecological habits, breeding and cultivation techniques and so on have been analyzed and summed up. On the other hand, its Ornamental value has been utilized in the modern landscape. At the same time, Edible and Medicinal value of D. hongkongensis has been discussed in the paper as well as Material value of D. hongkongensis. In addition, the future aspects of physiological and ecological research, domestication and breeding new varieties, resource protection and landscape application of D.
This paper develops a new algorithm for
sensor network self-localization, which is an enhanced version of the existing
Crocco’s method in . The algorithm explores the noisy time of flight (TOF)
measurements that quantify the distances between sensor nodes to be localized
and sources also at unknown positions. The newly proposed technique first
obtains rough estimates of the sensor node and source positions, and then it
refines the estimates via a least squares estimator (LSE). The LSE takes into
account the geometrical constraints introduced by the desired global coordinate
system to improve performance. Simulations show that the new technique offers
superior localization accuracy over the original Crocco’s algorithm under small
measurement noise condition.