The GNSS derived Zenith Tropospheric Delay (ZTD) plays today a very critical role in meteorological study and weather forecasts, as ZTDs of thousands of GNSS stations are operationally assimilated into numerical weather prediction models. Recently, the Chinese BeiDou Navigation Satellite System (BDS) was officially announced to provide operational services around China and its neighborhood and it was demonstrated to be very promising for precise navigation and positioning. In this contribution, we concentrate on estimating ZTD using BDS observations to assess its capacity for troposphere remote sensing. A local network which is about 250 km from Beijing and comprised of six stations equipped with GPS- and BDS-capable receivers is utilized. Data from 5 to 8 November 2012 collected on the network is processed in network mode using precise orbits and in Precise Point Positioning mode using precise orbits and clocks. The precise orbits and clocks are generated from a tracking network with most of the stations in China and several stations around the world. The derived ZTDs are compared with that estimated from GPS data using the final products of the International GNSS Service (IGS). The comparison shows that the bias and the standard deviation of the ZTD differences are about 2 mm and 5 mm, respectively, which are very close to the differences of GPS ZTD estimated using different software packages.
Figure 1: Retinal implant ('bionic eye') for restoring vision to people with visual impairment. A) Light captured by a camera is transformed into electrical pulses delivered through a microelectrode array to stimulate the retina (adapted with permission from [39]). B) To create meaningful artificial vision, we explored deep learning-based scene simplification as a preprocessing strategy for retinal implants (reproduced from doi:10.6084/m9.figshare.13652927 under CC-BY 4.0). As a proof of concept, we used a neurobiologically inspired computational model to generate realistic predictions of simulated prosthetic vision (SPV), and asked sighted subjects (i.e., virtual patients) to identify people and cars in a novel SPV dataset of natural outdoor scenes. In the future, this setup may be used as input to a real retinal implant.
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