Global Positioning System (GPS) has been used in many aerial and terrestrial high precision positioning applications. Multipath affects positioning and navigation performance. This paper proposes a convolutional neural network based carrier-phase multipath detection method. The method is based on the fact that the features of multipath characteristics in multipath contaminated data can be learned and identified by a convolutional neural network. The proposed method is validated with simulated and real GPS data and compared with existing multipath mitigation methods in position domain. The results show the proposed method can detect about 80% multipath errors (i.e., recall) in both simulated and real data. The impact of the proposed method on positioning accuracy improvement is demonstrated with two datasets, 18–30% improvement is obtained by down-weighting the detected multipath measurements. The focus of this paper is on the development and test of the proposed convolutional neural network based multipath detection algorithm.

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Global Navigation Satellite Systems (GNSS) Carrier Phase (CP)-based high-precision positioning techniques have been widely used in geodesy, attitude determination, engineering survey and agricultural applications. With the modernisation of GNSS, multi-constellation and multi-frequency data processing is one of the foci of current GNSS research. The GNSS development authorities have better designs for the new signals, which are aimed for fast acquisition for civil users, less susceptible to interference and multipath, and having lower measurement noise. However, how good are the new signals in practice? The aim of this paper is to provide an early assessment of the newly available signals as well as assessment of the other currently available signals. The signal quality of the multi-GNSS (GPS, GLONASS, Galileo, BDS and QZSS) is assessed by looking at their zero-baseline Double Difference (DD) CP residuals. The impacts of multi-GNSS multi-frequency signals on single-epoch positioning are investigated in terms of accuracy, precision and fixed solution availability with known short baselines.

Lung cancer is the major cause of cancer-related deaths globally. Mutant KRAS is a feature of 15-50% of lung cancer cases and represents one of the most prevalent oncogenic drivers in this disease. Unfortunately, although much effort has been spent on searching for small molecule inhibitors of KRAS, KRAS gene has proven extraordinarily difficult to target by current pharmacological agents. In the present study, we developed an alternative strategy to silence the so-called untargetable and undruggable KRAS gene by employing exosomemediated siRNA delivery. Particularly, we reprogrammed HEK293T cells to simultaneously express KRAS siRNA and Lamp2b, an exosomal membrane protein, in fusion with a tumor-penetrating internalizing RGD (iRGD) peptide (CRGDKGPDC), and then purified the tumor-targeting exosomes as KRAS siRNA delivery system. In agreement with the study design, intravenously injected iRGD-exosomes specifically targeted to tumor tissues in vivo. The therapeutic potential was revealed by the strong inhibition of tumor growth in a mouse model after intravenous injection of KRAS siRNA encapsulated in iRGD-exosomes. In conclusion, our results indicate that iRGD-tagged exosomes is an ideal delivery agent to transport KRAS siRNAs for lung cancer treatment. As an extension of this finding, the vast majority of mutated genes that are difficult to target by current pharmacological agents will be targetable and druggable in the future.

Mobile laser scanning (MLS) has been widely used in three-dimensional (3D) city modelling data collection, such as Google cars for Google Map/Earth. Building Information Modelling (BIM) has recently emerged and become prominent. 3D models of buildings are essential for BIM. Static laser scanning is usually used to generate 3D models for BIM, but this method is inefficient if a building is very large, or it has many turns and narrow corridors. This paper proposes using MLS for BIM 3D data collection. The positions and attitudes of the mobile laser scanner are important for the correct georeferencing of the 3D models. This paper proposes using three high-precision ultra-wide band (UWB) tags to determine the positions and attitudes of the mobile laser scanner. The accuracy of UWB-based MLS 3D models is assessed by comparing the coordinates of target points, as measured by static laser scanning and a total station survey.