SPOT GNSS in Emergency and Location Based Services

Location Based Systems (LBS) market has emerged exponentially since early 2000 in the wake of growing need for Emergency Relief Applications. The argument of course outstanding is which device outperforms all other in diverse scenarios without failure. While many purpose built LBS are in use, SPOT satellite messenger gained attention for its reliability. This paper summates the system architecture and experimental tests results with those of competing Assisted and Global Navigation Satellite Systems (A/GNSS). Our test bed comprised of 26 test points with pre-established database of GNSS difficulty levels in diverse environments in UNSW. Parameters of interest are availability, accuracy and Time to First Fix (TTFF). Relative benchmarking proves SPOT’s higher TTFF and higher failure rate in general. While High Sensitivity GNSS and Assisted GNSS (MS-Based and MS-Assisted) had higher availability, higher accuracy and lower TTFF. Altogether fewer failure scenarios, trustworthy coverage with cost effectiveness were observed for MS-Based AGNSS which is vital for LBS applications. However reliance on wired or wireless IP network potentially limits the performance in non-existent underlying infrastructure in remote applications. SPOT demonstrated higher TTFF and failure rates in test scenario. On the contrary Assisted GNSS (MS-Based or MS-Assisted) can provide a reliable, cost effective and open source alternative to SPOT satellite messenger with better TTFF, availability and accuracy for consumer and research applications

ChatGPT is a Potential Zero-Shot Dependency Parser

Pre-trained language models have been widely used in dependency parsing task and have achieved significant improvements in parser performance. However, it remains an understudied question whether pre-trained language models can spontaneously exhibit the ability of dependency parsing without introducing additional parser structure in the zero-shot scenario. In this paper, we propose to explore the dependency parsing ability of large language models such as ChatGPT and conduct linguistic analysis. The experimental results demonstrate that ChatGPT is a potential zero-shot dependency parser, and the linguistic analysis also shows some unique preferences in parsing outputs.Comment: 10 page

Measurement of SiPM gain and photon detection efficiency at different temperatures and bias voltages

Gain and photon detection efficiency (PDE) of silicon photomultipliers (SiPMs) are important characteristics to understand SiPM-based detector systems in low light level applications. In this work, experimental setups are developed to quantify SiPM gain and PDE at different temperatures and bias voltages with a light source of fixed wavelength 405 nm, where a novel light-tight connected device of two integrating spheres is implemented to produce weak light onto SiPM. We present methods and results of the breakdown voltage, gain and PDE measurements for a Hamamatsu S13360-2050VE MPPC. At 25 Celsius, consistent results are obtained with the datasheet from the manufacturer. The temperature and bias voltage dependence of SiPM performances can guide its usage, such as in gain compensation at readout circuits, optical modeling of SiPMs and optimization of operating conditions of SiPM-based detectors.Comment: 9 pages, 14 figure

Iterative Robust Visual Grounding with Masked Reference based Centerpoint Supervision

Visual Grounding (VG) aims at localizing target objects from an image based on given expressions and has made significant progress with the development of detection and vision transformer. However, existing VG methods tend to generate false-alarm objects when presented with inaccurate or irrelevant descriptions, which commonly occur in practical applications. Moreover, existing methods fail to capture fine-grained features, accurate localization, and sufficient context comprehension from the whole image and textual descriptions. To address both issues, we propose an Iterative Robust Visual Grounding (IR-VG) framework with Masked Reference based Centerpoint Supervision (MRCS). The framework introduces iterative multi-level vision-language fusion (IMVF) for better alignment. We use MRCS to ahieve more accurate localization with point-wised feature supervision. Then, to improve the robustness of VG, we also present a multi-stage false-alarm sensitive decoder (MFSD) to prevent the generation of false-alarm objects when presented with inaccurate expressions. The proposed framework is evaluated on five regular VG datasets and two newly constructed robust VG datasets. Extensive experiments demonstrate that IR-VG achieves new state-of-the-art (SOTA) results, with improvements of 25\% and 10\% compared to existing SOTA approaches on the two newly proposed robust VG datasets. Moreover, the proposed framework is also verified effective on five regular VG datasets. Codes and models will be publicly at https://github.com/cv516Buaa/IR-VG

Propagation and Wireless Channel Modeling Development on Wide-Sense Vehicle-to-X Communications

The need for improving the safety and the efficiency of transportation systems has become of extreme importance. In this regard, the concept of vehicle-to-X (V2X) communication has been introduced with the purpose of providing wireless communication technology in vehicular networks. Not like the traditional views, the wide-sense V2X (WSV2X) communications in this paper are defined by including not only vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications but also train-to-X (T2X) communications constituted of train-to-train (T2T) and train-to-infrastructure (T2I) communications. All the information related to the wide-sense V2X channels, such as the standardization, scenarios, characters, and modeling philosophies, is organized and summarized to form the comprehensive understanding of the development of the WSV2X channels