NTIRE 2023 Quality Assessment of Video Enhancement Challenge

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance

Sciences for The 2.5-meter Wide Field Survey Telescope (WFST)

The Wide Field Survey Telescope (WFST) is a dedicated photometric survey facility under construction jointly by the University of Science and Technology of China and Purple Mountain Observatory. It is equipped with a primary mirror of 2.5m in diameter, an active optical system, and a mosaic CCD camera of 0.73 Gpix on the main focus plane to achieve high-quality imaging over a field of view of 6.5 square degrees. The installation of WFST in the Lenghu observing site is planned to happen in the summer of 2023, and the operation is scheduled to commence within three months afterward. WFST will scan the northern sky in four optical bands (u, g, r, and i) at cadences from hourly/daily to semi-weekly in the deep high-cadence survey (DHS) and the wide field survey (WFS) programs, respectively. WFS reaches a depth of 22.27, 23.32, 22.84, and 22.31 in AB magnitudes in a nominal 30-second exposure in the four bands during a photometric night, respectively, enabling us to search tremendous amount of transients in the low-z universe and systematically investigate the variability of Galactic and extragalactic objects. Intranight 90s exposures as deep as 23 and 24 mag in u and g bands via DHS provide a unique opportunity to facilitate explorations of energetic transients in demand for high sensitivity, including the electromagnetic counterparts of gravitational-wave events detected by the second/third-generation GW detectors, supernovae within a few hours of their explosions, tidal disruption events and luminous fast optical transients even beyond a redshift of 1. Meanwhile, the final 6-year co-added images, anticipated to reach g about 25.5 mag in WFS or even deeper by 1.5 mag in DHS, will be of significant value to general Galactic and extragalactic sciences. The highly uniform legacy surveys of WFST will also serve as an indispensable complement to those of LSST which monitors the southern sky.Comment: 46 pages, submitted to SCMP

Maritime Semantic Labeling of Optical Remote Sensing Images with Multi-Scale Fully Convolutional Network

In current remote sensing literature, the problems of sea-land segmentation and ship detection (including in-dock ships) are investigated separately despite the high correlation between them. This inhibits joint optimization and makes the implementation of the methods highly complicated. In this paper, we propose a novel fully convolutional network to accomplish the two tasks simultaneously, in a semantic labeling fashion, i.e., to label every pixel of the image into 3 classes, sea, land and ships. A multi-scale structure for the network is proposed to address the huge scale gap between different classes of targets, i.e., sea/land and ships. Conventional multi-scale structure utilizes shortcuts to connect low level, fine scale feature maps to high level ones to increase the network’s ability to produce finer results. In contrast, our proposed multi-scale structure focuses on increasing the receptive field of the network while maintaining the ability towards fine scale details. The multi-scale convolution network accommodates the huge scale difference between sea-land and ships and provides comprehensive features, and is able to accomplish the tasks in an end-to-end manner that is easy for implementation and feasible for joint optimization. In the network, the input forks into fine-scale and coarse-scale paths, which share the same convolution layers to minimize network parameter increase, and then are joined together to produce the final result. The experiments show that the network tackles the semantic labeling problem with improved performance

Redundant Reader Elimination in large-scale IoT City Networks

© 2019 IEEE. Radio Frequency Identification (RFID) is an important communication technology for the Internet of Things (IoT). With the development of IoT, RFID technology is widely deployed in large-scaled city networks. Under the circumstances, how to effectively optimize RFID network to decrease the operational cost of IoT is an ongoing research direction. In this area, one of the problems is the redundant reader issue, which means multiple RFID readers cover and interact with one RFID tag or many same tags, thereby resulting in massive energy cost. In order to solve this problem, quite a lot redundant reader elimination algorithms were presented to reduce unnecessary RFID readers. In this paper, the author proposes and compares three existing redundant reader elimination algorithms, including Redundant Reader Elimination Algorithm (RRE), Three-Count Based Algorithm (TCBA) and Threshold Selection Algorithm (TSA), to observe their performances. In the simulations, the author designs three experiments to test RFID reader\u27s detection radius, detection accuracy and algorithmic efficiency by observing the number of readers eliminated in different environmental sets. The simulation results show that TCBA can detect more redundant readers than RRE and TSA, but it costs more time. Compared with TCBA and RRE, the performance of TSA is more practical and efficient to satisfy real RFID environment and market\u27s needs

Modeling the current and future distribution of Brucellosis under climate change scenarios in Qinghai Lake basin, China

Bruce llosis is a bacterial disease caused by various Brucella species, which infect primarily cattle, swine, goats, sheep, and dogs. The disease is typically transmitted to humans through direct contact with diseased animals, consumption of contaminated animal products, or inhalation of airborne pollutants. The majority of cases are caused by consuming unpasteurized goat or sheep milk or cheese. Based on observed Brucellosis occurrence data and ecogeographic variables, a MaxEnt algorithm was used to model the current and future distribution of Brucellosis in Qinghai Lake basin, P.R. China. Our model showed the Brucellosis current distribution and predicts suitable habitat shifts under future climate scenarios. In the new representatives; SSP 2.6 and SSP 4.5 for the year 2050s and 2070s, our model predicts an expansion in the current suitable areas. This indicates that under the possible climate changes in the future, the living space of Brucellosis in Qinghai Lake basin China will expand significantly. Ecogeographic variables that contributed significantly to the distribution of Brucellosis in Qinghai Lake basin are revealed by our model. The results of our study will promote comparisons with future research and provide a new perspective to inform decision-making in the field of public health in Qinghai province