Physical Layer Security in Wireless Ad Hoc Networks Under A Hybrid Full-/Half-Duplex Receiver Deployment Strategy

This paper studies physical layer security in a wireless ad hoc network with numerous legitimate transmitter-receiver pairs and eavesdroppers. A hybrid full-/half-duplex receiver deployment strategy is proposed to secure legitimate transmissions, by letting a fraction of legitimate receivers work in the full-duplex (FD) mode sending jamming signals to confuse eavesdroppers upon their information receptions, and letting the other receivers work in the half-duplex mode just receiving their desired signals. The objective of this paper is to choose properly the fraction of FD receivers for achieving the optimal network security performance. Both accurate expressions and tractable approximations for the connection outage probability and the secrecy outage probability of an arbitrary legitimate link are derived, based on which the area secure link number, network-wide secrecy throughput and network-wide secrecy energy efficiency are optimized respectively. Various insights into the optimal fraction are further developed and its closed-form expressions are also derived under perfect self-interference cancellation or in a dense network. It is concluded that the fraction of FD receivers triggers a non-trivial trade-off between reliability and secrecy, and the proposed strategy can significantly enhance the network security performance.Comment: Journal paper, double-column 12 pages, 9 figures, accepted by IEEE Transactions on Wireless Communications, 201

Zoom-VQA: Patches, Frames and Clips Integration for Video Quality Assessment

Video quality assessment (VQA) aims to simulate the human perception of video quality, which is influenced by factors ranging from low-level color and texture details to high-level semantic content. To effectively model these complicated quality-related factors, in this paper, we decompose video into three levels (\ie, patch level, frame level, and clip level), and propose a novel Zoom-VQA architecture to perceive spatio-temporal features at different levels. It integrates three components: patch attention module, frame pyramid alignment, and clip ensemble strategy, respectively for capturing region-of-interest in the spatial dimension, multi-level information at different feature levels, and distortions distributed over the temporal dimension. Owing to the comprehensive design, Zoom-VQA obtains state-of-the-art results on four VQA benchmarks and achieves 2nd place in the NTIRE 2023 VQA challenge. Notably, Zoom-VQA has outperformed the previous best results on two subsets of LSVQ, achieving 0.8860 (+1.0%) and 0.7985 (+1.9%) of SRCC on the respective subsets. Adequate ablation studies further verify the effectiveness of each component. Codes and models are released in https://github.com/k-zha14/Zoom-VQA.Comment: Accepted by CVPR 2023 Worksho

CFSv2-Based Seasonal Hydroclimatic Forecasts over the Conterminous United States

There is a long history of debate on the usefulness of climate model–based seasonal hydroclimatic forecasts as compared to ensemble streamflow prediction (ESP). In this study, the authors use NCEP's operational forecast system, the Climate Forecast System version 2 (CFSv2), and its previous version, CFSv1, to investigate the value of climate models by conducting a set of 27-yr seasonal hydroclimatic hindcasts over the conterminous United States (CONUS). Through Bayesian downscaling, climate models have higher squared correlation R2 and smaller error than ESP for monthly precipitation, and the forecasts conditional on ENSO have further improvements over southern basins out to 4 months. Verification of streamflow forecasts over 1734 U.S. Geological Survey (USGS) gauges shows that CFSv2 has moderately smaller error than ESP, but all three approaches have limited added skill against climatology beyond 1 month because of overforecasting or underdispersion errors. Using a postprocessor, 60%–70% of probabilistic streamflow forecasts are more skillful than climatology. All three approaches have plausible predictions of soil moisture drought frequency over the central United States out to 6 months, and climate models provide better results over the central and eastern United States. The R2 of drought extent is higher for arid basins and for the forecasts initiated during dry seasons, but significant improvements from CFSv2 occur in different seasons for different basins. The R2 of drought severity accumulated over CONUS is higher during winter, and climate models present added value, especially at long leads. This study indicates that climate models can provide better seasonal hydroclimatic forecasts than ESP through appropriate downscaling procedures, but significant improvements are dependent on the variables, seasons, and regions