Traditional physical layer authentication (PLA) schemes in wireless networks typically depend on individual nodes for feature observation, lacking cooperative gain and thus suffering from limited accuracy and robustness. This paper investigates a cooperative transmission and PLA framework for wireless networks, wherein multiple legitimate devices serve as both message relays and identity verifiers by extracting hardware fingerprints. We first establish a theoretical model for the system’s bit error rate (BER), false alarm rate (FAR), and detection probability (PD), and derive closed-form upper bounds to characterize the transmission reliability and authentication performance. Based on our theoretical model, we then define an accuracy improvement ratio (AIR) metric that quantifies FAR gain without compromising BER and PD performance, and derive channel conditions to ensure a positive AIR. To validate the proposed integrated framework, a time-division multiple access (TDMA)-based case study is conducted using carrier frequency offset as the authentication feature. Simulation results demonstrate that the proposed scheme can reduce FAR by up to 100.0% under favorable signal-to-noise ratio (SNR) conditions, while maintaining the BER and PD performance of the conventional non-cooperative scheme, thereby confirming its effectiveness for enhancing secure wireless communications.

Recently, correlation filter (CF)-based tracking methods have attracted considerable attention because of their high-speed performance. However, distortion, which refers to the phenomenon that the correlation outputs of CF-based trackers are distorted, remains a major obstacle for these methods. In this paper, we propose a distortion-aware correlation filter framework, which can detect distortions and recover from tracking failures. Our framework employs a simple yet effective feature termed normed correlation response to detect distortions. Meanwhile, we introduce a competition mechanism to handle distortions, in which we build a specialized graph to formulate and handle tracking under distortion as a maximum multi clique problem. Furthermore, a global-local context model is exploited to alleviate underlying distortions during the tracking process. Extensive experiments on the Online Tracking Benchmark show that our tracker can find the optimal target trajectory during the distortion period and retrieve the possibly missing target, consequently outperforms the state-of-the-art methods and improves the performance of CF-based trackers favorably.

Water scarcity and floods are the major challenges for human society both present and future. Effective and scientific management of water resources requires a good understanding of water cycles, and a systematic integration of observations can lead to better prediction results. This paper presents an integrated approach to water resource management based on geoinformatics including technologies such as Remote Sensing (RS), Geographical Information Systems (GIS), Global Positioning Systems (GPS), Enterprise Information Systems (EIS), and cloud services. The paper introduces a prototype IIS called Water Resource Management Enterprise Information System (WRMEIS) that integrates functions such as data acquisition, data management and sharing, modeling, and knowledge management. A system called SFFEIS (Snowmelt Flood Forecasting Enterprise Information System) based on the WRMEIS structure has been implemented. It includes operational database, Extraction-Transformation-Loading (ETL), information warehouse, temporal and spatial analysis, simulation/prediction models, knowledge management, and other functions. In this study, a prototype water resource management IIS is developed which integrates geoinformatics, EIS, and cloud service. It also proposes a novel approach to information management that allows any participant play the role as a sensor as well as a contributor to the information warehouse. Both users and public play the role for providing data and knowledge. This study highlights the crucial importance of a systematic approach toward IISs for effective resource and environment management.