Detection and anti-detection with multispectral camouflage are of pivotal importance, while suffer from significant challenges due to the inherent contradiction between detection and anti-detection and conflict microwave and infrared (IR) stealth mechanisms. Here, a strategy is proposed to asymmetrically control transmitted microwave wavefront under radar-IR bi-stealth scheme using composite metasurface. It is engineered composed of infrared stealth layer (IRSL), microwave absorbing layer (MAL), and asymmetric microwave transmissive structure (AMTS) with polarization conversion from top to bottom. Therein, IR emissivity, microwave reflectivity, and transmissivity are simultaneously modulated by elaborately designing the filling ratio of ITO square patches on IRSL, which ensures both efficient microwave transmission and IR camouflage. Furthermore, full-polarized backward microwave stealth is achieved on MAL by transmitting and absorbing microwaves under x- and y- polarization, respectively, while forward wavefront is controlled by precise curvature phase compensation on AMTS according to ray-tracing technology. For verification, a proof-of-concept metadevice is numerically and experimentally characterized. Both results coincide well, demonstrating spiral detective wavefront manipulation under y-polarized forward wave excitation while effective reduction of radar cross section within 8-18 GHz and low IR emissivity (<0.3) for backward detection. This strategy provides a new paradigm for integration of detection and anti-detection with multispectral camouflage.
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http://dx.doi.org/10.1002/advs.202410364 | DOI Listing |
Adv Sci (Weinh)
November 2024
Air and Missile Defense College, Air Force Engineering University, Xi'an, 710051, China.
Spontaneous infrared radiation dissipation is a critical factor in facilitating object cooling, which influences the thermal stability and stealth efficacy of infrared stealth devices. Furthermore, the compatibility between efficient visible, infrared, and radar stealth is challenging due to different camouflage principles in different bands. This Letter presents a five-layer etched film structure to achieve multispectral stealth, and the utilization of the high-quality ultrathin silver films enables highly efficient infrared selective emission.
View Article and Find Full Text PDFEntropy (Basel)
July 2023
Information Technology Institute, Information Engineering University, Zhengzhou 450001, China.
Traditional PDF document detection technology usually builds a rule or feature library for specific vulnerabilities and therefore is only fit for single detection targets and lacks anti-detection ability. To address these shortcomings, we build a double-layer detection model for malicious PDF documents based on an entropy method with multiple features. First, we address the single detection target problem with the fusion of 222 multiple features, including 130 basic features (such as objects, structure, content stream, metadata, etc.
View Article and Find Full Text PDFISME J
May 2022
Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
Microbes often secrete high levels of quorum sensing (QS) autoinducers into the environment to coordinate gene expression and biofilm formation, but risk detection and subsequent predation by bacterivorous predators. With such prominent signaling molecules acting as chemoattractants that diffuse into the environment at alarmingly high concentrations, it is unclear if bacterial cells can mask their chemical trails from predator detection. Here, we describe a microbial-based anti-detection adaptation, termed as "biofilm cloak", where the biofilm prey produced biofilm matrix exopolysaccharides that "locked" and reduced the leaching of autoinducers into the milieu, thereby concealing their trails to the detection by the bacterivorous Caenorhabditis elegans nematode.
View Article and Find Full Text PDFJ Supercomput
August 2021
School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China.
Malware has seriously threatened the safety of computer systems for a long time. Due to the rapid development of anti-detection technology, traditional detection methods based on static analysis and dynamic analysis have limited effects. With its better predictive performance, AI-based malware detection has been increasingly used to deal with malware in recent years.
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