RESUMEN
The increasing role of unmanned aerial vehicle (UAV) swarms in modern warfare poses a significant challenge to ground and air defense systems. Considering complex terrain environments and multi-sensor resources including radar and photoelectric systems constraints, a novel multi-sensor dynamic scheduling algorithm is proposed in this paper. Firstly, a transmission model with Fresnel zone under complex terrain and sensor models for radar/photoelectric systems are established. Considering the constraints of 6 factors, such as pitch angle, array scanning angle and threat levels, a detection model is developed subsequently. Secondly, to meet the real-time requirements of ground and air defense systems, a fast calculation method for Fresnel zone clearance using adaptive buffer is achieved. Thirdly, an improved Hungarian algorithm is proposed to solve the combinatorial optimization problem of sensor scheduling. Finally, simulation experiments are conducted to evaluate the algorithm performance under different conditions. The results demonstrate that the proposed approach significantly reduces the sensor switching rate while achieving a high sensor-UAV matching rate and high-threat matching rate. Furthermore, the simulation results verify the effectiveness of the proposed algorithm when applied to multi-sensor scheduling for defending UAV swarms.
RESUMEN
Information-Centric Networking (ICN) is the emerging next-generation internet paradigm. The Low Earth Orbit (LEO) satellite mega-constellation based on ICN can achieve seamless global coverage and provide excellent support for Internet of Things (IoT) services. Additionally, in-network caching, typically characteristic of ICN, plays a paramount role in network performance. Therefore, the in-network caching policy is one of the hotspot problems. Especially, compared to caching traditional internet content, in-networking caching IoT content is more challenging, since the IoT content lifetime is small and transient. In this paper, firstly, the framework of the LEO satellite mega-constellation Information-Centric Networking for IoT (LEO-SMC-ICN-IoT) is proposed. Then, introducing the concept of "viscosity", the proposed Caching Algorithm based on the Random Forest (CARF) policy of satellite nodes combines both content popularity prediction and satellite nodes location prediction, for achieving good cache matching between the satellite nodes and content. And using the matching rule, the Random Forest (RF) algorithm is adopted to predict the matching relationship among satellite nodes and content for guiding the deployment of caches. Especially, the content is cached in advance at the future satellite to maintain communication with the current ground segment at the time of satellite switchover. Additionally, the policy considers both the IoT content lifetime and the freshness. Finally, a simulation platform with LEO satellite mega-constellation based on ICN is developed in Network Simulator 3 (NS-3). The simulation results show that the proposed caching policy compared with the Leave Copy Everywhere (LCE), the opportunistic (OPP), the Leave Copy down (LCD), and the probabilistic algorithm which caches each content with probability 0.5 (prob 0.5) yield a significant performance improvement, such as the average number of hops, i.e., delay, cache hit rate, and throughput.
RESUMEN
Insight concerning the switch in HIV-1 coreceptor use will lead to a better understanding of HIV-1 pathogenesis and host-virus dynamics. Predicting CXCR4 utilization by analyzing HIV-1 envelope consensus sequences is highly specific, but minority variants in the viral population are often missed resulting in low sensitivity. Commercial phenotypic assays are costly, and the development of sensitive in-house phenotypic assays to detect CXCR4-using HIV may not be feasible for some laboratories. A sensitive, inexpensive genotyping assay was developed to detect viral sequences associated with CXCR4-utilizing virus (X4). Codon-specific primer pairs were used to detect X4-associated codons at five positions in the HIV-1 envelope V3 loop (11, 13, 24, 25, and 32). Sixty plasma samples from HIV-1-infected individuals were analyzed by consensus sequencing and codon-specific PCR (CS-PCR). Forty-six of these were also phenotyped by Trofile or Enhanced Sensitivity Trofile (ESTA). CS-PCR detected X4 variants 17% more often than 11/24/25 consensus sequencing alone (n=60), 30% more often than Trofile (n=27), and in a limited data set, 16% more often than ESTA (n=19). CS-PCR combined with consensus sequencing had approximately 80% concordance with ESTA.
