Visual SLAM for Unmanned Aerial Vehicles: Localization and Perception.

Localization and perception play an important role as the basis of autonomous Unmanned Aerial Vehicle (UAV) applications, providing the internal state of movements and the external understanding of environments. Simultaneous Localization And Mapping (SLAM), one of the critical techniques for localization and perception, is facing technical upgrading, due to the development of embedded hardware, multi-sensor technology, and artificial intelligence. This survey aims at the development of visual SLAM and the basis of UAV applications. The solutions to critical problems for visual SLAM are shown by reviewing state-of-the-art and newly presented algorithms, providing the research progression and direction in three essential aspects: real-time performance, texture-less environments, and dynamic environments. Visual-inertial fusion and learning-based enhancement are discussed for UAV localization and perception to illustrate their role in UAV applications. Subsequently, the trend of UAV localization and perception is shown. The algorithm components, camera configuration, and data processing methods are also introduced to give comprehensive preliminaries. In this paper, we provide coverage of visual SLAM and its related technologies over the past decade, with a specific focus on their applications in autonomous UAV applications. We summarize the current research, reveal potential problems, and outline future trends from academic and engineering perspectives.

Screening of highly discriminative microhaplotype markers for individual identification and mixture deconvolution in East Asian populations.

Microhaplotypes are forensic genetic markers that combine single nucleotide polymorphisms in close proximity to one another. Highly discriminative microhaplotype markers could be superior to short tandem repeats (STRs) in DNA mixture deconvolution investigations because they are not interfered by stutters. In this study, the effective number of alleles (Ae) and discrimination power values of microhaplotypes and STRs were compared. It was found that current microhaplotypes are not as discriminative as commonly used forensic STRs. Effective screening of highly discriminative microhaplotype markers were consequently conducted for East Asian populations. To satisfy different forensic application needs, four sets of microhaplotypes with Ae values ≥ 4 were screened for under different conditions that included marker length and physical distances between markers. While the four sets contained 703, 301, 337, and 190 microhaplotypes, their average Ae values reached 5.38, 6.30, 7.39, and 5.61, respectively. The microhaplotype group containing 301 markers (maximum length of 200 bp and separated by ≥ 5 million bases) was further investigated. The results showed that none of the 301 loci were exactly the same as those previously reported, while seven loci partially overlapped with known markers. While Ae values of 45 loci were ≥ 8, the Ae value of the mh17WL-008 locus reached a maximum of 93.57. Further analysis showed that the newly identified microhaplotype markers were also highly polymorphic in African, American, European, and South Asian populations.

Identification of cancer prognosis-associated functional modules using differential co-expression networks.

The rapid accumulation of cancer-related data owing to high-throughput technologies has provided unprecedented choices to understand the progression of cancer and discover functional networks in multiple cancers. Establishment of co-expression networks will help us to discover the systemic properties of carcinogenesis features and regulatory mechanisms of multiple cancers. Here, we proposed a computational workflow to identify differentially co-expressed gene modules across 8 cancer types by using combined gene differential expression analysis methods and a higher-order generalized singular value decomposition. Four co-expression modules were identified; and oncogenes and tumor suppressors were significantly enriched in these modules. Functional enrichment analysis demonstrated the significantly enriched pathways in these modules, including ECM-receptor interaction, focal adhesion and PI3K-Akt signaling pathway. The top-ranked miRNAs (mir-199, mir-29, mir-200) and transcription factors (FOXO4, E2A, NFAT, and MAZ) were identified, which play an important role in deregulating cellular energetics; and regulating angiogenesis and cancer immune system. The clinical significance of the co-expressed gene clusters was assessed by evaluating their predictability of cancer patients' survival. The predictive power of different clusters and subclusters was demonstrated. Our results will be valuable in cancer-related gene function annotation and for the evaluation of cancer patients' prognosis.