Hypovigilance Detection for UCAV Operators Based on a Hidden Markov Model

With the advance of military technology, the number of unmanned combat aerial vehicles (UCAVs) has rapidly increased. However, it has been reported that the accident rate of UCAVs is much higher than that of manned combat aerial vehicles. One of the main reasons for the high accident rate of UCAVs is the hypovigilance problem which refers to the decrease in vigilance levels of UCAV operators while maneuvering. In this paper, we propose hypovigilance detection models for UCAV operators based on EEG signal to minimize the number of occurrences of hypovigilance. To enable detection, we have applied hidden Markov models (HMMs), two of which are used to indicate the operators’ dual states, normal vigilance and hypovigilance, and, for each operator, the HMMs are trained as a detection model. To evaluate the efficacy and effectiveness of the proposed models, we conducted two experiments on the real-world data obtained by using EEG-signal acquisition devices, and they yielded satisfactory results. By utilizing the proposed detection models, the problem of hypovigilance of UCAV operators and the problem of high accident rate of UCAVs can be addressed

Hypovigilance Detection for UCAV Operators Based on a Hidden Markov Model

With the advance of military technology, the number of unmanned combat aerial vehicles (UCAVs) has rapidly increased. However, it has been reported that the accident rate of UCAVs is much higher than that of manned combat aerial vehicles. One of the main reasons for the high accident rate of UCAVs is the hypovigilance problem which refers to the decrease in vigilance levels of UCAV operators while maneuvering. In this paper, we propose hypovigilance detection models for UCAV operators based on EEG signal to minimize the number of occurrences of hypovigilance. To enable detection, we have applied hidden Markov models (HMMs), two of which are used to indicate the operators' dual states, normal vigilance and hypovigilance, and, for each operator, the HMMs are trained as a detection model. To evaluate the efficacy and effectiveness of the proposed models, we conducted two experiments on the real-world data obtained by using EEG-signal acquisition devices, and they yielded satisfactory results. By utilizing the proposed detection models, the problem of hypovigilance of UCAV operators and the problem of high accident rate of UCAVs can be addressed

AbGRI4, a novel antibiotic resistance island in multiply antibiotic-resistant Acinetobacter baumannii clinical isolates.

OBJECTIVES: To investigate the genomic context of a novel resistance island (RI) in multiply antibiotic-resistant Acinetobacter baumannii clinical isolates and global isolates. METHODS: Using a combination of long and short reads generated from the Oxford Nanopore and Illumina platforms, contiguous chromosomes and plasmid sequences were determined. BLAST-based analysis was used to identify the RI insertion target. RESULTS: Genomes of four multiply antibiotic-resistant A. baumannii clinical strains, from a US hospital system, belonging to prevalent MLST ST2 (Pasteur scheme) and ST281 (Oxford scheme) clade F isolates were sequenced to completion. A class 1 integron carrying aadB (tobramycin resistance) and aadA2 (streptomycin/spectinomycin resistance) was identified. The class 1 integron was 6.8 kb, bounded by IS26 at both ends, and embedded in a new target location between an α/β-hydrolase and a reductase. Due to its novel insertion site and unique RI composition, we suggest naming this novel RI AbGRI4. Molecular analysis of global A. baumannii isolates identified multiple AbGRI4 RI variants in non-ST2 clonal lineages, including variations in the resistance gene cassettes, integron backbone and insertion breakpoints at the hydrolase gene. CONCLUSIONS: A novel RI insertion target harbouring a class 1 integron was identified in a subgroup of ST2/ST281 clinical isolates. Variants of the RI suggested evolution and horizontal transfer of the RI across clonal lineages. Long- and short-read hybrid assembly technology completely resolved the genomic context of IS-bounded RIs, which was not possible using short reads alone

Large Deviations for Constrained Pattern Matching

In the constrained pattern matching one searches for a given pattern in a constrained sequence, which finds applications in communication, magnetic recording, and biology. We concentrate on the so-called (d, k) constrained binary sequences in which any run of zeros must be of length at least d and at most k, where 0≤d<k. In our previous paper [2] we established the central limit theorem (CLT) for the number of occurrences of a given pattern in such sequences. Here, we present precise large deviations results, often used in diverse applications. In particular, we apply our results to detect under- and over-represented patterns in neuronal data (spike trains), which satisfy structural constraints that match the framework of (d, k) binary sequences. Among others, we obtain justifiably accurate statistical inferences about their biological properties and functions. Throughout, we use techniques of analytic information theory such as combinatorial calculus, generating functions, and complex asymptotics