• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1339-1355
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• 2023

Localization is a hot research spot for many areas, especially in the mobile robot field. Due to the weak signal of the global positioning system (GPS), the alternative schemes in an indoor environment include wireless signal transmitting and receiving solutions, laser rangefinder to build a map followed by a re-localization stage and visual positioning methods, etc. Among all wireless signal positioning techniques, Wi-Fi is the most common one. Wi-Fi access points are installed in most indoor areas of human activities, and smart devices equipped with Wi-Fi modules can be seen everywhere. However, the localization of a mobile robot using a Wi-Fi scheme usually lacks orientation information. Besides, the distance error is large because of indoor signal interference. Another research direction that mainly refers to laser sensors is to actively detect the environment and achieve positioning. An occupancy grid map is built by using the simultaneous localization and mapping (SLAM) method when the mobile robot enters the indoor environment for the first time. When the robot enters the environment again, it can localize itself according to the known map. Nevertheless, this scheme only works effectively based on the prerequisite that those areas have salient geometrical features. If the areas have similar scanning structures, such as a long corridor or similar rooms, the traditional methods always fail. To address the weakness of the above two methods, this work proposes a coarse-to-fine paradigm and an improved localization algorithm that utilizes Wi-Fi to assist the robot localization in a geometrically similar environment. Firstly, a grid map is built by using laser SLAM. Secondly, a fingerprint database is built in the offline phase. Then, the RSSI values are achieved in the localization stage to get a coarse localization. Finally, an improved particle filter method based on the Wi-Fi signal values is proposed to realize a fine localization. Experimental results show that our approach is effective and robust for both global localization and the kidnapped robot problem. The localization success rate reaches 97.33%, while the traditional method always fails.

• The Journal of the Korean Society for Microbiology
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• v.34 no.6
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• pp.519-532
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• 1999

Helicobacter pylori is a causative agent of type B gastritis and plays a central role in the pathogenesis of gastroduodenal ulcer and gastric cancer. To elucidate the host-parasite relationship of the H. pylori infection on the basis of molecular biology, we tried to evaluate the genomic diversity of H. pylori. An ordered overlapping bacterial artificial chromosome (BAC) library of a Korean isolate, H. pylori 51 was constructed to set up a genomic map. A circular physical map was constructed by aligning ApaI, NotI and SfiI-digested chromosomal DNA. When the physical map of H. pylori 51 was compared to that of unrelated strain, H. pylori 26695, completely different restriction patterns were shown. Fifteen known genes were mapped on the chromosome of H. pylori 51 and the genetic map was compared with those of strain 26695 and J99, of which the entire genomic sequences were reported. There were some variability in the gene location as well as gene order among three strains. For further analysis on the genomic diversity of H. pylori, when comparing the genomic structure of 150 H. pylori Korean isolates with one another, genomic macrodiversity of H. pylori was characterized by several features: whether or not susceptible to restriction digestion of the chromsome, variation in chromosomal restriction fingerprint and/or high frequency of gene rearrangement. We also examined the extent of allelic variation in nucleotide or deduced amino acid sequences at the individual gene level. fucT, cagA and vacA were confirmed to carry regions of high variation in nucleotide sequence among strains. The plasticity zone and strain-specific genes of H. pylori 51 were analyzed and compared with the former two genomic sequences. It should be noted that the H. pylori 51-specific sequences were dispersed on the chromosome, not congregated in the plasticity zone unlike J99- or 26695-specific genes, suggesting the high frequency of gene rearrangement in H. pylori genome. The genome of H. pylori 51 shows differences in the overall genomic organization, gene order, and even in the nucleotide sequences among the H. pylori strains, which are far greater than the differences reported on the genomic diversity of H. pylori.

• The Plant Pathology Journal
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• v.34 no.5
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• pp.381-392
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• 2018

Clavibacter michiganensis subsp. michiganesis (Cmm) is a quarantine-worthy pest in $M{\acute{e}}xico$. The implementation and validation of new technologies is necessary to reduce the time for bacterial detection in laboratory conditions and Raman spectroscopy is an ambitious technology that has all of the features needed to characterize and identify bacteria. Under controlled conditions a contagion process was induced with Cmm, the disease epidemiology was monitored. Micro-Raman spectroscopy ($532nm\;{\lambda}$ laser) technique was evaluated its performance at assisting on Cmm detection through its characteristic Raman spectrum fingerprint. Our experiment was conducted with tomato plants in a completely randomized block experimental design (13 plants ${\times}$ 4 rows). The Cmm infection was confirmed by 16S rDNA and plants showed symptoms from 48 to 72 h after inoculation, the evolution of the incidence and severity on plant population varied over time and it kept an aggregated spatial pattern. The contagion process reached 79% just 24 days after the epidemic was induced. Micro-Raman spectroscopy proved its speed, efficiency and usefulness as a non-destructive method for the preliminary detection of Cmm. Carotenoid specific bands with wavelengths at 1146 and $1510cm^{-1}$ were the distinguishable markers. Chemometric analyses showed the best performance by the implementation of PCA-LDA supervised classification algorithms applied over Raman spectrum data with 100% of performance in metrics of classifiers (sensitivity, specificity, accuracy, negative and positive predictive value) that allowed us to differentiate Cmm from other endophytic bacteria (Bacillus and Pantoea). The unsupervised KMeans algorithm showed good performance (100, 96, 98, 91 y 100%, respectively).