Scalable Life-long Visual Place Recognition

Visual place recognition (VPR) is the task of using visual inputs to determine if mobile robots are visiting a previously observed place or exploring new regions. To perform convincingly, a practical VPR algorithm must be robust against appearance changes, due to not only short-term (e.g., weather, lighting) and long-term (e.g., seasons, vegetation growth, etc) environmental variations, but also "less cyclical" changes (construction and roadworks, updating of signage, facades and billboards, etc). Such appearance changes invariably occur in real life. It motivates our thesis to fill this research gap. To this end, we firstly investigate probabilistic frameworks to effectively exploit the temporal information from visual data which is in the form of videos. Inspired by Bayes Filter, we propose two VPR methods that respectively perform filtering on discrete and continuous domains, where the temporal information is efficiently used to improve VPR accuracy under appearance changes. Given the fact that the appearance of operational environments uninterruptedly and indefinitely changes, a promising solution for VPR to deal with appearance changes is to continuously accumulate images to incorporate new changes into the internal environmental representation. This demands a VPR technique that is scalable on an ever growing dataset. To this end, inspired by Hidden Markov Models (HMM), we develop novel VPR techniques, that can be efficiently updated and compressed, such that the recognition of new queries can exploit all available data (including recent changes) without suffering from the linear growth in time and space complexity. Another approach to address the scalability issue in VPR is map summarization, which only keeps informative 3D points in a topometric map, according to predefined constraints. In this thesis, we define timestamp as another constraint. Accordingly, we formulate a repeatability predictor (RP) as a regressor, that predicts the repeatability of an interest point as a function of time. We show that the RP can be used to significantly alleviate the degeneration of VPR accuracy from map summarization. The contributions of this thesis not only fill the gap within current state of VPR research; but, more importantly, also enable a wide range of applications, such as, self-driving cars, autonomous robots, augmented reality, and so on.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202

Reclamation of Marine Chitinous Materials for Chitosanase Production via Microbial Conversion by Paenibacillus macerans

[[abstract]]: Chitinous materials from marine byproducts elicit great interest among biotechnologists for their potential biomedical or agricultural applications. In this study, four kinds of marine chitinous materials (squid pens, shrimp heads, demineralized shrimp shells, and demineralized crab shells) were used to screen the best source for producing chitosanase by Paenibacillus macerans TKU029. Among them, the chitosanase activity was found to be highest in the culture using the medium containing squid pens as the sole carbon/nitrogen (C/N) source. A chitosanase which showed molecular weights at 63 kDa was isolated from P. macerans cultured on a squid pens medium. The purified TKU029 chitosanase exhibited optimum activity at 60 ◦C and pH 7, and was stable at temperatures under 50 ◦C and pH 3-8. An analysis by MALDI-TOF MS revealed that the chitosan oligosaccharides (COS) obtained from the hydrolysis of water-soluble chitosan by TKU029 crude enzyme showed various degrees of polymerization (DP), varying from 3–6. The obtained COS enhanced the growth of four lactic acid bacteria strains but exhibited no effect on the growth of E. coli. By specialized growth enhancing effects, the COS produced from hydrolyzing water soluble chitosan with TKU029 chitinolytic enzymes could have potential for use in medicine or nutraceuticals.[[sponsorship]]MOST[[notice]]補正完

Domain Adaptation for Satellite-Borne Hyperspectral Cloud Detection

The advent of satellite-borne machine learning hardware accelerators has enabled the on-board processing of payload data using machine learning techniques such as convolutional neural networks (CNN). A notable example is using a CNN to detect the presence of clouds in hyperspectral data captured on Earth observation (EO) missions, whereby only clear sky data is downlinked to conserve bandwidth. However, prior to deployment, new missions that employ new sensors will not have enough representative datasets to train a CNN model, while a model trained solely on data from previous missions will underperform when deployed to process the data on the new missions. This underperformance stems from the domain gap, i.e., differences in the underlying distributions of the data generated by the different sensors in previous and future missions. In this paper, we address the domain gap problem in the context of on-board hyperspectral cloud detection. Our main contributions lie in formulating new domain adaptation tasks that are motivated by a concrete EO mission, developing a novel algorithm for bandwidth-efficient supervised domain adaptation, and demonstrating test-time adaptation algorithms on space deployable neural network accelerators. Our contributions enable minimal data transmission to be invoked (e.g., only 1% of the weights in ResNet50) to achieve domain adaptation, thereby allowing more sophisticated CNN models to be deployed and updated on satellites without being hampered by domain gap and bandwidth limitations

Bioactivity-guided purification of novel herbal antioxidant and anti-NO compounds from Euonymus laxiflorus Champ

[[abstract]]Euonymus laxiflorus Champ., a medicinal herb collected in Vietnam, has been reported to show several potent bioactivities, including anti-NO, enzyme inhibition, hypoglycemic and antidiabetic effects. Recently, the antioxidant activity of Euonymus laxiflorus Champ. trunk bark (ELCTB) has also been reported. However, the active antioxidant and anti-NO constituents existing in ELCTB have not been reported in the literature. The objective of this study was to purify the active antioxidants from ELCTB and investigate the anti-NO effect of the major constituents. Twenty-two phenolics isolated from ELCTB, including 12 compounds newly isolated in this study (1–12) and 10 constituents obtained from our previous work, were evaluated for their antioxidant activity. Of these, 12 compounds (4–6, 9, 13–15, 18–22) showed a potent antioxidant capacity (FRS50 = 7.8–58.11 µg/mL), in comparison to α-tocopherol (FRS50 = 23 µg/mL). In the anti-NO activity tests, Walterolactone A (1a) and B (1b) β-D-glucopyranoside (13) demonstrated the most effective and comparable activity to that of quercetin with max inhibition and IC50 values of 100%, 1.3 µg/mL, and 100%, 1.21 µg/mL, respectively. The crude extract and its major compounds showed no cytotoxicity on normal cells. Notably, three constituents (9, 11, and 12) were identified as new compounds, another three constituents, including 1, 7, and 8, were found to be new natural products, constituents 9 and 13 were determined to be new antioxidants, and compound 13 was reported to have novel potent anti-NO activity for the first time. The results of this study contribute to the enrichment of new natural products and compounds, as well as the novel biological activity of constituents isolated from Euonymus laxiflorus Champ. The current study also indicates ELCTB as a rich natural source of active phenolics. It is suggested that ELCTB could be developed as a health food with promising antioxidant and anti-NO effects, as well as other beneficial biological activities.[[sponsorship]]科技部[[notice]]補正完

Potential application of rhizobacteria isolated from the Central Highland of Vietnam as an effective biocontrol agent of robusta coffee nematodes and as a bio-fertilizer

[[abstract]]Robusta coffee is a major commercial crop in the Central Highland of Vietnam with high economic and export value. However, this crop is adversely affected by various pathogens, particularly nematodes. This study aimed to screen active anti-nematode rhizobacterial strains for sustainable coffee production. Among more than 200 isolates, the isolate TUN03 demonstrated efficient biocontrol with nearly 100% mortality of J2 coffee nematodes Meloidogyne spp. and 84% inhibition of nematode egg hatching. This active strain was identified as Pseudomonas aeruginosa TUN03 based on its 16S rRNA gene sequence and phylogenetic analysis. In greenhouse tests, the strain TUN03 significantly reduced the coffee nematode population in the rhizome-soil with an 83.23% inhibition rate and showed plant growth-promoting effects. Notably, this is the first report of the nematicidal effect of P. aeruginosa against coffee nematodes. This potent strain further showed an antifungal effect against various crop-pathogenic fungi and was found to be the most effective against Fusarium solani F04 (isolated from coffee roots) with a 70.51% inhibition rate. In addition, high-performance liquid chromatography analysis revealed that this bacterial strain also secretes plant growth regulators including indole acetic acid (IAA), gibberellic acid (GA3), kinetin, and zeatin in significant amounts of 100, 2700, 37, and 9.5 µg/mL, respectively. The data from this study suggest that P. aeruginosa TUN03 may be a potential biocontrol agent and biofertilizer for the sustainable production of Robusta coffee and other crops.[[sponsorship]]科技部[[notice]]補正完