Machine Learning Methods for Autonomous Object Recognition and Restoration in Images

Image recognition and image restoration are important tasks in the field of image processing. Image recognition are becoming very popular due to the state-of-the-art deep learning methods. However, these models usually require big datasets and high computational costs, which could be challenging. This thesis proposes an online learning framework that deals with both small and big datasets. For small datasets, a Cauchy prior logistic regression classifier is proposed to provide a quick convergence, and the online weight updating scheme is efficient due to the previously trained weights being reused. For big datasets, convolutional neural network could be implemented. For image recognition, non-parametric classifiers are often used for image recognition such as K-nearest neighbours, however, K-nearest neighbours are vulnerable to noise and high dimensional features. This thesis proposes a non-parametric classifier based on Bayesian compressive sensing; the developed classifier is robust and it does not need a training stage. For image restoration, which is usually performed before image recognition as a preprocessing process. This thesis proposes such a joint framework that performs image recognition and restoration simultaneously. In image restoration, image rotation and occlusion are common problems but convolutional neural networks are not suitable to solve these due to the limitation of the convolutional process and pooling process. This thesis develops a joint framework based on capsule networks. The developed joint capsule framework could achieve a good result on recognition, image de-noising, recovering rotation and removing occlusion. The developed algorithms have been evaluated for vehicle logo restoration and recognition, however, they are transferable to other implementations. This thesis also developed an automatic detection and recognition framework for badger monitoring for the first time. Badger plays a key role in the transmission of bovine tuberculosis, which is described by government as the most pressing animal health problem in the UK. An automatic badger monitoring system could help researcher to understand the transmission mechanisms and thereby to develop methods to deal with the transmission between species

Min-max Submodular Ranking for Multiple Agents

In the submodular ranking (SR) problem, the input consists of a set of submodular functions defined on a ground set of elements. The goal is to order elements for all the functions to have value above a certain threshold as soon on average as possible, assuming we choose one element per time. The problem is flexible enough to capture various applications in machine learning, including decision trees. This paper considers the min-max version of SR where multiple instances share the ground set. With the view of each instance being associated with an agent, the min-max problem is to order the common elements to minimize the maximum objective of all agents -- thus, finding a fair solution for all agents. We give approximation algorithms for this problem and demonstrate their effectiveness in the application of finding a decision tree for multiple agents.Comment: To appear in AAAI 202

Protective effect of recombinant staphylococcal enterotoxin A entrapped in polylactic-co-glycolic acid microspheres against Staphylococcus aureus infection

Staphylococcus aureus is an important cause of nosocomial and community-acquired infections in humans and animals, as well as the cause of mastitis in dairy cattle. Vaccines aimed at preventing S. aureus infection in bovine mastitis have been studied for many years, but have so far been unsuccessful due to the complexity of the bacteria, and the lack of suitable vaccine delivery vehicles. The current study developed an Escherichia coli protein expression system that produced a recombinant staphylococcal enterotoxin A (rSEA) encapsulated into biodegradable microparticles generated by polylactic-co-glycolic acid (PLGA) dissolved in methylene chloride and stabilized with polyvinyl acetate. Antigen loading and surface properties of the microparticles were investigated to optimize particle preparation protocols. The prepared PLGA-rSEA microspheres had a diameter of approximately 5 μm with a smooth and regular surface. The immunogenicity of the PLGA-rSEA vaccine was assessed using mice as an animal model and showed that the vaccine induced a strong humoral immune response and increased the percent survival of challenged mice and bacterial clearance. Histological analysis showed moderate impairment caused by the pathogen upon challenge afforded by immunization with PLGA-rSEA microspheres. Antibody titer in the sera of mice immunized with PLGA-rSEA microparticles was higher than in vaccinated mice with rSEA. In conclusion, the PLGA-rSEA microparticle vaccine developed here could potentially be used as a vaccine against enterotoxigenic S. aureus

Fast automatic airport detection in remote sensing images using convolutional neural networks

Fast and automatic detection of airports from remote sensing images is useful for many military and civilian applications. In this paper, a fast automatic detection method is proposed to detect airports from remote sensing images based on convolutional neural networks using the Faster R-CNN algorithm. This method first applies a convolutional neural network to generate candidate airport regions. Based on the features extracted from these proposals, it then uses another convolutional neural network to perform airport detection. By taking the typical elongated linear geometric shape of airports into consideration, some specific improvements to the method are proposed. These approaches successfully improve the quality of positive samples and achieve a better accuracy in the final detection results. Experimental results on an airport dataset, Landsat 8 images, and a Gaofen-1 satellite scene demonstrate the effectiveness and efficiency of the proposed method

Enhancing Cyber-Resiliency of DER-based SmartGrid: A Survey

The rapid development of information and communications technology has enabled the use of digital-controlled and software-driven distributed energy resources (DERs) to improve the flexibility and efficiency of power supply, and support grid operations. However, this evolution also exposes geographically-dispersed DERs to cyber threats, including hardware and software vulnerabilities, communication issues, and personnel errors, etc. Therefore, enhancing the cyber-resiliency of DER-based smart grid - the ability to survive successful cyber intrusions - is becoming increasingly vital and has garnered significant attention from both industry and academia. In this survey, we aim to provide a systematical and comprehensive review regarding the cyber-resiliency enhancement (CRE) of DER-based smart grid. Firstly, an integrated threat modeling method is tailored for the hierarchical DER-based smart grid with special emphasis on vulnerability identification and impact analysis. Then, the defense-in-depth strategies encompassing prevention, detection, mitigation, and recovery are comprehensively surveyed, systematically classified, and rigorously compared. A CRE framework is subsequently proposed to incorporate the five key resiliency enablers. Finally, challenges and future directions are discussed in details. The overall aim of this survey is to demonstrate the development trend of CRE methods and motivate further efforts to improve the cyber-resiliency of DER-based smart grid.Comment: Submitted to IEEE Transactions on Smart Grid for Publication Consideratio

Non-Foster Impedance Wideband Matching Technique for Electrically Small Active Antenna

This paper investigates a non-Foster wideband circuit matching technique for an electrically small antenna (ESA). By introducing a negative impedance convertor into an active network, the active network can obtain an equivalent input gain at input port to improve gain, sensitivity and output ratio of signal to noise. In addition, it also increases the effective height of active antenna. The experimental results have verified the proposed method by using a 100 KHz-30 MHz wideband active receiving monopole antenna

A selective cascade reaction-based probe for colorimetric and ratiometric fluorescence detection of benzoyl peroxide in food and living cells

A novel colorimetric and ratiometric fluorescent probe (Cou-BPO) was readily prepared for specific detection of harmful benzoyl peroxide (BPO). The probe Cou-BPO reacted with BPO via a selective oxidation cleavage-induced cascade reaction of the pinacol phenylboronate group, which resulted in an observable colorimetric and ratiometric fluorescence response towards BPO with a fast response time (o15 min) and a low detection limit (56 nM). For practical application, facile, portable and sensitive test paper of Cou-BPO has been prepared for visual detection of BPO. Furthermore, we employed Cou-BPO as a probe to determine BPO in food samples and living cells.info:eu-repo/semantics/publishedVersio

Seasonal variations in carbon, nitrogen and phosphorus concentrations and C:N:P stoichiometry in the leaves of differently aged Larix principis-rupprechtii Mayr. plantations

The concentrations and stoichiometry of certain elements (carbon, nitrogen and phosphorus) are critical to the maintenance of plant functional and environmental adaptation during plant growth. We explore how the concentrations of C, N and P and the ratios of C:N, C:P, and N:P in the leaves of differently aged Larix principis-rupprechtii Mayr. plantations changed with growing season and stand age from 2012 to 2015 in the Qinling Mountains, China. The results showed that the element concentration and stoichiometric ratios in leaves were significantly affected by sampling month, stand age and sampling year; and multiple correlations with stand age were observed in different growing seasons. Compared to global element concentrations and stoichiometry in plants, the leaves of larch stands in the study region had higher C and P concentrations and C:N and C:P ratios but lower N concentrations and N:P ratios than global levels. The leaf N:P ratios of all of the larch stands were generally less than 14, suggesting that the growth of larch stands was limited by N in the study region. Our study facilitates the management and restoration of forest plantation and provides a valuable contribution to the global pool of leaf nutrition and stoichiometry data

Insights into technical challenges in the field of microplastic pollution through the lens of early career researchers (ECRs) and a proposed pathway forward

Early career researchers (ECR) face a series of challenges related to the inherent difficulties of starting their careers. Microplastic (MP) research is a topical field attracting high numbers of ECRs with diverse backgrounds and expertise from a wealth of disciplines including environmental science, biology, chemistry and ecotoxicology. In this perspective the challenges that could hinder scientific, professional, or personal development are explored, as identified by an international network of ECRs, all employed in MP research, that was formed following a bilateral workshop for scientists based in the UK and China. Discussions amongst the network were grouped into four overarching themes of technical challenges: in the field, in the laboratory, in the post data collection phase, and miscellaneous. The three key areas of representativeness, access to appropriate resources, training, and clean labs, and the use of databases and comparability, as well as the overarching constraint of available time were identified as the source of the majority of challenges. A set of recommendations for pathways forward are proposed based on the principles of research openness, access to information and training, and widening collaborations. ECRs have great capacity to promote research excellence in the field of MPs and elsewhere, when provided with appropriate opportunities and suitable support