Adaptive Unified Differential Evolution for Clustering

Various clustering methods to obtain optimal information continues to evolve one of its development is Evolutionary Algorithm (EA). Adaptive Unified Differential Evolution (AuDE), is the development of Differential Evolution (DE) which is one of the EA techniques. AuDE has self adaptive scale factor control parameters (F) and crossover-rate (Cr).. It also has a single mutation strategy that represents the most commonly used standard mutation strategies from previous studies.The AuDE clustering method was tested using 4 datasets. Silhouette Index and CS Measure is a fitness function used as a measure of the quality of clustering results. The quality of the AuDE clustering results is then compared against the quality of clustering results using the DE method.The results show that the AuDE mutation strategy can expand the cluster central search produced by ED so that better clustering quality can be obtained. The comparison of the quality of AuDE and DE using Silhoutte Index is 1:0.816, whereas the use of CS Measure shows a comparison of 0.565:1. The execution time required AuDE shows better but Number significant results, aimed at the comparison of Silhoutte Index usage of 0.99:1 , Whereas on the use of CS Measure obtained the comparison of 0.184:1

Agricultural scene understanding

The author has identified the following significant results. The LACIE field measurement data were radiometrically calibrated. Calibration enabled valid comparisons of measurements from different dates, sensors, and/or locations. Thermal band canopy results included: (1) Wind velocity had a significant influence on the overhead radiance temperature and the effect was quantized. Biomass and soil temperatures, temperature gradient, and canopy geometry were altered. (2) Temperature gradient was a function of wind velocity. (3) Temperature gradient of the wheat canopy was relatively constant during the day. (4) The laser technique provided good quality geometric characterization

Authentication of Amadeo de Souza-Cardoso Paintings and Drawings With Deep Learning

Art forgery has a long-standing history that can be traced back to the Roman period and has become more rampant as the art market continues prospering. Reports disclosed that uncountable artworks circulating on the art market could be fake. Even some principal art museums and galleries could be exhibiting a good percentage of fake artworks. It is therefore substantially important to conserve cultural heritage, safeguard the interest of both the art market and the artists, as well as the integrity of artists’ legacies. As a result, art authentication has been one of the most researched and well-documented fields due to the ever-growing commercial art market in the past decades. Over the past years, the employment of computer science in the art world has flourished as it continues to stimulate interest in both the art world and the artificial intelligence arena. In particular, the implementation of Artificial Intelligence, namely Deep Learning algorithms and Neural Networks, has proved to be of significance for specialised image analysis. This research encompassed multidisciplinary studies on chemistry, physics, art and computer science. More specifically, the work presents a solution to the problem of authentication of heritage artwork by Amadeo de Souza-Cardoso, namely paintings, through the use of artificial intelligence algorithms. First, an authenticity estimation is obtained based on processing of images through a deep learning model that analyses the brushstroke features of a painting. Iterative, multi-scale analysis of the images is used to cover the entire painting and produce an overall indication of authenticity. Second, a mixed input, deep learning model is proposed to analyse pigments in a painting. This solves the image colour segmentation and pigment classification problem using hyperspectral imagery. The result is used to provide an indication of authenticity based on pigment classification and correlation with chemical data obtained via XRF analysis. Further algorithms developed include a deep learning model that tackles the pigment unmixing problem based on hyperspectral data. Another algorithm is a deep learning model that estimates hyperspectral images from sRGB images. Based on the established algorithms and results obtained, two applications were developed. First, an Augmented Reality mobile application specifically for the visualisation of pigments in the artworks by Amadeo. The mobile application targets the general public, i.e., art enthusiasts, museum visitors, art lovers or art experts. And second, a desktop application with multiple purposes, such as the visualisation of pigments and hyperspectral data. This application is designed for art specialists, i.e., conservators and restorers. Due to the special circumstances of the pandemic, trials on the usage of these applications were only performed within the Department of Conservation and Restoration at NOVA University Lisbon, where both applications received positive feedback.A falsificação de arte tem uma história de longa data que remonta ao período romano e tornou-se mais desenfreada à medida que o mercado de arte continua a prosperar. Relatórios revelaram que inúmeras obras de arte que circulam no mercado de arte podem ser falsas. Mesmo alguns dos principais museus e galerias de arte poderiam estar exibindo uma boa porcentagem de obras de arte falsas. Por conseguinte, é extremamente importante conservar o património cultural, salvaguardar os interesses do mercado da arte e dos artis- tas, bem como a integridade dos legados dos artistas. Como resultado, a autenticação de arte tem sido um dos campos mais pesquisados e bem documentados devido ao crescente mercado de arte comercial nas últimas décadas.Nos últimos anos, o emprego da ciência da computação no mundo da arte floresceu à medida que continua a estimular o interesse no mundo da arte e na arena da inteligência artificial. Em particular, a implementação da Inteligência Artificial, nomeadamente algoritmos de aprendizagem profunda (ou Deep Learning) e Redes Neuronais, tem-se revelado importante para a análise especializada de imagens.Esta investigação abrangeu estudos multidisciplinares em química, física, arte e informática. Mais especificamente, o trabalho apresenta uma solução para o problema da autenticação de obras de arte patrimoniais de Amadeo de Souza-Cardoso, nomeadamente pinturas, através da utilização de algoritmos de inteligência artificial. Primeiro, uma esti- mativa de autenticidade é obtida com base no processamento de imagens através de um modelo de aprendizagem profunda que analisa as características de pincelada de uma pintura. A análise iterativa e multiescala das imagens é usada para cobrir toda a pintura e produzir uma indicação geral de autenticidade. Em segundo lugar, um modelo misto de entrada e aprendizagem profunda é proposto para analisar pigmentos em uma pintura. Isso resolve o problema de segmentação de cores de imagem e classificação de pigmentos usando imagens hiperespectrais. O resultado é usado para fornecer uma indicação de autenticidade com base na classificação do pigmento e correlação com dados químicos obtidos através da análise XRF. Outros algoritmos desenvolvidos incluem um modelo de aprendizagem profunda que aborda o problema da desmistura de pigmentos com base em dados hiperespectrais. Outro algoritmo é um modelo de aprendizagem profunda estabelecidos e nos resultados obtidos, foram desenvolvidas duas aplicações. Primeiro, uma aplicação móvel de Realidade Aumentada especificamente para a visualização de pigmentos nas obras de Amadeo. A aplicação móvel destina-se ao público em geral, ou seja, entusiastas da arte, visitantes de museus, amantes da arte ou especialistas em arte. E, em segundo lugar, uma aplicação de ambiente de trabalho com múltiplas finalidades, como a visualização de pigmentos e dados hiperespectrais. Esta aplicação é projetada para especialistas em arte, ou seja, conservadores e restauradores. Devido às circunstâncias especiais da pandemia, os ensaios sobre a utilização destas aplicações só foram realizados no âmbito do Departamento de Conservação e Restauro da Universidade NOVA de Lisboa, onde ambas as candidaturas receberam feedback positivo

Towards Zero Touch Next Generation Network Management

The current trend in user services places an ever-growing demand for higher data rates, near-real-time latencies, and near-perfect quality of service. To meet such demands, fundamental changes were made to the front and mid-haul and backbone networking segments servicing them. One of the main changes made was virtualizing the networking components to allow for faster deployment and reconfiguration when needed. However, adopting such technologies poses several challenges, such as improving the performance and efficiency of these systems by properly orchestrating the services to the ideal edge device. A second challenge is ensuring the backbone optical networking maximizes and maintains the throughput levels under more dynamically variant conditions. A third challenge is addressing the limitation of placement techniques in O-RAN. In this thesis, we propose using various optimization modeling and machine learning techniques in three segments of network systems towards lowering the need for human intervention targeting zero-touch networking. In particular, the first part of the thesis applies optimization modeling, heuristics, and segmentation to improve the locally driven orchestration techniques, which are used to place demands on edge devices throughput to ensure efficient and resilient placement decisions. The second part of the thesis proposes using reinforcement learning (RL) techniques on a nodal base to address the dynamic nature of demands within an optical networking paradigm. The RL techniques ensure blocking rates are kept to a minimum by tailoring the agents’ behavior based on each node\u27s demand intake throughout the day. The third part of the thesis proposes using transfer learning augmented reinforcement learning to drive a network slicing-based solution in O-RAN to address the stringent and divergent demands of 5G applications. The main contributions of the thesis consist of three broad parts. The first is developing optimal and heuristic orchestration algorithms that improve demands’ performance and reliability in an edge computing environment. The second is using reinforcement learning to determine the appropriate spectral placement for demands within isolated optical paths, ensuring lower fragmentation and better throughput utilization. The third is developing a heuristic controlled transfer learning augmented reinforcement learning network slicing in an O-RAN environment. Hence, ensuring improved reliability while maintaining lower complexity than traditional placement techniques

Earth Observation Data, Processing and Applications. Volume 2A. Processing - Basic Image Operations

Eds. Harrison, B.A., Jupp, D.L.B., Lewis, M.M, Sparks, T., Phinn, S.F., Mueller, N., Byrne, G

Molecular Mechanisms of Natural Compounds : Compound Kushen Injection (CKI) in Cancer

Chemotherapy is a treatment that uses cytotoxic drugs to kill rapidly dividing cancer cells. There are many anti-cancer chemotherapeutic drugs used alone or in combination with others to kill cancerous cells, and some of these, are of plant origin. Naturally occurring compounds, such as Taxol, are used in chemotherapy and have very specific, unique, molecular targets. However, according to the World Health Organization (Ekor, 2014), approximately eighty percent of the world’s population depends on natural compounds from traditional medicine and these compounds are widely used in complementary medicine as anti-cancer drugs (Foster et al., 2000). Traditional Chinese medicine (TCM) uses treatments that contain multiple natural compounds, a number of which have been claimed to be of therapeutic benefit to cancer sufferers (Chung et al., 2015). Some TCM preparations have shown anti-cancer, anti-migratory and anti-metastatic properties in laboratory settings (Wang et al., 2009;Pan et al., 2011;Qu et al., 2016). Research suggests that TCM natural compound mixtures might synergistically trigger therapeutic benefits through the action of multiple components affecting multiple regulatory signaling targets (Wang et al., 2008). Compound Kushen injection (CKI) is a TCM anticancer agent which has been approved by the Chinese State Food and Drug Administration to treat solid tumors in combination with chemotherapy drugs in clinics for pain relief, cancer metastasis and enhancement of the immune system since 1995, and is used to treat approximately 30,000 patients daily. Although a large body of evidence has suggested CKI has anti-cancer properties (Xu et al., 2011;Gao et al., 2018) the anti-cancer mechanisms attributable to specific compounds within the mixture remain unknown. CKI contains multiple alkaloid and flavonoid compounds and the main bioactive compounds such as matrine and oxymatrine have shown to affect cancer cells in the lab. However, other medicinal herbs containing these two compounds as main components have demonstrated patient toxicity. It is therefore important to better understand the effects of CKI, particularly with respect the contributions of individual compounds within the mixture. In this thesis, I describe a multi-disciplinary approach including analytical chemistry, cellular assays and transcriptome analysis to explore the effects of several major compounds present in CKI. Through the application of a subtractive fractionation method that removed individual compounds one, two or three at a time, I have been able to map these compounds and their interactions to specific pathways based on altered gene expression profiles. This has illuminated the roles of several major compounds of CKI, that on their own, have no, or minimal, activity in our bioassay. This approach has enabled us to identify the interactions between compounds in a mixture as shown by the response of cancer cell cultures. Using a systems biology approach along with cellular migration and invasion assays, I have mapped the activity of related proteins and pathways which may contribute to the migrastatic activity of CKI. Altogether, this thesis presents an initial characterization of the underlying mechanistic changes induced by CKI. First, by comparing differentially expressed genes across treatment combinations generated using our subtractive fractionation approach, I identified specific candidate pathways that were altered by the removal of compounds from the mixture. Second, by using transcriptome data of a breast cancer cell line, the effects of CKI on candidate anti-migratory pathways for six different cancer cell lines were assessed. These experiments identified specific candidate target pathways through which CKI might act. These approaches can be used to understand the roles and interactions of individual compounds from any complex natural compound mixture whose biological activity cannot be associated with purified compounds.Thesis (Ph.D.) -- University of Adelaide, School of Biological Sciences, 201

Automatic signal and image-based assessments of spinal cord injury and treatments.

Spinal cord injury (SCI) is one of the most common sources of motor disabilities in humans that often deeply impact the quality of life in individuals with severe and chronic SCI. In this dissertation, we have developed advanced engineering tools to address three distinct problems that researchers, clinicians and patients are facing in SCI research. Particularly, we have proposed a fully automated stochastic framework to quantify the effects of SCI on muscle size and adipose tissue distribution in skeletal muscles by volumetric segmentation of 3-D MRI scans in individuals with chronic SCI as well as non-disabled individuals. We also developed a novel framework for robust and automatic activation detection, feature extraction and visualization of the spinal cord epidural stimulation (scES) effects across a high number of scES parameters to build individualized-maps of muscle recruitment patterns of scES. Finally, in the last part of this dissertation, we introduced an EMG time-frequency analysis framework that implements EMG spectral analysis and machine learning tools to characterize EMG patterns resulting in independent or assisted standing enabled by scES, and identify the stimulation parameters that promote muscle activation patterns more effective for standing. The neurotechnological advancements proposed in this dissertation have greatly benefited SCI research by accelerating the efforts to quantify the effects of SCI on muscle size and functionality, expanding the knowledge regarding the neurophysiological mechanisms involved in re-enabling motor function with epidural stimulation and the selection of stimulation parameters and helping the patients with complete paralysis to achieve faster motor recovery

PRION CHARACTERIZATION USING CELL BASED APPROACHES

Prions are the causative agents of a group of lethal, neurodegenerative conditions that include sheep scrapie, bovine spongiform encephalopathy (BSE), and human Creutzfeldt-Jakob disease (CJD). Prions are derived from the conversion of a normal, primarily alpha-helical, cellular prion protein (PrPC), to an infectious, beta sheet-rich conformer (PrPSc). Many unresolved issues surround the process of PrP conversion, and we know very little about cellular responses to these unique pathogens. Our lack of knowledge relates, in part, to the difficulty of infecting cells in vitro with prions. While expression of PrPC is an absolute requirement for prion propagation, I show here that not all cells that express PrPC are capable of propagating PrPSc. The goal of this thesis is to understand the role that host factors play in sustaining prion infection and to develop systems in which the cellular response to prion infection can be assessed. We hypothesize that cellular permissiveness to prion infectivity is co-dependent on unidentified additional cellular factors. To study the role of PrPC expression in susceptibility to prion infectivity, and identify these cofactors in cell culture, we utilized cells which fail to express endogenous PrPC, but become susceptible to prions following stable expression of PrPC. Following transfection of a species specific PrP expression construct and isolation of single cell clones, we assessed PrP expression and susceptibility to prion infectivity by measuring accumulation of protease resistant PrPSc. Differential gene expression studies suggest significant transcriptional differences between susceptible and resistant clones. Using three independent gene expression databases our analyses suggest that the resistant transcriptional profile favors cell division/cycle and chromosomal regulation pathways, while the sensitive transcriptional profile is involved in protein homeostasis and quality control. The results of these studies will not only lead to a greater understanding of PrP cell biology and the mechanisms of prion pathogenesis, but should ultimately lead to sensitive and expedient methods for detecting and characterizing prion infectivity from a wide range of sources