Self-adjustable domain adaptation in personalized ECG monitoring integrated with IR-UWB radar

To enhance electrocardiogram (ECG) monitoring systems in personalized detections, deep neural networks (DNNs) are applied to overcome individual differences by periodical retraining. As introduced previously [4], DNNs relieve individual differences by fusing ECG with impulse radio ultra-wide band (IR-UWB) radar. However, such DNN-based ECG monitoring system tends to overfit into personal small datasets and is difficult to generalize to newly collected unlabeled data. This paper proposes a self-adjustable domain adaptation (SADA) strategy to prevent from overfitting and exploit unlabeled data. Firstly, this paper enlarges the database of ECG and radar data with actual records acquired from 28 testers and expanded by the data augmentation. Secondly, to utilize unlabeled data, SADA combines self organizing maps with the transfer learning in predicting labels. Thirdly, SADA integrates the one-class classification with domain adaptation algorithms to reduce overfitting. Based on our enlarged database and standard databases, a large dataset of 73200 records and a small one of 1849 records are built up to verify our proposal. Results show SADA\u27s effectiveness in predicting labels and increments in the sensitivity of DNNs by 14.4% compared with existing domain adaptation algorithms

Artificial Intelligence for the Edge Computing Paradigm.

With modern technologies moving towards the internet of things where seemingly every financial, private, commercial and medical transaction being carried out by portable and intelligent devices; Machine Learning has found its way into every smart device and application possible. However, Machine Learning cannot be used on the edge directly due to the limited capabilities of small and battery-powered modules. Therefore, this thesis aims to provide light-weight automated Machine Learning models which are applied on a standard edge device, the Raspberry Pi, where one framework aims to limit parameter tuning while automating feature extraction and a second which can perform Machine Learning classification on the edge traditionally, and can be used additionally for image-based explainable Artificial Intelligence. Also, a commercial Artificial Intelligence software have been ported to work in a client/server setups on the Raspberry Pi board where it was incorporated in all of the Machine Learning frameworks which will be presented in this thesis. This dissertation also introduces multiple algorithms that can convert images into Time-series for classification and explainability but also introduces novel Time-series feature extraction algorithms that are applied to biomedical data while introducing the concept of the Activation Engine, which is a post-processing block that tunes Neural Networks without the need of particular experience in Machine Leaning. Also, a tree-based method for multiclass classification has been introduced which outperforms the One-to-Many approach while being less complex that the One-to-One method.\par The results presented in this thesis exhibit high accuracy when compared with the literature, while remaining efficient in terms of power consumption and the time of inference. Additionally the concepts, methods or algorithms that were introduced are particularly novel technically, where they include: • Feature extraction of professionally annotated, and poorly annotated time-series. • The introduction of the Activation Engine post-processing block. • A model for global image explainability with inference on the edge. • A tree-based algorithm for multiclass classification

Interrogating the multi-stress tolerance of Oryza australiensis using novel genomic and phenomic strategies

Elite high-yielding rice cultivars are, by necessity, grown under high-input scenarios, requiring substantial nitrogen and water inputs to achieve their yield potential. As a result of intensive breeding for performance in the relatively benign conditions of the tropics, they are also susceptible to a number of abiotic stresses including heat wave events and drought. To keep pace with increasing food demand, domestic rice will need to be augmented such that its realised yield is close to its potential yield. Recently there has been great interest in exploring novel germplasm for traits associated with increased nitrogen use efficiency (NUE), water use efficiency (WUE), and stress tolerance. Part of this exploration includes the exploitation of wild Oryza species. There are ~24 recognised wild Oryza species, each occupying and adapted to different ecological niches. Amongst them, O. australiensis has received attention because of its tolerance to leaf brown hopper and extreme heat and its hypothesised tolerance to drought conditions. Native to the northern savannahs of Australia, this species has evolved in a relatively hot and seasonally dry environment, with high rainfall variability and poor soil nutrition. Because of this, we hypothesised that this species might also possess both high WUE and high NUE. However, knowledge of the mechanisms behind its tolerance to extreme heat are lacking, and no reliable experiments have been performed to substantiate claims of drought tolerance in O. australiensis. Further, the International Oryza Map Alignment Project, an initiative that aimed to sequence Oryza genomes for the benefit of domestic rice breeding, called for the genome of O. australiensis to be sequenced in 2003. However, there has been no publicly available O. australiensis genome since. This thesis presents the results of several novel phenotyping techniques including RACiR gas exchange and state of the art imaging techniques to resolve questions of both heat and drought tolerance in the wild rice. I also present the first long-read assembly of the O. australiensis genome, and a novel tool, CLAW (Chloroplast Long-read Assembly Workflow), developed for the assembly of chloroplast genomes derived from larger long-read libraries. Having identified several traits of interest in O. australiensis under stress conditions and having provided significant tools (the genome assembly and a tool for the assembly of chloroplast genomes), this thesis sets the stage for future studies hoping to identify candidate genes for the improvement of domestic rice performance under stress conditions.Thesis (Ph.D.) -- University of Adelaide, School of Agriculture, Food, and Wine, 202

Genetic Glass Ceilings

As the world’s population rises to an expected ten billion in the next few generations, the challenges of feeding humanity and maintaining an ecological balance will dramatically increase. Today we rely on just four crops for 80 percent of all consumed calories: wheat, rice, corn, and soybeans. Indeed, reliance on these four crops may also mean we are one global plant disease outbreak away from major famine. In this revolutionary and controversial book, Jonathan Gressel argues that alternative plant crops lack the genetic diversity necessary for wider domestication and that even the Big Four have reached a “genetic glass ceiling”: no matter how much they are bred, there is simply not enough genetic diversity available to significantly improve their agricultural value. Gressel points the way through the glass ceiling by advocating transgenics—a technique where genes from one species are transferred to another. He maintains that with simple safeguards the technique is a safe solution to the genetic glass ceiling conundrum. Analyzing alternative crops—including palm oil, papaya, buckwheat, tef, and sorghum—Gressel demonstrates how gene manipulation could enhance their potential for widespread domestication and reduce our dependency on the Big Four. He also describes a number of ecological benefits that could be derived with the aid of transgenics. A compelling synthesis of ideas from agronomy, medicine, breeding, physiology, population genetics, molecular biology, and biotechnology, Genetic Glass Ceilings presents transgenics as an inevitable and desperately necessary approach to securing and diversifying the world's food supply

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp