694 research outputs found
Hidden Markov Models
Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research
A Comprehensive Survey on Generative Diffusion Models for Structured Data
In recent years, generative diffusion models have achieved a rapid paradigm
shift in deep generative models by showing groundbreaking performance across
various applications. Meanwhile, structured data, encompassing tabular and time
series data, has been received comparatively limited attention from the deep
learning research community, despite its omnipresence and extensive
applications. Thus, there is still a lack of literature and its reviews on
structured data modelling via diffusion models, compared to other data
modalities such as visual and textual data. To address this gap, we present a
comprehensive review of recently proposed diffusion models in the field of
structured data. First, this survey provides a concise overview of the
score-based diffusion model theory, subsequently proceeding to the technical
descriptions of the majority of pioneering works that used structured data in
both data-driven general tasks and domain-specific applications. Thereafter, we
analyse and discuss the limitations and challenges shown in existing works and
suggest potential research directions. We hope this review serves as a catalyst
for the research community, promoting developments in generative diffusion
models for structured data.Comment: 20 pages, 1 figure, 2 table
Combining Synthesis of Cardiorespiratory Signals and Artifacts with Deep Learning for Robust Vital Sign Estimation
Healthcare has been remarkably morphing on the account of Big Data. As Machine Learning
(ML) consolidates its place in simpler clinical chores, more complex Deep Learning (DL) algorithms
have struggled to keep up, despite their superior capabilities. This is mainly attributed
to the need for large amounts of data for training, which the scientific community is unable to
satisfy.
The number of promising DL algorithms is considerable, although solutions directly targeting
the shortage of data lack. Currently, dynamical generative models are the best bet, but focus on
single, classical modalities and tend to complicate significantly with the amount of physiological
effects they can simulate.
This thesis aims at providing and validating a framework, specifically addressing the data
deficit in the scope of cardiorespiratory signals. Firstly, a multimodal statistical synthesizer was
designed to generate large, annotated artificial signals. By expressing data through coefficients of
pre-defined, fitted functions and describing their dependence with Gaussian copulas, inter- and
intra-modality associations were learned. Thereafter, new coefficients are sampled to generate
artificial, multimodal signals with the original physiological dynamics. Moreover, normal and
pathological beats along with artifacts were included by employing Markov models. Secondly,
a convolutional neural network (CNN) was conceived with a novel sensor-fusion architecture
and trained with synthesized data under real-world experimental conditions to evaluate how its
performance is affected.
Both the synthesizer and the CNN not only performed at state of the art level but also innovated
with multiple types of generated data and detection error improvements, respectively.
Cardiorespiratory data augmentation corrected performance drops when not enough data is available,
enhanced the CNN’s ability to perform on noisy signals and to carry out new tasks when
introduced to, otherwise unavailable, types of data. Ultimately, the framework was successfully
validated showing potential to leverage future DL research on Cardiology into clinical standards
Heartwave biometric authentication using machine learning algorithms
PhD ThesisThe advancement of IoT, cloud services and technologies have prompted heighten
IT access security. Many products and solutions have implemented biometric solution
to address the security concern. Heartwave as biometric mode offers the potential due
to the inability to falsify the signal and ease of signal acquisition from fingers. However
the highly variated heartrate signal, due to heartrate has imposed much headwinds in
the development of heartwave based biometric authentications.
The thesis first review the state-of-the-arts in the domains of heartwave
segmentation and feature extraction, and identifying discriminating features and
classifications. In particular this thesis proposed a methodology of Discrete Wavelet
Transformation integrated with heartrate dependent parameters to extract
discriminating features reliably and accurately.
In addition, statistical methodology using Gaussian Mixture Model-Hidden
Markov Model integrated with user specific threshold and heartrate have been proposed
and developed to provide classification of individual under varying heartrates. This
investigation has led to the understanding that individual discriminating feature is a
variable against heartrate.
Similarly, the neural network based methodology leverages on ensemble-Deep
Belief Network (DBN) with stacked DBN coded using Multiview Spectral Embedding
has been explored and achieved good performance in classification. Importantly, the
amount of data required for training is significantly reduce
A Causal Intervention Scheme for Semantic Segmentation of Quasi-periodic Cardiovascular Signals
Precise segmentation is a vital first step to analyze semantic information of
cardiac cycle and capture anomaly with cardiovascular signals. However, in the
field of deep semantic segmentation, inference is often unilaterally confounded
by the individual attribute of data. Towards cardiovascular signals,
quasi-periodicity is the essential characteristic to be learned, regarded as
the synthesize of the attributes of morphology (Am) and rhythm (Ar). Our key
insight is to suppress the over-dependence on Am or Ar while the generation
process of deep representations. To address this issue, we establish a
structural causal model as the foundation to customize the intervention
approaches on Am and Ar, respectively. In this paper, we propose contrastive
causal intervention (CCI) to form a novel training paradigm under a frame-level
contrastive framework. The intervention can eliminate the implicit statistical
bias brought by the single attribute and lead to more objective
representations. We conduct comprehensive experiments with the controlled
condition for QRS location and heart sound segmentation. The final results
indicate that our approach can evidently improve the performance by up to 0.41%
for QRS location and 2.73% for heart sound segmentation. The efficiency of the
proposed method is generalized to multiple databases and noisy signals.Comment: submitted to IEEE Journal of Biomedical and Health Informatics
(J-BHI
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Building trajectories through clinical data to model disease progression
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Clinical trials are typically conducted over a population within a defined time period
in order to illuminate certain characteristics of a health issue or disease process. These cross-sectional studies provide a snapshot of these disease processes over a large number of people but do not allow us to model the temporal nature of disease, which is essential for modeling detailed prognostic predictions. Longitudinal studies, on the other hand, are used to explore how these processes develop over time in a number of people but can be expensive and time-consuming, and many studies only cover a relatively small window within the disease process. This thesis describes the application of intelligent data analysis techniques for extracting information from time series generated by different diseases. The aim of this thesis is to identify intermediate stages
in a disease process and sub-categories of the disease exhibiting subtly different symptoms. It explores the use of a bootstrap technique that fits trajectories through the data generating “pseudo time-series”. It addresses issues including: how clinical variables interact as a disease progresses along the trajectories in the data; and how to automatically identify different disease states along these trajectories, as well as the transitions between them. The thesis documents how reliable time-series models can be created from large amounts of historical cross-sectional data and a novel relabling/latent variable approach has enabled the exploration of the temporal nature of disease progression. The proposed algorithms are tested extensively on simulated data and on three real clinical datasets. Finally, a study is carried out to explore whether we can “calibrate” pseudo time-series models with real longitudinal data in order to improve them. Plausible directions for future research are discussed at the end of the thesis
A framework for digitisation of manual manufacturing task knowledge using gaming interface technology
Intense market competition and the global skill supply crunch are hurting the manufacturing industry, which is heavily dependent on skilled labour. Companies must look for innovative ways to acquire manufacturing skills from their experts and transfer them to novices and eventually to machines to remain competitive. There is a lack of systematic processes in the manufacturing industry and research for cost-effective capture and transfer of human skills. Therefore, the aim of this research is to develop a framework for digitisation of manual manufacturing task knowledge, a major constituent of which is human skill.
The proposed digitisation framework is based on the theory of human-workpiece interactions that is developed in this research. The unique aspect of the framework is the use of consumer-grade gaming interface technology to capture and record manual manufacturing tasks in digital form to enable the extraction, decoding and transfer of manufacturing knowledge constituents that are associated with the task. The framework is implemented, tested and refined using 5 case studies, including 1 toy assembly task, 2 real-life-like assembly tasks, 1 simulated assembly task and 1 real-life composite layup task. It is successfully validated based on the outcomes of the case studies and a benchmarking exercise that was conducted to evaluate its performance.
This research contributes to knowledge in five main areas, namely, (1) the theory of human-workpiece interactions to decipher human behaviour in manual manufacturing tasks, (2) a cohesive and holistic framework to digitise manual manufacturing task knowledge, especially tacit knowledge such as human action and reaction skills, (3) the use of low-cost gaming interface technology to capture human actions and the effect of those actions on workpieces during a manufacturing task, (4) a new way to use hidden Markov modelling to produce digital skill models to represent human ability to perform complex tasks and (5) extraction and decoding of manufacturing knowledge constituents from the digital skill models
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