11,060 research outputs found
Passive Radio Frequency-based 3D Indoor Positioning System via Ensemble Learning
Passive radio frequency (PRF)-based indoor positioning systems (IPS) have
attracted researchers' attention due to their low price, easy and customizable
configuration, and non-invasive design. This paper proposes a PRF-based
three-dimensional (3D) indoor positioning system (PIPS), which is able to use
signals of opportunity (SoOP) for positioning and also capture a scenario
signature. PIPS passively monitors SoOPs containing scenario signatures through
a single receiver. Moreover, PIPS leverages the Dynamic Data Driven
Applications System (DDDAS) framework to devise and customize the sampling
frequency, enabling the system to use the most impacted frequency band as the
rated frequency band. Various regression methods within three ensemble learning
strategies are used to train and predict the receiver position. The PRF
spectrum of 60 positions is collected in the experimental scenario, and three
criteria are applied to evaluate the performance of PIPS. Experimental results
show that the proposed PIPS possesses the advantages of high accuracy,
configurability, and robustness.Comment: DDDAS 202
Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review
In this paper, a critical bibliometric analysis study is conducted, coupled
with an extensive literature survey on recent developments and associated
applications in machine learning research with a perspective on Africa. The
presented bibliometric analysis study consists of 2761 machine learning-related
documents, of which 98% were articles with at least 482 citations published in
903 journals during the past 30 years. Furthermore, the collated documents were
retrieved from the Science Citation Index EXPANDED, comprising research
publications from 54 African countries between 1993 and 2021. The bibliometric
study shows the visualization of the current landscape and future trends in
machine learning research and its application to facilitate future
collaborative research and knowledge exchange among authors from different
research institutions scattered across the African continent
A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms
Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data.
A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability.
To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity.
A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case.
The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change.
The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ‘problem of implementation’ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sector’s emergence
In vitro investigation of the effect of disulfiram on hypoxia induced NFκB, epithelial to mesenchymal transition and cancer stem cells in glioblastoma cell lines
A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Glioblastoma multiforme (GBM) is one of the most aggressive and lethal cancers with a poor prognosis. Advances in the treatment of GBM are limited due to several resistance mechanisms and limited drug delivery into the central nervous system (CNS) compartment by the blood-brain barrier (BBB) and by actions of the normal brain to counteract tumour-targeting medications. Hypoxia is common in malignant brain tumours such as GBM and plays a significant role in tumour pathobiology. It is widely accepted that hypoxia is a major driver of GBM malignancy. Although it has been confirmed that hypoxia induces GBM stem-like-cells (GSCs), which are highly invasive and resistant to all chemotherapeutic agents, the detailed molecular pathways linking hypoxia, GSC traits and chemoresistance remain obscure. Evidence shows that hypoxia induces cancer stem cell phenotypes via epithelial-to-mesenchymal transition (EMT), promoting therapeutic resistance in most cancers, including GBM.
This study demonstrated that spheroid cultured GBM cells consist of a large population of hypoxic cells with CSC and EMT characteristics. GSCs are chemo-resistant and displayed increased levels of HIFs and NFκB activity. Similarly, the hypoxia cultured GBM cells manifested GSC traits, chemoresistance and invasiveness. These results suggest that hypoxia is responsible for GBM stemness, chemoresistance and invasiveness. GBM cells transfected with nuclear factor kappa B-p65 (NFκB-p65) subunit exhibited CSC and EMT markers indicating the essential role of NFκB in maintaining GSC phenotypes. The study also highlighted the significance of NFκB in driving chemoresistance, invasiveness, and the potential role of NFκB as the central regulator of hypoxia-induced stemness in GBM cells. GSC population has the ability of self-renewal, cancer initiation and development of secondary heterogeneous cancer. The very poor prognosis of GBM could largely be attributed to the existence of GSCs, which promote tumour propagation, maintenance, radio- and chemoresistance and local infiltration.
In this study, we used Disulfiram (DS), a drug used for more than 65 years in alcoholism clinics, in combination with copper (Cu) to target the NFκB pathway, reverse chemoresistance and block invasion in GSCs. The obtained results showed that DS/Cu is highly cytotoxic to GBM cells and completely eradicated the resistant CSC population at low dose levels in vitro. DS/Cu inhibited the migration and invasion of hypoxia-induced CSC and EMT like GBM cells at low nanomolar concentrations.
DS is an FDA approved drug with low toxicity to normal tissues and can pass through the BBB. Further research may lead to the quick translation of DS into cancer clinics and provide new therapeutic options to improve treatment outcomes in GBM patients
Underwater optical wireless communications in turbulent conditions: from simulation to experimentation
Underwater optical wireless communication (UOWC) is a technology that aims to apply high speed optical wireless communication (OWC) techniques to the underwater channel. UOWC has the potential to provide high speed links over relatively short distances as part of a hybrid underwater network, along with radio frequency (RF) and underwater acoustic communications (UAC) technologies. However, there are some difficulties involved in developing a reliable UOWC link, namely, the complexity of the channel. The main focus throughout this thesis is to develop a greater understanding of the effects of the UOWC channel, especially underwater turbulence. This understanding is developed from basic theory through to simulation and experimental studies in order to gain a holistic understanding of turbulence in the UOWC channel.
This thesis first presents a method of modelling optical underwater turbulence through simulation that allows it to be examined in conjunction with absorption and scattering. In a stationary channel, this turbulence induced scattering is shown to cause and increase both spatial and temporal spreading at the receiver plane. It is also demonstrated using the technique presented that the relative impact of turbulence on a received signal is lower in a highly scattering channel, showing an in-built resilience of these channels. Received intensity distributions are presented confirming that fluctuations in received power from this method follow the commonly used Log-Normal fading model. The impact of turbulence - as measured using this new modelling framework - on link performance, in terms of maximum achievable data rate and bit error rate is equally investigated.
Following that, experimental studies comparing both the relative impact of turbulence induced scattering on coherent and non-coherent light propagating through water and the relative impact of turbulence in different water conditions are presented. It is shown that the scintillation index increases with increasing temperature inhomogeneity in the underwater channel. These results indicate that a light beam from a non-coherent source has a greater resilience to temperature inhomogeneity induced turbulence effect in an underwater channel. These results will help researchers in simulating realistic channel conditions when modelling a light emitting diode (LED) based intensity modulation with direct detection (IM/DD) UOWC link.
Finally, a comparison of different modulation schemes in still and turbulent water conditions is presented. Using an underwater channel emulator, it is shown that pulse position modulation (PPM) and subcarrier intensity modulation (SIM) have an inherent resilience to turbulence induced fading with SIM achieving higher data rates under all conditions. The signal processing technique termed pair-wise coding (PWC) is applied to SIM in underwater optical wireless communications for the first time. The performance of PWC is compared with the, state-of-the-art, bit and power loading optimisation algorithm. Using PWC, a maximum data rate of 5.2 Gbps is achieved in still water conditions
Towards A Graphene Chip System For Blood Clotting Disease Diagnostics
Point of care diagnostics (POCD) allows the rapid, accurate measurement of analytes near to a patient. This enables faster clinical decision making and can lead to earlier diagnosis and better patient monitoring and treatment. However, despite many prospective POCD devices being developed for a wide range of diseases this promised technology is yet to be translated to a clinical setting due to the lack of a cost-effective biosensing platform.This thesis focuses on the development of a highly sensitive, low cost and scalable biosensor platform that combines graphene with semiconductor fabrication tech-niques to create graphene field-effect transistors biosensor. The key challenges of designing and fabricating a graphene-based biosensor are addressed. This work fo-cuses on a specific platform for blood clotting disease diagnostics, but the platform has the capability of being applied to any disease with a detectable biomarker.Multiple sensor designs were tested during this work that maximised sensor ef-ficiency and costs for different applications. The multiplex design enabled different graphene channels on the same chip to be functionalised with unique chemistry. The Inverted MOSFET design was created, which allows for back gated measurements to be performed whilst keeping the graphene channel open for functionalisation. The Shared Source and Matrix design maximises the total number of sensing channels per chip, resulting in the most cost-effective fabrication approach for a graphene-based sensor (decreasing cost per channel from £9.72 to £4.11).The challenge of integrating graphene into a semiconductor fabrication process is also addressed through the development of a novel vacuum transfer method-ology that allows photoresist free transfer. The two main fabrication processes; graphene supplied on the wafer “Pre-Transfer” and graphene transferred after met-allisation “Post-Transfer” were compared in terms of graphene channel resistance and graphene end quality (defect density and photoresist). The Post-Transfer pro-cess higher quality (less damage, residue and doping, confirmed by Raman spec-troscopy).Following sensor fabrication, the next stages of creating a sensor platform involve the passivation and packaging of the sensor chip. Different approaches using dielec-tric deposition approaches are compared for passivation. Molecular Vapour Deposi-tion (MVD) deposited Al2O3 was shown to produce graphene channels with lower damage than unprocessed graphene, and also improves graphene doping bringing the Dirac point of the graphene close to 0 V. The packaging integration of microfluidics is investigated comparing traditional soft lithography approaches and the new 3D printed microfluidic approach. Specific microfluidic packaging for blood separation towards a blood sampling point of care sensor is examined to identify the laminar approach for lower blood cell count, as a method of pre-processing the blood sample before sensing.To test the sensitivity of the Post-Transfer MVD passivated graphene sensor de-veloped in this work, real-time IV measurements were performed to identify throm-bin protein binding in real-time on the graphene surface. The sensor was function-alised using a thrombin specific aptamer solution and real-time IV measurements were performed on the functionalised graphene sensor with a range of biologically relevant protein concentrations. The resulting sensitivity of the graphene sensor was in the 1-100 pg/ml concentration range, producing a resistance change of 0.2% per pg/ml. Specificity was confirmed using a non-thrombin specific aptamer as the neg-ative control. These results indicate that the graphene sensor platform developed in this thesis has the potential as a highly sensitive POCD. The processes developed here can be used to develop graphene sensors for multiple biomarkers in the future
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Multi-omics Characterisation of Myxoid Liposarcoma and Investigation of Trabectedin Mechanism of Action for Its Treatment
Myxoid liposarcoma (MLPS) is a rare soft tissue sarcoma of the mesenchymal cells characterised by the expression of the FUS-DDIT3 oncoprotein that impairs the adipocyte differentiation. Among the available therapeutic choices for the treatment of MLPS, trabectedin has shown significant anti-tumour activity. Although prolonged treatment is well-tolerated, recurrence of the resistant tumour invariably occurs even in initially responder patients. With the aim to characterise yet unknown molecular mechanisms of trabectedin mode of action and of resistance in MLPS, we integrated genomic, transcriptomic and protein-DNA binding data from patient-derived MLPS xenograft model, ML017, and its trabectedin-resistant derivate, ML017/ET.
DNA-Seq analysis showed that acquired-resistance to trabectedin may be due to the loss of genetic material in the 4p15.2, 4p16.3 and 17q21.3 cytobands and to the consequent inhibition of the genes mapping on these regions. Integration of longitudinal RNA-Seq and ChIP-Seq data revealed a two-phases mechanism of action of trabectedin. An early phase, characterised by the cytotoxic action of trabectedin and probably independent from FUS-DDIT3 activity; a second late phase, led by the DNA-binding activity of the chimera. Indeed, trabectedin displaces FUS-DDIT3 from most of its targets and modulates the transcription of the related genes involved in processes that sustain adipocyte differentiation. To note, no such differences were observed in ML017/ET resistant model.
These results shed light on the complex mechanism of action of trabectedin in MLPS. They provide insights into the process leading to drug resistance and can be the basis for the development of novel combinatorial strategies for the treatment of MLPS
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Brain signal recognition using deep learning
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityBrain Computer Interface (BCI) has the potential to offer a new generation of applications independent of
muscular activity and controlled by the human brain. Brain imaging technologies are used to transfer the
cognitive tasks into control commands for a BCI system. The electroencephalography (EEG) technology
serves as the best available non-invasive solution for extracting signals from the brain. On the other hand,
speech is the primary means of communication, but for patients suffering from locked-in syndrome, there
is no easy way to communicate. Therefore, an ideal communication system for locked-in patients is a
thought-to-speech BCI system.
This research aims to investigate methods for the recognition of imagined speech from EEG signals
using deep learning techniques. In order to design an optimal imagined speech recognition BCI, variety
of issues have been solved. These include 1) proposing new feature extraction and classification
framework for recognition of imagined speech from EEG signals, 2) grammatical class recognition of
imagined words from EEG signals, 3) discriminating different cognitive tasks associated with speech in
the brain such as overt speech, covert speech, and visual imagery. In this work machine learning, deep
learning methods were used to analyze EEG signals.
For recognition of imagined speech from EEG signals, a new EEG database was collected while the
participants mentally spoke (imagined speech) the presented words. Along with imagined speech, EEG
data was recorded for visual imagery (imagining a scene or an image) and overt speech (verbal speech).
Spectro-temporal and spatio-temporal domain features were investigated for the classification of imagined
words from EEG signals. Further, a deep learning framework using the convolutional network
and attention mechanism was implemented for learning features in the spatial, temporal, and spectral
domains. The method achieved a recognition rate of 76.6% for three binary word pairs. These experiments
show that deep learning algorithms are ideal for imagined speech recognition from EEG signals
due to their ability to interpret features from non-linear and non-stationary signals. Grammatical classes
of imagined words from EEG signals were also recognized using a multi-channel convolution network
framework. This method was extended to a multi-level recognition system for multi-class classification
of imagined words which achieved an accuracy of 52.9% for 10 words, which is much better in
comparison to previous work.
In order to investigate the difference between imagined speech with verbal speech and visual imagery
from EEG signals, we used multivariate pattern analysis (MVPA). MVPA provided the time segments
when the neural oscillation for the different cognitive tasks was linearly separable. Further, frequencies
that result in most discrimination between the different cognitive tasks were also explored. A framework
was proposed to discriminate two cognitive tasks based on the spatio-temporal patterns in EEG signals.
The proposed method used the K-means clustering algorithm to find the best electrode combination and
convolutional-attention network for feature extraction and classification. The proposed method achieved
a high recognition rate of 82.9% and 77.7%.
The results in this research suggest that a communication based BCI system can be designed using
deep learning methods. Further, this work add knowledge to the existing work in the field of communication
based BCI system
Mixed Criticality Systems - A Review : (13th Edition, February 2022)
This review covers research on the topic of mixed criticality systems that has been published since Vestal’s 2007 paper. It covers the period up to end of 2021. The review is organised into the following topics: introduction and motivation, models, single processor analysis (including job-based, hard and soft tasks, fixed priority and EDF scheduling, shared resources and static and synchronous scheduling), multiprocessor analysis, related topics, realistic models, formal treatments, systems issues, industrial practice and research beyond mixed-criticality. A list of PhDs awarded for research relating to mixed-criticality systems is also included
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