10 research outputs found
A Photoplethysmography System Optimised for Pervasive Cardiac Monitoring
Photoplethysmography is a non-invasive sensing technique which infers instantaneous
cardiac function from an optical measurement of blood vessels. This
thesis presents a photoplethysmography based sensor system that has been developed
speci fically for the requirements of a pervasive healthcare monitoring
system. Continuous monitoring of patients requires both the size and power
consumption of the chosen sensor solution to be minimised to ensure the patients
will be willing to use the device. Pervasive sensing also requires that
the device be scalable for manufacturing in high volume at a build cost that
healthcare providers are willing to accept. System level choice of both electronic
circuits and signal processing techniques are based on their sensitivity to
cardiac biosignals, robustness against noise inducing artefacts and simplicity
of implementation. Numerical analysis is used to justify the implementation
of a technique in hardware. Circuit prototyping and experimental data collection
is used to validate a technique's application. The entire signal chain
operates in the discrete-time domain which allows all of the signal processing
to be implemented in firmware on an embedded processor which minimised the
number of discrete components while optimising the trade-off between power
and bandwidth in the analogue front-end. Synchronisation of the optical illumination
and detection modules enables high dynamic range rejection of both
AC and DC independent light sources without compromising the biosignal.
Signal delineation is used to reduce the required communication bandwidth as
it preserves both amplitude and temporal resolution of the non-stationary photoplethysmography
signals allowing more complicated analytical techniques to
be performed at the other end of communication channel. The complete sensing
system is implemented on a single PCB using only commercial-off -the-shelf
components and consumes less than 7.5mW of power. The sensor platform
is validated by the successful capture of physiological data in a harsh optical
sensing environment
CMOS Hyperbolic Sine ELIN filters for low/audio frequency biomedical applications
Hyperbolic-Sine (Sinh) filters form a subclass of Externally-Linear-Internally-Non-
Linear (ELIN) systems. They can handle large-signals in a low power environment under half
the capacitor area required by the more popular ELIN Log-domain filters. Their inherent
class-AB nature stems from the odd property of the sinh function at the heart of their
companding operation. Despite this early realisation, the Sinh filtering paradigm has not
attracted the interest it deserves to date probably due to its mathematical and circuit-level
complexity.
This Thesis presents an overview of the CMOS weak inversion Sinh filtering
paradigm and explains how biomedical systems of low- to audio-frequency range could
benefit from it. Its dual scope is to: consolidate the theory behind the synthesis and design of
high order Sinh continuous–time filters and more importantly to confirm their micro-power
consumption and 100+ dB of DR through measured results presented for the first time.
Novel high order Sinh topologies are designed by means of a systematic
mathematical framework introduced. They employ a recently proposed CMOS Sinh
integrator comprising only p-type devices in its translinear loops. The performance of the
high order topologies is evaluated both solely and in comparison with their Log domain
counterparts. A 5th order Sinh Chebyshev low pass filter is compared head-to-head with a
corresponding and also novel Log domain class-AB topology, confirming that Sinh filters
constitute a solution of equally high DR (100+ dB) with half the capacitor area at the expense
of higher complexity and power consumption. The theoretical findings are validated by
means of measured results from an 8th order notch filter for 50/60Hz noise fabricated in a
0.35μm CMOS technology. Measured results confirm a DR of 102dB, a moderate SNR of
~60dB and 74μW power consumption from 2V power supply
A Photoplethysmography System Optimised for Pervasive Cardiac Monitoring
Photoplethysmography is a non-invasive sensing technique which infers instantaneous
cardiac function from an optical measurement of blood vessels. This
thesis presents a photoplethysmography based sensor system that has been developed
speci fically for the requirements of a pervasive healthcare monitoring
system. Continuous monitoring of patients requires both the size and power
consumption of the chosen sensor solution to be minimised to ensure the patients
will be willing to use the device. Pervasive sensing also requires that
the device be scalable for manufacturing in high volume at a build cost that
healthcare providers are willing to accept. System level choice of both electronic
circuits and signal processing techniques are based on their sensitivity to
cardiac biosignals, robustness against noise inducing artefacts and simplicity
of implementation. Numerical analysis is used to justify the implementation
of a technique in hardware. Circuit prototyping and experimental data collection
is used to validate a technique's application. The entire signal chain
operates in the discrete-time domain which allows all of the signal processing
to be implemented in firmware on an embedded processor which minimised the
number of discrete components while optimising the trade-off between power
and bandwidth in the analogue front-end. Synchronisation of the optical illumination
and detection modules enables high dynamic range rejection of both
AC and DC independent light sources without compromising the biosignal.
Signal delineation is used to reduce the required communication bandwidth as
it preserves both amplitude and temporal resolution of the non-stationary photoplethysmography
signals allowing more complicated analytical techniques to
be performed at the other end of communication channel. The complete sensing
system is implemented on a single PCB using only commercial-off -the-shelf
components and consumes less than 7.5mW of power. The sensor platform
is validated by the successful capture of physiological data in a harsh optical
sensing environment
UML-Based co-design framework for body sensor network applications
Ph.DDOCTOR OF PHILOSOPH
Next Generation Internet of Things – Distributed Intelligence at the Edge and Human-Machine Interactions
This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.The following chapters build on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT–EPI), the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects, presenting global views and state-of-the-art results regarding the next generation of IoT research, innovation, development, and deployment.The IoT and Industrial Internet of Things (IIoT) are evolving towards the next generation of Tactile IoT/IIoT, bringing together hyperconnectivity (5G and beyond), edge computing, Distributed Ledger Technologies (DLTs), virtual/ andaugmented reality (VR/AR), and artificial intelligence (AI) transformation.Following the wider adoption of consumer IoT, the next generation of IoT/IIoT innovation for business is driven by industries, addressing interoperability issues and providing new end-to-end security solutions to face continuous treats.The advances of AI technology in vision, speech recognition, natural language processing and dialog are enabling the development of end-to-end intelligent systems encapsulating multiple technologies, delivering services in real-time using limited resources. These developments are focusing on designing and delivering embedded and hierarchical AI solutions in IoT/IIoT, edge computing, using distributed architectures, DLTs platforms and distributed end-to-end security, which provide real-time decisions using less data and computational resources, while accessing each type of resource in a way that enhances the accuracy and performance of models in the various IoT/IIoT applications.The convergence and combination of IoT, AI and other related technologies to derive insights, decisions and revenue from sensor data provide new business models and sources of monetization. Meanwhile, scalable, IoT-enabled applications have become part of larger business objectives, enabling digital transformation with a focus on new services and applications.Serving the next generation of Tactile IoT/IIoT real-time use cases over 5G and Network Slicing technology is essential for consumer and industrial applications and support reducing operational costs, increasing efficiency and leveraging additional capabilities for real-time autonomous systems.New IoT distributed architectures, combined with system-level architectures for edge/fog computing, are evolving IoT platforms, including AI and DLTs, with embedded intelligence into the hyperconnectivity infrastructure.The next generation of IoT/IIoT technologies are highly transformational, enabling innovation at scale, and autonomous decision-making in various application domains such as healthcare, smart homes, smart buildings, smart cities, energy, agriculture, transportation and autonomous vehicles, the military, logistics and supply chain, retail and wholesale, manufacturing, mining and oil and gas
Biosensors
A biosensor is defined as a detecting device that combines a transducer with a
biologically sensitive and selective component. When a specific target molecule interacts
with the biological component, a signal is produced, at transducer level, proportional to the
concentration of the substance. Therefore biosensors can measure compounds present in the
environment, chemical processes, food and human body at low cost if compared with
traditional analytical techniques.
This book covers a wide range of aspects and issues related to biosensor technology,
bringing together researchers from 11 different countries. The book consists of 16 chapters
written by 53 authors. The first four chapters describe several aspects of nanotechnology
applied to biosensors. The subsequent section, including three chapters, is devoted to
biosensor applications in the fields of drug discovery, diagnostics and bacteria detection.
The principles behind optical biosensors and some of their application are discussed in
chapters from 8 to 11. The last five chapters treat of microelectronics, interfacing circuits,
signal transmission, biotelemetry and algorithms applied to biosensing
Next Generation Internet of Things – Distributed Intelligence at the Edge and Human-Machine Interactions
This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.The following chapters build on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT–EPI), the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects, presenting global views and state-of-the-art results regarding the next generation of IoT research, innovation, development, and deployment.The IoT and Industrial Internet of Things (IIoT) are evolving towards the next generation of Tactile IoT/IIoT, bringing together hyperconnectivity (5G and beyond), edge computing, Distributed Ledger Technologies (DLTs), virtual/ andaugmented reality (VR/AR), and artificial intelligence (AI) transformation.Following the wider adoption of consumer IoT, the next generation of IoT/IIoT innovation for business is driven by industries, addressing interoperability issues and providing new end-to-end security solutions to face continuous treats.The advances of AI technology in vision, speech recognition, natural language processing and dialog are enabling the development of end-to-end intelligent systems encapsulating multiple technologies, delivering services in real-time using limited resources. These developments are focusing on designing and delivering embedded and hierarchical AI solutions in IoT/IIoT, edge computing, using distributed architectures, DLTs platforms and distributed end-to-end security, which provide real-time decisions using less data and computational resources, while accessing each type of resource in a way that enhances the accuracy and performance of models in the various IoT/IIoT applications.The convergence and combination of IoT, AI and other related technologies to derive insights, decisions and revenue from sensor data provide new business models and sources of monetization. Meanwhile, scalable, IoT-enabled applications have become part of larger business objectives, enabling digital transformation with a focus on new services and applications.Serving the next generation of Tactile IoT/IIoT real-time use cases over 5G and Network Slicing technology is essential for consumer and industrial applications and support reducing operational costs, increasing efficiency and leveraging additional capabilities for real-time autonomous systems.New IoT distributed architectures, combined with system-level architectures for edge/fog computing, are evolving IoT platforms, including AI and DLTs, with embedded intelligence into the hyperconnectivity infrastructure.The next generation of IoT/IIoT technologies are highly transformational, enabling innovation at scale, and autonomous decision-making in various application domains such as healthcare, smart homes, smart buildings, smart cities, energy, agriculture, transportation and autonomous vehicles, the military, logistics and supply chain, retail and wholesale, manufacturing, mining and oil and gas
Antennas and Electromagnetics Research via Natural Language Processing.
Advanced techniques for performing natural language processing (NLP) are being utilised to devise a pioneering methodology for collecting and analysing data derived from scientific literature. Despite significant advancements in automated database generation and analysis within the domains of material chemistry and physics, the implementation of NLP techniques in the realms of metamaterial discovery, antenna design, and wireless communications remains at its early stages. This thesis proposes several novel approaches to advance research in material science. Firstly, an NLP method has been developed to automatically extract keywords from large-scale unstructured texts in the area of metamaterial research. This enables the uncovering of trends and relationships between keywords, facilitating the establishment of future research directions. Additionally, a trained neural network model based on the encoder-decoder Long Short-Term Memory (LSTM) architecture has been developed to predict future research directions and provide insights into the influence of metamaterials research. This model lays the groundwork for developing a research roadmap of metamaterials. Furthermore, a novel weighting system has been designed to evaluate article attributes in antenna and propagation research, enabling more accurate assessments of impact of each scientific publication. This approach goes beyond conventional numeric metrics to produce more meaningful predictions. Secondly, a framework has been proposed to leverage text summarisation, one of the primary NLP tasks, to enhance the quality of scientific reviews. It has been applied to review recent development of antennas and propagation for body-centric wireless communications, and the validation has been made available for comparison with well-referenced datasets for text summarisation. Lastly, the effectiveness of automated database building in the domain of tunable materials and their properties has been presented. The collected database will use as an input for training a surrogate machine learning model in an iterative active learning cycle. This model will be utilised to facilitate high-throughput material processing, with the ultimate goal of discovering novel materials exhibiting high tunability. The approaches proposed in this thesis will help to accelerate the discovery of new materials and enhance their applications in antennas, which has the potential to transform electromagnetic material research