Department of Computer & Software Engineeringhttps://research.thea.ie/handle/20.500.12065/24192024-03-28T19:06:56Z2024-03-28T19:06:56ZAdaptive intent realisation (AIR) - inductive intent realisation through NLOP enabled intent matchingMcNamara, Josephhttps://research.thea.ie/handle/20.500.12065/46772023-11-23T03:01:46Z2023-09-01T00:00:00ZAdaptive intent realisation (AIR) - inductive intent realisation through NLOP enabled intent matching
McNamara, Joseph
There is a strong interest in designing systems that can simplify the interactions between
humans and complex digital systems. Network Operators want more straightforward
mechanisms to engage with their networks and inform their actions and goals. Intent was
proposed to meet this challenge, but comes at a cost. Intent introduces large modelling
efforts, requiring Network Operators to gain expertise in formal model notation and the
integration of these models with their network. The cost is compounded by the speed
which modern networks evolve, requiring constant adaption to maintain intent-driven
features.
This work aims to leverage the concepts of intent-based management for private net works, without component and formal model expertise. This will be achieved through
the coordination of three enablers, Adaptive Policy, Machine Learning and Intent. Adap tive Policy provides a flexible framework for context-aware decision making, utilising a
state-based approach to policy execution. Machine Learning informs the decision mak ing process to produce impact-aware responses based on closed-loop reporting. Intent
structures the realisation process, how abstraction is handled through inductive pro cesses to generate actionable output. This work is highly experimental, developed on
site at the Network Management Lab in an Ericsson Product Development Unit based in
Ireland. This work concludes with the Adaptive Intent Realisation (AIR) reference ar chitecture successfully demonstrated in three use cases hosted in industrial grade private
5G networks.
2023-09-01T00:00:00ZReal-time processing of I-LOFAR data using signal and image-based artificial intelligence/machine learning methodsScully, Jeremiahhttps://research.thea.ie/handle/20.500.12065/46762023-11-23T03:01:41Z2023-08-01T00:00:00ZReal-time processing of I-LOFAR data using signal and image-based artificial intelligence/machine learning methods
Scully, Jeremiah
Solar flares discharge up to 1025J of magnetic energy into the solar atmosphere and
are often linked with high-intensity radio emissions known as Solar Radio Bursts
(SRBs). SRBs are commonly found in dynamic spectra and are classified into five
major spectral classes, ranging from Type I to Type V, based on their form and
frequency, and time extent. The automatic detection and classification of such radio
bursts is a challenge in solar radio physics due to their heterogeneous form. Near
real-time detection and classification of SRBs has become a necessity in recent
years due to large data rates generated by advanced radio telescopes such as the
LOw-FRequency ARray (LOFAR).
This thesis proposes a strategy for developing a very fast image and signal classification system that uses artificial intelligence algorithms to process gigabyte/sec
data streams in real-time using the Irish-LOFAR array as its prime data source.
Currently, the state-of-the-art systems in this area are falling short of the required
performance to process such high-bandwidth data streams. Real-time study of SRBs
is crucial for effective solar monitoring because it provides timely information about
dynamic solar phenomena, such as flares and coronal mass ejections, allowing us
to predict space weather impacts on Earth’s technology and infrastructure. This
real-time data helps spacecraft operators, scientists, and public safety officials make
informed decisions, validates and refines models of solar behavior, and drives advancements in monitoring technologies, ensuring accurate and proactive responses
to solar disturbances
2023-08-01T00:00:00ZPerceived factors informing the pre-acceptability of digital health innovation by again respiratory patients: a case study from the Republic of IrelandByrne, TaraMurray, NiallMCDonnell-Naughton, MaryRowan, Neil J.https://research.thea.ie/handle/20.500.12065/46382023-10-26T03:02:01Z2023-10-24T00:00:00ZPerceived factors informing the pre-acceptability of digital health innovation by again respiratory patients: a case study from the Republic of Ireland
Byrne, Tara; Murray, Niall; MCDonnell-Naughton, Mary; Rowan, Neil J.
It is appreciated that digital health is increasing in interest as an important
area for efficiently standardizing and developing health services in Ireland,
and worldwide. However, digital health is still considered to be in its infancy
and there is a need to understand important factors that will support the
development and uniform uptake of these technologies, which embrace
their utility and ensure data trustworthiness. This constituted the first study to
identify themes believed to be relevant by respiratory care and digital health
experts in the Republic of Ireland to help inform future decision-making
among respiratory patients that may potentially facilitate engagement with
and appropriate use of digital health innovation (DHI). The study explored and
identified expert participant perceptions, beliefs, barriers, and cues to action
that would inform content and future deployment of living labs in respiratory
care for remote patient monitoring of people with respiratory diseases using
DHI. The objective of this case study was to generate and evaluate appropriate
data sets to inform the selection and future deployment of an ICT-enabling
technology that will empower patients to manage their respiratory systems
in real-time in a safe effective manner through remote consultation with
health service providers. The co-creation of effective DHI for respiratory care
will be informed by multi-actor stakeholder participation, such as through a
Quintuple Helix Hub framework combining university-industry-governmenthealthcare-
society engagements. Studies, such as this, will help bridge the
interface between top-down digital health policies and bottom-up end-user
engagements to ensure safe and effective use of health technology. In addition,
it will address the need to reach a consensus on appropriate key performance
indicators (KPIs) for effective uptake, implementation, standardization, and
regulation of DHI.
2023-10-24T00:00:00ZShock shaping? Nebular spectroscopy of nova V906 CarinaeHarvey, E.J.Aydi, E.Izzo, L.Morisset, C.Darnley, M.J.Fitzgerald, KarolMolaro, P.Murphy-Glaysher, F.Redman, M.P.Shrestha, M.https://research.thea.ie/handle/20.500.12065/45222023-06-10T03:02:02Z2023-03-22T00:00:00ZShock shaping? Nebular spectroscopy of nova V906 Carinae
Harvey, E.J.; Aydi, E.; Izzo, L.; Morisset, C.; Darnley, M.J.; Fitzgerald, Karol; Molaro, P.; Murphy-Glaysher, F.; Redman, M.P.; Shrestha, M.
V906 Carinae was one of the best observed novae of recent times. It was a prolific dust producer and harboured shocks in the early evolving ejecta outflow. Here, we take a close look at the consequences of these early interactions through study of high-resolution Ultraviolet and Visual Echelle spectrograph spectroscopy of the nebular stage and extrapolate backwards to investigate how the final structure may have formed. A study of ejecta geometry and shaping history of the structure of the shell is undertaken following a spectral line SHAPE
model fit. A search for spectral tracers of shocks in the nova ejecta is undertaken and an analysis of the ionized environment. Temperature, density, and abundance analyses of the evolving nova shell are presented
2023-03-22T00:00:00Z