Wireless sensor node design for heterogeneous networks

Two complementary wireless sensor nodes for building two-tiered heterogeneous networks are presented. A larger node with a 25 mm by 25 mm size acts as the backbone of the network, and can handle complex data processing. A smaller, cheaper node with a 10 mm by 10 mm size can perform simpler sensor-interfacing tasks. The 25mm node is based on previous work that has been done in the Tyndall National Institute that created a modular wireless sensor node. In this work, a new 25mm module is developed operating in the 433/868 MHz frequency bands, with a range of 3.8 km. The 10mm node is highly miniaturised, while retaining a high level of modularity. It has been designed to support very energy efficient operation for applications with low duty cycles, with a sleep current of 3.3 μA. Both nodes use commercially available components and have low manufacturing costs to allow the construction of large networks. In addition, interface boards for communicating with nodes have been developed for both the 25mm and 10mm nodes. These interface boards provide a USB connection, and support recharging of a Li-ion battery from the USB power supply. This paper discusses the design goals, the design methods, and the resulting implementation.

Optimisation of Smart Grid performance using centralised and distributed control techniques

A massive change is currently taking place in the manner in which power networks are operated. Traditionally, power networks consisted of large power stations which were controlled from centralised locations. The trend in modern power networks is for generated power to be produced by a diverse array of energy sources which are spread over a large geographical area. As a result, controlling these systems from a centralised controller is impractical. Thus, future power networks will be controlled by a large number of intelligent distributed controllers which must work together to coordinate their actions. The term Smart Grid is the umbrella term used to denote this combination of power systems, artificial intelligence, and communications engineering. This thesis focuses on the application of optimal control techniques to Smart Grids with a focus in particular on iterative distributed MPC. A novel convergence and stability proof for iterative distributed MPC based on the Alternating Direction Method of Multipliers is derived. Distributed and centralised MPC, and an optimised PID controllers' performance are then compared when applied to a highly interconnected, nonlinear, MIMO testbed based on a part of the Nordic power grid. Finally, a novel tuning algorithm is proposed for iterative distributed MPC which simultaneously optimises both the closed loop performance and the communication overhead associated with the desired control.

The protection and security of vulnerable populations in complex emergencies using the Dadaab refugee camps in the north eastern province of Kenya as a case study

The past two decades has seen a dramatic upheaval in the international world order: the end of the Cold War, the 9/11 attacks and the subsequent 'War on Terror', increased Jihadist activities, the accelerated pace of globalization, climate change and the 2008 global financial crisis have contributed to fear, uncertainty, poverty, conflict, massive displacements of populations of asylum seekers and refugees globally and a proliferation of Protracted Refugee Situations (PRS), defined as situations in which refugees have been in exile 'for 5 years or more after their initial displacement, without immediate prospects for implementation of durable solutions. In the past two decades there has been a huge proliferation of these with more than 7.2 million refugees now trapped in these PRS, with a further 16 million internally displaced persons (IDPs) trapped in camps within their own countries. The Dadaab refugee complex in Kenya, which of as March 2012, holds over 463,000 refugees, is the most significant and extreme example in recent times of a PRS. It was established in 1991 following the collapse of the Somali Government of Dictator Siad Barre, and the disintegration of Somalia into the chaos that still exists today. PRS such as Dadaab raise particular issues about humanitarianism in terms of aid, protection, security, human rights and the actions (or inaction) of the various stakeholders on an international, national and local level. This thesis investigates these issues by the use of a case study methodology on Dadaab as a PRS, framed in the context of humanitarianism and in particular the issues that arise in terms of how the international community, the UN system and individual states provide assistance and protection to vulnerable populations. Although the refugee camps have been in existence (as of 2012) for over 20 years, there has never been such a detailed study of Dadaab (or any other PRS) undertaken to date and would be of interest to academics in the areas of international relations, refugee/migration studies and global Governance as well as practitioners in both humanitarian response and development

Comparing impacts of new cropping systems on biodiversity in traditional rural China

This study selected six geographically-similar villages with traditional and alternative cultivation methods (two groups of three, one traditional and two alternatives) in two counties of Henan Province, China—a representative area of the Huang-huai-hai Plain representing traditional rural China. Soil heavy metal concentrations, floral and faunal biodiversity, and socio-economic data were recorded. Heavy metal concentrations of surface soils from three sites in each village were analysed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS, chromium, nickel, copper, cadmium, and lead) and Atomic Absorption Spectrophotometer (AAS, zinc). The floral biodiversity of four land-use types was recorded following the Braun-Blanquet coverage-abundance method using 0.5×0.5m quadrats. The faunal biodiversity of two representative farmland plots was recorded using 0.3×0.3m quadrats at four 0.1m layers. The socio-economic data were recorded through face-to-face interviews of one hundred randomly selected households at each village. Results demonstrate different cultivation methods lead to different impact on above variables. Traditional cultivation led to lower heavy metal concentrations; both alternative managements were associated with massive agrochemical input causing heavy metal pollution in farmlands. Floral distribution was significantly affected by village factors. Diverse cultivation supported high floral biodiversity through multi-scale heterogeneous landscapes containing niches and habitats. Faunal distribution was also significantly affected by village factor nested within soil depth. Different faunal groups responded differently, with Acari being taxonomically diverse and Collembola high in densities. Increase in manual labour and crop number in villages using alternative cultivation may positively affect biodiversity. The results point to the conservation potential of diverse cultivation methods in traditional rural China and other regions under social and political reforms, where traditional agriculture is changing to unified, large-scale mechanized agriculture. This study serves as a baseline for conservation in small-holding agricultural areas of China, and points to the necessity of further studies at larger and longer scales.

The relevance of bacterial isoprenoid biosynthetic pathways for host-microbe interactions

This thesis was undertaken to investigate the relevance of two bacterial isoprenoid biosynthetic pathways (Mevalonate (MVAL) and 2-C-methyl-D-erythritol 4-phosphate (MEP)) for host-microbe interactions. We determined a significant reduction in microbial diversity in the murine gut microbiota (by next generation sequencing) following oral administration of a common anti-cholesterol drug Rosuvastatin (RSV) that targets mammalian and bacterial HMG-CoA reductase (HMG-R) for inhibition of MVAL formation. In tandem we identified significant hepatic and intestinal off-target alterations to the murine metabolome indicating alterations in inflammation, bile acid profiles and antimicrobial peptide synthesis with implications on community structure of the gastrointestinal microbiota in statin-treated animals. However we found no effect on local Short Chain Fatty Acid biosynthesis (metabolic health marker in our model). We demonstrated direct inhibition of bacterial growth in-vitro by RSV which correlated with reductions in bacterial MVAL formation. However this was only at high doses of RSV. Our observations demonstrate a significant RSV-associated impact on the gut microbiota prompting similar human analysis. Successful deletion of another MVAL pathway enzyme (HMG-CoA synthase (mvaS)) involved in Listeria monocytogenes EGDe isoprenoid biosynthesis determined that the enzyme is non-essential for normal growth and in-vivo pathogenesis of this pathogen. We highlight potential evidence for alternative means of synthesis of the HMG-CoA substrate that could render mvaS activity redundant under our test conditions. Finally, we showed by global gene expression analysis (Massive Analysis of cDNA Ends (MACE RNA-seq) a significant role for the penultimate MEP pathway metabolite (E)-4-hydroxy-3-methyl-2-but-2-enyl pyrophosphate (HMBPP) in significant up regulation of genes of immunity and antigen presentation in THP-1 cells at nanomolar levels. We infected THP-1 cells with wild type or HMBPP under/over-producing L. monoctyogenes EGDe mutants and determined subtle effects of HMBPP upon overall host responses to Listeria infection. Overall our findings provide greater insights regarding bacterial isoprenoid biosynthetic pathways for host-microbe/microbe-host dialogue.

A distributed architecture for the monitoring and analysis of time series data

It is estimated that the quantity of digital data being transferred, processed or stored at any one time currently stands at 4.4 zettabytes (4.4 × 2 70 bytes) and this figure is expected to have grown by a factor of 10 to 44 zettabytes by 2020. Exploiting this data is, and will remain, a significant challenge. At present there is the capacity to store 33% of digital data in existence at any one time; by 2020 this capacity is expected to fall to 15%. These statistics suggest that, in the era of Big Data, the identification of important, exploitable data will need to be done in a timely manner. Systems for the monitoring and analysis of data, e.g. stock markets, smart grids and sensor networks, can be made up of massive numbers of individual components. These components can be geographically distributed yet may interact with one another via continuous data streams, which in turn may affect the state of the sender or receiver. This introduces a dynamic causality, which further complicates the overall system by introducing a temporal constraint that is difficult to accommodate. Practical approaches to realising the system described above have led to a multiplicity of analysis techniques, each of which concentrates on specific characteristics of the system being analysed and treats these characteristics as the dominant component affecting the results being sought. The multiplicity of analysis techniques introduces another layer of heterogeneity, that is heterogeneity of approach, partitioning the field to the extent that results from one domain are difficult to exploit in another. The question is asked can a generic solution for the monitoring and analysis of data that: accommodates temporal constraints; bridges the gap between expert knowledge and raw data; and enables data to be effectively interpreted and exploited in a transparent manner, be identified? The approach proposed in this dissertation acquires, analyses and processes data in a manner that is free of the constraints of any particular analysis technique, while at the same time facilitating these techniques where appropriate. Constraints are applied by defining a workflow based on the production, interpretation and consumption of data. This supports the application of different analysis techniques on the same raw data without the danger of incorporating hidden bias that may exist. To illustrate and to realise this approach a software platform has been created that allows for the transparent analysis of data, combining analysis techniques with a maintainable record of provenance so that independent third party analysis can be applied to verify any derived conclusions. In order to demonstrate these concepts, a complex real world example involving the near real-time capturing and analysis of neurophysiological data from a neonatal intensive care unit (NICU) was chosen. A system was engineered to gather raw data, analyse that data using different analysis techniques, uncover information, incorporate that information into the system and curate the evolution of the discovered knowledge. The application domain was chosen for three reasons: firstly because it is complex and no comprehensive solution exists; secondly, it requires tight interaction with domain experts, thus requiring the handling of subjective knowledge and inference; and thirdly, given the dearth of neurophysiologists, there is a real world need to provide a solution for this domain

A corpus-driven error analysis of the oral and written production of Leaving Certificate Spanish learners

The Leaving Certificate (LC) is the national, standardised state examination in Ireland necessary for entry to third level education – this presents a massive, raw corpus of data with the potential to yield invaluable insight into the phenomena of learner interlanguage. With samples of official LC Spanish examination data, this project has compiled a digitised corpus of learner Spanish comprised of the written and oral production of 100 candidates. This corpus was then analysed using a specific investigative corpus technique, Computer-aided Error Analysis (CEA, Dagneaux et al, 1998). CEA is a powerful apparatus in that it greatly facilitates the quantification and analysis of a large learner corpus in digital format. The corpus was both compiled and analysed with the use of UAM Corpus Tool (O’Donnell 2013). This Tool allows for the recording of candidate-specific variables such as grade, examination level, task type and gender, therefore allowing for critical analysis of the corpus as one unit, as separate written and oral sub corpora and also of performance per task, level and gender. This is an interdisciplinary work combining aspects of Applied Linguistics, Learner Corpus Research and Foreign Language (FL) Learning. Beginning with a review of the context of FL learning in Ireland and Europe, I go on to discuss the disciplinary context and theoretical framework for this work and outline the methodology applied. I then perform detailed quantitative and qualitative analyses before going on to combine all research findings outlining principal conclusions. This investigation does not make a priori assumptions about the data set, the LC Spanish examination, the context of FLs or of any aspect of learner competence. It undertakes to provide the linguistic research community and the domain of Spanish language learning and pedagogy in Ireland with an empirical, descriptive profile of real learner performance, characterising learner difficulty.