A demonstration of wireless sensing for long-term monitoring of water quality

At a time when technological advances are providing new sensor capabilities, novel network capabilities, long-range communications technologies and data interpreting and delivery formats via the World Wide Web, we never before had such opportunities to sense and analyse the environment around us. However, the challenges exist. While measurement and detection of environmental pollutants can be successful under laboratory-controlled conditions, continuous in-situ monitoring remains one of the most challenging aspects of environmental sensing. This paper describes the development and test of a multi-sensor heterogeneous real-time water monitoring system. A multi-sensor system was deployed in the River Lee, County Cork, Ireland to monitor water quality parameters such as pH, temperature, conductivity, turbidity and dissolved oxygen. The R. Lee comprises of a tidal water system that provides an interesting test site to monitor. The multi-sensor system set-up is described and results of the sensor deployment and the various challenges are discussed.

A benchmark comparison between reconfigurable, intelligent and autonomous wireless inertial measurement and photonic technologies in rehabilitation

Advanced sensory systems address a number of major obstacles towards the provision for cost effective and proactive rehabilitation. Many of these systems employ technologies such as high-speed video or motion capture to generate quantitative measurements. However these solutions are accompanied by some major limitations including extensive set-up and calibration, restriction to indoor use, high cost and time consuming data analysis. Additionally many do not quantify improvement in a rigorous manner for example gait analysis for 5 minutes as opposed to 24 hour ambulatory monitoring. This work addresses these limitations using low cost, wearable wireless inertial measurement as a mobile and minimal infrastructure alternative. In cooperation with healthcare professionals the goal is to design and implement a reconfigurable and intelligent movement capture system. A key component of this work is an extensive benchmark comparison with the 'gold standard' VICON motion capture system.

Quantum behavior in mesoscopic systems

In this thesis I present the work done during my PhD. The Thesis is divided into two parts; in the first one I present the study of mesoscopic quantum systems whereas in the second one I address the problem of the definition of Markov regime for quantum system dynamics. The first work presented is the study of vortex patterns in (quasi) two dimensional rotating Bose Einstein condensates (BECs). I consider the case of an anisotropy trapping potential and I shall show that the ground state of the system hosts vortex patterns that are unstable. In a second work I designed an experimental scheme to transfer entanglement from two entangled photons to two BECs. This work is meant to propose a feasible experimental set up to bring entanglement from microscopic to macroscopic systems for both the study of fundamental questions (quantum to classical transition) and technological applications. In the last work of the first part another experimental scheme is presented in order to detect coherences of a mechanical oscillator which is assumed to have been previously cooled down to the quantum regime. In this regime in fact the system can rapidly undergo decoherence so that new techniques have to be employed in order to detect and manipulate their states. In the scheme I propose a micro-mechanical oscillator is coupled to a BEC and the detection is performed by monitoring the BEC with a negligible back-action on the cantilever. In the second part of the thesis I give a definition of Markov regime for open quantum dynamics. The importance of such definition comes from both the mathematical description of the system dynamics and from the understanding of the role played by the environment in the evolution of an open system. In the Markov regime the mathematical description can be simplified and the role of the environment is a passive one.

Investigation of polyhydroxyalkanoate production by an activated sludge microbial consortium treating artificial dairy wastewater

Petrochemical plastics/polymers are a common feature of day to day living as they occur in packaging, furniture, mobile phones, computers, construction equipment etc. However, these materials are produced from non-renewable materials and are resistant to microbial degradation in the environment. Considerable research has therefore been carried out into the production of sustainable, biodegradable polymers, amenable to microbial catabolism to CO2 and H2O. A key group of microbial polyesters, widely considered as optimal replacement polymers, are the Polyhydroxyalkaonates (PHAs). Primary research in this area has focused on using recombinant pure cultures to optimise PHA yields, however, despite considerable success, the high costs of pure culture fermentation have thus far hindered the commercial viability of PHAs thus produced. In more recent years work has begun to focus on mixed cultures for the optimisation of PHA production, with waste incorporations offering optimal production cost reductions. The scale of dairy processing in Ireland, and the high organic load wastewaters generated, represent an excellent potential substrate for bioconversion to PHAs in a mixed culture system. The current study sought to investigate the potential for such bioconversion in a laboratory scale biological system and to establish key operational and microbial characteristics of same. Two sequencing batch reactors were set up and operated along the lines of an enhanced biological phosphate removal (EBPR) system, which has PHA accumulation as a key step within repeated rounds of anaerobic/aerobic cycling. Influents to the reactors varied only in the carbon sources provided. Reactor 1 received artificial wastewater with acetate alone, which is known to be readily converted to PHA in the anaerobic step of EBPR. Reactor 2 wastewater influent contained acetate and skim milk to imitate a dairy processing effluent. Chemical monitoring of nutrient remediation within the reactors as continuously applied and EBPR consistent performances observed. Qualitative analysis of the sludge was carried out using fluorescence microscopy with Nile Blue A lipophillic stain and PHA production was confirmed in both reactors. Quantitative analysis via HPLC detection of crotonic acid derivatives revealed the fluorescence to be short chain length Polyhydroxybutyrate, with biomass dry weight accumulations of 11% and 13% being observed in reactors 1 and 2, respectively. Gas Chromatography-Mass Spectrometry for medium chain length methyl ester derivatives revealed the presence of hydroxyoctanoic, -decanoic and -dodecanoic acids in reactor 1. Similar analyses in reactor 2 revealed monomers of 3-hydroxydodecenoic and 3-hydroxytetradecanoic acids. Investigation of the microbial ecology of both reactors as conducted in an attempt to identify key species potentially contributing to reactor performance. Culture dependent investigations indicated that quite different communities were present in both reactors. Reactor 1 isolates demonstrated the following species distributions Pseudomonas (82%), Delftia acidovorans (3%), Acinetobacter sp. (5%) Aminobacter sp., (3%) Bacillus sp. (3%), Thauera sp., (3%) and Cytophaga sp. (3%). Relative species distributions among reactor 2 profiled isolates were more evenly distributed between Pseudoxanthomonas (32%), Thauera sp (24%), Acinetobacter (24%), Citrobacter sp (8%), Lactococcus lactis (5%), Lysinibacillus (5%) and Elizabethkingia (2%). In both reactors Gammaproteobacteria dominated the cultured isolates. Culture independent 16S rRNA gene analyses revealed differing profiles for both reactors. Reactor 1 clone distribution was as follows; Zooglea resiniphila (83%), Zooglea oryzae (2%), Pedobacter composti (5%), Neissericeae sp. (2%) Rhodobacter sp. (2%), Runella defluvii (3%) and Streptococcus sp. (3%). RFLP based species distribution among the reactor 2 clones was as follows; Runella defluvii (50%), Zoogloea oryzae (20%), Flavobacterium sp. (9%), Simplicispira sp. (6%), Uncultured Sphingobacteria sp. (6%), Arcicella (6%) and Leadbetterella bysophila (3%). Betaproteobacteria dominated the 16S rRNA gene clones identified in both reactors. FISH analysis with Nile Blue dual staining resolved these divergent findings, identifying the Betaproteobacteria as dominant PHA accumulators within the reactor sludges, although species/strain specific allocations could not be made. GC analysis of the sludge had indicated the presence of both medium chain length as well short chain length PHAs accumulating in both reactors. In addition the cultured isolates from the reactors had been identified previously as mcl and scl PHA producers, respectively. Characterisations of the PHA monomer profiles of the individual isolates were therefore performed to screen for potential novel scl-mcl PHAs. Nitrogen limitation driven PHA accumulation in E2 minimal media revealed a greater propensity among isoates for mcl-pHA production. HPLC analysis indicated that PHB production was not a major feature of the reactor isolates and this was supported by the low presence of scl phaC1 genes among PCR screened isolates. A high percentage distribution of phaC2 mcl-PHA synthase genes was recorded, with the majority sharing high percentage homology with class II synthases from Pseudomonas sp. The common presence of a phaC2 homologue was not reflected in the production of a common polymer. Considerable variation was noted in both the monomer composition and ratios following GC analysis. While co-polymer production could not be demonstrated, potentially novel synthase substrate specificities were noted which could be exploited further in the future.

Artefact detection and removal algorithms for EEG diagnostic systems

The electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of- use and mobility are important. Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours. However, as typical EEG signals are of the order of microvolts (μV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance. This thesis therefore, contributes to the further improvement of auto- mated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science. The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG. Subsequently, addtional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head- movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement arte- fact detection. The artefact detection methods developed for ambulatory EEG are subsequently adapted for use in an automated epileptiform activity detection system. Information from support vector machines classifiers used to detect epileptiform activity is fused with information from artefact-specific detection classifiers in order to significantly reduce the number of false detections in the epileptiform activity detection system. By this means, epileptiform activity detection which compares favourably with other state-of-the-art systems is achieved. Finally, the problem of false detections in automated neonatal seizure detection is approached in an alternative manner; blind source separation techniques, complimented with information from additional physiological signals are used to remove respiration artefact from the EEG. In utilising these methods, some encouraging advances have been made in detecting and removing respiration artefacts from the neonatal EEG, and in doing so, the performance of the underlying diagnostic technology is improved, bringing its deployment in the real-world, clinical domain one step closer.

The use of fault detection and diagnostics to reduce air handling unit energy consumption

The contribution of buildings towards total worldwide energy consumption in developed countries is between 20% and 40%. Heating Ventilation and Air Conditioning (HVAC), and more specifically Air Handling Units (AHUs) energy consumption accounts on average for 40% of a typical medical device manufacturing or pharmaceutical facility’s energy consumption. Studies have indicated that 20 – 30% energy savings are achievable by recommissioning HVAC systems, and more specifically AHU operations, to rectify faulty operation. Automated Fault Detection and Diagnosis (AFDD) is a process concerned with potentially partially or fully automating the commissioning process through the detection of faults. An expert system is a knowledge-based system, which employs Artificial Intelligence (AI) methods to replicate the knowledge of a human subject matter expert, in a particular field, such as engineering, medicine, finance and marketing, to name a few. This thesis details the research and development work undertaken in the development and testing of a new AFDD expert system for AHUs which can be installed in minimal set up time on a large cross section of AHU types in a building management system vendor neutral manner. Both simulated and extensive field testing was undertaken against a widely available and industry known expert set of rules known as the Air Handling Unit Performance Assessment Rules (APAR) (and a later more developed version known as APAR_extended) in order to prove its effectiveness. Specifically, in tests against a dataset of 52 simulated faults, this new AFDD expert system identified all 52 derived issues whereas the APAR ruleset identified just 10. In tests using actual field data from 5 operating AHUs in 4 manufacturing facilities, the newly developed AFDD expert system for AHUs was shown to identify four individual fault case categories that the APAR method did not, as well as showing improvements made in the area of fault diagnosis.