Displacement sensor using channelled spectrum dispersed on a linear CCD array

A novel form of low coherence interferometric sensor is described. The channelled spectrum produced by illuminating a sensing interferometer with a broadband source is analysed directly using a CCD array. The system currently provides unambiguous measurement over a range of 1.5 mm with an accuracy of better than 6 µm.

Non-linear hierarchical visualisation

This thesis applies a hierarchical latent trait model system to a large quantity of data. The motivation for it was lack of viable approaches to analyse High Throughput Screening datasets which maybe include thousands of data points with high dimensions. High Throughput Screening (HTS) is an important tool in the pharmaceutical industry for discovering leads which can be optimised and further developed into candidate drugs. Since the development of new robotic technologies, the ability to test the activities of compounds has considerably increased in recent years. Traditional methods, looking at tables and graphical plots for analysing relationships between measured activities and the structure of compounds, have not been feasible when facing a large HTS dataset. Instead, data visualisation provides a method for analysing such large datasets, especially with high dimensions. So far, a few visualisation techniques for drug design have been developed, but most of them just cope with several properties of compounds at one time. We believe that a latent variable model (LTM) with a non-linear mapping from the latent space to the data space is a preferred choice for visualising a complex high-dimensional data set. As a type of latent variable model, the latent trait model can deal with either continuous data or discrete data, which makes it particularly useful in this domain. In addition, with the aid of differential geometry, we can imagine the distribution of data from magnification factor and curvature plots. Rather than obtaining the useful information just from a single plot, a hierarchical LTM arranges a set of LTMs and their corresponding plots in a tree structure. We model the whole data set with a LTM at the top level, which is broken down into clusters at deeper levels of t.he hierarchy. In this manner, the refined visualisation plots can be displayed in deeper levels and sub-clusters may be found. Hierarchy of LTMs is trained using expectation-maximisation (EM) algorithm to maximise its likelihood with respect to the data sample. Training proceeds interactively in a recursive fashion (top-down). The user subjectively identifies interesting regions on the visualisation plot that they would like to model in a greater detail. At each stage of hierarchical LTM construction, the EM algorithm alternates between the E- and M-step. Another problem that can occur when visualising a large data set is that there may be significant overlaps of data clusters. It is very difficult for the user to judge where centres of regions of interest should be put. We address this problem by employing the minimum message length technique, which can help the user to decide the optimal structure of the model. In this thesis we also demonstrate the applicability of the hierarchy of latent trait models in the field of document data mining.

Analysis of magnetoencephaoographic data as a nonlinear dynamical system

This thesis presents the results from an investigation into the merits of analysing Magnetoencephalographic (MEG) data in the context of dynamical systems theory. MEG is the study of both the methods for the measurement of minute magnetic flux variations at the scalp, resulting from neuro-electric activity in the neocortex, as well as the techniques required to process and extract useful information from these measurements. As a result of its unique mode of action - by directly measuring neuronal activity via the resulting magnetic field fluctuations - MEG possesses a number of useful qualities which could potentially make it a powerful addition to any brain researcher's arsenal. Unfortunately, MEG research has so far failed to fulfil its early promise, being hindered in its progress by a variety of factors. Conventionally, the analysis of MEG has been dominated by the search for activity in certain spectral bands - the so-called alpha, delta, beta, etc that are commonly referred to in both academic and lay publications. Other efforts have centred upon generating optimal fits of "equivalent current dipoles" that best explain the observed field distribution. Many of these approaches carry the implicit assumption that the dynamics which result in the observed time series are linear. This is despite a variety of reasons which suggest that nonlinearity might be present in MEG recordings. By using methods that allow for nonlinear dynamics, the research described in this thesis avoids these restrictive linearity assumptions. A crucial concept underpinning this project is the belief that MEG recordings are mere observations of the evolution of the true underlying state, which is unobservable and is assumed to reflect some abstract brain cognitive state. Further, we maintain that it is unreasonable to expect these processes to be adequately described in the traditional way: as a linear sum of a large number of frequency generators. One of the main objectives of this thesis will be to prove that much more effective and powerful analysis of MEG can be achieved if one were to assume the presence of both linear and nonlinear characteristics from the outset. Our position is that the combined action of a relatively small number of these generators, coupled with external and dynamic noise sources, is more than sufficient to account for the complexity observed in the MEG recordings. Another problem that has plagued MEG researchers is the extremely low signal to noise ratios that are obtained. As the magnetic flux variations resulting from actual cortical processes can be extremely minute, the measuring devices used in MEG are, necessarily, extremely sensitive. The unfortunate side-effect of this is that even commonplace phenomena such as the earth's geomagnetic field can easily swamp signals of interest. This problem is commonly addressed by averaging over a large number of recordings. However, this has a number of notable drawbacks. In particular, it is difficult to synchronise high frequency activity which might be of interest, and often these signals will be cancelled out by the averaging process. Other problems that have been encountered are high costs and low portability of state-of-the- art multichannel machines. The result of this is that the use of MEG has, hitherto, been restricted to large institutions which are able to afford the high costs associated with the procurement and maintenance of these machines. In this project, we seek to address these issues by working almost exclusively with single channel, unaveraged MEG data. We demonstrate the applicability of a variety of methods originating from the fields of signal processing, dynamical systems, information theory and neural networks, to the analysis of MEG data. It is noteworthy that while modern signal processing tools such as independent component analysis, topographic maps and latent variable modelling have enjoyed extensive success in a variety of research areas from financial time series modelling to the analysis of sun spot activity, their use in MEG analysis has thus far been extremely limited. It is hoped that this work will help to remedy this oversight.

Exploratory data analysis with non-linear and missing data in geochemistry

Exploratory analysis of data seeks to find common patterns to gain insights into the structure and distribution of the data. In geochemistry it is a valuable means to gain insights into the complicated processes making up a petroleum system. Typically linear visualisation methods like principal components analysis, linked plots, or brushing are used. These methods can not directly be employed when dealing with missing data and they struggle to capture global non-linear structures in the data, however they can do so locally. This thesis discusses a complementary approach based on a non-linear probabilistic model. The generative topographic mapping (GTM) enables the visualisation of the effects of very many variables on a single plot, which is able to incorporate more structure than a two dimensional principal components plot. The model can deal with uncertainty, missing data and allows for the exploration of the non-linear structure in the data. In this thesis a novel approach to initialise the GTM with arbitrary projections is developed. This makes it possible to combine GTM with algorithms like Isomap and fit complex non-linear structure like the Swiss-roll. Another novel extension is the incorporation of prior knowledge about the structure of the covariance matrix. This extension greatly enhances the modelling capabilities of the algorithm resulting in better fit to the data and better imputation capabilities for missing data. Additionally an extensive benchmark study of the missing data imputation capabilities of GTM is performed. Further a novel approach, based on missing data, will be introduced to benchmark the fit of probabilistic visualisation algorithms on unlabelled data. Finally the work is complemented by evaluating the algorithms on real-life datasets from geochemical projects.

A solar-powered reverse osmosis system for high recovery of freshwater from saline groundwater

Desalination of groundwater is essential in arid regions that are remote from both seawater and freshwater resources. Desirable features of a groundwater desalination system include a high recovery ratio, operation from a sustainable energy source such as solar, and high water output per unit of energy and land. Here we propose a new system that uses a solar-Rankine cycle to drive reverse osmosis (RO). The working fluid such as steam is expanded against a power piston that actuates a pump piston which in turn pressurises the saline water thus passing it through RO membranes. A reciprocating crank mechanism is used to equalise the forces between the two pistons. The choice of batch mode in preference to continuous flow permits maximum energy recovery and minimal concentration polarisation in the vicinity of the RO membrane. This study analyses the sizing and efficiency of the crank mechanism, quantifies energy losses in the RO separation and predicts the overall performance. For example, a system using a field of linear Fresnel collectors occupying 1000 m2 of land and raising steam at 200 °C and 15.5 bar could desalinate 350 m3/day from saline water containing 5000 ppm of sodium chloride with a recovery ratio of 0.7.

Stand-alone groundwater desalination system using reverse osmosis combined with a cooled greenhouse for use in arid and semi-arid zones of India

In many areas of northern India, salinity renders groundwater unsuitable for drinking and even for irrigation. Though membrane treatment can be used to remove the salt, there are some drawbacks to this approach e.g. (1) depletion of the groundwater due to over-abstraction, (2) saline contamination of surface water and soil caused by concentrate disposal and (3) high electricity usage. To address these issues, a system is proposed in which a photovoltaic-powered reverse osmosis (RO) system is used to irrigate a greenhouse (GH) in a stand-alone arrangement. The concentrate from the RO is supplied to an evaporative cooling system, thus reducing the volume of the concentrate so that finally it can be evaporated in a pond to solid for safe disposal. Based on typical meteorological data for Delhi, calculations based on mass and energy balance are presented to assess the sizing and cost of the system. It is shown that solar radiation, freshwater output and evapotranspiration demand are readily matched due to the approximately linear relation among these variables. The demand for concentrate varies independently, however, thus favouring the use of a variable recovery arrangement. Though enough water may be harvested from the GH roof to provide year-round irrigation, this would require considerable storage. Some practical options for storage tanks are discussed. An alternative use of rainwater is in misting to reduce peak temperatures in the summer. An example optimised design provides internal temperatures below 30EC (monthly average daily maxima) for 8 months of the year and costs about €36,000 for the whole system with GH floor area of 1000 m2 . Further work is needed to assess technical risks relating to scale-deposition in the membrane and evaporative pads, and to develop a business model that will allow such a project to succeed in the Indian rural context.

Optimization of a 42.7 Gb/s wavelength tunable RZ transmitter using a linear spectrogram technique

The optimization of a wavelength tunable RZ transmitter, consisting of an electro-absorption modulator and a SG DBR tunable laser, is carried out using a linear spectrogram based characterization and leads to 1500 km transmission at 42.7 Gb/s independent of the operating wavelength. We demonstrate that, to ensure optimum and consistent transmission performance over a portion of the C-band, the RF drive and bias conditions of the EAM must be varied at each wavelength. The sign and magnitude of the pulse chirp (characterized using the linear spectrographic technique) is therefore tailored to suit the dispersion map of the transmission link. Results achieved show that by optimizing the drive and DC bias applied to the EAM, consistent transmission performance can be achieved over a wide wavelength range. Failure to optimize the EAM drive conditions at each wavelength can lead to serious degradation in system performance.

The effect of flying and low humidity on the admittance of the tympanic membrane and middle ear system

Many passengers experience discomfort during flight because of the effect of low humidity on the skin, eyes, throat, and nose. In this physiological study, we have investigated whether flight and low humidity also affect the tympanic membrane. From previous studies, a decrease in admittance of the tympanic membrane through drying might be expected to affect the buffering capacity of the middle ear and to disrupt automatic pressure regulation. This investigation involved an observational study onboard an aircraft combined with experiments in an environmental chamber, where the humidity could be controlled but could not be made to be as low as during flight. For the flight study, there was a linear relationship between the peak compensated static admittance of the tympanic membrane and relative humidity with a constant of proportionality of 0.00315 mmho/% relative humidity. The low humidity at cruise altitude (minimum 22.7 %) was associated with a mean decrease in admittance of about 20 % compared with measures in the airport. From the chamber study, we further found that a mean decrease in relative humidity of 23.4 % led to a significant decrease in mean admittance by 0.11 mmho [F(1,8) = 18.95, P = 0.002], a decrease of 9.4 %. The order of magnitude for the effect of humidity was similar for the flight and environmental chamber studies. We conclude that admittance changes during flight were likely to have been caused by the low humidity in the aircraft cabin and that these changes may affect the automatic pressure regulation of the middle ear during descent. © 2013 Association for Research in Otolaryngology.

A learning feed-forward current controller for linear reciprocating vapor compressors

Direct-drive linear reciprocating compressors offer numerous advantages over conventional counterparts which are usually driven by a rotary induction motor via a crank shaft. However, to ensure efficient and reliable operation under all conditions, it is essential that motor current of a linear compressor follows a sinusoidal current command with a frequency which matches the system resonant frequency. The design of a high-performance current controller for linear compressor drive presents a challenge since the system is highly nonlinear, and an effective solution must be low cost. In this paper, a learning feed-forward current controller for the linear compressors is proposed. It comprises a conventional feedback proportional-integral controller and a feed-forward B-spline neural network (BSNN). The feed-forward BSNN is trained online and in real time in order to minimize the current tracking error. Extensive simulation and experiment results with a prototype linear compressor show that the proposed current controller exhibits high steady state and transient performance. © 2009 IEEE.

Displacement sensor using channelled spectrum dispersed on a linear CCD array

A novel form of low coherence interferometric sensor is described. The channelled spectrum produced by illuminating a sensing interferometer with a broadband source is analysed directly using a CCD array. The system currently provides unambiguous measurement over a range of 1.5 mm with an accuracy of better than 6 µm.

Optical sensor interrogation with a blazed fiber Bragg grating and a charge-coupled device linear array

We present what is to our knowledge the first comprehensive investigation of the use of blazed fiber Bragg gratings (BFBGs) to interrogate wavelength division multiplexed (WDM) in-fiber optical sensor arrays. We show that the light outcoupled from the core of these BFBGs is radiated with sufficient optical power that it may be detected with a low-cost charge-coupled device (CCD) array. We present thorough system performance analysis that shows sufficient spectral-spatial resolution to decode sensors with a WDM separation of 75 ρm, signal-to-noise ratio greater than 45-dB bandwidth of 70 nm, and drift of only 0.1 ρm. We show the system to be polarization-state insensitive, making the BFBG-CCD spectral analysis technique a practical, extremely low-cost, alternative to traditional tunable filter approaches.

Psychological traits influence autonomic nervous system recovery following esophageal intubation in health and functional chest pain

Background: Esophageal intubation is a widely utilized technique for a diverse array of physiological studies, activating a complex physiological response mediated, in part, by the autonomic nervous system (ANS). In order to determine the optimal time period after intubation when physiological observations should be recorded, it is important to know the duration of, and factors that influence, this ANS response, in both health and disease. Methods: Fifty healthy subjects (27 males, median age 31.9 years, range 20-53 years) and 20 patients with Rome III defined functional chest pain (nine male, median age of 38.7 years, range 28-59 years) had personality traits and anxiety measured. Subjects had heart rate (HR), blood pressure (BP), sympathetic (cardiac sympathetic index, CSI), and parasympathetic nervous system (cardiac vagal tone, CVT) parameters measured at baseline and in response to per nasum intubation with an esophageal catheter. CSI/CVT recovery was measured following esophageal intubation. Key Results: In all subjects, esophageal intubation caused an elevation in HR, BP, CSI, and skin conductance response (SCR; all p < 0.0001) but concomitant CVT and cardiac sensitivity to the baroreflex (CSB) withdrawal (all p < 0.04). Multiple linear regression analysis demonstrated that longer CVT recovery times were independently associated with higher neuroticism (p < 0.001). Patients had prolonged CSI and CVT recovery times in comparison to healthy subjects (112.5 s vs 46.5 s, p = 0.0001 and 549 s vs 223.5 s, p = 0.0001, respectively). Conclusions & Inferences: Esophageal intubation activates a flight/flight ANS response. Future studies should allow for at least 10 min of recovery time. Consideration should be given to psychological traits and disease status as these can influence recovery. The psychological trait of neuroticism retards autonomic recovery following esophageal intubation in health and functional chest pain. © 2013 John Wiley & Sons Ltd.

A hybrid current controller for linear reciprocating vapor compressors

Direct-drive linear reciprocating compressors offer numerous advantages over conventional counterparts which are usually driven by a rotary induction motor via crank shaft However, to ensure efficient and reliable operation under all conditions, it is essential that the motor current of the linear compressor follows a sinusoidal command profile with a frequency which matches the system resonant frequency. This paper describes a hybrid current controller for the linear compressors. It comprises a conventional proportional-integral (PI) controller, and a B-spline neural network compensator which is trained on-line and in real-time in order to minimize the current tracking error under all conditions with uncertain disturbances. It has been shown that the hybrid current controller has a superior steady-state and transient performance over the conventional carrier based PI controller. The performance of the proposed hybrid controller has been demonstrated by extensive simulations and experiments. It has also been shown that the linear compressor operates stably under the current feedback control and the piston stroke can be adjusted by varying the amplitude of the current command. © 2007 IEEE.

A 10 Mb/s visible light communication system using a low bandwidth polymer light-emitting diode

In this paper we experimentally demonstrate a 10 Mb/s error free visible light communications (VLC) system using polymer light-emitting diodes (PLEDs) for the first time. The PLED under test is a blue emitter with ∼600 kHz bandwidth. Having such a low bandwidth means the introduction of an intersymbol interference (ISI) induced penalty at higher transmission speeds and thus the requirement for an equalizer. In this work we improve on previous literature by implementing a decision feedback equalizer, rather than a linear equalizer. Considering 7% and 20% forward error correction codes, transmission speeds up to ∼12 Mb/s can be supported.

Highly sensitive liquid level monitoring system utilizing polymer fiber Bragg gratings

A novel and highly sensitive liquid level sensor based on a polymer optical fiber Bragg grating (POFBG) is experimentally demonstrated. Two different configurations are studied and both configurations show the potential to interrogate liquid level by measuring the strain induced in a POFBG embedded in a silicone rubber diaphragm, which deforms due to hydrostatic pressure variations. The sensor exhibits a highly linear response over the sensing range and a good repeatability. For comparison, a similar sensor using a FBG inscribed in silica fiber is fabricated, which displays a sensitivity that is a factor of 5 smaller than the POFBG. The temperature sensitivity is studied and a novel multi-sensor arrangement proposed which has the potential to provide level readings independent of temperature and the liquid density.