Modelling context information with ORM

Context-aware applications rely on implicit forms of input, such as sensor-derived data, in order to reduce the need for explicit input from users. They are especially relevant for mobile and pervasive computing environments, in which user attention is at a premium. To support the development of context-aware applications, techniques for modelling context information are required. These must address a unique combination of requirements, including the ability to model information supplied by both sensors and people, to represent imperfect information, and to capture context histories. As the field of context-aware computing is relatively new, mature solutions for context modelling do not exist, and researchers rely on information modelling solutions developed for other purposes. In our research, we have been using a variant of Object-Role Modeling (ORM) to model context. In this paper, we reflect on our experiences and outline some research challenges in this area.

Scalable location management for context-aware systems

Location information is commonly used in context-aware applications and pervasive systems. These applications and systems may require knowledge, of the location of users, devices and services. This paper presents a location management system able to gather, process and manage location information from a variety of physical and virtual location sensors. The system scales to the complexity of context-aware applications, to a variety of types and large number of location sensors and clients, and to geographical size of the system. The proposed location management system provides conflict resolution of location information and mechanisms to ensure privacy.

Designing a Computer Vision System for Underwater Robotic Interventions

This thesis deals with the challenging problem of designing systems able to perceive objects in underwater environments. In the last few decades research activities in robotics have advanced the state of art regarding intervention capabilities of autonomous systems. State of art in fields such as localization and navigation, real time perception and cognition, safe action and manipulation capabilities, applied to ground environments (both indoor and outdoor) has now reached such a readiness level that it allows high level autonomous operations. On the opposite side, the underwater environment remains a very difficult one for autonomous robots. Water influences the mechanical and electrical design of systems, interferes with sensors by limiting their capabilities, heavily impacts on data transmissions, and generally requires systems with low power consumption in order to enable reasonable mission duration. Interest in underwater applications is driven by needs of exploring and intervening in environments in which human capabilities are very limited. Nowadays, most underwater field operations are carried out by manned or remotely operated vehicles, deployed for explorations and limited intervention missions. Manned vehicles, directly on-board controlled, expose human operators to risks related to the stay in field of the mission, within a hostile environment. Remotely Operated Vehicles (ROV) currently represent the most advanced technology for underwater intervention services available on the market. These vehicles can be remotely operated for long time but they need support from an oceanographic vessel with multiple teams of highly specialized pilots. Vehicles equipped with multiple state-of-art sensors and capable to autonomously plan missions have been deployed in the last ten years and exploited as observers for underwater fauna, seabed, ship wrecks, and so on. On the other hand, underwater operations like object recovery and equipment maintenance are still challenging tasks to be conducted without human supervision since they require object perception and localization with much higher accuracy and robustness, to a degree seldom available in Autonomous Underwater Vehicles (AUV). This thesis reports the study, from design to deployment and evaluation, of a general purpose and configurable platform dedicated to stereo-vision perception in underwater environments. Several aspects related to the peculiar environment characteristics have been taken into account during all stages of system design and evaluation: depth of operation and light conditions, together with water turbidity and external weather, heavily impact on perception capabilities. The vision platform proposed in this work is a modular system comprising off-the-shelf components for both the imaging sensors and the computational unit, linked by a high performance ethernet network bus. The adopted design philosophy aims at achieving high flexibility in terms of feasible perception applications, that should not be as limited as in case of a special-purpose and dedicated hardware. Flexibility is required by the variability of underwater environments, with water conditions ranging from clear to turbid, light backscattering varying with daylight and depth, strong color distortion, and other environmental factors. Furthermore, the proposed modular design ensures an easier maintenance and update of the system over time. Performance of the proposed system, in terms of perception capabilities, has been evaluated in several underwater contexts taking advantage of the opportunity offered by the MARIS national project. Design issues like energy power consumption, heat dissipation and network capabilities have been evaluated in different scenarios. Finally, real-world experiments, conducted in multiple and variable underwater contexts, including open sea waters, have led to the collection of several datasets that have been publicly released to the scientific community. The vision system has been integrated in a state of the art AUV equipped with a robotic arm and gripper, and has been exploited in the robot control loop to successfully perform underwater grasping operations.

Robust automatic mapping algorithms in a network monitoring scenario

Automatically generating maps of a measured variable of interest can be problematic. In this work we focus on the monitoring network context where observations are collected and reported by a network of sensors, and are then transformed into interpolated maps for use in decision making. Using traditional geostatistical methods, estimating the covariance structure of data collected in an emergency situation can be difficult. Variogram determination, whether by method-of-moment estimators or by maximum likelihood, is very sensitive to extreme values. Even when a monitoring network is in a routine mode of operation, sensors can sporadically malfunction and report extreme values. If this extreme data destabilises the model, causing the covariance structure of the observed data to be incorrectly estimated, the generated maps will be of little value, and the uncertainty estimates in particular will be misleading. Marchant and Lark [2007] propose a REML estimator for the covariance, which is shown to work on small data sets with a manual selection of the damping parameter in the robust likelihood. We show how this can be extended to allow treatment of large data sets together with an automated approach to all parameter estimation. The projected process kriging framework of Ingram et al. [2007] is extended to allow the use of robust likelihood functions, including the two component Gaussian and the Huber function. We show how our algorithm is further refined to reduce the computational complexity while at the same time minimising any loss of information. To show the benefits of this method, we use data collected from radiation monitoring networks across Europe. We compare our results to those obtained from traditional kriging methodologies and include comparisons with Box-Cox transformations of the data. We discuss the issue of whether to treat or ignore extreme values, making the distinction between the robust methods which ignore outliers and transformation methods which treat them as part of the (transformed) process. Using a case study, based on an extreme radiological events over a large area, we show how radiation data collected from monitoring networks can be analysed automatically and then used to generate reliable maps to inform decision making. We show the limitations of the methods and discuss potential extensions to remedy these.

In-fibre Bragg grating temperature sensor system for medical applications

A novel quasidistributed in-fiber Bragg grating (FBG) temperature sensor system has been developed for temperature proving in vivo in the human body for medical applications, e.g., hyperthermia treatment. This paper provides the operating principle of FBG temperature sensors and then the design of the sensor head. High-resolution detection of the wavelength-shifts induced by temperature changes are achieved using drift-compensated interferometric detection while the return signals from the FBG sensor array are demultiplexed with a simple monochromator which offers crosstalk-free wavelength-division-multiplexing (WDM). A “strain-free” probe is designed by enclosing the FBG sensor array in a protection sleeve. A four FBG sensor system is demonstrated and the experimental results are in good agreement with those obtained by traditional electrical thermocouple sensors. A resolution of 0.1°C and an accuracy of ±0.2°C over a temperature range of 30-60°C have been achieved, which meet established medical requirements.

Research activities arising from the University of Kent

In this paper I describe research activities in the field of optical fiber sensing undertaken by me after leaving the Applied Optics Group at the University of Kent. The main topics covered are long period gratings, neural network based signal processing, plasmonic sensors, and polymer fiber gratings. I also give a summary of my two periods of research at the University of Kent, covering 1985–1988 and 1991–2001.

A smart sensing platform for the discrimination of human movement

This thesis documents the design, implementation and testing of a smart sensing platform that is able to discriminate between differences or small changes in a persons walking. The distributive tactile sensing method is used to monitor the deflection of the platform surface using just a small number of sensors and, through the use of neural networks, infer the characteristics of the object in contact with the surface. The thesis first describes the development of a mathematical model which uses a novel method to track the position of a moving load as it passes over the smart sensing surface. Experimental methods are then described for using the platform to track the position of swinging pendulum in three dimensions. It is demonstrated that the method can be extended to that of real-time measurement of balance and sway of a person during quiet standing. Current classification methods are then investigated for use in the classification of different gait patterns, in particular to identify individuals by their unique gait pattern. Based on these observations, a novel algorithm is developed that is able to discriminate between abnormal and affected gait. This algorithm, using the distributive tactile sensing method, was found to have greater accuracy than other methods investigated and was designed to be able to cope with any type of gait variation. The system developed in this thesis has applications in the area of medical diagnostics, either as an initial screening tool for detecting walking disorders or to be able to automatically detect changes in gait over time. The system could also be used as a discrete biometric identification method, for example identifying office workers as they pass over the surface.

Respiratory function monitoring using a real-time three-dimensional fiber-optic shaping sensing scheme based upon fiber Bragg gratings

An array of in-line curvature sensors on a garment is used to monitor the thoracic and abdominal movements of a human during respiration. The results are used to obtain volumetric changes of the human torso in agreement with a spirometer used simultaneously at the mouth. The array of 40 in-line fiber Bragg gratings is used to produce 20 curvature sensors at different locations, each sensor consisting of two fiber Bragg gratings. The 20 curvature sensors and adjoining fiber are encapsulated into a low-temperature-cured synthetic silicone. The sensors are wavelength interrogated by a commercially available system from Moog Insensys, and the wavelength changes are calibrated to recover curvature. A three-dimensional algorithm is used to generate shape changes during respiration that allow the measurement of absolute volume changes at various sections of the torso. It is shown that the sensing scheme yields a volumetric error of 6%. Comparing the volume data obtained from the spirometer with the volume estimated with the synchronous data from the shape-sensing array yielded a correlation value 0.86 with a Pearson's correlation coefficient p <0.01.

Helicopter vibration sensor selection using data visualisation

The main objective of the project is to enhance the already effective health-monitoring system (HUMS) for helicopters by analysing structural vibrations to recognise different flight conditions directly from sensor information. The goal of this paper is to develop a new method to select those sensors and frequency bands that are best for detecting changes in flight conditions. We projected frequency information to a 2-dimensional space in order to visualise flight-condition transitions using the Generative Topographic Mapping (GTM) and a variant which supports simultaneous feature selection. We created an objective measure of the separation between different flight conditions in the visualisation space by calculating the Kullback-Leibler (KL) divergence between Gaussian mixture models (GMMs) fitted to each class: the higher the KL-divergence, the better the interclass separation. To find the optimal combination of sensors, they were considered in pairs, triples and groups of four sensors. The sensor triples provided the best result in terms of KL-divergence. We also found that the use of a variational training algorithm for the GMMs gave more reliable results.

An area localization scheme for large scale underwater wireless sensor networks

In this paper, we study an area localization problem in large scale Underwater Wireless Sensor Networks (UWSNs). The limited bandwidth, the severely impaired channel and the cost of underwater equipment all makes the underwater localization problem very challenging. Exact localization is very difficult for UWSNs in deep underwater environment. We propose a Mobile DETs based efficient 3D multi-power Area Localization Scheme (3D-MALS) to address the challenging problem. In the proposed scheme, the ideas of 2D multi-power Area Localization Scheme(2D-ALS) [6] and utilizing Detachable Elevator Transceiver (DET) are used to achieve the simplicity, location accuracy, scalability and low cost performances. The DET can rise and down to broadcast its position. And it is assumed that all the underwater nodes underwater have pressure sensors and know their z coordinates. The simulation results show that our proposed scheme is very efficient. © 2009 IEEE.

In-fibre Bragg grating temperature sensor system for medical applications

A novel quasidistributed in-flber Bragg grating (FBG) temperature sensor system has been developed for temperature profiling in vivo in the human body for medical applications, e.g., hyperthermia treatment. This paper provides the operating principle of FBG temperature sensors and then the design of the sensor head. High-resolution detection of the wavelength-shifts induced by temperature changes are achieved using drift-compensated interferometric detection while the return signals from the FBG sensor array are demultiplexed with a simple monochromator which offers crosstalk-free wavelength-division-multiplexing (WDM). A "strain-free" probe is designed by enclosing the FBG sensor array in a protection sleeve. A four FBG sensor system is demonstrated and the experimental results are in good agreement with those obtained by traditional electrical thermocouple sensors. A resolution of 0.1°C and an accuracy of ±0.2°C over a temperature range of 30-60°C have been achieved, which meet established medical requirements.

Respiratory function monitoring using a real-time three-dimensional fiber-optic shaping sensing scheme based upon fiber Bragg gratings

An array of in-line curvature sensors on a garment is used to monitor the thoracic and abdominal movements of a human during respiration. The results are used to obtain volumetric changes of the human torso in agreement with a spirometer used simultaneously at the mouth. The array of 40 in-line fiber Bragg gratings is used to produce 20 curvature sensors at different locations, each sensor consisting of two fiber Bragg gratings. The 20 curvature sensors and adjoining fiber are encapsulated into a low-temperature-cured synthetic silicone. The sensors are wavelength interrogated by a commercially available system from Moog Insensys, and the wavelength changes are calibrated to recover curvature. A three-dimensional algorithm is used to generate shape changes during respiration that allow the measurement of absolute volume changes at various sections of the torso. It is shown that the sensing scheme yields a volumetric error of 6%. Comparing the volume data obtained from the spirometer with the volume estimated with the synchronous data from the shape-sensing array yielded a correlation value 0.86 with a Pearson's correlation coefficient p <0.01.

Usability of prostaglandin monotherapy eye droppers

AIM: To determine the force needed to extract a drop from a range of current prostaglandin monotherapy eye droppers and how this related to the comfortable and maximum pressure subjects could exert. METHODS: The comfortable and maximum pressure subjects could apply to an eye dropper constructed around a set of cantilevered pressure sensors and mounted above their eye was assessed in 102 subjects (mean 51.2±18.7 years), repeated three times. A load cell amplifier, mounted on a stepper motor controlled linear slide, was constructed and calibrated to test the force required to extract the first three drops from 13 multidose or unidose latanoprost medication eye droppers. RESULTS: The pressure that could be exerted on a dropper comfortably (25.9±17.7 Newtons, range 1.2-87.4) could be exceeded with effort (to 64.8±27.1 Newtons, range 19.9-157.8; F=19.045, p<0.001), and did not differ between repeats (F=0.609, p=0.545). Comfortable and maximum pressures exerted were correlated (r=0.618, p<0.001), neither were influenced strongly by age (r=0.138, p=0.168; r=-0.118, p=0237, respectively), but were lower in women than in men (F=12.757, p=0.001). The force required to expel a drop differed between dropper designs (F=22.528, p<0.001), ranging from 6.4 Newtons to 23.4 Newtons. The force needed to exert successive drops increased (F=36.373, p<0.001) and storing droppers in the fridge further increased the force required (F=7.987, p=0.009). CONCLUSIONS: Prostaglandin monotherapy droppers for glaucoma treatment vary in their resistance to extract a drop and with some a drop could not be comfortably achieved by half the population, which may affect compliance and efficacy.

A self-referenced optical intensity sensor network using POFBGs for biomedical applications

This work bridges the gap between the remote interrogation of multiple optical sensors and the advantages of using inherently biocompatible low-cost polymer optical fiber (POF)-based photonic sensing. A novel hybrid sensor network combining both silica fiber Bragg gratings (FBG) and polymer FBGs (POFBG) is analyzed. The topology is compatible with WDM networks so multiple remote sensors can be addressed providing high scalability. A central monitoring unit with virtual data processing is implemented, which could be remotely located up to units of km away. The feasibility of the proposed solution for potential medical environments and biomedical applications is shown.

Functional carbon nanotubes for photonic applications

Carbon nanomaterials are an active frontier of research in current nanotechnology. Single wall Carbon Nanotube (SWNT) is a unique material which has already found several applications in photonics, electronics, sensors and drug delivery. This thesis presents a summary of the author’s research on functionalisation of SWNTs, a study of their optical properties, and potential for an application in laser physics. The first significant result is a breakthrough in controlling the size of SWNT bundles by varying the salt concentrations in N-methyl 2-pyrrolidone (NMP) through a salting out effect. The addition of Sodium iodide leads to self-assembly of CNTs into recognizable bundles. Furthermore, a stable dispersion can be made via addition polyvinylpyrrolidone (PVP) polymer to SWNTs-NMP dispersion, which indicates a promising direction for SWNT bundle engineering in organic solvents. The second set of experiments are concerned with enhancement of photoluminescence (PL), through the formation of novel macromolecular complexes of SWNTs with polymethine dyes with emission from enhanced nanotubes in the range of dye excitation. The effect appears to originate from exciton energy transfer within the solution. Thirdly, SWNT base-saturable absorbers (SA) were developed and applied to mode locking of fibre lasers. SWNT-based SAs were applied in both composite and liquid dispersion forms and achieved stable ultrashort generation at 1000nm, 1550nm, and 1800 nm for Ytterbium, Erbium and Thulium-doped fibre laser respectively. The work presented here demonstrates several innovative approaches for development of rapid functionalised SWNT-based dispersions and composites with potential for application in various photonic devices at low cost.