CamSim:a distributed smart camera network simulator

Smart cameras allow pre-processing of video data on the camera instead of sending it to a remote server for further analysis. Having a network of smart cameras allows various vision tasks to be processed in a distributed fashion. While cameras may have different tasks, we concentrate on distributed tracking in smart camera networks. This application introduces various highly interesting problems. Firstly, how can conflicting goals be satisfied such as cameras in the network try to track objects while also trying to keep communication overhead low? Secondly, how can cameras in the network self adapt in response to the behavior of objects and changes in scenarios, to ensure continued efficient performance? Thirdly, how can cameras organise themselves to improve the overall network's performance and efficiency? This paper presents a simulation environment, called CamSim, allowing distributed self-adaptation and self-organisation algorithms to be tested, without setting up a physical smart camera network. The simulation tool is written in Java and hence allows high portability between different operating systems. Relaxing various problems of computer vision and network communication enables a focus on implementing and testing new self-adaptation and self-organisation algorithms for cameras to use.

Magnification factors for the SOM and GTM algorithms

Magnification factors specify the extent to which the area of a small patch of the latent (or `feature') space of a topographic mapping is magnified on projection to the data space, and are of considerable interest in both neuro-biological and data analysis contexts. Previous attempts to consider magnification factors for the self-organizing map (SOM) algorithm have been hindered because the mapping is only defined at discrete points (given by the reference vectors). In this paper we consider the batch version of SOM, for which a continuous mapping can be defined, as well as the Generative Topographic Mapping (GTM) algorithm of Bishop et al. (1997) which has been introduced as a probabilistic formulation of the SOM. We show how the techniques of differential geometry can be used to determine magnification factors as continuous functions of the latent space coordinates. The results are illustrated here using a problem involving the identification of crab species from morphological data.

Mathematical and numerical modelling of dispersion-managed solitons, autosolitons and self-similar optical pulses

This thesis presents theoretical investigation of three topics concerned with nonlinear optical pulse propagation in optical fibres. The techniques used are mathematical analysis and numerical modelling. Firstly, dispersion-managed (DM) solitons in fibre lines employing a weak dispersion map are analysed by means of a perturbation approach. In the case of small dispersion map strengths the average pulse dynamics is described by a perturbation approach (NLS) equation. Applying a perturbation theory, based on the Inverse Scattering Transform method, an analytic expression for the envelope of the DM soliton is derived. This expression correctly predicts the power enhancement arising from the dispersion management.Secondly, autosoliton transmission in DM fibre systems with periodical in-line deployment of nonlinear optical loop mirrors (NOLMs) is investigated. The use of in-line NOLMs is addressed as a general technique for all-optical passive 2R regeneration of return-to-zero data in high speed transmission system with strong dispersion management. By system optimisation, the feasibility of ultra-long single-channel and wavelength-division multiplexed data transmission at bit-rates ³ 40 Gbit s-1 in standard fibre-based systems is demonstrated. The tolerance limits of the results are defined.Thirdly, solutions of the NLS equation with gain and normal dispersion, that describes optical pulse propagation in an amplifying medium, are examined. A self-similar parabolic solution in the energy-containing core of the pulse is matched through Painlevé functions to the linear low-amplitude tails. The analysis provides a full description of the features of high-power pulses generated in an amplifying medium.

Representation and parameterisation issues in genetic algorithms

A multi-chromosome GA (Multi-GA) was developed, based upon concepts from the natural world, allowing improved flexibility in a number of areas including representation, genetic operators, their parameter rates and real world multi-dimensional applications. A series of experiments were conducted, comparing the performance of the Multi-GA to a traditional GA on a number of recognised and increasingly complex test optimisation surfaces, with promising results. Further experiments demonstrated the Multi-GA's flexibility through the use of non-binary chromosome representations and its applicability to dynamic parameterisation. A number of alternative and new methods of dynamic parameterisation were investigated, in addition to a new non-binary 'Quotient crossover' mechanism. Finally, the Multi-GA was applied to two real world problems, demonstrating its ability to handle mixed type chromosomes within an individual, the limited use of a chromosome level fitness function, the introduction of new genetic operators for structural self-adaptation and its viability as a serious real world analysis tool. The first problem involved optimum placement of computers within a building, allowing the Multi-GA to use multiple chromosomes with different type representations and different operators in a single individual. The second problem, commonly associated with Geographical Information Systems (GIS), required a spatial analysis location of the optimum number and distribution of retail sites over two different population grids. In applying the Multi-GA, two new genetic operators (addition and deletion) were developed and explored, resulting in the definition of a mechanism for self-modification of genetic material within the Multi-GA structure and a study of this behaviour.

Queer enough? Contested terrains of identity deployment in the context of gay and lesbian public activism in Poland

Gay and lesbian prides and marches are of crucial relevance to the way in which non-heterosexual lives are imagined internationally despite regional and national differences. Quite often, these events are connected not only with increased activist mobilisation, but also with great controversy, which is the case of Poland, where gay and lesbian marches have been attacked by right-wing protesters and cancelled by right-wing city authorities on a number of occasions. Overall, the scholars analysing these events have largely focused on the macro-context of the marches, paying less attention to the movement actors behind these events. The contribution of this thesis lies not only in filling a gap when it comes to research on sexual minorities in Eastern Europe/Poland, but also in its focus on micro-level movement processes and engagement with theories of collective identity and citizenship. Furthermore, this thesis challenges the inscription of Eastern European/Polish movements into the narrative of victimhood and delayed development when compared to LGBT movements in the Global North. This thesis is grounded in qualitative research including participant observation of public activist events as well as forty semi-structured interviews with the key organisers of gay and lesbian marches in Warsaw, Poznan and Krakow between 2001 and 2007, and five of these interviews were further accompanied by photo-elicitation (self-directed photography) methods. Starting from the processes whereby from 2001 onwards, marches, pride parades and demonstrations became the most visible and contested activity of the Polish lesbian and gay movement, this thesis examines how the activists redefined the meanings of citizenship in the post-transformation context, by incorporating the theme of sexual minorities' rights. Using Bernstein's (1997, 2002, 2005, 2008) concept of identity deployment, I show how and when movement actors use identity tactically, depending on their goals. Specifically, in the context of movement-media interactions, I examine the ways in which the activists use marches to challenge the negative representations of sexual minorities in Poland. I also broaden Bernstein's framework to include the discussion of emotion work as relevant to public LGBT activism in Poland. Later, I discuss how the emotions of protests allowed the activists to inscribe their efforts into the "revolutionary" narrative of the Polish Solidarity movement and by extension, the frame of citizenship. Finally, this thesis engages with the dilemmas of identity deployment strategies, and seeks to problematise the dichotomy between identity-based gay and lesbian assimilationist strategies and the anti-identity queer politics.

Development and deployment of an autonomous micro-drilling system for cochleostomy

This thesis describes the design and development of an autonomous micro-drilling system capable of accurately controlling the penetration of complaint tissues and its application to the drilling of the cochleostomy; a key stage in the cochlea implant procedure. The drilling of the cochleostomy is a precision micro-surgical task in which the control of the burr penetration through the outer bone tissue of the cochlea is vital to prevent damage to the structures within and requires a high degree of skill to perform successfully. The micro-drilling system demonstrates that the penetration of the cochlea can be achieved consistently and accurately. Breakthrough can be detected and controlled to within 20µm of the distal surface and the hole completed without perforation of the underlying endosteal membrane, leaving the membranous cochlea intact. This device is the first autonomous surgical tool successfully deployed in the operating theatre. The system is unique due to the way in which it uses real-time data from the cutting tool to derive the state of the tool-tissue interaction. Being a smart tool it uses this state information to actively control the way in which the drilling process progresses. This sensor guided strategy enables the tool to self-reference to the deforming tissue and navigate without the need for pre-operative scan data. It is this capability that enables the system to operate in circumstances where the tissue properties and boundary conditions are unknown, without the need to restrain the patient.

Stabilization of self-coherent OFDM with injection locked laser

With the rebirth of coherent detection, various algorithms have come forth to alleviate phase noise, one of the main impairments for coherent receivers. These algorithms provide stable compensation, however they limit the DSP. With this key issue in mind, Fabry Perot filter based self coherent optical OFDM was analyzed which does not require phase noise compensation reducing the complexity in DSP at low OSNR. However, the performance of such a receiver is limited due to ASE noise at the carrier wavelength, especially since an optical amplifier is typically employed with the filter to ensure sufficient carrier power. Subsequently, the use of an injection-locked laser (ILL) to retrieve the frequency and phase information from the extracted carrier without the use of an amplifier was recently proposed. In ILL based system, an optical carrier is sent along with the OFDM signal in the transmitter. At the receiver, the carrier is extracted from the OFDM signal using a Fabry-Perot tunable filter and an ILL is used to significantly amplify the carrier and reduce intensity and phase noise. In contrast to CO-OFDM, such a system supports low-cost broad linewidth lasers and benefits with lower complexity in the DSP as no carrier frequency estimation and correction along with phase noise compensation is required.

Exploring ant-based algorithms for gene expression data analysis

Objective: Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. Methods and material: An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Results: Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource Database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Conclusions: Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules.

Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity

The standard reference clinical score quantifying average Parkinson's disease (PD) symptom severity is the Unified Parkinson's Disease Rating Scale (UPDRS). At present, UPDRS is determined by the subjective clinical evaluation of the patient's ability to adequately cope with a range of tasks. In this study, we extend recent findings that UPDRS can be objectively assessed to clinically useful accuracy using simple, self-administered speech tests, without requiring the patient's physical presence in the clinic. We apply a wide range of known speech signal processing algorithms to a large database (approx. 6000 recordings from 42 PD patients, recruited to a six-month, multi-centre trial) and propose a number of novel, nonlinear signal processing algorithms which reveal pathological characteristics in PD more accurately than existing approaches. Robust feature selection algorithms select the optimal subset of these algorithms, which is fed into non-parametric regression and classification algorithms, mapping the signal processing algorithm outputs to UPDRS. We demonstrate rapid, accurate replication of the UPDRS assessment with clinically useful accuracy (about 2 UPDRS points difference from the clinicians' estimates, p < 0.001). This study supports the viability of frequent, remote, cost-effective, objective, accurate UPDRS telemonitoring based on self-administered speech tests. This technology could facilitate large-scale clinical trials into novel PD treatments.

Supporting decision-making for self-adaptive systems:from goal models to dynamic decision networks

Context/Motivation - Different modeling techniques have been used to model requirements and decision-making of self-adaptive systems (SASs). Specifically, goal models have been prolific in supporting decision-making depending on partial and total fulfilment of functional (goals) and non-functional requirements (softgoals). Different goalrealization strategies can have different effects on softgoals which are specified with weighted contribution-links. The final decision about what strategy to use is based, among other reasons, on a utility function that takes into account the weighted sum of the different effects on softgoals. Questions/Problems - One of the main challenges about decisionmaking in self-adaptive systems is to deal with uncertainty during runtime. New techniques are needed to systematically revise the current model when empirical evidence becomes available from the deployment. Principal ideas/results - In this paper we enrich the decision-making supported by goal models by using Dynamic Decision Networks (DDNs). Goal realization strategies and their impact on softgoals have a correspondence with decision alternatives and conditional probabilities and expected utilities in the DDNs respectively. Our novel approach allows the specification of preferences over the softgoals and supports reasoning about partial satisfaction of softgoals using probabilities. We report results of the application of the approach on two different cases. Our early results suggest the decision-making process of SASs can be improved by using DDNs. © 2013 Springer-Verlag.

A modelling and simulation environment for self-aware and self-expressive systems

Self-awareness and self-expression are promising architectural concepts for embedded systems to be equipped with to match them with dedicated application scenarios and constraints in the avionic and space-flight industry. Typically, these systems operate in largely undefined environments and are not reachable after deployment for a long time or even never ever again. This paper introduces a reference architecture as well as a novel modelling and simulation environment for self-aware and self-expressive systems with transaction level modelling, simulation and detailed modelling capabilities for hardware aspects, precise process chronology execution as well as fine timing resolutions. Furthermore, industrial relevant system sizes with several self-aware and self-expressive nodes can be handled by the modelling and simulation environment.

Stabilization of self-coherent OFDM with injection locked laser

With the rebirth of coherent detection, various algorithms have come forth to alleviate phase noise, one of the main impairments for coherent receivers. These algorithms provide stable compensation, however they limit the DSP. With this key issue in mind, Fabry Perot filter based self coherent optical OFDM was analyzed which does not require phase noise compensation reducing the complexity in DSP at low OSNR. However, the performance of such a receiver is limited due to ASE noise at the carrier wavelength, especially since an optical amplifier is typically employed with the filter to ensure sufficient carrier power. Subsequently, the use of an injection-locked laser (ILL) to retrieve the frequency and phase information from the extracted carrier without the use of an amplifier was recently proposed. In ILL based system, an optical carrier is sent along with the OFDM signal in the transmitter. At the receiver, the carrier is extracted from the OFDM signal using a Fabry-Perot tunable filter and an ILL is used to significantly amplify the carrier and reduce intensity and phase noise. In contrast to CO-OFDM, such a system supports low-cost broad linewidth lasers and benefits with lower complexity in the DSP as no carrier frequency estimation and correction along with phase noise compensation is required.

Self-aware and self-expressive camera networks

Smart cameras perform on-board image analysis, adapt their algorithms to changes in their environment, and collaborate with other networked cameras to analyze the dynamic behavior of objects. A proposed computational framework adopts the concepts of self-awareness and self-expression to more efficiently manage the complex tradeoffs among performance, flexibility, resources, and reliability. The Web extra at http://youtu.be/NKe31-OKLz4 is a video demonstrating CamSim, a smart camera simulation tool, enables users to test self-adaptive and self-organizing smart-camera techniques without deploying a smart-camera network.

Self-adapting parallel metric-space search engine for variable query loads

This research focuses on automatically adapting a search engine size in response to fluctuations in query workload. Deploying a search engine in an Infrastructure as a Service (IaaS) cloud facilitates allocating or deallocating computer resources to or from the engine. Our solution is to contribute an adaptive search engine that will repeatedly re-evaluate its load and, when appropriate, switch over to a dierent number of active processors. We focus on three aspects and break them out into three sub-problems as follows: Continually determining the Number of Processors (CNP), New Grouping Problem (NGP) and Regrouping Order Problem (ROP). CNP means that (in the light of the changes in the query workload in the search engine) there is a problem of determining the ideal number of processors p active at any given time to use in the search engine and we call this problem CNP. NGP happens when changes in the number of processors are determined and it must also be determined which groups of search data will be distributed across the processors. ROP is how to redistribute this data onto processors while keeping the engine responsive and while also minimising the switchover time and the incurred network load. We propose solutions for these sub-problems. For NGP we propose an algorithm for incrementally adjusting the index to t the varying number of virtual machines. For ROP we present an ecient method for redistributing data among processors while keeping the search engine responsive. Regarding the solution for CNP, we propose an algorithm determining the new size of the search engine by re-evaluating its load. We tested the solution performance using a custom-build prototype search engine deployed in the Amazon EC2 cloud. Our experiments show that when we compare our NGP solution with computing the index from scratch, the incremental algorithm speeds up the index computation 2{10 times while maintaining a similar search performance. The chosen redistribution method is 25% to 50% faster than other methods and reduces the network load around by 30%. For CNP we present a deterministic algorithm that shows a good ability to determine a new size of search engine. When combined, these algorithms give an adapting algorithm that is able to adjust the search engine size with a variable workload.