All optical networking beyond 10 Gbits/s OTDM networks based on electro-optic modulators and fibre ring lasers

This thesis examines options for high capacity all optical networks. Specifically optical time division multiplexed (OTDM) networks based on electro-optic modulators are investigated experimentally, whilst comparisons with alternative approaches are carried out. It is intended that the thesis will form the basis of comparison between optical time division multiplexed networks and the more mature approach of wavelength division multiplexed networks. Following an introduction to optical networking concepts, the required component technologies are discussed. In particular various optical pulse sources are described with the demanding restrictions of optical multiplexing in mind. This is followed by a discussion of the construction of multiplexers and demultiplexers, including favoured techniques for high speed clock recovery. Theoretical treatments of the performance of Mach Zehnder and electroabsorption modulators support the design criteria that are established for the construction of simple optical time division multiplexed systems. Having established appropriate end terminals for an optical network, the thesis examines transmission issues associated with high speed RZ data signals. Propagation of RZ signals over both installed (standard fibre) and newly commissioned fibre routes are considered in turn. In the case of standard fibre systems, the use of dispersion compensation is summarised, and the application of mid span spectral inversion experimentally investigated. For green field sites, soliton like propagation of high speed data signals is demonstrated. In this case the particular restrictions of high speed soliton systems are discussed and experimentally investigated, namely the increasing impact of timing jitter and the downward pressure on repeater spacings due to the constraint of the average soliton model. These issues are each addressed through investigations of active soliton control for OTDM systems and through investigations of novel fibre types respectively. Finally the particularly remarkable networking potential of optical time division multiplexed systems is established, and infinite node cascadability using soliton control is demonstrated. A final comparison of the various technologies for optical multiplexing is presented in the conclusions, where the relative merits of the technologies for optical networking emerges as the key differentiator between technologies.

Digital video transmission over wireless networks

The advent of the Integrated Services Digital Network (ISDN) led to the standardisation of the first video codecs for interpersonal video communications, followed closely by the development of standards for the compression, storage and distribution of digital video in the PC environment, mainly targeted at CD-ROM storage. At the same time the second-generation digital wireless networks, and the third-generation networks being developed, have enough bandwidth to support digital video services. The radio propagation medium is a difficult environment in which to deploy low bit error rate, real time services such as video. The video coding standards designed for ISDN and storage applications, were targeted at low bit error rate levels, orders of magnitude lower than the typical bit error rates experienced on wireless networks. This thesis is concerned with the transmission of digital, compressed video over wireless networks. It investigates the behaviour of motion compensated, hybrid interframe DPCM/DCT video coding algorithms, which form the basis of current coding algorithms, in the presence of high bit error rates commonly found on digital wireless networks. A group of video codecs, based on the ITU-T H.261 standard, are developed which are robust to the burst errors experienced on radio channels. The radio link is simulated at low level, to generate typical error files that closely model real world situations, in a Rayleigh fading environment perturbed by co-channel interference, and on frequency selective channels which introduce inter symbol interference. Typical anti-multipath techniques, such as antenna diversity, are deployed to mitigate the effects of the channel. Link layer error control techniques are also investigated.

Strategic innovation networks

This thesis draws on two key areas of the innovation literature, the strategic management of technology (SMOT) and innovation networks. The aim is to integrate these two areas of the management of innovation literature to develop a framework which I describe as the Strategic Innovation Network (SIN). The key proposition that the revised framework (SIN) aims to address is based on the work of Chandler (1962). Chandler's (1962) conclusion that 'structure follows strategy' is examined in relation to the interaction between corporate/technology strategy and network structure. The SIN is intended to address weaknesses in both the SMOT and network literature. The research data is based on five detailed longitudinal case studies. The organisations are defined as mid-corporate firms operating in traditional manufacturing sectors. Each organisation was chosen on the basis that it was aiming to develop its innovative capacity through product or process innovation projects. The research was carried out over an 18 month period with interviews being held regularly to develop the longitudinal aspect of the study analysis. The data for each individual case study is examined using the SIN framework. The longitudinal approach addresses the objective to provide a dynamic model of the innovation processes by mapping the changes in network structure during the course of individual projects. The network structural changes are examined in relation to each organisation's strategy and five key dynamic network stages are identified in relation to the innovation process. These network stages show the influence strategy has on the structures adopted by the five case studies.

The application of artificial neural networks to the interpretation and classification of freshwater benthic invertebrate communities

This thesis presents a thorough and principled investigation into the application of artificial neural networks to the biological monitoring of freshwater. It contains original ideas on the classification and interpretation of benthic macroinvertebrates, and aims to demonstrate their superiority over the biotic systems currently used in the UK to report river water quality. The conceptual basis of a new biological classification system is described, and a full review and analysis of a number of river data sets is presented. The biological classification is compared to the common biotic systems using data from the Upper Trent catchment. This data contained 292 expertly classified invertebrate samples identified to mixed taxonomic levels. The neural network experimental work concentrates on the classification of the invertebrate samples into biological class, where only a subset of the sample is used to form the classification. Other experimentation is conducted into the identification of novel input samples, the classification of samples from different biotopes and the use of prior information in the neural network models. The biological classification is shown to provide an intuitive interpretation of a graphical representation, generated without reference to the class labels, of the Upper Trent data. The selection of key indicator taxa is considered using three different approaches; one novel, one from information theory and one from classical statistical methods. Good indicators of quality class based on these analyses are found to be in good agreement with those chosen by a domain expert. The change in information associated with different levels of identification and enumeration of taxa is quantified. The feasibility of using neural network classifiers and predictors to develop numeric criteria for the biological assessment of sediment contamination in the Great Lakes is also investigated.

Finding structures in information networks using the affinity network

This thesis proposes a novel graphical model for inference called the Affinity Network,which displays the closeness between pairs of variables and is an alternative to Bayesian Networks and Dependency Networks. The Affinity Network shares some similarities with Bayesian Networks and Dependency Networks but avoids their heuristic and stochastic graph construction algorithms by using a message passing scheme. A comparison with the above two instances of graphical models is given for sparse discrete and continuous medical data and data taken from the UCI machine learning repository. The experimental study reveals that the Affinity Network graphs tend to be more accurate on the basis of an exhaustive search with the small datasets. Moreover, the graph construction algorithm is faster than the other two methods with huge datasets. The Affinity Network is also applied to data produced by a synchronised system. A detailed analysis and numerical investigation into this dynamical system is provided and it is shown that the Affinity Network can be used to characterise its emergent behaviour even in the presence of noise.

A network coding based interference cancelation scheme for wireless ad hoc networks

The performance of wireless networks is limited by multiple access interference (MAI) in the traditional communication approach where the interfered signals of the concurrent transmissions are treated as noise. In this paper, we treat the interfered signals from a new perspective on the basis of additive electromagnetic (EM) waves and propose a network coding based interference cancelation (NCIC) scheme. In the proposed scheme, adjacent nodes can transmit simultaneously with careful scheduling; therefore, network performance will not be limited by the MAI. Additionally we design a space segmentation method for general wireless ad hoc networks, which organizes network into clusters with regular shapes (e.g., square and hexagon) to reduce the number of relay nodes. The segmentation methodworks with the scheduling scheme and can help achieve better scalability and reduced complexity. We derive accurate analytic models for the probability of connectivity between two adjacent cluster heads which is important for successful information relay. We proved that with the proposed NCIC scheme, the transmission efficiency can be improved by at least 50% for general wireless networks as compared to the traditional interference avoidance schemes. Numeric results also show the space segmentation is feasible and effective. Finally we propose and discuss a method to implement the NCIC scheme in a practical orthogonal frequency division multiplexing (OFDM) communications networks. Copyright © 2009 John Wiley & Sons, Ltd.

Making motes intelligent:an agent-based approach to wireless sensor networks

In-Motes is a mobile agent middleware that generates an intelligent framework for deploying applications in Wireless Sensor Networks (WSNs). In-Motes is based on the injection of mobile agents into the network that can migrate or clone following specific rules and performing application specific tasks. By doing so, each mote is given a certain degree of perception, cognition and control, forming the basis for its intelligence. Our middleware incorporates technologies such as Linda-like tuplespaces and federated system architecture in order to obtain a high degree of collaboration and coordination for the agent society. A set of behavioral rules inspired by a community of bacterial strains is also generated as the means for robustness of the WSN. In this paper, we present In-Motes and provide a detailed evaluation of its implementation for MICA2 motes.

Learning and change in interorganizational networks:the case for network learning and network change

The ALBA 2002 Call for Papers asks the question ‘How do organizational learning and knowledge management contribute to organizational innovation and change?’. Intuitively, we would argue, the answer should be relatively straightforward as links between learning and change, and knowledge management and innovation, have long been commonly assumed to exist. On the basis of this assumption, theories of learning tend to focus ‘within organizations’, and assume a transfer of learning from individual to organization which in turn leads to change. However, empirically, we find these links are more difficult to articulate. Organizations exist in complex embedded economic, political, social and institutional systems, hence organizational change (or innovation) may be influenced by learning in this wider context. Based on our research in this wider interorganizational setting, we first make the case for the notion of network learning that we then explore to develop our appreciation of change in interorganizational networks, and how it may be facilitated. The paper begins with a brief review of lite rature on learning in the organizational and interorganizational context which locates our stance on organizational learning versus the learning organization, and social, distributed versus technical, centred views of organizational learning and knowledge. Developing from the view that organizational learning is “a normal, if problematic, process in every organization” (Easterby-Smith, 1997: 1109), we introduce the notion of network learning: learning by a group of organizations as a group. We argue this is also a normal, if problematic, process in organizational relationships (as distinct from interorganizational learning), which has particular implications for network change. Part two of the paper develops our analysis, drawing on empirical data from two studies of learning. The first study addresses the issue of learning to collaborate between industrial customers and suppliers, leading to the case for network learning. The second, larger scale study goes on to develop this theme, examining learning around several major change issues in a healthcare service provider network. The learning processes and outcomes around the introduction of a particularly controversial and expensive technology are described, providing a rich and contrasting case with the first study. In part three, we then discuss the implications of this work for change, and for facilitating change. Conclusions from the first study identify potential interventions designed to facilitate individual and organizational learning within the customer organization to develop individual and organizational ‘capacity to collaborate’. Translated to the network example, we observe that network change entails learning at all levels – network, organization, group and individual. However, presenting findings in terms of interventions is less meaningful in an interorganizational network setting given: the differences in authority structures; the less formalised nature of the network setting; and the importance of evaluating performance at the network rather than organizational level. Academics challenge both the idea of managing change and of managing networks. Nevertheless practitioners are faced with the issue of understanding and in fluencing change in the network setting. Thus we conclude that a network learning perspective is an important development in our understanding of organizational learning, capability and change, locating this in the wider context in which organizations are embedded. This in turn helps to develop our appreciation of facilitating change in interorganizational networks, both in terms of change issues (such as introducing a new technology), and change orientation and capability.

High efficiency SiC AC chopper for LV networks

A proposal to increase the existing residential LV grid voltage from 230 V has been made in order to increase existing network capacity. A power-electronic AC-AC converter is then used to provide 230 V at each property. Several constraints such as temperature rise at the converter location lead to a converter design requiring very high efficiency. In this paper results from a recent feasibility study in terms of LV network capacity increase are presented along with the design and testing of a SiC based 1 kW, AC/AC prototype module, which forms the basis of a much larger 15 kW multi-module converter.

Classification of Data to Extract Knowledge from Neural Networks

A major drawback of artificial neural networks is their black-box character. Therefore, the rule extraction algorithm is becoming more and more important in explaining the extracted rules from the neural networks. In this paper, we use a method that can be used for symbolic knowledge extraction from neural networks, once they have been trained with desired function. The basis of this method is the weights of the neural network trained. This method allows knowledge extraction from neural networks with continuous inputs and output as well as rule extraction. An example of the application is showed. This example is based on the extraction of average load demand of a power plant.

Artificial neural networks for surface roughness prediction when face milling Al 7075-T7351

In this work, different artificial neural networks (ANN) are developed for the prediction of surface roughness (R a) values in Al alloy 7075-T7351 after face milling machining process. The radial base (RBNN), feed forward (FFNN), and generalized regression (GRNN) networks were selected, and the data used for training these networks were derived from experiments conducted using a high-speed milling machine. The Taguchi design of experiment was applied to reduce the time and cost of the experiments. From this study, the performance of each ANN used in this research was measured with the mean square error percentage and it was observed that FFNN achieved the best results. Also the Pearson correlation coefficient was calculated to analyze the correlation between the five inputs (cutting speed, feed per tooth, axial depth of cut, chip°s width, and chip°s thickness) selected for the network with the selected output (surface roughness). Results showed a strong correlation between the chip thickness and the surface roughness followed by the cutting speed. © ASM International.

Identifying spatially overlapping local cortical networks with MEG

Recent modelling studies (Hadjipapas et al. [2009]: Neuroimage 44:1290-1303) have shown that it may be possible to distinguish between different neuronal populations on the basis of their macroscopically measured (EEG/MEG) mean field. We set out to test whether the different orientation columns contributing to a signal at a specific cortical location could be identified based on the measured MEG signal. We used 1.5deg square, static, obliquely oriented grating stimuli to generate sustained gamma oscillations in a focal region of primary visual cortex. We then used multivariate classifier methods to predict the orientation (left or right oblique) of the stimuli based purely on the time-series data from this one location. Both the single trial evoked response (0-300 ms) and induced post-transient power spectra (300-2,300 ms, 20-70 Hz band) due to the different stimuli were classifiable significantly above chance in 11/12 and 10/12 datasets respectively. Interestingly, stimulus-specific information is preserved in the sustained part of the gamma oscillation, long after perception has occurred and all neuronal transients have decayed. Importantly, the classification of this induced oscillation was still possible even when the power spectra were rank-transformed showing that the different underlying networks give rise to different characteristic temporal signatures. © 2009 Wiley-Liss, Inc.

Trust and cooperation in buyer–seller relationships and networks. The co-evolution of structural balance and trust in iterated PD games

Our study has two aims: to elaborate theoretical frameworks and introduce social mechanisms of spontaneous co-operation in repeated buyer-seller relationships and to formulate hypotheses which can be empirically tested. The basis of our chain of ideas is the simple two-person Prisoner’s Dilemma game. On the one hand, its repeated variation can be applicable for the distinction of the analytical types of trust (iteration trust, strategy trust) in co-operations. On the other hand, it provides a chance to reveal those dyadic sympathy-antipathy relations, which make us understand the evolution of trust. Then we introduce the analysis of the more complicated (more than two-person) buyer-seller relationship. Firstly, we outline the possible role of the structural balancing mechanisms in forming trust in three-person buyer-seller relationships. Secondly, we put forward hypotheses to explain complex buyer-seller networks. In our research project we try to theoretically combine some of the simple concepts of game theory with certain ideas of the social-structural balance theory. Finally, it is followed by a short summary.

Application of support vector machines on the basis of the first Hungarian bankruptcy model

In our study we rely on a data mining procedure known as support vector machine (SVM) on the database of the first Hungarian bankruptcy model. The models constructed are then contrasted with the results of earlier bankruptcy models with the use of classification accuracy and the area under the ROC curve. In using the SVM technique, in addition to conventional kernel functions, we also examine the possibilities of applying the ANOVA kernel function and take a detailed look at data preparation tasks recommended in using the SVM method (handling of outliers). The results of the models assembled suggest that a significant improvement of classification accuracy can be achieved on the database of the first Hungarian bankruptcy model when using the SVM method as opposed to neural networks.

Pavement performance prediction using adaptive logic networks

Pavement performance is one of the most important components of the pavement management system. Prediction of the future performance of a pavement section is important in programming maintenance and rehabilitation needs. Models for predicting pavement performance have been developed on the basis of traffic and age. The purpose of this research is to extend the use of a relatively new approach to performance prediction in pavement performance modeling using adaptive logic networks (ALN). Adaptive logic networks have recently emerged as an effective alternative to artificial neural networks for machine learning tasks. ^ The ALN predictive methodology is applicable to a wide variety of contexts including prediction of roughness based indices, composite rating indices and/or individual pavement distresses. The ALN program requires key information about a pavement section, including the current distress indexes, pavement age, climate region, traffic and other variables to predict yearly performance values into the future. ^ This research investigates the effect of different learning rates of the ALN in pavement performance modeling. It can be used at both the network and project level for predicting the long term performance of a road network. Results indicate that the ALN approach is well suited for pavement performance prediction modeling and shows a significant improvement over the results obtained from other artificial intelligence approaches. ^