Effectiveness of landmark analysis for establishing locality in p2p networks

Locality to other nodes on a peer-to-peer overlay network can be established by means of a set of landmarks shared among the participating nodes. Each node independently collects a set of latency measures to landmark nodes, which are used as a multi-dimensional feature vector. Each peer node uses the feature vector to generate a unique scalar index which is correlated to its topological locality. A popular dimensionality reduction technique is the space filling Hilbert’s curve, as it possesses good locality preserving properties. However, there exists little comparison between Hilbert’s curve and other techniques for dimensionality reduction. This work carries out a quantitative analysis of their properties. Linear and non-linear techniques for scaling the landmark vectors to a single dimension are investigated. Hilbert’s curve, Sammon’s mapping and Principal Component Analysis have been used to generate a 1d space with locality preserving properties. This work provides empirical evidence to support the use of Hilbert’s curve in the context of locality preservation when generating peer identifiers by means of landmark vector analysis. A comparative analysis is carried out with an artificial 2d network model and with a realistic network topology model with a typical power-law distribution of node connectivity in the Internet. Nearest neighbour analysis confirms Hilbert’s curve to be very effective in both artificial and realistic network topologies. Nevertheless, the results in the realistic network model show that there is scope for improvements and better techniques to preserve locality information are required.

Nature protection in Greece: an appraisal of the factors shaping integrative conservation and policy effectiveness

The proliferation of designated areas following the implementation of Natura 2000 in Greece has initiated changes in the protected area design and conservation policy making aiming at delivering action for biodiversity and integrative planning on a wider landscape. Following the sustainability concept, an integrative approach cannot realistically take place simply by extending the protected area and designations. The paper addresses public involvement and inter-sectoral coordination as major procedural elements of integrative management and evaluates the nature and strength of their negative or positive influences on the fulfillment of an integrative vision of nature conservation. A review of the history of protected areas and administration developments in Greece provide useful input in the research. The analysis has shown that the selected network of Natura 2000 sites has been superimposed upon the existing system and resulted in duplication of administrative effort and related legislation. As a result the overall picture of protected areas in the country appears complex, confusing and fragmented. Major failures to integrated conservation perspective can be traced to structural causes rooted in politico-economic power structures of mainstream policy and in a rather limited political commitment to conservation. It is concluded that greater realisation. of integrated conservation in Greece necessitates policy reforms related mainly to sectoral legal frameworks to promote environmentalism as well as an increased effort by the managing authorities to facilitate a broader framework of public dialogue and give local communities incentives to sustainably benefit from protected areas. (C) 2006 Elsevier Ltd. All rights reserved.

A neurofuzzy network knowledge extraction and extended gram-Schmidt algorithm for model subspace decomposition

This paper introduces a new neurofuzzy model construction and parameter estimation algorithm from observed finite data sets, based on a Takagi and Sugeno (T-S) inference mechanism and a new extended Gram-Schmidt orthogonal decomposition algorithm, for the modeling of a priori unknown dynamical systems in the form of a set of fuzzy rules. The first contribution of the paper is the introduction of a one to one mapping between a fuzzy rule-base and a model matrix feature subspace using the T-S inference mechanism. This link enables the numerical properties associated with a rule-based matrix subspace, the relationships amongst these matrix subspaces, and the correlation between the output vector and a rule-base matrix subspace, to be investigated and extracted as rule-based knowledge to enhance model transparency. The matrix subspace spanned by a fuzzy rule is initially derived as the input regression matrix multiplied by a weighting matrix that consists of the corresponding fuzzy membership functions over the training data set. Model transparency is explored by the derivation of an equivalence between an A-optimality experimental design criterion of the weighting matrix and the average model output sensitivity to the fuzzy rule, so that rule-bases can be effectively measured by their identifiability via the A-optimality experimental design criterion. The A-optimality experimental design criterion of the weighting matrices of fuzzy rules is used to construct an initial model rule-base. An extended Gram-Schmidt algorithm is then developed to estimate the parameter vector for each rule. This new algorithm decomposes the model rule-bases via an orthogonal subspace decomposition approach, so as to enhance model transparency with the capability of interpreting the derived rule-base energy level. This new approach is computationally simpler than the conventional Gram-Schmidt algorithm for resolving high dimensional regression problems, whereby it is computationally desirable to decompose complex models into a few submodels rather than a single model with large number of input variables and the associated curse of dimensionality problem. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

A multi-level probabilistic neural network

Based on the idea of an important cluster, a new multi-level probabilistic neural network (MLPNN) is introduced. The MLPNN uses an incremental constructive approach, i.e. it grows level by level. The construction algorithm of the MLPNN is proposed such that the classification accuracy monotonically increases to ensure that the classification accuracy of the MLPNN is higher than or equal to that of the traditional PNN. Numerical examples are included to demonstrate the effectiveness of proposed new approach.

Neural network enhanced generalised minimum variance self-tuning controller for nonlinear discrete-time systems

A neural network enhanced self-tuning controller is presented, which combines the attributes of neural network mapping with a generalised minimum variance self-tuning control (STC) strategy. In this way the controller can deal with nonlinear plants, which exhibit features such as uncertainties, nonminimum phase behaviour, coupling effects and may have unmodelled dynamics, and whose nonlinearities are assumed to be globally bounded. The unknown nonlinear plants to be controlled are approximated by an equivalent model composed of a simple linear submodel plus a nonlinear submodel. A generalised recursive least squares algorithm is used to identify the linear submodel and a layered neural network is used to detect the unknown nonlinear submodel in which the weights are updated based on the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model therefore the nonlinear submodel is naturally accommodated within the control law. Two simulation studies are provided to demonstrate the effectiveness of the control algorithm.

A neural network enhanced generalized minimum variance self-tuning proportional, integral and derivative control algorithm for complex dynamic systems

A neural network enhanced proportional, integral and derivative (PID) controller is presented that combines the attributes of neural network learning with a generalized minimum-variance self-tuning control (STC) strategy. The neuro PID controller is structured with plant model identification and PID parameter tuning. The plants to be controlled are approximated by an equivalent model composed of a simple linear submodel to approximate plant dynamics around operating points, plus an error agent to accommodate the errors induced by linear submodel inaccuracy due to non-linearities and other complexities. A generalized recursive least-squares algorithm is used to identify the linear submodel, and a layered neural network is used to detect the error agent in which the weights are updated on the basis of the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model, and therefore the error agent is naturally functioned within the control law. In this way the controller can deal not only with a wide range of linear dynamic plants but also with those complex plants characterized by severe non-linearity, uncertainties and non-minimum phase behaviours. Two simulation studies are provided to demonstrate the effectiveness of the controller design procedure.

A mixture of experts network structure construction algorithm for modelling and control

This paper introduces a new fast, effective and practical model structure construction algorithm for a mixture of experts network system utilising only process data. The algorithm is based on a novel forward constrained regression procedure. Given a full set of the experts as potential model bases, the structure construction algorithm, formed on the forward constrained regression procedure, selects the most significant model base one by one so as to minimise the overall system approximation error at each iteration, while the gate parameters in the mixture of experts network system are accordingly adjusted so as to satisfy the convex constraints required in the derivation of the forward constrained regression procedure. The procedure continues until a proper system model is constructed that utilises some or all of the experts. A pruning algorithm of the consequent mixture of experts network system is also derived to generate an overall parsimonious construction algorithm. Numerical examples are provided to demonstrate the effectiveness of the new algorithms. The mixture of experts network framework can be applied to a wide variety of applications ranging from multiple model controller synthesis to multi-sensor data fusion.

Neurofuzzy mixture of experts network parallel learning and model construction algorithms

A connection between a fuzzy neural network model with the mixture of experts network (MEN) modelling approach is established. Based on this linkage, two new neuro-fuzzy MEN construction algorithms are proposed to overcome the curse of dimensionality that is inherent in the majority of associative memory networks and/or other rule based systems. The first construction algorithm employs a function selection manager module in an MEN system. The second construction algorithm is based on a new parallel learning algorithm in which each model rule is trained independently, for which the parameter convergence property of the new learning method is established. As with the first approach, an expert selection criterion is utilised in this algorithm. These two construction methods are equivalent in their effectiveness in overcoming the curse of dimensionality by reducing the dimensionality of the regression vector, but the latter has the additional computational advantage of parallel processing. The proposed algorithms are analysed for effectiveness followed by numerical examples to illustrate their efficacy for some difficult data based modelling problems.

Neurofuzzy network model construction using Bézier-Bernstein polynomial functions

Neurofuzzy modelling systems combine fuzzy logic with quantitative artificial neural networks via a concept of fuzzification by using a fuzzy membership function usually based on B-splines and algebraic operators for inference, etc. The paper introduces a neurofuzzy model construction algorithm using Bezier-Bernstein polynomial functions as basis functions. The new network maintains most of the properties of the B-spline expansion based neurofuzzy system, such as the non-negativity of the basis functions, and unity of support but with the additional advantages of structural parsimony and Delaunay input space partitioning, avoiding the inherent computational problems of lattice networks. This new modelling network is based on the idea that an input vector can be mapped into barycentric co-ordinates with respect to a set of predetermined knots as vertices of a polygon (a set of tiled Delaunay triangles) over the input space. The network is expressed as the Bezier-Bernstein polynomial function of barycentric co-ordinates of the input vector. An inverse de Casteljau procedure using backpropagation is developed to obtain the input vector's barycentric co-ordinates that form the basis functions. Extension of the Bezier-Bernstein neurofuzzy algorithm to n-dimensional inputs is discussed followed by numerical examples to demonstrate the effectiveness of this new data based modelling approach.

Generalized neurofuzzy network modeling algorithms using Bezier-Bernstein polynomial functions and additive decomposition

This paper introduces a new neurofuzzy model construction algorithm for nonlinear dynamic systems based upon basis functions that are Bezier-Bernstein polynomial functions. This paper is generalized in that it copes with n-dimensional inputs by utilising an additive decomposition construction to overcome the curse of dimensionality associated with high n. This new construction algorithm also introduces univariate Bezier-Bernstein polynomial functions for the completeness of the generalized procedure. Like the B-spline expansion based neurofuzzy systems, Bezier-Bernstein polynomial function based neurofuzzy networks hold desirable properties such as nonnegativity of the basis functions, unity of support, and interpretability of basis function as fuzzy membership functions, moreover with the additional advantages of structural parsimony and Delaunay input space partition, essentially overcoming the curse of dimensionality associated with conventional fuzzy and RBF networks. This new modeling network is based on additive decomposition approach together with two separate basis function formation approaches for both univariate and bivariate Bezier-Bernstein polynomial functions used in model construction. The overall network weights are then learnt using conventional least squares methods. Numerical examples are included to demonstrate the effectiveness of this new data based modeling approach.

Mid-term evaluation of African Technology Policy Studies Network Phase VI Strategic Plan: for the period January 1, 2009 to December 31, 2010

The African Technology Policy Studies Network (ATPS) is a multidisciplinary network of researchers, private sector actors, policymakers and civil society. ATPS has the vision to become the leading international centre of excellence and reference in science, technology and innovation (STI) systems research, training and capacity building, communication and sensitization, knowledge brokerage, policy advocacy and outreach in Africa. It has a Regional Secretariat in Nairobi Kenya, and operates through national chapters in 29 countries (including 27 in Africa and two Chapters in the United Kingdom and USA for Africans in the Diaspora) with an expansion plan to cover the entire continent by 2015. The ATPS Phase VI Strategic Plan aims to improve the understanding and functioning of STI processes and systems to strengthen the learning capacity, social responses, and governance of STI for addressing Africa's development challenges, with a specific focus on the Millennium Development Goals (MDGs). A team of external evaluators carried out a midterm review to assess the effectiveness and efficiency of the implementation of the Strategic Plan for the period January 1, 2009 to December 31, 2010. The evaluation methodology involved multiple quantitative and qualitative methods to assess the qualitative and quantitative inputs (human resources, financial resources, time, etc.) into ATPS activities (both thematic and facilitative) and their tangible and intangible outputs, outcomes and impacts. Methods included a questionnaire survey of ATPS members and stakeholders, key informant interviews, and focus group discussions (FGDs) with members in six countries. Effectiveness of Programmes Under all six strategic goals, very good progress has been made towards planned outputs and outcomes. This is evidenced by key performance indicators (KPIs) generated from desk review, ratings from the survey respondents, and the themes that run through the FGDs. Institutional and Programme Cost Effectiveness Institutional Effectiveness: assessment of institutional effectiveness suggests that adequate management frameworks are in place and are being used effectively and transparently. Also technical and financial accounting mechanisms are being followed in accordance with grant agreements and with global good practice. This is evidenced by KPIs generated from desk review. Programme Cost Effectiveness: assessment of cost-effectiveness of execution of programmes shows that organisational structure is efficient, delivering high quality, relevant research at relatively low cost by international standards. The evidence includes KPIs from desk review: administrative costs to programme cost ratio has fallen steadily, to around 10%; average size of research grants is modest, without compromising quality. There is high level of pro bono input by ATPS members. ATPS Programmes Strategic Evaluation ATPS research and STI related activities are indeed unique and well aligned with STI issues and needs facing Africa and globally. The multi-disciplinary and trans-boundary nature of the research activities are creating a unique group of research scientists. The ATPS approach to research and STI issues is paving the way for the so called Third Generation University (3GU). Understanding this unique positioning, an increasing number of international multilateral agencies are seeking partnership with ATPS. ATPS is seeing an increasing level of funding commitments by Donor Partners. Recommendations for ATPS Continued Growth and Effectiveness On-going reform of ATPS administrative structure to continue The on-going reforms that have taken place within the Board, Regional Secretariat, and at the National Chapter coordination levels are welcomed. Such reform should continue until fully functional corporate governance policy and practices are fully established and implemented across the ATPS governance structures. This will further strengthen ATPS to achieve the vision of being the leading STI policy brokerage organization in Africa. Although training in corporate governance has been carried out for all sectors of ATPS leadership structure in recent time, there is some evidence that these systems have not yet been fully implemented effectively within all the governance structures of the organization, especially at the Board and National chapter levels. Future training should emphasize practical application with exercises relevant to ATPS leadership structure from the Board to the National Chapter levels. Training on Transformational Leadership - Leading a Change Though a subject of intense debate amongst economists and social scientists, it is generally agreed that cultural mindsets and attitudes could enhance and/or hinder organizational progress. ATPS’s vision demands transformational leadership skills amongst its leaders from the Board members to the National Chapter Coordinators. To lead such a change, ATPS leaders must understand and avoid personal and cultural mindsets and value systems that hinder change, while embracing those that enhance it. It requires deliberate assessment of cultural, behavioural patterns that could hinder progress and the willingness to be recast into cultural and personal habits that make for progress. Improvement of relationship amongst the Board, Secretariat, and National Chapters A large number of ATPS members and stakeholders feel they do not have effective communications and/or access to Board, National Chapter Coordinators and Regional Secretariat activities. Effort should be made to improve the implementation of ATPS communication strategy to improve on information flows amongst the ATPS management and the members. The results of the survey and the FGDs suggest that progress has been made during the past two years in this direction, but more could be done to ensure effective flow of pertinent information to members following ATPS communications channels. Strategies for Increased Funding for National Chapters There is a big gap between the fundraising skills of the Regional Secretariat and those of the National Coordinators. In some cases, funds successfully raised by the Secretariat and disbursed to national chapters were not followed up with timely progress and financial reports by some national chapters. Adequate training in relevant skills required for effective interactions with STI key policy players should be conducted regularly for National Chapter coordinators and ATPS members. The ongoing training in grant writing should continue and be made continent-wide if funding permits. Funding of National Chapters should be strategic such that capacity in a specific area of research is built which, with time, will not only lead to a strong research capacity in that area, but also strengthen academic programmes. For example, a strong climate change programme is emerging at University of Nigeria Nsukka (UNN), with strong collaborations with Universities from neighbouring States. Strategies to Increase National Government buy-in and support for STI Translating STI research outcomes into policies requires a great deal of emotional intelligence, skills which are often lacking in the first and second generation universities. In the epoch of the science-based or 2GUs, governments were content with universities carrying out scientific research and providing scientific education. Now they desire to see universities as incubators of new science- or technology-based commercial activities, whether by existing firms or start-ups. Hence, governments demand that universities take an active and leading role in the exploitation of their knowledge and they are willing to make funds available to support such activities. Thus, for universities to gain the attention of national leadership they must become centres of excellence and explicit instruments of economic development in the knowledge-based economy. The universities must do this while working collaboratively with government departments, parastatals, and institutions and dedicated research establishments. ATPS should anticipate these shifting changes and devise programmes to assist both government and universities to relate effectively. New administrative structures in member organizations to sustain and manage the emerging STI multidisciplinary teams Second Generation universities (2GUs) tend to focus on pure science and often do not regard the application of their know-how as their task. In contrast, Third Generation Universities (3GUs) objectively stimulate techno-starters – students or academics – to pursue the exploitation or commercialisation of the knowledge they generate. They view this as being equal in importance to the objectives of scientific research and education. Administratively, research in the 2GU era was mainly monodisciplinary and departments were structured along disciplines. The emerging interdisciplinary scientific teams with focus on specific research areas functionally work against the current mono-disciplinary faculty-based, administrative structure of 2GUs. For interdisciplinary teams, the current faculty system is an obstacle. There is a need for new organisational forms for university management that can create responsibilities for the task of know-how exploitation. ATPS must anticipate this and begin to strategize solutions for their member institutions to transition to 3Gus administrative structure, otherwise ATPS growth will plateau, and progress achieved so far may be stunted.

Digital predistorter design using B-Spline neural network and inverse of De Boor algorithm

This contribution introduces a new digital predistorter to compensate serious distortions caused by memory high power amplifiers (HPAs) which exhibit output saturation characteristics. The proposed design is based on direct learning using a data-driven B-spline Wiener system modeling approach. The nonlinear HPA with memory is first identified based on the B-spline neural network model using the Gauss-Newton algorithm, which incorporates the efficient De Boor algorithm with both B-spline curve and first derivative recursions. The estimated Wiener HPA model is then used to design the Hammerstein predistorter. In particular, the inverse of the amplitude distortion of the HPA's static nonlinearity can be calculated effectively using the Newton-Raphson formula based on the inverse of De Boor algorithm. A major advantage of this approach is that both the Wiener HPA identification and the Hammerstein predistorter inverse can be achieved very efficiently and accurately. Simulation results obtained are presented to demonstrate the effectiveness of this novel digital predistorter design.

A radial basis function network classifier to maximise leave-one-out mutual information

tWe develop an orthogonal forward selection (OFS) approach to construct radial basis function (RBF)network classifiers for two-class problems. Our approach integrates several concepts in probabilisticmodelling, including cross validation, mutual information and Bayesian hyperparameter fitting. At eachstage of the OFS procedure, one model term is selected by maximising the leave-one-out mutual infor-mation (LOOMI) between the classifier’s predicted class labels and the true class labels. We derive theformula of LOOMI within the OFS framework so that the LOOMI can be evaluated efficiently for modelterm selection. Furthermore, a Bayesian procedure of hyperparameter fitting is also integrated into theeach stage of the OFS to infer the l2-norm based local regularisation parameter from the data. Since eachforward stage is effectively fitting of a one-variable model, this task is very fast. The classifier construc-tion procedure is automatically terminated without the need of using additional stopping criterion toyield very sparse RBF classifiers with excellent classification generalisation performance, which is par-ticular useful for the noisy data sets with highly overlapping class distribution. A number of benchmarkexamples are employed to demonstrate the effectiveness of our proposed approach.

B-spline neural network based single-carrier frequency domain equalisation for Hammerstein channels

A practical single-carrier (SC) block transmission with frequency domain equalisation (FDE) system can generally be modelled by the Hammerstein system that includes the nonlinear distortion effects of the high power amplifier (HPA) at transmitter. For such Hammerstein channels, the standard SC-FDE scheme no longer works. We propose a novel Bspline neural network based nonlinear SC-FDE scheme for Hammerstein channels. In particular, we model the nonlinear HPA, which represents the complex-valued static nonlinearity of the Hammerstein channel, by two real-valued B-spline neural networks, one for modelling the nonlinear amplitude response of the HPA and the other for the nonlinear phase response of the HPA. We then develop an efficient alternating least squares algorithm for estimating the parameters of the Hammerstein channel, including the channel impulse response coefficients and the parameters of the two B-spline models. Moreover, we also use another real-valued B-spline neural network to model the inversion of the HPA’s nonlinear amplitude response, and the parameters of this inverting B-spline model can be estimated using the standard least squares algorithm based on the pseudo training data obtained as a byproduct of the Hammerstein channel identification. Equalisation of the SC Hammerstein channel can then be accomplished by the usual one-tap linear equalisation in frequency domain as well as the inverse Bspline neural network model obtained in time domain. The effectiveness of our nonlinear SC-FDE scheme for Hammerstein channels is demonstrated in a simulation study.

Adaptive B-spline neural network based nonlinear equalization for high-order QAM systems with nonlinear transmit high power amplifier

High bandwidth-efficiency quadrature amplitude modulation (QAM) signaling widely adopted in high-rate communication systems suffers from a drawback of high peak-toaverage power ratio, which may cause the nonlinear saturation of the high power amplifier (HPA) at transmitter. Thus, practical high-throughput QAM communication systems exhibit nonlinear and dispersive channel characteristics that must be modeled as a Hammerstein channel. Standard linear equalization becomes inadequate for such Hammerstein communication systems. In this paper, we advocate an adaptive B-Spline neural network based nonlinear equalizer. Specifically, during the training phase, an efficient alternating least squares (LS) scheme is employed to estimate the parameters of the Hammerstein channel, including both the channel impulse response (CIR) coefficients and the parameters of the B-spline neural network that models the HPA’s nonlinearity. In addition, another B-spline neural network is used to model the inversion of the nonlinear HPA, and the parameters of this inverting B-spline model can easily be estimated using the standard LS algorithm based on the pseudo training data obtained as a natural byproduct of the Hammerstein channel identification. Nonlinear equalisation of the Hammerstein channel is then accomplished by the linear equalization based on the estimated CIR as well as the inverse B-spline neural network model. Furthermore, during the data communication phase, the decision-directed LS channel estimation is adopted to track the time-varying CIR. Extensive simulation results demonstrate the effectiveness of our proposed B-Spline neural network based nonlinear equalization scheme.