In this paper, we introduce a visualization tool for interactive and efficient exploration of high dimensional data using parallel coordinates. An algorithm is developed to find an optimal permutation of dimensions, which allows the data miner to immediately see the most important features or irregularities in the dataset. This is implemented as a genetic algorithm based on the travelling salesman problem using maximal correlation as fitness. Other features of the tool include selection operators to group the data such as selection by intersection or by angle, orthogonal and density plots complementing the parallel coordinates plot, manual arrangement of permutation order of the dimensions, possibility to show all plots necessary to see all dimensional relations and displaying a certain number of standard deviations for each dimension separately. The tool is applied to multiple gene prioritization cases in search of genes that are relevant to certain genetic disorders. The used datasets are obtained with the MerKator and Endeavour tools and include a Breast cancer, Cataract, Charcoth-Marie-Tooth and Cardiomyopathy dataset, as well as a dataset relating 29 diseases with 22206 genes. Our tool, manual and data can be downloaded from http://www.toomas.be/parcoord/.

It has been shown that kernel based regression (KBR) with a least squares loss has some undesirable properties from robustness point of view. KBR with more robust loss functions, e.g. Huber or Logistic losses, often give rise to more complicated computations. In classical statistics, robustness is improved by reweighting the original estimate. We study the influence of reweighting the LS-KBR estimate using three well-known weight functions and one new weight function called Myriad. Our results give practical guidelines in order to choose the weights, providing robustness and fast convergence. It turns out that Logistic and Myriad weights are suitable reweighting schemes when outliers are present in the data. In fact, the Myriad shows better performance over the others in the presence of extreme outliers (e.g. Cauchy distributed errors). These findings are then illustrated on toy example as well as on a real life data sets. Finally, we establish an empirical maxbias curve to demonstrate the ability of the proposed methodology.

Basic Circuits and Systems CAS education is crucial in the first phase of any electrical engineering curriculum, but increasingly under threat. This situation is in many ways similar to that of mathematics education in engineering. Creative shortcuts for teaching mathematics have recently been advocated in the “streetfighting mathematics approach”. Similar innovative, alternative ways can make the basic CAS education more effective. This special session aims to provide a forum where colleagues across the globe come together to share best practices, such as jewels or pedagogical shortcuts, and where they propose modern circuits, signals, and systems curricula for various target student audiences and discuss other related issues. Here we survey the contributions and discuss a case study, namely, the Thévenin/Helmholtz and Norton/Mayer equivalents.

In this brief, we propose a new method to reduce the number of support vectors of support vector machine (SVM) classifiers. We formulate the approximation of an SVM solution as a classification problem that is separable in the feature space. Due to the separability, the hard-margin SVM can be used to solve it. This approach, which we call the separable case approximation (SCA), is very similar to the cross-training algorithm explained in , which is inspired by editing algorithms . The norm of the weight vector achieved by SCA can, however, become arbitrarily large. For that reason, we propose an algorithm, called the smoothed SCA (SSCA), that additionally upper-bounds the weight vector of the pruned solution and, for the commonly used kernels, reduces the number of support vectors even more. The lower the chosen upper bound, the larger this extra reduction becomes. Upper-bounding the weight vector is important because it ensures numerical stability, reduces the time to find the pruned solution, and avoids overfitting during the approximation phase. On the examined datasets, SSCA drastically reduces the number of support vectors.

We discuss certain aspects of the state of core education in circuits, signals, and systems and list a few current problems and issues. The colleagues who have contributed to the two special sessions on education not only share their valuable experiences and innovations, but also express their opinions on how to address a number of salient problems. In other subjects, such as mathematics, some educators have taken to drastic approaches-one so unusual that it was dubbed "street-fighting mathematics". In a similar vein, we launch a debate on a collection of items for inclusion in, or exclusion from, basic circuits, signals, and systems education. We hope these ideas and novel practices will penetrate the diverse curricula around the globe.

The study of circuits and systems requires some basic reasoning with various equivalent interconnections of components like resistors, sources, ideal opamps, ideal transistors, short and open circuits.... Students should be able to handle a rich variety of such interconnections correctly and appropriately. Special components like nullators and norators bring in some valuable insights, flexibility and design methods based thereupon. From the practical experience of teaching nullators and norators in basic circuits courses for electrical engineering students, the authors stress a number of didactical goals and experiences. First, it is crucial that the students understand the notions of nullator and norator and are able to distinguish these from other components, such as short circuits, open circuits, resistors, and sources. Second, they should be able to reason correctly with interconnections that involve nullators and norators. Third, they should be able to find equivalents for circuits having nullators and norators. Analysis and design of circuits with nullators and norators will be presented. A particular case study of the use of nullators and norators for the implicit inversion of a 2 × 2 matrix will be used as an illustration of some unexpected results, which we will hardly And without nullators and norators. This will lead to an evaluation of the didactical processes of using nullators and norators.

It is the aim of this paper to discuss the various critical elements in the education of the mathematics of circuits and systems in the present context of a lean, efficient, and performant, but above all attractive electrical engineering education. The paper motivates the need for a solid mathematical basis, and strong concepts, that are more than shallow recipes, for conceptually interested students, at an early phase of the bachelor education. Such courses should be taught by motivated teachers in an attractive, motivational way.

The mathematics of circuits and systems requires some basic knowledge of linear algebra, differential equations, and transforms. Moreover many concepts and methods of basic circuits and systems courses can and should rely on sharp mathematical insight. Efficient and effective teaching of the basic mathematics and the basic circuits and systems courses for electrical engineering students can hence benefit from a strong link between the two. The paper gives an inventory of the relevant subjects and describes the different ways that such subjects can be taught and linked with circuits and systems Experience with this approach and interesting modules and references are presented.

We present a new class of methods for the global optimization of continuous variables based on simulated annealing (SA). The coupled SA (CSA) class is characterized by a set of parallel SA processes coupled by their acceptance probabilities. The coupling is performed by a term in the acceptance probability function, which is a function of the energies of the current states of all SA processes. A particular CSA instance method is distinguished by the form of its coupling term and acceptance probability. In this paper, we present three CSA instance methods and compare them with the uncoupled case, i.e., multistart SA. The primary objective of the coupling in CSA is to create cooperative behavior via information exchange. This aim helps in the decision of whether uphill moves will be accepted. In addition, coupling can provide information that can be used online to steer the overall optimization process toward the global optimum. We present an example where we use the acceptance temperature to control the variance of the acceptance probabilities with a simple control scheme. This approach leads to much better optimization efficiency, because it reduces the sensitivity of the algorithm to initialization parameters while guiding the optimization process to quasioptimal runs. We present the results of extensive experiments and show that the addition of the coupling and the variance control leads to considerable improvements with respect to the uncoupled case and a more recently proposed distributed version of SA.

It is the aim of reproducible research to provide mechanisms for objective comparison of methods, algorithms, software and procedures in various research topics. In this paper, we discuss the role of data sets, benchmarks and competitions in the fields of system identification, time series prediction, classification, and pattern recognition in view of creating an environment of reproducible research. Important elements are the data sets, their origin, and the comparison measures that will be used to rank the performance of the methods. The issues are discussed, a comparison is made and recommendations are given.

In this paper Josephson junctions are used in order to generate n-scroll and n-scroll hypercube attractors. We propose to use of the Josephson junction in a general Jerk circuit in such a way that there is no need for synthesizing the nonlinearity towards n-scroll and n-scroll hypercube attractors. The results are illustrated with computer simulations.

In signal processing systems, aliasing is normally treated as a disturbing signal. That motivates the need for effective analog, optical and digital anti-aliasing filters. However, aliasing also conveys valuable information on the signal above the Nyquist frequency. Hence, an effective processing of the samples, based on a model of the input signal, would virtually allow the sampling frequency to be increased using slower and cheaper converters. We present such an algorithm for bandlimited signals that are sampled below twice the maximum signal frequency. Using a subspace method in the frequency domain, we show that these signals can be reconstructed from multiple sets of samples. The offset between the sets is unknown and can have arbitrary values. This approach can be applied to the creation of super-resolution images from sets of low resolution images. In this application, registration parameters have to be computed from aliased images. We show that parameters and high resolution images can be computed precisely, even when high levels of aliasing are present on the low resolution images.

Kernel based methods suffer from exceeding time and memory requirements when applied on large datasets since the involved optimization problems typically scale polynomially in the number of data samples. As a remedy we propose both working on a reduced set (for fast evaluation) and at the same time keeping the number of model parameters small (for fast training). Departing from the Nystrom based feature approximation we describe fixed-size least squares support vector machine in the context of primal space least squares regression, to extend it with a supervised counterpart, sparse kernel partial least squares. The model is illustrated on a large scale example.

Presents the welcome message from the conference proceedings.

In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function is proposed so as to obtain a linear set of equations in the dual space. While the SVM classifier has a large margin interpretation, the LS-SVM formulation is related in this paper to a ridge regression approach for classification with binary targets and to Fisher's linear discriminant analysis in the feature space. Multiclass categorization problems are represented by a set of binary classifiers using different output coding schemes. While regularization is used to control the effective number of parameters of the LS-SVM classifier, the sparseness property of SVMs is lost due to the choice of the 2-norm. Sparseness can be imposed in a second stage by gradually pruning the support value spectrum and optimizing the hyperparameters during the sparse approximation procedure. In this paper, twenty public domain benchmark datasets are used to evaluate the test set performance of LS-SVM classifiers with linear, polynomial and radial basis function (RBF) kernels. Both the SVM and LS-SVM classifier with RBF kernel in combination with standard cross-validation procedures for hyperparameter selection achieve comparable test set performances. These SVM and LS-SVM performances are consistently very good when compared to a variety of methods described in the literature including decision tree based algorithms, statistical algorithms and instance based learning methods. We show on ten UCI datasets that the LS-SVM sparse approximation procedure can be successfully applied.

We describe a hardware implementation of an arithmetic processor which is efficient for bit-lengths suitable for both commonly used types of public key cryptography (PKC), i.e., elliptic curve (EC) and RSA cryptosystems. Montgomery modular multiplication in a systolic array architecture is used for modular multiplication. The processor consists of special operational blocks for Montgomery modular multiplication, modular addition/subtraction, EC point doubling/addition, modular multiplicative inversion, EC point multiplier, projective to affine coordinates conversion and Montgomery to normal representation conversion.

Discrete Fourier theory has been applied successfully in digital communication theory. In this correspondence, we prove a new inequality linking the number of nonzero components of a complex valued function defined on a finite Abelian group to the number of nonzero components of its Fourier transform. We characterize the functions achieving equality. Finally, we compare this inequality applied to Boolean functions to the inequality arising from the minimal distance property of Reed-Muller codes.

In this paper, we present a simple and straightforward primal-dual support vector machine formulation to the problem of principal component analysis (PCA) in dual variables. By considering a mapping to a high-dimensional feature space and application of the kernel trick (Mercer theorem), kernel PCA is obtained as introduced by Scholkopf et al. (2002). While least squares support vector machine classifiers have a natural link with the kernel Fisher discriminant analysis (minimizing the within class scatter around targets +1 and -1), for PCA analysis one can take the interpretation of a one-class modeling problem with zero target value around which one maximizes the variance. The score variables are interpreted as error variables within the problem formulation. In this way primal-dual constrained optimization problem interpretations to the linear and kernel PCA analysis are obtained in a similar style as for least square-support vector machine classifiers.

This article investigates if and how mobile agents can execute secure electronic transactions on untrusted hosts. An overview of the security issues of mobile agents is first given. The problem of untrusted (i.e., potentially malicious) hosts is one of these issues, and appears to be the most difficult to solve. The current approaches to counter this problem are evaluated, and their relevance for secure electronic transactions is discussed. In particular, a state-of-the-art survey of mobile agent-based secure electronic transactions is presented.

The Bayesian evidence framework has been successfully applied to the design of multilayer perceptrons (MLPs) in the work of MacKay. Nevertheless, the training of MLPs suffers from drawbacks like the nonconvex optimization problem and the choice of the number of hidden units. In support vector machines (SVMs) for classification, as introduced by Vapnik, a nonlinear decision boundary is obtained by mapping the input vector first in a nonlinear way to a high-dimensional kernel-induced feature space in which a linear large margin classifier is constructed. Practical expressions are formulated in the dual space in terms of the related kernel function, and the solution follows from a (convex) quadratic programming (QP) problem. In least-squares SVMs (LS-SVMs), the SVM problem formulation is modified by introducing a least-squares cost function and equality instead of inequality constraints, and the solution follows from a linear system in the dual space. Implicitly, the least-squares formulation corresponds to a regression formulation and is also related to kernel Fisher discriminant analysis. The least-squares regression formulation has advantages for deriving analytic expressions in a Bayesian evidence framework, in contrast to the classification formulations used, for example, in gaussian processes (GPs). The LS-SVM formulation has clear primal-dual interpretations, and without the bias term, one explicitly constructs a model that yields the same expressions as have been obtained with GPs for regression. In this article, the Bayesian evidence framework is combined with the LS-SVM classifier formulation. Starting from the feature space formulation, analytic expressions are obtained in the dual space on the different levels of Bayesian inference, while posterior class probabilities are obtained by marginalizing over the model parameters. Empirical results obtained on 10 public domain data sets show that the LS-SVM classifier designed within the Bayesian evidence framework consistently yields good generalization performances.

Watershed modeling is often faced with the difficulty of determining the flow contribution from the ungaged sections of the catchment. Where the main concern is making accurate streamflow forecasts at specific watershed locations, it is cost-effective and efficient to implement a simple system theoretic model. In this paper Artificial Neural Networks (ANNs) are used as system theoretic models to model the ungaged flows. Using data from the Kafue River sub-catchment in Zambia and a simple reservoir routing model, an estimate of the flow contribution from the ungaged sections is derived. Inputs: rainfall, evaporation, and previous-time-step flow are fed to a series of Feedforward-Backpropagation ANNs with target-output the current derived flow. Selected best performing ANNs are compared with Autoregressive Moving Average models with exogenous inputs (ARMAX) and they give accurate and more robust forecasts over long term than the best performing ARMAXs thereby making ANNs a viable alternative in forecasting.

When an artificial neural network (ANN) is trained to predict signals p steps ahead, the quality of the prediction typically decreases for large values of p. In this paper, we compare two methods for prediction with ANNs: the classical recursion of one-step ahead predictors and a new kind of chain structure. When applying both techniques to the prediction of the temperature at the end of a blast furnace, we conclude that the chaining approach leads to an improved prediction of the temperature and avoidance of instabilities, since the chained networks gradually take the prediction of their predecessors in the chain as an extra input. It is observed that instabilities might occur in the iterative case, which does not happen with the chaining approach. To select relevant inputs and decrease the number of weights in this approach, Automatic Relevance Determination (ARD) for multilayer perceptrons is applied.

The Bayesian evidence framework is applied in this paper to least squares support vector machine (LS-SVM) regression in order to infer nonlinear models for predicting a financial time series and the related volatility. On the first level of inference, a statistical framework is related to the LS-SVM formulation which allows one to include the time-varying volatility of the market by an appropriate choice of several hyper-parameters. The hyper-parameters of the model are inferred on the second level of inference. The inferred hyper-parameters, related to the volatility, are used to construct a volatility model within the evidence framework. Model comparison is performed on the third level of inference in order to automatically tune the parameters of the kernel function and to select the relevant inputs. The LS-SVM formulation allows one to derive analytic expressions in the feature space and practical expressions are obtained in the dual space replacing the inner product by the related kernel function using Mercer's theorem. The one step ahead prediction performances obtained on the prediction of the weekly 90-day T-bill rate and the daily DAX30 closing prices show that significant out of sample sign predictions can be made with respect to the Pesaran-Timmerman test statistic.

This paper deals with the extraction of the antepartum foetal electrocardiogram (ECG) from multilead cutaneous potential recordings, and the modelling of the transfer to the electrodes. We give an overview of a class of algebraic approaches, based on variants of the singular value decomposition (SVD): the concept, pros and cons of techniques relying on the ordinary SVD, quotient SVD and multilinear SVD are discussed.

In least squares support vector machines (LS-SVMs) for function estimation Vapnik's /spl epsiv/-insensitive loss function has been replaced by a cost function which corresponds to a form of ridge regression. In this way nonlinear function estimation is done by solving a linear set of equations instead of solving a quadratic programming problem. The LS-SVM formulation also involves less tuning parameters. However, a drawback is that sparseness is lost in the LS-SVM case. In this paper we investigate imposing sparseness by pruning support values from the sorted support value spectrum which results from the solution to the linear system.

This paper deals with the blind identification of multiple-input multiple-output finite impulse response filters. We limit ourselves to the case of 2 outputs and 2 inputs. After a classical prewhitening, the remaining problem is the blind identification of a paraunitary filter. For this task, we derive a multilinear algebraic algorithm. This procedure is a generalization of the algorithm for independent component analysis described in Comon (1994). The performance is illustrated by means of some numerical experiments.

This paper presents an algorithm to extract rules re- lating input/output data and including prior knowledge. The rules are created in the environment of fuzzy systems. The fuzzy sets describing the system are constructed within a framework of lin- guistic integrity to guarantee its interpretability in the linguistic context. Two algorithms are presented in this paper. The main al- gorithm is the autonomous fuzzy rule extractor with linguistic in- tegrity (AFRELI). This algorithm is complemented with the use of the FuZion algorithm created to merge consecutive member- ship functions, while guaranteeing the distinguish ability between fuzzy sets. Comparisons with other proposed methods show a good tradeoff between accuracy and interpretability.

In this paper we derive a condition for robust local stability of multilayer recurrent neural networks with two hidden layers. The stability condition follows from linking theory about linearization, robustness analysis of linear systems under nonlinear perturbation and matrix inequalities. A characterization of the basin of attraction of the origin is given in terms of the level set of a quadratic Lyapunov function. In a similar way like for NL theory, local stability is imposed around the origin and the apparent basin of attraction is made large by applying the criterion, while the proven basin of attraction is relatively small due to conservatism of the criterion. Modifying dynamic backpropagation by the new stability condition is discussed and illustrated by simulation examples.

We present an extension of least squares support vector machines (LS-SVMs) to the multiclass case. While standard SVM solutions involve solving quadratic or linear programming problems, the least squares version of SVMs corresponds to solving a set of linear equations, due to equality instead of inequality constraints in the problem formulation. In LS-SVMs the Mercer condition is still applicable. Hence several type of kernels such as polynomial, RBFs and MLPs can be used. The multiclass case that we discuss here is related to classical neural net approaches for classification where multi-classes are encoded by considering multiple outputs for the network. Efficient methods for solving large scale LS-SVMs are available.

We present absolute stability (global asymptotic) criteria for continuous time multilayer recurrent neural networks with two hidden layers. Such forms arise when considering recurrent neural models and neural controllers for a given plant, both parametrized by multilayer perceptrons with one-hidden layer. The one-hidden layer case corresponds to systems in Lur'e form. These results are related to the NLq theory which is a stability theory for q-layered discrete time multilayer recurrent neural networks with conditions for global asymptotic stability and input-output stability with finite L/sub 2/-gain. The criteria can be used to constrain dynamic backpropagation in order to impose closed-loop stability for neural control schemes.

In this paper we describe a training method for one hidden layer multilayer perceptron classifier which is based on the idea of support vector machines (SVM's). An upper bound on the Vapnik-Chervonenkis (VC) dimension is iteratively minimized over the interconnection matrix of the hidden layer and its bias vector. The output weights are determined according to the support vector method, but without making use of the classifier form which is related to Mercer's condition. The method is illustrated on a two-spiral classification problem.

In parameter estimation of closely-spaced sinusoids, prior knowledge of some known signal poles can be incorporated as done in the LCTLS-LP algorithm proposed by Dowling et al. (1992, 1994) and based on forward linear prediction and total least squares. In this paper, two better algorithms, HTLS-PK and HTLN-PK, derived respectively from Kung et al.'s (1983) state-space method using the TLS principle and the newly developed structured total least norm technique are presented that incorporate the same prior knowledge but clearly outperform the LCTLS-LP method in both resolution and parameter accuracy.

The aim of this paper is to show that discrete time Generalized Cellular Neural Networks, with feedforward, feedback or cascade interconnections between CNNs can be represented as NL/sub q/s. NL/sub q/s are nonlinear systems in state space form with the typical feature of having a number of q layers with alternating linear and nonlinear operators that satisfy a sector condition. It can be shown that many systems and problems arising in neural networks, systems and control are special cases of NL/sub q/s. Sufficient conditions for global asymptotic stability and dissipativity with finite L/sub 2/-gain are available. For q=1 the criteria are closely related to known results in H/sub /spl infin// and /spl mu/ control theory.

The various special features of cellular neural networks are presented. It is stated what has been achieved so far and what future work is still needed in order to obtain a truly universal building block for information processing systems.>

In this paper, we present a new model of discrete neural associative memories and its design rule. The most important feature of this new model is that a static mapping instead of the dynamic convergent process is used to retrieve the stored messages. The new model features a two-layer structure, with feedforward connections only and uses two kinds of neurons which implement different output functions. Another important feature is that this new model employs an extremely simple weight setup rule and all the resulted weights can only assume two different values, -1 and +1, which facilitates the VLSI implementation. Compared to the famous discrete Hopfield model designed with the well-known Hebbian rule or any other rule, the new model can guarantee all the given patterns to be stored as fixed points. Moreover, each fixed point is surrounded by an attraction basin (which is a ball in the Hamming distance sense) with the maximal possible radius. The performances of the new model are compared through some illustrative examples with those of the Hopfield associative memory designed using different methods.

Using the property of universal approximation of multilayer perceptron neural network, a class of discrete nonlinear dynamical systems are modeled by a perceptron with two hidden layers. A backpropagation algorithm is then used to train the model to identify the nonlinear systems to a desired level of accuracy. Based on the identified model, a one-step-ahead predictive control scheme is proposed in which the future control inputs are obtained through some nonlinear optimization process. Making use of the online learning properties of neural networks, the predictive control scheme is further developed into an adaptive one which is robust to the incompleteness of identification. Simulation results show that this neural control scheme works well even for some very complicated nonlinear systems.

This paper describes a differential attack on several hash functions based on a block cipher. The emphasis will be on the results for cases where DES [8] is the underlying block cipher. It will briefly discuss the case of FEAL-N [19, 21].

This paper tries to demonstrate how a heuristic neural control approach can be used to solve a complex nonlinear control problem. The control task is to swing up a pendulum mounted on a cart from its stable position (vertically down) to the zero state (up right) and keep it there by applying a sequence of two opposing constant forces of equal magnitude to the mass center of the cart. In addition, the displacement of the cart itself is confined to within a preset limit during the swinging up action and it will eventually be brought to the origin of the track. This is truly a nontrivial nonlinear regulation problem and is considerably difficult compared to the pendulum balancing problem (and its variations) widely adopted as a benchmarking test system for neural controllers. Through the solution of this specific control problem, we try to illustrate a heuristic neural control approach with task decomposition, control rule extraction and neural net rule implementation as its basic elements. Specializing to the pendulum problem, the global control task is decomposed into subtasks namely pendulum positioning and cart positioning. Accordingly, three separate neural subcontrollers are designed to cater to the subtasks and their coordination, i.e., pendulum subcontroller (PSC), cart subcontroller (CSC) and the switching subcontroller (SSC). Each of the subcontrollers is designed based on the rules and guidelines obtained from the experiences of a human operator. The simulation result is included to show the actual performance of the controller.

This article discusses a distributed implementation of the Bell-LaPadula security policy model. Implementation of a confidentiality service in the OSIRM is not sufficient for enforcing the Bell-LaPadula model. Also integrity services are necessary. In this article both public key systems (PKSs) as well as symmetric cryptographic systems are considered for the realisation of these security services. By concentrating on the key distribution, no cryptographic algorithms or protocols are excluded on beforehand. It is investigated how key-distributions can be found resulting in a minimum number of keys. Application of PKSs results in a key distribution which requires less keys than key-distributions going with the use of a symmetric system. Moreover, practical or viable key-distributions going with symmetric algorithms turn out to be more sensitive to the disclosure of a secret key than key-distributions going with PKSs. A combination of a PKS and a symmetric system is indicated which does not suffer from the disadvantages going with the use of symmetric systems alone.>

This paper is concerned with the formulation of neural associative memories. Centered around the fundamental issue of the memory storage, we examine the deficiencies associated with the standard Hopfield net. To overcome the problems, we pursue a data-driven design approach by modifying the configuration of the Hopfield net to allow hidden structures. As important results, we show how the well-known sum-of-outer product rule can be utilized to explore the freedom provided by the hidden structures leading to the desired memory performance.

The authors focus on an orthogonal-inverse updating algorithm, and show how a systolic implementation can be derived. They avoid the critical path problem by introducing a few additional computations such that the different algorithmic steps can be executed at the same time. The overall efficiency is then roughly 67%, and the obtained throughput is independent of the problem size.>

The BSB (brain-state-in-a-box) neural network is restructured as a multivariable linear system with a nonlinear feedback. This reconstruction allows some well-developed techniques in the field of circuit and control theory to be used for resolving issues such as stability. The authors specifically consider the stability of the asymmetric BSB nets in terms of the nonexpansivity of the whole system and derive simple conditions to be satisfied by the weight matrix in order to guarantee the stability. It is seen that this framework can reproduce and generalize some of the previous results on the subject, like those previously developed by the authors (1989)

A computer program for microcode compilation for custom digital signal processors is presented. This tool is part of the CATHEDRAL II silicon compiler. The following optimization problems are highlighted: scheduling, hardware assignment, and loop folding. Efficient techniques to solve these problems are developed. This allows for the automatic synthesis of processor architectures which simultaneously exploit pipelining and parallelism. A demonstrator design is presented.>

In this paper, we discuss a control-flow transformation called loop folding, during the scheduling of register-transfer code for DSP-systems. Loop folding is functionally equivalent to data-path pipelining. An iterative loop-folding procedure, implemented in the CATHEDRAL II compiler, is presented. This technique may significantly improve the utilization of parallel hardware, available in a data path.

A nonlinear echo canceller with Volterra series expansion is presented. A model of the data transmission system is used to study its performance. It is shown that the Volterra expansion method is a good solution to the problem of nonlinear echo cancellation. To sufficiently reduce the nonlinearities caused by A/D and D/A (analog/digital and digital/analog) converters, an adaptive third-order Volterra filter with a nonlinear memory of length three can be applied. The far-end signal has a very strong influence on the canceller. To reduce this, a training sequence without a far-end signal is used, and different values of the step-size mu are chosen for the transient period (larger mu ) and for the steady state. Simulations have shown that the canceller can achieve 15-dB improvement with a training sequence and 8-dB improvement without the training sequence.>

A structure for the subband acoustic echo canceler is proposed that avoids filter banks in the sending line and the consequent delay. In this arrangement of the filter banks, the conventional least-mean-square algorithm is modified and an additional adaptive filter is included. Simulation results are given, and a real application is reported.>

Some geometrically inspired concepts are studied for the identification of models for multivariable linear time-invariant systems from noisy input-output observations. Starting from a fundamental highly structured input-output matrix equation, it is shown how the singular value decomposition allows the order of the observable part of the system and its state-space model matrices to be estimated. Moreover, conditions for persistency of excitation of the inputs and the behavior of the algorithm when the data are perturbed by noise can easily be studied from a geometrical point of view. The singular values allow these concepts to be quantified. An example with an industrial plant identification is presented.>

CAD tools to support a top-down custom design methodology for integrated digital filters are presented. The methodology is based on a tool-box concept, which makes use of specialised analysis, synthesis and optimization programs at each design level: the network, the architecture and the circuit layout. In this paper, CAD tools for filter synthesis, network optimization and architecture optimization are developed. These tools complement design aids for architecture synthesis, and automatic layout generation (silicon compilers) to create a complete design environment. By combining both synthesis as well as optimization aids at each design level, it is possible to achieve complete automation while retaining efficient use of silicon area, speed and power consumption. Application of these tools to the custom integration of wave digital filters with bit-serial architectures is demonstrated.

An interactive CAD methodology is presented for designing digital filters with optimised discrete coefficients. The objective is to minimise the cost of integrationC(\undertilde{a})subject to the constraint that the desired specifications on the complex frequency transfer functionH(w,\undertilde{a})are satisfied. The vector a represents the fixed coefficients in the filter network. For VLSI DSP architectures, where multiplication is implemented using software controlled or hardwired shift-and-add operations,C(\undertilde{a})is defined as the total number of non-zero bits in the discrete representation of\undertilde{a}. An accurate multiparameter analysis technique, based on the bilinear property ofH(w,\undertilde{a}), is exploited to develop numerically reliable and computationally efficient tools for function evaluation and feasible region determination. These are built into a CAD framework together with tools for computingC(\undertilde{a})and predicting the location of the minima ofC(\undertilde{a})Extremely fast optimisation strategies using this framework are demonstrated, for different filter topologies with up to 10 coefficients.

The authors claim that the security of the Merkle-Hellman algorithm is greatly exaggerated. First, any enciphering key that is obtained from a superincreasing sequence has infinitely many superincreasing deciphering keys that can decipher all messages. This follows from the fact that the conditions on the transformation^{\ast} w \bmod mrequirew/mto lie in a restricted set of intervals. Second, it is claimed that iterative transformations^{\ast} w \bmod mmay not increase the security. In the example that Merkle and Hellman used for "proving" the benefits of the iterative transformation, the security is completely ruined. Third, techniques are presented to crack one bit of the plaintext. These techniques apply to sets of enciphering keys introduced in this text, which contain all the Merkle-Hellman enciphering keys. Such bit-by-bit techniques also allow the construction of new enciphering keys. Fourth, some knapsacks that allow a one-to-one deciphering cannot be obtained from easy deciphering keys, e.g., superincreasing keys, even with infinitely many transformations^{\ast} w \bmod m!If the worst cases of nondeterministic polynomial complete knapsack problems are always of this kind, the foundation of the security of the Merkle-Hellman algorithm is nonexistent. The cryptanalysis can be reduced to a problem of simultaneous diophantine approximations. A link is made with other recent results.

A repeated scanning algorithm for determining the minimax approximation to an arbitrary function by means of a piecewise linear function with a fixed number of variable knots is described, and it is compared to optimal and suboptimal methods.

In this paper a novel theory and algorithm for spectral factorization is presented. It is based on a criterion for minimal extraction of a so-called "elementary factor." Although not all positive para-hermitian matrices can be minimally factored into elementary factors, still the method can be adapted to fit the general case by increasing the degree in a well-controlled way and removing the nonminimal units of degree at the end. The method is, in this sense, strictly minimal. Moreover, the algorithm produces the spectral factor in ali cases where such a factorization does exist. Also, an independent proof of the famous spectral factorization result of Youla is obtained, so that the completeness of the method is ascertained. The procedure results in a workable and optimally minimal algorithm.

Modulo qiscalers or counters are one-input Moore machines whose state set contains a unique subset (called cycle) of states which occur in cyclic succession.