Computer Aided Systems Theory - EUROCAST 2009

Stream ciphers are essential tools for encrypting sensitive data. While having the limitation that a single key may never be used twice, they are often very fast and can offer a valuable alternative to block ciphers in many applications.

In this contribution we describe a novel stream cipher based on discrete Kolmogorov systems. Based on a theorem stating that discrete Kolmogorov systems can provide a perfect permutation operator, we develop a strong generator for pseudo-random bits or bytes. These bits or bytes are then added to the plaintext stream to produce the desired ciphertext stream in a straightforward manner.

The Morphotronic approach postulates a significant improvement to traditional system design thinking based on the Turing Machine model. The paper presents a range of important concepts and definitions supporting this proposition.The Morphotronic system represents an abstract universe of the objects. This universe of objects has two interpretations as in the case of the voltages and currents in the electrical circuit. For the space of the voltages the objects are the voltages at edges of the electrical circuit. For the current space of the currents the objects are the currents in any edge. The dimension of the object space is equal to the number of edges in the electrical circuit. Such a space allows dual interpretation of the current and voltages. Other possible dual variables can be used in the morphotronic system as forces and the fluxes in mechanics or dissipative thermodynamics, in a general way the dual interpretation of the object space will be denoted as causes and effects. The morphogenetic system can be modelled by samples of the causes and effects. The morphotronic system with the samples generates the algorithm to implement the purpose in the system. Providing that the samples of the effect and the purpose denote a virtual cause, the vector E can be computed so that it represents the effective origin of the causes inside the purpose map. With the

cause-effect

rule the effective causes can be computed obtaining results that are coherent with the samples. Providing that the virtual cause is given by purpose the effective causes can be generated in agreement with the samples. The described algorithm is denoted as the projection operator that transforms a virtual cause (purpose) into an effective cause.

In the past, the way of turning data into knowledge relied on manual analysis and interpretation. Nowadays computational techniques are used in order to extract knowledge from data. Changing data into knowledge is not a straightforward task. Data is generally disorganized, contains useless details and may be incomplete. Knowledge is the opposite, organized but expressed using a poorer language, which might even be imprecise or vague.

Knowledge Discovery in Databases is the nontrivial process of identifying valid, novel, potentially useful, and understandable patterns in data. The Multivalued Array Algebra does not handle raw data, it handles declarative descriptions of the data by means of a multivalued language. This paper proposes and addresses the use of the Multivalued Array Algebra for Knowledge Discovery in Databases.

The realization of local space-time models of retinal processes can be achieved by means of available typical dynamical systems simulation tools (like

Simulink

) because only a very small number of parallel channels is needed. In short, the aim is to simulate both the time and space dimensions as delay chains, where the travelling signals are available at different points of the delay chain to interact among them. These models provide an interesting and fruitful insight into the neurophysiological processes.

Directional sensitivity to local stimuli by retinal ganglion cells are related to processes which probably are located at the Inner Plexiform Layer of the retina, at the ganglion cells dendrites and it is the result of at least two mechanisms. First, at the ganglion dendrites, either by postsynaptic inhibition from amacrines or by presynaptic inhibition of bipolar synapses, also by amacrines. Second, there seems to be an “intrinsic” amacrine directionality by the so called “starbust” amacrines which is itself emphasized by amacrine-amacrine interaction and then transmitted, by inhibition, to presynaptic ganglia connections [1], [2], [3].

Jorma Rissanen developed in his papers [1],[2] a method to compute recursively for a matrix-valued data stream S of finite length the associated minimal linear system

$\it \Sigma$

=(F,G,H) which has S as its impulse response. The method of Rissanen is based on the fundamental algebraic theory of linear systems realization as developed earlier by the fundamental research in mathematical systems theory by the work of Rudolf Kalman [3],[4],[5]. In our presentation we show how the Rissanen method of Hankel matrix decomposition can be applied to measure the linear complexity profile of vector-valued cryptographic data streams as it is applied in stream cipher testing. Our method generalizes the well known Massey-Berlekamp algorithm which is applied in testing scalar-valued data streams. For this reason we call it the “Rissanen algorithm”. Although the author has been familiar already for a long time with the realization theory of Kalman and contributed to the topic earlier [6], only recently the reported applicability in cryptographic testing of pseudorandom sequences has been found. The result presented here proves that results of mathematical systems theory and automata theory, which were developed nearly half a century ago by Rudolf Kalman, Jorma Rissanen, Michael Arbib and others are until today of scientific interest and can successfully be applied to solve engineering problem of todays interest. Jochinger [7] gives a report on the software implementation of the Rissanen Method of recursive Hankel matrix decomposition and the effective computation of partial linear systems, following [1] and [2]. A more detailed presentation of the topic discussed here, which includes also the discussion of the theory of linear systems realization, has been given earlier by Pichler [8].

This work deals with the software solution for calculating the minimal partial realization of a discrete multi-variable linear system. The Rissanen method for the computation of the partial linear system is proposed. This method is based on a recursive Hankel matrix decomposition. It is implemented in the JAVA programming language to determine the linear system

$\it \Sigma$

=

(F,G,H)

over a finite field

K

=

GF(q)

. The method is illustrated by some cryptological experiments. The Rissanen method generalizes the Massey-Berlekamp algorithm.

Building better models is crucial for coping with complexity in general, and for the management of organizations in particular. This paper discusses the epistemological aspects of model validation for the achievement of high-quality models. Then it provides an overview of validation methods. The logic of validation is demonstrated by introducing the Structural Dominance Test as a means for testing the correspondence of the structural dominance between model and reality.

Hardware design is traditionally done by modeling finite state machines (FSMs). In this paper, we present how a basic round-robing scheduling mechanism, well-known from operating systems, can be applied to a design that needs several identical FSMs running (quasi) in parallel.

This approach allows exploiting the classical trade-off between chip area and operating frequency to severely cut down the hardware resources needed to implement the FSMs by increasing the operating frequency of the design. We additionally show that, in a system-on-a-chip design using only a single clock domain, the design’s overall operating frequency is dependent on the processor’s frequency, making especially low-speed communication cores already clocked faster than needed. This means that with regard to the design’s frequency, our approach may come at no additional cost.

The effort of verifying state-of-the-art hardware designs undeviatingly increases with the complexity of those designs. The design’s state space, directly related to its complexity, grows exponentially, while the computational performance for verifying the design grows only linearly. This so-called verification gap can, for example, be met by using methods such as assertion-based verification (ABV), which can be used for both specifying the system’s properties as well as verifying the relating implementation during simulation phase.

In this paper, we present an open-source tool which generates synthesizable HDL code from assertions specified in the Property Specification Language (PSL). This is done by first reducing the PSL formulas into base cases, called PSL

min

, and then generating automata which can be transformed to synthesizable HDL code and therefore into hardware.

In this paper we present a method to obtain a near optimal neuro-controller for the autonomous helicopter flight by means of an ad hoc evolutionary reinforcement learning method. The method presented here was developed for the Second Annual Reinforcement Learning Competition (RL2008) held in Helsinki-Finland. The present work uses a Helicopter Hovering simulator created in the Stanford University that simulates a Radio Control XCell Tempest helicopter in the flight regime close to hover. The objective of the controller is to hover the helicopter by manipulating four continuous control actions based on a 12-dimensional state space.

In this paper, we describe results of designing a communication space in WSN. To achieve this goal we propose notional system which can convey sophisticated WSN reality onto some mathematical abstraction. We concentrate on three of them; neighborhood, communication space and relations. Consequently our work concentrate on developing the formal methods and techniques necessary to model and evaluate communication space in the network. Proposed approach pointed that: neighborhoods are more useful than clusters because they provide more communication connections, communication space is better than routing paths because it spreads evenly energy losses, relations work better than functions because of topology based properties.

Boundary scan (JTAG) is a powerful testing scheme that is widely used in nowadays circuits to maintain and verify operation of the hardware. However, JTAG is not used in cryptographic hardware since it may be used to compromise security of the implemented cryptographic algorithm. This paper analyses different solutions proposed to overcome the threat of such attacks, presents requirements that have to be satisfied in order to construct effective security solution, and presents novel proposal that improves security of the boundary scan.

In this paper methods of designing of a class of highly reliable digital circuit - Totally Self Checking Sequential Machines are presented. The main problem in TSC sequential machines (TSC SM) designing is synthesis TSC functional excitation circuit. Formal condition of ST property for both AND-OR and AND-AND-OR structures are presented. The description of design methodology of TSC SM is presented. Owing to our methods we can design TSC circuits in a fully automatic way.

Industrial control applications are developed as ad hoc solutions. These solutions require specific programming and installation, and very often it takes large economic investments. We have developed a system that makes a special effort trying to offer versatility and a solution to a widespread of industrial installations. Our system understands an industrial plant as a compound of sensors, actuators and processes. The user will define its own processes, sensors and actuators. It will also be in his hand the design of the plants visualization diagram. In order to achieve this generalness it is of essence to offer a user interface as friendly as possible. Via a very intuitive user interface the implemented functionalities are far more powerful because the user understand them and makes use of them.

A stochastic model describing tumor growth based on Gompertz law is considered. We pay attention on the tumor size at time detection. We assume the initial state as a random variable since it may suffer from errors due to measurement and diagnostics. The aim of the present work is to study the first exit time problem for the resulting stochastic process. A numerical analysis is also performed for particular choices of the initial distribution.

A stochastic model for the firing activity of a neuronal unit has been recently proposed in [4]. It includes the decay effect of the membrane potential in the absence of stimuli, and the occurrence of excitatory inputs driven by a Poisson process. In order to add the effects of inhibitory stimuli, we now propose a Stein-type model based on a suitable exponential transformation of a bilateral birth-death process on

${\mathbb Z}$

and characterized by state-dependent nonlinear birth and death rates. We perform an analysis of the probability distribution of the stochastic process describing the membrane potential and make use of a simulation-based approach to obtain some results on the firing density.

The aim of the present paper is to provide some quantitative informations on the role of catastrophes in diffusion models. Analytical and computational results for the Wiener and for the Ornstein-Uhlenbeck processes are determined.

A neural network with characteristic parameters to recognize abnormalities in ultrasound images acquired from echographic tissue-mimicking materials is proposed. The neural network has been implemented in MATLAB and it can be used in real time to assist the clinical diagnoses in the early phases. The parameters are extracted from a database of B-mode ultrasound images. After training and testing the network, using a statistically significative set of experimental data and a non-commercial phantom, results show that the proposal can be successfully applied to efficiently deal with this problem.

Vision is considered form a theoretical view point stressing a traditional view concerning the 3D problem. Proprioceptive variables as well as cross correlation and regression analysis are considered. It is proposed a semantic model of logical processes based on the Theory of Artificial Neural Netwoks and a calculus of Signification and Intention developed by J. S. Fonseca and J. Mira y Mira in 1970. First, the cogency of semantic proposal is tested using a poem by Fernando Pessoa. Finally, it is shown that a visual counterpart of the approach has been used by Piet Mondrian.

Motivated by some experimental results of [13], the standard stochastic Leaky Integrate-and-Fire model for single neuron firing activity is generalized in a way to include evolutionary instantaneous time constant and resting potential. The main features of the ensuing Gauss-diffusion process are disclosed by making use of a space-time transformation leading to the Ornstein-Uhlenbeck process. On the grounds of simulations of the time course of the membrane potential, we are led to conclude that our generalized model well accounts for a variety of experimental recordings that appear to indicate that the standard model is inadequate to reproduce statistically reliable features of spike trains generated by certain types of cortical neurons.

In Computational Neuroscience, mathematical and computational modeling are differentiated. In this paper, both kinds of modeling are considered. In particular, modeling approaches to signal generation and processing in single neurons (i.e., membrane excitation dynamics, spike propagation, and dendritic integration) and to spatiotemporal activity patterns in neuronal ensembles are discussed.

In the present work a

Coq

based approach is taken to characterize the

foldl

using a functorial structure from which an inductive type is determined. With

μ

F

being an initial

F

–algebra and (

B

,

θ

) another

F

–algebra, two

F

–algebras with support

B

×

μ

F

are constructed and then coequalized. This coequalization morphism allows the definition of

foldl

structurally.

After examining some significant examples we propose the following methodology to define a

foldl

operator. Let

F

be a polynomial endofunctor and (

μ

F

,

in

F

) its initial algebra. We define two

F

–algebras with support

B

×

μ

F

, and

h

1

,

h

2

:

F

(

B

×

μ

F

) →

B

×

μ

F

constructed such that in one of them the argument of the initial type is syntactically (structurally) lower than that in the other. Then,

${\mathit{foldl}}:B\times\mu_F \rightarrow B$

can be defined as a specific morphism that coequalizes them

$(h_1;{\mathit{foldl}}=h_2;{\mathit{foldl}}).$

For an initial

F

–algebra with distinguished element (as in the case of lists),

foldl

is a coequalizer of

h

1

and

h

2

.

The proofs are performed using the

Coq

proof system. In this context, constructive approach stress that existence is constructive, therefore with a computational content.

Detailed structure for proofs in

Coq

is included but interactive code is omited. See the section 4 for a repository with the complete source code.

Given a basis of a vector space

V

over a field

$\mathbb{K}$

and a multiplication table which defines a bilinear map on

V

, we develop a computer program on Mathematica which checks if the bilinear map satisfies the Leibniz identity, that is, if the multiplication table endows

V

with a Leibniz algebra structure. In case of a positive answer, the program informs whether the structure corresponds to a Lie algebra or not, that is, if the bilinear map is skew-symmetric or not.

The algorithm is based on the computation of a Gröbner basis of an ideal, which is employed in the construction of the universal enveloping algebra of a Leibniz algebra. Finally, we describe a program in the NCAlgebra package which permits the construction of Gröbner bases in non commutative algebras.

The age-related macular degeneration (AMD) is the main cause of blindness among people over 50 years in developed countries and there are 150 million people affected worlwide. This disease can lead to severe loss central vision and adversely affect the patient’s quality of life. The appearance of drusen is associated with the early AMD, so we proposed a top-down methodology to detect drusen in initial stages to prevent AMD. The proposed methodology has several stages where the key issues are the detection and characterization of suspect areas. We test our method with a set of 1280 ×1024 images, obtaining a system with a high sensitivity in the localization of drusen, not just fake injuries.

Guideline documents offer a rich repository of information on clinical decisions, actions and prescriptions. However, clinicians do not use them as much as expected since health care organisations started to develop them. One alternative to promote the use of guidelines is to automatically select the relevant information at the point of care. But, extracting knowledge from a guideline document is an arduous and complex task. In this paper, we propose to apply the methodology CommonKADS in the analysis phase of a clinical practice guideline, with the aim of systematizing knowledge acquisition, providing a methodological support that helps to detect and document all the transformations from natural language to the structured representation of a knowledge model. When forcing to the knowledge engineer to keep these transformations, the knowledge modelling becomes more gradual.

In this work we propose a representation of graded algebraic structures and morphisms over them appearing in the field of Homological Algebra in the proof assistants Isabelle and Coq. We provide particular instances of these representations in both systems showing the correctness of the representation. Moreover the adequacy of such representations is illustrated by developing a formal proof of the Trivial Perturbation Lemma in both systems.

This paper describes a methodology for the detection of landmark points in the retinal vascular tree using eye fundus images. The procedure is fully automatic and is based in modified order filters, morphological operators and local analysis along the vascular tree. The results show a detection rate of 90% approx. using VARIA, a retinal image database designed to test techniques of retinal processing in heterogeneous conditions.

This paper covers some of the most advanced optimization techniques for web servers and web applications applied to a Modular Object Oriented Distance Learning Environment based on PHP 5 and Apache 2.

This paper presents a disambiguation method that diminishes the functional combinations of the words of a sentence taking into account the context in which they appear. This process uses an algorithm which does the syntactic analysis of every functional combination of the sentece. In order to control this analysis, a grammar with restrictions has been developed to model the valid syntactic structures of the Spanish language. The main target of our algorithm is the separation between the disambiguation method and the grammar which governs it.

This contribution deals with the importance of similarity in Case-based Reasoning and the application for problems in the field of Structural Health Monitoring. Case-based Reasoning and different methodologies for the retrieval of knowledge stored in a case base are presented. Furthermore, different approaches for object representation and applicable similarity measures including indexing techniques to reduce the number of required distance-calculations are introduced, particularly the M-tree approach. Finally, a prototype for a Case-based Decision Support System for Structural Health Monitoring is described.

Audio-visual interactive content is very common nowadays. It has many applications in very different fields, from videogames to visualization of scientific data. However, there are environments such as digital television in which the delivery of interactive content is of interest, but are limited by the shortcomings of the players. For example, in cable TV environments users access content through a set-top box, which is usually very limited in computing power due to cost, power consumption and the need to keep a moderate size. Furthermore, set-top boxes do not usually have specific hardware for graphics processing (GPU, Graphics Processing Unit) desirable for high quality interactive content, but rather are optimized for real time decoding of video in hardware (usually Mpeg-2, in very recent ones h.264). In this work we describe a distributed system for the creation of synthetic content and its encoding to digital video to send it to the clients. The most important features to provide are scalability and fault tolerance, in order to support a large number of concurrent users with an uninterrupted service.

Achieving high effectiveness in Shot Boundary Detection (SBD) paves the way for high-level analysis of the video (keyframe extraction, story segmentation, etc.), which makes this step very important. Thus, the SBD problem has been extensively addressed, and many approaches have been proposed. As these approaches have their own different strengths and weaknesses, merging the outcomes of different detectors in order to obtain a better detector comes naturally. In this paper we propose an approach to SBD which takes into account the outcomes of two shot boundary detectors, using a rank fusion technique. This new detector is tested with videos from the TRECVid initiative, finding that it outperforms the two original methods. Moreover, the computation of this merging method is very fast, so it is very attractive for an operational environment.

Health-care quality control is a challenge that medical services and their information systems will deal with in the following years. Clinical Practice Guidelines are a structured set of recommendations for physicians to solve a medical problem. The correct fulfilment of a guideline seems to be a good indicator of health-care quality. However, the guideline fulfilment checking is an open clinical problem that requires collaborative efforts. In this sense, clinical workflows have demonstrated to be an effective approach to partially model a clinical guideline. Moreover, some efforts have been done in consistency checking and debugging management in order to obtain a correct description of the clinical processes. However, the clinical practice not always strictly fulfils clinical workflows, since patients require personalised care and unexpected situations occur. Therefore, in order to obtain a workflow fulfilment degree, it seems reasonable to compare

a posteriori

the evolution of the patient records and the clinical workflow, providing a flexible fulfilment measure. In this work we present a general framework to classify and study the development of these measures, and we propose a workflow fulfilment function, illustrating its suitability in the clinical domain by an example of a real medical problem.

Multi-agent systems are systems composed of multiple interacting autonomous agents forming complex systems. Verifying multi-agent systems is a challenging task due to their dynamic nature, and the complex interactions between agents. In this paper, we propose the use of the McErlang model checker as a testing tool, as it affords precise control of the scheduling of agents, and provides convenient access to the internal states and actions of the agents. We illustrate the suitability of the approach by discussing our experiences in applying this verification technique to RoboCup teams. The experiments we conducted discovered a number of bugs in two such teams.

There are many issues to overcome when integrating different data sources due to the number of variables that are involved in the integration phase. However we are interested in the integration of temporal and spatial information due to the nature of modern Information Systems. We have previously developed a model, called STOWL, which is a spatio-temporal extension of OWL. We use this model as the common model for defining the schemes of the data sources in order to ease their integration. This paper presents the part of STOWL which has to do with the definition of n-ary relations.

Model-based testing generates test cases from a high-level model. Current models employ extensions to finite-state machines. This work proposes a separation of transitions in the model and their corresponding actions in the target implementation, and also includes special treatment of exceptional states.

Testing is the most used technique for software verification: it is easy to use and even if no error is found, it can release a set of tests certifying the (partial) correctness of the compiled system. Moreover, in order to increase the confidence of the correctness of the compiled system, it is often required that the provided set of tests covers 100% of the code. This requirement, however, substantially increases the costs associated to the testing phase, since it may involve the manual generation of tests. In this paper we show how to use a Bounded Model Checker for C programs (CBMC) as an automatic test generator for the Coverage Analysis, and we show how its use can substantially reduce the costs of the testing phase.

This paper presents an approach of using bounded model checking for healing assurance within a framework for self-healing of concurrent Java programs. In this framework, dynamic (i.e., runtime) analysis is used to detect possible data races for which some pre-defined healing strategy may subsequently be applied. Before applying such a strategy, it is desirable to confirm that the detected possible error is indeed a real error and that the suggested healing strategy will solve it without introducing an even worse problem (namely, a deadlock). For this purpose, we suggest bounded model checking to be applied in the neighbourhood of the state in which the possible error is detected. In order to make this possible, we record certain points in the trace leading to the suspicious state within a run of the tested system, and then replay the trace in the chosen model checker (in particular, Java PathFinder) using its state space exploration capabilities to navigate between the recorded points.

Recently, it has been proposed to use approximation techniques in the context of decision procedures for the quantifier-free theory of fixed-size bit-vectors. We discuss existing and novel variants of under-approximation techniques. Under-approximations produce smaller models and may reduce solving time significantly. We propose a new technique that allows early termination of an under-approximation refinement loop, although the original formula is unsatisfiable. Moreover, we show how over-approximation and under-approximation techniques can be combined. Finally, we evaluate the effectiveness of our approach on array and bit-vector benchmarks of the SMT library.

For the efficient and effective use of automated static analysis of software systems it is crucial to know what kind of errors can be detected and how seriously a reported problem can or should be taken. In the study conducted for this paper we applied a widely used tool (

PC

-

lint

) for automated static analysis (ASA) to check C++ code fragments from student exercises. The goal of this research was to discover which types of defects can be identified by automated static analysis. In this paper we present our findings; furthermore the results from classifying the defects are set in relation to detection rules and severity levels provided by ASA, in order to derive insights for calibrating ASA tools in a specific application context.

This paper describes a real-time vision-based system that detects vehicles approaching from the rear in order to anticipate possible rear-end collisions. A camera mounted on the rear of the vehicle provides images which are analysed by means of computer vision techniques. The detection of candidates is carried out using the top-hat transform in combination with intensity and edge-based symmetries. The candidates are classified by using a Support Vector Machine-based classifier (SVM) with Histograms of Oriented Gradients (HOG features). Finally, the position of each vehicle is tracked using a Kalman filter and template matching techniques. The proposed system is tested using image data collected in real traffic conditions.

In this paper we present a new real-time hierarchical (topological/metric) Visual SLAM system focusing on the localization of a vehicle in large-scale outdoor urban environments. It is exclusively based on the visual information provided by both a low-cost wide-angle stereo camera and a low-cost GPS. Our approach divides the whole map into local sub-maps identified by the so-called fingerprint (reference poses). At the sub-map level (Low Level SLAM), 3D sequential mapping of natural landmarks and the vehicle location/orientation are obtained using a top-down Bayesian method to model the dynamic behavior. A higher topological level (High Level SLAM) based on references poses has been added to reduce the global accumulated drift, keeping real-time constraints. Using this hierarchical strategy, we keep local consistency of the metric sub-maps, by mean of the EKF, and global consistency by using the topological map and the MultiLevel Relaxation (MLR) algorithm. GPS measurements are integrated at both levels, improving global estimation. Some experimental results for different large-scale urban environments are presented, showing an almost constant processing time.

New trends in High Performance Computing Architectures are arising an old concept,

concurrence

. But concurrence is not parallelism. To afford parallelism in the new computing arena, parallel applications need to consider this or just being completely rewritten in such a way that parallelism can be expressed by means of concurrence. Abstractions may help to keep performance on the new, and also on the legacy, platforms. This paper shows how abstractions may play an important role when used to model the problem.

Understanding the biological variability of anatomical objects is essential for statistical shape analysis and to distinguish between healthy and pathological structures. Statistical Shape Modelling (SSM) can be used to analyse the shapes of sub-structures aiming to describe their variation across individual objects and between groups of them [1]. However, when the shapes exhibit self-similarity or are intrinsically fractal, such as often encountered in biomedical problems, global shape models result in highly non-linear shape spaces and it can be difficult to determine a compact set of modes of variation. In this work, we present a method for

local

shape modelling and analysis that uses Diffusion Maps [2] for non-linear, spectral clustering to build a set of linear shape spaces for such analysis. The method uses a curvature scale-space (CSS) description of shape to partition them into sets of self-similar parts and these are then linearly mixed to more compactly model the global shape.

The objective of this paper is to analyse the influence of the different parameters used for an overall approach to forecasting a future position of the mobile objects of an image sequence after processing the previous images to it. Our approximation uses classical techniques such as optical flow to extract object’s trajectories and velocities and autoregressive algorithms to build the predictive model. Applications to outdoor scenarios are possible, for videos where stationary cameras are used and moving objects follow an affine displacement field. In this work, traffic sequences with different meteorological conditions are studied.

This present work presents a parameterization system based on angles from signature edge (2D-shape) for off-line signature identification. We have used three different classifiers, the Nearest Neighbor classifier (K-NN), Neural Networks (NN) and Hidden Markov Models (HMM). Our off-line database has 800 writers with 24 samples per each writer; in total, 19200 images have been used in our experiments. We have got a success rate of 84.64%, applying as classifier Hidden Markov Model, and only used the information from this edge detection method.

We present an optical flow based method for noise reduction in image sequences. To prevent artefacts caused by optical flow imperfections, we propose a method to estimate these imperfections. We use the estimation to adaptively choose either a temporal or a spatial based noise reduction algorithm to be applied in different image zones. Our results have shown that an important noise reduction can be achieved with the proposed method, without the drawbacks of the simpler methods. The method has provided important noise reductions even with complex image sequences.

Robotics is a very fast growing field especially in the last years. In the late seventies the first industrial applications of stationary unintelligent industrial robots were realised. Begin of the 90‘s a new generation of mobile, intelligent, cooperative robots grows up. This new generation opens new applications areas like in construction, in agriculture, in the food industry, in the household, for medical and rehabilitation applications, in the entertainment industry as well as for leisure and hobby. Current developing trends are humanoid robots and robots supporting humans in every day life. In the future probably ubiquitous robots will support us.

The framework of this paper is robot localization inside buildings using WiFi signal strength measure. This localization is usually made up of two phases: training and estimation stages. In the former the WiFi signal strength of all visible Access Points (APs) are collected and stored in a database or Wifi map, while in the latter the signal strengths received from all APs at a certain position are compared with the WiFi map to estimate the robot location. This work proposes the use of Fuzzy Rule-based Classification in order to obtain the robot position during the estimation stage, after a short training stage where only a few significant WiFi measures are needed. As a result, the proposed method is easily adaptable to new environments where triangulation algorithms can not be applied since the AP physical location is unknown. It has been tested in a real environment using our own robotic platform. Experimental results are better than those achieved by other classical methods.

This paper describes the detection of moving obstacles using laser radar in road environments. This application is designed to be implemented in further research on data fusion technologies. The developed application uses only a laser radar which provides information to sort objects according to their shape and movement. The subsequent detection and classification provide higher level tracking.

This work introduces the internally and externally grounded core structure of intelligent behaviour in complete robots. It integrates reactive as well as emotional subsystems into a multi-agent network in order to realize emergent intentional behaviour. The architecture of this controller is structured in four layers that interact in a decentralized and reciprocal way. Of special importance is the internal grounding which is based on monitoring internal processes of the robotic system. The integration of these internal states enables the robot to response differently to the same stimulus pattern at different times. Emotional behaviour may be regarded as a bundle of concerted activities that facilitate the successful survival in a potentially unpredictable environment. Additionally, an extension for the controller that may help to achieve long-ranging and aim-oriented behaviour is outlined. Rational behaving robots should be based on grounded cognition.

In this work we describe the main aspects of an information system for travelers of public transport by road, specifically its architecture and functionalities. In the paper we explain how the ubiquitous computing paradigm has been applied to achieve the system goals. Relevant properties of the system are: Its distributed architecture, its capacity to work using local communications infrastructures such as Bluetooth and Wifi, the interaction with the travelers is made using their own mobile devices’ communications, such as: cellular phones, PDAs, etc., etc.

We have applied ANLAGIS to a coordination problem Multi-robot Systems, specifically the storage of a set of elements is in the warehouses. We have combined ANLAGIS along with Reinforcement Learning for each of the behaviors that stem from this task, besides drawing up the coordination of these behaviors in order to perform the task in a satisfactory way.

Driving through crossroads is one of the most dangerous maneuvers. The European community goal of reducing the vehicle accident rate will require a reduction of accidents in crossroads. This paper presents a method of cooperation among vehicles getting into crossroads in order to avoid accidents.

Vehicular Ad-hoc NETworks (VANETs) will provide many interesting services in the near future. One of the most promising is commercial application. In such a case, there will be necessary to motivate drivers to cooperate and contribute to packet forwarding in Vehicle-TO-Vehicle and Vehicle-TO-Roadside communications. This paper examines the problem, analyzes the drawbacks of known schemes, and proposes a new secure incentive scheme to stimulate cooperation in VANETs.

In this paper we present a control architecture for multi-robot systems in dynamic environments, where the low level behaviors are obtained through artificial neural networks and evolutionary algorithms to achieve collaborative behaviors in a multi-robot system. As an example, we have cooperative tasks establishing a surveillance scenario stressing cooperation and competition between them.

This work is related to the analysis of reactive obstacle avoidance in general, and specifically to ND algorithms family. Contrary to many previous methods, the ND approach is not aimed at devising a general motion law; instead, it operates over a reduced set of possible situations that are treated by a particular motion law. The big earning of this idea is that it eases the design of control, as now motion laws are specific to every identifiable situation. However, it also raises new issues as nothing guarantees the control action continuity when the diagnostic changes. In this paper a modification of the ND approach, along with experimental results, is presented in order to improve this aspect of the method.

Traffic Circles are frequently used in cities, to control vehicular traffic at intersections. As said in [1], their main advantages can be the provision of an adequate throughput and the improvement of user safety, by slower vehicle speeds and reducing traffic conflicts.

In this work, performance aspects of a high-precision radiolocation system are evaluated. An integrated simulation environment is introduced, which allows the co-evaluation of both physical layer parameters, such as measurement error, as well as network-related performance figures like the time-to-fix and throughput. Simulation results for different strategies and algorithms are presented and analyzed for their practicability.

Low Density Parity Check (LDPC) codes are considered in many future communication systems for error correction coding. Optimal decoding of LDPC codes is usually too costly to be done in practice. For this reason, sub-optimal algorithms are used. A state-of-the-art algorithm for decoding of LDPC codes is called belief propagation (BP). For short LDPC codes and for codes with an implementation efficient structure, the performance of this algorithm can be far from optimum.

We present a graphical model for representing the decoding problem, called configuration graph. We show the construction of a configuration graph and describe how the decoding problem can be represented as maximum weighted vertex problem (VP) on a configuration graph. We describe decoding approaches utilizing this representation and show the improvements in terms of decoding performance as well as the complexity/performance trade-offs possible with these algorithms.

Spatial Multiplexing (SM) is an effective means for enhancing the transmission data rate in Multiple-Input Multiple-Output (MIMO) systems, particularly when used in combination with precoding techniques. However, it is not always obvious to connect the performance of such a system to its number of data streams and antennas. In this paper, the diversity order of a SM MIMO system using a Minimum Mean Square Error (MMSE) receiver is analytically calculated when a precoder based on transmit antenna correlation is included at the transmitter. It is shown that it is given by

d

 = 

N

r

 − 

N

s

 + 1 where

N

r

is the number of receive antennas and

N

s

is the number of data streams. This result is confirmed by simulations. Although enabling a Signal-to-Noise Ratio (SNR) gain, such a precoder is thus not able to improve the diversity order with respect to a non-precoded SM MIMO system.

Nowadays, applications for marine autonomous systems are increasing due to the interest caused by different issues: global warming, surveillance, monitoring, etc. These systems need to work twenty four hours without interruption. For this reason, it is indispensable to have an efficient energy system. In order to characterize the consumption and energy production is necessary to have a good model [1]. This model must take into account the different elements that work in the system to obtain the maximum power and efficiency.

This paper concerns with the variation in discrete time systems driven by a random walk, in contrast with the ordinary Malliavin calculus based on a Brownian motion. A derivative of random functionals with respect to a random walk is introduced and some its fundamental properties are shown. Theories parallel to Malliavin calculus are also discussed in view of applications for discrete time phenomena in signal processing, mathematical finance, and systems science and engineering.

This paper discusses relationships among convolution matrices and fixed-polarity matrices for polynomial expressions of discrete functions on finite groups. Switching and multiple-valued functions are considered as particular examples of discrete functions on finite groups. It is shown that if the negative literals for variables are defined in terms of the shift operators on domain groups, then there is a relationship between the polarity matrices and convolution matrices. Therefore, the recursive structure of polarity matrices follows from the recursive structure of convolution matrices. This structure is determined by the assumed decomposition of the domain groups for the considered functions.

Reversible logic synthesis gained much attention recently, primarily due to its applications in low-power computing and quantum computing. There are many synthesis approaches including those using spectral techniques. The algorithm presented in this paper uses the Reed-Muller expansion of a reversible function represented by a Shared Functional Decision Diagram (FDD) to synthesize the function as a network of Toffoli gates. In each step of the algorithm the Toffoli gate with smallest cost is selected, where the cost is defined through complexity of the Shared FDD.

The paper introduces a signal adaptive Haar-like transform for ternary functions. The term adaptive means, given the signal, we design a transform after a brief analysis of signal features such as appearance of identical patterns or periods of constancy. The proposed transform possesses a fast FFT-like computation algorithm similar to fast algorithms for the classical Haar transform on finite dyadic groups as well as the generalized Haar transforms for multiple-valued functions. The proposed transform is utilized in reduction of the number of nonzero coefficients in spectral representation of ternary functions. The method shows good results, especially, when the given ternary signal contains intervals of constancy or repeated patterns having relatively high frequency of appearance.

The calculation of Hamiltonian Circuits is an

NP

-complete task. This paper uses slightly modified complete sets of Hamiltonian circuits for the classification of documents. The known solution method is based on a SAT-instance with a huge number of clauses which is flattening the knowledge about the problem. We suggest an even more compact model of Boolean equations that preserves the knowledge by summarizing restrictions and requirements. The presented

implicit two-phase SAT-solver

finds efficiently the solution using operations of the XBOOLE library. This solver can be included easily as signal processing unit into the device where the classification of the documents is required.

Traditionally IIR digital filters are designed by using analog filters described in time or transform domain, then by converting the analog filters to digital filters using appropriate transformation from

s

-domain to

z

-domain. For many engineers analog filters mean certain circuits or a netlist of components, and digital filters are a set of statements in certain software. In this work, we show how to obtain a digital filter from a given netlist of an analog filter by skipping the transfer function description.

This paper describes a decision diagram package for efficient computation with large matrices. The heterogeneous decision diagrams supported by the package do not require the selection variables to have a single domain. Computations can be over a selected field up to the complex numbers including finite fields. Implementation strategies supporting this level of flexibility are presented. Applications are outlined taken from diverse areas such as reversible and quantum logic, spectral transformations and operations over finite fields.

Discrete Walsh functions are well known in digital signal processing, telecommunications, and logic design. In this paper we show that they also appear “naturally” in matrices representing forms of interaction among different factors involved in the design of industrial experiments.

Myoelectric hand-prostheses are used by patients with either above- or below-elbow amputations and actuated with a minimal microvolt-threshold myoelectric signal (MES). Prehensile motions or patterns are deduced from the MES by classification. Current approaches act on the assumption, that MES is adiabatic-invariant and unaffected by fatigue of contributory muscles. However, classifiers fail on the onset of muscle fatigue and cannot distinguish between voluntary-, submaximal-contraction and an intentional release of muscle tension. As a result, patients experience a gradual loss of control over their prostheses. In this contribution we show, that the probability distributions of extracted time- and frequency-domain features are fatigue dependent with regard to locality, skewness and time. Also, we examine over which time-frame, established classifiers provide unambiguous results and how classifiers can be improved by the selection of a proper sampling-window size and an appropriate threshold for select features.

This paper presents a new cost-efficient realization scheme of pattern matching circuits in FPGA structures with

embedded memory blocks

(EMB). The general idea behind the proposed method is to implement combinational circuits using a net of

finite state machines

(FSM) instead. The application of functional decomposition method reduces the utilization of resources by implementing FSMs using both EMBs and LUT-based programmable logic blocks available in contemporary FPGAs. Experimental results for the proposed method are also shown. A comparison with another dedicated method yields extremely encouraging results: with a comparable number of EMBs, the number of logic cells has been reduced by 95%.

Technology of smart hand prosthesis control based on myoelectric signals strongly depends on signal processing algorithms. The application forces specific requirements on computational complexity (for dexterity of prosthesis), processing speed (for fast reaction) and size (for portability). The paper refers to a concept of selected DFT points of EMG signal analysis for patient intention recognition. Signals are acquisited from 6 channels with 1 kHz sampling frequency. Specialized digital hardware is proposed, capable of parallel processing of series of signals. The design was implemented in VHDL, verified and synthesized for FPGA. In-house developed floating point arithmetic was applied. Satisfying processing speed was obtained at a reasonable cost.

This paper describes methods of automatic analysis and classification of biological signals. Polysomnographic (PSG) recordings encompass a set of heterogeneous biological signals (e.g. EEG, EOG, EMG, ECG, PNG) recorded simultaneously. These signals, especially EEG, are very complex and exhibit nonstationarity and stochasticity. Thus their processing represents a challenging multilevel procedure composed of several methods. Used methods are illustrated on examples of PSG recordings of newborns and sleep recordings of adults and can be applied to similar tasks in other problem domains. Analysis was performed using real clinical data.

A collection of algorithms implemented in Mathematica 7.0, freely available over the internet, and capable to manipulate rational functions and solve related control problems using polynomial analysis and design methods is presented. The package provides all the necessary functionality and tools in order to use the theory of

$\it \Omega-$

stable functions, and is expected to provide the necessary framework for the development of several other algorithms that solve specific control problems.

The modern development of nonlinear control theory is related mainly to the use of algebraic methods which show great applicability to solve a number of nonlinear control problems. Recently, the power of such methods were extended by introducing the concept of transfer functions of nonlinear systems. Such a concept represents a generalization, for the transfer functions of nonlinear systems have many analogical properties like those of linear systems. In this chapter some basic properties of the transfer function formalism of nonlinear systems are briefly discussed and references to possible applications are given.

The paper deals with design of a robust controller via algebraic

μ

-synthesis for a two tank system, which is a well known benchmark problem. The controller is obtained by decoupling two-input two-output system into two identical SISO (Single-Input Single-Output) plants. The task of robust controller design is then performed by finding a suitable pole placement for the SISO systems. The robustness is measured by the structured singular value denoted

μ

. The final controller is verified through simulation for plants perturbed by worst case perturbations.

This paper compares two approaches to the control design for a thermal plant. This is dominant by two modes of the heat transfer - the fast mode (radiation) and the slow mode (conduction), by the nonlinear state dependent dynamics and by necessity to apply robust control design approaches. The algebraic and the constrained pole assignment controllers are compared both from the point of view of the nominal tuning required for achieving fastest possible monotonic transient responses and from the availability and easy of the controller robustification. The real experiment is used to verify the results.

This chapter deals with an application of the algebraic formalism in nonlinear control systems on the nonlinear controller design for a fluid tank system. A nonlinear discrete-time model of a fluid tank system and its transfer function are derived. Then, nonlinear continuous- and discrete-time controllers are designed using transfer function formalism for nonlinear systems which was developed recently. Verification on the real plant is also included and it suggested the modification of the original model of one-tank system to achieve better performance on the real plant.

The paper deals with the algorithm named as pre-identification, which denotes the simple general identification algorithm used for the system identification. The identification is realized before the system is controlled. It can be used in case the controlled system is time-invariant or slightly time-variant. Furthermore, the identified system might be nonlinear. Pre-identification provides a priori system description which is necessary for switching self-tuning control or useful for nonlinear control. The verification of the pre-identification usefulness was realized on several laboratory apparatuses in real-time using PC.

The aim of the paper is to apply the polynomial methods to nonlinear realization problem. A new formula is presented which allows to compute the differentials of the state coordinates directly from the polynomial description of the nonlinear system, yielding a shorter and more compact program code in

Mathematica

implementation.

Solving the segmentation problem for music is a key issue in music information retrieval (MIR). Structural information about a composition achieved by music segmentation can improve several tasks related to MIR such as searching and browsing large music collections, visualizing musical structure, lyric alignment, and music summarization. Various approaches using genetic algorithms have already been introduced to the field of media segmentation including image and video segmentation as segmentation problems usually have complex fitness landscapes. The authors of this paper present an approach to apply genetic algorithms to the music segmentation problem.

In this work we consider the application of metaheuristic algorithms to the problem of fitting rectangular signals to time-data series. The application background is to search for transit signals of exoplanets in stellar photometric observation data. The presented algorithms include an Evolution Strategy and Differential Evolution; both algorithms use an efficient reduction of the search space by exactly solving a subproblem. The presented results affirm the presented methods to be promising and effective tools for the discovery of the first multi-transit planetary system.

In this paper we discuss the generation of models for emissions of a Diesel engine, produced by genetic programming based evolutionary system identification: Models for the formation of NO

x

and particulate matter emissions are identified and analyzed. We compare these models to models designed by experts applying variables section and the identification of local polynomial models; analyzing the results summarized in the empirical part of this paper we see that the use of enhanced genetic programming yields models for emissions that are valid not only in certain parts of the parameter space but can be used as global virtual sensors.

The bounded diameter minimum spanning tree problem is an

$\mathcal{NP}$

-hard combinatorial optimization problem arising in particular in network design. There exist various exact and metaheuristic approaches addressing this problem, whereas fast construction heuristics are primarily based on Prim’s minimum spanning tree algorithm and fail to produce reasonable solutions in particular on large Euclidean instances.

In this work we present a method based on hierarchical clustering to guide the construction process of a diameter constrained tree. Solutions obtained are further refined using a greedy randomized adaptive search procedure. Especially on large Euclidean instances with a tight diameter bound the results are excellent. In this case the solution quality can also compete with that of a leading metaheuristic.

In this paper we propose an iterative hybrid heuristic approach consisting of two phases to solve the Rectangle Packing Problem. In the first phase, a strip width value

W

is fixed and the corresponding Strip Packing Problem is solved using an efficient hybrid GRASP-VNS heuristic. In the second one, a new value

W

is determined. The above phases are repeated until the stopping condition is met. Then, the results obtained by this iterated heuristic are compared with the results given by a Simulated Annealing given in the literature. The comparative analysis corroborates the effectiveness of the proposed hybrid approach.

In this paper we present new local search algorithms for the Probabilistic Traveling Salesman Problem (PTSP) using sampling and ad-hoc approximation. These algorithms improve both runtime and solution quality of state-of-the-art local search algorithms for the PTSP.

We applied an Island Model Genetic Algorithm (GA) to a Multi-Agent System (MAS) modeled in Cellular Automata (CA) in order to find the optimal behavior of the agents. The agents’ task is to visit all free cells in a cellular grid containing obstacles as fast as possible. For this investigation we used a previously defined set of five different environments. The agents are controlled by a finite state machine with a restricted number of states and outputs (actions of the agents). Finite state machines with 4 to 7 states have been evolved by the GA. We compared the effectiveness (quality of solutions) and efficiency of the GA to an exhaustive search of all possible solutions. A special hardware (FPGA logic) has been used to enumerate and evaluate all 6-state finite state machines. The results show that the GA is much faster but almost as effective as the exhaustive search.

In the paper we carried out the analysis of the properties of the Harmony Search Algorithm (

HSA

) on a well known one-dimensional binary knapsack problem. Binary knapsack problems are among the most widely studied problems in discrete optimization. Since the optimization versions of these problems are nP-hard, practical solution techniques do not ask for optimality, but are heuristics that generate feasible, suboptimal solutions. In this paper we describe the 0-1 knapsack problem itself, the backgrounds of the

HSA

, Baldwin and Lamarck Effects and the numerical tests. The result of the tests performed is surprised a bit.

In this paper a certain modification of an evolutionary algorithm is characterized, which is intended for planning a schedule for tanker drivers working for a petrol base. The description of a computational algorithm is presented, starting from the description of a particular genotype and phenotype. The most important element of the algorithm is the procedure of projection of the genotype on the set of phenotypes based on the Baldwin Effect. The final part of the paper presents computational tests and plans for the future.

The rooted delay-constrained minimum spanning tree problem is an NP-hard combinatorial optimization problem arising for example in the design of centralized broadcasting networks where quality of service constraints are of concern. We present a construction heuristic based on Kruskal’s algorithm for finding a minimum cost spanning tree which eliminates some drawbacks of existing heuristic methods. To improve the solution we introduce a greedy randomized adaptive search procedure (GRASP) and a variable neighborhood descent (VND) using two different neighborhood structures. Experimental results indicate that our approach produces solutions of better quality in shorter runtime when having strict delay-bounds compared to an existing centralized construction method based on Prim’s algorithm. Especially when testing on Euclidian instances our Kruskal-based heuristic outperforms the Prim-based approach in all scenarios. Moreover our construction heuristic seems to be a better starting point for subsequent improvement methods.

We present an optimisation algorithm called “King of The Hill” ACO (KoTH-ACO) based on the MAX-MIN Ant System for a TSP problem extended for the dynamic pickup and delivery problem. The KoTH algorithm shows faster convergence and better solution qualities than the MAX-MIN Ant System in our benchmark instances. In addition, the runtime performance of ACO systems could be improved with approximate probability calculation.

In this paper the authors describe a model driven approach for the development of heuristic optimization algorithms. Based on a generic algorithm model, several operators are presented which can be used as algorithm building blocks. In combination with a graphical user interface, this approach provides an interactive and declarative way of engineering complex optimization heuristics. By this means, it also enables users with little programming experience to develop, tune, test, and analyze heuristic optimization techniques.

Workflows are used nowadays in different areas of application. Emergency services are one of these areas where explicitly defined workflows help to increase traceability, control, efficiency, and quality of rescue missions. In this paper, we introduce a generic workflow model for describing fire fighting operations in different scenarios. Based on this model we also describe heuristics for calculating the similarity of workflows which can be used for searching and clustering.

This work is devoted to the problem of building a sample classifier based on data from microarray gene expression experiments. Two specific issues related to this are tackled in this paper: (a) selection of parameters of a classification model to ensure best generalization power, and (b) variability of expected prediction error (EPE) for new data as a function of the model parameters. A method is presented for selection of model parameters minimizing the EPE in studies where the number of samples (

n

) is much smaller then the number of attributes (

d

). Due to very unstable behaviour of the EPE in the space of model parameters, it seems essential that microarray studies involve systematic search for the right model parameters, as shown in this work.

The design of intelligent and sensor-based autonomous agents learning by themselves to perform complex real-world tasks is a still-open challenge for artificial and computational intelligence. In this paper a concept of a framework for conflict resolution in an autonomous robotic agent system is presented. The structure of an intelligent robotic agent consists of two independent subsystems: the action and motion planning system and the action and motion reactive control system with integrated conflict resolution methods.

In this work we examine the effect of elitist and non-elitist selection on a supply chain problem. The problem is characterized by an output constraint which in turn separates the search space in a feasible and a non-feasible region. Additionally the simulation output is noisy due to a stochastic demand model. We will show analyze which strategy is able to perform a walk on the boundary between the feasible and infeasible space. Additionally a new selection scheme is introduced based on a statistical test to evaluate the difference between two solutions given a number of noisy quality values. This selection scheme is described and evaluated on the problem situation.

The process of feature selection is an important first step in building machine learning models. Feature selection algorithms can be grouped into wrappers and filters; the former use machine learning models to evaluate feature sets, the latter use other criteria to evaluate features individually. We present a new approach to feature selection that combines advantages of both wrapper as well as filter approaches, by using logistic regression and the area under the ROC curve (AUC) to evaluate

pairs

of features. After choosing as starting feature the one with the highest individual discriminatory power, we incrementally rank features by choosing as next feature the one that achieves the highest AUC in combination with an already chosen feature. To evaluate our approach, we compared it to standard filter and wrapper algorithms. Using two data sets from the biomedical domain, we are able to demonstrate that the performance of our approach exceeds that of filter methods, while being comparable to wrapper methods at smaller computational cost.

This paper discusses some aspects of the general convergence behavior of genetic algorithms. Careful attention is given to how different selection strategies influence the progress of genetic diversity in populations. For being able to observe genetic diversity over time measures are introduced for estimating pairwise similarities as well as similarities among populations; these measures allow different perspectives to the similarity distribution of a genetic algorithm’s population during its execution. The similarity distribution of populations is illustrated exemplarily on the basis of some routing problem instances.

Frequency planning is a very important task in the design and operation of current GSM networks. For this reason, the frequency assignment problem (FAP) is a well-known problem in the Operations Research which includes different mathematical models depending on the specific conditions of the application which is being designed. However, most of these models are not close from considering current technologies aspects which are deployed in GSM networks. In this work, we use a formulation of FAP, developed in published work, which focuses on aspects which are used in real-word GSM networks. We focus on solving this problem for a realistic-sized, real-world GSM network, using the Scatter Search algorithm. We have analyzed and fixed the SS algorithm to the FAP problem and, after a detailed statistical study, the obtained results prove that this approach can compute accurate frequency plans for real-world instances in an optimum way. In fact, our results surpass all the results previously published in the literature.

Genetic programming is a powerful heuristic search technique that is used for a number of real world applications to solve amongst others regression, classification, and time-series forecasting problems. A lot of progress towards a theoretic description of genetic programming in form of schema theorems has been made, but the internal dynamics and success factors of genetic programming are still not fully understood. In particular, the effects of different crossover operators in combination with offspring selection are largely unknown.

This contribution sheds light on the ability of well-known GP crossover operators to create better offspring when applied to benchmark problems. We conclude that standard (sub-tree swapping) crossover is a good default choice in combination with offspring selection, and that GP with offspring selection and random selection of crossover operators can improve the performance of the algorithm in terms of best solution quality when no solution size constraints are applied.

In-door localization of mobile devices is a common problem for many current and future applications, for example to control infrastructure services or for personalized in-building navigation systems. Sufficiently capable Bluetooth support is often available in off-the-shelf mobile devices such as mobile phones, which makes Bluetooth an attractive technology for cheap and widely available in-door localization systems. However, Bluetooth has been optimized to deal with effects of radio frequency transmission such as reflection and multi-path propagation. It therefore produces highly non-linear relationships between the distance of devices and their perceived signal strength. In this paper, we aim to identify these relationships for a specific dataset of 2D device positions using structural identification methods. Driven by an extended genetic algorithm, we aim to find optimal mappings in form of non-linear equations for

x

and

y

coordinates, thus producing formal regression functions.

Fusion community is becoming more important as long as fusion energy is considered the next generation of energy. However, many problems are presented in fusion devices. One of these problems consists of improving the equilibrium of confined plasma. Some modelling tools can be used to improve the equilibrium, but the computational cost of these tools and the number of different configurations to simulate make impossible to perform the required tests to obtain optimal designs. With grid computing we have the computational resources needed for running all these tests and with genetic algorithms (GAs) we can look for an approximate result without exploring all the solution space. This work joins all these ideas. The obtained results are very encouraging.

Setup costs are a crucial factor in many branches of industry and frequently sequence dependent. However, the empirical acquisition of setup costs is inaccurate and not practicable for companies with large product portfolios operating in volatile markets. We therefore propose an abstract model for the estimation of such sequence dependent setup costs and subsequently apply dispatching and scheduling strategies to generate optimized production sequences. Both approaches are tested on randomly generated test instances and a real-world production scenario.

We consider the Vehicle Routing Problem with time windows where travel times are triangular fuzzy numbers. The weighted possibility and necessity measure of fuzzy relations is used to specify a confidence level at which it is desired that the travel times to reach each customer fall into their time windows. In this paper we propose and analyze a solution procedure consisting in hybridizing a Variable Neighborhood Search (VNS) and a Greedy Randomize Adaptive Search Procedure (GRASP) for the corresponding optimization problem.

The new paradigm of Integrated Modular Avionics (IMA) [1] necessitates the analysis and validation of non-functional requirements for IMA systems. This includes the analysis of their performability. In this paper we present an initial approach of a performance modelling framework, based on the SAE standardised modelling and analysis language AADL [2,3], to integrate performance analysis in the toolchain of this practical context. The proposed framework is a hybrid of static and dynamic systems analysis and includes aspects of performance evaluation.

Project engineering is a domain where suitable formal models and tools are still needed. The paper presents a way how the dynamically instantiable, multilevel Petri nets can be applied in all significant processes of project engineering. The main emphasis is put on the resources modeling, simulation, and scheduling. We use the

Object oriented Petri nets

(OOPN) formalism which is a kind of

multi-level Petri nets

with a possibility to synchronize events at different levels. In the case of the project modeling domain, the first level corresponds to the

project plans

and the second level corresponds to the

resources

.

Current model-based design methodologies use executable semi-formal models allowing for transformations including code generation. Nevertheless, the code should be finalized manually and further development or debugging by means of prime models is impossible. The paper introduces an approach to the system design called Simulation Based Design which uses fromalisms of DEVS (Discrete-Event Systems Specification) and Object Oriented Petri Nets (OOPN) allowing for clear modeling, a possibility to check correctness by means of simulation as well as by formal verification. The approach is based on techniques such as incremental development in the simulation, reality-in-the-loop simulation, and model-continuity. The model is understood as an executable program valid through all development stages including the deployment (the target system).

The support of the engineering process for computer-based systems by Co-Design frameworks is the key to lower production cost and the time-to-market for complex devices. Early in the design cycle, system models must be rated, concerning design constraints and decisions must be made. To enable system engineers to gauge concurring system-realizations approaches, such frameworks must allow the prediction of key characteristics for the system under development, like performance, battery consumption and heat production. Hereby, the prediction of the system performance constitutes the vital constraint.

In this work, we demonstrate, how an EQN-based performance simulation method can be seamlessly embedded into a Hardware / Software Co-Design framework based on UML system descriptions. The presented approach yields a performance simulation model, ready for the automated simulation process, and therefore fosters the performance evaluation of the system under development.

The article is focused on a model-based design and verification of reactive systems. In opposite to common approaches to developing reliable systems, at first a model of a system is constructed in high level visual language, then it is verified on this level of abstraction and consequently a low level code for target platform is generated. In approach discussed in this article an UML statechart formalism is used for construction of the model. This model is translated into Promela model and verified by the SPIN model checker.

In this present how to use SPICE for transient analysis of Boolean logic circuits based on monostable–bistable transition logic element (MOBILE). MOBILE circuit is composed of negative differential resistance (NDR) and takes advantages of negative resistance in

I

 − 

V

characteristic of NDR. We also propose and verify a method how to construct NDRs library for SPICE. Our method generates GTG for a given Boolean function of up to

n

variables.

The recognition of the importance of verification and validation tasks, within the software development process or life cycle, is growing significantly. Still, its unarguably complexity and the great amount of time and resources needed to perform testing properly, together with the industry’s unawareness of the most powerful and versatile testing tools, makes that, in practise, these activities are often underestimated and diminished, or just simply ignored and skipped, sometimes due to client’s demands or hard time-to-market constraints.

Integration testing is a specific kind of testing, which is gathering more and more attention within a software engineering industry that has been for quite some time already relying in structuring application and systems in different modules and components. In this paper, we propose a generic and re-usable model-based methodology for testing integration between different components, and illustrate it using a real case study, LiveScheduler, a scheduler and control tool for transmissions on live broadcast channels through the Internet.

The discussed training system employs several means for encouraging safe behavior during laparoscopic surgery procedures. The elements of no-fly zones, magnetic position sensing and expert systems are tied together to form a complex system that provides guidance and performance assessment. A 3D reconstruction algorithm is used for the purpose of defining no-fly zones and has been tested in a simulator developed for the purpose of this work. An expert system has been built in cooperation with surgeons that, based on simple rules, can assess the risk of the trainee’s actions. Despite the shortcomings of the 3D reconstruction process, the training system performed as expected during experiments. Simple exercises like touching points in 3D space were performed and scored appropriately to whether a no-fly zone has been breached or not. Also simple advice could be provided to the trainee in order to help improve the results.

This paper discusses a methodology to manage wireless sensor networks (WSN) with self-organising feature maps, using co-operative Extended Kohonen Maps (EKMs). EKMs have been successfully demonstrated in other machine-learning contexts such as learning sensori-motor control and feedback tasks. Through a quantitative analysis of the algorithmic process, an indirect-mapping EKM can self-organise from a given input space, such as the WSN’s external factors, to administer the WSN’s routing and clustering functions with a control parameter space.

Preliminary results demonstrate indirect mapping with EKMs provide an economical control and feedback mechanism by operating in a continuous sensory control space when compared with direct mapping techniques. By training the control parameter, a faster convergence is made with processes such as the recursive least squares method. The management of a WSN’s clustering and routing procedures are enhanced by the co-operation of multiple self-organising EKMs to adapt to actively changing conditions in the environment.

This paper discusses a newly proposed Morphotronic System paradigm that can be used as a general computation model for construction of software. The Morphotronic System allows for a definition of very flexible software prototypes, in which a processing path can be interpreted as an extremum principle. This approach offers a significant improvement to traditional software practices. The system purpose is stated as a conceptual input to the computer system at a starting point of the computation process while the local machine state is completely ignored. The system context and its rules are generated as resources to allocate the computational components. The morphotronic system applies non-Euclidean geometry which allows to shape the context and to define the projection operators for an ideal network of forms.

Wireless Sensor Networks (WSN) is fast becoming the holy grail of digital surveillance, data monitoring and analysis. Being relatively cheap, low-powered and easy to deploy WSNs is being adopted in a range of different fields. Currently, the focus is towards optimizing techniques used to form sensor clusters and to route data within a network. In order to satisfy these goals a significant amount of research is being done globally and what tends to be lacking at times is the right tools to make such research work less time consuming and inexpensive. The use of simulation tools is one such adaptation that can help researchers closely analyse a particular aspect of WSN while sticking with a known environment that is applicable to other scenarios in a similar way. This paper presents the performance and features of WSNSim, a WSN Simulator and the immediate advantages that can be experienced by researchers working on various realms in this area.

Embedded Fortress system is the tool designed for electronics engineers and designers, who wish to secure the Intellectual Property (IP) in their embedded systems. The main tasks of Embedded Fortress software include providing designer with a ready-made security.

The solutions in the system will allow the user to select the security that meets his/her expectations from the point of view of security and hardware requirements.

Document formats based on XML are widely used in today’s office collaboration. However, most supporting tools like version control systems, or business process systems do not handle these documents adequately. Parallel editing of documents across network and system borders is almost impossible.

Our recent research showed that versions of XML documents can be merged in a reliable way using deltas with context fingerprints. In this paper, we present a collaboration strategy based on these deltas that allows for a highly dynamic distributed collaboration among XML documents.

In many Multi-Objective Optimization Problems it is required to evaluate a great number of objective functions and constraints and the calculation effort is very high. The use of parallelism in Multi-Objective Genetic Algorithms is one of the solutions of this problem. In this work we propose an algorithm, based on parallelization scheme using island model with spatially isolated populations. The intent of the proposed paper is to illustrate that modifications made to a selection and resolution processes and to a migration scheme have further improved the efficiency of the algorithm and good distribution of Pareto front.