Irina-Gabriela Lolu

Mihai Aurelian

Stanescu

Comparative Study of Self-Organizing Robotic Systems Regarding Basic Architecture

Keywords: self-organization, robotic systems, architecture

Self-organization has become an intensely researched area, being considered the new control system science. Different self-organizing algorithms (bio and non-bio inspired) have been developed by scientist worldwide with general applicability in autonomous systems, multi-robot systems, autonomic networking, sensor and actor networks and mobile ad hoc networks. This paper presents a comparative analysis of basic architectures for multi-robot systems from the point of view of self-organization capabilities. We identify requirements for implementing efficiently self-organization mechanisms with high applicability in multi-robot systems.

Introduction

Nature has always inspired scientists to develop techniques, algorithms and mechanisms and to apply them in artificial systems. This is also the case of selforganization; inspired from biology, physics and chemistry and used in computer science and robotics. This paper presents a critical analysis of basic architectures for robotic systems from the point of view of self-organization capabilities. We also propose a simple architecture that enables efficient implementation of self-organizing mechanisms.

Since it was introduced in 1947 by W. Ross Ashby [START_REF] Ashby | Principles of the Self-Organizing Dynamic System[END_REF], self-organization has caught the interest of the research community in various fields (ex. autonomous systems, autonomic networking, sensor and actor networks, mobile ad hoc networks), including robotics. The generally accepted definition for self-organization is: the mechanism enabling a system to change its internal organization according to environmental changes without explicit external command.

There are two approaches in self-organization: stigmergy and social. The concept of stigmergy was introduced by Grassé [START_REF] Grassé | La reconstruction du nid et les coordinations interindividuelles chez Bellicositermes natalensis et Cubitermes sp., La théorie de la stigmergie : essais d'interprétation du comportement des termites constructeurs[END_REF] to explain the social behavior of termites. It was demonstrated that coordination in ants and termites colonies is achieved through indirect communication by the means of a chemical substance, pheromone, deposited in the environment. The important characteristics of pheromones that have to be kept in mind when implementing stigmergy are: they evaporate in time (evaporation), the concentration increases when new pheromones are released at a marked location (aggregation) and they propagate through environment (propagation).

The bio-inspired stigmergy mechanisms aim to achieve self-organization trough simple rules and interactions between robots/agents, using indirect communication by the means of artificial "pheromones". Stigmergy has been used in applications for hierarchical task networks [START_REF] Parunak | Stigmergic Reasoning over Hierarchical Task Networks In[END_REF], manufacturing control [START_REF] Karuna | Emergent Forecasting using a stigmergy approach in manufacturing coordination and control[END_REF], coordination of unmanned vehicles [START_REF] Parunak | Digital pheromone mechanisms for coordination of unmanned vehicles[END_REF] a.s.o.

In contrast with stigmergy, the social approach uses direct communication,. These techniques are inspired from natural social behaviors encountered in human societies, business organizations and even economic systems. The most popular social selforganization mechanisms are market-based (in which entities act to increase their profit) [START_REF] Hassas | Self-organizing Mechanisms from Social and Business/Economics Approaches[END_REF] and delegation based mechanisms (in which delegation is based on trust or reputation level, authority and voting ) [START_REF] Schillo | Self-organization in multiagent systems: from agent interaction to agent organization[END_REF].

Contributions to Technological Innovation

The adaptive and robust self-organizing systems are an increasing trend nowadays, especially in software application, although the developers have to face great challenges: non-linearity, unpredictability, instability and sensitivity. When designing a self-organizing robotic system the challenges are even more difficult due to hardware limitations and time restrictions. Our work is a contribution to the design of such systems and concerns with one of the first steps of developing a robotic system: choosing the basic architecture. We are contributing with an analysis of existing architectures and we also propose a new architecture that ensures the implementation of different types of self-organizing mechanisms. We hope this paper will encourage further research in self-organizing robotic systems, enabling the development of the future robot societies.

Comparative Analysis of Basic Architectures

In this chapter we will discuss several robot architectures from the point of view of self-organization capabilities. Even though there are essential differences between stigmergy and the social approach, both have the same following proprieties:

• Absence of external control

• Decentralization • Emergence • Self-maintenance • Self-building • Adaptive • Sensitivity • Low predictability • Robustness.
There are two different types of implementation for stigmergy: software and hardware. The software implementation has no special requirements (small processing power, shared memory for environment simulation), but the hardware implementation is restricted by the physical proprieties of pheromones. Still some solutions have been proposed by Johansson and Saffiotti [START_REF] Johansson | Navigating by Stigmergy: A Realization on an RFID Floor for Minimalistic Robots[END_REF], [START_REF] Edelen | Swarm intelligence and stigmergy: Robotic implementation of foraging behavior[END_REF].

The social techniques are agent-oriented, thus the basic requirement is an agentoriented architecture. Therefore we will concentrate on studying architectures based on multi-agent systems (MAS). For efficient implementation we also consider the following requirements: fast, long distance communication capabilities and considerably more processing power and memory than necessary for implementing stigmergy.

While the social approach is mostly used in more complex heterogeneous systems that require hierarchy, social learning, and knowledge propagation through gossiping [START_REF] Jelasity | Engineering emergence through gossip[END_REF], the stigmergy approach has some important advantages:

• Simplicity (robots/agents don't need a complex deliberative mechanism to implement the simple rules of stigmergy) • Asynchrony (robots/agents act asynchronous according to the information found in the environment) • Anonymity(robots/agents are not aware of each other) • Public knowledge (all knowledge is public and can be found in the environment) • Low -cost We are considering the following criteria for analyzing existing MAS architectures for robotic systems: coordination -centralized/decentralized, resource requirements and communication capabilities.

There have been designed architectures for self-organizing multi-robot systems, but they implement a simple, specific self-organization mechanism using none or very little direct communication. From this category we mention ALLIANCE [START_REF] Parker | ALLIANCE: An Architecture for Fault Tolerant Multi-Robot Cooperation[END_REF], Cebot [START_REF] Fukuda | Cellular robotic system (CEBOT) as one of the realization of self-organizing intelligent universal manipulator[END_REF], SWARM-bots [START_REF] Dorigo | Evolving selforganizing behaviors for a swarm-bot[END_REF] and ABBA [START_REF] Jung | An architecture for Distributed Cooperative Planning in a Behavior-Based Multi-Robot System[END_REF]. These architectures cannot be used for developing more complex applications that require a social self-organization mechanism demanding high-level, message-oriented communication.

The first category of architectures that we present is the layered architectures category, depicted in Figure 1.These architectures have two [START_REF] Eze | Distributed Control Architecture for Collaborative Physical Robot Agents[END_REF], three [START_REF] Neves | A multi-agent approach for a mobile robot control system[END_REF] or even eight layers [START_REF] Shaw | Software Architecture: Perspectives on an Emerging Discipline[END_REF], that can communicate only with the neighboring layers using an inter-layer communication protocol (ex. TCP/IP). The drawback of this architecture is that the superior layers have to use the intermediate layers to communicate with the inferior layers, causing latencies which could be significant in real-time applications.

The top layer is usually called the Cognitive Layer and is represented by one or more deliberative agents that could enable self-organization, if chosen properly. These architectures were designed for cooperative robots, but were not meant especially for self-organization mechanisms, thus even if they have a multi-agent system these agents were not chosen in a way that enables emergent behavior.

Most of the architectures have a single agent that coordinates and controls the other agents ("Global Manager" or "Coordinator" [START_REF] Eze | Distributed Control Architecture for Collaborative Physical Robot Agents[END_REF], [START_REF] Busquets | A multiagent approach to qualitative landmark-based navigation[END_REF]), which means centralizing the decision, contrary to the distributed coordination essential for self-organization. Table 1 summarizes the studied architectures.

An Architecture for Self-Organizing Robotic Systems

The disadvantages we identified in our analysis of the above architectures motivated us to design a simple, flexible architecture, with low resource requirements and decentralized control. Therefore we propose the two layered architecture with simple inter-layer communication (plain function call) depicted in Figure 3. Removing the intermediate layers we eliminate message passing, we reduce inter-layer communication (by embedding the intermediate layers in the superior or inferior layers), although at the cost of increasing intra-layer communication.

The Action Layer is a simple implementation of tasks (reactive) and its design does not influence the self-organization capabilities, but it has an impact over the performance of the system. The top layer (Cognitive Layer) is a multi-agent system with three or more agents, depending on the self-organization mechanism we want to implement. This ensures the flexibility of the architecture.

The DF and AMS agents implement the analogue components defined in the FIPA standard [START_REF]The Foundation of Intelligent Physical Agents[END_REF], and they are necessary to ensure self-organization in a multi-robot system. The DF agent is responsible for maintaining a list of all agents and their services in the system and offers the following services: updating and search. The AMS agent is responsible for registering and unregistering agents. Choosing the other agents depends directly on the application and on the robots capabilities. In our opinion there is no need for "body" agents corresponding to sensors, but agents that control actuators could be useful for resolving resource concurrency. An example for emergent behavior that could be implemented using this architecture is navigation with obstacle avoidance. In this case we must define the following agents: motor control agent -controls the motors and decides which agent has priority, move forward agent -moves forward the robot, avoid obstacle agentwhen sensing an obstacle stops the robot and changes the direction of the robot.

Our current implementation of this architecture is on a Lego NXT robot (we wanted to underline the advantages of this architecture by implementing it on a machine with low computational power), using leJOS NXJ (a Java programming environment). We implemented a simple agent platform that uses shared memory for local communication and Bluetooth communication for inter-robot communication (a Bluetooth Agent). In our system every robot has an agent platform and the agents have the possibility of registering on multiple platforms (when agents communicate they use the local platform's communication service). At the moment we are developing a practical application that gives the possibility to test different selforganization algorithms in different scenarios in order to have consistent results.

Conclusions

In this paper we presented our analysis of the studied architectures for self-organizing robotic systems with respect to the identified requirements for the implementation of self-organizing mechanisms. The problems we encountered are: centralized coordination, high resource requirements and inter-layer slow communication protocols. Therefore we proposed an architecture that haves none of these disadvantages. We intend to further develop this architecture and test it with different self-organization mechanisms in our further work.

Fig. 1 .

 1 Fig. 1. Generic layered architecture.Another category of architectures are the architectures with one layer composed of different types of agents: Body Agents, Deliberative Agents, Service Agents, Behavioral Agents a.s.o.(Figure2) We mention here the ARMADiCO[START_REF] Innocenti | Resource coordination deployment for physical agents[END_REF] architecture which has distributed coordination among agents (based on a utility function) that could enable emergent behavior.

Fig. 2 .

 2 Fig. 2. One layer architecture based on multi-agent system.

Fig. 3 .

 3 Fig. 3. A two layered architecture for self-organizing multi-robot systems.

Table 1 .

 1 Layered architectures examples.

	Name	No. of	Description	Disadvantages
		layers
	Physical Robot	2	• Action Layer: Executor,	• Centralized control
	Agent [15]		Repository of Tasks and State
			Monitor
			• Cognitive Layer: Decision Maker,
			Coordinator and Negotiator
	Acromovi [19] 4	• Applications and Middleware	• Inter-layer
			Layer -multi-agent systems	communication -
			implemented in Java	Java JNI and Java
			• ARIA and Saphira Layers -	RMI
			implemented in C++	• High resource
				requirements
	ARA [16]	3	• Reflexive Layer -on the robot	• Separation on two
			• Reactive and Cognitive Layers -	machines
			on another machine
			• High-level behaviors composed of
			elementary behaviors
	Busquets [18]	2	• Executive and Deliberative	• Centralized control
			systems
			• Coordination through bidding
			executed by a Coordinator


Carlos Matos
email: cmatos@est.ips.pt

Manuel Duarte Ortigueira

Fractional Filters: An Optimization Approach

Keywords: Fractional Filters, Fractional Derivative, Optimization

The design and optimization of fractional filters is considered in this paper. Some of the classic filter architectures are presented and their performances relatively to an ideal amplitude spectrum evaluated. The fractional filters are designed using the differential evolution optimization algorithm for computing their parameters. To evaluate the performances of all the filters the quadratic error between the computed amplitude is calculated against an ideal (goal) response. The fractional filters have a better behavior, both in the pass and reject-band.

Introduction

The importance of filters in signal processing and other engineering areas is unquestionable. Continuous time filters are widely used functional blocks, from simple anti-aliasing filters preceding ADCs to high-spec channel-select filters in integrated RF transceivers. Four classical classes of filters are currently used: Butterworth, Chebyshev, elliptic and Bessel. Even in the integer order case, filter design is challenging, mainly when the system has to meet a wide set of constrains [START_REF] Damera-Venkata | An automated framework for multicriteria optimization of analog filter designs[END_REF]. Most tools for filter design are based on the transfer functions of the above classes, which impose only requirements related to the magnitude or phase responses.

The design of fractional filters was considered for low orders, [START_REF] Radwan | First Order Filters Generalized to the Fractional Domain[END_REF] and [START_REF] Radwan | On the Generalizations of Second-Order Filters to the Fractional-Order Domain[END_REF]. Here, we will consider the problem with all the generality.

We are dealing essentially with an optimization problem that we will face by using the differential evolution (DE) algorithm [START_REF] Haupt | Practical Genetic Algorithms -Second Edition[END_REF]. DE is a stochastic, population-based optimization algorithm.

With this algorithm we were able to design fractional filters. To do it we used the poles and zeros as design parameters, together with the fractional order, α. This inserts in the problem a new parameterthe fractional derivative orderthat gives a new degree of freedom. This may increase the computational burden associated with the optimization algorithm, but leads to more flexible solutions.

The papers outlines as follows. In section 2 we summarize the contribution to technological innovation of this work. In section 3 we describe the main characteristics of the filters. The optimization algorithm is described shortly in section 4 after which we present the simulation results. Finally we will present some conclusions.

Contribution to Technological Innovation

This algorithm opens a way to the design of fractional filters, it allows the calculus and optimization of all the design parameters, including the fractional parameter.

Fractional filters better approximate the ideal response than the classical ones. Their applicability in Industrial Control Systems can be very wide.

The presented results are an incentive in itself to fractional capacitor manufacturers and filter designers, allowing the generalised use of fractional filters and the use of this technology.

Integer Order Filters

Specification of the Filter

The specifications for a low pass filter are expressed in figure 1. The specifications of the other kinds of filters are similar. So, we will not consider them. Usually we fix the band pass (BP) amplitude equal to 1. The obtained filter will have amplitude oscillations in the interval [1-δ p , 1+δ p ]. Other interesting values are: δ s -amplitude of stop band ripple ω c -pass band edge frequency ω s -stop band edge frequency ∆ω i -width of the transition band:

∆ω i = ω s -ω c A p -band-pass attenuation p 1 A =20Log 1 p p δ δ + - (1)
A s -stop-band attenuation For other kinds of filters the specifications are similar. In the presented work we considered the filter specifications as described in figure 2.

Simulation Results

The Genetic Differential Evolution Algorithm was introduced by [START_REF] Ortigueira | Introduction to Fractional Signal Processing. Part 2: Discrete-Time Systems[END_REF]. It was developed to optimize real parameters of a real valued function. It finds the true global minimum, regardless of the initial parameters, It has fast convergence and it uses a small set of control parameters.

Applying (DE) algorithm to fractional filter design, will lead to filters with minimum quadratic error to a defined goal. The minimization of the quadratic error of the frequency response, supply us with the values of the zeroes, poles and fractional order α.

rd Order Integer low-pass filter

Suppose that we want to approximate the ideal low pass filter defined by:

1 if 0 1 -20Log( ) if 1 ω ω ω ≤ ≤   >  (2)
With the usual 3 rd order Low-Pass filters, Butterworth, Bessel, Elliptic and Chebyshev. The quadratic error of these filters compared to the ideal response, for ω∈]0,10] is: Butterworth: 1.90658, Bessel: 18.4252, Elliptic: 5649.03, Chebyshev: 282.28

rd Order fractional Filter

We define the fractional Butterworth filter, α the fractional parameter, as:

3 2 1 H(s)= s +2s +2s +1 α α α (3)
First we will use a 3 rd order all-pole fractional filter (2). We impose that we have two complex conjugate poles and a real pole. The optimization algorithm is intended to calculate the poles and α that minimize the quadratic error using the genetic differential evolution algorithm.

For the above defined ideal filter approximation, we obtained, Pole 1 = -0.386955±0.890131, Pole 2 = -1.13249, α = 1.00031 and a quadratic error: 0.331989

The fractional transfer function is given by: The responses are depicted in figure 3 for the amplitude and figure 4 for the phase. The parameter α is very close to 1 as the filter, a 3 rd order is very close to the integer response given by the Butterworth filter. Now we are going to use the same 3 rd order fractional filter to approximate an ideal 4 th order Low-Pass filter defined by:

1 if 0 1 -20Log( ) if 1 ω ω ω ≤ ≤   >  (5)
Proceeding as above we calculate α and the poles: Pole 1 = -0.0000449527+ j0.817145, Pole2 = -1.67915, α = 1.34776 with a quadratic error of: 24.6592. And the transfer function is: This is a case where α>1, because the ideal filter decay is bigger than the integer Butterworth capacity. The responses are depicted in figure 5 for the amplitude and figure 6 for the phase.

Conclusions

We just considered the design of fractional filters and compared their performances with similar classic filters, from the point of view of the amplitude spectrum. The fractional filters approximate much better the ideal filter than the classic ones.

One rule of design should be starting by the use of the fractional filter derived from the integer that better approximates the goal. In our study, we used Butterworth filters as the starting point. We demonstrated that if the integer filter order is smaller than the needed response α will be bigger than one, on the other hand, if we start with an integer filter that has an order higher than needed then α will be smaller than one.

We have shown that the fractional filters outperform the integer ones. By changing poles location and finding a suitable α a fractional filter can perform as a higher or lower order integer filter with advantages. One of the main advantages is that we can have a smaller quadratic error between the goal ideal filter and the designed filter. Another important advantage is that a N order fractional filter with α>1 performs as an integer filter of order>N, so we can have a filter of order>N but with the complexity of a N order.

The same fractional approach will be applied to other type of classical filters and the results will be presented in future work.

Fig. 1 .

 1 Fig. 1. Specifications of a low pass filter.

Fig. 2 .

 2 Fig. 2. Specifications of an ideal low pass filter.

Fig. 3 .

 3 Fig. 3. Amplitude response of 3 rd order fractional filter. Goal in solid Black and designed in dashed gray.

Fig. 4 .

 4 Fig. 4. Phase response of 3rd order fractional filter (dashed gray) and 3 rd order LP Butterworth (solid black).

Fig. 5 .

 5 Fig.5. Amplitudes of fractional 3rd order fractional low pass filter (dashed grey) and ideal 4th order LP response (solid black).

Fig. 6 .

 6 Fig. 6. Phase response of 3rd order fractional Low Pass filter (dashed grey) compared with Butterworth (solid black).


José I Santos
email: jisantos@ubu.es

Ricardo Del Olmo
email: rdelolmo@ubu.es

Javier Pajares
email: pajares@insisoc.org

Evolutionary Model of an Innovative and Differentiated Industry

Keywords: evolutionary economics, industrial dynamics, economic geography

In this paper, we propose an agent based model that describes the spatial and temporal evolution of an industry composed of a set of heterogeneous firms distributed in different regions. The model formalizes a particular hypothesis about spatial agglomeration and industrial concentration phenomena in which innovation occupies the central place of economic and geographical growth explanation. Each company owns one or more manufacturing divisions that produce an exclusive variety of product. Economic selection is modeled as a monopolistic competition market where competitive pressure depends on consumers' preference for variety. Moreover, firms may enjoy more competitive advantages innovating in processes, product characteristics and new commodities. The purpose of the model is integrating theories which come from research areas traditionally separated into a single formal proposal.

Introduction

Traditionally, Economics theory has left aside the spatial dimension of economic phenomena, and in particular agglomeration and industrial concentration phenomena 1 , with the aim of developing a tractable and analytic framework [START_REF] Fujita | The spatial economy: cities, regions and international trade[END_REF]. However, industrial dynamics are usually characterized by economic forces that tend to agglomerate firms in particular locations and concentrate market power in a few group of companies. Because of this, different scientific disciplines, not exclusively economic, have given increased attention to these phenomena in the last two decades.

Due to the complexity of these phenomena, there are few and quite heterogeneous formal models that address the problem. A significant contribution comes from some economists of the new Economic Geography, who propose a new family of microeconomic models [START_REF] Fujita | The spatial economy: cities, regions and international trade[END_REF]. The main assumption of these models of spatial economic agglomeration is based primarily on the effect of local market size and the relationship between scale economies and transportation costs.

However, new Economic Geography models intentionally forget the dynamic dimension of industries to make the problem analytically tractable. In contrast, selection and innovation processes are of fundamental importance to other scholars [START_REF] Boschma | Evolutionary economics and economic geography[END_REF]. For example, Audretsch and Feldman [START_REF] Audretsch | R&D Spillovers and the Geography of Innovation and Production[END_REF] demonstrate how R&D activities of many knowledge-based industries tend to be agglomerated in a few locations. Similarly, Baptista and Swann [START_REF] Baptista | Do firms in clusters innovate more?[END_REF] show empirically the greater intensity of innovation activity in UK industrial clusters.

Evolutionary dynamics of industries are characterized by complex spatialeconomic interdependence, insofar as firms' economic performance is conditioned by geographical factors, which at the same time are influenced by firms' performance. Industrial dynamics evolve simultaneously in both economic and geographic dimensions, making quite difficult, or even impossible, any intent to separate one from another.

Frenken and Boschma [START_REF] Frenken | A theoretical framework for evolutionary economic geography: industrial dynamics and urban growth as a branching process[END_REF] integrate evolutionary theories of industrial dynamics with other geographical theories of regional growth and innovation to propose a new theoretical framework for developing geographic and evolutionary formal models, and this theoretical approach is the starting point of our work. We try to develop an evolutionary formal model of industrial dynamics in which innovation plays a key role as the engine of economic and geographical growth.

Cumulative Causality of Industrial Agglomeration and Concentration Phenomena

The model is built on the following assumptions:

The industry is formed by heterogeneous firms which are different in their capacities, knowledge and routines [START_REF] Nelson | An evolutionary theory of economic change[END_REF].

Selection processes operate through a monopolistic competition market in which firms enjoy an imperfect monopolistic position producing differentiated products [START_REF] Santos | Selection processes in a monopolistic competition market[END_REF]. Innovation is the source of diversity and competitive advantages. Firms do not only seek to improve processes and products, but also develop new products, and hence they can grow and strengthen their position in the industry [START_REF] Frenken | A theoretical framework for evolutionary economic geography: industrial dynamics and urban growth as a branching process[END_REF].

The synergy of knowledge within the firm as well as the knowledge diffusion (knowledge spillovers) outside the firm through labor mobility and personal contacts influence innovation results, reducing uncertainty and increasing the chances of success [START_REF] Audretsch | R&D Spillovers and the Geography of Innovation and Production[END_REF].

All these assumptions can be summarize in a hypothesis about spatial agglomeration and industrial concentration phenomena that we call it cumulative causality: a positive reinforcing chain of causes and effects. Figure 1 depicts graphically this cumulative causality: (1) innovation generates improvements in manufacturing processes (process innovation) and in the characteristics of products (product innovation), and therefore provides firms with competitive advantages; [START_REF] Boschma | Evolutionary economics and economic geography[END_REF] innovation also makes possible the creation of new products (product differentiation) that promote the expansion of firms; (3) the growth of firms may have a positive effect on their R&D activities through scale economies; (4) the growth of firms entails consequently the growth of the region where they are located; [START_REF] Frenken | A theoretical framework for evolutionary economic geography: industrial dynamics and urban growth as a branching process[END_REF] the geographical proximity between firms facilitates knowledge externalities (knowledge spillovers) that influence all types of innovative activities.

Innovation

Agent Based Model of a Differentiated Industry

We use the Agent-Based Modeling (ABM) paradigm to formalize the theoretical hypothesis outlined before. The model2 describes the spatial and temporal evolution of an industry initially composed of a group of firms distributed in different locations (regions). Each company owns one or more manufacturing divisions that produce an exclusive variety of product. Economic selection is modeled as a monopolistic competition market, where consumers' preferences are determined by an aggregate utility function with constant elasticity of substitution (CES) between varieties. The income that consumers spend on products evolves exogenously according to a logistic curve, characteristic of the life cycle of the industry. Firms may enjoy competitive advantage innovating in processes and product characteristics or creating new products.

Monopolistic Competition Market

Selection processes in the industry is modeled as a competition between imperfect substitute varieties of products, each one is a monopoly of a single division of a firm. We incorporate a particular abstraction of imperfect monopolistic competition borrow from Dixit and Stiglitz [START_REF] Dixit | Monopolistic Competition and Optimum Product Diversity[END_REF] where consumers' behavior is fully described by an aggregate utility function U (Eq.1) with CES between varieties 1, 2,..., jm

1 1 1 11 ( ) ( ) ( ) ( ) 0 1 mm k k k k kk U f t q t f t q t ( 1
)
where k q denotes the quantity of commodity k and f k (t) the consumers' predilection for it. The parameter (0,1) governs consumers' preference for variety, and therefore the degree of monopolistic competition in the market. Higher values of denote weaker preferences for diversity. In particular, 1 represents the situation where consumers do not distinguish between products, and the competition is similar to a homogeneous product market.

The family of CES utility functions in Eq.1 facilitates the derivation of the demand curve for each product j in Eq.2, where Y(t) represents consumers' income. We assume that consumer's income evolves exogenously according to a logistic equation that characterizes the life cycle of the industry [START_REF] Klepper | Industry life cycles[END_REF].

1 1 () ( ) ( ) ( ) ( ) ( ) j j m j k k k ft p t Y t q t f t q t (2)
It is interesting to observe how the demand for each commodity is affected by the total number of products in the industry (see Fig. 2), due to the particular abstraction of monopolistic competition used in the model. Note that new commodities in the market push down demand curves of all products, and this competitive pressure is higher as consumers' preference for variety declines, i.e., as 1 .

Firm behavior

The firm i is formed by one or more divisions ji responsible for a variety of product, all them located in a region r. At time period t, the division j belonging to the firm i produces Q ij (t) units of the corresponding commodity according to the production function of Eq.3, which relates the output with the productivity of the division A ij (t) and its stock of physical capital K ij (t).

(

( ) ( ) ij ij ij Q t A t K t (3) Fig. 2. )
Family of demand curves of products (Eq.2) for two scenarios of a monopolistic competition market, 0.5 on the left and 0.9 on the right, where 1, ( ) 1 , ( ) 1 ii Y f t i q t i j . In each graph, the demand curve of a product j has been drawn for different values of the number of products n in the market. Looking at both graphs simultaneously we observe that the sensitivity of the demand curve to changes in the number n is modulated by consumers' preference for variety .

The division j invests part of its capital () pc j rt in process innovations, which may improve the productivity A ij (t), and other part () pd j rt in product innovations, which may improve consumers' predilection f k (t). Similarly, the firm i invests part of its capital (the sum of capital over its divisions) () nv i rt in innovation of new varieties of products.

We assume that the total production of the division j, Q ij (t), is sold at price p j (t), which is determined by Eq.2. Division j's profits

( ) ( ) ( ) ( ) ( ) ij j ij ij ij
t p t Q t c t K t are the difference between revenues and costs, where c ij (t) represents the cost per unit of capital of the division (Eq.4); the parameter c quantifies the cost of capital, which is identical for all divisions.

( ) ( ) ( ) ( )

pc pd nv ij ij ij i c t c r t r t r t (4)
At time period t, the division j estimates the capital investment rate I ij (t) according to a simple adaptive rule [START_REF] Nelson | An evolutionary theory of economic change[END_REF], expressed in Eq.5. max ( ) max 0, min ( ), ( )

des ij ij ij I t I t I t (5)
The maximum capital investment rate max () ij It is limited by division j's profits in the period, and the desired investment rate ()

des ij
It depends on how far division j is from the optimal situation in which its marginal revenue is equal to marginal costs. This deviation from the optimum can be expressed easily, Eq.6, by means of a desired margin () des mg t , which depends on the elasticity of the demand curve, and an expected margin exp () mg t , which depends on expected price and marginal costs.

( ) 1 ()

des des ij esp mg It mg t (6)
Then, division j's stock of capital is updated according to Eq.7, where modulates the depreciation of capital. Whenever the division j gets an expected margin higher than the desired margin, it decides to increase its stock of capital and consequently the quantity to be produced and sold in the next period.

( 1) ( ) 1 ( )

ij ij ij K t K t I t (7)

Innovation and Knowledge Spillovers

We model innovation as a stochastic process of two steps. First, a division/firm has a chance of innovating according to a probability of innovation. Second, if it achieves success, it assesses the scope of the innovation. The probability of innovation (for the three types of innovations considered in the model) is defined by Eq.8. probability of innovating, determine the technological opportunity regime in the model -innovating can be more difficult in some industries than in others [START_REF] Klevorick | On the sources and significance of interindustry differences in technological opportunities[END_REF]-. The parameter in , which governs the growth rate of the probability function, represents the productivity of innovative efforts in X . Not every division/firm invests the same resources in innovative activities, and they are not equally efficient due to returns to scale and knowledge externalities [START_REF] Santos | Innovation and knowledge spillovers in a networked industry[END_REF]. The innovative effort in X is modeled as a Cobb-Douglas function of increasing returns (Eq.9), where

( ) min( ( )), max( ( )) ( ) min( ( )), max( ( )) ij ik ik j k k A t U A t A t k i f t U f t f t k i (12)
We incorporate a noise in the model with the entry of new firms at each time period according to a Poisson distribution of parameter entry , which it is assumed to be constant throughout the simulation. Finally, if a division reduces its stock of capital below a minimum threshold, it exits the industry; consequently, if all divisions of a firm exit, the company leaves de industry too.

Conclusions

We have proposed a formal model of an innovative and differentiated industry where a set of heterogeneous firms localized in different regions compete in an imperfect monopolistic market. The model is built on a particular hypothesis about spatial agglomeration and industrial concentration phenomena -that we call it cumulative causality-in which innovation occupies the central place of economic and geographical growth explanation. Innovation is modeled as a stochastic process where firm's probability of innovating depends not only on its innovative effort, but also on knowledge externalities within the firm and knowledge spillovers in the region.

The main contribution of the model is that we integrate theories which come from research areas traditionally separated into a single formal proposal. Thus, we incorporate the concepts of diversity, development and selection from Evolutionary Economics, and some theories about innovation and knowledge spillovers from Economic Geography and Geography of Innovation.

Fig. 1 .

 1 Fig. 1. Main hypothesis of the model: cumulative causality of spatial agglomeration and industrial concentration phenomena [5].

  represent the maximum and minimum

Economic Geography often uses the term agglomeration to describe the spatial distribution of an industry over regions and countries. In this paper, we keep this assumption and use the term concentration to refer to the distribution of the market shares within an industry.

The model has been implemented in Repast[START_REF] North | Experiences Creating Three Implementations of the Repast Agent Modeling Toolkit[END_REF].

Acknowledgments

The authors have benefited from the financial support of the Junta de Castilla y León (project BU034A08 and VA006A09) and Caja Burgos (project 2009/00148/001 and project 2009/00199/001).

The parameters firm and region govern the importance of knowledge externalities within the firm and the region respectively. We assume that the former are more important than the latter, insofar as firms have more tools to manage them, and that both do not condition excessively the probability of innovation ( 1) region firm , which depends mainly on each division's innovative effort.

In short, the probability of innovating is positively reinforced by: (1) returns to scale in the division's expenditure in R&D; (2) knowledge externalities within the firm [START_REF] Frenken | A theoretical framework for evolutionary economic geography: industrial dynamics and urban growth as a branching process[END_REF]; and (3) knowledge spillovers in the region [START_REF] Audretsch | R&D Spillovers and the Geography of Innovation and Production[END_REF].

The scope of innovations in processes and products are modeled as a random leap forward from the current state of the division's productivity A ij (t) and consumers' predilections f j (t) according to a uniform distribution defined in Eq.10.

( ) ( ), ( 1) ( )

The parameters pc and pd are considered to be constant and equal to all firms in the industry. Moreover, we suppose that any innovation in processes or products gives the same order of competitive advantage, so it is not difficult to demonstrate a relation between them (Eq.11).

( 1) [START_REF] Fujita | The spatial economy: cities, regions and international trade[END_REF] pc pd [START_REF] Klevorick | On the sources and significance of interindustry differences in technological opportunities[END_REF] A firm can also create a new product, starting a new division responsible for its manufacturing. The new division j* replicates the capacities of its parent according to a stochastic process (Eq.12) that is function of all divisions' features of the firm. Furthermore, there is a probability spinoff p that the new division becomes a new independent spin-off [START_REF] Klepper | Employee startips in high-tech industries[END_REF].


Bruno Nascimento
email: brunonascimento@netcabo.pt

Arnaldo Batista

Luis Brandão Alves

Manuel Ortigueira

Raul Rato

MicroECG: An Integrated Platform for the Cardiac Arrythmia Detection and Characterization

Keywords: Cardiac Arrhythmia, Fibrillation, Wavelets and Time-Frequency

A software tool for the analysis of the High-Resolution Electrocardiogram (HR-ECG) for Arrhythmia detection is introduced. New algorithms based on Wavelet analysis are presented and compared with the classic Simson protocol over the P and QRS segments of the Electrocardiogram (EEG). A novel procedure based on a two step wavelet analysis and synthesis is performed in order to obtain a frequency description of the P, T or QRS segments. This frequency "signature" is useful for the detection of otherwise asymptomatic Arrhythmia patients. The tool has been developed in Matlab, and deployed for a standalone C application.

Introduction

Atrial fibrillation is the most common sustained heart arrhythmia. It is estimated to occur in more than 0.4 percent of the adult population and perhaps as many as 10 percent of the population who are 60 years or older [START_REF] Cox | Electrophysiology, Pacing and Arrhythmia, Operations for Atrial Fibrillation[END_REF]. Atrial arrhythmia may be transient or persistent. While most atrial arrhythmia occurs in individuals having other forms of underlying heart disease, some atrial arrhythmias occur independently. While atrial arrhythmias do not directly cause death as frequently as ventricular arrhythmias, they increase the risk factor for a number of other diseases such as strokes, thrombosis, atherosclerosis, systemic and cerebral embolism and cause a number of additional medical problems [START_REF] Chang | National Implications for Rhythm Management and Stroke Prevention: the AnTicoagulation and Risk Factors In: Atrial Fibrillation (ATRIA) Study[END_REF]. A two-step wavelet method is introduced for the detection and reconstruction of the cardiac micro-potentials [START_REF] Kuchar | Late potentials detected after myocardial infarction. Natural history and prognostic significance[END_REF] that account for the atrial and ventricular arrhythmia [START_REF] Zandi | Quantitative evaluation of a wavelet-based method in ventricular late potential detection[END_REF]. These algorithms are incorporated in a software platform tool (MicroECG) for the integrated High Resolution Electrocardiogram (HR-ECG) processing. A continuous wavelet method is herein applied for the detection of the micro potentials and a wavelet packet methodology for the reconstruction of these signals. MicroECG includes the following pre-processing tools: QRS detection, versatile peak detection, signal averaging or beat-by-beat analysis and ECG delineation with wavelet processes [5] [6]. These pre-processing tools are presented to the medical user or researcher in an intuitive and user-friendly interface where all the parameters can be viewed and changed. After this classical pre-processing is performed, an innovative wavelet methodology is applied in two steps. The first step consists in the application of the continuous wavelet transformation for the micro potentials detection via time-frequency scalograms. The next step includes the wavelet packet transform applied to the user selection in the scalogram plane in order to obtain both the reconstructed micro potentials and a frequency signature for each patient. This frequency signature contains information about the micro potentials presence in the patient's HR-ECG [START_REF] Berbari | A practical guide to the use of the high-resolution electrocardiography[END_REF]. This presence is confirmed by higher frequency components. This procedure is user selected and is applied to the P-wave or the QRS complex. Fig. 1 shows a screen-shot of the MicroECG user interface presenting the Frank leads [START_REF] Frank | An accurate, clinically practical system for spatial vectorcardiography[END_REF] automatically delineated ECG [START_REF] Almeida | Multilead ECG Delineation Using Spatially Projected Leads From Wavelet Transform Loops[END_REF]. The system is versatile to the user and offers the pre-processing tools necessary to the wavelet processing [START_REF] Tompkins | Biomedical Digital Signal Processing: C Language Examples and Laboratory Experiments for the IBM PC[END_REF].

Contribution to Technological Innovation

In this paper we present a tool with the following advanced features:

1. Incorporation under the same roof all the processing steps necessary to the high resolution HR-ECG processing thus saving the user all the time consuming pre-processing procedures. 2. User selection of all the HR-ECG analysis parameters. 3. Innovative wavelet packet methodology for the detection and reconstruction of micro-potentials. 4. User driven (mouse-click) selection of signal features in the wavelet timescale plan for subsequent reconstruction. 5. Cardiac micro-potential syntheses and representation.

Methods and Results

A. Continuous wavelet transform

The continuous wavelet transform (CWT) is based on a set of analyzing wavelets [START_REF] Couderc | Contribution of wavelets to the non-invasive Electrocardiology[END_REF] that allow the decomposition of an electrocardiographic signal in a series of coefficients. Each wavelet used to analyze a signal has its own duration, temporal localization and frequency band. The resulting coefficients from the application of a continuous wavelet transformation corresponds to a measure of the components, on a given temporal and frequency band, of the HR-ECG segment under study. The resulting coefficients are obtained through two major steps:

1. The multiplication of an analyzing wavelet and the HR-ECG segment.

2. The calculation of the degree of correlation between the two signals above. This transformation is given by:

(

) 1
where a is the scale, b is the temporal segment, ECG (t) is the electrocardiogram and Ψ is the analyzing wavelet. The P-wave scalogram shows an intense component below the 80 Hz mark and a weak component signal post this mark. Figure 2 shows the scalogram for a 40 Hz high-pass filtered simulated ECG signal with a micro-potential between 33 and 54.ms. [START_REF] Afonso | ECG beat detection using filter banks[END_REF] The rounded whiter patch represents micro-potential energy The colour intensity of the scalogram is proportional to the degree of correlation between the filtered signal and the detection wavelet at play. This means that the lighter zone in the scalogram represents the area where the signal is highly matched the detection wavelet [START_REF] Natwong | Wavelet Entropy Analysis of the High Resolution ECG, Industrial Electronics and Applications[END_REF]. It is known by the authors' experience that the detection wavelet cmor1-1.5 (Complex Morlet Wavelet) produces the best results regarding the detection of late potentials in the HR-ECG data, so it is set by default in the MicroECG software.

B. Discrete wavelet transform

Unlike the CWT, the Discrete Wavelet Transform (DWT) allows for signal reconstruction. Calculating wavelet coefficients at every possible scale produces a redundant signal representation. If the scales are based on powers of two -so-called dyadic grid -then the analysis will be much more efficient. This is known as the DWT. As seen in Fig. 3, the approximations (cA n ) are the high-scale, low-frequency components of the signal. The details (cD n ) are the low-scale, high-frequency components. The decomposition process can be iterated, with successive approximations being decomposed in turn, so that one signal is broken down into many lower resolution components. This is called the wavelet decomposition tree. Since the analysis process is iterative, in theory it can be continued indefinitely. In reality, the decomposition can proceed only until the individual details consist of a single sample or pixel.

C. Wavelet packet transform

The Discrete Wavelet Packet Transform (DWPT) offers a more complex and flexible analysis, because the details as well as the approximations are split. The difference between DWT and DWPT is that the former presents the same frequency resolution in all the nodes in the same level, since the signal's approximation and detail nodes are all split into two new signals, instead of only the approximation node being split as it happens in the DWT as seen in Fig. 4. Therefore the DWPT turns out to be more efficient to study high resolution cases because there is no loss of resolution in the frequency axis. Another important thing to take notice is that in the final level of this multi-level decomposition "tree" there are the coefficients that can be used to reconstruct the original signal. However these coefficients or "leaves" of this "tree" are in no sequential order in terms of frequency, and one most take notice of this fact if signal reconstruction from this coefficients is required.

In MicroECG, these decomposition levels are calculated via the length of the signal and its sampling frequency. The number of "leaves" of this wavelet packet decomposition "tree" is always a power of 2, for example, if a number of 8 levels are calculated there will be 256 "leaves" or coefficients in the end. However, all these 256 "leaves" will be shown only if the user selects all frequencies to be analyzed from zero to the Nyquist frequency. If the user only selects a sub-set of the frequencies available to be analyzed the 256 "leaves" will be reduced accordingly. For better exemplify these same principles, Fig. 5 is shown where the micro-potential lies around 33-44ms with less than 10µV amplitude and bandwidth between 65 and 187 Hz.

Conclusions and Future Work

The DWPT is a promising tool in the detection of small amplitude, short lived signals present in the HR-ECG. A spectral signature is obtained and the reconstruction of the micro-potentials is possible through the selection of the most energetic nodes. In this work, interesting results have been obtained with simulated data, and will now progress with real data from patients with Paroxistic Atrial Fibrillation. The MicroECG system will serve as a platform for this research work relieving the user from the time consuming pre-processing steps along with all the wavelet algorithms. This platform is design to be flexible and open to improvements.

Fig. 1 .

 1 Fig. 1. A screen-shot of the MicroECG user interface showing the Frank leads automatically delineated ECG. The user can change a significant number of parameters.

Fig. 2 .

 2 Fig. 2. Scalogram from a simulated signal showing a micro-potential event. Top plot: P wave with 380 µV amplitude and the same wave after filtering with micro-potential around 33 and 54 ms, and around 12 µV amplitude.

Fig. 3 .

 3 Fig. 3. Wavelet decomposition tree.

Fig. 4 .

 4 Fig. 4. -Wavelet packet decomposition.

Fig. 5 .

 5 Fig. 5. DPWT reconstruction for the micro-potential of Fig. 2 (dashed) and original filtered P wave. The micro-potential lies around 33-44ms with less than 10µV amplitude and bandwidth between 65 and 187 Hz.

Figure 6

 6 Figure 6 shows the colour maps DPWT energy map of: Top: a patient with Paroxistic Atrial Fibrillation; Bottom: a normal subject. Lighter colour means increased node energy, therefore indicating micro-potential activity.

FreqFig. 6 .

 6 Fig. 6. DPWT energy map of: Top: a patient with Paroxistic Atrial Fibrillation; Bottom: a normal subject.


Rui Gomes

Arnaldo Batista

Manuel Ortigueira

Raul Rato

Marco Baldeiras
email: mlbaldeiras@refer.pt

Railscan: A Tool for the Detection and Quantification of Rail Corrugation

Keywords: Rail Corrugation, Wavelets, Time-Frequency

Rail corrugation is a phenomenon that leads to a waving in the rails with wavelengths typically between 3 cm and 100 cm and amplitude levels of several microns. The genesis of this waving is complex. Rail corrugation is a recognized problem that leads to excess vibration on the rails and vehicles to a point of reducing their life span and compromising safety. In urban areas excess vibration noise is also a problem. A software tool was developed to analyze accelerometer signals acquired in the boggies of rail vehicles in order to quantify the rail corrugation according to their frequency and amplitude. A wavelet packet methodology was used in this work and compared with the One Third Octave Filter (OTOF) power representations, which is currently used in the industry. It is shown that the former produces better results.

Introduction

Rail corrugation is a problem extensively felt by railway companies. This phenomenon is due to the railway traffic conditions that produce corrugation wavelengths in the railhead between 3cm and 100cm [START_REF] Grassie | Measurement of railhead longitudinal profiles: a comparison between different techniques[END_REF]. These rail irregularities are a matter of concern due to excess vibration loads in the vehicles and noise. Vibration may compromise safety and reduce the life span of the equipment, therefore requiring some type of rail maintenance. Early detection of the phenomenon to improve rail security and for economical reasons is a desired goal.

Corrugation measurement may be done using the direct and the indirect approach [START_REF]Railway applications -Acoustics -Measurements of noise emitted by railbound vehicles[END_REF], [START_REF] Verheijen | E, A survey on roughness measurements[END_REF]. In this work an indirect measurement approach in which the corrugation levels are obtained through the signals from axle-box accelerometers [START_REF] Verheijen | E, A survey on roughness measurements[END_REF].

In this paper is presented a software tool, RailScan that integrates under the same roof the necessary signal processing steps and procedures for the rail corrugation detection and quantification using the EN ISO 3095 parameters, exploring the wavelet's superior ability for non-stationary signal analysis.

The RailScan user interface is designed to allow for the system parameters to be modified and adjusted for research purposes. The results are presented in a series of interactive results representations. The results numerical values may be exported for later comparative analysis. The software has been developed in Matlab [4]. RailScan corrugation signal analysis includes: 1. Time-scale representation with user selected wavelets; 2. Base-line removal; 3. Mouse driven feature selection in the Time-Scale plan; 4. Wavelet-Packet implementation that results in power spectrum in the corrugation wavelengths, rail corrugation localization in the rail and its signal recovering in selected wavelet nodes; 5 .One Third Octave Filter (OTOF) power representations.

Contribution to Technological Innovation

Wavelet analysis is herein used as a tool for the analysis on the vibration signals due to the rail corrugation, following a new trend [START_REF] Grassie | Measurement of railhead longitudinal profiles: a comparison between different techniques[END_REF] in this research area. Wavelet analysis has been proved to be more adequate for the processing of non-stationary signals, such as these, for which the classical Fourier analysis presents limited results. However, the application of this tool lacks standardization procedures and overall validation. This work is a contribution for that goal, also being a preparation for the implementation of a version of the EN ISO 3095 with wavelets.

Methods

RailScan uses the Continuous Wavelet Transform (CWT) and the Wavelet Packet Transform, to analyze the axle-box accelerometer data. These methods will be described in this section. It should be emphasized that the CWT is used in the RailScan interface with a mouse driven selection tool to analyze user elected signal details, which is not represented were for lack of space.

A. Continuous Wavelet Transform

The Continuous Wavelet Transform is used in this work to perform time-scale analysis of corrugation signals. This is done multiplying a signal x(t) by scaled and translated versions of ( ) τ ψ the mother wavelet:

( ) ( ) . 1 , * dt s t t x s s CWT x       - = ∫ τ ψ τ ψ (1)
CWT is a function of two variables, the scale s and position τ which is related with the localization of the Wavelet, as the wavelet is shifted through the signal. The scale parameter can be seen as a scale in a map, larger scale show global views and smaller scales show detailed information of a hidden pattern in a signal [START_REF] Daubechies | Ten Lectures on Wavelets[END_REF]. A time-scale signal representation is obtained where features are exposed and localized both in time and in the frequency, since scale can be converted to frequency. RailScan interface allows for the user to mouse select features in the time-scale plan and then automatically to synthesize the underling signal using Wavelet Packets, within the selected frequency boundaries. The synthesized signal may be the corrugation component which has been recovered from the whole signal, for analysis purposes. In this work it was found that the application of the complex Morlet wavelet gives better results for the real corrugation tested signals.

B. Wavelet Packets

The CWT is generally a representation with a high degree of redundancy that doesn't allow for signal reconstruction, but permits time-scale signal representations with a user defined frequency resolution. However, the Discrete Wavelet Transform (DWT) allows for multiresolution signal reconstruction, although its time-scale representation being defined consecutive frequencies that are a power of two related, in consecutive levels. This constraint may be avoided if the Wavelet Packet Transform (WPT) is used. The general representation of the wavelet tree of a second order, three levels Wavelet Packet tree is shown in Figure 1. The filters sequence has been altered in order to give the last tree nodes (the tree leaves) a frequencial order. The last line numbers sequence represents the nodes natural order. A 1 stands for signal approximation in level 1 due to the Low Pass (LP) filtering, and D 1 stands for signal detail in level 1 due to the High Pass (HP) filtering level 1. Likewise DA 2 , for instance stands for the detail of approximation signal A 1 . For the other nodes the same rule is applied.

The leaves frequency nodes progress linearly between zero and the Nyquist frequency, a feature that makes the Wavelet Packet transform a desirable tool in this application. The frequency resolution may be increased, and is limited by the signal length. Of course time resolution decreases in the leave´s nodes, as frequency resolution increases. Fig. 1. wavelet tree with level 3 decomposition (in the frequencial order).

A scheme was used with 64 leaves which cover the corrugation wavelength band with enough resolution. For this a six level WPT is implemented with a precision of 3.9 Hz. Applying expression (7) with v=1m/s the wavelength scale is obtained as shown in Fig. 3 (horizontal axis). The Daubechies D10 wavelet was used [START_REF] Daubechies | Ten Lectures on Wavelets[END_REF].

C. One Third Octave Filter (OTOF)

The European Standard EN ISO 3095 [START_REF]Railway applications -Acoustics -Measurements of noise emitted by railbound vehicles[END_REF] was followed in this work, regarding the indirect measurement of the corrugation level. This chapter includes the case of the data being acquired with an axle-box accelerometer. Accordingly, the OTOF is used to identify the frequencies contents of the vibration due to corrugation, in a predefined scale [START_REF]Railway applications -Acoustics -Measurements of noise emitted by railbound vehicles[END_REF] shown in the first column of Table 1. This procedure will allow for result comparison between the classical OTOF spectra and the one derived from Wavelet Packets application.

A series of band-pass filters for each central frequency ( c f ) are defined, for each the lower cutoff frequency ( lcut f ) and higher cutoff frequency ( hcut f ) is give by the expressions:

. 10

1 20 1 c lcut f f ⋅ = (5)
. f 10 [START_REF]American National Standard Specifications for Octave-Band and Fractional-Octave-Band Analog and Digital Filters[END_REF] .

c 20 1 ⋅ = t f hcut
λ v f = (7)
Using expression [START_REF] Caprioli | Rail inspection in track maintenance: a benchmark between the wavelet approach and the more conventional Fourier analysis[END_REF] the pre-defined ISO wavelengths (for v=1 m/s) were converted to frequencies. Third order Butterworth filters are defined as in [START_REF]American National Standard Specifications for Octave-Band and Fractional-Octave-Band Analog and Digital Filters[END_REF]. Table 1 contains the OTOF bands for the 23 steps according to [START_REF]American National Standard Specifications for Octave-Band and Fractional-Octave-Band Analog and Digital Filters[END_REF] For frequencies close to zero or the Nyquist value an interpolation factor is applied to the data to improve filter stability. For each band the resulting filtered signal power is obtained to be compared with the pre-defined ISO power values [START_REF]Railway applications -Acoustics -Measurements of noise emitted by railbound vehicles[END_REF]. Following the mentioned ISO rule the rail roughness limit spectrum is represented in Fig. 2 by the solid line.

D. Pre Processing

For the wavelet processing, the higher wavelength band extends to infinity. Since the corrugation signal may contain higher energetic long wavelengths components that would result in power peaks, which would hide the power in the interest bands, a base line removal step is required. Having in account that the higher ISO wavelength value is 0.63 m and the long wavelength corrugation extends to 1.00 m [START_REF] Grassie | Measurement of railhead longitudinal profiles: a comparison between different techniques[END_REF], the data is filtered with a wavelet packet filter to remove signal components with wavelengths higher than 1.00 m.

The wavelet packet filter was implemented using the reconstruction of the signal associated to the few base-line nodes and then subtracting this residue from the original corrugation signal. Since only a few nodes are involved, the resulting algorithm is rather fast. Moreover, the wavelet packet frequency structure allows for a flexible selection of the cutoff frequency value.

Application to a Simulated Signal

A simulated corrugation signal with three sinusoids corresponding to a 1000 m rail section, with sampling frequency of 500 Hz at an average speed of 1 m/s is obtained:

( ) ( ) ( ) ( ) [ ] N 2 2 2 3 3 2 2 1 1 × + + = n f sen n f sen n f sen A n y π π π (5) With 1
f corresponding to 10 Hz (λ=10cm), 2 f corresponding to 33.33 Hz (λ=3cm) and 3 f corresponding to 100 Hz (λ=1cm),

[ ] 125000 , 1 1 ∈ n , [ ] 312500 , 187500 2 ∈ n , [ ] 500001 , 375000 3 ∈ n
and A=100 µm being the corrugation amplitude value. A random noise N with zero mean and standard deviation of 150 µm is added. Fig. 2 shows that the noise added signal in the OTOF power spectrum no longer shows the signal components (arrow marked) whereas in fig. 3 plot (WPT) the peaks are clearly detected (arrow marked).

Application to a Corrugation Signal

Fig. 4 shows the OTOF power spectrum of a real corrugation signal and the spectrum of the same signal added to a simulated noise of standard deviation of 135 µm. The noisy signal spectrum no longer detects the relevant components b and c. However the WPT plot, in Fig. 5, detects components b and c, with component a clearer defined. This shows the superior detection ability of the WPT algorithm.

Corrugation Spatial Localization

One of the advantages of the Wavelet Transform is the ability of the time localization along with the frequency (scale) description. This feature is used in the WPT to locate in the rail, the places where corrugation in a particular wavelength occurs [START_REF] Caprioli | Rail inspection in track maintenance: a benchmark between the wavelet approach and the more conventional Fourier analysis[END_REF], an information important for railway companies that will then perform some type of remediation procedures [START_REF] Grassie | Rail Corrugation: advances in measurement, understanding and treatment[END_REF]. Fig. 6 shows that corrugation power in the wave-length band 25.6-12.8 cm is higher between wavelet coefficients 481 and 1226 which corresponds to the rail track between 61 and 157 m.

Conclusions and Further Developments

Wavelet analysis has produced promising results both for simulated data and corrugation detection for accelerometer signals acquired in the axle-box of a railway vehicle. Wavelets outperform the classical OTOF method for signals contaminated with high levels of noise. This feature seems important in the development of portable in-vehicle corrugation detectors as well as improved accelerometer data analysis. For future work it is planned to represent wavelet analysis results in the EN ISO 3095 representation, as well as address issues of standardization and referencing.

Fig. 5 .

 5 Fig. 5. Power Spectrum of the Roughness Level, Wavelet Packet Spectrum for signal + noise.

  Power estimation in level 6 of Node: 1(1) Frequency: 3.9063-7.8125Hz Wavelength: 25.6000-12.8000cm

Fig. 6 .

 6 Fig. 6. Wavelet Coefficient Power estimation showing that excess corrugation in the wavelength 25.6-12.8 cm band exists, in the rail track between 61 and 157 m, see text.

Table 1 .

 1 One third octave band structure.

	Wavelength (m)	Central frequency (Hz)	One third octave band (Hz)
	λ	f c	f lcut -f hcut
	0.63	1.5873	1.4147 -1.7810
	0.5	2.0	1.7825 -2.2440
	0.4	2.5	2.2281 -2.8050
	0.315	3.1746	2.8294 -3.5620
	0.25	4.0	3.5650 -4.4881
	0.2	5.0	4.4563 -5.6101
	0.16	6.25	5.5703 -7.0126
	0.125	8.0	7.1300 -8.9761
	0.1	10.0	8.9125 -11.2202
	0.08	12.5	11.1406 -14.0252
	0.063	15.8730	14.1468 -17.8098
	0.05	20.0	17.8250 -22.4404
	0.04	25.0	22.2813 -28.0505
	0.0315	31.7460	28.2937 -35.6196
	0.025	40.0	35.6500 -44.8807
	0.02	50.0	44.5625 -56.1009
	0.016	62.5	55.7032 -70.1262
	0.00125	80.0	71.3001 -89.7615
	0.001	100.0	89.1251 -112.2018
	0.008	125.0	111.4064 -140.2523
	0.0063	158.7302	141.4684 -178.0982
	0.005	200.0	178.2502 -224.4037
	0.004	250.0	222.8127 -280.5046
	0.00315	317.4603	282.9368 -356.1963

  Power Spectrum of the Roughness Level, 1/3 Octave Power Spectrum for simulated signal and simulated sinal added to noise. Power Spectrum of the Roughness Level, Wavelet Packet Spectrum for signal + noise. Power Spectrum of the Roughness Level, 1/3 Octave Power Spectrum.

		30			Signal + Noise
		25
	Roughness Level (dB)	5 10 15 20	Signal + Noise	a	b	c	Signal
		0
	Wavelength, cm 3.15 4 5 6.3 Wavelength, cm 2.5 0.9850 8 1.0240 1.0670 10 1.1130 1.1640 12.5 1.2800 16 1.4220 20 1.6000 25 1.8290 31.5 Signal 40 50 3.2000 3.6570 63 4.2670 5.1200 6.4000 8.5330 12.8000 25.6000 Inf Fig. 2. 8400 8600 8800 9000 9200 9400 9600 9800 10000 10200 10400 Roughness Level 2.8440 2.5600 2.3270 2.1330 1.9690 1.7070 1.5060 1.3470 1.2190 0.9480 0.9140 0.8830 0.8530 0.8260 0.8000 0.7760 0.7530 0.7310 0.7110 0.6920 2 0.6740 0.6560 1.6 0.6400 0.6240 1.25 0.6100 0.5950 1 0.5820 0.5690 0.8 0.5570 0.5450 0.63 0.5330 0.5220 0.5 0.5120 0.5020 0.4 0.4920 0.4830 0.315 0.4740 0.4650 0.4570 0.4490 0.4410 0.4340 0.4270 0.4200 0.4130 0.4060 0.4000 Wavelength, cm 0.315 0.4 0.5 0.63 0.8 1 1.25 1.6 2 2.5 3.15 4 5 6.3 8 10 12.5 16 20 25 31.5 40 50 63 Wavelength, cm Inf 25.6000 12.8000 8.5330 6.4000 5.1200 4.2670 3.6570 3.2000 2.8440 2.5600 2.3270 2.1330 1.9690 1.8290 1.7070 1.6000 1.5060 1.4220 1.3470 1.2800 1.2190 1.1640 1.1130 1.0670 1.0240 0.9850 0.9480 0.9140 0.8830 0.8530 0.8260 0.8000 0.7760 0.7530 0.7310 0.7110 0.6920 0.6740 0.6560 0.6400 0.6240 0.6100 0.5950 0.5820 0.5690 0.5570 0.5450 0.5330 0.5220 0.5120 0.5020 0.4920 0.4830 0.4740 0.4650 0.4570 0.4490 0.4410 0.4340 0.4270 0.4200 0.4130 0.4060 0.4000 Fig. 3. -5 350 360 370 380 390 400 420 Roughness Level Fig. 4. 340 410 Signal + Noise

Acknowledgements. The collaboration of Dr. Stuart Grassie of Railmeasurement in this work is acknowledge.


Victoria Mª

De-La-Fuente

Lorenzo Ros
email: lorenzo.ros@upct.es

Cold Supply Chain Processes in a Fruit-and-Vegetable Collaborative Network

Keywords: Cold chain, Collaborative network, Perishable goods

The work presented in this paper focuses on the construction of a prototype for strategic design of nodes in a logistics network (PROCONET project). It incorporates the necessary concepts, models and supporting tools related to the design of the process flow and the choice of technology. The operation of the prototype will be validated with global supply chain processes for perishable products. Global supply chain processes will consider both full loads and groupages. Special attention will be given to the impact of INCOTERM agreements. The prototype will support intermodal logistics processes as an added value.

Introduction

In the last thirty years the fruit-and-vegetable sector has become the first Spanish agriculture sector in terms of production and export value. Driven by strong competition, this dynamic area must continuously incorporate new methods and technologies.

As such, an ongoing effort has to be made by agricultural companies to implement innovative measures which add value to the sector and make it capable of appropriately responding to consumers' demands, of successfully facing competition and of maintaining leadership via new products and new forms of presenting them.

It is in this context of innovation and new technologies where the PROCONET Research Project is defined, initiated due to the need for improvements in logistics and transport of perishable goods (as discovered by the companies in this segment). A prototype of strategic storage nodes will be designed in a collaborative network framework, which facilitates the operation of the cold chain and transportation of products while ensuring traceability and food safety throughout the whole logistic process.

This paper is organized as follows: Section 2 presents a brief review of collaborative networks, their characteristics, their coordination of activities and the information mechanisms inside them. Section 3 deals with current issues in the Spanish fruit-and-vegetable sector. Section 4 discusses the objectives, the integration methodology and the framework for the fruit-and-vegetable collaborative network to be developed in the project, and proposes the PROCONET architectural design and components of the fruit-and-vegetable collaborative network, information systems and network data exchange techniques. Section 5 presents the main conclusions and innovation aspects of the PROCONET project.

Collaborative Networked Organizations

Participation in networks has become very important for any organization that strives to achieve a differentiated competitive advantage. Among the wide variety of existing networks, collaborative networks are especially relevant. These have emerged over the last few years as a result of the challenges faced by both the business and scientific world, since collaboration has become the key issue to rapidly answer market demands in manufacturing companies through sharing competence and resources [START_REF] Camarinha-Matos | Collaborative networked organizations -Concepts and practice in manufacturing enterprises[END_REF].

A collaborative network (CN) is constituted by a variety of entities (e.g. organizations and people) that are largely autonomous, geographically distributed, and heterogeneous in terms of their operating environment, culture, social capital and goals. Nevertheless, these entities collaborate to better achieve compatible goals, using ICT for supporting the development of collaborative business opportunities [START_REF] Camarinha-Matos | Collaborative networks: a new scientific discipline[END_REF].

As dynamic inter-organizational models, CNs must display a distributed business process management system to support the means for obtaining, providing and managing production-related information from and about CNs, enabling enterprises to conduct their logistics more efficiently, and working better in an integrated and virtual environment [START_REF] Pereira | Managing distributed Business processes in the virtual enterprise[END_REF]. In this context, communication mechanisms play a vital role, alongside coordination to support the exchange of information between interdependent activities / processes and among CN enterprises [START_REF] Camarinha-Matos | Cooperation coordination in virtual enterprises[END_REF].

For this reason, the advantages in the ICT that support the concept of collaborative networks have allowed enterprises to move to more cooperative information-driven environments. Enterprise knowledge sharing (know-how), open source / web-based applications, and common best practices are ways to achieve both the concept of integrated enterprise and the implementation of collaborative networked enterprises for the manufacturing industry [START_REF] Camarinha-Matos | Collaborative networked organizations -Concepts and practice in manufacturing enterprises[END_REF].

Finally, it must be noted that a growing number of collaborative networked organization forms are emerging as a result of the continuous advances in the field of ICT, which contributes to help SMEs face current social and market challenges. CNs are already recognized as an important survival instrument for organizations in a period of turbulent socio-economic changes [START_REF] Camarinha-Matos | Collaborative networks: a new scientific discipline[END_REF][START_REF] Camarinha-Matos | Collaborative networked organizations -Concepts and practice in manufacturing enterprises[END_REF].

3

The Fruit-and-Vegetable Spanish Sector: Problems and Necessities

In the last thirty years the fruit and vegetable sector has become the first sector of Spanish agriculture in terms of production value. Spain is the major producer of fruits and vegetables for consumption in fresh and the major exporter of these products.

The fruit-and-vegetable sector is extremely dynamic. It is continuously incorporating new methods and modern technologies, due to heavy domestic competition, and especially because of the development that is occurring in other regions and countries. An increasingly globalized market and competition from producers and distributors from other regions within, and outside of, the European Union compels a continuous effort to adopt competitive measures which add value to the sector and make it possible to respond to consumers' demands, to face foreign competition, to maintain the leadership of the Spanish sector and to increase its market share by introducing new products, and new ways of presenting them [START_REF] Gómez-Espín | Estrategias de innovación en el sector hortofrutícola español y en las empresas encargadas de la logística y transporte de estos productos perecederos[END_REF].

Spanish exportation of fruit and vegetables in recent campaigns has totalled more than 9 million tons. The main countries of destination are other member countries of the European Union, which receive more than 87% of these exports. Most of these shipments are dispatched by land, especially by road, using refrigerated vehicles to transport loads at a controlled temperature.

Analysis of Spanish production areas, trends in consumption and markets; experience in distribution and logistics; even the expansion to 27 European Union member states, with a market of 500 million consumers, all seems to confirm an increase in the demand of transport by trucks at controlled temperatures (refrigerated lorries), for shipments of fruit-and-vegetable produce and other logistic services associated to it [START_REF] Gómez-Espín | Estrategias de innovación en el sector hortofrutícola español y en las empresas encargadas de la logística y transporte de estos productos perecederos[END_REF][START_REF] Luengo | El sector hortofrutícola español: a la espera de su racionalización[END_REF].

However, the sector must also confront a number of problems, both internal (precarious generational relief, progressive decrease of resources such as water and land, etc.) and external (production from third countries at lower costs and less Customs control, etc.).

A conclusion can be drawn from all the above: by trying to balance market preferences and agricultural reality, the fruit-and-vegetable sector faces constant challenge. For the transformation of this sector the development of innovative solutions will be of great importance to keep its leadership positioning. This goal can be achieved by primarily focusing on aspects which enable producers to have an advantageous position over large distribution chains.

The PROCONET Project

When analyzing the fruit-and-vegetable marketing process as a whole, from the point where the decision is made to grow a certain kind of product to the stage when it reaches the final consumer, a wide range of innovations are used as a response to demands from markets and consumers, as well as new proposals in production, distribution and transport.

In this process, relations are established among the agents involved in the food supply chain: producers, growers, exporters, importers, haulers, etc., which must be adapted and managed from a collaborative business model.

The organizational model "Collaborative Network" is composed of a variety of entities (organizations and individuals) largely autonomous, geographically distributed, all collaborating to better achieve common / compatible goals, with the interactions among them supported by computer networks; and with common patterns, such as autonomous entities from varied locations, driven by common goals/intentions to be achieved by collaboration, and operationally based on agreed principles and inter-operable infrastructures which allow them to cope with their heterogeneity [START_REF] Camarinha-Matos | Collaborative networks: a new scientific discipline[END_REF][START_REF] Camarinha-Matos | Collaborative networked organizations -Concepts and practice in manufacturing enterprises[END_REF].

As aforementioned, internal and external competition in the fruit and vegetable industry obliges those companies involved to continually innovate in terms of range of products, growing techniques and product presentation, and to improve in such aspects as food handling, storage systems, loading and unloading techniques, etc. There are various reasons behind all these innovations, but they are mainly determined by consumers and food safety rules, and must respond to the different problems posed by the agents/companies in the sector.

The pursuit of the PROCONET Project research team may be defined as "comprehensive traceability of agri-food road transport at controlled temperature", which, on the basis of logistic processes (either full loads or groupages) may grant access to experimenting with electronic CMRs by means of controlling technological variables involved in the said logistical processes.

Today's existing traceability models for the food sector focus on certain products (milk, eggs, beef, transgenic products, etc.). The food safety and traceability model presented in this paper results from the lack of legislation in the agri-food sector and also from recommendations issued by both Spanish and European organizations during the last decade [START_REF] Benner | A chain information model for structured knowledge management: towards effective and efficient food product improvement[END_REF][START_REF] Moe | Perspectives on traceability in food manufacture[END_REF][START_REF] Sarkar | Dynamics of open innovation in the food industry[END_REF].

The PROCONET Project focuses on the construction of a prototype for strategic design of nodes in a logistics network. It incorporates the necessary concepts, models and supporting tools related to the design of the process flow and the choice of technology. The operation of the prototype will be validated with global supply chain processes for perishable products. Global supply chain processes will consider both full loads and groupages. Special attention will be given to the impact of INCOTERM agreements. The prototype will support intermodal logistics processes as an added value.

The project will develop a platform for knowledge management in a collaborative fruit-and-vegetable network, which supports the communication processes within that network (see Figure 1). Connected to the Operations Planning and Control System application run by each of the member companies in the collaborative network, this platform will design and manage such crucial elements as: (a) Route maps (scheduled departures and arrivals, checkpoints, etc.) linked to existing applications may display real-time information on the location of transport units and related data (dispatch time, average speed, cargo temperature, etc.). (b) Route maps connected with invoicing systems in such a way that the logistics company may invoice for the service given at the time of collection / delivery. (c) Transport management and management of fleet condition, resource utilization, reduction of polluting emissions, data control. The PROCONET platform, or integrated model for communication between transport systems and logistics companies, will allow, through GPS (Global Positioning System), to know where the merchandise is at any given time, facilitating its transport and delivery in terms of traceability and food safety.

Technical Objectives f the PROCONET Project

The technical objectives targeted by this project form a continuum from business requirements up to evaluation of alternatives: 1. To inventory systems and equipment being used, likely to be used or likely to be developed for the Cold Supply Chain. 2. To design the necessary processes required for handling of loads in the groupages storage node for transportation by the Cold Supply Chain. 3. To define a model for traceability and food safety of fruit-and-vegetable products in the transport process of the cold supply chain. 4. To define a methodology which relates business decisions on transport (inventory records, suppliers' orders records and customers' orders records) with the technical requirements of the technology used in the Cold Chain. 5. To Identify and parameterize those variables likely to be present in a cold supply chain and their interaction with other agents in the chain. 6. To develop an initial traceability and food safety prototype for fruit-andvegetable products in the logistic processes of the cold supply chain. 7. To develop a second transport safety prototype, based on INCOTERMS and demonstrate the feasibility of an electronic CMR.

8. To integrate both prototypes in such a way that a small transport business may evolve into a logistics company. 9. To assess and evaluate the developed prototypes. 10. To diffuse the conclusions of the completed prototyping work.

Methodology for the PROCONET Project

As stated by several authors [START_REF] Camarinha-Matos | A comprehensive modeling framework for collaborative networked organizations[END_REF][START_REF] Camarinha-Matos | Collaborative networked organizations -Concepts and practice in manufacturing enterprises[END_REF], in Collaborative Networks there is not a single formal modelling methodology, tool or approach that may adequately cover all perspectives (no universal language for all problems). Therefore, for the development of a fruit-and-vegetable collaborative network which allows an appropriate paradigm and approach for different enterprise information systems of cold chain integration and collaboration, the research team are working with the ERE-GIO methodology [START_REF] Ros | Enterprise Engineering versus Cycle Re-Engineering Methods[END_REF], based on Integrated Information Architectures, and also on their own experience in designing and managing different enterprise information systems. The ERE-GIO methodology will allow the establishment of a modelling framework for CNs which integrates multiple perspectives, because this methodology presents a life cycle approach based on the "As-is" model as well as on the "To-be" model (Figure 2). This model will be used to develop an approach to the cold supply chain from an endogenous CN perspective, made and mapped into different dimensions (structural, functional, etc.). This double-model methodology will also be used to integrate the GPS system and the platform that will support cold chain processes, whilst analyzing the performance and constraints of the traceability and safety prototype, and further implementation in the collaborative network.

Innovation in the Cold Supply Chain

Innovation is a complex and diversified activity involving many components interacting and acting as sources of new ideas. The PROCONET project is focused on incremental technological innovations, i.e. improvements that are made to a service or an existing process in order to increase the benefits for the chain or the network.

In this project, innovation is oriented towards four large areas (see Table 1), which for the fruit-and-vegetable collaborative network translates into the following factors: renewal and expansion of the productive processes and the range of services, changes in organization and in management, and consequences of the feasibility of the electronic CMR. • Development of a prototype that allows and enables traceability of fruit-and-vegetable products whilst on route and its interaction with logistical companies.

Storage Nodes

• Design of processes that allow small farmers' groupages to access Cold Supply Chain transport services in the best conditions. ICTs

• Application in the traceability of fruit-and-vegetable products.

• Application in transport safety.

• Application in the fruit-and-vegetable sector and cold chain.

• Development under open systems.

Conclusions

Alongside the main objective of the PROCONET project, to achieve comprehensive traceability of fruit-and-vegetable transport with controlled temperature (in a collaborative network organization), the research team is pursuing a set of long-term goals which, once the project is completed, may be applied to different areas, depending on the type of enterprise and the business sector addressed:

• The project's consortium has established as a challenge to demonstrate the feasibility of the electronic CMR and its legal implications. If it proves to be feasible, the challenge is to propose the different Spanish institutions the modification of the current European normative and INCOTERMS agreements.

• Road export companies would be greatly interested in the results of the prototyping oriented to "on-route transport" because it is a constant source of conflict between logistic operators, customers and insurance companies. • Logistics companies in the fruit-and-vegetable sector and in other sectors also working in the cold supply chain will have a great interest in the results of the parameterization of traceability, motivated by option to know the actual characteristics of how their transport has been carried out. • Small farmers, the most common users of groupages, will be supplied the possibility to incorporate the added value of traceability of their products, which is usually restricted to and monopolized by large companies. • National transport companies will be motivated to incorporating the technology involved in the prototype, to be developed in this project. This technology will initially involve investment in equipment and facilities, but it will mean an added value and differentiating feature against competition from foreign transport.

Fig. 1 .

 1 Fig. 1. Interconnected electronic information PROCONET platform. .

Fig. 2 .

 2 Fig. 2. Phases and stages of the ERE-GIO methodology.

Table 1 .

 1 Technological innovation of the PROCONET project.

	Fruit-and-	• Traceability control of fruit-and-vegetable products during the
	vegetable	transport and handling phases.
	Sector	• INCOTERM-based improvements in CMR application.
	Cold Supply	• Transport model in Cold Supply Chain, definition of indicators,
	Chain	implementation, measuring, evaluation and development of
		scoreboard.