Peter Gundelach

Democracy and Denomination: Democratic values among Muslim minorities and the majority population in Denmark

Keywords: Immigrants, Islam, survey data, national identity, Denmark, Democracy Immigrants, Islam, survey data, national identity, Denmark, democracy. Islam, immigrants and democracy

Democracy and Denomination: Democratic values among Muslim minorities and the majority population in Denmark

Huntington's argument refers to large cultural entities -civilizations -but he also argues that clashes of civilizations might be found inside Western European countries as a consequence of the increase in Muslim immigrants. Huntington claims that Europe is threatened by 'Islamization' (Huntington 1996, p. 204) and 'Muslims pose the immediate problem to Europe' (Huntington 1996, p. 204). According to Huntington, democracy is one of the important issues that divide civilizations. In particular, he notes that Islam is associated with authoritarian rule and Christianity with democracy. However, we still lack studies that analyze a possible clash of civilisations (i.e. sizeable value differences) at the individual level in Western countries (for an exception based on the World Values Survey and consequently with very few respondents from ethnic minorities, see Pettersson 2007). This article addresses this need, using a relatively unique data set: A Danish survey of values among eight ethnic groups: Ethnic Danes and seven ethnic minorities in which all or some members are Muslims.

If Huntington's thesis were correct, we would expect a lower level of support for democratic values among Muslim minorities in Western countries compared to the support shown by the Christian majority population. Huntington's fear is shared by [START_REF] Fukuyama | Identity, Immigration, and Liberal Democracy[END_REF] who is concerned that immigration to Western Europe -particularly the increasing number of Muslim immigrants -will result in a struggle between radical Islamism and liberal democracy.

Democratic values are fundamental to Western societies. Most European countries have references to democracy and human rights written into their constitutions, and democracy is codified as a principle of the European Union. The preamble of the 2004 draft treaty mentions 'the inviolable and inalienable rights of the human person, freedom, democracy, equality and the rule of law' (Eur-Lex, vol. 47, December 16, 2004, p. 3). Even for supporters of multiculturalism, who argue that societies should accommodate a large variety of values based on cultural differences, the acceptance of parliamentary democracy and freedom of speech and religion are seen as a non-negotiable principles (Borowski 2000).

Scholars like Fukuyama and Huntington fear that immigration may create problems for Western democracies for at least two reasons. Firstly, in general immigrants come from countries that are less democratic than the Western European countries they immigrate to (Puddington and Piano 2005). This observation is documented in The Freedom House Survey that measures a country's level of democracy according to two criteria: Political rights and civil liberties. There is some variation these countries' total score but in general the score is lower than that of any Western European country. Even though we may be able to estimate the character of the regime we have very little reliable evidence about the attitudes in the general population in these countries. Perhaps the support for democracy among the general public is higher than one would expect, judging by the character of regime. At least this result was found by [START_REF] Tessler | Islam, Gender, Culture, and Democracy[END_REF] in a study of Arab countries but even so it seems reasonable to argue that in general the general support for democracy in these countries is lower than in the Western world.

Secondly, most of the immigrants to Western Europe come from countries that are dominated by Islam. Some critics argue that Islam's relation to democracy is ambiguous, if not dubious. Nasr claims that the 'Islamic view of democracy is not something deeply legitimate, but at best … a tool or tactic that may be useful in gaining the power to build an Islamic state' (Nasr 2005, p. 13). By contrast, [START_REF] Ibrahim | Universal Values and Muslim Democracy[END_REF] argues that 'several crucial elements of constitutional democracy and civil society are also moral imperatives of Islam' (Ibrahim 2006, p. 7). The same difference of opinion is found at a public discursive level in Western societies both among politicians and in the general population. This article explores whether the Fukuyama/Huntington thesis about different sets of values among Muslim minorities and the Christian majority population can be substantiated in the case of Denmark. Our analysis is based on a survey among immigrants, descendants, and Danes undertaken in spring, 2006. According to the Fukuyama/Huntington thesis, a comparison of these groups would reflect a higher level of democratic values among Danes than among Muslim minorities. However, the Fukuyama/Huntington thesis does not allow us to determine whether the level of democratic sentiment differs among immigrants and descendants; nor does it allow for differences among ethnic minority groups. That is why a set of alternative hypotheses is needed. An alternative to the religious hypothesis is the assumption that political socialization will change the values of the individual. Almond and Verba's famous study, The Civic Culture (1963) argues that political socialization is a crucial mechanism for creating political values and skills among citizens. According to [START_REF] Sapiro | Not Your Parent's Political Socialization: Introduction for a New Generation[END_REF], the concept of political socialization had its heyday in the 1980s but has received less attention in recent decades. However, political socialization is becoming increasingly relevant to the development of political involvement and values among immigrants. A general socialization hypothesis suggests that the mere fact of living in a society with democratic institutions will teach individual citizens democratic values.

Alternative hypotheses: Socialization and nation

In order to analyse political socialization, we need to differentiate between two groups: Immigrants and descendants. Immigrants (including family-reunited individuals) are people who were born abroad and have come to Denmark either to work or as refugees from their home country. In other words, they have lived only part of their lives in Denmark. They are expected to have a lower level of democratic awareness than native Danes when arriving in Denmark. Based on the political socialization hypothesis, however, we would expect that the longer an immigrant lives in Denmark, the stronger his/her democratic values will become.

Descendants are people who have lived in Denmark all their lives. Their socialization has taken place in a society with democratic institutions and a democratic culture. More specifically, they have gone to Danish kindergartens and schools where one of the official goals (formally stated in a law from 1975) is to teach pupils fundamental democratic principles. A comparative analysis has found that Danish schools emphasize 'collaboration and consensus' and concern themselves particularly with 'education for citizenship and democracy, as well as with the academic goals of education' (Osborn 2001, p. 274). Following the political socialization hypothesis, this emphasis on democracy will have an impact on the values of pupils. For this reason, we would expect descendants to have a more positive attitude towards democracy than their parents (the immigrants). There would only small differences in democratic values between descendants of immigrants and Danes because both groups have lived in a democratic society all their lives. [START_REF] Gundelach | Etniske gruppers vaerdier, København: Ministeriet for flygtninge, indvandrere og integration HUNTINGTON, SAMUEL P. 1993 'The Clash of Civilizations?[END_REF]. This finding may be interpreted as the result of a number of processes: Often, people from ethnic minorities come from the same region in the country of origin; they have a common destiny (be it as refugees or immigrants), and they are interrelated by family relations, common language and their daily interactions in their host country.

To be able to test the hypothesis about the relationship between democratic values and ethnicity (irrespective of religion), the ethnic group needs to reflect a variation of religious faiths. In some ethnic groupings, however, such differences do not exist. Among Turks or Pakistanis more than 95 per cent of the respondents present themselves as Muslims. In such cases, it would be impossible to ascertain whether it is the respondent's ethnicity or his/her religion that has had the greatest influence on his/her democratic values or lack of such. The ethnicity hypothesis can only be tested in ethnic groups with a varied composition of denominations. This limits the investigation to immigrants from West Balkan, where our survey reflects 69 per cent to be Muslims; to immigrants from Iran, where 55 per cent of respondents are Muslims; and to immigrants from Iraq where 82 per cent of respondents are Muslims.

To summarize, we operate with the following three hypotheses:

• A religious hypothesis that claims that Muslims have fewer democratic values than Christians.

• A political socialization hypothesis, which suggests that the longer a person has lived in Denmark the stronger his/her democratic values become. This means that Danes and descendants have more democratic values than immigrants, and that there will be little or no differences between descendants and Danes in terms of democratic mindset.

• An ethnicity hypothesis that argues that in ethnic groups with different denominations, ethnicity is a stronger indicator of democratic values than religion. Each of the hypotheses above will be explored in the following. As a precursor to such an analysis, a short introduction to immigration in Denmark appears relevant, describing the data and definitions of the dependent variables.

Immigration to Denmark

There has always been immigration to Denmark. Until the 1960s, the proportion of ethnic minorities was very small, making Denmark a very homogeneous country in terms of ethnicity. During the late 1960s, the first immigrants from Pakistan, Turkey and the former Yugoslavia came to Denmark as so-called 'guest workers". By the early 1970s, immigration laws prevented more labour immigration, but from the mid-1980s onwards, Denmark has accepted a number of refugees from countries such as Iraq, Iran, Somalia and Vietnam.

According to the Danish Ministry of Integration (2006a), a total of six per cent of the Danish population are immigrants or descendants from non-Western countries. It is not easy to estimate the number of Muslims in Denmark because the authorities do not register religious denomination. Based on a careful analysis of a number of sources, Jacobsen (2007) estimates that the number of Muslims in Denmark ranged around 207.000 in 2006. This corresponds to 3.8 per cent of the Danish population.

The structural societal position of minorities is clearly less favourable than that of native Danes. The rate of employment as well as the level of education and income is considerably lower among immigrants -and among the descendants (Ministry of Integration 2006b).

Definitions

Concepts such as 'ethnicity', 'ethnicity' and 'immigrant' are contentious and difficult to define. In this article, we 'ethnic groups'is used in accordance with Smith's (2001, p. 13) 'working definition' where ethnie is defined as "a named human community connected to a homeland, possessing common myths of ancestry, shared memories, one or more elements of shared culture, and a measure of solidarity, at least among the elite". The concepts of immigrant and descendant are regrettably limited to the technical definitions provided by Statistics Denmark since these definitions constitute the foundation for our data collection. According to Statistics Denmark an immigrant is defined as a person born abroad whose parents are both (or one of them if there is no available information on the other parent) foreign citizens or were both born abroad. If there is no available information on either of the parents and the person was born abroad, the person is also defined as an immigrant. This means that the category immigrant also includes refugees.

Statistics Denmark (2008) defines a descendant as a person born in Denmark whose parents (or one of them if there is no information on the other parent) are either immigrants or descendants with foreign citizenship. If there is no available information on either of the parents and the person in question is a foreign citizen, the person is also defined as a descendant.

Ethnic group refers to the country of origin as registered by Statistics Denmark. If a descendants has parents with different ethnicities, the descendants is assigned the ethnicity of his/her mother (for details, see Statistics Denmark 2008). This assigned ethnicity does not necessarily correspond with the respondent's perceived ethnic belonging. However, the data contain no information about the respondent's self-reported ethnic origin -with one important exception: Respondents were asked if they were of Kurdish origin. A statistical analysis [START_REF] Gundelach | Etniske gruppers vaerdier, København: Ministeriet for flygtninge, indvandrere og integration HUNTINGTON, SAMUEL P. 1993 'The Clash of Civilizations?[END_REF] has shown that people who say they are Kurds do not differ significantly from other immigrants from the respective country of origin. A similar result was found in a survey among Turkish immigrants in Denmark (Necef 1996). A special problem relates to immigrants and refugees from the Balkans since Statistics Denmark only registers the state where the immigrant comes from. The first immigrants came to Denmark in the early 1970s from former Yugoslavia, and in the 1990s, Denmark accepted a number of refugees primarily from Bosnia-Herzegovina.

A comparison (not shown here) of the so-called guest workers from ex-Yugoslavia, their reunited family members and the refugees from the Balkan countries in the 1990s shows no significant difference in democratic values among these groups. This may be due to the fact that they come from roughly the same region of ex-Yugoslavia [START_REF] Fledelius | [END_REF]. For the purposes of this article, it seems acceptable to consider respondents from West Balkan as a single group.

Finally, 'Danes' refer to people who are not immigrants or descendants. It would be more correct to use the term 'ethnic Danes' as many immigrants are in fact Danish citizens, but the term 'Danes' has been chosen for the sake of brevity.

Data

Our survey was based on phone interviews with nine ethnic groups: Immigrants and descendants from Pakistan and Turkey, immigrants or refugees from West Balkan, Iraq, Iran and Vietnam, and Danes. Since this article concerns the possible relationship between Islam and democracy, the Vietnamese immigrants have not been included in the subsequent analysis because none of the Vietnamese respondents were Muslims. Among all the other ethnic minorities (except for the Danes), some or all of the respondents have stated that they are Muslims.

Among the ethnic minorities, only people who have lived in Denmark for three years or more were included in the survey. The three-year limit was chosen because Danish law grants the right to vote at local elections after three years residency in Denmark.

Compared to the general population, the age distribution among the immigrant groups is skewed. Almost all of the respondents were below 50 years of age. Consequently, the survey population was defined as persons aged 18-50. The largest immigrant group was the Turkish immigrants where the population was 22.587; the smallest group of immigrants was the Vietnamese (5.910 persons). Among descendants, only two groups had a size that permitted further study: Turkish descendants amounted to 5.967, and Pakistani descendants numbered 3.268.

Respondents were randomly chosen by civil registration number. Interviewers were bilingual and both immigrants and descendants could choose to be interviewed in Danish or in their mother tongue. About 500 persons in each ethnic group were interviewed. The average response rate was 54 per cent, varying between 67 per cent for Danes and 41 per cent for Pakistani descendants. The main reason for the relatively low response rate was the difficulty encountered when having to establish a clear Once contact was established, the number of rejects was relatively small. In the tables, weights are used to compensate for the low response rate. In the statistical analyses, no weights were applied.

The number of 'don't know' answers varied from question to question and between ethnic groups. In general, the 'don't know' answers were lowest among Danes. As it is difficult to establish the reasons for such differences, 'don't know' answers have been excluded from the analysis. All data may be obtained from the Danish Data Archive (DDA-21171).

The dependent variables: Democratic values

Democracy theory tells us that the term 'democracy' has at least two meanings: 'popular sovereignty' and 'the liberty of the individual members of society' [START_REF] Thomassen | Citizens and the State[END_REF]. The interpretation of these meanings and their interrelation varies in the different normative democracy theories, but that is of less relevance to the concerns of this article. For our purposes, the two-dimensional structure that these meanings suggest is of greater importance.

The first -'popular sovereignty'-will be termed democratic principles. This variable is operationalized in the following question: 'What do you think about each of the following ways of governing Denmark? Having a democratic system' ('Very good', 'Fairly good', 'Fairly bad', 'Bad', 'Don't know'). This question is part of a battery of four questions that has been used by the World Values Survey and the European Values Survey. The other three questions in the battery ask about support for 'Having a strong leader who does not have to bother with parliament and elections", 'Having experts, not government, make decisions according to what they think is best for the country", 'Having the army rule the country". These questions are sometimes combined in an index but in our data we could not find a satisfactory one dimensional structure for all groups. Therefore, the analysis only concerns the 'democratic system' question -the only question that use the word 'democracy". Analyses show that the [START_REF] Fuchs | Citizens and the State[END_REF][START_REF] Klingemann | Critical Citizens. Global Support for Democratic Governance[END_REF]).

The second dimension, democratic rights, is just as important for democracy as free elections. In the present survey, the variable is an index based on two questions: one on religious freedom and one on freedom of speech. Religious freedom has been called 'the first freedom' in the sense that freedom of conscience is both chronologically the first and may be seen as a precondition for other freedoms (Casanova 1994, p. 40). It is measured by the following question: 'To what extent do you agree or disagree with the following statement: It is important that all people in Denmark have the possibility of carrying out their religious rituals and wearing religious symbols'('Strongly agree', 'Agree', 'Neither agree nor disagree', 'Disagree', 'Strongly disagree', 'Don't know').

Freedom of speech and association is measured by the following question: 'To what extent do you agree or disagree that all groups -even groups with extreme attitudes -should have the right to hold meetings and state their cause?'('Strongly agree', 'Agree', 'Neither agree nor disagree', 'Disagree', 'Strongly disagree', 'Don't know').

The two questions are positively correlated for all ethnic groups (γ between 0,12 and 0,36. The γ's are significant except for the Pakistani descendants (cf. Table A.1).

The low insignificant correlation in this group is due to the fact that the distribution of responses is very skewed: almost everybody answers 'agrees strongly". For each respondent the answers are added up. The sum varies between 2 and 10. This is recoded as follows: 2=1, 3=2, 4 or 5 = 3, other values = 4. If a question is answered 'don't know', the index is coded 'don't know'. Democratic values are not just related to the formal structural arrangements in society but also to its political culture (Almond and Verba, 1963). Democratic culture has been measured in many ways. In our survey, we decided to develop a measurement for democratic values that was family related. If democratic culture pervades all institutions in society, it would also encompass the private sphere. Democratic culture in the family would mean that the individual members of the family have influence on their own life and are not subjected to any authoritarian family structure. Giddens have suggested a close relation between societal and family democracy. There is only one story to tell about the family today, and that is of democracy..... Democracy in the public sphere involves formal equality, individual rights, public discussion of issues free from violence, and authority which is negotiated rather than given by tradition. The democratized family shares these characteristics. (Giddens 1998, p. 93).

Family democracy may be studied through the relations between spouses or through the relationship between parents and their children. For the purpose of this article, values concerning the husband-wife relationship are less useful because marital relationships are heavily infused with gender roles issues. The parent-child relationship, by contrast, is less contaminated when it comes to democratic values. The index is based on the following question: 'How much influence should a young person/parents have on the young person's a) choice of partner or b) choice of education". The respondents were asked to grade their answer according to a 10-point scale, where 1 reflected total decision power to the young person, and 10 suggested that parents should decide totally. There is a very high correlation (γ between 0,53 and 0,70) between the two variables for all ethnic groups, cf. Table A1 . The two variables were entered into an index by adding the values of each variable, and the result subsequently divided into three groups: Index value 2 = the young person has very high influence (code 1), Index value 3-6 = the young person has high influence (code 2). Index value 7-20 = the young person has less influence (code 3). If one question is answered with a 'don't know', the index is coded 'don't know'.

Are Muslims less democratic than Christians?

Table 1 reflects an initial overview of democratic values in the Christian majority and in the Muslim minorities. The denomination of the respondents is measured by a question on self-reported religiosity ('What is your religion?"). Non-believers have been excluded from the table. The table presents an interesting and quite surprising result. The respective positions of the three groups vary according to which dimension of democratic values we investigate. When it comes to democratic principles -i.e. whether the respondent believes that a democratic system is good thing -the Danes score higher than the two other groups. The Danes' high score on this variable is similar to what [START_REF] Svensson | Demokrati[END_REF] has reported from the European Values Survey 1999. The two minority groups' score are somewhat lower but still higher than what is found in many European countries and in the immigrants' homelands (cf. Inglehart et al 2004). Consequently, we may conclude that the support for democratic principles is high among Danes and the ethnic minorities.

It is thought provoking that the support for democracy is considerably lower when it comes to the second aspect of democracy: Democratic rights. It is perhaps even more surprising that in a country that prides itself on democratic principles, Danes show a low level of acceptance of democratic rights when these rights concern the liberties of minorities. More than twice as many among the ethnic minorities are strongly in favour of democratic rights compared to the Danes. As we will discuss below, this discrepancy is probably related to the general dissociation from Islam found among many Danes.

Finally, the data shows a lower support for family democracy among the ethnic minorities that among the Danes. Although the difference is not that large, it is still significant. It may be explained by differences in family structure and family values among Danes and the ethnic minorities. In general, immigrant families are less individualistic, reflecting a more authoritarian culture than we see in Danish families. This initial presentation of data shows that there is no reason to believe that there is a clash of civilizations among Danes and ethnic minorities. On the contrary, in two out of three democracy measures, the differences are fairly small. In the third dimension (democratic rights), the ethnic minorities are more democratic than the Danes. Since the ranking varies for different measures of democratic values, the pattern cannot be explained by reference to a single variable such as the religion of the respondent. Instead, the differences are probably best explained by the framework in which Danes' generally perceive Islam.

The perception of Islam and the granting of democratic rights

It is indeed striking that the Danes are the most positive group in relation the democratic system but the most reluctant when it come to accepting the constitutional rights of all citizens in relation to civil liberties. One explanation for this may be found in the framing of the questions and the survey itself. Based on in-depths interviews about democracy among Americans, [START_REF] Chong | How People Think, Reason, and Feel about Rights and Liberties[END_REF] shows that people's perception of concepts and their immediate social context greatly influence their way of answering survey questions. Finding themselves within the framework of a democratic constitution, people are generally positive when asked about relatively abstract democratic ideals. When it comes to specific civil liberties, however, the framing appears to become group-specific; respondents fear that their own liberties will be affected -even infringed -when applied to people of a different faith.

Since our survey was framed within the context of immigration it is likely that the respondents (Danes as well as ethnic minorities) read 'neutral' questions through the lens of current immigration debates and attitudes in Denmark. The wording of the question on religiosity may point in the same direction. Part of the question concerned the issue of whether people should have the right to carry religious symbols. Since the wearing of religious symbols is of little significance to most Danes, the perception of the question has probably been coloured by the Muslim headscarf debate, leading respondents to assume that the question only related to Islam. This may explain the low percentage of Danes in favour of religious freedom. An overview of survey evidence [START_REF] Andersen | Danskernes holdning til indvandrere, Aalborg: Amid BOROWSKI, ALLAN 2000 'Creating a Virtuous Society: Immigration and Australia's Policies of Multiculturalism[END_REF] has concluded that Danes seem to have particular resentment towards Islam. For many Danes, religion and ethnicity are intimately related. Therefore, an unfamiliar religion such as Islam might be mistrusted because it is perceived as offering an alternative allegiance -a potential negation of what it means to be Danish.

Does living in Denmark breed democratic values?

According to theories of political socialization, living in a society with democratic institutions and democratic rule will educate the individual in democratic values.

Following this hypothesis, the individual's democratic values will become stronger the 1. Immigrants: the longer an immigrant has lived in Denmark, the more positive his/her democratic values will be.

Comparing immigrants and descendants: Since immigrants have lived in

Denmark for a shorter period, they are expected to have fewer democratic values than descendants.

Comparing descendants and Danes:

Since people in these two groups have lived in Denmark all their lives, there will be only minor differences in their democratic values.

Immigrants: Length of stay and democratic values

A socialization hypothesis predicts that immigrants who have stayed in Denmark for a longer period will have stronger democratic values than immigrants who have only recently arrived in Denmark. Pettersson's (2007) analysis of world values survey data suggest, however, that the values among immigrants in Europe only change slowly. In contrast [START_REF] Camp | Learning Democracy in Mexico and the United States[END_REF] study of Mexican Americans found that 'Mexican Americans begin to adopt the American definition of democracy after having resided in the United States for only one year' (Camp 2003, p. 14). Later in the text, however, Camp modifies his conclusion. It appears that it is not the length of stay as such but the immigrant's language proficiency that is the more important determinant of his/her changing values.

Camp does not present an interpretation of this conclusion, but his study implies that immigrants who command the language of the host country are more committed to adapting to the ways of their new country and will develop a stronger sense of belonging towards the host country.

We used Camp's finding to create a statistical model where the three measures of democratic values are dependent variables. The intervening variables are: The respondent's sense of belonging vis a vis Danish society, the respondent's proficiency in Danish, and the length of the respondent's stay in Denmark. The independent variable is ethnicity. Data was analyzed by so-called 'chain graphical models' (Lauritzen 1996) using strategies and techniques described by Kreiner (1996) integrating analyses of data with graphical theoretic analyses of Markov graphs. The basic idea underlying analysis by graphical models is nearly identical with [START_REF] Rosenberg | The Logic of Survey Analysis[END_REF] classic concepts of elaboration and specification in connection with analyses of conditional relationships. Rosenbergs' concept of explanation corresponds to conditional independence of variables. The Markov graph is a statistical model that has no links between variables if the association between the variables can be completely explained by the remaining variables in the model. While Rosenberg and colleagues had to limit the analysis to fairly few variables the use of modern computers and allow for analysis of high-dimensional contingency tables. The statistical analysis uses the DIGRAM computer program [START_REF] Kreiner | User Guide to DIGRAM[END_REF]. Partial γ coefficients were used throughout the analysis to measure association among ordinal and/or dichotomous variables. Associations involving nominal variables were investigated using χ 2 tests of conditional independence. The significance of test results was in all cases evaluated by Monte Carlo tests as described in Kreiner (1987). This statistical analysis is chosen because of the character of the data. Ethnicity, the core variable, is a nominal variable and the other variables are ordinal: This makes it problematic to use for instance traditional regression analysis. The advantage of the graphical model technique is that there are there are no statistical requirements for the character and distributions of the variables. Another advantage is that all variables can be included in the model from the outset of the analysis including all three dependent variables..

Figure 1 shows the result of our statistical analysis.

Figure 1. Model of democratic values for immigrants

The analysis renders the following result: Only ethnic group influences all three dependent variables. Furthermore, the analysis confirms Camp's findings. The length of the respondent's stay in Denmark correlates positively with language skills, but there is no direct relationship between length of stay and democratic values. The variable language skill only correlates with family democracy. The respondent's sense of belonging to Danish society correlates positively with democratic principles and family democracy, but the attitude towards democratic rights only correlates with ethnicity. This leads us to conclude that ethnicity is the most important determinant of democratic This result shows that primary socialization seem to be stronger than secondary socialization. Another explanation is that the living conditions of ethnic minorities may limit the impact of secondary socialization from the Danish society. The survey shows that only about half of the immigrants in Denmark have a paid job -in some cases, the immigrant's job will not involve direct contact with Danes at all -and only 30 per cent have regular communication with Danes. Moreover, immigrants are typically married to someone with the same ethnicity as themselves. This indicates that immigrants' contact with Danish society is relatively limited and consequently that the probability for political socialization is relatively small.

Immigrants and descendants

Since descendants have been born in Denmark, the socialization hypothesis would predict that they have a stronger democratic mindset than that of their immigrant counterparts. Descendants have joined Danish schools, kindergartens and have generally had more intimate contact with Danish society. At the same time, however, they have grown up in a family that is different from the average Danish family, implying that their family socialization may conflict with the impact of societal institutions.

Only the Pakistani and Turkish minorities can be used for the comparison of immigrants and descendants as these are the only two groups that have lived in Denmark long enough for there to be second generation ethnic minority groups of a sufficient size to undertake analysis. A comparison of immigrants and descendants is complicated by the different age structure of the two groups. There are no descendants over 35 years of age in the data. For this reason, it is only possible to compare descendants and immigrants between the ages of 18 and 34. Table 2 shows the percentage of high the level of democracy values in the four ethnic groups; these groups have been further subdivided into two age groups (18-24 years of age and 25-24 years of age). The scenario reflected by the table is somewhat complicated. Firstly, there are no significant differences between immigrants and descendants in relation to democratic principles, which support the general findings in relation to this variable. Secondly, in relation to family values, we might expect that descendants are more democratic than immigrants, but the table shows no general pattern here. Among the 18-24 year-olds, the Pakistani descendants are more democratic than their immigrant counterparts, but for the Turks, it is the other way around. Among the 25-34 year-olds, there is only a noticeable difference for the Turkish minorities, but here the correlation is reversed: Turkish descendants are more democratic than Turkish immigrants. These differences appear difficult to explain, but it looks as if their family values depend on the specific situation of the ethnic group. Finally, as regards democratic rights there is no difference between descendants and immigrants in the 18-24 year age group. Among the 25-35 year-olds, descendants are, surprisingly enough, less democratic than the immigrants. In fact, it seems as if descendants are becoming more like the Danes (who are very low on democratic rights) than their parents' generation (cf. Table 1). This leads us to a closer analysis of the differences between descendants and Danes of the same age.

Danes and descendants

The best way of testing the denomination and socialization hypotheses is by comparing people of different denomination, who are all socialized in Danish society. We matched the descendants of Muslim faith with Christian Danes of the same age (18-34 years).

Table 3 presents an overview of the distribution of the three dependent democracy variables in these groups, Table 3

about here

The figures in the table present a picture that we recognize from our general description. The Danes are generally more positive towards democratic rule, but their tolerance towards freedom of speech and religious faith is lower than that of the ethnic minorities. When it comes to young people's influence on their own life (choice of education and spouse), the difference between the ethnic groups is much smaller than when discussing more abstract democratic values. There is a significant difference between all the ethnic groups.

As the figures in the table reflect, value differences exist in spite of the fact that all three ethnic groups have gone through public education in Denmark. Thus, the hypothesis about formal socialization cannot be supported. The differences must be explained by other factors. One possibility is family socialization. Descendants are not only socialized by social institutions, they also received specific ethnic socialization through their family and network.

We lack general information about family socialization in Denmark. However, it would be reasonable to expect that at least some of the descendants have been exposed to relatively strong Islamic values in their families. Some of them may also have attended private Islam-oriented schools but we have no information about this in the data. In the immigrant family, values are shaped both by the country of origin as well as the host society [START_REF] Foner | The Immigrant Family: Cultural Legacies and Cultural Changes[END_REF]. In many immigrant families, religion plays a much stronger role than in Danish families, whose lifestyle is generally quite secular. This returns us once more to Huntington's 'clash of civilizations' hypothesis. Is it plausible to assume that devout descendants have less democratic values, whereas religious faith among the Danes has no affect on their democratic values?

A multivariate statistical analysis -not shown here because space does not allow it -reflects that for Danes, there is no relationship between religious values and democratic values. Among descendants, however, democratic values are influenced by the religion of the respondent, but there is no difference in democratic values between the Pakistani or Turkish respondents. Figure 2 shows the distribution of democratic values for people with strong religious faith (who report themselves to be 'very religious' or 'somewhat religious') and people who are less devout (who say that they are 'not very religious'). The differences between 'very' and 'less devout' descendants are relatively small, but there is a tendency for the more religious respondents to be less attracted to democratic principles and family democracy and to value religious freedom and freedom of speech higher. This result may be interpreted with reference to [START_REF] Fukuyama | Identity, Immigration, and Liberal Democracy[END_REF] hypothesis about identity politics among young Muslims. Fukuyama argues that when Muslims move from traditional Muslims societies to Western societies, they experience an existential disparity between their inner identity as member of a Muslim cultural community and their exterior lives and behaviour in their new European context.

According to Fukuyama, this disparity is particularly prominent among descendants.

'Stuck between two cultures with which they cannot identify, they find a strong appeal in the universalistic ideology offered by contemporary jihadism' (Fukuyama 2006, p. 11).

While there is no indication in the data that there is a strong fundamentalist Islamic minority in Denmark, Fukuyama's observation may contain some truth, especially for the more religious second generation immigrants for the simple reason that this group is more exposed to the pressures of different value systems. Such an interpretation is supported by an interesting finding in our survey: When asked whether they feel that they have become more religious during the last three years, 37 per cent of the descendants answer in the affirmative, compared to 26 per cent of the immigrants from the same ethnic groups, and 9 per cent of the Danes. Another viable explanation is found in the fact that the descendants may feel that their religion is being discriminated against by the native majority; this leads them to put more emphasis on their right to religious freedom and freedom of speech.

Is religion more important than ethnicity in relation to democratic values?

Generally, it is difficult to distinguish between ethnicity and religion because most of the immigrants only belong to one denomination: Islam. As has been shown above, the respondent's degree of devoutness has an influence on his/her democratic values, but in the following we shall explore whether differences in democratic values are higher within ethnic groups or within religious groups. If a religious explanation were to be similar irrespective of their ethnicity. This hypothesis is challenged by an ethnicity hypothesis, which claims that differences among ethnic groups are greater than those among denominations due to the impact of common history and destiny. An analysis of these hypotheses can only be conducted among ethnic groups with a varied composition of denomination, i.e. immigrants from Iran, Iraq and West Balkan. The statistical tests show that there are no significant differences in democratic values among the denominational groups in each ethnicity group. This strongly supports the ethnicity hypothesis -i.e. that the destiny and living conditions of a certain group are the more important determinants of a democratic mindset. However, if an ethnicity hypothesis should be supported, it would also require significant differences among the Muslims in all ethnic groups. This is precisely what we see in Figure 3. For each variable, there are significant differences among the ethnic groups. Thus, there is no basis for arguing that Muslim respondents are less democratic than non-Muslims respondents.

In all three cases, Muslims from Iraq have the strongest democratic values, followed by Muslims from Iran and Muslims from West Balkan. This result is difficult to interpret but might be explained by the history of the three countries or perhaps with reference to the different types of Islam that characterize the groups (e.g. Sunni, Shia).

Unfortunately, no data exists that may support such a conclusion.

Conclusion

Besides being of interest in their own right, studies of ethnic minorities tell us a lot about the host society [START_REF] Davie | The Sociology of Religion[END_REF]. Often, the conclusions drawn by such studies have been relatively unclear because researchers only study the ethnic minorities and not the majority. In this respect, the present study is an exception because it includes data on the ethnic majority (the Danes) as well as data on a number of ethnic minorities. Note: The differences between the high and low religiosity categories are significant.

Democratic principles γ = -0,21 p = 0,009; democratic rights γ = 0,17 p = 0,008; family democracy = -0,18, p = 0,006. Note: The differences between the national groups are significant. Democratic principles χ 2 = 41,018, df=6, p=0,000; democratic rights χ 2 = 99,702, df=6, p=0,000; family democracy χ 2 = 14,867, df=4, p=0,005.

  hypothesis focuses on the role played by ethnicity rather than religion in determining democratic values. Earlier research has shown that ethnicity has a significant influence on the individual's values

  respondent's civil registration number and his/her phone number.

  principles is generally high in most Western countries

Figure 1 .

 1 Figure 1. Model of democratic values for immigrants

  lives in a democratic country. Let us apply this general hypothesis to the three groups concerned here:

  furthermore that the length of stay in Denmark has only an indirect or no impact on respondents' democratic values. In other words, our analysis refutes the socialization hypothesis (a result that supports Pettersson's (2007) findings from Europe).

Figure 2 .

 2 Figure 2. Democratic values by religiosity among Pakistani and Turkish descendants. Per cent.

  would mean that the democratic values of Muslims are relatively

Figure 3

 3 Figure3shows the distribution of democratic values among the mixed ethnicreligious groups.

Figure 3 .

 3 Figure 3. Democratic values among three groups of Muslim immigrants. Per cent.

  symbols and customs need to be understood in the context of the individual country's history. Denmark has a history of a strong nation-religion relations, and has been dominated by a single denomination. It would be interesting to study whether other countries sharing the same characteristics also appear to be less willing to accept the democratic rights of all citizens.

F

Figure 2 .

 2 Figure 2. Democratic values by religiosity amongPakistani and Turkish descendants. Per cent.

Figure 3 .

 3 Figure 3. Democratic values among three groups of Muslim immigrants. Per cent.

  Table 2 about here.

Table 1 .

 1 Democratic values among religious groups in Denmark. Per cent.Notes: The entries show the percentage in the most democratic category. Data is weighted to compensate for bias in the data collection but not according to the size of the national minorities in the Danish population.

	o r
	P
	e
	e r
	R
	e
	v i e
	w
	O n l
	y

Table 2 .

 2 Percentage with high democratic values by age and national group. Ellipses show significant difference between descendents and immigrants in the same ethnic group (χ 2 , p<0,05) when controlled for age.

	Democratic principles 18-24 25-34 18-24 25-34 18-24 Democratic rights Family democracy 25-34 71 68 54 51 52 45 76 73 59 65 28 40 75 78 55 49 54 61 77 72 54 65 66 49 Note: Page 28 of 33 Age Pakistan descendant Pakistan immigrant Turkey descendant Turkey immigrant Ethnic and Racial Studies

URL: http://mc.manuscriptcentral.com/rers ethnic@surrey.ac.uk

Table 3 .

 3 Democratic values among Turkish and Pakistani descendants and Danes aged 18-34.Figure 1. Model of democratic values for immigrantsNote: The analysis is based on graphical modelling. A line shows a significant relationship. Figures are partial γ-coefficients. Such cannot be computed with national group because the variable is nominal. In these cases, the measure of association is χ2. Only significant γ's above 0,10 are reported. The analysis includes five immigrant groups (from Iran, Iraq, Pakistan, Turkey and West Balkan).

			Ethnic and Racial Studies
	Per cent.
		Democratic	Democratic	Family	Number of
		Principles	rights	democracy	respondents
		Very high	Very high	Very high
	Danes	90	23	61	231-235
	Pakistan	69	53	49	469-488
	descendants
	Turkey	76	53	56	477-504
	descendants
		χ 2 = 39,732,	χ 2 = 106,816,	χ 2 = 36,591,
		df = 6;	df= 6;	df = 4;
		p= 0,000	p = 0,000	p= 0,000.

Page 29 of 33 URL: http://mc.manuscriptcentral.com/rers ethnic@surrey.ac.uk

33 of 33 URL: http://mc.manuscriptcentral.com/rers ethnic@surrey.ac.uk Ethnic and Racial Studies

  Table A.1 Correlations between the variables in the index. Don't know answers are excluded. For wording of questions see text.

	Turkey immigrant Turkey descendant Pakistan immigrant Pakistan descendant West Balkan immigrant Irak immigrant Iran immigrant Danes Note: Page	Correlations between the two questions on democratic rights γ p 0,35 0,002 0,21 0,047 0,36 0,008 0,12 0,313 0,21 0,001 0,36 0,01 0,14 0,06 0,31 0,00	Correlations between the two questions on family democracy γ p 0,60 0,000 0,53 0,000 0,57 0,000 0,62 0,000 0,66 0,000 0,67 0,000 0,70 0,000 0,69 0,000	Number of respondents 486-504 498-505 420-434 477-488 483-503 491-513 492-506 515-521

Our study undertaken has used three variables to measure democratic values: The support for democratic principles, democratic rights and family democracy. The study concludes that the support for democratic rights is high among the ethnic minorities as well as the majority population. Even though minorities show less support for democratic principles, they still score higher than the populations of many West European countries. As regards family democracy, there appears to be a similar pattern as for democratic principles (but we have no information about the distribution of family values in other European countries). The most important difference is found in the support for democratic rights -the freedom of speech and freedom of religion.

Here, the Danish majority population is much less democratic than the ethnic minorities. Thus, democratic values do not give rise to a clash of civilizations, as Huntington would have it -at least not when we consider immigrants in Western Societies. Contrary to what Huntington's thesis suggests, Christian Danes are less democratic than Muslim immigrants when it comes to granting democratic rights to all members of society.

Generally, democratic values do not differ between immigrants and descendants of the same ethnic group, and a change in values does not appear to depend on the length of stay in the host society. These results emphasize the role of ethnic belonging rather than the denomination or socio-economic factors. For instance, we found that among ethnic minorities with a mixed religious composition, the differences between Muslims are greater than the in-minority differences.

These results support our general findings: Ethnicity is more important for democratic values than denomination. Ethnicity is important because of the common history and destiny of the various ethnic groups. This 'ethnic habitus' means that democratic values are based on a common history and fate and reproduced within ethnic groups primarily through social interaction and a shared language within the group.

The importance of ethnicity might not come as a surprise to sociologists. As noted by [START_REF] Davie | The Sociology of Religion[END_REF], immigrant populations arrive in Europe and their experiences vary for specific historical reasons. The Muslim headscarf, for instance, is an acceptable part of public life in Britain, but not in France. In Denmark, the headscarf is quite common in public life but also a highly contested symbol. The difference in


R Reyna-Rojas

D Houzet
email: houzet@insa-rennes.fr

F Carlier

S Ouadjaout

Keywords: Parallel Architecture, Pattern Recognition, Support Vector Machines, Hardware Design Language, Systems

The first aim of this work is to propose the design of a System on Chip (SoC) platform dedicated to digital image and signal processing, which is tuned to implement efficiently multiply-and-accumulate (MAC) matrix/vector operations. The second aim of this work is to implement a recent promising neural network method, namely the Support Vector Machine (SVM) used for real-time object recognition, in order to build a vision machine. With such a reconfigurable and programmable SoC platform, it is possible to implement any SVM function dedicated to any object recognition problem. The final aim is to obtain an automatic reconfiguration of the SoC platform, based on the results of the learning phase on an objects' database, which makes it possible to recognize practically any object without manual programming. Recognition can be of any kind that is from image to signal data. Such a system is a generalpurpose automatic classifier. Many applications can be considered as a classification problem, but are usually treated specifically in order to optimize the cost of the implemented solution. The cost of our approach is more important than a dedicated one, but in a near future, hundreds of millions of gates will be common and affordable compared to the design cost. What we are proposing here is a general-purpose classification neural network implemented on a reconfigurable SoC platform. The first version presented here is limited in size and thus in object recognition performances, but can be easily upgraded according to technology improvements.

thus used when there is an insufficient knowledge of the studied process. It will be also possible, as mentioned in the abstract, to use them when the design time is shortened as it is the case with time to market constraints. The neural networks by themselves represent a significant research subject in the scientific and technological world since a few tens of years. Theoretical bases, performances, architectures, applications, hardware implementations, are some of the studied axis [START_REF] Bishop | Neural Networks for Pattern Recognition[END_REF].

A machine vision design relates also to the hardware part of a system. For some particular applications, hardware design goes from the study and the design of image sensors and optics to computing units. This work is rather centered on the computing units dedicated to application algorithms, using a standard camera for image acquisition. In commercial systems we frequently find architectures using traditional processors, which provide the necessary performances to applications. We also can find architectures with specialized digital signal processing circuits (DSP), which have suitable arithmetic units for the necessary precision. Nevertheless, the regularity of image processing and neural network algorithms cannot be completely exploited by these types of architectures. Parallel architectures are best adapted for hardware implementation of vision systems and neural calculations due to their ability to exploit the parallel nature of algorithms.

The growing scale of integration has allowed designers to include in the same chip several parts of a system and even the entire system. Systems-on-Chip is one of the latest ideas on system integration. Circuits cannot be designed in a classical way because they are more complex and different functions (subsystems) are being integrated. Technology allows more flexible architectures: a larger number of integrated gates, less power consumption, higher speeds, bigger and faster integrated memories, processors cores, communications interfaces, etc. Object recognition systemon-chip is a natural perspective in the machine perception domain.

In section II of this paper we present the basic idea of the support vector machine, in particular for classification. In section III we explain the algorithm complexity and the software performances of the SVM method. We briefly present neural architectures on section V and some application results in section VI. From section VII to section VIII we will give the details of our proposed architecture of the SoC platform solution and we will end our paper with the conclusion and perspectives. Twenty years ago, the neural networks knew a very significant importance in scientific and engineering worlds. Nowadays, industrial products are offered on the market with real success even if we do not have the associated physical model within the automation or the diagnosis. It is necessary to consider the neural networks as a tool for building an empirical model with what that supposes of inaccuracy and risk for the application. The theory of the statistical learning became more interesting with new results in generalization and with the proposal of the SVM model. Vapnik in the AT&T Bell laboratories proposed the theory of the statistical learning [START_REF] Boser | A learning algorithm for optimal margin classifier[END_REF] [START_REF] Vapnik | Statistical. Learning Theory[END_REF]. We will very briefly present this theory in order to introduce the generalization function. The details of the theory can be consulted in [START_REF] Vapnik | Statistical. Learning Theory[END_REF].

The Theory of the SVM

The Support Vector Machines model is the most recent proposition on neural network structures. This model is based on the statistical learning Theory. The Support Vector Machine model consists on a transformation of the input vectors X in a space of higher dimension Z through a nonlinear transformation, selected a priori. It is in this new space Z that we can build an optimal hyperplane [START_REF] Vapnik | Statistical. Learning Theory[END_REF]. For the particular case of pattern recognition, the SVM make a distinction of two classes by finding a decision surface constructed from certain points of the entire learning database, called Support Vectors [START_REF] Burges | A tutorial on Support Vector Machines for Pattern Recognition[END_REF].

Vapnik proposes a representation of a SVM in the form of one-hidden layer neural network whose number of cells is equal to the number of "support vectors", and not to the dimension of the space of the internal representations, as we could have supposed it initially. In this manner the number of neurons is obtained in an automatic way with the resolution of a quadratic problem. The support vectors are the input vectors x i for which equality y i ((w 0 x i +b 0 )=1 holds. Concretely, they are the closest points to the optimal hyperplane. For all the other examples, there is thus a factor α=0 that eliminates them from the solution. We thus know that the decision function is calculated from the examples that are on the margin. In the non-linear case, it is enough to replace the scalar products (x⋅x i ) by kernels k(x,x i ). The kernel functions were proposed to build nonlinear algorithms from linear algorithms by calculating the inner product not in the input space but in the feature space. Figure 1 shows this transformation.

The three most common options for the selection of the kernel function of the SVM method are the polynomial, RBF and sigmoid neural networks. The sigmoid neural network kernel function option was rejected in this work because of the difficulty of hardware implementation. Moreover in the literature the performances obtained with this kernel function are less interesting than those obtained with the two others. The results on the applications (cf. section IV) showed that, with the polynomial kernel function we obtain a solution, for different databases, with the minimum number of support vectors. In terms of generalization we observed, particularly in the first application, that the best performances were also obtained with the polynomial kernel.

III. COMPLEXITY AND PERFORMANCES

The general equation of the SVM generalization function for classification is:

( ) ( )         + = ∑ tors SupportVec i i i b x x k y sign x f , , α α (1)
Where: y i α i =w i , are the network weights, x i , are the support vectors of the solution, b, is the threshold of the function, and k(x,x i ).is the kernel function.

As we can see, the solution is the sign of the sum, which is the generalization function for two-class's classification.

In our case, the kernel function is the polynomial function of degree d:

d c y x y x k ) ( ) , ( + ⋅ = (2)
The principal parameter of the polynomial kernel function is the polynomial degree. We take as a priori choice a polynomial of degree 2 (a higher degree implied the use of wider data buses in the hardware implementation).

A. Complexity

Let us suppose that the image size is t m x t m and that t b x t b is the detection window size. t b 2 is thus the number of pixels to be processed by the window of classification. Here we consider a decision function of SVM with a polynomial kernel of degree d:

( ) ( ) [ ]         + + ⋅ = ∑ tors SupportVec d i i i b x x y sign x f 1 , α α (3) If we write i i i y w α = we have: ( ) ( ) [ ]         + + ⋅ = ∑ tors SupportVec d i i b x x w sign x f 1 ,α (4)
To make the classification of all the windows of pixels of one 512x512 image, with no sweeping, and a 8x8 detection window, we have 64x64 (tm/tb) 2 windows to process. Each window (or input vector) requires t b 2 operations (operation = multiplication + addition) for the scalar product of the kernel function (x•x i ) and d multiplications for power operation, which we also consider as one operation for simplicity, we have then: ) (

) ( )

2 2 2 1 2 1 3 2 1 2 1 3 2 , 2 , : , , , R R : x x x x z z z x x = → Φ a F R 2 R 3 2 x 3 z 1 x 2 z 1 z
Fig. 1. Kernel functions are used to transform the input space into feature space where the optimal hyperplane is constructed.

The additional operation is due to the multiplication between the weight w i and the result of the polynomial and the addition of the threshold b. Let N be the number of support vectors obtained during learning, we will then have:

( )

1 2 + + × d t N b operations per block.
For (tm/tb) 2 windows per image we obtain:

( ) 2 2 1         × + + × b m b t t d t N
operations per image.

By making a simplification and knowing that in general That means that the number of operations to be calculated depends on the image size and on the number of support vectors. The size of the window thus does not have a significant influence on the complexity of the algorithm. Nevertheless, this size will represent a fundamental factor during the material implementation because it will be used to dimension part of the circuit. Now, if we use a sweeping classification window over the image, we will classify pixels several times. In this case, there will be more windows to analyze per image: (tm/p) 2 ; where p is the number of sweeping pixels (can also be seen as the classification resolution). For example for p=2, i.e. we move in the image with a step of 2 pixels at a time, horizontally and vertically. We then get:

( ) 2 2 2 2 1 m b m b t N p t p t d t N × ×         ≈         × + + ×
operations per image.

In the case of a 8x8 detection window and a sweeping step of 2 pixels, we will make 16 times more calculations than without sweeping. The advantage of using sweeping would be to increase the image sampling and to classify several times each pixel or window of pixels and thus to obtain a more robust decision, and also to increase at the same time the localization precision. The complexity for a traditional image processing algorithm like filtering by a convolution direct method, depends on the size of the convolution mask (MxM for example) and on the size of the processed image, therefore the number of operations is given by: 2 2 m t M × operations by image.

Table 1 summarizes the algorithm complexity analysis. Applying a convolution mask to an image is less expensive in computing requirements than the other algorithms if the size of the mask M is higher than 9. Nevertheless, applying the convolution mask is only the first step to solve the problem of object detection and localization.

In general, if we use a classical method for object recognition, the complexity of the system will be the addition of the complexity of each subsystem. It will also depend on different parameters of the processed image, for example: edges density, line density and the ratio between the object and the image size. For the SVM method, the complexity depends only on a priori chosen parameters.

B. Performances

We carried out some measurements of execution times. As we have shown, the number of operations and the computing time increases proportionally to the number of support vectors. We thus find the main disadvantage of the Support Vector Machine method: the number of support vectors. This number is automatically obtained during learning; we cannot control this parameter without modifying the generalization performances.

These measurements of execution times were made on a Sun Microsystems ultra 5 workstation.

For the estimation of the computing time, we obtained that a multiplication-addition operation is executed in 470ns. We obtained this time from a program carrying out a loop of 10 6 iterations. In this loop as in the software implementation of the function of generalization of the SVM we used the mathematical function pow(). Estimated times are slightly larger than measured times. This is due to the use of the indices in the estimation program. Table II shows some results.

C. Learning Performances

The learning algorithm uses a decomposition method to increase the learning performance and to reduce the necessary resources of the machine on which we execute the learning algorithm, in particular memory resources. This algorithm calls the generalization function and supposes that we can define a working set (vectors or examples) B such as |B|≤L (L is equal to the number of examples or vectors of all the learning database, and |B| the number of B elements). This set is sufficiently large to contain all the support vectors (α i >0), but sufficiently small so that the hardware platform (PC, workstation, etc.) can handle them and optimize them by using the quadratic optimization algorithm.

The decomposition technique can be written in the following manner: 1. Choose in a random way |B| points of the database. 2. Resolve the sub-problem defined by the elements in B. 3. Repeat the 3 steps while there exist a j ∈ N, such as g(x j ).y j <1 (which corresponds to a bad classification), where The algorithm, at each iteration, improves the objective (optimization) function and is not, in this sense, recursive. Since the objective function is limited, the algorithm converges towards the optimal solution in a finite number of iteration [START_REF] Osuna | Reducing the run-time Complexity of Support Vector Machines[END_REF].

( ) ( ) ∑ = + = l p p j p p j b x x K y x g 1 , α (5) TABLE I SUMMARY OF ALGORITHM COMPLEXITY Algorithm Number of operations SVM 2 m t N × SVM Sweeping window 2 2 m b t N p t × ×         Convolution 2 2 m t M ×
The function g(x j ) is in fact the SVM generalization function. And for instance, if we are able to reduce of two orders of magnitude the execution time of this part of the algorithm will improve the learning performances and we could have a real-time learning algorithm.

We can observe the experimental results of the execution time of the learning algorithm according to the size of the working subset B in Fig. 2. The learning process is clearly accelerated with this decomposition method. According to these simulation results, for a real-time learning system and for subsets of average sizes, it would be necessary to increase the performances of the execution of the quadratic optimization algorithm. We can also observe in Fig. 2 that the execution time of the SVM generalization function is practically constant, that is approximately 100 seconds. This is because the calculation of g(x j ) is made for all j of the database and thus does not depend on B. For B lower than 200, the execution time of the learning algorithm is practically dominated by the generalization subroutine.

As we also can see, the software execution times are prohibited for real-time applications. This is the reason of the hardware implementation. We are now presenting some of the results on one of the three tested applications and then we are going to detail the architecture at different levels.

IV. APPLICATIONS

The excellent performances of the SVM for classification problems were very attractive from the beginning of their proposal. This is true especially if we consider that the method can be applied directly on pixel values, and it does not need to take into account any other a priori problem knowledge and "a permutation of the images by a fixed transformation does not modify the SVM classifier performances" [START_REF] Lecun | Comparison of learning algorithms for handwritten digit recognition[END_REF].

The performance analysis of the SVM methods on databases used as «benchmarks» by the scientific community were already reported in literature [3][7]. Other evaluations were made on synthetic databases [START_REF] Brailovsky | On global, local, mixed and neighborhood kernels for support vector machines[END_REF]. The principal interest of our contribution is to study this method for real-life applications (matrix bar codes detection, face detection in an automobile cockpit and the white lines detection). We have found that the SVM method makes possible to build very powerful classifiers (polynomial, RBF or perceptron).

A. Detection and localization of matrix barcodes. Bar codes are essential as product identification, either during manufacturing or marketing. The market requirements made very important the fine resolution of questions like reading robustness under very diverse conditions. The effectiveness of barcodes is so interesting that the vendors would wish to be able to put more information on them. A linear bar code, for example EAN13 code, can code 11 characters (numerical 0-9), this code is generally used like reference for a product index. The aim of matrix barcodes is to be able to code more than 2000 alphanumeric characters, and to thus be able to have product information like its price and its principal features. That supposes to evolve from a one-dimensional code to a twodimensional code. And two-dimensional codes suppose image processing and recognition.

This study was made with the collaboration of INTERMEC Company. INTERMEC provided a base of 78 images with different types of matrix barcodes and various image sizes. The study was based on the DATAMATRIX code. We have also shown results of generalization on other types of codes. Each pixel value is coded on eight bits, i.e. in 256-gray levels from 0 to 255.

The images show different scenarios like projective deformations, different image backgrounds, different scales, etc. For this application we find the object by segmenting the image and not by finding directly the whole object, i.e. we benefit from the texture regularity of matrix bar codes to locate them. In [START_REF] Marcel | Study on the automatic reading of two-dimensional codes by image processing[END_REF], the author proposes, for the localization and the automatic reading of matrix bar codes, to use the texture to validate the different zones found by the localization algorithm. The objective thus for this first application is to learn texture from a matrix barcode DATAMATRIX, and to make a localization of these codes in new images through image segmentation.

Databases creation is a delicate task for the methods that use supervised learning algorithms. The solution of the neural network will depend exclusively on the examples of the learning database. Since the SVM method is also based on learning from examples, a given "optimal" learning database provides an "optimal" solution.

In this application, we feed the learning algorithm with examples of the "positives" parts of the image (a matrix barcode), and with other textures (text, images, etc.) as "negative" examples. Two classes are thus defined (see Fig. 3): a block of pixels with the texture of the matrix barcode (class +1), and a block of pixels with a different texture (class -1). Two detection window sizes were tested: 8x8 pixels and 16x16 pixels.

We present the learning results over one database and the respective result in generalization. The first database was created from the image shown in Fig. 3 We created a test database from three different images. This test database consists of 12288 examples, including 10% of positive examples. In generalization we obtained 91,8% of good classifications (positive or negative), 3,8% of not-detection (when the example is positive and the result of generalization is negative), and 4,2% of false detection (when the example is negative and the result of generalization is positive).

For a second test database with 10465 examples, including 25% positive, we had 85% of success or good classifications, 11% of not-detection and 4% of false alarm. The fact of having a relatively small percentage of false alarms compared to the number of not-detection led us to define a post-processing module based on a morphological processing for this particular application. In the images of Fig. 4, we show some qualitative results of detection, i.e., we show two examples of the output binary images.

We seek to use the best solution with a minimal number of support vectors. These results were obtained with the second-degree polynomial kernel function solution and with the penalization parameter C=200. The first image shows the result of the test since the learning database was created using this image. More detailed information can be found in [START_REF] Reyna-Rojas | Segmenting images with support vector machines[END_REF].

V. PARALLEL NEURAL ARCHITECTURES

The regularity of image processing and neural network algorithms encourages the use of parallel VLSI circuits. Parallelism is an intrinsic notion of the neural networks, which are regarded as massively parallel systems [START_REF] Jain | Statistical Pattern Recognition: In Review[END_REF]. In spite of the enormous computing power obtained with new sequential processors, it is possible that these types of processors are not sufficient for real time applications. There are some solutions with neural networks, which use classical sequential processors. For example, the optical character recognition algorithms (OCR), whose performances are acceptable for applications that do not require a real time operation.

A significant number of analog implementations were proposed, exploiting the biological origin of neural networks, which illustrates the use of individual simple cells but interconnected by a network and functioning in a massively parallel way. In the particular case of the integrated artificial retinas, the use of analog circuits is a choice impossible to avoid. Because we want to be able to bring processing as near as possible to the photosensitive circuit and to be able to manage the interconnections more easily (each pixel interacts with its closer neighbors) [START_REF] Koch | Vision Chips: Implementing Vision Algorithms with Analog VLSI Circuits[END_REF] [START_REF] Chua | Cellular Neural Networks: Applications[END_REF]. Many neural implementations in numerical integrated circuits have been proposed. The finality of these circuits is to be used within traditional workstations like neural coprocessors, in acquisition and signal processing cards, in order to make more intelligent sensors, or to be used as specialized parallel-processing machines. They are generally dedicated to a single neural model, and all do not propose a learning integrated procedure [START_REF] Emirian | Study and design of a parallel machine multi-models for the neural networks[END_REF]. There are thus several types of neural systems: Application Specific Architecture implement a model, a topology and a set of weights, mostly by analog means. Problem Specific Architecture implement a model and a given topology; the weights of the network are programmable. The learning is done most of the time offline. With Algorithm Specific Architectures, the model is selected a priori. Topology can be modified, and the learning is carried out by the system itself. Neural Processor Architectures are also called multi-model accelerators. They are much closer to a generic processor [16][17]. VLIW Digital Signal Processors (DSP) can also be used to implement neural networks, but they are more generic processors. Many DSP chips are available, like EQUATOR MAP-CA BSP, NEC SPXK5 or Analog TigerSHARC that include a small degree of parallelism. Some are built around a large parallel processor structure (VLIW) linked to a scalar RISC processor, in a single core structure. For example, the SIROYAN SRA328 [START_REF] Clark | Siroyan implements clustered DSP architecture[END_REF], which is much more a real multiprocessor. The CHIPWRIGHTS CWv8 processor core [START_REF] Long | ChipWrights Intros New Visual Signal Processor[END_REF] is much more a SIMD processor. The RC MODULE NeuroMatrix NM6403 core [START_REF]VLIW/SIMD NeuroMatrix Core[END_REF], which is a real full vector/matrix parallel processor provides scalable performances and a programmable operand width of 1 to 64 bits. This flexibility allows designers to trade precision for performance to suit their applications. The NM6403 processor includes a 32/64-bit RISC processor and a 1-to 64-bit vector coprocessor that supports vector operations with elements of variable bit lengths. The vector coprocessor, with SIMD (single-instruction-multiple-data) architecture, works on packed integer-data comprising 64-bit blocks in the form of variable 1-to 64-bit words. The device is limited to vector-matrix or matrix-matrix multiplications.

The Vector coprocessor's core looks like an array of multipliers comprising cells that include a 1-bit memory (flip-flop) surrounded by several logical elements. Designers can combine the cells into several macrocells with two 64bit programmable registers. These registers define the borders between rows and columns with macrocells. Each macrocell performs the multiplication on variable-input words using preloaded coefficients and accumulates the result from the macrocells in the column above it. The columns simultaneously calculate the results in one processor cycle. For 8-bit data and coefficients, the vector coprocessor performs 24 MAC (multiply-accumulate) operations with 21-bit results in one processor cycle. The number of MAC operations depends on the length and number of words packed into a 64-bit block. The engine's configuration can change dynamically during calculations. An application can start with maximum precision and minimum performance and dynamically increase performance by reducing the data-word lengths.

VI. THE OBJECT RECOGNITION SYSTEM

If we take the classical and simplified architecture of an object recognition system, we have the following modules: image sensor, detection, localization and diagnosis. For our implementation we propose a PC-based recognition system, and use a standard camera as image sensor. Therefore, it is the detection module that we will hardware-implement using the SVM as its core. In order to be able to integrate the detection module in the PC-based system we will use the PCI interface.

VII. THE SOC PLATFORM ARCHITECTURE

A particular SoC category concerns the SoC platforms [START_REF] Sangiovanni-Vincentelli | Platform-Based Design and Software Design Methodology for Embedded Systems[END_REF], an emerging technology which main purpose is to provide a reusable silicon platform for many applications, either for several versions of a single application or even for several different applications in the same field. This is due to the growing design and fabrication costs of ASICs, which thus impose large amounts of chips. The only solution is to have more general reusable chips. The Xilinx VirtexPro II can be considered as a general purpose SoC platform, which associates dedicated blocks such as PowerPC processors, RAM and multipliers, and a classic FPGA part that can be dynamically reconfigured.

The platform we are proposing here is dedicated to fixedpoint vector/matrix operations, which are the basic operations of many signal and image processing functions. We have concentrated our effort on neural network applications.

Figure 5 depicts the general architecture of our proposed SoC platform. This platform is built around a RISC 32-bit processor linked to a parallel vector coprocessor. Both are connected to a Network-on-Chip (NoC) [START_REF] Benini | Networks on Chip: A New SoC Paradigm[END_REF] that controls communications between the different parts of the system. Here the NoC is a PCI-X On Chip Bus (OCB) version. We have simplified the LEON-2 SPARC processor (1) in order to communicate directly from its cache memory (2) to the dual-ported RAM (3) used to store LEON-2's binary code and data. A second data RAM ( 4) is accessible in the memory address space of both the LEON-2 processor and the external I/O subsystem [START_REF] Osuna | Reducing the run-time Complexity of Support Vector Machines[END_REF], which is here a simple On Chip Bus with its wrappers (light-gray boxes). This dualported RAM is the storage unit of the CP CoProcessing vector/matrix unit (6) which performs ALU/MAC operations loops on vector/matrix fixed-point data from the RAM, according to the instruction register [START_REF] Schölkopf | Support Vector Learning[END_REF] which provides the configuration of the processing units. This register is detailed in figure 6. The ALU allows any kind of operations to be executed, leading to a richer instruction set than the simple MAC operations of most similar approaches such as the NeuroMatrix chip [START_REF]VLIW/SIMD NeuroMatrix Core[END_REF]. This is a double register operating in ping-pong mode. This register is reconfigured for each new matrix operation. The configuration that is provided to the vector processing Processing units(6)

(7) (11)
Fig. 5. SoC Platform Architecture unit is, the size, step and addresses of the loops, the precision of data and the operations performed with or without accumulation.

Here it is an example of matrix/vector operation with accumulation: The ( 8) and ( 9) registers are used to shift input and output data in and out of the vector RAM. These registers can also broadcast input and output data in the case of vector/matrix operations to be treated as matrix/matrix operations.

For (J=start2; J<size2; J+=step2) { For (I=start1; I<size1; I+=step1) { Res[J] = Res[J] op2 (RAM1[I] op1 RAM2[I]); } }
The multi-precision unit is presented on figure 8. This is a version with only two different input precisions (8-bit and 16-bit), in order to simplify the presentation. The first OP1 operator is either a 8x8 multiply or a 8-bit ALU. The 16-bit result can be accumulated with the 32-bit OP2 operator. The two 8-bit multiply operators (OP1) can also be combined to perform a 16-bit multiply in two clock cycles, using the accumulation operator (OP2) to perform the two additions. A 16-bit MAC is thus performed in four clock cycles, that is two for the four multiply operations, one for the last addition of the 16-bit multiply results and one for the final accumulation. The accumulation is pipelined with the preceding operation, which is thus treated every clock cycle for an 8-bit MAC. Every 3 cycles for a 16-bit MAC and every 5 cycles for a 32-bit MAC, that is every N+2 cycles with N the number of bytes of data precision. The main limitation of our proposed architecture is the vector data precision which must be a multiple of 8 bits, which however is often the case in image processing. The counterpart is the lower complexity of the logic which lead to higher clock frequencies, compared to the Neuromatrix solution which is a 1-bit multiple with a lower clock frequency.

The last part of the system is the I/O subsystem, which has to feed the processor with data. The OCB is used as the central communication subsystem between the processor/coprocessor and the external analog [START_REF] Reyna-Rojas | Segmenting images with support vector machines[END_REF] and digital [START_REF] Moerland | Neural Network Adaptations to Hardware Implementations[END_REF] ports. Other components can also be integrated on the OCB [START_REF] Bermak | Sysneuro : Un circuit Systolique Neural multipécision pour des tâches de classification[END_REF], like other processor/coprocessor couples in order to build a complex system. A single large coprocessor with many processing units would be difficult to manage due to the limited available data and instruction parallelism as well as the long distances between units which would affect their communications and the clock frequency.

Here it is figure 9 an example of configuration obtained from the original and adapted C SVM source codes of figure 10 and 11. The CoProcessor here executes only the two internal loops. These internal loops are packed in a function.

The Acquisition() function start a DMA with the CoProcessor RAM, and the Sync() function waits the end of the CoProcessor treatment and switch the configuration register and RAM banks to prepare the next treatment. The 7. This preprocessing links the parameters of the loops to the dedicated C library function which will dynamically, that is at run-time, fill the fields of a configuration instruction register which will then be launched to the CP core ( CoProcessor). Those configurations represent the nature of the processing, that is the SVM processing. This vector coprocessor architecture is a good compromise between a fully hardwired solution and a fully programmable general-purpose solution. A first small C library has been designed for our SVM experiments. This approach can be compared to the Neuromatrix one [START_REF]VLIW/SIMD NeuroMatrix Core[END_REF], which is the only comparable product on the market. Their approach is based on a static compile-time generation of configurations, which is most of the time sufficient and as easy to program as our solution, but dynamicity becomes more and more important. This is particularly important when the reconfiguration needs to be dependent of the previous results of a processing, in a non-predictable way. The heart of our learning procedure is based on the classification procedure, which is evaluated here. The dynamic nature of the parameters is used here in the learning phase, which calculates the interesting support vectors, their number, and their size according to the quality of the classification. These results can be re-injected in the classifier treatment in order to size the final classifier parameters. Thus, this non-supervised approach leads to an automatic parameterization of the classification treatment. This SVM solution, which is optimal in terms of database classification, can thus be used as an automatic solution to many treatments, which can be adapted and solved by means of classification. This kind of approach is only possible if a large size hardware is available, that is in a near future. This application could be also implemented on the Neuromatrix chip, but with lower execution time (or higher costs). The scalability of the application, which is linear for matrix operations can be dealt in two ways. First, when the size of the loops is higher than the size of the CoProcessor, a second internal level of loop is performed in the coprocessor structure by means of RAM address management, that is with a circular data mapping in the RAM array. Second, when the data size is higher than the RAM size, the treatment has to be divided in smaller parts manually at compile-time, either on the same CoProcessor by serialization, or on several different CoProcessors linked together through the Network on Chip, that is here the PCI-X On Chip Bus. A future data cache RAM array architecture is under study in order to mask this limitation to the programmer. Both solutions lead to performances or cost impact due to serialization of operations.

VIII. RESULTS

A. The choice on SVM parameters.

The retained kernel function of the SVM machine is the polynomial. Because of the obtained performances and also because its hardware implementation is relatively easier. The principal parameter of the polynomial kernel function is the degree of the polynomial. Although it is possible to make an implementation with a variable polynomial degree, we took as basic choice a polynomial of degree 2 (a higher degree would impose the use of wider data buses). Considering that our principal application is the matrix bar codes detection, we took 16x16 pixels as the detection window size for the hardware implementation. This size is not important to the application level. Table VI summarizes the behavioral specification of the SVM classifier.

B. The choice of the hardware parameters.

We have chosen to implement the SVM classifier on a SoC platform in order to exploit the parallel nature of the SVM algorithm. The active logical blocks and the interconnection buses normally consume the surface of silicon of an ASIC or FPGA circuit. For a few years, interconnection busses became the main consumers of silicon surface, due to the complexity of algorithms and circuits. Input Data: the values of pixels are coded in 256-gray levels, therefore all memories with pixel values will be used as a multiple of 8 bits. So each element of the support vectors will correspond to an 8-bit value. Weight Data: It is the only data whose size is not defined by the specification. It is significant to define its size precisely, in order not to modify the recognition performances significantly. Although the values in the software implementation are float values, in the hardware implementation we use fixed-point to avoid the use of the floating-point operators. The results of weights precision analysis were obtained from the same database used for testing the SVM algorithm. We vary the precision (number of bits) of the weights and we obtain the percentage of good detection and of bad classifications, the rest corresponding to false alarms. In opposition to the results obtained by A. Bermak [START_REF] Bermak | Sysneuro : Un circuit Systolique Neural multipécision pour des tâches de classification[END_REF] and those shown in literature [START_REF] Moerland | Neural Network Adaptations to Hardware Implementations[END_REF] where the average is 8 bits, the necessary precision to have the same success rate that we obtained by software is near 16 bits. It is a relatively high precision compared to the implementations shown in literature. Different factors can explain this result: for offline learning we have in general a more significant precision [START_REF] Moerland | Neural Network Adaptations to Hardware Implementations[END_REF]. Afterwards, the weights obtained during this learning process must be approximated to their hardware precision. In our case, the learning precision is maximal. Other models have fewer neurons than the SVM: this requires less precision for a hardware implementation. And finally, let us recall that the hyperplane of separation in the case of the SVM is in a dimension, which is much higher than the input dimension, and that the solution is built up using the support vectors. The 32 bits of the processor outputs are sufficient to provide the result to the generalization function, which operates on the data weights. The LEON processor performs this last function, which is limited in complexity, sequentially and in pipeline with the matrix product.

C. Prototyping Platform.

We have designed a general rapid prototyping platform dedicated to SoC emulation. The central board connects a CAN/CNA module with a Xilinx XC2V3000 FPGA and a PCI-X controller. This general-purpose board is presented on figure 12. A more complex system can be built with several boards on the PCI-X bus, which corresponds, to the OCB of our final SoC. We have implemented and validated the presented application on this single board. The synthesis results obtained are presented on table V. The vector coprocessor RAM is organized in two 1Kbytes RAM per processing unit. The peak performances of 10 Giga MAC/sec have been reached with this application. As a comparison, the number of gates of our chip is nearly the double compared to the Neuromatrix core. Also, the main vector/matrix product consists of 256*88 8-bit multiplies, that is 256*88/64=352 clock cycles compared to the 16*88=1408 clock cycles with the evaluated Neuromatrix chip. We have thus obtained an efficient solution, easy to program. A large SoC will be studied on CMOS 0.13µm technology ASIC in order to obtain real-time execution with more important applications.

IX. CONCLUSION

Platform Based Design (PBD) is the best-validated industrial approach for achieving high reuse in SoC design, and incurs the lowest risk in derivative creation via user programmability. Although these platforms already exist in some application domains, their design process is largely ad hoc. Furthermore, despite high development costs, such platforms tend to be difficult to program, and very little software support is available. Our proposition attempts to fill this gap. Our approach is to provide a general-purpose neural network application customized by a learning phase instead of explicit programming which avoid tedious designing effort. Such a solution is only possible with large hardware platforms. We have proposed in this paper a sizeable SoC platform dedicated to regular image and signal processing involving matrix operations. We have illustrated its implementation capabilities with the SVM neural network application, which performs object recognition of any kind (image or signal). A user-friendly interface is under construction. Also a future ASIC SoC implementation will demonstrate the feasibility of our approach on realistic objects recognition. With such a system, it is possible to obtain an automatic object recognition/classification based on a learning phase, which automatically configures the recognition engine, and then obtain a real-time toolbox for any object classification.

(

Fig. 2 . 1 Fig. 3 .

 213 Fig. 2. Execution time of the learning program, the optimization and generalization subroutines of the SVM method. Obtained by using a database of 4096 examples of dimension 64.

Fig. 4 .

 4 Fig.4. Image segmentation results using the SVM as detection system. The window size is 8x8 pixels. A post-processing algorithm is used in order to erase the bad classifications of the SVM.

LEON-light ( 1 )

 1

Fig. 7 .

 7 Fig. 7. General parallel loop pattern

  Fig. 8. Multi-precision Processing Unit

Fig. 9 .

 9 Fig. 9. CP_Recognition() function and equivalent configuration.

Fig. 12 .

 12 Fig. 12. FPGA-based Prototyping Platform

  Roberto Reyna-Rojas, Dominique Houzet, Daniela Dragomirescu, IEEE Members, Florent Carlier, IEEE Student Member, Salim Ouadjaout, ACM student Member

	II. THE SUPPORT VECTOR MACHINES
	Object Recognition System on Chip using the
	Support Vectors Machine
	T

TABLE II EXECUTION

 II TIMES FOR DIFERENT IMAGE SIZES. 16X16 WINDOW SIZE, 88

			SUPPORT VECTORS
	Image Size	Execution Time	Execution Time
				Sweeping Window
		Estimated	Measured	Estimated	Measured
	128x128	0.7s	0.6s	2.2s	2.7s
	256x256	2.5s	2.7s	9.2s	10.8s
	512x512	10.2s	11.0s	37.0s	43.9s

  . In the following table, we show learning results with a database of 4096 input vectors of dimension 64 (8x8 pixels), with 240 positive examples. The number of support vectors is indicated in Table III for different kernel functions and different values of the penalization parameter C.

  8. Multi-precision Processing Unit

	@start1 Size1	@start2	Size2	@Dest.	Precision OP1	OP2	Broad	Accum.
		Fig. 6. Configuration Instruction Register
								int CP_Recognition(int nb_sensors, int nb_supports)
								{ int k,j;
								for(j=0; j<nb_supports; j++)
								for(k=0; k<nb_sensors, k++)
									oo[j] += support_vectors[nb_sensors*j+k]*sample[k]; }
								Support_vector	nb_supports	sample nb_sensors oo	8	+	x no	acc.

ACKNOWLEDGMENT

We thank INTERMEC for the image databases used to show the performances of the SVM method for a real application. Roberto Reyna thanks the Mexican Research Council for its financial support through the studentship 111030.

This work was supported in part by the Mexican Research Council CONACYT under Student Scholarship 111030.