undertaking of a task. The crowd participates by bringing their work, money, knowledge and experience, which always entails mutual benefit. [START_REF] Estellés-Arolas | Towards an integrated crowdsourcing definition[END_REF] Slide 3-A question Libraries have fewer and fewer resources to do the work necessary to complete their projects.

So, they could outsource to the crowd of web users, rather than outsourcing some of their tasks to providers using the workforce in low-cost countries. The online crowd includes specialists in all domains and individuals prepared to become involved. They could even fulfill objectives it would have been impossible to imagine and achieve before.

Slide 4-Philosophy

The philosophical origin of crowdsourcing is to give humans a central place on the web as a means and a purpose. So, it may as well be regarded as being humanism or socialism. It may also be regarded as anarchism and a rejection of authority because the contribution of amateurs is equal to that of professionals and experts. Finally, it may be regarded as liberalism and its love of individual freedom, its initiative and spirit of enterprise. This confusion in the philosophical origins of crowdsourcing is particularly evident in the field of gamification. Gamification is used to collect contributions and data users and to encourage their participation in making them play games on the web. Indeed, gamification could be a sort of Stakhanovism and socialist emulation which organized competition between factories, rewarding with medals and titles. But gamification is also like the capitalist slogan of "fun at work" and its rewards for top employees, and also its concept of "weisure", a mixture of "work" and "leisure".

According to some theorists, crowdsourcing allows the emergence of a contribution economy, the end of wage labor, the end of the distinction between amateurs and professionals, between leisure and work (because leisure becomes work and work a hobby), between consumption and production (because consumption itself becomes productive of value).

Slide 5-Cons

Other scholars believe that behind the development of the ideology of Web 2.0 is hiding a new totalitarian nightmare. This movement would encourage nihilistic relativism and the denial of authority. It would lead free or underpaid labor and should be qualified as servuction because beyond any rules. For example, the Amazon Mechanical Turk Marketplace allows companies to sell microtasks, for "microsalaries" to connected workers without any legal framework and making unfair competition with traditional providers.

Others promise to tax the data in order to return to the public a portion of the value of the data they have freely produced in the form of "invisible work" for YouTube, Facebook or Google.

For professional cultural institutions, crowdsourcing could also mean the individual appropriation of the collective heritage by some Internet users to tag or give their personal and mediocre views.

You can have a look at a painting before and after restoration by an amateur.

If a manager wants to conduct the change in a library with crowdsourcing, he will have to consider also all these arguments. However, the user who has a look at a digitized document often knows much more about the document than the librarian. He or she is, in any case, usually more qualified to do so, than the subcontractor of a low-wage country. The quality of information that the user is likely to bring is far from negligible as shown in several comparative studies.

Slide 6-Tasks to be crowdsourced

In the field of digital libraries, almost every step of the scan chain is likely to be done by volunteers online :

• Selection of books to be digitized • Scanning activity

• OCR correction • Cataloging & indexing activities • Curation • Resource enrichment Slide 7-Participative digitization
Thus, the documentary selection, acquisition and digitization can be outsourced to the crowd of users: [1914][1915][1916][1917][1918] When I was in charge of digitization in Sainte-Genevieve Library, one of the two biggest academic libraries in France, we diffused about two thousand digitized books on Internet Archive, one of the three biggest digital libraries on the web.

• Internet Archive • Commons Wikimedia • Europeana
Rather than developing a costly digital library with low visibility and unsatisfactory features, we chose to participate in a sustainable and pooled digital library, on which it is possible to download the books to EPUB and MOBI for Kindle. So you can read them on your eReaders, and not only on a computer screen. Like it was suggested by [START_REF] Waibel | You're more social than you think[END_REF], the old school librarians try to boost their Google ranking so people come interact with their content on their own sites. And the new school librarians will allow people to interact with their content in the places where they already work and play.

Slide 8-Digitization on demand

Digitization funding can also be outsourced to the crowd. It is digitization on demand through crowdfunding:

• Phénix Editions • Chapitre.com • Ebooks on Demand • Adopt a book (Gallica) • Yabé (Numalire)
Slide 9-OCR correction Insofar as character recognition by OCR software gives very different results depending on the fonts and the state of original documents and depending on the quality of scanning, participatory projects can, for correction, obtain high quality articles better indexed by search engines and most importantly, compatible with eReaders.

Slide 10-Wikisource Participatory correction OCR has been experimented in too many projects to be able to evoke all of them. However, we can mention… Wikisource. In 2008, when I was in charge of a National Veterinary School Library, I found a sponsorship with Wikipedia. One hundred thesis were digitized and diffused on Wikisource. And people have corrected texts. In 2010, 1400 books from the French National Library (BnF) were diffused on Wikisource to be corrected by the crowd.

Slide 11-Digitalkoot

Digitalkoot involve users in the form of games on the web. In this game you have to recapture each word from the OCR to build each brick of a bridge allowing moles to across a river, avoiding a maximum of drowning and errors invariably punished by the explosion of a bridge brick.

Slide 12-Recaptcha

With Recaptcha, users must type the distorted images of two words to prove that they are not malicious robots and to create an account on a website. In doing so, they contribute to correct OCR texts from Google Books. One of the two words comes from Google Books and is not recognized by a dictionary while another word is used for safety reasons. With the slogan "Stop span, read books", reCaptcha allows about 200 million words to be corrected each day:

• 12,000 hours of work • 2000 ebooks Corrected in countries with low-cost labour, it would take about 1 million euros per day.

Slide 13-Other OCR projects

There are many other OCR projects:

• Australian Newspapers Digitisation Program (TROVE) • Distributed Proofreader • Transcribe Bentham • What's on the menu? • Ozalid (France)
Slide 14-Folksonomy Folksonomy indexing may also be requested to users as proposed, for example by steve.museum or Filckr the commons.

Slide 15-Google Image Labeler

Google Image Labeler is a game with a purpose. With this game, you have to try to guess and find the same keyword as another partner on the web to describe and index the same image in order to score maximum points. In doing so, indexing of optimal quality is obtained.

Slide 16-New activities for librarians

For cultural institutions that benefit from volunteer work, the development of such projects requires significant investments to communicate, recruit, manage community, motivate, reward, train, mitigate, monitor, evaluate quality and most importantly, return the data produced to digital libraries.

Slide 17-Why users contribute?

Crowds may respond to library calls for participation. Libraries serve common goods, have a good reputation, and have a volunteer tradition. They might get work, skills, knowledge, creativity and money and so contribute to the digitization projects for reasons as diverse as personal development, entertainment, game, self-promotion or altruism.

Users can contribute for intrinsic or extrinsic reasons. Some of them want to feel useful for a group, for society, for the country, for science, for the general interest, or for a cause, or do something selfless, in a spirit of altruism and philanthropy with a sense of accountability. Others seek personal development, to grow and learn, to satisfy their thirst for knowledge. Some are looking to have fun, to play or to test an innovative approach. Others are driven by the spirit of competition, the challenge or the need to prove something. And others want to have good self-esteem, have power over things, being an author and actor or simply looking to improve their e-reputation on the web.

Slide 18-End of crowdsourcing for OCR?

As you can see on the diagram of the Australian projet Trove, maybe a threshold has been reached and it becomes difficult to develop more crowdsourcing, especially as advances in character recognition software could progress in the future.

And can one really talk about crowdsourcing for libraries? Contributors to cultural projects using crowdsourcing are often a defined community of volunteers whose profile is often retired genealogists for local and family history. So, it would be more appropriate to speak about communitysourcing rather than crowdsourcing.

Slide 19-Crowdfunding experimentation at INRA

Other projects finally appeal to other types of online resources: funding resources. One speaks, in this case, of crowdfunding. From online library catalogs or from bibliographic metadata referenced on platforms, we ask the crowd, institutions or societies to fund the digitization of books via links referring to a payment interface. Documents are then digitized on site by an operator, or sent to a scanning workshop by shuttle or by mail.

At INRA, we are experimenting the Numalire Project. For my PhD, I am working with this company to know more about scanning without prior agreement with the library, scanning without a preliminary quote, and in situ scanning with mobile scanning equipment.

Slide 20-Crowdfunding

Once scanned and put online, documents can be labeled with their sponsors and offer a return on investment in terms of advertising and web traffic. Return on investment may be particularly interesting in the case of books which can be accessed a lot of times. So, public money can focus on documents of historic or scientific interest and let private money fund digitization of books of business interest.

Libraries are now able to offer their users digital copying services, without having to bear the cost and so they are able to complete their digitization programs. They can now outsource the difficult work of identification and selection of documents which, in their documentary heritage, should be scanned and thus opened up and shared with the general public.

Slide 21-Conclusion

Opening to amateurs can represent for libraries, as for businesses, an important source of innovations and inventions. Amateurs are not trying to reproduce the established business models with which professionals have been trained, they can bring innovative breakthroughs. Thus, according to Von Hippel, forty six percent of U.S. companies which have survived for at least five years were created by a single user.

Acknowledgements

Many thanks to Christine Jung for her corrections and for entertainment for the English speech.


F Gazier
email: cazier@univ-littoral.fr

A De/Bende

H Nouali

B Hanoune

D Pillot

R Vidon

P Perret

P Tasse

Determination of VOC components in the exhaust of light vehicles fuelled with different biofuels

. In the framework of this study, the first results show that an addition of ethanol in the unleaded gasoline leads to a decrease of the emissions of aromatics especially of benzene and an increase of the 1,3-butadiene emission. An increase of aromatics emissions but no specific trend for carbonyl compounds emission factors was observed when partially substituting biodiesel to diesel. Pure HVO leads to higher carbonyl emissions than pure or substituted diesel fuel.

Introduction

Volatile organic compounds (VOCs) play a major role in urban atmospheric chemistry. They are important precursors of photochemically formed secondary pollutants, such as ozone, which poses a serious air pollution problem . They are also compounds that adversely affect the human health due to their high toxicity (e.g. benzene, 1,3-butadiene). Vehicular traffic highly contributes to these VOCs emissions and, to reduce these emissions, biofuels are introduced in EU, partially substituting petroleum diesel by fatly acid methyl esters (FAMEs), commonly known as biodiesel, and gasoline by ethanol. Biodiesel blends up to 30% by volume can be used for diesel engine without modification because of their properties similar to those of diesel. It is recognized that there is a substantial reduction in regulated emissions such as HC, CO, PM with the exception of NOx emissions when diesel vehicles are fuelled with biodiesel. With the increasing percentage of biofuels in the petroleum based fuels supplied at gas stations and the development of the synthetic biofuels, it is relevant to assess the pollutants in car exhaust with these alternative fuels, under various traffic conditions . In this work, we report the emission factors (EF) of the speciated gas page 260 from 274 th International Symposium Transport and Air Pollution Session 6: Biofuels 2 phase hydrocarbons and carbonyls collected from recent technology (Euro 4) light vehicles fuelled with conventional fuel or biofuel, to determine the effect of biofuel on the unregulated exhaust emissions, with a special interest in toxic and carcinogenic compounds. The blends selected in this . study were a blend of 10% ethanol in gasoline (E10) and blend of 5 % and 30 % of biodiesel (FAMEs) in the diesel fuel (called B5 and B30). A 100% pure HVO was also tested. The emission factors of unregulated pollutants emission were measured using real-world test driving cycles performed on a chassis dynamometer with constant volume sampling (CVS) at INRETS facility. The tailpipe emissions were sampled for light hydrocarbons (C 2 -C 6 ), semi-volatile hydrocarbons (C 7 -C 19 ) and carbonyl compounds. The results obtained in this work are compared to the data obtained in the previous Artemis Project study [START_REF] Cazier | Emissions of unregulated pollutants from European gasoline and diesel passenger cars[END_REF], which investigated the emissions of cars complying to less restrictive standards (Euro 3 and before).

Materials and methods

Vehicles, fuels and driving cycles

The unregulated pollutants (VOCs) were sampled and analysed for gasoline (GDI) and diesel (D1, DPF, LDD) vehicles, ail Euro-4 certified, using real-world test driving cycles. The gasoline car is equipped with a direct injection and turbocharged engine. This type of spark ignition engine is an emergent and promising technology to reach better performances and decrease pollutant emissions. For diesel engines, the first one (D1) has direct and common rail injection. The second one (DPF) is equipped with a fuel-borne catalyst (FBC) type particulate filter on the exhaust. The third vehicle (LDD) is a 3.5 t gross weight light-duty diesel van. Ali three Diesel vehicles have turbocharged engines. The GDI car was fuelled with the reference gasoline and with E10. The three diesel vehicles were run successively on the 4 following fuels: reference diesel fuel, B5, B30 and HVO. When shifting fuels, the vehicle was run dry and the fuel pipes "rinsed" with the new fuel. The vehicle was then driven along dozens of kilometres till the tank was empty again. The gasoline and diesel reference fuels are used as bases for comparisons. They are both neat petroleum based fuels, and are used currently in Europe for vehicle certification. The driving cycles were the Artemis cycles (urban , rural and motorway) analysed separately and an INRETS specific short urban cycle (IRSUC) (André et aL, 2006) whose aim is to assess the effect of "cold start" and "hot start" in urban traffic.

Sampling and analysis

The vehicle exhaust gas is diluted with filtered ambient air using a constant volume sampler (CVS). This method of sampling reflects the natural dilution and cooling of the exhaust gas as it leaves the tailpipe and eliminates the problem of water condensation during the sampling . VOCs are sampled in the dilution tunnel using sorbent tubes: Carbotrap Band C, Carbosieve III for "light" hydrocarbons (C T C 6 ), Tenax for semi-volatile hydrocarbons (C r C 19 ), and DNPH-coated silica cartridges for carbonyl compounds. Before each set of sampling of exhaust gas the background contribution of the dilution air was measured . The procedure for the sampling and the analytical methods used for the analysis of the light and semi-volatile hydrocarbons were already applied for the Primequal -Predit Research Programs (Caplain et aL, 2006) and improved in this work the Carbotrap and Tenax samples were analysed by a thermal desorption preconcentration method, followed by quantification by high resolution gas chromatography with a flame ionisation detector (GC/FID) for compounds from C 2 to C 6 and with a mass spectrometer detector (GC/MS) for the compounds from C 7 to C 19 • DNPH cartridges were analysed by the standard UV-HPLC method.

Results and Discussion

Compound fam ilies characterised in the exhaust gas

The different compound families characterised by GC are alkanes (C 2 to C 19 ) , alkenes (C 2 to C 6 ) and aromatics compounds (BTEX, propylbenzene and butylbenzene (Iinear or branched -chain alkyl). For the GDI vehicle fueled with E10, depending on the driving cycle, the aromatics mass proportion in the tota l hydrocarbon species (except ca rbonyls) varies from 31 to 66%, the alkanes page 261 from 274 Session 6: 8iofuels 2 proportion from 33 to 59%, and the alkenes proportion from 1 to 11 %. The same car fuelled with the reference gasoline emits from 19 to 78% aromatics species, 2 to 72% alkanes and 2 to 10% alkenes. The fraction of alkanes and olefins decreases from Artemis-urban to Artemis-motorway while the proportion of aromatics increases when the GOI vehicle is fuelled with the reference gasoline. The trend is reversed with E10.

For the emission factors of the different compound families emitted by diesel engines, no trends were observed with respect to the four fuels under test. As for gasoline engine, the major VOC groups emitted by the diesel vehicles fuelled with different biodiesel blends are alkanes and aromatic compounds. For example with the LOD vehicle, the emission of aromatics decreases from 85 to HVO (e.g. cold cycle 65% -19% -16%) and the emission of alkanes increases from 85 to HVO (e.g. cold cycle 18 -82%).

The carbonyl emissions are dominated by formaldehyde, acetaldehyde, butyraldehyde and acroleine, as previously observed [START_REF] Corrêa | Carbonyl emissions in diesel and biodiesel exhaust[END_REF]. Propionaldehyde, crotonaldehyde and methacroleine were also present in the exhaust gases. The other carbonyl compounds (benzaldehyde, valeraldehyde, tolualdehyde, hexaldehyde) are negligible. The emission factors of ail the aldehydes are strongly correlated.

Influence of cold and hot starts on the emissions

The influence of cold or hot starts on the VOCs composition was determined by the difference of the emissions obtained during the IRSUC cold and IRSUC hot cycles. The measured emission factors of the VOCs grouped in different substance classes are given in Table 1. The total VOCs emission factor is larger for the hot IRSUC whatever the technology of diesel vehicle and the type of blended diesel fuel, but for carbonyls the situation is reversed with an overproduction in the cold cycle. For the diesel vehicle Di fuelled with blended fuels 85 and 830, the amount of total emitted VOCs is twice more important in a hot start than a cold one while for the OPF diesel car fuelled with blended fuel 85, the amount of emitted VOCs is 11 times more important. A six-fold increase of VOCs emission is also observed with the LOD diesel vehicle wh en it is fuelled with blended biodiesel 85. For these two latter vehicles and the two other blended fuels, the variation of the emission factor is not marked. We can also notice that for the two IRSUC cycles the emission factors of the diesel vehicles fuelled with biodiesel blends are higher than those obtained with regular diesel fuel, except in the case of 85 tested in cold start condition, where the use of biofuel decreases the emissions. The emission factor obtained with the IRSUC cold driving cycle also increases from the diesel vehicle Di to LOD with 830 and HVO fuel, showing an influence of the vehicle technology (engine, particulate filter) on the emissions. In the case of LOD the emission of aromatics compounds decreases from cold to hot driving cycle (e.g. 85: 65 % -15%) and the emission of alkanes increases from cold to hot driving cycle (e.g . 85: 18% -79%). For carbonyl compounds, the emission factor decreases when increasing the content of biofuel in the blends, though the use of pure HVO leads to higher emissions.

The gasoline engine VOCs emissions (Table 2) are not influenced by fuel blend (E10) in the case of cold and hot IRSUC cycles (EF ::::: 110 mg.km-1

) . Nevertheless, an addition of ethanol increases the emission of alkanes (3 to 74 mg.km-1

) and reduces the emission of aromatics (86 to 39 mg.km-

1 ),
especially for the IRSUC cold cycle, and of carbonyl compounds. For the IRSUC hot cycle, the difference between E10 and gasoline is reversed (alkanes decrease from 66 to 35 mg.km-1 , while aromatics increase from 50 to 69 mg.km-1

• A higher emission of aromatics is observed with the reference gasoline fuel in cold start conditions, and in hot start conditions with E10. Whatever the fuel used, alkenes emissions are 10 to 20 times larger for IRSUC cold than for IRSUC hot.

Influence of driving cycle on the emissions

The use of 85 and 830 blends leads to a reduction of total VOCs emission factor for Artemis-urban driving cycle (OPF 85 21.48 mg.km-\ 830 5.85 mg .km- OPF 36 .72 mg .km-1 ) (Table 3). For ail the vehicles under test, the highest emissions of every page 262 from 274 pollutant analysed in this study are observed during the Artemis urban driving cycle. The pollutants with the highest emission factors are ethane, propane, toluene, ethylbenzene, C 3 benzene and saturated alkanes C lO to C 14 . The reduction observed with B5 and B30 may be due to the intrinsic oxygen contained in the methyl ester (Sureshkumaret aL, 2008). -------------.-------------------------------------------------------------------------------------- *except benzene, formaldehyde and 1,3-butadiene in j.Jg.km-' .

---------------------------------------_.--------------------.-------------------------------------....
The results obtained with the reference diesel (16, 37, 41 mg.km-1 respectively for 01, DPF and LOD) may be compared with the data on Euro-3 diesel vehicles [START_REF] Cazier | Emissions of unregulated pollutants from European gasoline and diesel passenger cars[END_REF]. Compared to Euro-3 vehicles the emission factors of Euro-4 vehicles are divided by a factor 10 to 100 for IRSUC cold, 15 to 30 for IRSUC hot, 30 to 100 for the Artemis-urban and motorway driving cycles. Whatever the Artemis cycle and type of fuel, for gasoline vehicle the total VOCs emissions page 263 from 274 decrease in the following order: Artemis-urban > Artemis-rural > Artemis-motorway, and especially the light saturated and unsaturated COV (C 2 -C 6 ) (Table 2). Compared to diesel car real-world test driving cycles, VOCs emission levels are higher (e.g. IRSUC cold GDI E10 126 mg.km-\ 01 B5 14.9 mg.km-1 ) . The same observation holds for the carbonyl compounds.

For ail the vehicles, driving cycles and fuels used in this study, the emission factor observed for 1,3butadiene, benzene and formaldehyde is quite low compared to emission factors obtained in previous studies [START_REF] Aakko | Emission factors of unregulated atmospheric pollutants for passengers cars[END_REF].

Influence of biofuels

An addition of ethanol to the reference fuel seems to reduce the emissions of volatile organic compounds in the exhaust gas, especially for the Artemis-urban driving cycle (from 192 to 47 mg .km-\ Formaldehyde and acetaldehyde emissions also decrease when switching from reference gasoline to E10. Changes are observed in the aromatics and alkanes emissions when fuelling diesel engines with biodiesel blends instead of reference diesel. For 01 under IRSUC cold and hot cycles, the amount of aromatics and alkanes emitted increases significantly when the percentage of biodiesel increases in the fuel (Ref < 85 < B30 < HVO). The trend is less pronounced for the other driving cycles. For this vehicle, the biodiesel content seems to have a positive influence on the emission factors . The results obtained for the two others diesel vehicles (OPF , LOD) are not so clear, and a weak increase of the emission factors from reference diesel to HVO is noted. The use of biofuel decreases slightly the production of carbonyls compared to reference gasoil, except for the Artemis urban cycle, but pure HVO leads also to high emissions of carbonyls.

Conclusions

The analysis presented in this study shows that the use of biofuel pure or in blends at different concentration (E10, 85, 830, HVO) in gasoline or diesel engines and under different driving cycle conditions, has an impact on the tailpipe emissions. Non-regulated pollutants mainly emitled are alkanes and aromatics compounds whatever the type of engine. On the whole, the addition of ethanol to gasoline reduces the aromatics emissions but does not affect the total VOCs emissions as regards IRSUC cold and IRSUC hot cycles, where the emissions levels stay almost similar. With biodiesel blends, the emission factors are higher with the hot start driving cycle than with the cold start one. For the Artemis driving cycles, the emission factors of the gasoline vehicle are reduced wh en going from urban to rural to motorway driving cycle, but no clear trend is noted with the diesel vehicles . The emission factors obtained during this study are significantly reduced compared to the ones obtained with Euro-3 compliant diesel and gasoline vehicles.

1)

  while the use of HVO increases the emissions (Di 76.27 mg.km-\ OPF 53.7 mg.km-1 ) compared to reference diesel (Di 16.1 mg .km-1 ,

  ----------------...... _---_ .. ------------------. ..... ----... -----------_ ... -------------------------------_.. ------_ .. --------_ ....

Table 1 ,

 1 ' IRSUC cold and IRSUC hot Diesel vehicles emission/actors (mg.km-l)

			IRSUC cold			IRSUC hot
			Ref.	B5	B30	HVO	Ref.	B5	B30	HVO
		Vehicle 01
		Alkenes	1.77	4.55	2.07	0.54	13.19 10.19	0.65	2.25
		Alkanes	1.61	5.54	5.40	17.95 8.86	9.07	18.61 33.79
		Aromatics	3.81	4.82	8.81	22.29 5.87	8.85	20.24 13.58
	J	Carbonyls	4.87	3.29	3.17	1.29	3.67	1.40	1.08	3.29

Table 2 :

 2 Artemis and IRSUC driving cycles gasoline vehicle emissionfactors (mg.km-1

	) *

Table 3 :

 3 Artemis driving cycle Diesel vehicle emissionfactors (mg.km-1

	) *

  ......... .. _ ............. .. ........... . . . ............. .. -_. -. . .......... .. ...... ... ....... .. .. .... ~ ...... -. . . .. -.... .............. .... _ ............. _ ............... ..... .. .......... . . . .... .. ..... . . . ........

	1,3-butadiene*	N.D .	N.D.	N.D.	4.7	N.D.	N.D.	N.D .	4.9	5.8	9.5	N.D .	0.1
	Benzene*	22.2	19.8	15.5	20.5	N.D.	9.5	5.1	3.7	N.D.	2.4	5.5	0.2
	Formaldehyde	1965.	1687.	Nodat	3532.	226.	267.	362.	656.	377.	214.	228.	357.
		6		a	9	7	9	6	2	4	7	9	7
	Vehicle LDD
	Alkenes	0.63	1.61	N.D.	0.75	0.77	0.14	0.23	0.19	0.73
	Alkanes	30.47	39.26	6.26	21.33	3.04	0.15	4.05	0.06	0.38
	Aromatics	9.93	9.40	N.D .	4.21	1.25	2.44	1.27	N.D.	5.94	No data
	Carbonyls	4.20	7.82	7.69	13.89	0.92	1.91	0.81	3.22	1.07
	1,3-butadiene*	N.D.	2.9	N.D .	1.5	N.D.	N.D.	N.D.	N.D .	N.D.
	Benzene*	N.D.	20.1	N.D.	30.7	N.D.	7.2	15.1	N.D.	4.9
	Formaldehyde	930.1	2301 . 5	1624.6	3445. 7	163. 7	485. 3	161 . 8	826. 0	164. 8

Acknowledgements

This study was supported by ADEME, the French environment agency. page 265 from 274 References


Emmanuel Boutillon

Senior Member, IEEE José-Luis Sanchez-Rojas

Simplified Compression of Redundancy Free Trellis Sections in Turbo Decoder

It has been recently shown that a sequence of R = q(M -1) redundancy free trellis stages of a recursive convolutional decoder can be compressed in a sequence of L = M -1 trellis stages, where M is the number of states of the trellis and q is a positive integer. In this paper, we show that for an M state Turbo decoder, among the L compressed trellis stages, only m = 3 or even m = 2 are necessary. The so-called m-min algorithm can either be used to increase the throughput for decoding a high rate turbo-code and/or to reduce its power consumption. 1

I. INTRODUCTION

The quality of an error control code design can be evaluated in terms of decoding performance, implementation cost (area, power dissipation) and decoding throughput. A general overview of error control code decoders can be found in [START_REF] Kienle | On Complexity, Energy-and Implementation-Efficiency of Channel Decoders[END_REF]. The recent specifications of wireless systems (LTE, HSPA [2]) propose the use of turbo-codes with very high code rates (typically, between 0.8 and 0.98). In contrast to code construction, there are very few papers dedicated to decoders with such high rates. Most of the reported architectures propose the basic structure of a rate 1/3 decoder, with parameters (i.e. window lengths) optimized for high rates to tradeoff performance and decoding throughput. In [START_REF] Boutillon | Compression of redundancy free trellis stages in Turbo-Decoder[END_REF], a method is proposed to directly exploit the existence of long sequences (up to 100) of Redundancy Free Trellis Sections (RFTS, or sequences of bits without redundancy) to reduce the complexity of part of the decoder: during the acquisition process, any RFTS of size R is replaced by a shorter RFTS sequence of size L = M -1, with additional (R mod L) steps of shuffling. In this paper, we show that, in the context of a Max-Log-MAP decoder [START_REF] Boutillon | Iterative decoding of concatenated convolutional codes: Implementation Issues[END_REF], among the L steps of the compressed RFTS, only m = 3 or m = 2 are really useful, which allows further architectural optimization.

The remainder of the paper is divided into four sections. Sec. II gives enough information about the trellis compression to have a self-consistent paper. Then, Sec. III presents the sub-optimal 3-min simplification; followed in Sec. IV by a discussion about hardware implementation. Finally, Sec. V concludes the paper.

II. PRINCIPLE OF THE RTFS TRELLIS COMPACTION

In this section, we will first recall the problem of acquisition for high rate turbo-codes. Then, we will present the principle of trellis compaction at the encoding level before deriving it at the decoding level.

A. Acquisition for high rate turbo-codes

The implementation of a turbo-code is a well investigated area. The standard implementation uses the Log-MAP algorithm or the Max-Log-MAP algorithm [START_REF] Boutillon | Iterative decoding of concatenated convolutional codes: Implementation Issues[END_REF] along with the sliding window algorithm [START_REF] Viterbi | An intuitive justification and a simplified implementation of the MAP decoder for convolutional codes[END_REF]. This algorithm consists in dividing the frame of length K into windows of length W and processing the forward-backward steps on a W -sized block instead of a whole K-sized block. To process the p th window, an accurate estimation of the initial forward state metrics α pW and backward state metrics β pW +W are required. One possible scheduling is to perform the backward recursion directly from index K down to 1 to obtain naturally the initial β pW +W states and to obtain the α pW initial states by an acquisition of size W ′ , starting from state α 0 pW -W ′ to state α pW (see Fig. 1). This pre-processing is called "acquisition". The initial value α 0 pW -W ′ can be either the all-zero state vector (if there is no a priori knowledge on the initial state) or a forward state vector stored from the previous iteration. This last method is commonly called Next Iteration Initialization (NII) [START_REF] Dielissen | State vector reduction for initialization of sliding window MAP[END_REF].

For high code rates, the length of the acquisition L ′ needs to be high enough to contain some non RFTS (i.e. trellis sections associated with non-punctured redundancy bits). In fact, starting from the all-zero state vector, if the acquisition processes only RFTS, the final state will also be the allzero state vector and the acquisition process will thus be useless. By simulation, it is verified that a high value of L ′ is required, even if the NII technique is used. This means that the acquisition step consumes a significant portion of the decoder's processing effort, which reduces the efficiency and throughput of the hardware implementation.

B. Compression of RFTS at the encoding level

Let us consider the trellis compression in the case of hard information bits (a decision is made on the value of the received bit, i.e., no soft value is available). For an 8-state convolutional encoder, the state-space representation of [START_REF] Weiss | Turbo decoding with tail-biting trellises[END_REF] gives

X k+1 = AX k + BD k , (1a) V k = CX k + DD k , (1b)
where X k , D k and V k are respectively the state of the encoder, the input information bit and the output vector at time k. The matrices (A, B, C, D) are respectively the state matrix, the entry matrix, the output matrix and the feedforward matrix. In (1a) and (1b), arithmetic is over GF(2). Moreover, for a recursive code, the matrix A verifies A L = Id, where Id is the identity matrix. Fig. 2.a shows the structure of the encoder (the generation of output bits, corresponding to (1.b), is omitted). Starting from a state X 0 and the bit sequence D k , k = 0, 1, . . . , R -1, the final state of the encoder is given by

X R = A R • X 0 + R-1 ∑ k=0 A k • B • D R-1-k . (2)
Let us replace index k with qL + l, where l = k mod L and q = k-l L . For the sake of simplicity, k mod L will be denoted k |L . Then (2) can be rewritten as

X R = A R X 0 + L-1 ∑ l=0 ⌊ R-1-l L ⌋ ∑ q=0 A qL+l BD R-1-qL-l . (3) Since A L = Id, A qL+l = A l , thus (3
) can be expressed as:

X R = A R |L X 0 + L-1 ∑ l=0 A l B    ⌊ R-1-l L ⌋ ∑ q=0 D R-1-qL-l    , (4)
where Let D a h be the "aggregated bit" corresponding to the bits having the same residue h mod L: for h = 0, 1, . . . , L -1 I).

R |L = R mod L. We perform the change of variable p = ⌊ R-1-l L ⌋ -q in the last summation term of (4). Let h(l) = (R -1 -l) mod L. We have ⌊ R-1-l L ⌋ ∑ q=0 D R-1-qL-l = ⌊ R-1-h(l) L ⌋ ∑ p=0 D h(l)+pL . (5)
D a h = ⌊ R-1-h L ⌋ ∑ p=0 D h+pL . (6)
Note: when h ≥ L, D a h is equal to D a h mod L . Then, the last summation of (4) can be expressed as

D a h(l) . Let X ′ 0 = A R |L X 0 . Using the fact that A L X ′ 0 = A R |L X 0 , (4) can be expressed recursively starting from state X ′ 0 to state X ′ L = X R as, for l = 0, 1, . . . , L -1 X ′ l+1 = AX ′ l + BD a h(L-1-l) . (7)
Since [START_REF] Dielissen | State vector reduction for initialization of sliding window MAP[END_REF] can be expressed simply as:

h(L-1-l) = (R-1-(L-1-l)) mod L = (R |L +l) mod L,
X ′ l+1 = AX ′ l + BD a R |L +l . (8)
Note that (8) has the same structure as (1a). Moreover, the computation of X ′ 0 = A R |L X 0 can be performed in R |L steps of (1a) with dummy null input bits D d k = 0, k = 0, 1, . . . , R |L -1. In other words, every RFTS sequence of length R (i.e. R trellis stages with no redundancy) can be reduced to an RFTS sequence of length R |L + L. The first R |L RFTS are associated with a null dummy bit D d k = 0, k = 0, 1, . . . , R |L -1 and the last L trellis stages are associated with the aggregated bit D a l , l = R |L , . . . , L -1 + R |L . One should note that shifting the position of an aggregated bit by a multiple of L does not affect the result. Swapping the subblock of the last R |L aggregated bits with the sub-block of the first R |R dummy bits leads to another valid compaction of the trellis. In that case, the last R |L stages are associated with dummy null bits. This solution was originally presented in [START_REF] Boutillon | Compression of redundancy free trellis stages in Turbo-Decoder[END_REF].

C. Trellis compaction

Let us recall the classical forward recursion of the MAP decoder. Let α k be the forward state metrics at time k and γ k the branch metrics at time k. The recursion is given by

α k+1 (s) = max * (α k (s 0 ) + γ k (s 0 , s), α k (s 1 ) + γ k (s 1 , s)), ( 9
)
where s is the current state, (s 0 , s) is the branch in the trellis associated with the input bit D k = 0 connecting state s 0 to the state s. Similarly, (s 1 , s) is the branch in the trellis associated with the input bit D k = 1 connecting state s 1 to state s. The max * function is defined as

max * (a, b) = max(a, b) + e -|a-b| . ( 10
)
When the Max-Log-MAP algorithm is used, the max * operator in ( 9) is simply replaced by the max operator. Fig. 2.b shows the computation of the state metric α k+1 (s).

For an RFTS, there is no associated redundancy bit and the branch metrics are simply γ(s 0 , s) = LLR(D k ) and γ(s 1 , s) = -LLR(D k ), where LLR(D k ) is the Log-Likelihood Ratio of the received bit, i.e. LLR(D k ) = ln

( P (D k =0) P (D k =1)
)

. In the sequel, for the sake of simplicity, LLR(D k ) will be denoted γ k . Similarly, γ a l will represent the LLR of the aggregated bit D a l and γ d l the LLR of the dummy null bit (γ d l = +∞ since D d l = 0). As shown in Sec. II.B, at the encoder side, a sequence of length R can be reduced to a sequence of length R |L with dummy zero inputs and a sequence of length L of aggregated bits. At the receiver side, this implies that the RFTS sequence can be compressed into R |L steps of shuffling (using the branch metric γ d l = +∞), followed by L trellis sections of aggregated bits γ a l . Since summation in ( 6) is over GF(2), at the receiver side, γ a l = LLR(D a l ) is given by

γ a l = 2 tanh -1    ⌊ R-1-l L ⌋ ∏ q=0 tanh ( γ qL+l 2 )    . (11)
Thanks to the min-sum approximation, (11) can be simplified by separating the modulus of γ a l and the sign of γ a l :

|γ a l | = min { |γ qL+l |, q = 0, 1, . . . , ⌊ R -1 -l L ⌋} , (12)
sign(γ a l ) = ⌊ R-1-l L ⌋ ∏ q=0 sign(γ qL+l ). (13)

D. Example of RFTS compaction

Let us consider the M = 8-state encoder defined in Fig. 2.a, a RFTS sequence of length R = 16 with initial state metrics at time 0 equal to α 0 = [35, 0, 20, 25, 17, 3, 16, 31] ′ . The LLRs of the D k values are equal to {γ k } k=0,1,...,15 = {-14, 31, 24, 12, 31, 20, 6, -31, 19, 15, -19, -8, 15, 12, 5, -11}2 . Let us assume an RFTS sequence of length R = 16. Table I shows the first forward state metrics and the last forward state metrics of the RFTS sequence. In order to bound the increasing values of the state metrics, at each stage, the minimum value of the state metrics are subtracted out (α k = α kmin(α k (i), i = 0, 1, . . . , 7)). After this subtraction, the minimum metric is always equal to zero. shows the 9 steps of the compressed trellis. As expected, the last stage of the classical trellis (last column of Table I) and the last stage of the compressed trellis are equal (last column of Table II). This algorithm implies the use of L minima.

It is called the L-min algorithm. In terms of performance, processing acquisition sequences of the sliding window algorithm without or with trellis compression gives identical final results. In the latter case, the number of clock cycles can be significantly reduced, leading to a more efficient architecture, as described briefly in [START_REF] Boutillon | Compression of redundancy free trellis stages in Turbo-Decoder[END_REF]. Now the question arises whether it is possible to further decrease the complexity of the L-min algorithm by trading-off complexity and performance.

III. m-MIN a AND m-MIN g COMPUTATION

The analysis of the Max-Log-MAP algorithm for a sequence of RFTS shows that a high magnitude of γ implies simply a shuffling of the state metrics value α, while a low magnitude of γ decreases significantly the dynamics of the α terms (i.e. a low reliability bit gives more uncertainty on the current state of the encoder). This indicates that it may be sufficient to consider only a small subset of the initial γ values to process the RFTS sequence. Let us define the m-min a method as an extension of the L-min method where among the L LLRs of the aggregated bits, the L-m highest γ a modules are saturated to sign(γ)×∞. Using this method with the example of the previous section, the 3-min a method now consists of replacing the aggregated γ a values {15, -12, -8, 15, 6, 5, -11} by keeping only the 3 smallest magnitude values and saturating the others, i.e., the set {+∞, -∞, -8, +∞, 6, 5, -∞}. general case, the final state metrics can differ slightly, even using the 4-min or 5-min methods.

In practice, the only pertinent criterion to evaluate an algorithm is the performance loss. To this end, we have run several simulations performing all acquisitions using the mmin a algorithm. We also tested a modified version of the mmin a method, performing the saturation before the aggregation of the bits. In this case, the Rm highest values of the RFTS sequence are saturated prior to the aggregation method. This variation of the m-min a method is called m-min g method. Note that the m-min a and the m-min g methods lead to the same result if m = 1. For m > 1, the two methods give the same result only if the m minimum values before aggregation have all distinct indices modulo L.

Table IV shows bit-true simulation results of an HSPA Turbo decoder with K = 5144 using the sliding window technique associated with the NII technique. For r = 0.98, various acquisition lengths W ′ are given to illustrate the need of high values of W ′ for very high code rate. In all cases, the 3-min algorithm (not shown in the table) does not have significant performance degradation. The 2-min g and 1-min g algorithms degrade the performance by less than 0.02 dB and 0.2 dB respectively for all code rates.

IV. ARCHITECTURE OPTIMIZATION

In [START_REF] Boutillon | Compression of redundancy free trellis stages in Turbo-Decoder[END_REF], the authors proposed to perform the bit aggregation "on the fly" during the previous iteration. The time for processing the acquisition is thus reduced and the decoding throughput is increased. In this paper, we present new applications of RFTS compression to reduce the power consumption of the decoder. Let us focus on the 2-min g aggregation in an RFTS acquisition sequence of length R. The RFTS compression can be done on the fly in three steps. Step two: during the next R |L clock cycles, permutations of state metrics are done (processing of R |L dummy bits). Step 3: during the last L clock cycles, the forward unit is processed with the aggregated bit computed on the fly by algorithm 1. For R = 100 (code rate 0.98), this method allows the decoder to freeze the acquisition forward unit 90% of the time while the low complexity bit aggregation algorithm works. This method does not increase the throughput but helps to save power dissipation.

V. CONCLUSION

In this paper, we showed that the compression of redundancy free trellis stages using the L-min algorithm can be further simplified by considering only 3, or even 2, minima among the L values. Simulations on 3-min show no performance loss and simulation on 2-min shows minor performance loss (around 0.02 dB). The 3-min (or 2-min) algorithm opens new architecture optimization, either to save power during the acquisition or to increase the overall decoding throughput.

Fig. 1 .

 1 Fig. 1. Schematic representation of the sliding-window algorithms, with parameters W = K/4 and W ′ = W/2. The x-axis represents the time index and the y-axis the index of the bit.

  Let us give an example to illustrate (5) with R = 23, L = 7 and l = 3. The left term of (5) gives D 19 + D 12 + D 5 . Since h(3) = (23 -1 -3) mod 7 = 5, the right term of (5) gives D 5 + D 12 + D 19 and both terms are equal.

Fig. 2 .

 2 Fig. 2. (a) 8-state LTE encoder structure, (b) Step of the trellis (c) Example of RFTS to compute α 1 from α 0 and γ 0 (see tableI).

  Fig. 2.c shows the details of the computation of α 1 from α 0 and γ 0 . Let us compute γ a 2 . From (12), we obtain |γ a 2 | = min{|D 2 |, |D 9 |} = min{|24|, |15|} = 15. From (13), we obtain sign(γ a 2 )) = 1. The computation for the other values gives {γ a l } l=2,3,...,8 = {15, -12, -8, 15, 6, 5, -11}. TableII

1 :

 1 Step one: during the Initialisation: sign(l) = +1, for l = 0, 1, . . . , L -1; (min 1 , ind 1 ) = (+∞, 0); (min 2 , ind 2 ) = (+∞, 1)for k = 0 to R -1 do l = k mod L; sign(l) = sign(l) × sign(γ k ) if |γ k | < min 1 then min 2 = min 1 , min 1 = |γ k | ind 2 = ind 1 , ind 1 = l else if |γ k | < min 2 then min 2 = |γ k |, ind 2 = l end AlgorithmOn the fly aggregation of the bit for the 2-min g algorithm. first R -(R |L ) -L clock cycles, the acquisition forward unit is frozen (and thus saving energy).

  Table III shows the corresponding trellis. Compared to Table II, intermediate state metrics can differ from the L-min and the 3-min methods, but finally, both methods lead to the same final state. In the

TABLE IV REQUIRED

 IV E b /No TO OBTAIN A FER OF 10 -2 . HSPA TURBO-CODE OF LENGTH K = 5144 FOR SEVERAL CODE RATES. FB = FORWARD-BACKWARD, SW = SLIDING WINDOW, ACQUISITION WITH NII AND W = W ′

						TABLE III
			COMPRESSED TRELLIS (3-MIN ALGORITHM)
	l	0	γ d l	1	2	3	4	5	γ a l	6	7	8	9
	γ d,a l α l (0)	∞ 35	+∞ 35	∞ 35	-∞ 35 16 -8 ∞ 24 24 18 6 5	-∞ 6	. . . 16
	α l (1)	0		25	31	16 25	0 30 13	16	12
	α l (2)	20		17	0	25	0	18 15	0	12	6
	α l (3)	25		31	16	3 17	30 19 28	4	2
	α l (4)	17		0	25	31 35	15		0 24	1	6
	α l (5)	3		20	17	0	3	16 18	9	6	4
	α l (6)	16		3	20	17 31	34 16 12	2	1
	α l (7)	31		16	3	20 20	19 34 16	0	0
	rate		W ′ = W		FB	FB-SW	2-ming	1-ming
	r = 0.8			32		3.89 dB 3.92 dB 3.94 dB 4.05 dB
	r = 0.9			64		4.87 dB 4.89 dB 4.91 dB 5.02 dB
	r = 0.94		128		5.49 dB 5.51 dB 5.53 dB 5.71 dB
	r = 0.98		32		6.73 dB 8.17 dB 8.18 dB 8.20 dB
				64		6.73 dB 7.09 dB 7.10 dB 7.15 dB
				128		6.73 dB 6.83 dB 6.84 dB 6.92 dB
				256		6.73 dB 6.75 dB 6.77 dB 6.87 dB

This work has been supported by the GIGADEC project from Brittany region, as well as the CPER project PALMYRE II (Brittany region and FEDER funding).

The MATLAB code used for this example can be downloaded at[START_REF] Boutillon | [END_REF]