INHERENT ID: A NOVEL APPROACH TO DETECT COUNTERFEITCONSUMER GOODS USING PRODUCT INHERENT FEATURES

Christian Horn, Matthias Blankenburg and Jorg KrugerDepartment of Industrial Automation Technology

Technische Universitat Berlin, Pascalstrasse 8, 10587 Berlin, Germanyhorn@iwf.tu-berlin.de, blankenburg@iwf.tu-berlin.de, krueger@iwf.tu-berlin.de

ABSTRACT

Product-Piracy or counterfeiting is a well knownproblem which leads to economic damage that af-fects in particular countries that use advanced pro-duction and manufacturing processes based on in-tensive research and development to produce highquality goods. The existence of counterfeitingleads to the development of methods and technolo-gies to secure high-quality products. The presentway to secure top-quality products is often fol-lowed by an application of artificial security fea-tures.In this text we show a novel approach in detail tosecure products against counterfeiting using fea-tures of the product itself instead of additional la-bels. The specific conditions of production, manu-facturing technologies and materials generate spe-cific features, which identify the product uniquely.

KEYWORDS

Automated Counterfeit Detection, Product Finger-printing, Pattern Recognition, Sensor Fusion, Clas-sification

1 INTRODUCTION

The annual ”Report on EU customs enforce-ment of intellectual property rights” of the Eu-

ropean Union in 2012 [1] shows a continiousupward trend in the number of shipments sus-pected of violating intellectual property rights.As for the Year 2011 the value of detained ar-ticles and their equivalent genuine products isestimated to be over 1.2 billion euro. Thisnumber only includes the value of productsactually detained only at the european bor-der. The OECD report ”The Economic Im-pact of counterfeiting and piracy” [2] of 2008estimates a total loss of 250 billion dollarsin the year 2007 worldwide. In comparisonthe EU-Report [1] states 43.671 cases in 2007and 91.254 cases in 2011. The OECD re-port covers the analysis of international tradein counterfeit and pirated products.These es-timates do not include domestically producedand consumed counterfeit and pirated digitalproducts being distributed via the Internet. Ifthese were also considered, the magnitude ofcounterfeiting and piracy worldwide could beseveral hundred billion dollars more than pre-viously thought, and this increasing trend isquite alarming.

It is self-evident that counterfeiting and piracyare businesses from which criminal networksthrive. The OECD report shows further thatthe items counterfeiters and pirates produceand distribute are often of minor quality andcan even be dangerous and health hazards.

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With the magnitude of counterfeiting andpiracy in mind, these reports emphasize theneed for more effective enforcement to com-bat the counterfeiting and piracy on the partof governments and businesses alike. A keycomponent for this enforcement is the devel-opment of new methods for automated coun-terfeit detection.

The effect of counterfeiting and piracy is anintermission of innovation and thus impair-ment of economic growth. The economicdamage affects in particular countries that useadvanced production and manufacturing pro-cesses based on intensive research and devel-opment to produce high quality goods. Thereview of copyright infringement of registeredtrademarks and products is not easy to imple-ment. Due to the high number of pendingtrademarks and constantly added new applica-tions it is very difficult for the executive bod-ies, such as customs, to register violations oftrademark rights immediately and in a com-prehensive manner. The awareness to all reg-istered brands and products is for the execu-tive organs not possible and therefore neces-sarily, trademark infringement remains unno-ticed. The way to secure top-quality coun-terfeit products is often followed by an ap-plication of artificial security features. Theissues of such security labels are in part thehigh cost, and additionally the integration intothe product. High-quality branded products,as the target of counterfeiting, have usually,due to the production processes and materialsused, and in view of its processing machin-ery and equipment, a grade of high quality.The specific conditions of production, man-ufacturing technologies and materials gener-ate specific features, which identify the prod-uct uniquely. These features may be detectedmultimodal by man, including tactile (plas-ticity, elasticity, thermal conductivity, surfacestructure), visual (shape, colour, surface tex-ture, transparency), olfactory (smell) or acous-

tic (sound) perceptions. In general, only theperson familiar with the manufacture of theproduct can combine these inherent character-istics in their entirety so that it can differenti-ate the genuine product from a clear counter-feit. In the project Inherent-ID two propertiesof a product have been identified as the mostpromising ones suitable for identification: theolfactory and the optical features.

2 STATE-OF-THE-ART-TECHNOLOGY

Common automated counterfeit detectionmethods require nowadays additional securityfeatures at the product itself. Several methodshave been developed, but main advantages anddisadvantages remain similar.Additional security features require furthersteps in production to add these features to theproduct. This raises expenses, manufacturingtime and development efforts, which is clearlya disadvantage. On the other hand the securityis enhanced and an original brand is easy todetect in an automated fashion, since there is aspecific feature to look for. But this could alsobe a main disadvantage, if the security featureitself is easy to reproduce and could be addedto any forged product. Another challenge isto link the securtiy label to the brand productin a way it cannot be removed or stolen. Thisway product pirates could label their counter-feits easily as an original with an original se-curity label.Figure 1 shows examples of different labelswhich are commonly used on products for dif-ferent purposes. One purpose is the use as alogical security feature where the security la-bel contains unique information and cannot becopied. Counterfeit detection without artificialsecurity tags is a solution to these problems,if the counterfeit is distinguishable from theoriginal brand.

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(a) QR-Code (b) RFID

(c) FraunhoferSecuriFlex

Figure 1: Different Types of Security-Labels

2.1 Security Labels

The Anthology [3] gives a comprehensiveoverview of the latest efforts in product pro-tection. A reasonably well studied approachis the extensive supervision of supply-chains.Here the application of RFID tags plays a sig-nificant role, as the latest form of artificialsecurity tags, which can easily be integratedwith existing logistic chains. The applica-tion of Data Matrix Codes (DMC) is discussedas well as a cost-effective alternative. Muchwork has been done to link these tags insepa-rably with the corresponding product to hinderproduct pirates from transferring these tags totheir counterfeits. But in general it is observedthat this protection method holds only withtremendous logistic implications, since todaysproducts cover various stations during the dis-tribution process. Up to now there has been nocommon standard available and the customsauthorities’ integration is still open. Evenwhen the cost of these artificial tags could bereduced by advances in the production pro-cess, as e. g. the introduced direct printing ofRFID antennas onto packaging, additional ex-penses with no direct use for the customer willarise. Security Tags like holograms found at-tached to various consumer goods give nearlyno protection against counterfeiting since ma-

chine readability is poor and knowledge of thecorrect appearance is scarce.

2.2 Product-Inherent Features

The Inherent ID Project adopts a novel ap-proach to protecting high-value products fromcounterfeiting. The approach is based on thestationary and mobile capture of key prod-uct features indissolubly linked with the prod-uct which enable its production process to betraced. This not only renders obsolete the ap-plication of security tags but also gives en-hanced protection against counterfeiting as theinherent characteristics that the high-qualityproduction process impregnate in the genuineproduct are combined with one another toserve as proof of product identity. They formthe basis on which electronic certificates of au-thenticity can be issued without the need forcomplicated explicit security markings. Meth-ods for the capture and control of identity char-acteristics are being elaborated in the InherentID project for system integration using intel-ligent cameras and an electronic nose. Theidentity characteristics captured by this rangeof sensors serve both for the product identi-fication and product authentication. At thesame time this also offers opportunities for im-proving documentation of product flows in thesupply chain. Full documentation serves as acomplement to the inherent characteristics ofthe authentic product and offers valuable in-formation of verification of the genuine arti-cle, thus serving to safeguard against counter-feits. The Project aimes to answer the ques-tion: Which inherent features allow separationof genuine products from counterfeits in an au-tomated fashion? The motivation of this ques-tion is the assumption that genuine productsmust differ in its properties from its counter-feit, since the product pirate tries to maximizeits profit by using material of inferior qualityand misusing a trademark of a genuine man-

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Figure 2: Concept of the Project Inherent ID

ufacturer to feint the customer. One result ofthe project is that only a combination of fea-tures can detect counterfeits at a decent ratefor different products.

3 INHERENT ID IN DETAIL

Optical 2D and 3D characteristics as well asolfactory characteristics are combined withone another to serve as proof of product iden-tity, as shown in figure 2. They form the basison which electronic certificates of authentic-ity can be issued without the need for com-plicated explicit security markings. The iden-tity characteristics captured by this range ofsensors serve both for product identificationand product authentication. At the same timethis also offers opportunities for improvingdocumentation of product flows in the supplychain. Within the scope of Inherent ID is thesuccessful establishment of a laboratory pro-viding multi-modal measurement equipmentcomprising multigas sensor array for olfactoryanalysis, high resolution camera for textureanalysis and stereo vision, as well as rangecameras for 3D feature extraction. Further re-search is conducted with the aim for increas-ing robustness of the sole test methods espe-cially under ambiguous environments, integra-

tion into portable devices, implementing sen-sor data fusion for increased detection ratio,effortless integration into supply chains anddeveloping efficient data models for storageof various features depending on the regardedproduct.

3.1 Texture Features

The ability to characterise visual textures andextract the features inherent to them is consid-ered to be a powerful tool and has many rel-evant applications. A textural signature capa-ble of capturing these features, and in partic-ular capable of coping with various changesin the environment would be highly suited todescribing and recognising image textures [6].As humans, we are able to recognise textureintuitively. However, in the application ofComputer Vision it is incredibly difficult to de-fine how one texture differs from another. Inorder to understand, and manipulate texturalimage data, it is important to define what tex-ture is. Image texture is defined as a functionof the spatial variation of pixel intensities [5].Furthermore, the mathematical description ofimage texture should incorporate, identify anddefine the textural features that intuitively al-low humans to differentiate between differ-ent textures. Numerous methods have beendesigned, which in the past have commonlyutilised statistical models, however most ofthem are sensitive to changes in viewpoint andillumination conditions [6]. For the purposesof mobile counterfeit detection, it is clear thatthis would be an important characteristic forthe signature to have, as these conditions cannot be entirely controlled. Recently a descrip-tion method based on fractal geometry knownas the multifractal spectrum has grown in pop-ularity and is now considered to be a use-ful tool in characterising image texture. Oneof the most significant advantages is that themultifractal spectrum is invariant to the bi-

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UserInput

Gaussian Smoothing

Gradient Filtering Laplacian Filtering

Calculate LocalFractal Dimension

Define Setsfrom LFD Map

Calculate GlobalFractal Dimension for Sets

TextureSignature

Figure 3: Workflow for generating a Texture-Signature

Lipschitz transform, which is a very generaltransform that includes perspective and texturesurface deformations [6].Another advantage of Multrifractal Spectra isthat it has low dimension and is very efficientto compute [6] in comparison to other methodswhich achieve invariancy to viewpoint and il-lumination changes such as those detailed in[7], [8]. One of the key advantages of mul-tifractal spectra, which is utilised here is thatthey can be defined by many different cat-egorisations or measures, which means thatmultiple spectra can be produced for the sameimage.This is achieved through the use of filtering,

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Figure 4: Multi-Fractal-Spectra of texture of a textileproduct (top) and its counterfeit (bottom)

whereby certain filters are applied to enhancecertain aspects of the texture, to create a newmeasure. Certain measures are more or less in-variant to certain transforms, and the combina-tion of a number of spectra achieves a greaterrobustness to these. The worklfow is depictedin Figure 3 and an example is given in Fig-ure 4.

3.2 Shape Features

Since manual detection is often done visualby customs officials, visual features are alsoimportant for any automatic detection mecha-nism. Besides detecting features through twodimensional image processing, three dimen-sional data capture is necessary for counterfeitdetection, because it provides important addi-tional information.To capture a real-world object in three di-mensions a 3D scanner, or range camera, canbe used. The basic principles of 3D scan-ners available on the market are triangulation,time-of-flight or interferometric approaches,whereas each principle has its advantages or

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Compute Differenceof Gaussian (DoG)

Detect Key-Pointsin DoG Sets

User Input/3D Model of Object

Shape Signature

Compute LocalSpin Images around

Key-Points

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Figure 5: Workflow for generating a Shape-Signature

disadvantages. For a profound insight into thattopic refer to [9]. We use a mobile structured-light 3D scanner for our application, but ingeneral any three dimensional data acquisitionmethod can be used to capture a real-worldobject. But using different kinds of scanningtechniques results may vary.One distinguishable feature of brand productsis the shape itself. Shape matching is a wellstudied topic and several publications can befound over the last 15 years. Feature-basedapproaches have become very popular sincesome years in image analysis (2D) due to ro-bustness and less computational effort com-pared to other approaches. In shape match-ing (3D) feature-based approaches have beenintroduced more recently and are gaining pop-ularity in shape retrieval applications for the

same reasons. The major difference is whetherthe approach uses global or local features. In[10] an overview of shape matching principlesand algorithms can be found.Many shape matching approaches use digitalhuman made data like the Princeton-Shape-Benchmark [11] or the SHREC datasets [12]to evaluate their algorithms. Scanned datafrom real world objects is different in a sensethat holes1 and variations between two scansof the same object can appear.For that reason most approaches are not suit-able for counterfeit detection, where minordetails of an object can be highly important.Therefore only approaches detecting local fea-tures were taken into consideration. Figure 5shows the required steps for our shape match-ing algorithm using real world objects. Theshape matching algorithm requires a three di-mensional model of the product as input whichcan be matched to an abstract model of thebrand product. The abstract model is a de-scription of features that render the brandunique.One major challenge for three dimensional ob-ject capture is the huge amount of data thathas to be processed. The 3D scanner we usehas an accuracy of 20 to 50 µm and generatesaround 300, 000 vertices per object. Assum-ing a point per point matching algorithm withO(nc) and c > 1 growth rate and a calcula-tion time of 1ms per point match, it would takenearly 3 years to calculate a match of two ob-jects. This simple example demonstrates thechallenge. Optimized algorithms, data reduc-tion, parallel processing or transformation isnecessary to achieve acceptable results.In our approach the concept of Key-Points orPoints-Of-Interest in combination with trans-formation is used. To do so, a feature detector[12] has to be applied and the area surroundingthe detected Key-Points is transformed into a

1Holes are areas on the scanned object where theused scanning technique has troubles to capture data.

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Figure 6: Transformation of Shape Features

meaningful descriptor.Figure 6 shows a transformation of the areasurrounding Key-Points into a 2D dense mapusing Spin Images [13]. A set of Spin Im-ages is then transformed into a description ofthe object that can be matched to the abstractbrand model.

3.3 Odour Features

Much effort has been spent on how odourcould be measured. The European StandardEN-13725 [14] defines a method for the objec-tive determination of the odour concentrationof a gaseous sample using so called dynamicolfactometry. It is currently the only standard-ized method for the evaluation of odour im-pressions.The dynamic olfactometry is a method wherea panel of human assessors evaluates the con-centration of odour in a series of standard-ized presentations of a gas sample. Here theemission rate of odours emanating from pointsources, area sources with outward flow andarea sources without outward flow are consid-ered. The primary application of this standardis to provide a common basis for evaluationof odorant emissions in the member states ofthe European Union. Every method claimingthe ability to detect arbitrary odour emissionshas to benchmark against this standard. Anoverview of the development and applicationof electronic noses is given in Gardner andBartlett [15].In general it was observed that electronicnoses do not react to human inodorous gasesand were also unable to detect some gaseshumans are able to smell naturally. Begin-

Figure 7: Olfactory pattern of a genuine jersey (top)and a counterfeit (bottom)

ning with the working principle of specific gassensors the concept of electronic noses as acombination of sensor array and diverse pat-tern recognition algorithms for classificationis introduced. In principle the sensor con-cepts could be divided into three categories.The commercially available electronic noseArtinos basing on the KAMINA (KArlsuherMIkroNase) [16] is a representative of metalconductance sensors. Here the sample gasflowing alongside the sensor surface is chang-ing the concentration and configuration of ox-ide containing compounds, thus changing theconductance of the metal-oxide, which is thenused as a measurement signal. The sensorelements differ by the thickness of silicondioxide coating. Additionally the temperatureis changed over time producing 38 analoguechannels containing also transient responses,which are to be analysed. Due to its workingprinciple these sensors deliver the most unspe-cific data, which is both an advantage and adisadvantage at the same time, since the sen-sors are suitable for a broad variety of samples,but the signal processing is harder to realise.A metal-oxide conductance sensor using 16channels was utilized in the project Inherent-ID [4].

A similar sensor setup is used in [17], the

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difference being that the sensor elements arecoated with different polymers, which inducea change in conductance to specific gas com-ponents. It was shown that with four differ-ent sensor types held at four different temper-atures, so a total of 16 channels and follow-ing linear discriminant analysis ovarian can-cer could be detected from tissue samples.There are still some issues with falsely re-jected samples, but the results were quite im-pressive with respect to the use of ad-hocmethods. Another sensor concept utilisingpolymer coatings are the quartz microbalancesensor arrays as described in [18]. These sen-sors detect the change of frequency when agas is flowing over the sensor surface. Inprinciple these arrays are very sensitive butalso very susceptible to disturbances. Mostof recently published results in odour detec-tion are based on linear discriminant analy-sis and derivatives thereof. These methodsare efficient in classification of complex sen-sor data, but with a manageable number ofclasses. And these methods need a significantamount of data present and are therefore notsuitable for the here elaborated problem of oneto many matching, as needed for the applica-tion in counterfeit detection. An additional ob-stacle is the sensitivity to ambient conditionswhich result in wide variance of measurementdata from the same class of samples. Effortis made in the extraction of relevant featuresfor the purpose of reducing the dimensional-ity and the suppression of ambient influenceswhich was done by independent componentanalysis. An attempt of designing a generalodour model was made in [19], but was notsuccessful due to the sensors used and the factthat nonlinear behaviour was excluded in ad-vance. So the usage of specific models is morepromising.

UserInput

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Figure 8: Feature Fusion Concept

4 WORKFLOW

With the features described above there is astrong basis for automated classification ofpatterns. The key point for a robust and re-liable counterfeit detection is the combinationof these features and additional user informa-tion with the aim to derive a decision wetherthe probe is likely to be a counterfeit. An ad-vantage of the proposed algorithms for featureextraction is the possibility to utilize statisticalframeworks since the features are representedby probability distributions.In general there are various approaches possi-ble. Starting with a direct fusion of the fea-tures as proposed in [20] and shown in fig-ure 8, or a more sophisticated approach whichis taking the process of probing into account.Such a workflow is depicted in 9.Here the decision process is not necissarilybased on the utilization of all features, sincesome of them are dispensible or could be mis-leading. Think of the probing of shirt, obvi-ously the 3D geometry cannot give a relevantcontribution to the decision process and the3D scanning can therefore be ommitted. Theclassification itself is done with an adjustedBayesian approach where special account wasgiven to the detection of novel and thereforeunknown patterns. This was done with estima-tion of the Level of Signifcance distribution,

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UserInput

Logo Matching Textur Analysis

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Figure 9: Sophisticated Workflow for Counterfeit De-tection

which gives a decision information and an ad-ditional value of the plausibilty of this deci-sion, cf. [21].

5 CONCLUSION

It was shown that the Inherent-ID Projectadopts a novel approach to protecting high-value products from counterfeiting. The ap-proach is based on the stationary and mobilecapture of key product features indissolublylinked with the product which enable its pro-duction process to be traced. This not onlyrenders the application of security tags obso-lete but also gives enhanced protection against

counterfeiting as the inherent characteristicsthat the high-quality production process im-pregnate in the genuine product are combinedwith one another to serve as proof of prod-uct identity. They form the basis on whichelectronic certificates of authenticity can be is-sued without the need for complicated explicitsecurity markings. Methods for the captureand control of identity characteristics are be-ing elaborated in the Inherent-ID project forsystem integration using intelligent camerasand an electronic nose. The identity character-istics captured by this range of sensors serveboth for the product identification and prod-uct authentication. At the same time this alsooffers opportunities for improving documenta-tion of product flows in the supply chain. Fulldocumentation serves as a complement to theinherent characteristics of the authentic prod-uct and offers valuable information of verifi-cation of the genuine article, thus serving tosafeguard against counterfeits.

6 ACKNOWLEDGEMENTS

The authors would like to acknowledge thefunding of the research project Inherent-ID bythe senate of the state Berlin and the EuropeanRegional Development Fund. The project isembedded in the Fraunhofer Cluster of Inno-vation Secure Identity.

7 THE AUTHORS

The authors are working at the departmentof Industrial Automation Technology, whichis an integral part of the Institute for Ma-chine Tools and Factory Management at theSchool of Mechanical Engineering and Trans-port Systems of the Technische UniversitatBerlin. Main tasks are fundamental researchand lecturing in a broad band of topics re-garding industrial automation such as processautomation and robotics, process monitoring

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and simulation, image processing and patternrecognition.

8 REFERENCES

[1] European Comission, Report on EU customs en-forcement of intellectual property rights – Re-sults at the EU border - 2011, ISBN 978-92-79-25362-1, pp. 10-19, 2012

[2] OECD, The Economic Impact of Counterfeitingand Piracy, OECD, Paris, 2008, www.oecd.org/sti/counterfeiting

[3] M. Abramovici, L. Overmeyer, B. Wirnitzer,Kennzeichnungstechnologien zum wirksamenSchutz gegen Produktpiraterie - Band 2 derReihe Innovationen gegen Produktpiraterie,ISBN 978-3-8163-0602-3, pp. 1-196, 2010

[4] J. Kruger, M. Blankenburg Secure Identity - asource for innovative it-systems and processesFuture Security, 5th Security Research Confer-ence, Berlin, September 7th - 9th, 2010. Ed.:Fraunhofer VVS, Freiburg, 2010

[5] M. Tuceryan, A. K. Jain, Texture Analysis,Handbook of Pattern Recognition & ComputerVision, 2nd Edition, World Scientifc PublishingCo. Ptc. Ltd., 2001

[6] Y. Xu, H. Ji, C. Fermuller, Viewpoint InvariantTexture Description Using Fractal Analysis, IntJ Comput Vision 83, pp. 85-100, 2009

[7] M. Varma, A. Zisserman, Classifying images ofmaterials: Achieving viewpoint and illuminationindependence, ECCV Volume 3, pp. 255-271,2002

[8] M. Varma, A. Zisserman, Texture Classification:are filter banks necessary?, CPVR Volume 2,pp. 691-698, 2003

[9] B. Jahne, Digital Image Processing, ISBN 3-540-24035-7, 2005

[10] J. W. Tangelder, R. C. Veltkamp A Survey ofContent Based 3D Shape Retrieval Methods,Multimedia Tools and Applications Vol. 39 No.3. , pp. 441-471, 2007

[11] P. Shilane, P. Min, M. Kazhdan, T. FunkhouserThe princeton shape benchmark, Shape Model-ing International, 2004

[12] A. M. Bronstein et al.SHREC 2010, Proc. EU-ROGRAPHICS Workshop on 3D Object Re-trieval (3DOR), 2010

[13] A. Johnson, M. Hebert, Using spin images for ef-ficient object recognition in cluttered 3d scenes,IEEE PAMI 21, pp. 433-449, 1999

[14] EN 13725, Air quality. Determination of odourconcentration by dynamic olfactometry, DINEN 13725:2003

[15] J. W. Gardner, P. N. Bartlett, Electronic Noses -Principles and Applications, Measurement Sci-ence and Technology Volume 11, 1999. OxfordUniversity Press, Oxford

[16] D. Haeringer, J. Goschnick, Characterization ofsmelling contaminations on textiles using a gra-dient microarray as an electronic nose, Sensorsand actuators B-Chemical Vol. 132, Nr. 2, 2008

[17] J. Chilo, G. Horvath, T. Lindblad, R. Olsson,Electronic Nose Ovarian Carcinoma DiagnosisBased on Machine Learning Algorithms, Lec-ture Notes in Computer Science Volume 5633,2009

[18] A. S. Yuwono, T. Hamacher, J. Nieß, P. Boeker,P. S. Lammers, Implementation of a quartz mi-crobalance (QMB) sensor array - based instru-ment and olfactometer for monitoring the per-formance of an odour biofilter, 2nd IWA Inter-national Workshop & Conference on Odour &VOC’s, Singapore, 2003

[19] F. Bitter, Modell zur Bestimmung der Geruchsin-tensitat der Raumluft mit Multigassensorsyste-men, doctoral thesis, TU Berlin, 2009

[20] H. B. Mitchell Multi-Sensor Data Fusion: AnIntroduction, Springer publishing, 2007

[21] S. Kuhn, Stochastic Engineering – Berechnung,Entwicklung und Modellierung bei unsichererInformation, doctoral thesis, TU Berlin, ISBN978-3-8322-9188-4, pp. 103-115, 2010

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