Cognitive Computation

, Volume 5, Issue 1, pp 136–151 | Cite as

Biometric Applications Related to Human Beings: There Is Life beyond Security

  • Marcos Faundez-Zanuy
  • Amir Hussain
  • Jiri Mekyska
  • Enric Sesa-Nogueras
  • Enric Monte-Moreno
  • Anna Esposito
  • Mohamed Chetouani
  • Josep Garre-Olmo
  • Andrew Abel
  • Zdenek Smekal
  • Karmele Lopez-de-Ipiña
Article
First Online:
Received:
Accepted:

Abstract

The use of biometrics has been successfully applied to security applications for some time. However, the extension of other potential applications with the use of biometric information is a very recent development. This paper summarizes the field of biometrics and investigates the potential of utilizing biometrics beyond the presently limited field of security applications. There are some synergies that can be established within security-related applications. These can also be relevant in other fields such as health and ambient intelligence. This paper describes these synergies. Overall, this paper highlights some interesting and exciting research areas as well as possible synergies between different applications using biometric information.

Keywords

Biometrics Security Healthcare Ambient intelligence 
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Notes

Acknowledgments

This work was supported by FEDER and MEC, TEC2009-14123-C04-04. SIX (CZ.1.05/2.1.00/03.0072), CZ.1.07/2.3.00/20.0094, VG20102014033, GACR 102/12/1104 and KONTAKT ME10123. We also thank Francesc Viñals and Mari Luz Puente for providing the examples in Figs. 12 and 13.

References

  1. 1.
    Aarts E, Harwig R, Schuurmans M. Ambient intelligence. In: Denning PJ, editor. The Invisible future: the seamless integration of technology into everyday life. New York: McGraw-Hill; 2001. p. 235–50.Google Scholar
  2. 2.
    Abdullah R. Intelligent methods for complex systems control engineering. PhD thesis with Dr Amir Hussain. UK: The University of Stirling; 2007.Google Scholar
  3. 3.
    Abdullah R, Hussain A, Warwick K, Zayed A. Autonomous intelligent cruise control using a novel multiple-controller framework incorporating fuzzy-logic-based switching and tuning. Neurocomputing. 2008;71:2727–41.CrossRefGoogle Scholar
  4. 4.
    Abel A, Hussain A, Nguyen QD, Ringeval F, Chetouani M, Milgram M. Maximising audiovisual correlation with automatic lip tracking and vowel based segmentation. In: Biometric ID, editor. Management and multimodal communication. Berlin: Springer; 2009. p. 65–72.CrossRefGoogle Scholar
  5. 5.
    Ackermann H, Hertich I, Daum I, Scharf G, Spieker S. Kinematic analysis of articularoty movements in central motor disorders. Mov Disord. 1997;12(6):1019–27.PubMedCrossRefGoogle Scholar
  6. 6.
    Almajai I, Milner B. Effective visually-derived Wiener filtering for audio-visual speech processing. In: Proceedings of the interspeech. Brighton, UK; 2009.Google Scholar
  7. 7.
    Almajai I, Milner B. Maximising audio-visual speech correlation. In: Proceedings of the AVSP. 2007.Google Scholar
  8. 8.
    Barker JP, Berthommier F. Evidence of correlation between acoustic and visual features of speech. In: Proceedings of the ICPhS ‘99. 1999; p. 199–202.Google Scholar
  9. 9.
    Beatty WW, Orbelo DM, Sorocco KH, Ross ED. Comprehension of affective prosody in multiple sclerosis. Multiple Scler J. 2003;9(2):148–53.CrossRefGoogle Scholar
  10. 10.
    Bermond B, Nieuwenhuyse B, Fasotti L, Schuerman J. Spinal cord lesions, peripheral feedback, and intensities of emotional feelings. Cognit Emot. 1991;5:201–20.CrossRefGoogle Scholar
  11. 11.
    Bidet-Ildei C, Pollak P, Kandel S, Fraix V, Orliaguet J-P. Handwriting in patients with parkinson disease: effect of l-dopa and stimulation of the sub-thalamic nucleus on motor anticipation. Hum Mov Sci. 2011;30(4):783–91.PubMedCrossRefGoogle Scholar
  12. 12.
    Birren JE, Botwinick J. The relation of writing speed to age and to the senile psychoses. J Consult Psychol. 1951;15(3):243–9.PubMedCrossRefGoogle Scholar
  13. 13.
    Caccioppo JT, Klein DJ, Bernston GC, Hatfield E. The psychophysiology of emotion. In: Lewis JM, Haviland-Jones M, editors. Handbook of emotion. New York: Guilford Press; 1993. p. 119–42.Google Scholar
  14. 14.
    Cambria E, Hussain A, Havasi C, Eckl C. Sentic computing: exploitation of common sense for the development of emotionsensitive systems. Lect Notes Comput Sci. 2010;5967:148–56.CrossRefGoogle Scholar
  15. 15.
    Cambria E, Hussain A, Durrani T, Havasi C, Eckl C, Munro J. Sentic computing for patient centered applications. In: 10th IEEE international conference on signal processing (ICSP), 2010. p. 1279–82.Google Scholar
  16. 16.
    Cambria E, Hussain A, Eckl C. Bridging the gap between structured and unstructured health-care data through semantics and sentics. In: Proceedings of the ACM WebSci'11; 2011. p. 1–4.Google Scholar
  17. 17.
    Cambria E, Hupont I, Hussain A, Cerezo E, Baldassarri S. Sentic avatar: multimodal affective conversational agent with common sense. LNCS, vol. 6456. Berlin, Heidelberg: Springer; 2011. p. 81–95.Google Scholar
  18. 18.
    Cifani S, Abel A, Hussain A, Squartini S, Piazza F. An investigation into audiovisual speech correlation in reverberant noisy environments. Lect Notes Comput Sci. 2009;5641:331–43.CrossRefGoogle Scholar
  19. 19.
    Chan D, Fox NC, Scahill RI, Crum WR, Whitwell JL, Leschziner G, Rossor AM, Stevens JM, Ciplolotti L, Rossor MN. Patterns on temporal lobe atrophy in semantic dementia and Alzheimer’s disease. Ann Neurol. 2001;49:433–42.PubMedCrossRefGoogle Scholar
  20. 20.
    Delaherche E, Chetouani M. Multimodal coordination: exploring relevant features and measures. In: SSPW '10 Proceedings of the 2nd international workshop on Social signal processing; 2010. p. 47–52.Google Scholar
  21. 21.
    Eichhorn TE, Gasser T, Mai N, Marquardt C, Arnold G, Schwarzy J, Oertel WH. Computational analysis of open loop handwriting movements in Parkinson’s disease: a rapid method to detect dopamimetic effects. Mov Disord. 1996;11(3):289–97.PubMedCrossRefGoogle Scholar
  22. 22.
    Ekman P. An argument for basic emotions. Cognit Emot. 1992;6:169–200.CrossRefGoogle Scholar
  23. 23.
    Ericsson K, Forssell LG, Holmén K, Viitanen M, Winblad B. Copying and handwriting ability in the screening of cognitive dysfunction in old age. Arch Gerontol Geriatr. 1996;22:103–21.PubMedCrossRefGoogle Scholar
  24. 24.
    Esposito A. The amount of information on emotional states conveyed by the verbal and nonverbal channels: some perceptual data. In: Stilianou Y, et al., editors. Progress in nonlinear speech processing. Lecture notes in computer science, vol. 4392. Berlin: Springer; 2007. p. 45–264.Google Scholar
  25. 25.
    Esposito A. Affect in multimodal information. In: Tao J, Tan T, editors. Affective information processing. Heidelberg: Springer; 2008. p. 211–34.Google Scholar
  26. 26.
    Esposito A. The perceptual and cognitive role of visual and auditory channels in conveying emotional information. Cogn Comput J. 2009;1(2):268–78.CrossRefGoogle Scholar
  27. 27.
    Faruk A, Turan N. Handwritten changes under the effect of alcohol. Forensic Sci Int. 2003;132(3):201–10.CrossRefGoogle Scholar
  28. 28.
    Ferrand C. Harmonics-to-noise ratio: an indexing of vocal aging. J Voice. 2002;16(4):480–7.PubMedCrossRefGoogle Scholar
  29. 29.
    Foley RG, Miller L. The effects of marijuana and alcohol usage on handwriting. Forensic Sci Int. 1979;14(3):159–64.PubMedCrossRefGoogle Scholar
  30. 30.
    Folstein MF, Folstein SE, McHugh PR. Mini mental state: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189–98.PubMedCrossRefGoogle Scholar
  31. 31.
    Forbes KE, Shanks MF, Venneri A. The evolution of dysgraphia in Alzheimer’s disease. Brain Res Bull. 2004;63:19–24.PubMedCrossRefGoogle Scholar
  32. 32.
    Fotiou DF, Stergiou V, Tsiptsios D, Lithari C, Nakou M, Karlovasitou A. Cholinergic deficiency in Alzheimer’s and Parkinson’s disease: evaluation with pupillometry. Int J Psychophysiol. 2009;73(2):143–9.PubMedCrossRefGoogle Scholar
  33. 33.
    Fountoulakis K, Fotiou F, Iacovides A, Tsiptsios J, Goulas A, Tsolaki M, Ierodiakonou C. Changes in pupil reaction to light in melancholic patients. Int J Psychophysiol. 1999;31(2):121–8. ISSN 0167-8760.Google Scholar
  34. 34.
    Frijda NH. Moods, emotion episodes, and emotions. In: Lewis JM, Haviland-Jones M, editors. Handbook of emotion. New York: Guilford Press; 1993. p. 381–402.Google Scholar
  35. 35.
    Girin L, Schwartz JL, Feng G. Audio-visual enhancement of speech in noise. J Acoust Soc Am. 2001;109(6):3007–20.PubMedCrossRefGoogle Scholar
  36. 36.
    Gobermana AM, Coelho C. Acoustic analysis of Parkinsonian speech I: speech characteristics and L-Dopa therapy. NeuroRehabilitation. 2002;17:237–46.Google Scholar
  37. 37.
    Gobermana AM, Coelho C. Acoustic analysis of Parkinsonian speech II: L-Dopa related fluctuations and methodological issues. NeuroRehabilitation. 2002;17:247–54.Google Scholar
  38. 38.
    Goecke R, Potamianos G, Neti C. Noisy audio feature enhancement using audio-visual speech data. In: Acoustics, speech, and signal processing, Proceedings (ICASSP’02), vol. 2. IEEE International Conference; 2002. p. 2025–2028.Google Scholar
  39. 39.
    Gorriz JM, Segovia F, Ramirez J, Lassl A, Salas-Gonzalez D. GMM based SPECT image classification for the diagnosis of Alzheimer’s disease. Appl Soft Comput. 2011;11:2313–25.CrossRefGoogle Scholar
  40. 40.
    Groves-Wright K, Neils-Strunjas J, Burnett R, O’Neill MJ. A comparison of verbal and written language in Alzheimer’s disease. J Commun Disord. 2004;37(2):109–30.PubMedCrossRefGoogle Scholar
  41. 41.
    Gustaw K, Gonet W. Speech disorders in multiple system atrophy of parkinson type. Clin Res. 2008;1(2):185–8.Google Scholar
  42. 42.
    Heinik J, Werner P, Dekel T, Gurevitz I, Rosenblum S. Computerized kinematic analysis of the clock drawing task in elderly people with mild major depressive disorder: an exploratory study. Int Psychogeriatr. 2010;22(3):479–88.PubMedCrossRefGoogle Scholar
  43. 43.
    Holz FG, Piguet B, Minassian DC, Bird AC, Weale RA. Decreasing stromal iris pigmentation as a risk factor for age-related macular degeneration. Am J Ophthalmol. 1994;117(1):19–23.PubMedGoogle Scholar
  44. 44.
    Iliadou V, Kaprinis S. Clinical psychoacoustics in Alzheimer’s disease central auditory processing disorders and speech deterioration. Ann Gen Hospital Psychiatr. 2003;2:12.CrossRefGoogle Scholar
  45. 45.
    Illán IA, Górriz JM, Ramírez J, Salas-Gonzalez D, López MM, Segovia F, Chaves R, Gómez-Rio M, Puntonet CG, the Alzheimer’s Disease Neuroimaging Initiative. 18F-FDG PET imaging analysis for computer aided Alzheimer’s diagnosis. Inf Sci. 2011;181(4):903–16.CrossRefGoogle Scholar
  46. 46.
    Izard CE. Innate and universal facial expressions: evidence from developmental and cross-cultural research. Psycholog Bull. 1994;115:288–99.CrossRefGoogle Scholar
  47. 47.
    Kempler D, Curtiss S. Catherine jackson “synthactic preservation in Alzheimer’s disease”. J speech Hearing Res. 1987;30:343–50.PubMedGoogle Scholar
  48. 48.
    Kushki A, Chau T, Anagnostou E. Handwriting difficulties in children with autism spectrum disorders: a scoping review. J Autism Dev Disord. 2011;41(12):1706–16.PubMedCrossRefGoogle Scholar
  49. 49.
    Lee L, Grimson WEL. Gait analysis for recognition and classification. Automatic face and gesture recognition, 2002. In: Proceedings of the fifth IEEE international conference; 2002. p. 148–55.Google Scholar
  50. 50.
    Levenson RW. Human emotion: a functional view. In: Ekman PP, Davidson RJ, editors. The nature of emotion: fundamental questions. New York: Oxford University Press; 1994. p. 123–6.Google Scholar
  51. 51.
    Liu R, Zhou J, Liu M, Hou X. A wearable acceleration sensor system for gait recognition. In: 2nd IEEE Conference on industrial electronics and applications, ICIEA 2007; 2007. p. 2654–59.Google Scholar
  52. 52.
    Liu L, Popescu M, Rantz M, Skubic M, Cuddihy P, Yardibi T. Automatic fall detection based on Doppler radar motion signature. In: 5th International conference on pervasive computing technologies for healthcare; 2011. p. 222–5.Google Scholar
  53. 53.
    Llau Arcusa MJ, Gonzalez Alvarez J. Medida de la inteligibilidad en el habla disaártrica. Rev Logop Foniatr Audiol. 2004;24:33–43.Google Scholar
  54. 54.
    Maltoni D, Maio D, Jain AK, Prabhakar S. Handbook of fingerprint recognition. 1st ed. New York: Springer; 2003.Google Scholar
  55. 55.
    Maternaghan C, Turner KJ. A component framework for telecare and home automation. In: CCNC'10 Proceedings of the 7th IEEE conference on consumer communications and networking conference; 2009. p. 886–870.Google Scholar
  56. 56.
    McGurk H, MacDonald J. Hearing lips and seeing voices. Nature. 1976;264:746–8.PubMedCrossRefGoogle Scholar
  57. 57.
    Mekyska J, Smekal Z, Kostalova M, Mrackova M, Skutilova S, Rektorova I. Motor aspects of speech imparment in Parkinson’s disease and their assessment. Cesk Slov Neurol N. 2011;74:662–8.Google Scholar
  58. 58.
    Moreau C, Ozsancak C, Blatt J-L, Derambure P, Destee A, Defebvre L. Oral festination in parkinson’s disease: biomechanical analysis and correlation with festination and freezing of gait. Mov Disord. 2007;22(10):1503–6.PubMedCrossRefGoogle Scholar
  59. 59.
    Nagulic M, Davidovic J, Nagulic I. Parkinsonian voice acoustic analysis in real-time after stereotactic thalamotomy. Stereotact Funct Neurosurg. 2005;83(2–3):115–21.PubMedCrossRefGoogle Scholar
  60. 60.
    Neils-Strunjas J, Groves-Wright K, Mashima P, Harnish S. Dyspgraphia in Alzheimer’s disease: a review for clinical and research purposes. J speech Lang Hearing Res. 2006;49(6):1313–30.CrossRefGoogle Scholar
  61. 61.
    Oatley K, Jenkins JM. Understanding emotions. 2nd ed. Oxford: Blackwell; 2006.Google Scholar
  62. 62.
    Ohn TG, Braak H. Auditory brainstem nuclei in Alzheimer’s disease. Neurosci Lett. 1989;2:60–3.Google Scholar
  63. 63.
    Ozsancak C, Auzou P, Jan M, Defebvre L, Derambure P, Destee A. The place of perceptual analysis of dysarthria in the differential diagnosis of corticobasal degeneration and Parkinson’s disease. J Neurol. 2006;253(1):92–7.PubMedCrossRefGoogle Scholar
  64. 64.
    Panksepp J. Emotions as natural kinds within the mammalian brain. In: Lewis JM, Haviland-Jones M, editors. Handbook of emotions. 2nd ed. New York: Guilford Press; 2000. p. 137–56.Google Scholar
  65. 65.
    Phillips JG, Ogeil RP, Muller F. Alcohol consumption and handwriting: a kinematic analysis. Hum Mov Sci. 2009;28:619–32.PubMedCrossRefGoogle Scholar
  66. 66.
    Plutchik R. Emotions as adaptive reactions: implications for therapy. Psychoanal Rev LIII. 1966;2:105–10.Google Scholar
  67. 67.
    Ramlee RA, Ranjit S. Using iris recognition algorithm, detecting cholesterol presence. In: International conference on information management and engineering; 2009. p. 714–7.Google Scholar
  68. 68.
    Rantz MJ, Skubic M, Koopman RJ, Phillips L, Alexander GL, Miller SJ, Guevara RD. Using sensor networks to detect urinary tract infections in older adults. In: Proceedings of the IEEE 13th international conference on e-health networking, applications and services. 2011.Google Scholar
  69. 69.
    Rapcan V, D’Arcy S, Yeap S, Afzal N, Thakore J, Reilly RB. Acoustic and temporal analysis of speech: a potential biomarker for schizophrenia. Med Eng Phys. 2010;32(9):1074–9.PubMedCrossRefGoogle Scholar
  70. 70.
    Ringeval F, Demouy J, Szaszak G, Chetouan M, Robel L, Xavier J, Cohen D, Plaza M. Automatic intonation recognition for the prosodic assessment of language impaired children. IEEE Trans Audio Speech Lang Process. 2011;19(5):1328–42.CrossRefGoogle Scholar
  71. 71.
    Roberts VJ, Ingram SM, Lamar M. Prosody impairment and associated affective and behavioral disturbances in Alzheimer’s disease. Neurology. 1996;47:1482–8.PubMedCrossRefGoogle Scholar
  72. 72.
    Rosenblum S, Parush S, Weiss PL. The in air phenomenon: temporal and spatial correlates of the handwriting process. Percept Mot Skills. 2003;96(3):933–54.PubMedCrossRefGoogle Scholar
  73. 73.
    Russell JA. A circumplex model of affect. J Pers Soc Psychol. 1980;39:1161–78.CrossRefGoogle Scholar
  74. 74.
    Sargin ME, Yemez Y, Erzin E, Tekalp AM. Audiovisual synchronization and fusion using canonical correlation analysis”. IEEE Trans Multimed. 2007;9(7):1396–403.CrossRefGoogle Scholar
  75. 75.
    Saunder-Pullman R, Derbym C, Stanley K, Floyd A, Bressman S, Lipton RB, Deligtisch A, Severt L, Qiping Yu, Kurtis M, Pullman SL. Validity of spiral analysis in early Parkinson’s disease. Mov Disord. 2008;23(4):531–7.CrossRefGoogle Scholar
  76. 76.
    Scherer KR, Banse R, Wallbott HG. Emotion inferences from vocal expression correlate across languages and cultures. J Cross Cult Psychol. 2001;32:76–92.CrossRefGoogle Scholar
  77. 77.
    Schlosberg H. Three dimensions of emotion. Psychol Rev. 1953;61(2):81–8.CrossRefGoogle Scholar
  78. 78.
    Sesa E, Faundez-Zanuy M. Biometric recognition using online uppercase handwritten text. Pattern Recognit. 2012;45(1):128–44.CrossRefGoogle Scholar
  79. 79.
    Sesa E, Faundez-Zanuy M, Mekyska J. An information analysis of in-air and on-surface trajectories in online handwriting. Cognit Comput. 2012;4:195–205.CrossRefGoogle Scholar
  80. 80.
    Skodda S, Schlegel U. Speech rate and rhythm in parkinson’s disease. Mov Disord. 2008;23(7):985–92.PubMedCrossRefGoogle Scholar
  81. 81.
    Sodoyer D, Schwartz JL, Girin L, Klinkisch J, Jutten C. Separation of audio-visual speech sources: a new approach exploiting the audio-visual coherence of speech stimuli. EURASIP J Appl Signal Process. 2002;11(1):1165–73.Google Scholar
  82. 82.
    Sodoyer D, Girin L, Jutten C, Schwartz JL. Developing an audio-visual speech source separation algorithm. Speech Commun. 2004;44(1–4):113–25.CrossRefGoogle Scholar
  83. 83.
    Stewart C, Winfield L, Junt A, Bressman SB, Fahn S, Blitzer A, Brin MF. Speech dysfunction in early Parkinson’s disease. Mov Disord. 1995;10(5):562–5.PubMedCrossRefGoogle Scholar
  84. 84.
    Sunderland T, Hill JL, Mellow AM, et al. Clock drawing in Alzheimer’s disease: a novel measure of dementia severity. J Am Geriatr Soc. 1989;37:725–9.PubMedGoogle Scholar
  85. 85.
    Trombetti A, Hars M, Herrmann FR, Kressig RW, Ferrari S, Rizzoli R. Effect of music-based multitask training on gait, balance, and fall risk in elderly people: a randomized controlled trial. Arch Intern Med. 2011;171(6):525–33.PubMedCrossRefGoogle Scholar
  86. 86.
    Stone EE, Skubic M. Evaluation of an inexpensive depth camera for passive in-home fall risk assessment. In: Proceedings of the 4th International conference on pervasive computing technologies for healthcare. Dublin; 2011.Google Scholar
  87. 87.
    Sumby WH, Pollack I. Visual contribution to speech intelligibility in noise. J Acoust Soc Am. 1954;26(2):212–5.CrossRefGoogle Scholar
  88. 88.
    Tripolitia EE, Fotiadisb DI, Argyropoulou M. A supervised method to assist the diagnosis and monitor progression of Alzheimer’s disease using data from an fMRI experiment. Artif Intell Med. 2011;53(1):35–45.CrossRefGoogle Scholar
  89. 89.
    Tucha O, Mecklinger L, Thome J, Reiter A, Alders GL, Sartor H, Naumann M, Lange KW. Kinematic analysis of dopaminergic effects on skilled handwriting movements in Parkinson’s disease. J Neural Transm. 2006;113:609–23.PubMedCrossRefGoogle Scholar
  90. 90.
    Tucha O, Mecklinger L, Walitza S, Lange KW. The effect of caffeine on handwriting movements in skilled writers. Hum Mov Sci. 2006;25(4–5):523–35.PubMedCrossRefGoogle Scholar
  91. 91.
    Verghese J, Holtzer R, Lipton RB, Wang C. Quantitative gait markers and incident fall risk in older adults. J Gerontol Ser A Biol Sci Med Sci. 2009;64A(8):896–901.CrossRefGoogle Scholar
  92. 92.
    Viñals Carrera F, Puente Balsells ML. “Grafología criminal”, capítulo 3, alteraciones neurológicas y biológicas. Editorial Herder, 2009.Google Scholar
  93. 93.
    Warkentin S, Erikson C, Janciauskiene S. rCBF pathology in Alzheimer’s disease is associated with slow processing speed. Neuropsychologia. 2008;46(5):1193–200.PubMedCrossRefGoogle Scholar
  94. 94.
    Werner Perla, Rosenblum Sara, Bar-On Gady, Heinik J, Korczyn A. Handwriting process variables discriminating mild Alzheimer’s disease and mild cognitive impairment”. J Gerontol. 2006;61B(4):228–36.Google Scholar
  95. 95.
    Woodward J. Biometrics: identifying law and policy concerns. In: Jain AK, Bolle RM, Pankanti S, editors. Biometrics: personal identification in networked society. New York: Springer; 2005. p. 385–405.Google Scholar
  96. 96.
    Zelkha E, Epstein B, Birrell S, Dodsworth C. From devices to “ambient intelligence”. Digital Living Room Conference, June 1998. http://www.epstein.org/brian/ambient_intelligence/DLR%20Final%20Internal.ppt.

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Marcos Faundez-Zanuy
    • 1
  • Amir Hussain
    • 2
  • Jiri Mekyska
    • 3
  • Enric Sesa-Nogueras
    • 1
  • Enric Monte-Moreno
    • 4
  • Anna Esposito
    • 5
  • Mohamed Chetouani
    • 6
  • Josep Garre-Olmo
    • 7
  • Andrew Abel
    • 2
  • Zdenek Smekal
    • 3
  • Karmele Lopez-de-Ipiña
    • 8
  1. 1.Escola Universitària Politècnica de MataróMataróSpain
  2. 2.Department of Computing Science and MathematicsUniversity of StirlingStirlingUK
  3. 3.Department of TelecommunicationsBrno University of TechnologyBrnoCzech Republic
  4. 4.TALP Research CenterUPCBarcelonaSpain
  5. 5.IIASS (International Institute for Advanced Scientific Studies)Vietri sul MareItaly
  6. 6.Pierre Marie Curie UniversityParisFrance
  7. 7.IAS (Sanitary Assistance Institute)SaltSpain
  8. 8.EHU (Basque Country University)DonostiaSpain

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