554,890 research outputs found

    Donning/Doffing and Arm Positioning Influence in Upper Limb Adaptive Prostheses Control

    Full text link
    [EN] New upper limb prostheses controllers are continuously being proposed in the literature. However, most of the prostheses commonly used in the real world are based on very old basic controllers. One reason to explain this reluctance to change is the lack of robustness. Traditional controllers have been validated by many users and years, so the introduction of a new controller paradigm requires a lot of strong evidence of a robust behavior. In this work, we approach the robustness against donning/doffing and arm position for recently proposed linear filter adaptive controllers based on myoelectric signals. The adaptive approach allows to introduce some feedback in a natural way in real time in the human-machine collaboration, so it is not so sensitive to input signals changes due to donning/doffing and arm movements. The average completion rate and path efficiency obtained for eight able-bodied subjects donning/doffing five times in four days is 95.83% and 84.19%, respectively, and for four participants using different arm positions is 93.84% and 88.77%, with no statistically significant difference in the results obtained for the different conditions. All these characteristics make the adaptive linear regression a potential candidate for future real world prostheses controllers.This work is partially supported by Ministerio de Educacion, Cultura y Deporte (Spain) under grant FPU15/02870. The authors would like to thank Lucas Parra for the Myo device and Janne M. Hahne for discussions about the subject of the paper.Igual, C.; Camacho-García, A.; Bernabeu Soler, EJ.; Igual García, J. (2020). Donning/Doffing and Arm Positioning Influence in Upper Limb Adaptive Prostheses Control. Applied Sciences. 10(8):1-19. https://doi.org/10.3390/app10082892S119108Esquenazi, A. (2004). Amputation rehabilitation and prosthetic restoration. From surgery to community reintegration. Disability and Rehabilitation, 26(14-15), 831-836. doi:10.1080/09638280410001708850Ziegler-Graham, K., MacKenzie, E. J., Ephraim, P. L., Travison, T. G., & Brookmeyer, R. (2008). Estimating the Prevalence of Limb Loss in the United States: 2005 to 2050. Archives of Physical Medicine and Rehabilitation, 89(3), 422-429. doi:10.1016/j.apmr.2007.11.005Igual, C., Pardo, L. A., Hahne, J., & Igual, J. M. (2019). Myoelectric Control for Upper Limb Prostheses. Electronics, 8(11), 1244. doi:10.3390/electronics8111244Biddiss, E., & Chau, T. (2007). Upper-Limb Prosthetics. American Journal of Physical Medicine & Rehabilitation, 86(12), 977-987. doi:10.1097/phm.0b013e3181587f6cBiddiss, E. A., & Chau, T. T. (2007). Upper limb prosthesis use and abandonment. Prosthetics & Orthotics International, 31(3), 236-257. doi:10.1080/03093640600994581Davidson, J. (2002). A survey of the satisfaction of upper limb amputees with their prostheses, their lifestyles, and their abilities. Journal of Hand Therapy, 15(1), 62-70. doi:10.1053/hanthe.2002.v15.01562Datta, D., Selvarajah, K., & Davey, N. (2004). Functional outcome of patients with proximal upper limb deficiency–acquired and congenital. Clinical Rehabilitation, 18(2), 172-177. doi:10.1191/0269215504cr716oaVujaklija, I., Farina, D., & Aszmann, O. (2016). New developments in prosthetic arm systems. Orthopedic Research and Reviews, Volume 8, 31-39. doi:10.2147/orr.s71468Scheme, E. J., Englehart, K. B., & Hudgins, B. S. (2011). Selective Classification for Improved Robustness of Myoelectric Control Under Nonideal Conditions. IEEE Transactions on Biomedical Engineering, 58(6), 1698-1705. doi:10.1109/tbme.2011.2113182Englehart, K., & Hudgins, B. (2003). A robust, real-time control scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 50(7), 848-854. doi:10.1109/tbme.2003.813539Parker, P., Englehart, K., & Hudgins, B. (2006). Myoelectric signal processing for control of powered limb prostheses. Journal of Electromyography and Kinesiology, 16(6), 541-548. doi:10.1016/j.jelekin.2006.08.006Fougner, A., Stavdahl, Ø., Kyberd, P. J., Losier, Y. G., & Parker, P. A. (2012). Control of Upper Limb Prostheses: Terminology and Proportional Myoelectric Control—A Review. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(5), 663-677. doi:10.1109/tnsre.2012.2196711Resnik, L., Huang, H. (Helen), Winslow, A., Crouch, D. L., Zhang, F., & Wolk, N. (2018). Evaluation of EMG pattern recognition for upper limb prosthesis control: a case study in comparison with direct myoelectric control. Journal of NeuroEngineering and Rehabilitation, 15(1). doi:10.1186/s12984-018-0361-3Sartori, M., Durandau, G., Došen, S., & Farina, D. (2018). Robust simultaneous myoelectric control of multiple degrees of freedom in wrist-hand prostheses by real-time neuromusculoskeletal modeling. Journal of Neural Engineering, 15(6), 066026. doi:10.1088/1741-2552/aae26bVelliste, M., Perel, S., Spalding, M. C., Whitford, A. S., & Schwartz, A. B. (2008). Cortical control of a prosthetic arm for self-feeding. Nature, 453(7198), 1098-1101. doi:10.1038/nature06996Amsuess, S., Vujaklija, I., Goebel, P., Roche, A. D., Graimann, B., Aszmann, O. C., & Farina, D. (2016). Context-Dependent Upper Limb Prosthesis Control for Natural and Robust Use. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(7), 744-753. doi:10.1109/tnsre.2015.2454240Kuiken, T. A., Miller, L. A., Turner, K., & Hargrove, L. J. (2016). A Comparison of Pattern Recognition Control and Direct Control of a Multiple Degree-of-Freedom Transradial Prosthesis. IEEE Journal of Translational Engineering in Health and Medicine, 4, 1-8. doi:10.1109/jtehm.2016.2616123Phinyomark, A., N. Khushaba, R., & Scheme, E. (2018). Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors. Sensors, 18(5), 1615. doi:10.3390/s18051615Asghari Oskoei, M., & Hu, H. (2007). Myoelectric control systems—A survey. Biomedical Signal Processing and Control, 2(4), 275-294. doi:10.1016/j.bspc.2007.07.009Spanias, J. A., Perreault, E. J., & Hargrove, L. J. (2016). Detection of and Compensation for EMG Disturbances for Powered Lower Limb Prosthesis Control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(2), 226-234. doi:10.1109/tnsre.2015.2413393Castellini, C., & van der Smagt, P. (2008). Surface EMG in advanced hand prosthetics. Biological Cybernetics, 100(1), 35-47. doi:10.1007/s00422-008-0278-1Ameri, A., Akhaee, M. A., Scheme, E., & Englehart, K. (2019). Regression convolutional neural network for improved simultaneous EMG control. Journal of Neural Engineering, 16(3), 036015. doi:10.1088/1741-2552/ab0e2eHahne, J. M., Biebmann, F., Jiang, N., Rehbaum, H., Farina, D., Meinecke, F. C., … Parra, L. C. (2014). Linear and Nonlinear Regression Techniques for Simultaneous and Proportional Myoelectric Control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22(2), 269-279. doi:10.1109/tnsre.2014.2305520Ameri, A., Scheme, E. J., Kamavuako, E. N., Englehart, K. B., & Parker, P. A. (2014). Real-Time, Simultaneous Myoelectric Control Using Force and Position-Based Training Paradigms. IEEE Transactions on Biomedical Engineering, 61(2), 279-287. doi:10.1109/tbme.2013.2281595Hochberg, L. R., Serruya, M. D., Friehs, G. M., Mukand, J. A., Saleh, M., Caplan, A. H., … Donoghue, J. P. (2006). Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature, 442(7099), 164-171. doi:10.1038/nature04970Interface Prostheses With Classifier-Feedback-Based User Training. (2017). IEEE Transactions on Biomedical Engineering, 64(11), 2575-2583. doi:10.1109/tbme.2016.2641584Thomas, N., Ung, G., McGarvey, C., & Brown, J. D. (2019). Comparison of vibrotactile and joint-torque feedback in a myoelectric upper-limb prosthesis. Journal of NeuroEngineering and Rehabilitation, 16(1). doi:10.1186/s12984-019-0545-5Guémann, M., Bouvier, S., Halgand, C., Borrini, L., Paclet, F., Lapeyre, E., … de Rugy, A. (2018). Sensory and motor parameter estimation for elbow myoelectric control with vibrotactile feedback. Annals of Physical and Rehabilitation Medicine, 61, e467. doi:10.1016/j.rehab.2018.05.1090Markovic, M., Schweisfurth, M. A., Engels, L. F., Farina, D., & Dosen, S. (2018). Myocontrol is closed-loop control: incidental feedback is sufficient for scaling the prosthesis force in routine grasping. Journal of NeuroEngineering and Rehabilitation, 15(1). doi:10.1186/s12984-018-0422-7Pasquina, P. F., Perry, B. N., Miller, M. E., Ling, G. S. F., & Tsao, J. W. (2015). Recent advances in bioelectric prostheses. Neurology: Clinical Practice, 5(2), 164-170. doi:10.1212/cpj.0000000000000132NING, J., & Dario, F. (2014). Myoelectric control of upper limb prosthesis: current status, challenges and recent advances. Frontiers in Neuroengineering, 7. doi:10.3389/conf.fneng.2014.11.00004Lendaro, E., Mastinu, E., Håkansson, B., & Ortiz-Catalan, M. (2017). Real-time Classification of Non-Weight Bearing Lower-Limb Movements Using EMG to Facilitate Phantom Motor Execution: Engineering and Case Study Application on Phantom Limb Pain. Frontiers in Neurology, 8. doi:10.3389/fneur.2017.00470Mastinu, E., Ortiz-Catalan, M., & Hakansson, B. (2015). Analog front-ends comparison in the way of a portable, low-power and low-cost EMG controller based on pattern recognition. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). doi:10.1109/embc.2015.7318805BECK, T. W., HOUSH, T. J., CRAMER, J. T., MALEK, M. H., MIELKE, M., HENDRIX, R., & WEIR, J. P. (2008). Electrode Shift and Normalization Reduce the Innervation Zone’s Influence on EMG. Medicine & Science in Sports & Exercise, 40(7), 1314-1322. doi:10.1249/mss.0b013e31816c4822Pasquina, P. F., Evangelista, M., Carvalho, A. J., Lockhart, J., Griffin, S., Nanos, G., … Hankin, D. (2015). First-in-man demonstration of a fully implanted myoelectric sensors system to control an advanced electromechanical prosthetic hand. Journal of Neuroscience Methods, 244, 85-93. doi:10.1016/j.jneumeth.2014.07.016Fougner, A., Scheme, E., Chan, A. D. C., Englehart, K., & Stavdahl, Ø. (2011). Resolving the Limb Position Effect in Myoelectric Pattern Recognition. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19(6), 644-651. doi:10.1109/tnsre.2011.2163529Hwang, H.-J., Hahne, J. M., & Müller, K.-R. (2017). Real-time robustness evaluation of regression based myoelectric control against arm position change and donning/doffing. PLOS ONE, 12(11), e0186318. doi:10.1371/journal.pone.0186318Young, A. J., Hargrove, L. J., & Kuiken, T. A. (2011). The Effects of Electrode Size and Orientation on the Sensitivity of Myoelectric Pattern Recognition Systems to Electrode Shift. IEEE Transactions on Biomedical Engineering, 58(9), 2537-2544. doi:10.1109/tbme.2011.2159216Prahm, C., Schulz, A., Paaben, B., Schoisswohl, J., Kaniusas, E., Dorffner, G., … Aszmann, O. (2019). Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(5), 956-962. doi:10.1109/tnsre.2019.2907200Cipriani, C., Sassu, R., Controzzi, M., & Carrozza, M. C. (2011). Influence of the weight actions of the hand prosthesis on the performance of pattern recognition based myoelectric control: Preliminary study. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. doi:10.1109/iembs.2011.6090468Amsuss, S., Paredes, L. P., Rudigkeit, N., Graimann, B., Herrmann, M. J., & Farina, D. (2013). Long term stability of surface EMG pattern classification for prosthetic control. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). doi:10.1109/embc.2013.6610327Scheme, E., & Englehart, K. (2011). Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use. The Journal of Rehabilitation Research and Development, 48(6), 643. doi:10.1682/jrrd.2010.09.0177Scheme, E., Fougner, A., Stavdahl, Ø., Chan, A. D. C., & Englehart, K. (2010). Examining the adverse effects of limb position on pattern recognition based myoelectric control. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. doi:10.1109/iembs.2010.5627638Dohnalek, P., Gajdos, P., & Peterek, T. (2013). Human activity recognition on raw sensor data via sparse approximation. 2013 36th International Conference on Telecommunications and Signal Processing (TSP). doi:10.1109/tsp.2013.6614027Marasco, P. D., Hebert, J. S., Sensinger, J. W., Shell, C. E., Schofield, J. S., Thumser, Z. C., … Orzell, B. M. (2018). Illusory movement perception improves motor control for prosthetic hands. Science Translational Medicine, 10(432). doi:10.1126/scitranslmed.aao6990Mastinu, E., Doguet, P., Botquin, Y., Hakansson, B., & Ortiz-Catalan, M. (2017). Embedded System for Prosthetic Control Using Implanted Neuromuscular Interfaces Accessed Via an Osseointegrated Implant. IEEE Transactions on Biomedical Circuits and Systems, 11(4), 867-877. doi:10.1109/tbcas.2017.2694710Igual, C., Igual, J., Hahne, J. M., & Parra, L. C. (2019). Adaptive Auto-Regressive Proportional Myoelectric Control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(2), 314-322. doi:10.1109/tnsre.2019.2894464Huang, Y., Englehart, K. B., Hudgins, B., & Chan, A. D. C. (2005). A Gaussian Mixture Model Based Classification Scheme for Myoelectric Control of Powered Upper Limb Prostheses. IEEE Transactions on Biomedical Engineering, 52(11), 1801-1811. doi:10.1109/tbme.2005.856295Hahne, J. M., Dahne, S., Hwang, H.-J., Muller, K.-R., & Parra, L. C. (2015). Concurrent Adaptation of Human and Machine Improves Simultaneous and Proportional Myoelectric Control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23(4), 618-627. doi:10.1109/tnsre.2015.240113

    Finding Resonant Frequencies for High Loss Dielectrics in Cylindrical Cavities

    Full text link
    This article proposes the use of argument principle method (APM) to find all complex resonant frequencies in a three layer cylindrical cavity. APM guarantees that no root is lost and frequencies can be associated with the resonant mode. The roots can be used to find permittivity of a material inside a cavityResults are obtained under the Project TEC2012-37532-C02-01, funded by Ministerio de Economia y Competitividad (MINECO) and cofunded by European Regional Development Funds (ERDF).Penaranda-Foix, FL.; Catalá Civera, JM.; Canós Marín, AJ.; García Baños, B. (2015). Finding Resonant Frequencies for High Loss Dielectrics in Cylindrical Cavities. International Journal of RF and Microwave Computer-Aided Engineering. 25(6):530-535. https://doi.org/10.1002/mmce.20889S530535256F.L. Penaranda-Foix J.M. Catala-Civera A.J. Canos-Marin B. Garcia-BanosF.L. Penaranda-Foix J.M. Catala-Civera http://sciyo.com/books/show/title/passive-microwave-components-and-antennasPeng, Z., Hwang, J.-Y., & Andriese, M. (2014). Maximum Sample Volume for Permittivity Measurements by Cavity Perturbation Technique. IEEE Transactions on Instrumentation and Measurement, 63(2), 450-455. doi:10.1109/tim.2013.2279496Baker-Jarvis, J., Vanzura, E. J., & Kissick, W. A. (1990). Improved technique for determining complex permittivity with the transmission/reflection method. IEEE Transactions on Microwave Theory and Techniques, 38(8), 1096-1103. doi:10.1109/22.57336Bussey, H. E. (1980). Dielectric Measurements in a Shielded Open Circuit Coaxial Line. IEEE Transactions on Instrumentation and Measurement, 29(2), 120-124. doi:10.1109/tim.1980.4314884S. Kaneko H. Kawabata Y. Kobayashi 010 020H. Kawabata Y. Kobayashi S. KanekoC.A. Balanis Advanced engineering electromagnetics. John Wiley & Sons, NewYork, 1989; ISBN-13: 978-0471621942Peñaranda-Foix, F. L., Catalá-Civera, J. M., Contelles-Cervera, M., & Canós-Marín, A. J. (2006). Solving the cutoff wave numbers in partially filled rectangular waveguides by the Cauchy integral method. International Journal of RF and Microwave Computer-Aided Engineering, 16(5), 502-509. doi:10.1002/mmce.20170Rodríguez-Berral, R., Mesa, F., & Medina, F. (2003). Systematic and efficient root finder for computing the modal spectrum of planar layered waveguides. International Journal of RF and Microwave Computer-Aided Engineering, 14(1), 73-83. doi:10.1002/mmce.10120F.L. Peñaranda-Foix J.M. Catalá-Civera J.G. Bogado P.J. Plaza-González J.I. Herranz-HerruzoLi, L., & Liang, C.-H. (2004). ANALYSIS OF RESONANCE AND QUALITY FACTOR OF ANTENNA AND SCATTERING SYSTEMS USING COMPLEX FREQUENCY METHOD COMBINED WITH MODEL-BASED PARAMETER ESTIMATION. Progress In Electromagnetics Research, 46, 165-188. doi:10.2528/pier0309150

    Real-time agreement and fulfilment of SLAs in Cloud Computing environments

    Full text link
    A Cloud Computing system must readjust its resources by taking into account the demand for its services. This raises the need for designing protocols that provide the individual components of the Cloud architecture with the ability to self-adapt and to reach agreements in order to deal with changes in the services demand. Furthermore, if the Cloud provider has signed a Service Level Agreement (SLA) with the clients of the services that it offers, the appropriate agreement mechanism has to ensure the provision of the service contracted within a specified time. This paper introduces real-time mechanisms for the agreement and fulfilment of SLAs in Cloud Computing environments. On the one hand, it presents a negotiation protocol inspired by the standard WSAgreement used in web services to manage the interactions between the client and the Cloud provider to agree the terms of the SLA of a service. On the other hand, it proposes the application of a real-time argumentation framework for redistributing resources and ensuring the fulfilment of these SLAs during peaks in the service demand.This work is supported by the Spanish government Grants CONSOLIDER-INGENIO 2010 CSD2007-00022, TIN2011-27652-C03-01, TIN2012-36586-C03-01 and TIN2012-36586-C03-03.De La Prieta, F.; Heras Barberá, SM.; Palanca Cámara, J.; Rodríguez, S.; Bajo, J.; Julian Inglada, VJ. (2014). Real-time agreement and fulfilment of SLAs in Cloud Computing environments. AI Communications. 1-24. doi:10.3233/AIC-140626S124[1]V. Aleven and K.D. Ashley, Teaching case-based argumentation through a model and examples, empirical evaluation of an intelligent learning environment, in: Artificial Intelligence in Education, AIED-97, Frontiers in Artificial Intelligence and Applications, Vol. 39, IOS Press, 1997, pp. 87–94.[2]M. Alhamad, W. Perth, T. Dillon and E. Chang, Conceptual SLA framework for cloud computing, in: 4th IEEE International Conference on Digital Ecosystems and Technologies (DEST), IEEE Press, 2010, pp. 606–610.Armbrust, M., Stoica, I., Zaharia, M., Fox, A., Griffith, R., Joseph, A. D., … Rabkin, A. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50. doi:10.1145/1721654.1721672Ashley, K. D. (1991). Reasoning with cases and hypotheticals in HYPO. International Journal of Man-Machine Studies, 34(6), 753-796. doi:10.1016/0020-7373(91)90011-u[6]P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris, A. Ho, R. Neugebauer, I. Pratt and A. Warfield, Xen and the art of virtualization, in: 9th ACM Symposium on Operating Systems Principles (SOSP-03), ACM Press, 2003, pp. 164–177.Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing. Future Generation Computer Systems, 28(5), 755-768. doi:10.1016/j.future.2011.04.017[8]A. Beloglazov and R. Buyya, Energy efficient allocation of virtual machines in cloud data centers, in: 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, IEEE Computer Society, 2010, pp. 577–578.[9]A. Beloglazov and R. Buyya, Energy efficient resource management in virtualized cloud data centers, in: 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, IEEE Computer Society, 2010, pp. 826–831.Bench-Capon, T., & Sartor, G. (2003). A model of legal reasoning with cases incorporating theories and values. Artificial Intelligence, 150(1-2), 97-143. doi:10.1016/s0004-3702(03)00108-5[11]T.J. Bench-Capon, Specification and implementation of Toulmin dialogue game, in: International Conferences on Legal Knowledge and Information Systems, JURIX-98, Frontiers of Artificial Intelligence and Applications, IOS Press, 1998, pp. 5–20.[12]R. Buyya, R. Ranjan and R.N. Calheiros, Intercloud: Utility-oriented federation of cloud computing environments for scaling of application services, in: 10th International Conference on Algorithms and Architectures for Parallel Processing – Volume Part I, ICA3PP’10, Springer-Verlag, 2010, pp. 13–31.[13]R. Buyya, C.S. Yeo and S. Venugopal, Market-oriented cloud computing: Vision, hype, and reality for delivering it services as computing utilities, in: High Performance Computing and Communications, 2008. HPCC’08. 10th IEEE International Conference, September 2008, IEEE, 2008, pp. 5–13.Chen, C., Li, S. S., Chen, B., & Wen, D. (2011). Agent Recommendation for Agent-Based Urban-Transportation Systems. IEEE Intelligent Systems, 26(6), 77-81. doi:10.1109/mis.2011.94[15]Y.Y. Cheng, M. Low, S. Zhou, W. Cai and C.S. Choo, Evolving agent-based simulations in the clouds, in: 3rd International Workshop on Advanced Computational Intelligence (IWACI), 2010, pp. 244–249.[16]F. Dignum and H. Weigand, Communication and Deontic Logic, in: Information Systems – Correctness and Reusability. Selected Papers from the IS-CORE Workshop, R. Wieringa and R. Feenstra, eds, World Scientific Publishing Co., 1995, pp. 242–260.Erdogmus, H. (2009). Cloud Computing: Does Nirvana Hide behind the Nebula? IEEE Software, 26(2), 4-6. doi:10.1109/ms.2009.31[19]J.O. Fitó, I. Goiri and J. Guitart, SLA-driven elastic cloud hosting provider, in: 18th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), IEEE Computer Society, 2010, pp. 111–118.Fuentes-Fernández, R., Hassan, S., Pavón, J., Galán, J. M., & López-Paredes, A. (2012). Metamodels for role-driven agent-based modelling. Computational and Mathematical Organization Theory, 18(1), 91-112. doi:10.1007/s10588-012-9110-5Heras, S., Botti, V., & Julián, V. (2009). Challenges for a CBR framework for argumentation in open MAS. The Knowledge Engineering Review, 24(4), 327-352. doi:10.1017/s0269888909990178Heras, S., Jordán, J., Botti, V., & Julián, V. (2013). Argue to agree: A case-based argumentation approach. International Journal of Approximate Reasoning, 54(1), 82-108. doi:10.1016/j.ijar.2012.06.005[24]M. Jensen, J. Schwenk, N. Gruschka and L. Iacono, On technical security issues in cloud computing, in: IEEE International Conference on Cloud Computing, IEEE Press, 2009, pp. 109–116.Kakas, A., Maudet, N., & Moraitis, P. (2005). Modular Representation of Agent Interaction Rules through Argumentation. Autonomous Agents and Multi-Agent Systems, 11(2), 189-206. doi:10.1007/s10458-005-2176-4[26]M.J. Kim, H.G. Yoon and H.K. Lee, MAV: An intelligent Multi-agent model based on Cloud computing for resource virtualization, in: Computers, Networks, Systems, and Industrial Engineering, Studies in Computational Intelligence, Vol. 365, Springer, 2011, pp. 99–111.Kraus, S., Sycara, K., & Evenchik, A. (1998). Reaching agreements through argumentation: a logical model and implementation. Artificial Intelligence, 104(1-2), 1-69. doi:10.1016/s0004-3702(98)00078-2[28]W.-Y. Lin, G.-Y. Lin and H.-Y. Wei, Dynamic auction mechanism for cloud resource allocation, in: 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, CCGRID’10, IEEE Computer Society, Washington, DC, USA, 2010, pp. 591–592.[29]S. Liu, G. Quan and S. Ren, On-line scheduling of real-time services for cloud computing, in: 6th World Congress on Services, SERVICES’10, IEEE Computer Society, 2010, pp. 459–464.Navarro, M., Heras, S., Botti, V., & Julián, V. (2013). Towards real-time agreements. Expert Systems with Applications, 40(10), 3906-3917. doi:10.1016/j.eswa.2012.12.087Ontañón, S., & Plaza, E. (2011). An argumentation framework for learning, information exchange, and joint-deliberation in multi-agent systems1. Multiagent and Grid Systems, 7(2-3), 95-108. doi:10.3233/mgs-2011-0169Palanca, J., Navarro, M., García-Fornes, A., & Julian, V. (2013). Deadline prediction scheduling based on benefits. Future Generation Computer Systems, 29(1), 61-73. doi:10.1016/j.future.2012.05.007[33]C. Pautasso, O. Zimmermann and F. Leymann, Restful web services vs. “big”’ web services: making the right architectural decision, in: Proceedings of the 17th International Conference on World Wide Web, WWW’08, ACM, New York, NY, USA, 2008, pp. 805–814.[34]J. Peng, X. Zhang, Z. Lei, B. Zhang, W. Zhang and Q. Li, Comparison of several cloud computing platforms, in: 2nd International Symposium on Information Science and Engineering, ISISE’09, IEEE Computer Society, 2009, pp. 23–27.Prakken, H., & Sartor, G. (1998). Artificial Intelligence and Law, 6(2/4), 231-287. doi:10.1023/a:1008278309945[36]I. Rahwan and G. Simari, eds, Argumentation in Artificial Intelligence, Springer, 2009.Ross, J. W., & Westerman, G. (2004). Preparing for utility computing: The role of IT architecture and relationship management. IBM Systems Journal, 43(1), 5-19. doi:10.1147/sj.431.0005Schaffer, H. E. (2009). X as a Service, Cloud Computing, and the Need for Good Judgment. IT Professional, 11(5), 4-5. doi:10.1109/mitp.2009.112[39]K.M. Sim, Agent-based cloud commerce, in: IEEE International Conference on Industrial Engineering and Engineering Management, IEEE Press, 2009, pp. 717–721.Soh, L.-K., & Tsatsoulis, C. (2005). A Real-Time Negotiation Model and A Multi-Agent Sensor Network Implementation. Autonomous Agents and Multi-Agent Systems, 11(3), 215-271. doi:10.1007/s10458-005-0539-5Talia, D. (2012). Clouds Meet Agents: Toward Intelligent Cloud Services. IEEE Internet Computing, 16(2), 78-81. doi:10.1109/mic.2012.28Tolchinsky, P., Modgil, S., Atkinson, K., McBurney, P., & Cortés, U. (2011). Deliberation dialogues for reasoning about safety critical actions. Autonomous Agents and Multi-Agent Systems, 25(2), 209-259. doi:10.1007/s10458-011-9174-5[44]A. Toniolo, T. Norman and K. Sycara, An empirical study of argumentation schemes in deliberative dialogue, in: 20th European Conference on Artificial Intelligence, ECAI-12, Frontiers in Artificial Intelligence and Applications, Vol. 242, IOS Press, 2012, pp. 756–761.[45]W.-T. Tsai, Q. Shao, X. Sun and J. Elston, Real-time service-oriented cloud computing, in: IEEE 6th World Congress on Services, SERVICES’10, IEEE Press, 2010, pp. 473–478.[46]D. Walton, C. Reed and F. Macagno, Argumentation Schemes, Cambridge University Press, 2008.[47]L. Wang, J. Tao, M. Kunze, A. Castellanos, D. Kramer and W. Karl, Scientific cloud computing: Early definition and experience, in: 10th IEEE International Conference on High Performance Computing and Communications (HPCC-08), IEEE Press, 2008, pp. 825–830.[48]Y.O. Yazir, C. Matthews, R. Farahbod, S. Neville, A. Guitouni, S. Ganti and Y. Coady, Dynamic resource allocation in computing clouds using distributed multiple criteria decision analysis, in: IEEE 3rd International Conference on Cloud Computing (CLOUD), IEEE Computer Society, 2010, pp. 91–98.[49]Y. Yu, S. Ren, N. Chen and X. Wang, Profit and penalty aware (pp-aware) scheduling for tasks with variable task execution time, in: ACM Symposium on Applied Computing, SAC’10, ACM, 2010, pp. 334–339

    Experimental Assessment of Time Reversal for In-Body to In-Body UWB Communications

    Full text link
    [EN] The standard of in-body communications is limited to the use of narrowband systems. These systems are far from the high data rate connections achieved by other wireless telecommunication services today in force. The UWB frequency band has been proposed as a possible candidate for future in-body networks. However, the attenuation of body tissues at gigahertz frequencies could be a serious drawback. Experimental measurements for channel modeling are not easy to carry out, while the use of humans is practically forbidden. Sophisticated simulation tools could provide inaccurate results since they are not able to reproduce all the in-body channel conditions. Chemical solutions known as phantoms could provide a fair approximation of body tissues¿ behavior. In this work, the Time Reversal technique is assessed to increase the channel performance of in-body communications. For this task, a large volume of experimental measurements is performed at the low part of UWB spectrum (3.1-5.1 GHz) by using a highly accurate phantom-based measurement setup. This experimental setup emulates an in-body to in-body scenario, where all the nodes are implanted inside the body. Moreover, the in-body channel characteristics such as the path loss, the correlation in transmission and reception, and the reciprocity of the channel are assessed and discussed.This work was supported by the Programa de Ayudas de Investigacion y Desarrollo (PAID-01-16) from Universitat Politecnica de Valencia and by the Ministerio de Economia y Competitividad, Spain (TEC2014-60258-C2-1-R), by the European FEDER funds.Andreu-Estellés, C.; Garcia-Pardo, C.; Castelló-Palacios, S.; Cardona Marcet, N. (2018). Experimental Assessment of Time Reversal for In-Body to In-Body UWB Communications. Wireless Communications and Mobile Computing (Online). (8927107):1-12. https://doi.org/10.1155/2018/8927107S1128927107Fireman, Z. (2003). Diagnosing small bowel Crohn’s disease with wireless capsule endoscopy. Gut, 52(3), 390-392. doi:10.1136/gut.52.3.390Burri, H., & Senouf, D. (2009). Remote monitoring and follow-up of pacemakers and implantable cardioverter defibrillators. Europace, 11(6), 701-709. doi:10.1093/europace/eup110Scanlon, W. G., Burns, B., & Evans, N. E. (2000). Radiowave propagation from a tissue-implanted source at 418 MHz and 916.5 MHz. IEEE Transactions on Biomedical Engineering, 47(4), 527-534. doi:10.1109/10.828152Chavez-Santiago, R., Garcia-Pardo, C., Fornes-Leal, A., Valles-Lluch, A., Vermeeren, G., Joseph, W., … Cardona, N. (2015). Experimental Path Loss Models for In-Body Communications within 2.36-2.5 GHz. IEEE Journal of Biomedical and Health Informatics, 1-1. doi:10.1109/jbhi.2015.2418757Khaleghi, A., Chávez-Santiago, R., & Balasingham, I. (2010). Ultra-wideband pulse-based data communications for medical implants. IET Communications, 4(15), 1889. doi:10.1049/iet-com.2009.0692Khaleghi, A., Chávez-Santiago, R., & Balasingham, I. (2011). Ultra-wideband statistical propagation channel model for implant sensors in the human chest. IET Microwaves, Antennas & Propagation, 5(15), 1805. doi:10.1049/iet-map.2010.0537Kurup, D., Scarpello, M., Vermeeren, G., Joseph, W., Dhaenens, K., Axisa, F., … Vanfleteren, J. (2011). In-body path loss models for implants in heterogeneous human tissues using implantable slot dipole conformal flexible antennas. EURASIP Journal on Wireless Communications and Networking, 2011(1). doi:10.1186/1687-1499-2011-51Floor, P. A., Chavez-Santiago, R., Brovoll, S., Aardal, O., Bergsland, J., Grymyr, O.-J. H. N., … Balasingham, I. (2015). In-Body to On-Body Ultrawideband Propagation Model Derived From Measurements in Living Animals. IEEE Journal of Biomedical and Health Informatics, 19(3), 938-948. doi:10.1109/jbhi.2015.2417805Shimizu, Y., Anzai, D., Chavez-Santiago, R., Floor, P. A., Balasingham, I., & Wang, J. (2017). Performance Evaluation of an Ultra-Wideband Transmit Diversity in a Living Animal Experiment. IEEE Transactions on Microwave Theory and Techniques, 65(7), 2596-2606. doi:10.1109/tmtt.2017.2669039Anzai, D., Katsu, K., Chavez-Santiago, R., Wang, Q., Plettemeier, D., Wang, J., & Balasingham, I. (2014). Experimental Evaluation of Implant UWB-IR Transmission With Living Animal for Body Area Networks. IEEE Transactions on Microwave Theory and Techniques, 62(1), 183-192. doi:10.1109/tmtt.2013.2291542Chou, C.-K., Chen, G.-W., Guy, A. W., & Luk, K. H. (1984). Formulas for preparing phantom muscle tissue at various radiofrequencies. Bioelectromagnetics, 5(4), 435-441. doi:10.1002/bem.2250050408Cheung, A. Y., & Koopman, D. W. (1976). Experimental Development of Simulated Biomaterials for Dosimetry Studies of Hazardous Microwave Radiation (Short Papers). IEEE Transactions on Microwave Theory and Techniques, 24(10), 669-673. doi:10.1109/tmtt.1976.1128936YAMAMOTO, H., ZHOU, J., & KOBAYASHI, T. (2008). Ultra Wideband Electromagnetic Phantoms for Antennas and Propagation Studies. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E91-A(11), 3173-3182. doi:10.1093/ietfec/e91-a.11.3173Lazebnik, M., Madsen, E. L., Frank, G. R., & Hagness, S. C. (2005). Tissue-mimicking phantom materials for narrowband and ultrawideband microwave applications. Physics in Medicine and Biology, 50(18), 4245-4258. doi:10.1088/0031-9155/50/18/001Yilmaz, T., Foster, R., & Hao, Y. (2014). Broadband Tissue Mimicking Phantoms and a Patch Resonator for Evaluating Noninvasive Monitoring of Blood Glucose Levels. IEEE Transactions on Antennas and Propagation, 62(6), 3064-3075. doi:10.1109/tap.2014.2313139Gezici, S., Zhi Tian, Giannakis, G. B., Kobayashi, H., Molisch, A. F., Poor, H. V., & Sahinoglu, Z. (2005). Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks. IEEE Signal Processing Magazine, 22(4), 70-84. doi:10.1109/msp.2005.1458289Marinova, M., Thielens, A., Tanghe, E., Vallozzi, L., Vermeeren, G., Joseph, W., … Martens, L. (2015). Diversity Performance of Off-Body MB-OFDM UWB-MIMO. IEEE Transactions on Antennas and Propagation, 63(7), 3187-3197. doi:10.1109/tap.2015.2422353SHI, J., ANZAI, D., & WANG, J. (2012). Channel Modeling and Performance Analysis of Diversity Reception for Implant UWB Wireless Link. IEICE Transactions on Communications, E95.B(10), 3197-3205. doi:10.1587/transcom.e95.b.3197Pajusco, P., & Pagani, P. (2009). On the Use of Uniform Circular Arrays for Characterizing UWB Time Reversal. IEEE Transactions on Antennas and Propagation, 57(1), 102-109. doi:10.1109/tap.2008.2009715Chavez-Santiago, R., Sayrafian-Pour, K., Khaleghi, A., Takizawa, K., Wang, J., Balasingham, I., & Li, H.-B. (2013). Propagation models for IEEE 802.15.6 standardization of implant communication in body area networks. IEEE Communications Magazine, 51(8), 80-87. doi:10.1109/mcom.2013.6576343Andreu, C., Castello-Palacios, S., Garcia-Pardo, C., Fornes-Leal, A., Valles-Lluch, A., & Cardona, N. (2016). Spatial In-Body Channel Characterization Using an Accurate UWB Phantom. IEEE Transactions on Microwave Theory and Techniques, 64(11), 3995-4002. doi:10.1109/tmtt.2016.2609409Pahlavan, K., & Levesque, A. H. (2005). Wireless Information Networks. doi:10.1002/0471738646Qiu, R. C., Zhou, C., Guo, N., & Zhang, J. Q. (2006). Time Reversal With MISO for Ultrawideband Communications: Experimental Results. IEEE Antennas and Wireless Propagation Letters, 5, 269-273. doi:10.1109/lawp.2006.875888Ando, H., Takizawa, K., Yoshida, T., Matsushita, K., Hirata, M., & Suzuki, T. (2016). Wireless Multichannel Neural Recording With a 128-Mbps UWB Transmitter for an Implantable Brain-Machine Interfaces. IEEE Transactions on Biomedical Circuits and Systems, 10(6), 1068-1078. doi:10.1109/tbcas.2016.251452

    On the detection of SOurce COde re-use

    Full text link
    © {Owner/Author | ACM} {2014}. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in FIRE '14 Proceedings of the Forum for Information Retrieval Evaluation, http://dx.doi.org/10.1145/2824864.2824878"This paper summarizes the goals, organization and results of the first SOCO competitive evaluation campaign for systems that automatically detect the source code re-use phenomenon. The detection of source code re-use is an important research field for both software industry and academia fields. Accordingly, PAN@FIRE track, named SOurce COde Re-use (SOCO) focused on the detection of re-used source codes in C/C++ and Java programming languages. Participant systems were asked to annotate several source codes whether or not they represent cases of source code re-use. In total five teams submitted 17 runs. The training set consisted of annotations made by several experts, a feature which turns the SOCO 2014 collection in a useful data set for future evaluations and, at the same time, it establishes a standard evaluation framework for future research works on the posed shared task.PAN@FIRE (SOCO) has been organised in the framework of WIQ-EI (EC IRSES grantn. 269180) and DIANA-APPLICATIONS (TIN2012-38603-C02- 01) research projects. The work of the last author was supported by CONACyT Mexico Project Grant CB-2010/153315, and SEP-PROMEP UAM-PTC-380/48510349.Flores Sáez, E.; Rosso, P.; Moreno Boronat, LA.; Villatoro-Tello, E. (2014). On the detection of SOurce COde re-use. En FIRE '14 Proceedings of the Forum for Information Retrieval Evaluation. ACM. 21-30. https://doi.org/10.1145/2824864.2824878S2130C. Arwin and S. Tahaghoghi. Plagiarism detection across programming languages. Proceedings of the 29th Australian Computer Science Conference, Australian Computer Society, 48:277--286, 2006.N. Baer and R. Zeidman. Measuring whitespace pattern sequence as an indication of plagiarism. Journal of Software Engineering and Applications, 5(4):249--254, 2012.M. Chilowicz, E. Duris, and G. Roussel. Syntax tree fingerprinting for source code similarity detection. In Program Comprehension, 2009. ICPC '09. IEEE 17th International Conference on, pages 243--247, 2009.D. Chuda, P. Navrat, B. Kovacova, and P. Humay. The issue of (software) plagiarism: A student view. Education, IEEE Transactions on, 55(1):22--28, 2012.G. Cosma and M. Joy. Evaluating the performance of lsa for source-code plagiarism detection. Informatica, 36(4):409--424, 2013.B. Cui, J. Li, T. Guo, J. Wang, and D. Ma. Code comparison system based on abstract syntax tree. In Broadband Network and Multimedia Technology (IC-BNMT), 3rd IEEE International Conference on, pages 668--673, Oct 2010.J. A. W. Faidhi and S. K. Robinson. An empirical approach for detecting program similarity and plagiarism within a university programming environment. Comput. Educ., 11(1):11--19, Jan. 1987.Fire, editor. FIRE 2014 Working Notes. Sixth International Workshop of the Forum for Information Retrieval Evaluation, Bangalore, India, 5--7 December, 2014.J. L. Fleiss. Measuring nominal scale agreement among many raters. Psychological bulletin, 76(5):378, 1971.E. Flores, A. Barrón-Cedeño, L. Moreno, and P. Rosso. Uncovering source code reuse in large-scale academic environments. Computer Applications in Engineering Education, pages n/a--n/a, 2014.E. Flores, A. Barrón-Cedeño, P. Rosso, and L. Moreno. DeSoCoRe: Detecting source code re-use across programming languages. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstration Session, NAACL-HLT, pages 1--4. Association for Computational Linguistics, 2012.E. Flores, A. Barrón-Cedeño, P. Rosso, and L. Moreno. Towards the Detection of Cross-Language Source Code Reuse. Proceedings of 16th International Conference on Applications of Natural Language to Information Systems, NLDB-2011, Springer-Verlag, LNCS(6716), pages 250--253, 2011.E. Flores, M. Ibarra-Romero, L. Moreno, G. Sidorov, and P. Rosso. Modelos de recuperación de información basados en n-gramas aplicados a la reutilización de código fuente. In Proc. 3rd Spanish Conf. on Information Retrieval, pages 185--188, 2014.D. Ganguly and G. J. Jones. Dcu@ fire-2014: an information retrieval approach for source code plagiarism detection. In Fire [8].R. García-Hernández and Y. Lendeneva. Identification of similar source codes based on longest common substrings. In Fire [8].M. Joy and M. Luck. Plagiarism in programming assignments. Education, IEEE Transactions on, 42(2):129--133, May 1999.A. Marcus, A. Sergeyev, V. Rajlich, and J. Maletic. An information retrieval approach to concept location in source code. In Reverse Engineering, 2004. Proceedings. 11th Working Conference on, pages 214--223, Nov 2004.S. Narayanan and S. Simi. Source code plagiarism detection and performance analysis using fingerprint based distance measure method. In Proc. of 7th International Conference on Computer Science Education, ICCSE '12, pages 1065--1068, July 2012.M. Potthast, M. Hagen, A. Beyer, M. Busse, M. Tippmann, P. Rosso, and B. Stein. Overview of the 6th international competition on plagiarism detection. In L. Cappellato, N. Ferro, M. Halvey, and W. Kraaij, editors, Working Notes for CLEF 2014 Conference, Sheffield, UK, September 15-18, 2014., volume 1180 of CEUR Workshop Proceedings, pages 845--876. CEUR-WS.org, 2014.L. Prechelt, G. Malpohl, and M. Philippsen. Finding plagiarisms among a set of programs with JPlag. Journal of Universal Computer Science, 8(11):1016--1038, 2002.I. Rahal and C. Wielga. Source code plagiarism detection using biological string similarity algorithms. Journal of Information & Knowledge Management, 13(3), 2014.A. Ramírez-de-la Cruz, G. Ramírez-de-la Rosa, C. Sánchez-Sánchez, W. A. Luna-Ramírez, H. Jiménez-Salazar, and C. Rodríguez-Lucatero. Uam@soco 2014: Detection of source code reuse by means of combining different types of representations. In Fire [8].F. Rosales, A. García, S. Rodríguez, J. L. Pedraza, R. Méndez, and M. M. Nieto. Detection of plagiarism in programming assignments. IEEE Transactions on Education, 51(2):174--183, 2008.K. Sparck and C. van Rijsbergen. Report on the need for and provision of an "ideal" information retrieval test collection. British Library Research and Development Report, 5266, University of Cambridge, 1975.G. Whale. Software metrics and plagiarism detection. Journal of Systems and Software, 13(2):131--138, 1990

    Evaluation of Clustering Algorithms on GPU-Based Edge Computing Platforms

    Get PDF
    [EN] Internet of Things (IoT) is becoming a new socioeconomic revolution in which data and immediacy are the main ingredients. IoT generates large datasets on a daily basis but it is currently considered as "dark data", i.e., data generated but never analyzed. The efficient analysis of this data is mandatory to create intelligent applications for the next generation of IoT applications that benefits society. Artificial Intelligence (AI) techniques are very well suited to identifying hidden patterns and correlations in this data deluge. In particular, clustering algorithms are of the utmost importance for performing exploratory data analysis to identify a set (a.k.a., cluster) of similar objects. Clustering algorithms are computationally heavy workloads and require to be executed on high-performance computing clusters, especially to deal with large datasets. This execution on HPC infrastructures is an energy hungry procedure with additional issues, such as high-latency communications or privacy. Edge computing is a paradigm to enable light-weight computations at the edge of the network that has been proposed recently to solve these issues. In this paper, we provide an in-depth analysis of emergent edge computing architectures that include low-power Graphics Processing Units (GPUs) to speed-up these workloads. Our analysis includes performance and power consumption figures of the latest Nvidia's AGX Xavier to compare the energy-performance ratio of these low-cost platforms with a high-performance cloud-based counterpart version. Three different clustering algorithms (i.e., k-means, Fuzzy Minimals (FM), and Fuzzy C-Means (FCM)) are designed to be optimally executed on edge and cloud platforms, showing a speed-up factor of up to 11x for the GPU code compared to sequential counterpart versions in the edge platforms and energy savings of up to 150% between the edge computing and HPC platforms.This work has been partially supported by the Spanish Ministry of Science and Innovation, under the Ramon y Cajal Program (Grant No. RYC2018-025580-I) and under grants RTI2018-096384-B-I00, RTC-2017-6389-5 and RTC2019-007159-5 and by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18.Cecilia-Canales, JM.; Cano, J.; Morales-García, J.; Llanes, A.; Imbernón, B. (2020). Evaluation of Clustering Algorithms on GPU-Based Edge Computing Platforms. Sensors. 20(21):1-19. https://doi.org/10.3390/s20216335S1192021Gebauer, H., Fleisch, E., Lamprecht, C., & Wortmann, F. (2020). Growth paths for overcoming the digitalization paradox. Business Horizons, 63(3), 313-323. doi:10.1016/j.bushor.2020.01.005Guillén, M. A., Llanes, A., Imbernón, B., Martínez-España, R., Bueno-Crespo, A., Cano, J.-C., & Cecilia, J. M. (2020). Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning. The Journal of Supercomputing, 77(1), 818-840. doi:10.1007/s11227-020-03288-wWang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 48, 144-156. doi:10.1016/j.jmsy.2018.01.003Gretzel, U., Sigala, M., Xiang, Z., & Koo, C. (2015). Smart tourism: foundations and developments. Electronic Markets, 25(3), 179-188. doi:10.1007/s12525-015-0196-8Pramanik, M. I., Lau, R. Y. K., Demirkan, H., & Azad, M. A. K. (2017). Smart health: Big data enabled health paradigm within smart cities. Expert Systems with Applications, 87, 370-383. doi:10.1016/j.eswa.2017.06.027Weber, M., & Podnar Žarko, I. (2019). A Regulatory View on Smart City Services. Sensors, 19(2), 415. doi:10.3390/s19020415Ghosh, A., Chakraborty, D., & Law, A. (2018). Artificial intelligence in Internet of things. CAAI Transactions on Intelligence Technology, 3(4), 208-218. doi:10.1049/trit.2018.1008Monti, L., Vincenzi, M., Mirri, S., Pau, G., & Salomoni, P. (2020). RaveGuard: A Noise Monitoring Platform Using Low-End Microphones and Machine Learning. Sensors, 20(19), 5583. doi:10.3390/s20195583Kumar, P., Sinha, K., Nere, N. K., Shin, Y., Ho, R., Mlinar, L. B., & Sheikh, A. Y. (2020). A machine learning framework for computationally expensive transient models. Scientific Reports, 10(1). doi:10.1038/s41598-020-67546-wMittal, S., & Vetter, J. S. (2015). A Survey of CPU-GPU Heterogeneous Computing Techniques. ACM Computing Surveys, 47(4), 1-35. doi:10.1145/2788396Singh, D., & Reddy, C. K. (2014). A survey on platforms for big data analytics. Journal of Big Data, 2(1). doi:10.1186/s40537-014-0008-6Khayyat, M., Elgendy, I. A., Muthanna, A., Alshahrani, A. S., Alharbi, S., & Koucheryavy, A. (2020). Advanced Deep Learning-Based Computational Offloading for Multilevel Vehicular Edge-Cloud Computing Networks. IEEE Access, 8, 137052-137062. doi:10.1109/access.2020.3011705Satyanarayanan, M. (2017). The Emergence of Edge Computing. Computer, 50(1), 30-39. doi:10.1109/mc.2017.9Capra, M., Peloso, R., Masera, G., Roch, M. R., & Martina, M. (2019). Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World. Future Internet, 11(4), 100. doi:10.3390/fi11040100Lu, H., Gu, C., Luo, F., Ding, W., & Liu, X. (2020). Optimization of lightweight task offloading strategy for mobile edge computing based on deep reinforcement learning. Future Generation Computer Systems, 102, 847-861. doi:10.1016/j.future.2019.07.019Mimmack, G. M., Mason, S. J., & Galpin, J. S. (2001). Choice of Distance Matrices in Cluster Analysis: Defining Regions. Journal of Climate, 14(12), 2790-2797. doi:10.1175/1520-0442(2001)0142.0.co;2Gimenez, C. (2006). Logistics integration processes in the food industry. International Journal of Physical Distribution & Logistics Management, 36(3), 231-249. doi:10.1108/09600030610661813Chang, P.-C., Liu, C.-H., & Fan, C.-Y. (2009). Data clustering and fuzzy neural network for sales forecasting: A case study in printed circuit board industry. Knowledge-Based Systems, 22(5), 344-355. doi:10.1016/j.knosys.2009.02.005Zheng, B., Yoon, S. W., & Lam, S. S. (2014). Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Systems with Applications, 41(4), 1476-1482. doi:10.1016/j.eswa.2013.08.044Woodley, A., Tang, L.-X., Geva, S., Nayak, R., & Chappell, T. (2019). Parallel K-Tree: A multicore, multinode solution to extreme clustering. Future Generation Computer Systems, 99, 333-345. doi:10.1016/j.future.2018.09.038Kwedlo, W., & Czochanski, P. J. (2019). A Hybrid MPI/OpenMP Parallelization of KK -Means Algorithms Accelerated Using the Triangle Inequality. IEEE Access, 7, 42280-42297. doi:10.1109/access.2019.2907885Liu, B., He, S., He, D., Zhang, Y., & Guizani, M. (2019). A Spark-Based Parallel Fuzzy cc -Means Segmentation Algorithm for Agricultural Image Big Data. IEEE Access, 7, 42169-42180. doi:10.1109/access.2019.2907573Baydoun, M., Ghaziri, H., & Al-Husseini, M. (2018). CPU and GPU parallelized kernel K-means. The Journal of Supercomputing, 74(8), 3975-3998. doi:10.1007/s11227-018-2405-7Li, Y., Zhao, K., Chu, X., & Liu, J. (2013). Speeding up k-Means algorithm by GPUs. Journal of Computer and System Sciences, 79(2), 216-229. doi:10.1016/j.jcss.2012.05.004Cuomo, S., De Angelis, V., Farina, G., Marcellino, L., & Toraldo, G. (2019). A GPU-accelerated parallel K-means algorithm. Computers & Electrical Engineering, 75, 262-274. doi:10.1016/j.compeleceng.2017.12.002Al-Ayyoub, M., Abu-Dalo, A. M., Jararweh, Y., Jarrah, M., & Sa’d, M. A. (2015). A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation. The Journal of Supercomputing, 71(8), 3149-3162. doi:10.1007/s11227-015-1431-yAit Ali, N., Cherradi, B., El Abbassi, A., Bouattane, O., & Youssfi, M. (2018). GPU fuzzy c-means algorithm implementations: performance analysis on medical image segmentation. Multimedia Tools and Applications, 77(16), 21221-21243. doi:10.1007/s11042-017-5589-6Timón, I., Soto, J., Pérez-Sánchez, H., & Cecilia, J. M. (2016). Parallel implementation of fuzzy minimals clustering algorithm. Expert Systems with Applications, 48, 35-41. doi:10.1016/j.eswa.2015.11.011Cebrian, J. M., Imbernón, B., Soto, J., García, J. M., & Cecilia, J. M. (2020). High-throughput fuzzy clustering on heterogeneous architectures. Future Generation Computer Systems, 106, 401-411. doi:10.1016/j.future.2020.01.022Cecilia, J. M., Timon, I., Soto, J., Santa, J., Pereniguez, F., & Munoz, A. (2018). High-Throughput Infrastructure for Advanced ITS Services: A Case Study on Air Pollution Monitoring. IEEE Transactions on Intelligent Transportation Systems, 19(7), 2246-2257. doi:10.1109/tits.2018.2816741Sriramakrishnan, P., Kalaiselvi, T., & Rajeswaran, R. (2019). Modified local ternary patterns technique for brain tumour segmentation and volume estimation from MRI multi-sequence scans with GPU CUDA machine. Biocybernetics and Biomedical Engineering, 39(2), 470-487. doi:10.1016/j.bbe.2019.02.002Fang, Y., Chen, Q., & Xiong, N. (2019). A multi-factor monitoring fault tolerance model based on a GPU cluster for big data processing. Information Sciences, 496, 300-316. doi:10.1016/j.ins.2018.04.053Rodriguez, M. Z., Comin, C. H., Casanova, D., Bruno, O. M., Amancio, D. R., Costa, L. da F., & Rodrigues, F. A. (2019). Clustering algorithms: A comparative approach. PLOS ONE, 14(1), e0210236. doi:10.1371/journal.pone.0210236Pandove, D., Goel, S., & Rani, R. (2018). Systematic Review of Clustering High-Dimensional and Large Datasets. ACM Transactions on Knowledge Discovery from Data, 12(2), 1-68. doi:10.1145/3132088Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2-3), 191-203. doi:10.1016/0098-3004(84)90020-7Soto, J., Flores-Sintas, A., & Palarea-Albaladejo, J. (2008). Improving probabilities in a fuzzy clustering partition. Fuzzy Sets and Systems, 159(4), 406-421. doi:10.1016/j.fss.2007.08.016Kolen, J. F., & Hutcheson, T. (2002). Reducing the time complexity of the fuzzy c-means algorithm. IEEE Transactions on Fuzzy Systems, 10(2), 263-267. doi:10.1109/91.99512

    Automatic classification of human facial features based on their appearance

    Full text link
    [EN] Classification or typology systems used to categorize different human body parts have existed for many years. Nevertheless, there are very few taxonomies of facial features. Ergonomics, forensic anthropology, crime prevention or new human-machine interaction systems and online activities, like e-commerce, e-learning, games, dating or social networks, are fields in which classifications of facial features are useful, for example, to create digital interlocutors that optimize the interactions between human and machines. However, classifying isolated facial features is difficult for human observers. Previous works reported low inter-observer and intra-observer agreement in the evaluation of facial features. This work presents a computer-based procedure to automatically classify facial features based on their global appearance. This procedure deals with the difficulties associated with classifying features using judgements from human observers, and facilitates the development of taxonomies of facial features. Taxonomies obtained through this procedure are presented for eyes, mouths and noses.Fuentes-Hurtado, F.; Diego-Mas, JA.; Naranjo Ornedo, V.; Alcañiz Raya, ML. (2019). Automatic classification of human facial features based on their appearance. PLoS ONE. 14(1):1-20. https://doi.org/10.1371/journal.pone.0211314S120141Damasio, A. R. (1985). Prosopagnosia. Trends in Neurosciences, 8, 132-135. doi:10.1016/0166-2236(85)90051-7Bruce, V., & Young, A. (1986). Understanding face recognition. British Journal of Psychology, 77(3), 305-327. doi:10.1111/j.2044-8295.1986.tb02199.xTodorov, A. (2011). Evaluating Faces on Social Dimensions. Social Neuroscience, 54-76. doi:10.1093/acprof:oso/9780195316872.003.0004Little, A. C., Burriss, R. P., Jones, B. C., & Roberts, S. C. (2007). Facial appearance affects voting decisions. Evolution and Human Behavior, 28(1), 18-27. doi:10.1016/j.evolhumbehav.2006.09.002Porter, J. P., & Olson, K. L. (2001). Anthropometric Facial Analysis of the African American Woman. Archives of Facial Plastic Surgery, 3(3), 191-197. doi:10.1001/archfaci.3.3.191Gündüz Arslan, S., Genç, C., Odabaş, B., & Devecioğlu Kama, J. (2007). Comparison of Facial Proportions and Anthropometric Norms Among Turkish Young Adults With Different Face Types. Aesthetic Plastic Surgery, 32(2), 234-242. doi:10.1007/s00266-007-9049-yFerring, V., & Pancherz, H. (2008). Divine proportions in the growing face. American Journal of Orthodontics and Dentofacial Orthopedics, 134(4), 472-479. doi:10.1016/j.ajodo.2007.03.027Mane, D. R., Kale, A. D., Bhai, M. B., & Hallikerimath, S. (2010). Anthropometric and anthroposcopic analysis of different shapes of faces in group of Indian population: A pilot study. Journal of Forensic and Legal Medicine, 17(8), 421-425. doi:10.1016/j.jflm.2010.09.001Ritz-Timme, S., Gabriel, P., Tutkuviene, J., Poppa, P., Obertová, Z., Gibelli, D., … Cattaneo, C. (2011). Metric and morphological assessment of facial features: A study on three European populations. Forensic Science International, 207(1-3), 239.e1-239.e8. doi:10.1016/j.forsciint.2011.01.035Ritz-Timme, S., Gabriel, P., Obertovà, Z., Boguslawski, M., Mayer, F., Drabik, A., … Cattaneo, C. (2010). A new atlas for the evaluation of facial features: advantages, limits, and applicability. International Journal of Legal Medicine, 125(2), 301-306. doi:10.1007/s00414-010-0446-4Kong, S. G., Heo, J., Abidi, B. R., Paik, J., & Abidi, M. A. (2005). Recent advances in visual and infrared face recognition—a review. Computer Vision and Image Understanding, 97(1), 103-135. doi:10.1016/j.cviu.2004.04.001Tavares, G., Mourão, A., & Magalhães, J. (2016). Crowdsourcing facial expressions for affective-interaction. Computer Vision and Image Understanding, 147, 102-113. doi:10.1016/j.cviu.2016.02.001Buckingham, G., DeBruine, L. M., Little, A. C., Welling, L. L. M., Conway, C. A., Tiddeman, B. P., & Jones, B. C. (2006). Visual adaptation to masculine and feminine faces influences generalized preferences and perceptions of trustworthiness. Evolution and Human Behavior, 27(5), 381-389. doi:10.1016/j.evolhumbehav.2006.03.001Boberg M, Piippo P, Ollila E. Designing Avatars. DIMEA ‘08 Proc 3rd Int Conf Digit Interact Media Entertain Arts. ACM; 2008; 232–239. doi: https://doi.org/10.1145/1413634.1413679Rojas Q., M., Masip, D., Todorov, A., & Vitria, J. (2011). Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models. PLoS ONE, 6(8), e23323. doi:10.1371/journal.pone.0023323Laurentini, A., & Bottino, A. (2014). Computer analysis of face beauty: A survey. Computer Vision and Image Understanding, 125, 184-199. doi:10.1016/j.cviu.2014.04.006Alemany S, Gonzalez J, Nacher B, Soriano C, Arnaiz C, Heras H. Anthropometric survey of the Spanish female population aimed at the apparel industry. Proceedings of the 2010 Intl Conference on 3D Body scanning Technologies. 2010. pp. 307–315.Vinué, G., Epifanio, I., & Alemany, S. (2015). Archetypoids: A new approach to define representative archetypal data. Computational Statistics & Data Analysis, 87, 102-115. doi:10.1016/j.csda.2015.01.018Jee, S., & Yun, M. H. (2016). An anthropometric survey of Korean hand and hand shape types. International Journal of Industrial Ergonomics, 53, 10-18. doi:10.1016/j.ergon.2015.10.004Kim, N.-S., & Do, W.-H. (2014). Classification of Elderly Women’s Foot Type. Journal of the Korean Society of Clothing and Textiles, 38(3), 305-320. doi:10.5850/jksct.2014.38.3.305Sarakon P, Charoenpong T, Charoensiriwath S. Face shape classification from 3D human data by using SVM. The 7th 2014 Biomedical Engineering International Conference. IEEE; 2014. pp. 1–5. doi: https://doi.org/10.1109/BMEiCON.2014.7017382PRESTON, T. A., & SINGH, M. (1972). Redintegrated Somatotyping. Ergonomics, 15(6), 693-700. doi:10.1080/00140137208924469Lin, Y.-L., & Lee, K.-L. (1999). Investigation of anthropometry basis grouping technique for subject classification. Ergonomics, 42(10), 1311-1316. doi:10.1080/001401399184965Malousaris, G. G., Bergeles, N. K., Barzouka, K. G., Bayios, I. A., Nassis, G. P., & Koskolou, M. D. (2008). Somatotype, size and body composition of competitive female volleyball players. Journal of Science and Medicine in Sport, 11(3), 337-344. doi:10.1016/j.jsams.2006.11.008Carvalho, P. V. R., dos Santos, I. L., Gomes, J. O., Borges, M. R. S., & Guerlain, S. (2008). Human factors approach for evaluation and redesign of human–system interfaces of a nuclear power plant simulator. Displays, 29(3), 273-284. doi:10.1016/j.displa.2007.08.010Fabri M, Moore D. The use of emotionally expressive avatars in Collaborative Virtual Environments. AISB’05 Convention:Proceedings of the Joint Symposium on Virtual Social Agents: Social Presence Cues for Virtual Humanoids Empathic Interaction with Synthetic Characters Mind Minding Agents. 2005. pp. 88–94. doi:citeulike-article-id:790934Sukhija, P., Behal, S., & Singh, P. (2016). Face Recognition System Using Genetic Algorithm. Procedia Computer Science, 85, 410-417. doi:10.1016/j.procs.2016.05.183Trescak T, Bogdanovych A, Simoff S, Rodriguez I. Generating diverse ethnic groups with genetic algorithms. Proceedings of the 18th ACM symposium on Virtual reality software and technology—VRST ‘12. New York, New York, USA: ACM Press; 2012. p. 1. doi: https://doi.org/10.1145/2407336.2407338Vanezis, P., Lu, D., Cockburn, J., Gonzalez, A., McCombe, G., Trujillo, O., & Vanezis, M. (1996). Morphological Classification of Facial Features in Adult Caucasian Males Based on an Assessment of Photographs of 50 Subjects. Journal of Forensic Sciences, 41(5), 13998J. doi:10.1520/jfs13998jTamir, A. (2011). Numerical Survey of the Different Shapes of the Human Nose. Journal of Craniofacial Surgery, 22(3), 1104-1107. doi:10.1097/scs.0b013e3182108eb3Tamir, A. (2013). Numerical Survey of the Different Shapes of Human Chin. Journal of Craniofacial Surgery, 24(5), 1657-1659. doi:10.1097/scs.0b013e3182942b77Richler, J. J., Cheung, O. S., & Gauthier, I. (2011). Holistic Processing Predicts Face Recognition. Psychological Science, 22(4), 464-471. doi:10.1177/0956797611401753Taubert, J., Apthorp, D., Aagten-Murphy, D., & Alais, D. (2011). The role of holistic processing in face perception: Evidence from the face inversion effect. Vision Research, 51(11), 1273-1278. doi:10.1016/j.visres.2011.04.002Donnelly, N., & Davidoff, J. (1999). The Mental Representations of Faces and Houses: Issues Concerning Parts and Wholes. Visual Cognition, 6(3-4), 319-343. doi:10.1080/135062899395000Davidoff, J., & Donnelly, N. (1990). Object superiority: A comparison of complete and part probes. Acta Psychologica, 73(3), 225-243. doi:10.1016/0001-6918(90)90024-aTanaka, J. W., & Farah, M. J. (1993). Parts and Wholes in Face Recognition. The Quarterly Journal of Experimental Psychology Section A, 46(2), 225-245. doi:10.1080/14640749308401045Wang, R., Li, J., Fang, H., Tian, M., & Liu, J. (2012). Individual Differences in Holistic Processing Predict Face Recognition Ability. Psychological Science, 23(2), 169-177. doi:10.1177/0956797611420575Rhodes, G., Ewing, L., Hayward, W. G., Maurer, D., Mondloch, C. J., & Tanaka, J. W. (2009). Contact and other-race effects in configural and component processing of faces. British Journal of Psychology, 100(4), 717-728. doi:10.1348/000712608x396503Miller, G. A. (1994). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 101(2), 343-352. doi:10.1037/0033-295x.101.2.343Scharff, A., Palmer, J., & Moore, C. M. (2011). Evidence of fixed capacity in visual object categorization. Psychonomic Bulletin & Review, 18(4), 713-721. doi:10.3758/s13423-011-0101-1Meyers, E., & Wolf, L. (2007). Using Biologically Inspired Features for Face Processing. International Journal of Computer Vision, 76(1), 93-104. doi:10.1007/s11263-007-0058-8Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001). Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6), 681-685. doi:10.1109/34.927467Ahonen, T., Hadid, A., & Pietikainen, M. (2006). Face Description with Local Binary Patterns: Application to Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12), 2037-2041. doi:10.1109/tpami.2006.244Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 711-720. doi:10.1109/34.598228Turk, M., & Pentland, A. (1991). Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 3(1), 71-86. doi:10.1162/jocn.1991.3.1.71Klare B, Jain AK. On a taxonomy of facial features. IEEE 4th International Conference on Biometrics: Theory, Applications and Systems, BTAS 2010. IEEE; 2010. pp. 1–8. doi: https://doi.org/10.1109/BTAS.2010.5634533Chihaoui, M., Elkefi, A., Bellil, W., & Ben Amar, C. (2016). A Survey of 2D Face Recognition Techniques. Computers, 5(4), 21. doi:10.3390/computers5040021Ma, D. S., Correll, J., & Wittenbrink, B. (2015). The Chicago face database: A free stimulus set of faces and norming data. Behavior Research Methods, 47(4), 1122-1135. doi:10.3758/s13428-014-0532-5Asthana A, Zafeiriou S, Cheng S, Pantic M. Incremental face alignment in the wild. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2014. pp. 1859–1866. doi: https://doi.org/10.1109/CVPR.2014.240Bag S, Barik S, Sen P, Sanyal G. A statistical nonparametric approach of face recognition: combination of eigenface & modified k-means clustering. Proceedings Second International Conference on Information Processing. 2008. p. 198.Doukas, C., & Maglogiannis, I. (2010). A Fast Mobile Face Recognition System for Android OS Based on Eigenfaces Decomposition. Artificial Intelligence Applications and Innovations, 295-302. doi:10.1007/978-3-642-16239-8_39Huang P, Huang Y, Wang W, Wang L. Deep embedding network for clustering. Proceedings—International Conference on Pattern Recognition. 2014. pp. 1532–1537. doi: https://doi.org/10.1109/ICPR.2014.272Dizaji KG, Herandi A, Deng C, Cai W, Huang H. Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization. Proceedings of the IEEE International Conference on Computer Vision. 2017. doi: https://doi.org/10.1109/ICCV.2017.612Xie J, Girshick R, Farhadi A. Unsupervised deep embedding for clustering analysis [Internet]. Proceedings of the 33rd International Conference on International Conference on Machine Learning—Volume 48. JMLR.org; 2016. pp. 478–487. Available: https://dl.acm.org/citation.cfm?id=3045442Nousi, P., & Tefas, A. (2017). Discriminatively Trained Autoencoders for Fast and Accurate Face Recognition. Communications in Computer and Information Science, 205-215. doi:10.1007/978-3-319-65172-9_18Sirovich, L., & Kirby, M. (1987). Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America A, 4(3), 519. doi:10.1364/josaa.4.00051

    Recent advances on recursive filtering and sliding mode design for networked nonlinear stochastic systems: A survey

    Get PDF
    Copyright © 2013 Jun Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Some recent advances on the recursive filtering and sliding mode design problems for nonlinear stochastic systems with network-induced phenomena are surveyed. The network-induced phenomena under consideration mainly include missing measurements, fading measurements, signal quantization, probabilistic sensor delays, sensor saturations, randomly occurring nonlinearities, and randomly occurring uncertainties. With respect to these network-induced phenomena, the developments on filtering and sliding mode design problems are systematically reviewed. In particular, concerning the network-induced phenomena, some recent results on the recursive filtering for time-varying nonlinear stochastic systems and sliding mode design for time-invariant nonlinear stochastic systems are given, respectively. Finally, conclusions are proposed and some potential future research works are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61329301, 61333012, 61374127 and 11301118, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant no. GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

    Fuzzy-logic-based control, filtering, and fault detection for networked systems: A Survey

    Get PDF
    This paper is concerned with the overview of the recent progress in fuzzy-logic-based filtering, control, and fault detection problems. First, the network technologies are introduced, the networked control systems are categorized from the aspects of fieldbuses and industrial Ethernets, the necessity of utilizing the fuzzy logic is justified, and the network-induced phenomena are discussed. Then, the fuzzy logic control strategies are reviewed in great detail. Special attention is given to the thorough examination on the latest results for fuzzy PID control, fuzzy adaptive control, and fuzzy tracking control problems. Furthermore, recent advances on the fuzzy-logic-based filtering and fault detection problems are reviewed. Finally, conclusions are given and some possible future research directions are pointed out, for example, topics on two-dimensional networked systems, wireless networked control systems, Quality-of-Service (QoS) of networked systems, and fuzzy access control in open networked systems.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374039, 61473163, and 61374127, the Hujiang Foundation of China under Grants C14002 andD15009, the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
    corecore