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

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. 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A review of mobile robots: Concepts, methods, theoretical framework, and applications

[EN] Humanoid robots, unmanned rovers, entertainment pets, drones, and so on are great examples of mobile robots. They can be distinguished from other robots by their ability to move autonomously, with enough intelligence to react and make decisions based on the perception they receive from the environment. Mobile robots must have some source of input data, some way of decoding that input, and a way of taking actions (including its own motion) to respond to a changing world. The need to sense and adapt to an unknown environment requires a powerful cognition system. Nowadays, there are mobile robots that can walk, run, jump, and so on like their biological counterparts. Several fields of robotics have arisen, such as wheeled mobile robots, legged robots, flying robots, robot vision, artificial intelligence, and so on, which involve different technological areas such as mechanics, electronics, and computer science. In this article, the world of mobile robots is explored including the new trends. These new trends are led by artificial intelligence, autonomous driving, network communication, cooperative work, nanorobotics, friendly human-robot interfaces, safe human-robot interaction, and emotion expression and perception. Furthermore, these news trends are applied to different fields such as medicine, health care, sports, ergonomics, industry, distribution of goods, and service robotics. These tendencies will keep going their evolution in the coming years.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Spanish Ministry of Economy and Competitiveness, which has funded the DPI2013-44227-R project.Rubio Montoya, FJ.; Valero Chuliá, FJ.; Llopis Albert, C. (2019). A review of mobile robots: Concepts, methods, theoretical framework, and applications. 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Finding Resonant Frequencies for High Loss Dielectrics in Cylindrical Cavities

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

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

[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

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

[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. 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Evaluation of Clustering Algorithms on GPU-Based Edge Computing Platforms

[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). 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Recreating Daily life in Pompeii

[EN] We propose an integrated Mixed Reality methodology for recreating ancient daily life that features realistic simulations of animated virtual human actors (clothes, body, skin, face) who augment real environments and re-enact staged storytelling dramas. We aim to go further from traditional concepts of static cultural artifacts or rigid geometrical and 2D textual augmentations and allow for 3D, interactive, augmented historical character-based event representations in a mobile and wearable setup. This is the main contribution of the described work as well as the proposed extensions to AR Enabling technologies: a VR/AR character simulation kernel framework with real-time, clothed virtual humans that are dynamically superimposed on live camera input, animated and acting based on a predefined, historically correct scenario. We demonstrate such a real-time case study on the actual site of ancient Pompeii.The work presented has been supported by the Swiss Federal Office for Education and Science and the EU IST programme, in frame of the EU IST LIFEPLUS 34545 and EU ICT INTERMEDIA 38417 projects.Magnenat-Thalmann, N.; Papagiannakis, G. (2010). Recreating Daily life in Pompeii. Virtual Archaeology Review. 1(2):19-23. https://doi.org/10.4995/var.2010.4679OJS192312P. MILGRAM, F. KISHINO, (1994) "A Taxonomy of Mixed Reality Visual Displays", IEICE Trans. Information Systems, vol. E77-D, no. 12, pp. 1321-1329R. AZUMA, Y. BAILLOT, R. BEHRINGER, S. FEINER, S. JULIER, B. MACINTYRE, (2001) "Recent Advances in Augmented Reality", IEEE Computer Graphics and Applications, November/December http://dx.doi.org/10.1109/38.963459D. STRICKER, P. DÄHNE, F. SEIBERT, I. CHRISTOU, L. ALMEIDA, N. IOANNIDIS, (2001) "Design and Development Issues for ARCHEOGUIDE: An Augmented Reality-based Cultural Heritage On-site Guide", EuroImage ICAV 3D Conference in Augmented Virtual Environments and Three-dimensional Imaging, Mykonos, Greece, 30 May-01 JuneW. WOHLGEMUTH, G. TRIEBFÜRST, (2000)"ARVIKA: augmented reality for development, production and service", DARE 2000 on Designing augmented reality environments, Elsinore, Denmark http://dx.doi.org/10.1145/354666.354688H. TAMURA, H. YAMAMOTO, A. KATAYAMA, (2001) "Mixed reality: Future dreams seen at the border between real and virtual worlds", Computer Graphics and Applications, vol.21, no.6, pp.64-70 http://dx.doi.org/10.1109/38.963462M. PONDER, G. PAPAGIANNAKIS, T. MOLET, N. MAGNENAT-THALMANN, D. THALMANN, (2003) "VHD++ Development Framework: Towards Extendible, Component Based VR/AR Simulation Engine Featuring Advanced Virtual Character Technologies", IEEE Computer Society Press, CGI Proceedings, pp. 96-104 http://dx.doi.org/10.1109/cgi.2003.1214453Archaeological Superintendence of Pompeii (2009), http://www.pompeiisites.orgG. PAPAGIANNAKIS, S. SCHERTENLEIB, B. O'KENNEDY , M. POIZAT, N.MAGNENAT-THALMANN, A. STODDART, D.THALMANN, (2005) "Mixing Virtual and Real scenes in the site of ancient Pompeii",Journal of CAVW, p 11-24, Volume 16, Issue 1, John Wiley and Sons Ltd, FebruaryEGGES, A., PAPAGIANNAKIS, G., MAGNENAT-THALMANN, N., (2007) "Presence and Interaction in Mixed Reality", The Visual Computer, Springer-Verlag Volume 23, Number 5, MaySEO H., MAGNENAT-THALMANN N. (2003), An Automatic Modeling of Human Bodies from Sizing Parameters. In ACM SIGGRAPH, Symposium on Interactive 3D Graphics, pp19-26, pp234. http://dx.doi.org/10.1145/641480.641487VOLINO P., MAGNENAT-THALMANN N. (2006), Resolving Surface Collisions through Intersection Contour Minimization. In ACM Transactions on Graphics (Siggraph 2006 proceedings), 25(3), pp 1154-1159. http://dx.doi.org/10.1145/1179352.1142007http://dx.doi.org/10.1145/1141911.1142007PAPAGIANNAKIS, G., SINGH, G., MAGNENAT-THALMANN, N., (2008) "A survey of mobile and wireless technologies for augmented reality systems", Journal of Computer Animation and Virtual Worlds, John Wiley and Sons Ltd, 19, 1, pp. 3-22, February http://dx.doi.org/10.1002/cav.22

Automatic classification of human facial features based on their appearance

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

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

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