Estudio de la clase de matrices {K,s+1}-potentes

En esta tesis doctoral se han introducido y analizado de manera exhaustiva una nueva clase de matrices denominada matrices {K,s+1}-potentes. Estas matrices contienen como casos particulares las matrices {s+1}-potentes, periódicas, centrosimétricas, mirrorsimétricas, circulantes, etc. Estos últimos tipos de matrices son de gran utilidad en diferentes áreas tales como transmisión de líneas multiconductor, antenas, ondas, sistemas eléctricos y mecánicos, y teoría de la comunicación, entre otros. En el capítulo 1 se han presentado algunos resultados básicos. En el capítulo 2 se han obtenido diferentes propiedades de las matrices {K,s+1}-potentes relacionadas con la suma, el producto, la inversa, la adjunta, la semejanza y la suma directa. Posteriormente, se han encontrado caracterizaciones de las matrices {K,s+1}-potentes desde distintos puntos de vista: usando teoría espectral, mediante potencias de matrices, a partir de inversas generalizadas, y mediante una representación por bloques de una matriz de índice 1. Luego, en el capítulo 3, se ha relacionado la clase de matrices introducida con diferentes clases de matrices complejas conocidas en la literatura, a saber: matrices {K}-hermíticas, proyectores {s+1}-generalizados, matrices unitarias, matrices normales, centrosimétricas {K}-generalizadas, etc. Con la intención de construir de manera efectiva matrices de esta clase, en el capítulo 4 se han diseñado algoritmos tanto en el caso s mayor o igual a 1 y el caso s=0. Primero se construyen matrices en esta clase a partir de información espectral de la matriz involutiva K. Utilizando este algoritmo se pueden construir más ejemplos. Concretamente, se hallan matrices {K,s+1}-potentes que conmutan con las encontradas anteriormente, y mediante estos dos algoritmos, se puede realizar el análisis de combinaciones lineales de matrices de este tipo. Por otra parte, para los casos s mayor o igual a 1 y s=0 se ha resuelto el problema inverso de calcular las matrices involutivas K que satisfacen la ecuación matricial que se está tratando. También en este caso se han presentado métodos numéricos que lo resuelven. Por último, en este capítulo se incluyen ejemplos numéricos para mostrar las prestaciones de los métodos desarrollados. En el capítulo 5, se extiende el estudio anterior al caso de matrices {K,-(s+1)}-potentes, completando así todos los valores de s enteros posibles. Especial énfasis se ha puesto en el análisis espectral de estas clases de matrices. La tesis finaliza con un anexo en el que se indican las conclusiones finales y las líneas futuras.Romero Martínez, JO. (2012). Estudio de la clase de matrices {K,s+1}-potentes [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/15974Palanci

On the solution of strong nonlinear oscillators by applying a rational elliptic balance method

AbstractA rational elliptic balance method is introduced to obtain exact and approximate solutions of nonlinear oscillators by using Jacobi elliptic functions. To illustrate the applicability of the proposed rational elliptic forms in the solution of nonlinear oscillators, we first investigate the exact solution of the non-homogenous, undamped Duffing equation. Then, we introduce first and second order rational elliptic form solutions to obtain approximate solutions of two nonlinear oscillators. At the end of the paper, we compare the numerical integration values of the angular frequencies with approximate solution results, based on the proposed rational elliptic balance method

Energy Method to Obtain Approximate Solutions of Strongly Nonlinear Oscillators

We introduce a nonlinearization procedure that replaces the system potential energy by an equivalent representation form that is used to derive analytical solutions of strongly nonlinear conservative oscillators. We illustrate the applicability of this method by finding the approximate solutions of two strongly nonlinear oscillators and show that this procedure provides solutions that follow well the numerical integration solutions of the corresponding equations of motion

Multimedia Data Flow Traffic Classification Using Intelligent Models Based on Traffic Patterns

[EN] Nowadays, there is high interest in modeling the type of multimedia traffic with the purpose of estimating the network resources required to guarantee the quality delivered to the user. In this work we propose a multimedia traffic classification model based on patterns that allows us to differentiate the type of traffic by using video streaming and network characteristics as input parameters. We show that there is low correlation between network parameters and the delivered video quality. Because of this, in addition to network parameters, we also add video streaming parameters in order to improve the efficiency of our system. Finally, it should be noted that, based on the objective video quality received by the user, we have extracted traffic patterns that we use to perform the development of the classification model.This work has been supported by the Ministerio de Economia y Competitividad in the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento within the Project with reference TIN2017-84802-C2-1-P.Canovas Solbes, A.; Jimenez, JM.; Romero Martínez, JO.; Lloret, J. (2018). Multimedia Data Flow Traffic Classification Using Intelligent Models Based on Traffic Patterns. IEEE Network. 32(6):100-107. doi:10.1109/MNET.2018.180012110010732

Identifying Polymeric Constitutive Equations for Incremental Sheet Forming Modelling

AbstractRecent publications have revealed an increasing interest in forming polymer materials using Incremental Sheet Forming. Therefore, several constitutive material models are being developed in an attempt to predict the physical response of polymeric materials during the process. This paper discuss several material models that could be used to predict experimental data collected on samples of PVC and PC subjected to simple uniaxial test performed at various temperatures and testing speeds. The results have shown that the Marlow and the rule of mixture material models could be used to describe viscoelastic and softening and permanent set effects, respectively, to predict the behaviour of a part formed by Incremental Sheet Forming

System for monitoring the wellness state of people in domestic environments employing emoticon-based HCI

[EN] Wellness state is affected by the habitability state of the domestic environment. Monitoring it can help to discover the causes of a low wellness levels aiding people in the improvement of their quality of life. In this paper, we propose a system to monitor the wellness state of people utilizing Likert¿s scale to determine the state of the user through an emoticon-based human¿computer interaction. The system is intended for domestic environments and measures the habitability conditions of the dwelling (such as temperature, humidity, luminosity and noise) employing sensors. An algorithm is designed in order to establish how to measure those conditions and to calculate the statistics that allows tracking their progress. The obtained information is presented to the user to compare his/her wellness state with the habitability conditions. Measures in a real domestic environment were performed in order to determine the configuration of our system. The energy efficiency of the algorithm provides an improvement between 99.36 and 99.62% in the energy consumption depending on the selected parameters.This work has been partially supported by the “Ministerio de Ciencia e Innovación”, through the “Plan Nacional de I+D+i 2008–2011” and by the “Ministerio de Educación, Cultura y Deporte”, through the grand “Ayudas para contratos predoctorales de Formación del Profesorado Universitario FPU14/02953”.García-García, L.; Parra-Boronat, L.; Romero Martínez, JO.; Lloret, J. (2017). System for monitoring the wellness state of people in domestic environments employing emoticon-based HCI. The Journal of Supercomputing. 1-25. https://doi.org/10.1007/s11227-017-2214-4S125Sendra S, Parra L, Lloret J, Tomás J (2017) Smart system for children’s chronic illness monitoring. Inf Fusion 40:76–86Lloret J, Parra L, Taha M, Tomás J (2017) An architecture and protocol for smart continuous eHealth monitoring using 5G. Comput Netw. https://doi.org/10.1016/j.comnet.2017.05.018 (in press)Hettler B (1976) The six dimensions of wellness. National Wellness Institute. http://c.ymcdn.com/sites/www.nationalwellness.org/resource/resmgr/docs/sixdimensionsfactsheet.pdf . Accessed 12 Dec 2017Dunn HL (1959) What high-level wellness means. Can J Public Health 50(11):447–457Herbes DJ, Mulder CH (2016) Housing and subjective well-being of older adults in Europe. J Hous Built Environ. https://doi.org/10.1007/s10901-016-9526-1OECD (2015) How’s life? measuring well-being. http://www.oecd-ilibrary.org/economics/how-s-life_23089679;jsessionid=55pjippucpjrq.x-oecd-live-02 . Accessed 12 Dec 2017Donaldson GC, Seemungal T, Jeffries DJ, Wedzicha JA (1999) Effect of temperature on lung function and symptoms in chronic obstructive pulmonary disease. Eur Respir J ERS 13(4):844–849Schwartz J, Samet J, Patz J (2004) Hospital admissions for heart disease: the effects of temperature and humidity. Epidemiology 15(6):755–761National Institute of Statistics of Spain (2005) Defunciones según causa de muerte en 2003. http://www.ine.es/prensa/np393.pdf . Accessed 12 Dec 2017Grimes A, Denne T, Howden-Dhapman P, Arnold R, Telfar-Barnard L, Preval N, Young C (2012) Cost benefit analysis of the warm up New Zealand: heat smart programme. University of Wellington, Wellington. http://sustainablecities.org.nz/wp-content/uploads/NZIF_CBA_report2.pdf . Accessed 12 Dec 2017Martínez-Pérez B, de la Torre-Díez I, Candelas-Plasencia S, López-Coronado M (2013) Developement and evaluation of tools for measuring the quality of experience (QoE) in mHealth applications. J Med Syst 37(5):9976Walther JB, D’addario KP (2001) The impacts of emotions on message interpretation in computer-mediated communication. Soc Sci Comput Rev 19(3):324–347Ghayvat H, Liu J, Mukhopadhay SC, Gui X (2015) Wellness sensor networks: a proposal and implementation for smart home for assisted living. IEEE Sens J 15(12):7341–7348Forkan ARM, Hu W (2016) A context-aware, predictive and protective approach for wellness monitoring of cardiac patients. In: Computing in Cardiology Conference, Vancouver, Canada, pp 369–372Booc CER, San Diego CMD, Tee ML, Caro JDL (2016) A mobile application for campus-based psychosocial wellness program. In: 7th International Conference on Information, Systems and Applications, Chalkidiki, Greece, pp 1–4Khan WA, Idris M, Ali T, Ali R, Hussain S, Hussain M, Amin MB, Khattak AM, Weiwei Y, Afzal M, Lee S, Kang BH, (2015) Correlating health and wellness analytics for personalized decision making. Boston, USA, pp 256–261Lim C, Kim ZM, Choi H (2017) Context-based healthy lifestyle recommendation for enhancing user’s wellness. In: IEEE International Conference on Big Data and Smart Computing, Jeju, South Korea, pp 418–421Tulu B, Strong D, Wang L, He Q, Agu E, Pedersen P, Djamasbi S (2016) Design implications of user experience studies: the case of a diabetes wellness app. In: 49th Hawaii International Conference on System Sciences, Koloa, USA, pp 3473–3482Kaur D, Siddaraju GS (2016) Experimental study of cardiac functionality for the wellness of individual by developing an android application. In: International Conference on Computation System and Information Technology for Sustainable Solutions, Bangalore, India, pp 174–183Arshad A, Khan S, Alam AHMZ, Tasnim R, Boby RI (2016) Health and wellness monitoring of elderly people using intelligent sensing technique. In: International Conference on Computer and Communications Engineering, Kuala Lumpur, Malaysia, pp 231–235Martin CJ, Platt SD, Hunt SM (1987) Housing conditions and ill health. Br Med J (Clin Res Ed) 294(6580):1125–1127Evans GW, Wells NM, Moch A (2003) Housing and mental health: a review of the evidence and a methodological and conceptual critique. J Soc Issues 59(3):475–500Shaw M (2004) Housing and public health. Annu Rev Public Health 25:397–418Thomson H, Thomas S (2015) Developing empirically supported theories of change for housing investment and health. Soc Sci Med 124:205–214Gustafson CJ, Feldman SR, Quandt SA, Isom S, Chem H, Spears CR, Arcury TA (2014) The association of skin conditions with housing conditions among North Carolina Latino migrant farm workers. Int J Dermatol 53(9):1091–1097Laquesta R, Garcia L, Garcia-Magarino I, Lloret J (2017) System to recommend the best place to life based on wellness state of the user employing the heart rate variability. IEEE Access 5:10594–10604Isiaka F, Mwitondi K, Ibrahim A (2015) Automatic prediction and detection of affect state based on invariant human computer interaction and human physiological response. In: Seventh International Conference on Computational Intelligence, Modelling and Simulation, Kuantan, Malaysia, pp 19–25Han S, Liu R, Zhu C, Soo YG, Yu H, Liu T, Duan F (2016) Development of a human computer interaction system based on multi-modal gaze tracking methods. In: IEEE International Conference on Robotics and Biomimetics, Qingdao, China, pp 1894–1899Chen B, Huang S, Tsai W (2017) Eliminating driving distractions: human–computer interaction with built-in applications. IEEE Veh Technol Mag 12(1):20–29Kamal S, Sayeed F, Rafeeq M (2016) Facial emotion recognition for human–computer interactions using hybrid feature extraction technique. In: International Conference on Data Mining and Advanced Computing, Ernakulam, India, pp 180–184Agrawal R, Gupta N (2016) Real time hand gesture recognition for human computer interaction. In: IEEE 6th International Conference on Advanced Computing, Bhimavaram, India, pp 470–475Sánchez CS, Mavrogianni A, González FJN (2017) On the minimal thermal habitability conditions in low income dwellings in Spain for a new definition of fuel poverty. Build Environ 114:344–356Ministry of Health, Social Services and Equality of Spain (2015) Plan Nacional de Actuaciones Preventivas de los Efectos del Exceso de Temperaturas Sobre la Salud. http://www.msssi.gob.es/ciudadanos/saludAmbLaboral/planAltasTemp/2015/docs/Plan_Nacional_de_Exceso_de_Temperaturas_2015.pdf . Accessed 12 Dec 2017Bornehag CG, Blomquist G, Gyntelberg F, Järvholm B, Malmberg P, Nordvall L, Nielsen A, Pershagen G, Sundell J (2001) Dampness in buildings and health. Indoor Air 11(2):72–86Garret MH, Rayment PR, Hooper MA, Abramson MJ, Hooper BM (1997) Indoor airborne fungal spores, house dampness and associations with environmental factors and respiratory health in children. Clin Exp Allergy 28:459–467Ariës MBC, Zonneveldt L (2004) Architectural aspects of healthy lighting. In: 21th Conference on Passive and Low Energy Architecture, The Netherlands, pp 1–5Boubekri M, Cheung IN, Reid KJ, Wang C, Zee PC (2014) Impact of windows and daylight exposure on overall health and sleep quality of office workers: a case-control pilot study. J Clin Sleep Med 10(6):603–611Beute F, de Kort YAW (2014) Salutogenic effects of the environments: review of health protective effects of nature and daylight. Appl Psychol Health Well Being 6(1):67–95Boyce P, Hunter C, Howlett O (2003) The benefits of daylight through windows. Rensselaer Polytechnic Institute, TroyHoogendijk WJG, Lips P, Dik MG, Deeg DJH, Beekman ATF, Penninx BWJH (2008) Depression is associated with decreased 25-hydroxyvitamin D and increased parathyroid hormone levels in older adults. Arch Gen Psychiatry 65(5):508–512Ising H, Kruppa B (2004) Health effects caused by noise: evidence in the literature from the past 25 years. Noise Health 6(22):5–13Sandra S, Lloret J, Garcia M, Toledo JF (2011) Power saving and energy optimization techniques for wireless sensor networks. J Commun 6(6):439–459Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the IEEE 33rd Annual Hawaii International Conference on System Sciences, Maui, HawaiiKaps JP, Sunar B (2006) Energy comparison of AES and SHA-1 for ubiquitous computing. In: Proceedings of the EUC 2006 Workshops: NCUS, SecUbiq, USN, TRUST, ESO, and MSA, Seoul, KoreaParra L, Sendra S, Jiménez JM, Lloret J (2016) Multimedia sensors embedded in smartphones for ambient assisted living and e-health. Multimed Tools Appl 75(21):13271–1329

Driving Assistance System for Ambulances to Minimise the Vibrations in Patient Cabin

[EN] The ambulance service is the main transport for diseased or injured people which suffers the same acceleration forces as regular vehicles. These accelerations, caused by the movement of the vehicle, impact the performance of tasks executed by sanitary personnel, which can affect patient survival or recovery time. In this paper, we have trained, validated, and tested a system to assess driving in ambulance services. The proposed system is composed of a sensor node which measures the vehicle vibrations using an accelerometer. It also includes a GPS sensor, a battery, a display, and a speaker. When two possible routes reach the same destination point, the system compares the two routes based on previously classified data and calculates an index and a score. Thus, the index balances the possible routes in terms of time to reach the destination and the vibrations suffered in the patient cabin to recommend the route that minimises those vibrations. Three datasets are used to train, validate, and test the system. Based on an Artificial Neural network (ANN), the classification model is trained with tagged data classified as low, medium, and high vibrations, and 97% accuracy is achieved. Then, the obtained model is validated using data from three routes of another region. Finally, the system is tested in two new scenarios with two possible routes to reach the destination. The results indicate that the route with less vibration is preferred when there are low time differences (less than 6%) between the two possible routes. Nonetheless, with the current weighting factors, the shortest route is preferred when time differences between routes are higher than 20%, regardless of the higher vibrations in the shortest route.This work was supported by the Researchers Supporting Project number (RSP-2021/295), King Saud University, Riyadh, Saudi Arabia.Aldegheishem, A.; Alrajeh, N.; Parra, L.; Romero Martínez, JO.; Lloret, J. (2022). Driving Assistance System for Ambulances to Minimise the Vibrations in Patient Cabin. Electronics. 11(23):1-19. https://doi.org/10.3390/electronics11233965119112

A robust multimedia traffic SDN-Based management system using patterns and models of QoE estimation with BRNN

[EN] Nowadays, network infrastructures such as Software Defined Networks (SDN) achieve a huge computational power. This allows to add a high processing on the network nodes. In this paper, a multimedia traffic management system is presented. This system is based on estimation models of Quality of Experience (QoE) and also on the traffic patterns classification. In order to achieve this, a QoE estimation method has been modeled. This method allows for classifying the multimedia traffic from multimedia transmission patterns. In order to do this, the SDN controller gathers statistics from the network. The patterns used have been defined from a lineal combination of objective QoE measurements. The model has been defined by Bayesian regularized neural networks (BRNN). From this model, the system is able to classify several kind of traffic according to the quality perceived by the users. Then, a model has been developed to determine which video characteristics need to be changed to provide the user with the best possible quality in the critical moments of the transmission. The choice of these characteristics is based on the quality of service (QoS) parameters, such as delay, jitter, loss rate and bandwidth. Moreover, it is also based on subpatterns defined by clusters from the dataset and which represents network and video characteristics. When a critical network situation is given, the model selects, by using network parameters as entries, the subpattern with the most similar network condition. The minimum Euclidean distance between these entries and the network parameters of the subpatters is calculated to perform this selection. Both models work together to build a reliable multimedia traffic management system perfectly integrated into current network infrastructures, which is able to classify the traffic and solve critical situations changing the video characteristics, by using the SDN architecture.This work has been partially supported by the "Ministerio de Educacion, Cultura y Deporte", through the "Ayudas para contratos predoctorales de Formation del Profesorado Universitario FPU (Convocatoria 2015)", grant number FPU15/06837 and by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigation Cientffica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" within the project under Grant TIN2017-84802-C2-1-P.Canovas Solbes, A.; Rego Mañez, A.; Romero Martínez, JO.; Lloret, J. (2020). A robust multimedia traffic SDN-Based management system using patterns and models of QoE estimation with BRNN. Journal of Network and Computer Applications. 150:1-14. https://doi.org/10.1016/j.jnca.2019.102498S114150Cánovas, A., Taha, M., Lloret, J., & Tomás, J. (2018). Smart resource allocation for improving QoE in IP Multimedia Subsystems. Journal of Network and Computer Applications, 104, 107-116. doi:10.1016/j.jnca.2017.12.020Canovas, A., Jimenez, J. M., Romero, O., & Lloret, J. (2018). Multimedia Data Flow Traffic Classification Using Intelligent Models Based on Traffic Patterns. IEEE Network, 32(6), 100-107. doi:10.1109/mnet.2018.1800121Burden, F., & Winkler, D. (2008). Bayesian Regularization of Neural Networks. Artificial Neural Networks, 23-42. doi:10.1007/978-1-60327-101-1_3Goodman, S. N. (2005). Introduction to Bayesian methods I: measuring the strength of evidence. Clinical Trials, 2(4), 282-290. doi:10.1191/1740774505cn098oaHirschen, K., & Schäfer, M. (2006). Bayesian regularization neural networks for optimizing fluid flow processes. Computer Methods in Applied Mechanics and Engineering, 195(7-8), 481-500. doi:10.1016/j.cma.2005.01.015Huang, X., Yuan, T., Qiao, G., & Ren, Y. (2018). Deep Reinforcement Learning for Multimedia Traffic Control in Software Defined Networking. IEEE Network, 32(6), 35-41. doi:10.1109/mnet.2018.1800097Lin, Y. (2002). Data Mining and Knowledge Discovery, 6(3), 259-275. doi:10.1023/a:1015469627679Lopez-Martin, M., Carro, B., Lloret, J., Egea, S., & Sanchez-Esguevillas, A. (2018). Deep Learning Model for Multimedia Quality of Experience Prediction Based on Network Flow Packets. IEEE Communications Magazine, 56(9), 110-117. doi:10.1109/mcom.2018.1701156Hagan, M. T., & Menhaj, M. B. (1994). Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5(6), 989-993. doi:10.1109/72.329697Nguyen, T. T. T., & Armitage, G. (2008). A survey of techniques for internet traffic classification using machine learning. IEEE Communications Surveys & Tutorials, 10(4), 56-76. doi:10.1109/surv.2008.080406Queiroz, W., Capretz, M. A. M., & Dantas, M. (2019). An approach for SDN traffic monitoring based on big data techniques. Journal of Network and Computer Applications, 131, 28-39. doi:10.1016/j.jnca.2019.01.016Rego, A., Canovas, A., Jimenez, J. M., & Lloret, J. (2018). An Intelligent System for Video Surveillance in IoT Environments. IEEE Access, 6, 31580-31598. doi:10.1109/access.2018.2842034Seshadrinathan, K., Soundararajan, R., Bovik, A. C., & Cormack, L. K. (2010). Study of Subjective and Objective Quality Assessment of Video. IEEE Transactions on Image Processing, 19(6), 1427-1441. doi:10.1109/tip.2010.2042111Soysal, M., & Schmidt, E. G. (2010). Machine learning algorithms for accurate flow-based network traffic classification: Evaluation and comparison. Performance Evaluation, 67(6), 451-467. doi:10.1016/j.peva.2010.01.001Tan, X., Xie, Y., Ma, H., Yu, S., & Hu, J. (2019). Recognizing the content types of network traffic based on a hybrid DNN-HMM model. Journal of Network and Computer Applications, 142, 51-62. doi:10.1016/j.jnca.2019.06.004Tongaonkar, A., Torres, R., Iliofotou, M., Keralapura, R., & Nucci, A. (2015). Towards self adaptive network traffic classification. Computer Communications, 56, 35-46. doi:10.1016/j.comcom.2014.03.02

MHCP: Multimedia Hybrid Cloud Computing Protocol and Architecture for Mobile Devices

[EN] Multimedia cloud computing has appeared as a very attractive environment for the business world in terms of providing cost-effective services with a minimum of entry costs and infrastructure requirements. There are some architecture proposals in the related literature, but there is no multimedia cloud computing architecture with hybrid features specifically designed for mobile devices. In this article, we propose a new multimedia hybrid cloud computing architecture and protocol. It merges existing private and public clouds and combines IaaS, SaaS and SECaaS cloud computing models in order to find a common platform to deliver real time traffic from heterogeneous multimedia and social networks for mobile users. The developed protocol provides suitable levels of QoS, while providing a secure and trusted cloud environment.Jimenez, JM.; Díaz Santos, JR.; Lloret, J.; Romero Martínez, JO. (2019). MHCP: Multimedia Hybrid Cloud Computing Protocol and Architecture for Mobile Devices. IEEE Network. 33(1):106-112. https://doi.org/10.1109/MNET.2018.1300246S10611233

Architecture and Protocol to Optimize Videoconference in Wireless Networks

[EN] In the past years, videoconferencing (VC) has become an essential means of communications. VC allows people to communicate face to face regardless of their location, and it can be used for different purposes such as business meetings, medical assistance, commercial meetings, and military operations. There are a lot of factors in real-time video transmission that can affect to the quality of service (QoS) and the quality of experience (QoE). The application that is used (Adobe Connect, Cisco Webex, and Skype), the internet connection, or the network used for the communication can affect to the QoE. Users want communication to be as good as possible in terms of QoE. In this paper, we propose an architecture for videoconferencing that provides better quality of experience than other existing applications such as Adobe Connect, Cisco Webex, and Skype. We will test how these three applications work in terms of bandwidth, packets per second, and delay using WiFi and 3G/4G connections. Finally, these applications are compared to our prototype in the same scenarios as they were tested, and also in an SDN, in order to improve the advantages of the prototype.This work has been supported by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" within the project under Grant TIN2017-84802-C2-1-P.Jimenez, JM.; García-Navas, JL.; Lloret, J.; Romero Martínez, JO. (2020). Architecture and Protocol to Optimize Videoconference in Wireless Networks. Wireless Communications and Mobile Computing. 2020:1-22. https://doi.org/10.1155/2020/4903420S122202