يعرض 1 - 20 نتائج من 85 نتيجة بحث عن '"aprendizaje incremental"', وقت الاستعلام: 0.87s تنقيح النتائج
  1. 1
    Dissertation/ Thesis

    المؤلفون: Freitas Dos Santos, Thiago

    Thesis Advisors: Osman , Nardine, Schorlemmer , Wernher Marco, Sierra García, Carlos

    المصدر: TDX (Tesis Doctorals en Xarxa)

    وصف الملف: application/pdf

  2. 2
    Dissertation/ Thesis
  3. 3
    Academic Journal
  4. 4
    Academic Journal
  5. 5
    Dissertation/ Thesis
  6. 6
    Dissertation/ Thesis
  7. 7
    Dissertation/ Thesis

    المؤلفون: Del Campo-Ávila, José

    Thesis Advisors: Lenguajes y Ciencias de la Computación, Ramos Jiménez, Gonzalo, Morales-Bueno, Rafael

  8. 8
    Conference

    Relation: Martínez-Prieto, Miguel A., et al. “Hacia la consolidación de las aulas ágiles”. En: Badía Contelles, José Manuel; Grimaldo Moreno, Francisco (eds.). Actas de las XXVI Jornadas sobre la Enseñanza Universitaria de la Informática, València, 8-9 de julio de 2020. València: Asociación de Enseñantes Universitarios de la Informática, 2020, pp. 29-36; http://hdl.handle.net/10045/125027

  9. 9
    Academic Journal
  10. 10
    Academic Journal
  11. 11
    Dissertation/ Thesis

    المساهمون: Orozco-Alzate, Mauricio, Londoño Bonilla, John Makario, Grupo de Control y Procesamiento Digital de Señales, P.A.Castro-Cabrera 0000-0002-4442-0715, Paola Alexandra Castro, Castro-Cabrera Paola Alexandra 36717473000, Paola Alexandra Castro Cabrera

    وصف الملف: xix, 147 páginas; application/pdf

    Relation: Ade, R. and Deshmukh, P. (2013). Methods for incremental learning: a survey. International Journal of Data Mining & Knowledge Management Process, 3(4):119.; Agliz, D., Atmani, A., et al. (2013). Seismic signal classification using multi-layer perceptron neural network. International Journal of Computer Applications, 79(15).; Akhouayri, E.-S., Agliz, D., Zonta, D., Atmani, A., et al. (2015). A fuzzy expert system for automatic seismic signal classification. Expert Systems with Applications, 42(3):1013-1027.; Alarcón, A., Rodriguez, E., and Escallón, J. (2000). Atlas de amenaza volcánica en Colombia. Ingeominas.; Allen, R. V. (1978). Automatic earthquake recognition and timing from single traces. Bulletin of the seismological society of America, 68(5):1521-1532.; Álvarez, I., Cortés, G., De la Torre, A., Benitez, C., García, L., Lesage, P., Arámbula, R., and González, M. (2009). Improving feature extraction in the automatic classification of seismic events. Application to Colima and Arenal volcanoes. In 2009 IEEE International Geoscience and Remote Sensing Symposium, volume 4, pages IV-526. IEEE.; Álvarez, I., García, L., Cortes, G., Benitez, C., and De la Torre, A. (2012). Discriminative feature selection for automatic classification of volcano-seismic signals. IEEE Geoscience and Remote Sensing Letters, 9(2):151-155.; Amirat, Y., Daney, D., Mohammed, S., Spalanzani, A., Chibani, A., and Simonin, O. (2016). Assistance and service robotics in a human environment. Robotics and Autonomous Systems, 75(PA):1-3.; Anantrasirichai, N., Biggs, J., Albino, F., and Bull, D. (2019). A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets. Remote Sensing of Environment, 230:111179.; Ansari, A., Noorzad, A., and Zafarani, H. (2009). Clustering analysis of the seismic catalog of Iran. Computers & Geosciences, 35(3):475-486.; Ashenden, C. L., Lindsay, J. M., Sherburn, S., Smith, I. E., Miller, C. A., and Malin, P. E. (2011). Some challenges of monitoring a potentially active volcanic field in a large urban area: Auckland volcanic field, New Zealand. Natural Hazards, 59(1):507-528.; Avesani, R., Azzoni, A., Bicego, M., and Orozco-Alzate, M. (2012). Automatic classification of volcanic earthquakes in HMM-induced vector spaces. In Iberoamerican Congress on Pattern Recognition, pages 640-647. Springer.; Avossa, C., Giudicepietro, F., Marinaro, M., and Scarpetta, S. (2003). Supervised and unsupervised analysis applied to Strombolian EQ. In Italian Workshop on Neural Nets, pages 173-178. Springer.; Awadallah, S., Moure, D., and Torres-González, P. (2019). An internet of things (IoT) application on volcano monitoring. Sensors, 19(21):4651.; Batista, G. E., Prati, R. C., and Monard, M. C. (2004). A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsletter, 6(1):20-29.; Bellahsene, H. and Taleb-Ahmed, A. (2018). ARMA order model detection using minimum of kurtosis: Application on seismic data. Arabian Journal of Geosciences, 11(24):776.; Benítez, M. C., Ramírez, J., Segura, J. C., Ibanez, J. M., Almendros, J., García-Yeguas, A., and Cortes, G. (2006). Continuous HMM-based seismic-event classification at Deception Island, Antarctica. IEEE Transactions on Geoscience and Remote Sensing, 45(1):138-146.; Benson, P. M., Vinciguerra, S., Meredith, P. G., and Young, R. P. (2010). Spatio-temporal evolution of volcano seismicity: A laboratory study. Earth and Planetary Science Letters, 297(1-2):315-323.; Beyreuther, M., Carniel, R., and Wassermann, J. (2008). Continuous hidden Markov models: Application to automatic earthquake detection and classification at Las Canãdas caldera, Tenerife. Journal of Volcanology and Geothermal Research, 176(4):513-518.; Beyreuther, M. and Wassermann, J. (2008). Continuous earthquake detection and classification using discrete hidden Markov models. Geophysical Journal International, 175(3):1055-1066.; Bhatti, S. M., Khan, M. S., Wuth, J., Huenupan, F., Curilem, M., Franco, L., and Yoma, N. B. (2016). Automatic detection of volcano-seismic events by modeling state and event duration in hidden Markov models. Journal of Volcanology and Geothermal Research, 324:134-143.; Bicego, M., Acosta-Munoz, C., and Orozco-Alzate, M. (2012). Classification of seismic volcanic signals using hidden Markov models-based generative embeddings. IEEE Transactions on Geoscience and Remote Sensing, 51(6):3400-3409.; Bicego, M., Londoño-Bonilla, J. M., and Orozco-Alzate, M. (2015). Volcano-seismic events classification using document classification strategies. In International Conference on Image Analysis and Processing, pages 119-129. Springer.; Bifet, A. and Gavalda, R. (2007). Learning from time-changing data with adaptive windowing. In Proceedings of the 2007 SIAM international conference on data mining, pages 443-448. SIAM.; Bifet, A. and Gavalda, R. (2009). Adaptive learning from evolving data streams. In Advances in Intelligent Data Analysis VIII: 8th International Symposium on Intelligent Data Analysis, IDA 2009, Lyon, France, August 31-September 2, 2009. Proceedings 8, pages 249-260. Springer.; Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.; Bormann, P. and Wielandt, E. (2013). Seismic signals and noise. In New Manual of Seismological Observatory Practice 2 (NMSOP2), pages 1-62. Deutsches GeoForschungsZentrum GFZ.; Brown, S. K., Jenkins, S. F., Sparks, R. S. J., Odbert, H., and Auker, M. R. (2017). Volcanic fatalities database: Analysis of volcanic threat with distance and victim classification. Journal of Applied Volcanology, 6(1):1-20.; Bueno, A., Benítez, C., De Angelis, S., Moreno, A. D., and Ibanez, J. M. (2019). Volcano-seismic transfer learning and uncertainty quantification with bayesian neural networks. IEEE Transactions on Geoscience and Remote Sensing, 58(2):892-902.; Bunke, H. (1993). Structural and syntactic pattern recognition. Handbook of Pattern Recognition and Computer Vision, pages 163-209.; Canario, J. P., Mello, R., Curilem, M., Huenupan, F., and Rios, R. (2020). In-depth comparison of deep artificial neural network architectures on seismic events classification. Journal of Volcanology and Geothermal Research, 401:106881.; Carmona, E., Almendros, J., Serrano, I., Stich, D., and Ibáñez, J. M. (2012). Results of seismic monitoring surveys of Deception Island volcano, Antarctica, from 1999-2011. Antarctic Science, 24(5):485.; Carniel, R. (2014). Characterization of volcanic regimes and identification of significant transitions using geophysical data: a review. Bulletin of Volcanology, 76(8):1-22.; Carniel, R. and Guzmán, S. (2020). Chapter machine learning in volcanology: A review.; Castaño, L. M., Ospina, C. A., Cadena, O. E., Galvis-Arenas, B., Londono, J. M., Laverde, C. A., Kaneko, T., and Ichihara, M. (2020). Continuous monitoring of the 2015-2018 Nevado del Ruiz activity, Colombia, using satellite infrared images and local infrasound records. Earth, Planets and Space, 72:1-18.; Castro Cabrera, P. A. (2011). Extracción y selección de características discriminantes para la detección de TDAH en registros de potenciales evocados cognitivos. Master’s thesis, Universidad Nacional de Colombia.; Catalan, L., Araiz, M., Aranguren, P., Padilla, G. D., Hernandez, P. A., Perez, N. M., Garcia de la Noceda, C., Albert, J. F., and Astrain, D. (2020). Prospects of autonomous volcanic monitoring stations: Experimental investigation on thermoelectric generation from fumaroles. Sensors, 20(12):3547.; Caudron, C., Lecocq, T., Syahbana, D. K., McCausland, W., Watlet, A., Camelbeeck, T., Bernard, A., et al. (2015). Stress and mass changes at a "wet" volcano: Example during the 2011-2012 volcanic unrest at Kawah Ijen volcano (Indonesia). Journal of Geophysical Research: Solid Earth, 120(7):5117-5134.; Chen, Y., Liu, W., Zhang, G., Cheng, Z., and Chen, W. (2016). Seismic time-frequency analysis using improved complete ensemble empirical mode decomposition. In 78th EAGE Conference and Exhibition 2016, pages 1-5. European Association of Geoscientists & Engineers.; Chouet, B. A. and Matoza, R. S. (2013). A multi-decadal view of seismic methods for detecting precursors of magma movement and eruption. Journal of Volcanology and Geothermal Research, 252:108-175.; Chu-Salgado, C. A., Orozco-Alzate, M., and Londoño-Bonilla, J. M. (2009). Combinación fija de clasificadores para la discriminación de señales sísmicas volcánicas. Boletín de Ciencias de la Tierra, (27):37-48.; Cortés, G., García, L., Álvarez, I., Benítez, C., de la Torre, Á., and Ibáñez, J. (2014). Parallel system architecture (PSA): An efficient approach for automatic recognition of volcano-seismic events. Journal of Volcanology and Geothermal Research, 271:1-10.; Curilem, G., Vergara, J., Fuentealba, G., Acuña, G., and Chacón, M. (2009). Classification of seismic signals at Villarrica volcano (Chile) using neural networks and genetic algorithms. Journal of Volcanology and Geothermal Research, 180(1):1-8.; Curilem, M., Vergara, J., San Martin, C., Fuentealba, G., Cardona, C., Huenupan, F., Chacón, M., Khan, M. S., Hussein, W., and Yoma, N. B. (2014). Pattern recognition applied to seismic signals of the Llaima volcano (Chile): An analysis of the events’ features. Journal of Volcanology and Geothermal Research, 282:134-147.; Dai, H. and MacBeth, C. (1995). Automatic picking of seismic arrivals in local earthquake data using an artificial neural network. Geophysical journal international, 120(3):758-774.; Das, A. K., Sengupta, S., and Bhattacharyya, S. (2018). A group incremental feature selection for classification using rough set theory based genetic algorithm. Applied Soft Computing, 65:400-411.; Davis, S. and Mermelstein, P. (1980). Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on Acoustics, Speech, and Signal Processing, 28(4):357-366.; Dayan, P., Sahani, M., and Deback, G. (1999). Unsupervised learning. The MIT Encyclopedia of`the Cognitive Sciences, pages 857-859.; Debnath, L. (2003). Wavelets and signal processing. Springer Science & Business Media.; Del Pezzo, E., Esposito, A., Giudicepietro, F., Marinaro, M., Martini, M., and Scarpetta, S. (2003). Discrimination of earthquakes and underwater explosions using neural networks. Bulletin of the Seismological Society of America, 93(1):215-223.; Dhar, P. (2023). Liquid neural network adapts on the go. IEEE Spectrum.; Di Stefano, R., Aldersons, F., Kissling, E., Baccheschi, P., Chiarabba, C., and Giardini, D. (2006). Automatic seismic phase picking and consistent observation error assessment: application to the Italian seismicity. Geophysical Journal International, 165(1):121-134.; Diaz-Chito, K., Ferri, F. J., and Diaz-Villanueva, W. (2014). Incremental generalized discriminative common vectors for image classification. IEEE Transactions on Neural Networks and Learning Systems, 26(8):1761-1775.; Diersen, S., Lee, E.-J., Spears, D., Chen, P., and Wang, L. (2011). Classification of seismic windows using artificial neural networks. Procedia Computer Science, 4:1572-1581.; Ditzler, G., Roveri, M., Alippi, C., and Polikar, R. (2015). Learning in nonstationary environments: A survey. IEEE Computational Intelligence Magazine, 10(4):12-25.; Dong, L., Wesseloo, J., Potvin, Y., and Li, X. (2016). Discrimination of mine seismic events and blasts using the Fisher classifier, naive Bayesian classifier and logistic regression. Rock Mechanics and Rock Engineering, 49(1):183-211.; Donovan, A., Oppenheimer, C., and Bravo, M. (2012). Science at the policy interface: Volcanomonitoring technologies and volcanic hazard management. Bulletin of Volcanology, 74(5):1005-1022.; Duda, R. O., Hart, P. E., et al. (2006). Pattern classification. John Wiley & Sons.; Duin, R. P., Orozco-Alzate, M., and Londono-Bonilla, J. M. (2010). Classification of volcano events observed by multiple seismic stations. In 2010 20th International Conference on Pattern Recognition, pages 1052-1055. IEEE.; Duin, R. P. and Pekalska, E. (2005). Open issues in pattern recognition. In Computer Recognition Systems, pages 27-42. Springer.; Duin, R. P. and Pekalska, E. (2007). The science of pattern recognition. Achievements and perspectives. In Challenges for Computational Intelligence, pages 221-259. Springer.; Duque Escobar, G. (2012). Gestión del riesgo por sismos, volcanes y laderas en la política ambiental de Manizales. Boletín Ambiental Instituto de Estudios Ambientales (IDEA), (104).; Dymarski, P. (2011). Hidden Markov models: theory and applications. BoD-Books on Demand.; Elwell, R. and Polikar, R. (2011). Incremental learning of concept drift in nonstationary environments. IEEE Transactions on Neural Networks, 22(10):1517-1531.; Erazo Bravo, Y. J. (2019). Estudio comparativo de algoritmos para la segmentación de señales volcánicas orientado a la clasificación de sismos. Master’s thesis, Universidad de Nariño.; Esmaili, S., Krishnan, S., and Raahemifar, K. (2004). Content based audio classification and retrieval using joint time-frequency analysis. In 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, volume 5, pages V-665. IEEE.; Espinoza Lara, P. E., Rolim Fernandes, C. A., Inza, A., Mars, J. I., Métaxian, J.-P., Dalla Mura, M., and Malfante, M. (2020). Automatic multichannel volcano-seismic classification using machine learning and EMD. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13:1322-1331.; Esposito, A., Giudicepietro, F., Scarpetta, S., D’auria, L., Marinaro, M., and Martini, M. (2006). Automatic discrimination among landslide, explosion-quake, and microtremor seismic signals at Stromboli volcano using neural networks. Bulletin of the Seismological Society of America, 96(4A):1230-1240.; Esposito, A. M., D’Auria, L., Giudicepietro, F., Peluso, R., and Martini, M. (2013). Automatic recognition of landslides based on neural network analysis of seismic signals: An application to the monitoring of Stromboli volcano (southern Italy). Pure and Applied Geophysics, 170(11):1821-1832.; Esposito, A. M., Giudicepietro, F., D’Auria, L., Scarpetta, S., Martini, M. G., Coltelli, M., and Marinaro, M. (2008). Unsupervised neural analysis of very-long-period events at Stromboli volcano using the self-organizing maps. Bulletin of the Seismological Society of America, 98(5):2449-2459.; Ezin, E. C., Giudicepietro, F., Petrosino, S., Scarpetta, S., and Vanacore, A. (2002). Automatic discrimination of earthquakes and false events in seismological recording for volcanic monitoring. In Italian Workshop on Neural Nets, pages 140-145. Springer.; Fagerlund, S. (2007). Bird species recognition using support vector machines. EURASIP Journal on Advances in Signal Processing, 2007:1-8.; Falcin, A., Métaxian, J.-P., Mars, J., Stutzmann, É., Komorowski, J.-C., Moretti, R., Malfante, M., Beauducel, F., Saurel, J.-M., Dessert, C., et al. (2021). A machine-learning approach for automatic classification of volcanic seismicity at La Soufrière volcano, Guadeloupe. Journal of Volcanology and Geothermal Research, 411:107151.; Falsaperla, S., Graziani, S., Nunnari, G., and Spampinato, S. (1996). Automatic classification of volcanic earthquakes by using multi-layered neural networks. Natural Hazards, 13(3):205-228.; Favereau, M., Robledo, L. F., and Bull, M. T. (2018). Analysis of risk assessment factors of individuals in volcanic hazards: Review of the last decade. Journal of Volcanology and Geothermal Research, 365:57-64.; Feng, F., Chan, R. H., Shi, X., Zhang, Y., and She, Q. (2019). Challenges in task incremental learning for assistive robotics. IEEE Access, 8:3434-3441.; Fernández, J., Pepe, A., Poland, M. P., and Sigmundsson, F. (2017). Volcano geodesy: Recent developments and future challenges. Journal of Volcanology and Geothermal Research, 344:1-12.; Firoozabadi, A. D., Seguel, F., Soto, I., Guevara, D., Huenupan, F., Curilem, M., and Franco, L. (2017). Evaluation of Llaima volcano activities for localization and classification of LP, VT and TR events. Journal of Electrical Engineering, 68(5):325-338.; Fujinaga, I. and MacMillan, K. (2000). Realtime recognition of orchestral instruments. In 2000 International Computer Music Conference, ICMC 2000. Berlin, Germany. August 27 - September 1.; Fujiwara, T., Chou, J.-K., Shilpika, S., Xu, P., Ren, L., and Ma, K.-L. (2019). An incremental dimensionality reduction method for visualizing streaming multidimensional data. IEEE Transactions on Visualization and Computer Graphics, 26(1):418-428.; Gaddes, M., Hooper, A., and Bagnardi, M. (2019). Using machine learning to automatically detect volcanic unrest in a time series of interferograms. Journal of Geophysical Research: Solid Earth,124(11):12304-12322.; Gama, J., Medas, P., Castillo, G., and Rodrigues, P. (2004). Learning with drift detection. In Advances in Artificial Intelligence-SBIA 2004: 17th Brazilian Symposium on Artificial Intelligence, Sao Luis, Maranhao, Brazil, September 29-October 1, 2004. Proceedings 17, pages 286-295. Springer.; Gama, J., Zliobait_e, I., Bifet, A., Pechenizkiy, M., and Bouchachia, A. (2014). A survey on concept drift adaptation. ACM Computing Surveys, 46(4):44.; García, C. and Mendez-Fajury, R. (2017). If I understand, I am understood: Experiences of volcanic risk communication in Colombia. In Observing the Volcano World, pages 335-351. Springer.; Gepperth, A. and Hammer, B. (2016). Incremental learning algorithms and applications. In European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium.; Giacco, F., Esposito, A., Scarpetta, S., Guidicepetro, F., and Marinaro, M. (2009). Support vector machines and MLP for automatic classification of seismic signals at Stromboli volcano. In Neural Nets WIRN09: Proceedings of the 19th Italian Workshop on Neural Nets, Vietri Sul Mare, Salerno, Italy May 28-30 2009, volume 204, page 116. IOS Press.; Giraud-Carrier, C. (2000). A note on the utility of incremental learning. AI Communications, 13(4):215-223.; Grangeon, J. and Lesage, P. (2019). A robust, low-cost and well-calibrated infrasound sensor for volcano monitoring. Journal of Volcanology and Geothermal Research, 387:106668.; Gunn, I. A., Arnaiz-González, Á., and Kuncheva, L. I. (2018). A taxonomic look at instance-based stream classifiers. Neurocomputing, 286:167-178.; Gutiérrez, L., Ibañez, J., Cortés, G., Ramírez, J., Benítez, C., Tenorio, V., and Isaac, A. (2009). Volcano-seismic signal detection and classification processing using hidden Markov models. Application to San Cristóbal volcano, Nicaragua. In 2009 IEEE International Geoscience and Remote Sensing Symposium, volume 4, pages IV-522. IEEE.; Gutiérrez, L., Ramírez, J., Benítez, C., Ibañez, J., Almendros, J., and García-Yeguas, A. (2006). HMM-based classification of seismic events recorded at Stromboli and Etna volcanoes. In 2006 IEEE International Symposium on Geoscience and Remote Sensing, pages 2765-2768. IEEE.; Han, N. C., Muniandy, S. V., and Dayou, J. (2011). Acoustic classification of Australian anurans based on hybrid spectral-entropy approach. Applied Acoustics, 72(9):639-645.; Havskov, J. and Alguacil, G. (2016). Seismic sensors. In Instrumentation in Earthquake Seismology, pages 13-100. Springer.; Havskov, J. and Ottemoller, L. (2010). Instruments and waveform data. In Routine data processing in earthquake seismology: With sample data, exercises and software. Springer Science & Business Media.; He, X., Beauseroy, P., and Smolarz, A. (2015). Dynamic feature subspaces selection for decision in a nonstationary environment. International Journal of Pattern Recognition and Artificial Intelligence, 29(06):1551009.; Heiken, G. (2016). Understanding volcanoes and volcanic hazards. In Oxford Research Encyclopedia of Natural Hazard Science.; Herrmann, R. B. (2013). Computer programs in seismology: An evolving tool for instruction and research. Seismological Research Letters, 84(6):1081-1088.; Hibert, C., Provost, F., Malet, J.-P., Maggi, A., Stumpf, A., and Ferrazzini, V. (2017). Automatic identification of rockfalls and volcano-tectonic earthquakes at the Piton de La Fournaise volcano using a random forest algorithm. Journal of Volcanology and Geothermal Research, 340:130-142.; Hoogenboezem, R. M. (2010). Automatic classification of segmented seismic recordings at the Nevado del Ruiz volcano, Columbia. Master’s thesis, Delft University of Technology, Delft, The Netherlands.; Ibáñez, J. M., Benítez, C., Gutiérrez, L. A., Cortés, G., García-Yeguas, A., and Alguacil, G. (2009). The classification of seismo-volcanic signals using hidden Markov models as applied to the Stromboli and Etna volcanoes. Journal of Volcanology and Geothermal Research, 187(3-4):218-226.; Ibs-von Seht, M. (2008). Detection and identification of seismic signals recorded at Krakatau volcano (Indonesia) using artificial neural networks. Journal of Volcanology and Geothermal Research, 176(4):448-456; Japkowicz, N. and Shah, M. (2011). Evaluating learning algorithms: A classification perspective. Cambridge University Press.; Jiao, P. and Alavi, A. H. (2020). Artificial intelligence in seismology: Advent, performance and future trends. Geoscience Frontiers, 11(3):739-744.; Joseph, A. A. and Ozawa, S. (2014). A fast incremental kernel principal component analysis for data streams. In 2014 International Joint Conference on Neural Networks (IJCNN), pages 3135-3142. IEEE.; Kalra, M., Kumar, S., and Das, B. (2020). Seismic signal analysis using empirical wavelet transform for moving ground target detection and classification. IEEE Sensors Journal, 20(14):7886-7895.; Karpatne, A., Ebert-Uphoff, I., Ravela, S., Babaie, H. A., and Kumar, V. (2018). Machine learning for the geosciences: Challenges and opportunities. IEEE Transactions on Knowledge and Data Engineering, 31(8):1544-1554.; Khamassi, I., Sayed-Mouchaweh, M., Hammami, M., and Ghédira, K. (2018). Discussion and review on evolving data streams and concept drift adapting. Evolving Systems, 9(1):1-23.; Kira, K. and Rendell, L. A. (1992). A practical approach to feature selection. In Machine learning proceedings, pages 249-256. Elsevier.; Kong, Q., Trugman, D. T., Ross, Z. E., Bianco, M. J., Meade, B. J., and Gerstoft, P. (2019). Machine learning in seismology: Turning data into insights. Seismological Research Letters, 90(1):3-14.; Kononenko, I. (1994). Estimating attributes: Analysis and extensions of Relief. In European conference on machine learning, pages 171-182. Springer.; Kortström, J., Uski, M., and Tiira, T. (2016). Automatic classification of seismic events within a regional seismograph network. Computers & Geosciences, 87:22-30.; Kuncheva, L. (2010). Teaching and research practices in pattern recognition (personal views and experiences). Report: School of Computer Science. Bangor University, UK.; Kuncheva, L. (2019). Pattern Recognition and Neural Networks. Lulu.com.; Kuncheva, L. I. (2013). Change detection in streaming multivariate data using likelihood detectors. IEEE Transactions on Knowledge and Data Engineering, 25(5):1175-1180.; Kuncheva, L. I. and Faithfull, W. J. (2014). PCA feature extraction for change detection in multidimensional unlabeled data. IEEE Transactions on Neural Networks and Learning Systems, 25(1):69-80.; Lamb, O. D., Varley, N. R., Mather, T. A., Pyle, D. M., Smith, P. J., and Liu, E. J. (2014). Multiple timescales of cyclical behaviour observed at two dome-forming eruptions. Journal of Volcanology and Geothermal Research, 284:106-121.; Langer, H. and Falsaperla, S. (2003). Seismic monitoring at Stromboli volcano (Italy): A case study for data reduction and parameter extraction. Journal of Volcanology and Geothermal Research, 128(1-3):233-245.; Langer, H., Falsaperla, S., Masotti, M., Campanini, R., Spampinato, S., and Messina, A. (2009). Synopsis of supervised and unsupervised pattern classification techniques applied to volcanic tremor data at Mt Etna, Italy. Geophysical Journal International, 178(2):1132-1144.; Langer, H., Falsaperla, S., Powell, T., and Thompson, G. (2006). Automatic classification and aposteriori analysis of seismic event identification at Soufriere Hills volcano, Montserrat. Journal of Volcanology and Geothermal Research, 153(1-2):1-10.; Lara, F., Lara-Cueva, R., Larco, J. C., Carrera, E. V., and León, R. (2021). A deep learning approach for automatic recognition of seismo-volcanic events at the Cotopaxi volcano. Journal of Volcanology and Geothermal Research, 409:107142.; Lara-Cueva, R., Benítez, D. S., Paillacho, V., Villalva, M., and Rojo-Álvarez, J. L. (2017). On the use of multi-class support vector machines for classification of seismic signals at Cotopaxi volcano. In 2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), pages 1-6. IEEE.; Lary, D. J. (2010). Artificial intelligence in geoscience and remote sensing. Geoscience and Remote Sensing: New Achievements, page 105.; Lary, D. J., Alavi, A. H., Gandomi, A. H., and Walker, A. L. (2016). Machine learning in geosciences and remote sensing. Geoscience Frontiers, 7(1):3-10.; Lehr, J., Eckel, F., Thorwart, M., and Rabbel, W. (2019). Low-frequency seismicity at Villarrica volcano: Source location and seismic velocities. Journal of Geophysical Research: Solid Earth, 124(11):11505-11530.; Leng, Y., Zhang, L., and Yang, J. (2014). Locally linear embedding algorithm based on OMP for incremental learning. In 2014 International Joint Conference on Neural Networks (IJCNN), pages 3100-3107. IEEE.; Li, P., Chen, Z., Yang, L. T., Gao, J., Zhang, Q., and Deen, M. J. (2018). An incremental deep convolutional computation model for feature learning on industrial big data. IEEE Transactions on Industrial Informatics, 15(3):1341-1349.; Lokmer, I., Saccorotti, G., Di Lieto, B., and Bean, C. J. (2008). Temporal evolution of long-period seismicity at Etna volcano, Italy, and its relationships with the 2004-2005 eruption. Earth and Planetary Science Letters, 266(1-2):205-220.; Lomax, A., Satriano, C., and Vassallo, M. (2012). Automatic picker developments and optimization: Filterpicker-a robust, broadband picker for real-time seismic monitoring and earthquake early warning. Seismological Research Letters, 83(3):531-540.; Londoño, J. M. (2010). Aspectos relevantes de la actividad del volcán Nevado del Ruiz. 1985-2008. Glaciares, nieves y hielos de América Latina. Cambio climático y amenazas, page 261.; Londoño, J. M. (2016). Evidence of recent deep magmatic activity at Cerro Bravo-Cerro Machín volcanic complex, central Colombia. Implications for future volcanic activity at Nevado del Ruiz, Cerro Machín and other volcanoes. Journal of Volcanology and Geothermal Research, 324:156-168.; López-Pérez, M., García, L., Benítez, C., and Molina, R. (2020). A contribution to deep learning approaches for automatic classification of volcano-seismic events: Deep gaussian processes. IEEE Transactions on Geoscience and Remote Sensing, 59(5):3875-3890.; Losing, V., Hammer, B., and Wersing, H. (2018). Incremental on-line learning: A review and comparison of state of the art algorithms. Neurocomputing, 275:1261-1274.; Loughlin, S. C., Sparks, R. S. J., Brown, S. K., Jenkins, S. F., and Vye-Brown, C. (2015). Global volcanic hazards and risk. Cambridge University Press.; Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., and Zhang, G. (2018). Learning under concept drift: A review. IEEE Transactions on Knowledge and Data Engineering, 99:1-18.; Malfante, M., Dalla Mura, M., Métaxian, J.-P., Mars, J. I., Macedo, O., and Inza, A. (2018). Machine learning for volcano-seismic signals: Challenges and perspectives. IEEE Signal Processing Magazine, 35(2):20-30.; Manley, G. F., Mather, T. A., Pyle, D. M., Clifton, D. A., Rodgers, M., Thompson, G., and Londoño, J. M. (2022). A deep active learning approach to the automatic classification of volcano-seismic events. Frontiers in Earth Science, 10:78.; Martínez, V. L., Titos, M., Benítez, C., Badi, G., Casas, J. A., Craig, V. H. O., and Ibáñez, J. M. (2021). Advanced signal recognition methods applied to seismo-volcanic events from Planchon Peteroa volcanic complex: Deep Neural Network classifier. Journal of South American Earth Sciences, 107:103115.; Maxwell, A. E., Warner, T. A., and Fang, F. (2018). Implementation of machine-learning classification in remote sensing: An applied review. International Journal of Remote Sensing, 39(9):2784-2817.; McNutt, S. R. (1996). Seismic monitoring and eruption forecasting of volcanoes: A review of the state-of-the-art and case histories. In Monitoring and Mitigation of Volcano Hazards, pages 99-146. Springer.; McNutt, S. R. (2002). Volcano seismology and monitoring for eruptions. International Geophysics Series, 81(A):383-406.; McNutt, S. R. (2005). Volcanic seismology. Annual Review of Earth and Planetary Sciences, 32:461-491.; Mehrkanoon, S., Agudelo, O. M., and Suykens, J. A. (2015). Incremental multi-class semi-supervised clustering regularized by Kalman filtering. Neural Networks, 71:88-104.; Mera, C., Orozco-Alzate, M., and Branch, J. (2019). Incremental learning of concept drift in Multiple Instance Learning for industrial visual inspection. Computers in Industry, 109:153-164.; Messina, A. and Langer, H. (2011). Pattern recognition of volcanic tremor data on Mt. Etna (Italy) with KKAnalysis - A software program for unsupervised classification. Computers & Geosciences, 37(7):953-961.; Minakami, T. (1974). Seismology of volcanoes in Japan. Physical Volcanology, 6:1-27.; Nallaperuma, D., Nawaratne, R., Bandaragoda, T., Adikari, A., Nguyen, S., Kempitiya, T., De Silva, D., Alahakoon, D., and Pothuhera, D. (2019). Online incremental machine learning platform for big data-driven smart traffic management. IEEE Transactions on Intelligent Transportation Systems, 20(12):4679-4690.; Ogata, K. and Yang, Y. (2002). Modern control engineering, volume 4. Prentice Hall India.; Ohrnberger, M. (2001). Continuous automatic classification of seismic signals of volcanic origin at Mt. Merapi, Java, Indonesia. Dissertation, page 168.; Oppenheim, A. V., Willsky, A. S., and Nawab, S. H. (1998). Señales y sistemas segunda edición. Prentice-Hall.; Orozco-Alzate, M. (2008). Generalized dissimilarity representations for pattern recognition. Universidad Nacional de Colombia - Sede Manizales. Tesis de doctorado.; Orozco-Alzate, M., Acosta-Muñoz, C., and Londoño-Bonilla, J. M. (2012). Earthquake Research and Analysis - Seismology, Seismotectonic and Earthquake Geology, chapter 19: The Automated Identification of Volcanic Earthquakes: Concepts, Applications and Challenges, pages 377-402. InTech.; Orozco-Alzate, M., Castro-Cabrera, P. A., Bicego, M., and Londoño-Bonilla, J. M. (2015). The DTW-based representation space for seismic pattern classification. Computers & Geosciences, 85:86-95.; Orozco-Alzate, M., García, M. E., Duin, R. P., and Castellanos, C. G. (2006). Dissimilarity-based classification of seismic signals at Nevado del Ruiz volcano. Earth Sciences Research Journal,10(2):57-66.; Orozco-Alzate, M., Londoño-Bonilla, J. M., Nale, V., and Bicego, M. (2019). Towards better volcanic risk-assessment systems by applying ensemble classification methods to triaxial seismicvolcanic signals. Ecological Informatics, 51:177-184.; Orozco-Alzate, M., Skurichina, M., and Duin, R. P. (2008). Spectral characterization of volcanic earthquakes at Nevado del Ruiz volcano using spectral band selection/extraction techniques. In Iberoamerican Congress on Pattern Recognition, pages 708-715. Springer.; O’Shaughnessy, D. (1988). Linear predictive coding. IEEE Potentials, 7(1):29-32.; Oussous, A., Benjelloun, F.-Z., Lahcen, A. A., and Belfkih, S. (2018). Big data technologies: A survey. Journal of King Saud University-Computer and Information Sciences, 30(4):431-448.; Ozawa, S., Kawashima, Y., Pang, S., and Kasabov, N. (2009). Adaptive incremental principal component analysis in nonstationary online learning environments. In 2009 International Joint Conference on Neural Networks, pages 2394-2400. IEEE.; Pekalska, E., Duin, R. P., and Paclik, P. (2006). Prototype selection for dissimilarity-based classifiers. Pattern Recognition, 39(2):189-208.; Polikar, R., Upda, L., Upda, S. S., and Honavar, V. (2001). Learn++: An incremental learning algorithm for supervised neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 31(4):497-508.; Porro-Muñoz, D., Duin, R. P., Orozco-Alzate, M., Talavera, I., and Londoño-Bonilla, J. M. (2010a). Classifying three-way seismic volcanic data by dissimilarity representation. In 20th International Conference on Pattern Recognition, pages 814-817. IEEE.; Porro-Munoz, D., Duin, R. P., Orozco-Alzate, M., Talavera, I., and Londono-Bonilla, J. M. (2010b). The dissimilarity representation as a tool for three-way data classification: A 2d measure. In Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), pages 569-578. Springer.; Porro-Muñoz, D., Duin, R. P., Talavera, I., and Orozco-Alzate, M. (2011). Classification of threeway data by the dissimilarity representation. Signal Processing, 91(11):2520-2529.; Ramírez-Rojas, A., Flores-Márquez, E. L., Sarlis, N. V., and Varotsos, P. A. (2018). The complexity measures associated with the fluctuations of the entropy in natural time before the deadly México m8. 2 earthquake on 7 september 2017. Entropy, 20(6):477.; Richardson, J. P., Waite, G. P., and Palma, J. L. (2014). Varying seismic-acoustic properties of the fluctuating lava lake at Villarrica volcano, Chile. Journal of Geophysical Research: Solid Earth, 119(7):5560-5573.; Riggelsen, C., Ohrnberger, M., and Scherbaum, F. (2007). Dynamic bayesian networks for realtime classification of seismic signals. In European Conference on Principles of Data Mining and Knowledge Discovery, pages 565-572. Springer.; Robnik-Sikonja, M. and Kononenko, I. (2003). Theoretical and empirical analysis of ReliefF and RReliefF. Machine learning, 53:23-69.; Rodriguez, I. V. (2011). Automatic time-picking of microseismic data combining STA\LTA and the stationary discrete wavelet transform. In GeoConvention, 2011.; Roman, D., De Angelis, S., Latchman, J., and White, R. (2008). Patterns of volcanotectonic seismicity and stress during the ongoing eruption of the Soufrière Hills volcano, Montserrat (1995-2007). Journal of Volcanology and Geothermal Research, 173(3-4):230-244.; Romeo, G. (1994). Seismic signals detection and classification using artiricial neural networks. Annals of Geophysics, 37(3).; Romeo, G., Mele, F., and Morelli, A. (1995). Neural networks and discrimination of seismic signals. Computers & Geosciences, 21(2):279-288.; Rouland, D., Legrand, D., Zhizhin, M., and Vergniolle, S. (2009). Automatic detection and discrimination of volcanic tremors and tectonic earthquakes: An application to Ambrym volcano, Vanuatu. Journal of Volcanology and Geothermal Research, 181(3-4):196-206.; Ruano, A. E., Madureira, G., Barros, O., Khosravani, H. R., Ruano, M. G., and Ferreira, P. M. (2014). Seismic detection using support vector machines. Neurocomputing, 135:273-283.; Salazar, A., Arroyo, R., Pérez, N., and Benítez, D. (2020). Deep-learning for volcanic seismic events classification. In 2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020), pages 1-6. IEEE.; San-Martin, C., Melgarejo, C., Gallegos, C., Soto, G., Curilem, M., and Fuentealba, G. (2010). Feature extraction using circular statistics applied to volcano monitoring. In Iberoamerican Congress on Pattern Recognition, pages 458-466. Springer.; Scarpetta, S., Giudicepietro, F., Ezin, E. C., Petrosino, S., Del Pezzo, E., Martini, M., and Marinaro, M. (2005). Automatic classification of seismic signals at Mt. Vesuvius volcano, Italy, using neural networks. Bulletin of the Seismological Society of America, 95(1):185-196.; Schapire, R. E. (2013). Explaining AdaBoost. In Empirical Inference, pages 37-52. Springer.; SERNAGEOMIN, RNVV, and OVDAS (2015). Reportes de actividad volcánica. http://sitiohistorico.sernageomin.cl/volcan.php?pagina=5&iId=22.; Sharma, B., Kumar, A., and Murthy, V. (2010). Evaluation of seismic events detection algorithms. Journal of the Geological Society of India, 75(3):533-538.; Sheldrake, T. E., Sparks, R., Cashman, K., Wadge, G., and Aspinall, W. (2016). Similarities and differences in the historical records of lava dome-building volcanoes: Implications for understanding magmatic processes and eruption forecasting. Earth-science reviews, 160:240-263.; Sherburn, S., Bryan, C. J., Hurst, A. W., Latter, J. H., and Scott, B. J. (1999). Seismicity of Ruapehu volcano, New Zealand, 1971-1996: A review. Journal of Volcanology and Geothermal Research, 88(4):255-278.; Soto, R., Huenupan, F., Meza, P., Curilem, M., and Franco, L. (2018). Spectro-temporal features applied to the automatic classification of volcanic seismic events. Journal of Volcanology and Geothermal Research, 358:194-206.; Sparks, R., Biggs, J., and Neuberg, J. (2012). Monitoring volcanoes. Science, 335(6074):1310-1311.; Tárraga, M., Martí, J., Abella, R., Carniel, R., and López, C. (2014). Volcanic tremors: Good indicators of change in plumbing systems during volcanic eruptions. Journal of Volcanology and Geothermal Research, 273:33-40.; Titos, M., Bueno, A., García, L., and Benítez, C. (2018). A deep neural networks approach to automatic recognition systems for volcano-seismic events. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(5):1533-1544.; Titos, M., Bueno, A., García, L., Benítez, C., and Segura, J. C. (2019). Classification of isolated volcano-seismic events based on inductive transfer learning. IEEE Geoscience and Remote Sensing Letters, 17(5):869-873.; Torres, C., Gómez, M., Narváez, M., et al. (1996). Unusual seismic signals associated with the activity at Galeras volcano, Colombia, from July 1992 to September 1994. Annals of Geophysics, 39(2):299-310.; Triastuty, H., Iguchi, M., and Tameguri, T. (2009). Temporal change of characteristics of shallow volcano-tectonic earthquakes associated with increase in volcanic activity at Kuchinoerabujima volcano, Japan. Journal of Volcanology and Geothermal Research, 187(1-2):1-12.; Trnkoczy, A. (2009). Understanding and parameter setting of STA/LTA trigger algorithm. In New Manual of Seismological Observatory Practice (NMSOP), pages 1-20. Deutsches GeoForschungsZentrum GFZ.; Trombley, R. (2006). The Forecasting of Volcanic Eruptions. iUniverse.; Trujillo-Castrillón, N., Valdés-González, C. M., Arámbula-Mendoza, R., and Santacoloma-Salguero, C. C. (2018). Initial processing of volcanic seismic signals using hidden Markov models: Nevado del Huila, Colombia. Journal of Volcanology and Geothermal Research, 364:107-120.; Tucker, S. and Brown, G. J. (2005). Classification of transient sonar sounds using perceptually motivated features. IEEE Journal of Oceanic Engineering, 30(3):588-600.; Ursino, A., Langer, H., Scarfi, L., Di Grazia, G., and Gresta, S. (2001). Discrimination of quarry blasts from tectonic microearthquakes in the Hyblean Plateau (Southeastern Sicily). Annals of Geophysics, 44(4).; Valade, S., Ley, A., Massimetti, F., D’Hondt, O., Laiolo, M., Coppola, D., Loibl, D., Hellwich, O., and Walter, T. R. (2019). Towards global volcano monitoring using multisensor sentinel missions and artificial intelligence: The mounts monitoring system. Remote Sensing, 11(13):1528.; Van Daele, M., Moernaut, J., Silversmit, G., Schmidt, S., Fontijn, K., Heirman, K., Vandoorne, W., De Clercq, M., Van Acker, J., Wolff, C., et al. (2014). The 600 yr eruptive history of Villarrica volcano (Chile) revealed by annually laminated lake sediments. Geological Society of America Bulletin, 126(3-4):481-498.; Vargas, C. A., Caneva, A., Monsalve, H., Salcedo, E., and Mora, H. (2018). Geophysical networks in Colombia. Seismological Research Letters, 89(2A):440-445.; Venzke, E. (2013). Global volcanism program. Volcanoes of the world, v. 4.11. 0 (08 jun 2022).; Verleysen, M. and Fran¸cois, D. (2005). The curse of dimensionality in data mining and time series prediction. In International work-conference on artificial neural networks, pages 758-770. Springer.; Vila, J., Macià, R., Kumar, D., Ortiz, R., Moreno, H., and Correig, A. M. (2006). Analysis of the unrest of active volcanoes using variations of the base level noise seismic spectrum. Journal of Volcanology and Geothermal Research, 153(1-2):11-20.; Wang, J., Xiao, Z., Liu, C., Zhao, D., and Yao, Z. (2019). Deep learning for picking seismic arrival times. Journal of Geophysical Research: Solid Earth, 124(7):6612-6624.; West, M. E. (2013). Recent eruptions at Bezymianny Volcano ─ A seismological comparison. Journal of Volcanology and Geothermal Research, 263:42-57.; Wilson, G., Wilson, T., Deligne, N., and Cole, J. (2014). Volcanic hazard impacts to critical infrastructure: A review. Journal of Volcanology and Geothermal Research, 286:148-182.; Withers, M., Aster, R., Young, C., Beiriger, J., Harris, M., Moore, S., and Trujillo, J. (1998). A comparison of select trigger algorithms for automated global seismic phase and event detection. Bulletin of the Seismological Society of America, 88(1):95-106.; Wu, H., Xiao, W., and Ren, H. (2022). Automatic time picking for weak seismic phase in the strong noise and interference environment: An hybrid method based on array similarity. Sensors, 22(24):9924.; Yıldırım, E., Gülbag, A., Horasan, G., and Dogan, E. (2011). Discrimination of quarry blasts and earthquakes in the vicinity of Istanbul using soft computing techniques. Computers & Geosciences, 37(9):1209-1217.; Ying, C., Qi-Guang, M., Jia-Chen, L., and Lin, G. (2013). Advance and prospects of AdaBoost algorithm. Acta Automatica Sinica, 39(6):745-758.; Yu, M., Yang, C., and Li, Y. (2018). Big data in natural disaster management: A review. Geosciences, 8(5):165.; Yu, Z., Luo, P., You, J., Wong, H.-S., Leung, H., Wu, S., Zhang, J., and Han, G. (2015). Incremental semi-supervised clustering ensemble for high dimensional data clustering. IEEE Transactions on Knowledge and Data Engineering, 28(3):701-714.; Zaugg, S., Van Der Schaar, M., Houégnigan, L., Gervaise, C., and André, M. (2010). Real-time acoustic classification of sperm whale clicks and shipping impulses from deep-sea observatories. Applied Acoustics, 71(11):1011-1019.; Zeng, X.-Q. and Li, G.-Z. (2014). Incremental partial least squares analysis of big streaming data. Pattern recognition, 47(11):3726-3735.; Zhang, J., Chen, L., Wang, C., Zhuo, L., Tian, Q., and Liang, X. (2017). Road recognition from remote sensing imagery using incremental learning. IEEE Transactions on Intelligent Transportation Systems, 18(11):2993-3005.; Zhu, L., Peng, Z., and McClellan, J. (2018). Deep learning for seismic event detection of earthquake aftershocks. In 2018 52nd Asilomar Conference on Signals, Systems, and Computers, pages 1121-1125. IEEE.; Zimroz, R., Madziarz, M., Zak, G., Wy lomanska, A., and Obuchowski, J. (2015). Seismic signal segmentation procedure using time-frequency decomposition and statistical modelling. Journal of Vibroengineering, 17(6):3111-3121.; Zliobait_e, I., Pechenizkiy, M., and Gama, J. (2016). An overview of concept drift applications. In Big data analysis: new algorithms for a new society, pages 91-114. Springer.; Zobin, V. M. (2012). Volcano-tectonic earthquakes at andesitic volcanoes. In Introduction to volcanic seismology, volume 6. Elsevier.; Zúñiga, M. D., Bremond, F., and Thonnat, M. (2013). Hierarchical and incremental event learning approach based on concept formation models. Neurocomputing, 100:3-18.; https://repositorio.unal.edu.co/handle/unal/85512; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.co/

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    المصدر: DYNA; Vol. 81 Núm. 185 (2014); 28-35 ; DYNA; Vol. 81 No. 185 (2014); 28-35 ; 2346-2183 ; 0012-7353

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    Relation: https://revistas.unal.edu.co/index.php/dyna/article/view/34867/pdf_4; https://revistas.unal.edu.co/index.php/dyna/article/view/34867/53973; Marculescu R., Ogras U. Y., Peh L. S., Jerger N. E., Hoskote Y. Outstanding research problems in NoC design: system, microarchitecture, and circuit perspectives. Trans. Comp.-Aided Des. Integ. Cir. Sys., vol. 28, no. 1, pp. 3–21, Jan. 2009.; Huang J., Buckl C., Raabe A., Knoll A. Energy-aware task allocation for network-on-chip based heterogeneous multiprocessor systems. In Parallel, Distributed and Network-Based Processing (PDP), 2011 19th Euromicro International Conference on, pp. 447 –454, Feb. 2011.; Rajaei R., Hessabi S., Vahdat B. V. An energy-aware methodology for mapping and scheduling of concurrent applications in MPSOC architectures. In Electrical Engineering (ICEE), 2011 19th Iranian Conference on, pp. 1 –6, May 2011.; Ghosh P., Sen A., Hall A. Energy efficient application mapping to noc processing elements operating at multiple voltage levels. In Networks-on-Chip, 2009. NoCS 2009. 3rd ACM/IEEE International Symposium on, pp. 80 –85, May 2009.; Mandelli M., Ost L., Carara E., Guindani G. , Gouvea T., Medeiros G., Moraes F. Energy-aware dynamic task mapping for NoC-based MPSoCs. In Circuits and Systems (ISCAS), 2011 IEEE International Symposium on, pp. 1676 –1679, May 2011.; Marculescu R. Networks-on-chip: The quest for on-chip fault-tolerant communication. In VLSI, 2003 Proceedings of the IEEE Computer Society Annual Symposium on, pp. 8 – 12, Feb. 2003.; Schranzhofer A., Chen J. J., Santinelli L., Thiele L. Dynamic and adaptive allocation of applications on mpsoc platforms. In Design Automation Conference (ASP-DAC), 2010 15th Asia and South Pacific, pp. 885 –890, Jan. 2010.; Wildermann S., Ziermann T., Teich J. Run time mapping of adaptive applications onto homogeneous noc-based reconfigurable architectures. In Field-Programmable Technology 2009. FPT 2009. International Conference on, pp. 514 –517, Dec. 2009.; Carvalho E. de S., Calazans N., Moraes F. Dynamic task mapping for MPSoCs. Design Test of Computers, IEEE, vol. 27, no. 5, pp. 26 –35, Oct. 2010.; Singh A. K., Srikanthan T., Kumar A., Jigang W. Communication aware heuristics for run-time task mapping on NoC-based MPSoC platforms. J. Syst. Archit., vol. 56, no. 7, pp. 242–255, Jul. 2010.; Carvalho E., Calazans N., Moraes F. Heuristics for dynamic task mapping in NoC-based heterogeneous MPSoCs. In Proceedings of the 18th IEEE/IFIP International Workshop on Rapid System Prototyping, ser. RSP '07. Washington, DC, USA: IEEE Computer Society, pp. 34–40, 2007.; Derin O., Kabakci D., Fiorin L. Online task remapping strategies for fault-tolerant network-on-chip multiprocessors. In Networks on Chip (NoCS), 2011 Fifth IEEE/ACM International Symposium on, pp. 129 –136, May 2011.; Tafesse B., Raina A., Suseela J. , Muthukumar V. Efficient scheduling algorithms for MPSoC systems. In Information Technology: New Generations (ITNG), 2011 Eighth International Conference on, pp. 683 –688, April 2011.; Russell S. J., Norvig P. Artificial Intelligence: A Modern Approach. 2nd ed. Pearson Education, 2003.; Jang W., Pan D. Z. A3Map: Architecture-aware analytic mapping for Networks-on-Chip. ACM Trans. Des. Autom. Electron. Syst., vol. 17, pp. 26:1–26:22, July 2012.; Singh A. K., Kumar A., Srikanthan T. A hybrid strategy for mapping multiple throughput-constrained applications on MPSoCs. In Proceedings of the 14th international conference on Compilers, architectures and synthesis for embedded systems, CASES '11, (New York, NY, USA), pp. 175–184, ACM, 2011.; Antunes E., Soares M., Aguiar A., Filho S. J., Sartori M., Hessel F., Marcon C. A. M. Partitioning and dynamic mapping evaluation for energy consumption minimization on noc-based MPSoC. In ISQED (K. A. Bowman, K. V. Gadepally, P. Chatterjee, M. M. Budnik, and L. Immaneni, eds.), pp. 451–457, IEEE, 2012.; Antunes E., Aguiar A., Johann F. S., Sartori M. , Hessel F., Marcon C. Partitioning and mapping on NoC-based MPSoC: an energy consumption saving approach. In Proceedings of the 4th International Workshop on Network on Chip Architectures, NoCArc '11, (New York, NY, USA), pp. 51–56, ACM, 2011.; He O., Dong S., Jang W., Bian J., Pan D. Z. UNISM: Unified scheduling and mapping for general Networks on Chip. IEEE Trans. VLSI Syst., vol. 20, no. 8, pp. 1496–1509, 2012.; Hosseinabady M., Nunez-Yanez J. L. Run-time stochastic task mapping on a large scale Network-on-Chip with dynamically reconfigurable tiles. IET Computers and Digital Techniques, vol. 6, no. 1, pp. 1–11, 2012.; Hamedani P. K., Hessabi S., Sarbazi-Azad H., Jerger N. D. E. Exploration of temperature constraints for thermal aware mapping of 3D Networks on Chip. In PDP (R. Stotzka, M. Schiffers, and Y. Cotronis, eds.), pp. 499–506, IEEE, 2012.; Wang C., Yu L., Liu L., Chen T. Packet triggered prediction based task migration for Network-on-Chip. In Proceedings of the 2012 20th Euromicro International Conference on Parallel, Distributed and Network-based Processing, PDP '12, (Washington, DC, USA), pp. 491–498, IEEE Computer Society, 2012.; Kaushik S., Singh A. K., Jigang W., Srikanthan T. Run-time computation and communication aware mapping heuristic for NoC based heterogeneous MPSoC platforms. In Proceedings of the 2011 Fourth International Symposium on Parallel Architectures, Algorithms and Programming, PAAP '11, (Washington, DC, USA), pp. 203–207, IEEE Computer Society, 2011.; Zhe L., Xiang L. NoC mapping based on chaos artificial bee colony optimization. In Computational Problem-Solving (ICCP), 2011 International Conference on, pp. 518 –521, oct. 2011.; Mandelli M., Amory A., Ost L., Moraes F. G. Multi-task dynamic mapping onto NoC-based MPSoCs. In Proceedings of the 24th symposium on Integrated circuits and systems design, SBCCI '11, (New York, NY, USA), pp. 191–196, ACM, 2011.; Habibi A., Arjomand M., Sarbazi-Azad H. Multicast-aware mapping algorithm for on-chip networks. In Proceedings of the 2011 19th International Euromicro Conference on Parallel, Distributed and Network-Based Processing, PDP '11, (Washington, DC, USA), pp. 455–462, IEEE Computer Society, 2011.; Liu Y., Ruan Y., Lai Z., Jing W. Energy and thermal aware mapping for mesh-based NoC architectures using multi-objective ant colony algorithm. In Computer Research and Development (ICCRD), 2011 3rd International Conference on, vol. 3, pp. 407 –411, march 2011.; Zhong L., Sheng J., Jing M., Yu Z., Zeng X. , Zhou D. An optimized mapping algorithm based on simulated annealing for regular NoC architecture. In ASIC (ASICON), 2011 IEEE 9th International Conference on, pp. 389 –392, oct. 2011.; Sepulveda J., Strum M., Chau W. J., Gogniat G. A multiobjective approach for multi-application NoC mapping. In Circuits and Systems (LASCAS), 2011 IEEE Second Latin American Symposium on, pp. 1 –4, feb. 2011.; Sheng J., Zhong L., Jing M., Yu Z. , Zeng X. A method of quadratic programming for mapping on NoC architecture. In ASIC (ASICON), 2011 IEEE 9th International Conference on, pp. 200 –203, Oct. 2011.; Sangiovanni-Vincentelli A. Is a unified methodology for system-level design possible? IEEE Des. Test, vol. 25, pp. 346–357, July 2008.; Bonatti P. A., Lutz C., Murano A., Vardi M. ISO IEC 13818-2 MPEG2. Information Technology - Generic Coding of Moving Pictures and Associated Audio Information: Video," in ICALP 2006. LNCS, pp. 540–551, Springer, 2006.; Fan L. J., Li B., Zhuang Z. Q., Fu Z. Q. An approach for dynamic Hardware/Software partitioning based on DPBIL. In Proceedings of the Third International Conference on Natural Computation. Volume 05, ser. ICNC '07. Washington, DC, USA: IEEE Computer Society, 2007.; Bolanos F., Aedo J., Rivera F. System-level partitioning for embedded systems design using Population-based Incremental Learning. In CDES, H. R. Arabnia and A. M. G. Solo, Eds. CSREA Press, pp. 74–80, 2010.; White R. H. Competitive hebbian learning: Algorithm and demonstrations. Neural Networks, vol. 5, no. 2, pp. 261 – 275, 1992.; Thiele L., Bacivarov I., Haid W., Huang K. Mapping applications to tiled multiprocessor embedded systems. In Proceedings of the Seventh International Conference on Application of Concurrency to System Design, ser. ACSD '07. Washington, DC, USA: IEEE Computer Society, pp. 29–40, 2007.; https://revistas.unal.edu.co/index.php/dyna/article/view/34867

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