publications

2020:

Journal papers:

J. M. Vazquez, E. Zamora, I. Gonzalez, R. Lozano, H. Sossa (2020). PD+SMC Quadorotor Control for Altitude and Crack Recognition using Deep Learning. International Journal of Control, Automation and Systems 18:834-844. ISSN: 1598-6446. ISSN online:2005-4092. FI 2.173. Q2. http://dx.doi.org/10.1007/s12555-018-0852-9

C. D. Virgilio, H. Sossa, J. M. Antelis, L. E. Falcón. Spiking Neural Network applied to the Classification of Motor Tasks in EEG Signals. Neural Networks 122:130-143. ISSN: 0893-6080. https://doi.org/10.1016/j.neunet.2019.09.037

S. Valadez, H. Sossa and R. Santiago. On the Accuracy and Computational Cost of Spiking Neuron Implementation. Neural Networks 122:196-217. ISSN: 0893-6080. https://doi.org/10.1016/j.neunet.2019.09.026.

G. Hernández, E. Zamora, H. Sossa, G. Tellez, F. Furlán. Hybrid neural networks for big data classification. Neurocomputing 390:327-340. ISSN: 0925-2312. https://doi.org/10.1016/j.neucom.2019.08.095

R. Santiago, H. Sossa, D. A. Gutiérrez, V. Zamudio, I. Hernádez, S. Valadez. Novel Mathematical Model of Breast Cancer Diagnostics using an Associative Pattern Classification. Diagnostics 10, 136. doi:10.3390/diagnostics10030136

R. Ochoa, G. Olague and H. Sossa. Expert knowledge for the recognition of leukemic cells. Applied Optics. 59(14):4448-4460. https://doi.org/10.1364/AO.385208

José A. Barraza and H. Sossa. 3D Motion Tracking of the Shoulder Joint with Respect to the Thorax Using MARG Sensors and Data Fusion Algorithm. Biocybernetics and Biomedical Engineering 40(3):1205-1224.

E. Bribiesca, U-D. Braumann, A. Carrillo, H. Sossa. An approach to the computation of the Euler number by means of the Vertex Chain Code. Computational and Mathematical Methods in Medicine. ISSN 1748-670X. Article ID 5632159, 13 pages.

F. Furlán, E. Rubio, H. Sossa and V. H. Ponce. CNN detectors on planetary environments, a performance evaluation. Frontiers in Neurorobotics. Octobre 2020. ISSN: 1662-5218. Manuscript ID: 590371. FI=2.574. Q2. DOI: 10.3389/fnbot.2020.590371

Books:

J. A. Mendoza, V. Gonzalez, G. Sepulveda, M. Mendez, H. Sossa. Advanced Robotic Vehicles Programming: An Ardupilot and Pixhawk Approach. Apress. ISBN 978-1-4842-5530-8. eBook ISBN 978-1-4842-5531-5. https://www.apress.com/gp/book/9781484255308. Nota: Por su impacto, este libro ya fue traducido al corano. Será publicado por la casa editorial GoldenBell Corporation en 2020, con ISBN 979-11-58-06-456-3. En el siguiente link, se puede adquieir en libro en Coreano: https://gbbook.co.kr/yc5/shop/item.php?it_id=1588927681

Conference Papers:

W. Gómez and H. Sossa. Towards Dendrite Spherical Neurons for Pattern Classification. In: Figueroa Mora K., Anzurez Marín J., Cerda J., Carrasco-Ochoa J., Martínez-Trinidad J., Olvera-López J. (eds) Pattern Recognition. MCPR 2020. Lecture Notes in Computer Science, LNCS 12088. Pp. 14-24. Springer, Cham. 12th Mexican Conference on Pattern Recognition (MCPR2020). Morelia, Michoacán, México, June 24-27, 2020. https://doi.org/10.1007/978-3-030-49076-8_2.

V. R. Virgilio, H. Sossa and E. Zamora E. (2020) Vision-Based Blind Spot Warning System by Deep Neural Networks. In: Figueroa Mora K., Anzurez Marín J., Cerda J., Carrasco-Ochoa J., Martínez-Trinidad J., Olvera-López J. (eds) Pattern Recognition. MCPR 2020. Lecture Notes in Computer Science, LNCS 12088. Pp. 185-194. Springer, Cham. 12th Mexican Conference on Pattern Recognition (MCPR2020). Morelia, Michoacán, México, June 24-27, 2020. https://doi.org/10.1007/978-3-030-49076-8_18.

E. López, E. Rubio, H. Sossa and V. H. Ponce. Mobile Robotic Navigation System with Improved Autonomy Under Diverse Scenarios. 19th Mexican International Conference on Artificial Intelligence (MICAI 2020). 12th to 17th October

2019:

Patents:

Aerotorso. J. A. Mendoza, J. J. González, P. Matabuena, M. H. Reyes, O. A. Domínguez, G. Sepúlveda, J A. Flores, H. S. Socconini, C. F. Aguilar, I. García, M. Méndez, J. R. Encinas, L. Fonseca, G. Mandujano, H. Sossa, M. S. Saurez, M. A. Sámano, M. A. Jimenez, G. De J. Lopez, V. J. González. Número de expediente: MX/a/2015/015160. Fecha de presentación: 30 de octubre de 2015. Publicación en gaceta junio 21 de 2017. Fecha de expedición: 10 de junio de 2019. Título de patente No. 365878 B.

Journal papers:

Ch. Gil, H. Calvo and H. Sossa. Learning an efficient Gait Cycle of a Biped Robot Based on Reinforcement Learning and Artificial Neural Networks. Applied Sciences-Basel 9(502):1-24. DOI: 10.3390/app9030502.

R. Rodriguez, Y. Garcer, E. Torres, H. Sossa and R. Tovar. A vision from a physical point of view and the information theory on the image segmentation. Journal of Intelligent & Fuzzy Systems 37(2):2835-2845. DOI: 10.3233/JIFS-190030.

G. Puriel, W. Yu, H. Sossa. Aprendizaje por Reforzamiento con Compensación de un Controlador PD para un doble Péndulo Invertido. IEEE Latin American Transactions. 17(2):323-329.

R. Martínez, E. Zamora, H. Sossa. Efficient FPGA hardware implementation for robot manipulator kinematic modelling. IEEE Latin American Transactions 17(9):1524-1535.

Book Chapters:

R. Rodríguez and H. Sossa. Mathematical Techniques for Biomedical Image Segmentation. Encyclopedia of Biomedical Engineering. First Edition. Pp. 64-78. ISSN 9780128048290. Elsevier. B978-0-12-801238-3.99989-6.

Conference Papers:

C. D. Virgilio, H. Sossa, J. M. Antelis and L. E. Falcón. Motor Imagery Task Classification in EEG Signals with Spiking Neural Network. 11th Mexican Conference on Pattern Recognition (MCPR-2019), Querétaro, Mexico, June 26 to June 29, 2019. LNCS 11524 Springer. Pp. 14-24. https://doi.org/10.1007/978-3-030-21077-9_2

F. Arce, O. Mendoza, E. Zamora, J. M. Antelis, H. Sossa, J. Cantillo, R. I. Carino, L. G. Hernandez and L. Falcon, Dendrite Ellipsoidal Neuron Trained by Stochastic Gradient Descent for Motor Imagery Classification. 11th Mexican Conference on Pattern Recognition (MCPR-2019), Querétaro, Mexico, June 26 to June 29, 2019. LNCS 11524 Springer. Pp. 80-89. https://doi.org/10.1007/978-3-030-21077-9_8

F. Furlan, E. Rubio, H. Sossa and V. Ponce. Rock Detection in a Mars-like Environment Using a CNN. 11th Mexican Conference on Pattern Recognition (MCPR-2019), Querétaro, Mexico, June 26 to June 29, 2019. LNCS 11524 Springer. Pp. 149-158. https://doi.org/10.1007/978-3-030-21077-9_14

R. Ochoa, O. Florez, H. Sossa and G. Olague. Automatic contrast enhacement with diferential evolution for identification of leukemia cells. 11th Mexican Conference on Pattern Recognition (MCPR-2019), Querétaro, Mexico, June 26 to June 29, 2019. LNCS 1124 Springer. Pp. 282-291. https://doi.org/10.1007/978-3-030-21077-9_26

G. Hernandez, L. G. Hernandez, J. M. Antelis, H. Sossa, O. Mendoza and L. E. Falcon. Using Morphological-Linear Neural Network for upper limb movement intention recognition from EEG signals. 11th Mexican Conference on Pattern Recognition (MCPR-2019), Querétaro, Mexico, June 26 to June 29, 2019. LNCS 11524 Springer Pp. 389-397. https://doi.org/10.1007/978-3-030-21077-9_36.

J. Cruz, J. Montiel and H. Sossa. 3-D Human body posture reconstruction by computer vision. 18th Mexican International Conference on Artificial Intelligence (MICAI 2019), 27th October to 2nd November in Xalapa, Veracruz, Mexico.

H. Sossa, E. Rubío, V. Ponce and H. Sánchez. Vertex Codification Applied to 3-D Binary Image Euler Number Computation. 18th Mexican International Conference on Artificial Intelligence (MICAI 2019), 27th October to 2nd November in Xalapa, Veracruz, Mexico.

2018:

Journal papers:

F. Arce, E. Zamora, C. Fócil and H. Sossa. Dendrite ellipsoidal neurons based on k-means optimization. Evolving Systems. July 2018. https://doi.org/10.1007/s12530-018-9248-6.

A. Sancen, R. Santiago, H. Sossa, F. J. Perez, J. J. Martínez, J. A. Padilla. Quantitative evaluation of binary digital region asymetry with application to skin detection. BMC Medical Informatics and Decision Making.https://doi.org/10.1186/s12911-018-0641-7.

F. Arce, E. Zamora, H. Sossa and R. Barrón. Differential Evolution Training Algorithm for Dendrite Morphological Neural Networks. Applied Soft Computing 68:303-313. https://doi.org/10.1016/j.asoc.2018.03.033.

J. M. Antelis, B. Gudiño, L. E. Falcón, G. Sánchez, H. Sossa. Dendral morphological neural networks for motor task recognition from elevtroencephalographic signals. Biomedical Signal Processing and Control 44:12-24. https:doi.org/10.2016/j.bspc.2018.03.010.

A. Luviano, E. Amaya, O. Gutiérrez, H. Sossa. Design and construction of a robotic platform for 3D reconstruction through an embedded processing system. IEEE Latin America Transactions 16(1):19-24.

E. Cabrera and H. Sossa. Generating exponentially stable states for a Hopfield Neural Network. Neurocomputing 275:358-365. https://doi.org.10.1016/j.neucom.2017.08.032

Book Chapters:

H. Sossa, F. Arce, E. Zamora and E. Guevara (2018). Chapter 2: Morphological Neural Networks with Dendritic Processing for Pattern Classification. Advanced Topics on Computer Vision, Control and Robotics in Mechatronics. Osslan O. Vergara, M. Nandayapa and I. Soto (Eds). Springer. Pp. 27-47. https://doi.org/10.1007/978-3-319-77770-2_2.

Conference Papers:

F. Arce, E. Zamora, G. Hernández, J. M. Antelis, H. Sossa. Recognizing Motor Imagery Tasks Using Deep Multi-Layer Perceptrons. MLDM 2018, LNAI 10935, pp. 468–482, https://doi.org/10.1007/978-3-319-96133-0_35

J. García, E. Zamora, H. Sossa. Supervised and unsupervised neural networks: Experimental study for anomaly detection in electrical consumption. Mexican International Conference on Artificial Intelligence (MICAI 2018). Guadalajara, Jalisco. Octubre 22-27, 2018. LNAL 11288. Pp. 91-105. DOI: 10.1007/978-3-030-04491-6_8

C. D. Virgilio, H. Sossa, J. M. Antelis. Artificial Neural Networks and Common Spatial Patterns for the recognition of motor electroencephalographic signals from EEG signals. Mexican International Conference on Artificial Intelligence (MICAI 2018). Guadalajara, Jalisco. Octubre 22-27, 2018. LNAL 11288. Pp. 106-117. DOI: 10.1007/978-3-030-04491-6_9

C. D. Virgilio, H. Sossa, E. Rubio, V. Ponce. Classification of Motor Imagery EEG Signals with CSP filtering through Neural Networks Models. Mexican International Conference on Artificial Intelligence (MICAI 2018). Guadalajara, Jalisco. Octubre 22-27, 2018. LNAL 11288. Pp. 118-129. DOI: 10.1007/978-3-030-04491-6_10

O. Tapia, H. Sánchez, H. López, H. Sossa. Coding 3D connected regions with F26 chain code. Mexican International Conference on Artificial Intelligence (MICAI 2018). Guadalajara, Jalisco. Octubre 22-27, 2018. LNAL 11289. Pp. 1-12. DOI: 10.1007/978-3-030-04497-8_1.

J. M. Vazquez, E. Zamora, I. Gonzalez, R. Lozano, H. Sossa. Towards automatic inspection: crack recognition based on Quadrotor UAV-taken images. 2018 International Conference on Unmanned Aircraft Systems (ICUAS 2918). June 12-15, 2018. Dallas, TX, USA. Pp. 654-659.

G. Hernández, E. Zamoray, H. Sossa. Morphological-Linear Neural Network. 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Windsor Convention Centre, Rio de Janeiro, BRAZIL. 08-13 July 2018. Pp. 470-475.

M. Mejía, J. J. Paredes, D. Paredes, H. Sossa. Neural Network for Shortest Path Problems Accelerated with Parakkek Multi-core Architecture. International Conference on Mechatronics, Electronics and Automotive Engineering 2018 (ICMEAE 2018), Cuernavaca, Morelos, Mexico. November 27-30, 2018.

2017:

Journal papers:

Y. Campos, H. Sossa, G. Pajares. Comparative analysis of texture descriptors in maize fields with plants, soil and object discrimination. Precision Agriculture 1-19.

E. Zamora and H. Sossa. Dendrite Morphological Neurons Trained by Stochastic Gradient Descent. Neurocomputing.doi.org/10.1016/j.neucom.2017.04.044.

E. Ortiz, M. Mejía, H. Sossa. Using Pulse Coupled Neural Networks to improve image filtering contamined with Gaussian Noise. Computación y Sistemas 21(2):381-395. doi: 10.13053/CyS-21-2-2742.

Book Chapters:

H. Sossa and H. Sánchez. Computing the Number of Bubbles and Tunnels of a 3-D Binary Object. LNCS 10163. A. Fred et al. (Eds.). Pattern Recognition Applications and Methods. 5th International Conference, ICPRAM 2016, Rome, Italy, February 24-26, 2016, Revised Selected Papers. Springer International Publishing. Pp. 194-211.

A. Peña, L. A. Cárdenas and H. Sossa. Chapter 3: A Landscape of Learning Analytics: An Exercise to Highlight the Nature of an Emergent Field. Learning Analytics: Fundaments, Applications and Trends. Studies in Systems, Decision and Control 94, A. Peña (Editor). Springer-Verlag.

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Conference Papers:

L. Delgado, G. Hernandez, E. Zamora, H. Sossa, A. Barreto, F. Ramos and R. Reyes. Classification of the Estrous Cycle through Texture and Shape Features. 2017 IEEE Symposium Series on Computational Intelligence (SSCI 2017). November 27-December 1, 2017. Hawaii, USA.

F. Arce, E. Zamora, G. Hernández, H. Sossa (2017). Efficient lane detection based on artificial neural networks. UDMS-SDSC_2017. 2nd International Conference on Smart Data and Smart Cities. October 4–6, 2017, Puebla, Mexico.

F. Furlán, E. Rubio, H. Sossa and V. Ponce (2017). Humanoid Robot Hierarchical Navigation Using Petri Nets and Fuzzy Logic. SICE 2017 Annual Conference. Kanazawa, Japan, September 19-22, 2017.

I. Chavez, H. Sanchez and H. Sossa (2017). Hyperspectral image compression using chain codes. Fifth International Conference on Advances in Computing, Communication and Information Technology (CCIT 2017). Zurich, Switzerland during 02-03 September, 2017. Pp. 93-98.

S. Valadez, H. Sossa and R. Santiago (2017). The Step Size Impact on the Computational Cost of Spiking Neuron Simulation. Computing Conference 2017, London, UK, July 18-20, 2017. Pp. 722-728.

F. Arce, E. Zamora and H. Sossa. Dendrite Ellipsoidal Neuron. IJCNN 2017. Anchorage, Alaska, USA, May 14-19, 2017.

E. de la Rosa, W. Yu and H. Sossa. Fuzzy Modeling from Black-Box Data with Deep Learning Techniques. Accepted 14^th International Symposium on Neural Networks (ISNN 2017). Sapporo, Hokkaido, Japan. June 21-26, 2017.

G. Hernández. E. Zamora, H. Sossa. Comparing Deep and Dendrite Neural Networks: A case Study. Mexican Conference in Pattern Recognition (MCPR 2017). Huatulco, México, Junio 21-24, 2017.

S. Valadez, J. González and H. Sossa. Efficient Pattern Recognition Using the Frequency Response of a Spiking Neuron. Mexican Conference in Pattern Recognition (MCPR 2017). Huatulco, México, Junio 21-24, 2017.

J. U. González, E. Rubio and H. Sossa. Visión por Computadora en un Robot Móvil Tipo Oruga. COMIA 2017.

F. Furlán, E. Rubio, H. Sossa and V. Ponce. Navegación jerárquica de un robot humanoide usando redes de Petri y lógica difusa. COMIA 2017.

2016:

Patents:

Levitador magnético para el equilibrio de robots bípedos. D. Zaldivar, E. Cuevas, M. Pérez, R. Rojas, H. Sossa, J. A. López. Número de solicitud: MX/a/2014/011236. Fecha de presentación 19 de septiembre de 2014. Fecha de comunicación de procedencia de otorgamiento de patente: 18 de octubre de 2016. Título de patente No. 343515 B.

Journal papers:

Y. Campos, H. Sossa, Gonzalo Pajares. Spatio-temporal analysis for obstacle detection in agricultural videos. Applied Soft Computing 45:86-97.

R. Ocampo, G. Sanchez, M. A. de Luna, R. Vega, L. E. Falcón, and H. Sossa. Using PCA and Logistic Regression to improve feature selection of DNA Microarray Data. Journal of Intelligent Data Analysis 20:S53-S67.

V. Osuna, E. Cuevas, D. Oliva, H. Sossa and M. Pérez. A bio-inspired evolutionary algorithm: Allostatic Optimization. International Journal of Bio-Inspired Computation 8(3):154-169.

Book Chapters:

V. Osuna, V. Zuñiga, D. Oliva, E. Cuevas, H. Sossa. Image Segmentation Using an Evolutionary Method Based on Allostatic Mechanisms. Studies in Computational Intelligence Vol. 630: Image Feature Detectors and Descriptors: Foundations and Applications. Eds. Ali Ismail and M. Hassaballah. Pp. 255-279.

Conference Papers:

H. Sossa (2016). On the Computation of the Number of Bubbles and Tunnels of a 3-D Binary Object, 5th International Conference on Pattern Recognition Applications and Methods, Roma, Italia, February 24-26, 2016. Pp. 17-23.

H. Sossa, A. Carreon and R. Santiago (2016). Training a Multilayered Perceptron to Compute the Euler Number of a 2-D Binary Image. MCPR 2016. LNCS 9703. Pp. 44-53.

R. Ocampo, G. Sanchez, L. E. Falcon and H. Sossa (2016).

Automatic Construction of Radial-Basis Function Networks through an Adaptive Partition Algorithm. MCPR 2016. LNCS 9703. Pp. 198-20'7.

B. Gudiño, H. Sossa, G. Sanchez and J. M. Antelis (2016). Classication of Motor States from Brain Rhythms Using Lattice Neural Networks. MCPR 2016. LNCS 9703. Pp. 303-312.

H. Sossa, A. Carreón, E. Guevara, R. Santiago (2016). Computing the 2-D Image Euler Number by an Artificial Neural Network, International Joint Conference on Neural Networks (IJCNN 2016), Vancouver, Canada. Pp. 1609-1616.

Y. Campos, E. Rodner, J. Denzler, H. Sossa, and G. Pajares (2016). Vegetation segmentation in cornfield images using Bag of Words. Advanced Concepts for Intelligent Vision Systems (ACIVS 2016). Museo Storico Della Citta' Di Lecce, Italy. Oct. 24-27, 2016. LNCS 10016. Pp. 193-204.

H. Sossa, A. Carreón, R. Santiago, E. Bribiesca, A. Petrilli (2016). Efficient Computation of the Euler Number of a 2-D Binary of Image. Mexican International Conference on Artificial Intelligence (MICAI 2016), Cancún, Quintana Roo, México, Oct. 23-29, 2016. Pp.

H. Sossa, A. Carreón, R. Santiago, A. Petrilli (2016). Support Vector Machines Applied to 2-D Binary Image Euler Number Computation, International Conference on Mechatronics, Electronics and Automotive Engineering 2016 (ICMEAE 2016), Cuernavaca, Morelos, Mexico. Pp 3-8.

F. Arce, E. Zamora, H. Sossa and R. Barrón (2016). Dendrite Morphological Neural Networks Trained by Differential Evolution. 2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016), Athens, Greece, December 6-9, 2016. Pp.

E. Zamora and H. Sossa (2016). Dendrite Morphological Neurons Trained by Stochastic Gradient Descent. 2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016), Athens, Greece, December 6-9, 2016. Pp.

2015:

Journal papers:

R. Vega, G. Sánchez, L. E. Falcón, H. Sossa and E. Guevara (2015). Retinal vessel extraction using Lattice Neural Networks with dendritic processing. Computers in Biology and Medecine 58:20-30.

V. Osuna, E. Cuevas, D. Oliva, H. Sossa and M. Pérez (2015). A bio-inspired evolutionary algorithm: Allostatic Optimization. International Journal of Bio-Inspired Computation.

R. Santiago, S. Valadez, H. Sossa, D. Asael, M. Ornelas (2015). A study of the Associative Pattern Classifier Method for Multi-Class Processes. Journal of Optoelectronics and Advanced Material 17(5-6):713-719. May 2015.

C. Aguilar, H. Sossa and S. Suárez. A Backstepping based procedure with saturation functions to control the PVTOL system. Nonlinear Dynamics. DOI 10.1007/s11071-015-2400-y

R. Ocampo, G. Sanchez, M. A. de Luna, R. Vega, L. E. Falcón, and H. Sossa. Using PCA and Logistic Regression to Improve Feature Selection of DNA Microarray Data. Journal of Intelligent Data Analysis 20:S53-S67.

Conference Papers:

L. Ojeda, R. Vega, L. E. Falcon, G. Sanchez-Ante, H. Sossa, and J. M. Antelis (2015). Classification of hand movements from non-invasive brain signals using lattice neural networks with dendritic processing. 7^th Mexican Conference on Pattern Recognition (MCPR 2015). LNCS 9116: 23-32. Springer Verlag.

C. Garibay, G. Sanchez-Ante, L. E. Falcon-Morales, and H. Sossa (2015). Modified Binary Inertial Particle Swarm Optimization for Gene Selection in DNA Microarray Data. 7^th Mexican Conference on Pattern Recognition (MCPR 2015). LNCS 9116: 271-281. Springer Verlag

S. Valadez, H. Sossa, R. Santiago-Montero, and E. Guevara (2015). Encoding polysomnographic signals into spike firing rate for sleep staging. 7^th Mexican Conference on Pattern Recognition (MCPR 2015). LNCS 9116: 282-291. Springer Verlag.

H. Sossa (2015). On the number of holes of a 2-D binary object. International Conference on Machine Vision (MVA 2015). Tokyo, Japan, May 18-22, 2015.

2014:

Journal papers:

A. Peña, H. Sossa, I. Méndez (2014). Activity theory as a framework for building adaptive E-Learning Systems: A case to provide empirical evidence. Computers in Human Behavior. 30:131-145.

H. Sossa and E. Guevara (2014). Efficient Efficient training for dendrite morphological neural networks. Neurcomputing 131:132-142.

H. Sossa, E. Rubio, A. Peña, E. Cuevas and R. Santiago (2014). Alternative Formulations to Compute the Binary Shape Euler Number. IET-Computer Vision. 8(3):171-181.

R. Santiago, M. A. López, H. Sossa (2014). Digital Shape Compactness measure by means of Perimeter Ratios. Electronics Letters 50(3):171-173.

Confrence Papers:

H. Sossa, G. Cortés and E. Guevara (2014). New Radial Basis Function Neural Network Architecture for Pattern Classification: First Results. 19^th Iberoamerican Congress on Pattern Recognition (CIARP 2014). Puerto Vallarta, México, 2-5 de noviembre de 2014. LNCS 8827, pp. 706-713.

R. Ocampo, M. A. de Luna, R. Vega, G. Sanchez-Ante, L. E. Falcon-Morales and H. Sossa (2014). Pattern Analysis in DNA Microarray Data through PCA-Based Gene Selection. 19^th Iberoamerican Congress on Pattern Recognition (CIARP 2014). Puerto Vallarta, México, 2-5 de noviembre de 2014. LNCS 8827, pp. 532-539.

G. Hernández, H. Sossa, E. Rubio, J. A. Escareño (2014). Identificación de objetos mediante un quadrotor equipado con una cámara y redes neuronales. XVI Congreso Latinoamericano de Control Automático (CLCA 2014). Octubre 14-17, Cancún, Quintana Roo, México. Pp. 1475-1480.

2013:

Books:

H. Sossa. Visión Artificial: Rasgos descriptores para el reconocimiento de objetos. Editorial RAMA. ISBN 978-84-9964-142-3.

Journal papers:

V. Osuna, E. Cuevas, H. Sossa (2013). A Comparison of Nature Inspired Algorithms for Multi-Threshold Image Segmentation. Expert Systems with Applications 40:1213-1219.

J. F. Serrano, C. Avilés, J. Villegas, H. Sossa (2013). Self Organizing Natural Scene Image Retrieval. Expert Systems with Applications 40:2398-2409.

H. Sánchez, H. Sossa, Ulf-Dietrich, E. Bribiesca (2013). The Euler-Poincaré Formula Through Contact Surfaces of Voxelized Objects. JART 11:58-78.

L. E. Gómez, H. Sossa, R. Barrón, F. Cuevas, J. F. Jiménez (2013). Redes neuronales dinámicas aplicadas a la recomendación musical optimizada. Polibits 45:83-92.

B. A. Garro, H. Sossa, R. A. Vázquez (2013). Diseño automatizado de redes neuronales mediante el uso del algoritmo de evolución diferencial (ED). Polibits 46:13-27.

E. Cuevas, V. Osuna, H. Sossa (2013). Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC). Applied Soft Computing 13:3047-3059.

H. Sossa, R. Santiago, E. Rubio, M. Pérez (2013). Computing the Euler Number of a Binary Image based on a Vertex Codification.JART 11:360-370.

D. Zaldivar, E. Cuevas, M. A. Pérez, H. Sossa, J. G. Rodríguez, E. O. Palafox (2013). An educational fuzzy-based control platform using LEGO robots. International Journal of Electrical Engineering Education. 50(2):157-171. ISSN 0200-7209 (Print). ISSN 2050-4578 (Online)

L. E. Gómez, H. Sossa, R. Barrón, F. Cuevas and J. F. Jiménez (2013). Redes neuronales dinámicas aplicadas a la recomendación musical optimizada. Polibits 47:89-95.

F. Pérez, H. Sossa, R. Martínez, D. Lorias and A. Minor (2013). Video-Based Tracking of Laparoscopic Instruments using an Orthogonal Webcams System. International Journal of Medical, Pharmaceutical Science and Engineering 7(8):1-4.

Book chapters:

A. Peña and H. Sossa (2013). Chapter 6: Fuzzy Cognitive maps for Applied Sciences and Engineering. Book: Fuzzy Cognitive Maps for Applied Sciences and Engineering. Springer.

Conference papers:

H. Sossa and E. Guevara. Modified Dendrite Morphological Neural Network Applied to 3D Object Recognition. Mexican Conference on Pattern Recognition (MCPR 2013). LNCS 7914, pp. 314-324.

H. Sossa and E. Guevara. Modified Dendrite Morphological Neural Network Applied to 3D Object Recognition on RGB-D Data. 8th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2013). LNAI 8073, pp. 304.313.

R. Vega, E. Guevara, L. E. Falcon, G. Sanchez-Ante and H. Sossa. Blood Vessel Segmentation in Retinal Images using Lattice Neural Networks. 12^th Mexican International Conference on Artificial Intelligence (MICAI 2013).

R. Ocampo, G. Sánchez, L. Falcón and H. Sossa. Automatic Reading of Electro-echanical Utility Meters. 12^th Mexican International Conference on Artificial Intelligence (MICAI 2013).

2012:

Journal papers:

E. Cuevas, D. Oliva, D. Zaldivar, M. Pérez and H. Sossa (2012). Circle detection using Electromagnetism-like optimization. Information Sciences 182(1):40-55.

A. Peña and H. Sossa, F. Cervantes (2012). Predictive Student Model Supported by Fuzzy-Causal Knowledge and Inference. Expert Systems with Applications 39:4690-4709.

R. Rodríguez, E. Torres and H. Sossa (2012). Image Segmentation via an iterative algorithm of the mean shift filtering for different values of the stoping thershold. International Journal of Imaging and Robotics 7(1):27-43.

E. Cuevas, F. Sención, D. Zaldivar. M. Perez and H. Sossa (2012). A Multi-threshold Segmentation Approach Based on Artificial Bee Colony Optimization. Applied Intelligence. DOI 10.1007/s10489-011-0330-z

L. E. Gómez, H. Sossa, R. Barrón and J. F. Sánchez (2012). A New Approach for Music Retrieval based on Dynamic Neural Network. International Journal of Hybrid Intelligent Systems 9:1-11. DOI 10.3233/HIS-2011-0143.

E. Cuevas, V. Osuna, D. Zaldivar, M. Perez and H. Sossa (2012). Multi-threshold Segmentation Based on Artificial Immne Systems. Mathematical Problems in Engineering. DOI: 10.1155/2012/874761.

Book chapters:

L. E. Toledo, F. J. Cuevas, J. Vielma, H. Sossa (2012). Chapter 5: Fringe Pattern Demodulation using Evolutionary Algorithms. Book Title: Advanced Topics in Measurements. Pp. 79-102. InTech. ISBN 978-953-51-0128-4.

A. Peña, I. Méndez, R. Mizoguchi, H. Sossa (2012), Proactive Sequencing based on a Causal and Fuzzy Student Model. Book Title: Intelligent and Adaptive Educational-Learning Systems: Achievements and Trends. A. Peña-Ayala, (Ed.). Springer-Verlag, New York, Series: Smart Innovation, Systems and Technologies Series, No. 17. Pp. 49-76. ISBN: 978-3-642-642-30170-4.

J. Villegas, G. Olague, H. Sossa and C. Avilés (2012). Evolutionary Associative Memories through Genetic Programming. Book Title: Parallel Architectures and Bioinspired Algorithms. Springer-Verlag. Series: Studies in Computational Intelligence. Vol. 415. Pp. 171-188. ISBN 978-3-642-28788-6.

R. Rodríguez, D. Domínguez, E. Torres and H. Sossa (2012). Image Segmentation through an Iterative Algorithm of the Mean Shift. Book Title: Advances in Image Segmentation, Pei-Gee Peter Ho(Ed.) INTECH. ISBN 980-953-307-581-0.

H. Sossa, B. A. Garro, J. Villegas, G. Olague and C. Avilés (2012). Evolutionary Computation Applied to the Automatic Design of Artificial Neural Networks and Associative Memories. AISC 175, pp. 285–297.

V. Osuna, E. Cuevas and H. Sossa (2012). Segmentation of Blood Cell Images Using Evolutionary Methods. AISC 175, pp. 299–311.

E. Cuevas, H. Sossa, V. Osuna, D. Zaldivar and M. Pérez (2012). Fast Circle Detection Using Harmony Search Optimization. AISC 175, pp. 313–325.

J. Jiménez, H. Sossa and F. Cuevas. Particle Swarm Optimization Applied to Interferogram Demodulation. AISC 175, pp. 327–337.

2011:

Books:

R. Rodríguez y H. Sossa (2011). Procesamiento y análisis digital de imágenes. Editorial RAMA. ISBN 978-84-9964-077-8.

Journal papers:

J. Villegas, H. Sossa, C. Avilés and G. Olague (2011). Automatic Synthesis of Associative Memories through Genetic Programming: a co-evolutionary approach. Revista Mexicana de Física 57(2):110-116.

R. Rodríguez, A. G. Suárez, H. Sossa (2011). A Segmentation Algorithm based on an Iterative Computation of the Mean Shift Filtering. Journal of Intelligent Robotic Systems 63(3):447-463.

R. A. Vázquez and H. Sossa (2011). Behavioral Study of Median Associative Memory under True Color Image Patterns. Neurocomputing 74:2985-2997.

H. Sossa, E. Cuevas and D. Zaldivar (2011). Alternative Way to Compute the Euler Number of a Binary Image. Journal of Applied Research and Technology 9(3):335-341.

R. Rodríguez, E. Torres and H. Sossa (2011). Image Segmentation based on an Iterative Computation of the Mean Shift Filtering for different values of window sizes. International Journal of Imaging and Robotics 6(11):1-19.

B. Garro, H. Sossa and R. A. Vazquez (2011). Back-Propagation vs Particle Swarm Optimization Algorithm: which Algorithm is better to adjust the Synaptic Weights of a Feed-Forward ANN? International Journal of Artificial Intelligence 7(11):208-218.