Department of Mathematics
Technical University of Catalunya
Barcelona Est Engineering School
Eduard Maristany 16
08019, Barcelona, Spain
e-mail: jose.rodellar@upc.edu
Professor
Director of the Department of Mathematics
Director of the Research Group Control, Modelling, Identification and Applications (CoDAlab)
J. Rodellar has achieved international recognition, publishing extensively in the above lines (around 250 references in JCR), including three books and six patents. He has been principal investigator of 27 competitive projects. He has supervised 26 doctoral thesis (4 in progress) and a dozen of postdocs and visiting researchers.
A full organized list is available by CLICKING to the web of the UPC Scientific Production.
Journals
Acevedo, A.; Alférez, S.; Merino, A.; Puigví, L.; Rodellar, J. Recognition of peripheral blood cell images using convolutional neural networks. Computer Methods and Programs in Biomedicine 2019. doi.org/10.1016/j.cmpb.2019.105020105020.
Alférez, S.; Merino, A.; Acevedo, A.; Puigví, L.; Rodellar, J. Colour clustering segmentation framework for image analysis of malignant lymphoid cells in peripheral blood. Medical & Biological Engineering & Computing 2019. DOI:10.1007/s11517-019-01954-7.
Puigví, L.; Merino, A.; Boldú L.; Acevedo, A.; Alférez, S.; Rodellar, J. Quantitative cytological descriptors to differentiate CLL, Sézary, granular and villous lymphocytes through image analysis. American Journal of Clinical Pathology 2019. DOI: 10.1093/ajcp/aqz025.
Mújica, L.E.; Gharibnezhad, F.; Rodellar, J.; Todd, M. Considering temperature effect on robust principal component analysis orthogonal distance as a damage detector. Structural Health Monitoring 2019. https://doi.org/10.1177/1475921719861908.
Rodellar, J.; Alférez, S.; Acevedo, A.; Molina A.; Merino A. Image processing and machine learning in the morphological analysis of blood cells. International Journal of Laboratory Hematology 2018. DOI: 10.1111/ijlh.12818.
Merino, A.; Puigví, L.; Boldú, L.; Alferez, S.; Rodellar, J. Optimizing morphology through blood cell image analysis. International Journal of Laboratory Hematology 2018. DOI:10.1111/ijlh.12832.
Ruiz, M.; Mujica, L.E.; Alférez, S.; Acho, L.; Tutivén, C.; Vidal, Y.; Rodellar, J.; Pozo, F.; Wind turbine fault detection and classification by means of image texture analysis. Mechanical Systems and Signal Processing 2018. DOI:10.1016/j.ymssp.2017.12.035.
Ruiz, M.; Mújica, L.E.; Sierra, J.; Pozo, F.; Rodellar, J. Multiway principal component analysis contributions for structural damage location. Structural Health Monitoring-an International Journal, 2018; 17(5), 1151-1165.
Puigví, L.; Merino, A.; Alférez, S.; Acevedo, A.; Rodellar, J. New quantitative features for the morphological differentiation of abnormal lymphoid cell images from peripheral blood. Journal of Clinical Pathology 2017; 70: 1038-48. DOI: 10.1136/jclinpath-2017-204389.
Alferez, E.; Merino, A.; Bigorra, L.; Rodellar, J. Characterization and automatic screening of reactive and abnormal neoplastic B lymphoid cells from peripheral blood. International Journal of Laboratory Hematology 2016; 38(2): 209-219. DOI: 10.1111/ijlh.12473.
Bigorra, L.; Merino, A.; Alferez, E.; Rodellar, J. Feature Analysis and Automatic Identification of Leukemic Lineage Blast Cells and Reactive Lymphoid Cells from Peripheral Blood Cell Images. Journal of Clinical Laboratory Analysis 2016. DOI: 10.1002/jcla.22024.
Alferez, E.; Merino, A.; Bigorra, L.; Mujica, L.E.; Ruiz, M.; Rodellar, J. Automatic recognition of atypical lymphoid cells from peripheral blood by digital image analysis. American Journal of Clinical Pathology 2015; 143(2): 168-76. DOI: 10.1309/AJCP78IFSTOGZZJN.
Alferez, E.; Merino, A.; Mujica, L.E.; Ruiz, M.; Bigorra, L.; Rodellar, J. Automatic classification of atypical lymphoid B cells using digital blood image processing. International Journal of Laboratory Hematology 2014; 36(4): 472-80. DOI: 10.1111/ijlh.12175.
Gharibnezhad, F.; Mújica, L.E.; Rodellar, J. Applying robust variant of Principal Component Analysis as a damage detector in the presence of outliers. Mechanical Systems and Signal Processing 2015; 50-51: 467-479.
Mújica, L.E.; Ruiz, M.; Pozo, F.; Rodellar, J.; Güemes, A. A structural damage detection indicator based on principal component analysis and statistical hypothesis testing, Smart Materials and Structures 2014; 23(2).
Torres-Arredondo, A.; Tibaduiza, D.; Mújica, L.E.; Rodellar, J.; Fritzen, C.P. Data-driven multivariate algorithms for damage detection and identification: Evaluation and comparison, Structural Health Monitoring-an International Journal 2014; 13(1): 19-32.
Tibaduiza, D.; Torres-Arredondo, M.A.; Mújica, l.E.; Rodellar, J.; Fritzen. C.P. A study of two unsupervised data driven statistical methodologies for detecting and classifying damages in structural health monitoring, Mechanical Systems and Signal Processing 2013; 41: 467-484.
Mújica, L.E.; Rodellar, J.; Fernández, A.; Güemes, A. Q-statistic and T-2-statistic PCA-based measures for damage assessment in structures, Structural Health Monitoring-an International Journal 2011; 10(5): 539-553.