Mintic

Measurement Analysis and Decision-Making Support Laboratory
– AMYSOD Lab –

Parque i, F201- ITM Campus Robledo, 050034, Medellin-Colombia

Director: Edilson Delgado-Trejos https://orcid.org/0000-0002-4840-478X
Correo electrónico: edilsondelgado@itm.edu.co

Research Group on Quality, Metrology and Production
(A1-MinCiencias-Colombia)

Presentation

The Measurement Analysis and Decision Support Laboratory – AMYSOD Lab – is a scientific laboratory of the Integrated Center of Scientific Laboratories –Parque i of the ITM University of Medellin-Colombia, where scientific research principles are applied to topics, such as: scientific metrology, soft metrology, measurement analysis using multivariate statistical methods, decision support systems based on measurements, smart measurements based on machine learning and, in general, measurement schemes based on artificial intelligence and soft computing. The scientific and technological production of this lab is derived from the relationship with social and productive sectors, through projects in Science, Technology, and Innovation (STI).

AMYSOD Lab plans to consolidate the cooperative and collaborative relationship with regional and global partners, by developing projects and applying for funding with the aim of increasing the laboratory´s capital budgeting. In this sense, AMYSOD Lab is creating links within an international network of working groups in STI, with the aim of generating high impact products of greater scientific and technological potential. From an academic formation perspective, AMYSOD Lab supports productive and qualified human resources, in different levels, from undergraduate to master or doctorate programs.

AMYSOD Lab

This laboratory offers the following specialized services:

  1. Formulation, execution, and validation of scientific research projects on decision support systems based on measurements, smart measurements based on machine learning and measurement schemes based on artificial intelligence and soft computing.
  2. Advisements and consultancies on scientific metrology, soft metrology, measurement analysis from multivariate statistical methods, decision support systems based on measurements, smart measurements based on machine learning and measurement schemes based on artificial intelligence and soft computing.
  3. Scientific solutions using soft metrology and measurement processes based on artificial intelligence and soft computing for decision support systems.

The AMYSOD lab has focused a significant portion of its scientific research challenges on the fields of soft metrology and soft sensors. Selected works that develop and further expand these concepts are listed below:

  • (2020) “Soft metrology based on machine learning: a review”, Measurement Science and Technology, 31 032001 (16pp).

DOI: https://doi.org/10.1088/1361-6501/ab4b39

  • (2023). “Soft Metrology: Concept and Challenges from Uncertainty Estimation”. In Handbook of Metrology and Applications (pp. 1–31). Springer Nature Singapore.

DOI: https://doi.org/10.1007/978-981-19-1550-5_67-1

  • (2023) “Aleatoric and Epistemic Uncertainty in Soft Metrology: A Perspective Based on Ensuring the Validity of Results”, Ingeniería, 28(2), pp. e18883.

DOI: https://doi.org/10.14483/23448393.18883

amysod lab

Relevant publications

  • (2018) “Diagonal time dependent state space models for modal decomposition of non-stationary signals”, Signal Processing, 147, 208–223.

DOI  https://doi.org/10.1016/j.sigpro.2018.01.031

  • (2018) “Preprocessing of 2-Dimensional Gel Electrophoresis Images Applied to Proteomic Analysis: A Review”, Genomics, Proteomics & Bioinformatics, 16(1): 63-72.

DOI  https://doi.org/10.1016/j.gpb.2017.10.001

  • (2018) “Neuromuscular disease detection by neural networks and fuzzy entropy on time‐frequency analysis of electromyography signals”, Expert Systems, e12274.

DOI  https://doi.org/10.1111/exsy.12274

(2018) “Exploratory Study of the Effects of Cardiac Murmurs on Electrocardiographic-Signal-Based Biometric Systems”. In the book H. Yin et al. (Eds.): Lecture Notes in Computer Science – IDEAL 2018, LNCS 11314, pp. 410–418. Springer-Verlag.

DOI  https://doi.org/10.1007/978-3-030-03493-1_43

  • (2019) “Embedded Dimension and Time Series Length. Practical Influence on Permutation Entropy and Its Applications”, Entropy 21(1), 385-410.

DOI  https://doi.org/10.3390/e21040385

  • (2019) “Cardiac Murmur Effects on Automatic Segmentation of ECG Signals for Biometric Identification: Preliminary Study”. In the book N. T. Nguyen et al. (Eds.): Lecture Notes in Artificial Intelligence – ACIIDS 2019, LNAI 11431, pp. 269–279. Springer-Verlag.

DOI  https://doi.org/10.1007/978-3-030-14799-0_23

  • (2020) “Soft metrology based on machine learning: a review”, Measurement Science and Technology, 31 032001 (16pp)

DOI https://doi.org/10.1088/1361-6501/ab4b39

  • (2020) “Speckle Noise Reduction in Ultrasound Images for Improving the Metrological Evaluation of Biomedical Applications: An Overview”, IEEE Access, 8, pp. 15983-15999.

DOI  https://doi.org/10.1109/ACCESS.2020.2967178

  • (2020) “Muscle fatigue analysis during dynamic contractions based on biomechanical features and Permutation Entropy”, Mathematical Biosciences and Engineering, 17(3), 2592–2615.

DOI  http://dx.doi.org/10.3934/mbe.2020142

  • (2020) “Joint pre-processing framework for two-dimensional gel electrophoresis images based on nonlinear filtering, background correction and normalization techniques,” BMC Bioinformatics, 21(131):376.

DOI https://doi.org/10.1186/s12859-020-03713-0

  • (2020) “Traffic characterization in a communication channel for monitoring and control in real-time systems,” Indonesian Journal of Electrical Engineering and Informatics, 8(4):683-695.

DOI https://doi.org/10.11591/ijeei.v8i4.1695

  • (2021) “Uncertainty estimation in the sphygmomanometers calibration according to OIML R16-1 from a legal metrology perspective,” Ingeniería y Universidad, 25, pp. 1-23.

DOI https://doi.org/10.11144/javeriana.iued25.uesc

  • (2021) “Dataset of two-dimensional gel electrophoresis images of acute myeloid leukemia patients before and after induction therapy,” Data, 6(2):1-5.

DOI https://doi.org/10.3390/data6020020

  • (2021) “State-space modal representations for decomposition of multivariate non-stationary signals,” IFAC-PapersOnLine, 54(7):475–480.

DOI https://doi.org/10.1016/j.ifacol.2021.08.405

  • (2021) “Dataset of flow-induced vibrations on a pipe conveying cold water,” Data 6(9):1-13.

DOI https://doi.org/10.3390/data6090100

  • (2021) “Metrological Advantages of Applying Vibration Analysis to Pipelines: A Review,” Scientia et Technica, 26(1):28–35.

DOI  https://doi.org/10.22517/23447214.24351

  • (2021) “Automatic sign language recognition based on accelerometry and surface electromyography signals: A study for Colombian sign language,” Biomedical Signal Processing and Control, 71, 103201.

DOI https://doi.org/10.1016/j.bspc.2021.103201

  • (2022) “Bayesian Evaluation for Uncertainty of Indirect Measurements in Comparison with GUM and Monte Carlo,” Ingeniería y Universidad, 26, pp. 1–26.

DOI  https://doi.org/10.11144/javeriana.iued26.beui

  • (2022) “Uniform and Non-uniform Embedding Quality Using Electrocardiographic Signals”. In J. M. Ferrández Vicente, J. R. Álvarez-Sánchez, F. de la Paz López, & H. Adeli (Eds.), Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications (pp. 605–614). Springer International Publishing.

DOI  https://doi.org/https://doi.org/10.1007/978-3-031-06242-1_60

  • (2022) “Variable Embedding Based on L–statistic for Electrocardiographic Signal Analysis”. In J. M. Ferrández Vicente, J. R. Álvarez-Sánchez, F. de la Paz López, & H. Adeli (Eds.), Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications (pp. 595–604). Springer International Publishing.

DOI  https://doi.org/https://doi.org/10.1007/978-3-031-06242-1_59

  • (2023) “State space model-based harmonic decomposition of pseudo-periodic non-stationary multivariate signals”, Signal Processing, 213, pp. 109192.

Available at  https://doi.org/10.1016/j.sigpro.2023.109192

  • (2023) “Soft Metrology: Concept and Challenges from Uncertainty Estimation”. In: Aswal, D.K., Yadav, S., Takatsuji, T., Rachakonda, P., Kumar, H. (eds) Handbook of Metrology and Applications (pp. 1–31). Springer Nature Singapore.

DOI  https://doi.org/10.1007/978-981-19-1550-5_67-1

  • (2024) “Analysis of Impedance Vector Trajectory when Applying Physical Effort: A Case Study,” 2024 3rd International Congress of Biomedical Engineering and Bioengineering (CIIBBI), Cali, Colombia, IEEE

DOI https://doi.org/10.1109/CIIBBI63846.2024.10785183

  • (2025) “Machine Learning and Soft Sensors of Electronic Nose and Tongue Type for Cancer Detection,” TecnoLógicas, 28(63):e3296

DOI https://doi.org/10.22430/22565337.3296

  • (2026) Flow rate estimation from flow-induced vibration using signal decomposition and advanced regression models,” Flow Measurement and Instrumentation, 107, pp. 103084

DOI https://doi.org/10.1016/j.flowmeasinst.2025.103084

Laboratorio de Análisis de Medición y Soporte de Decisión

– Laboratorio AMYSOD –

Presentación

El Laboratorio de Análisis de Medición y Soporte de Decisión – Laboratorio AMYSOD – es un laboratorio de investigación del Centro Integrado de Laboratorios Científicos –Parque i del Instituto Tecnológico Metropolitano ITM de Medellín, donde se investigan temas relacionados con metrología científica, soft metrología, análisis de medición usando métodos estadísticos multivariados, sistemas de soporte de decisión basado en mediciones, mediciones inteligentes basadas en máquinas de aprendizaje y, en general, esquemas de medición basados en inteligencia artificial y técnicas de soft computing. La producción científica y tecnológica de este laboratorio se deriva del relacionamiento con los sectores sociales y productivos, a través de proyectos de Ciencia, Tecnología e Innovación (CTeI).

El Laboratorio AMYSOD proyecta consolidar el relacionamiento cooperativo y colaborativo con aliados del ámbito regional o global, mediante el sometimiento de proyectos a convocatorias que permitan aumentar el capital de financiación del laboratorio. En este sentido, el laboratorio está fortaleciendo vínculos en una red internacional de grupos de trabajo en CTeI, de cara a la producción de alto impacto en las áreas de estudio con mayor potencial científico y tecnológico. Complementariamente, desde la perspectiva de formación, el laboratorio contribuye a una infraestructura de calidad competitiva, donde se fortalece el talento humano productivo y calificado del territorio, en los niveles de pregrado, maestría y doctorado.

Servicios (Técnicos, divulgativos)

Este laboratorio ofrece los siguientes servicios especializados a entidades públicas o privadas:

  1. Formulación, ejecución, y validación de proyectos de investigación sobre sistemas de soporte de decisión basados en mediciones, mediciones inteligentes basadas en máquinas de aprendizaje, y esquemas de medición basados en inteligencia artificial y soft computing.
  2. Asesorías y consultorías relacionadas con metrología científica, soft metrología, análisis de medición usando métodos estadísticos multivariados, sistemas de soporte de decisión basado en mediciones, mediciones inteligentes basadas en máquinas de aprendizaje y esquemas de medición basados en inteligencia artificial y técnicas de soft computing.
  3. Soluciones científicas usando soft metrología y procesos de medición basados en inteligencia artificial y técnicas de soft computing para sistemas de soporte de decisión.

Ubicación

Instituto Tecnológico Metropolitano ITM

Parque i, F201

ITM Campus Robledo, 050034, Medellín-Colombia

Grupos de Investigación

Líder del laboratorio

Edilson Delgado Trejos
https://orcid.org/0000-0002-4840-478X
edilsondelgado@itm.edu.co

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