Signal Analysis for Assessment and Prediction of the Artificial Habitat in Shrimp Aquaculture

  • José Juan Carbajal Hernández
  • Luis Pastor Sanchez Fernandez
  • José Luis Oropeza Rodríguez
  • Edgardo Manuel Felipe Riverón
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)


This paper presents a novel work for prediction of artificial habitat in shrimp aquaculture based on environmental signal analysis. The physical-chemical variables that are involved into the system are studied for modeling and predicting environmental patterns.The prediction model is built using AR models that reconstruct a partial section of a particular measured signal. The physical-chemical variables are classified based on the negative ecological impact using a new statistical model that calculates the frequency and the deviation of the measurements. A fuzzy inference system processes the level classifications using aquaculture rules that define all the cases calculating the condition of the shrimp habitat.


fuzzy inference systems prediction signal analysis Assessment 


  1. 1.
    Martínez Córdova Rafael, M.: Cultivo de Camarones Pendidos. In: Principios y Practicas, AGT Editor S.A. (1994)Google Scholar
  2. 2.
    Hirono, Y.: Current practices of water quality management in shrimp farming and their limitations. In: Proceedings of the Special Session on Shrimp Farming. World Aquaculture Society, USA (1992)Google Scholar
  3. 3.
    [ACA] Agencia Catalana del Agua, Catalonia, Spain (2005), (accessed August 2007)
  4. 4.
    [NSF] National Sanitation Foundation International (2005), (accessed August 2007)
  5. 5.
    [CCME] Canadian Council of Ministers of the Environment (Canada). An assessment of the application and testing of the water quality index of the Canadian Council of Ministers of the Environment for selected water bodies in Atlantic Canada. National indicators and reporting office (2004), (accessed August 2007)
  6. 6.
    Kenneth, H.: Water Quality Prediction and Probability Network Models. North Carolina State University (1998)Google Scholar
  7. 7.
    Emmanuel, C.: Digital signal processing: a practical approach. Addison-Wesley, Reading (1993)Google Scholar
  8. 8.
    Chapra, S., Canale, R.: Métodos Numéricos para Ingenieros. McGraw-Hill, México (1999)Google Scholar
  9. 9.
    Cohen, L.: Time-frequency signal analysis. Prentice Hall PTR, Englewood Cliffs (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • José Juan Carbajal Hernández
    • 1
  • Luis Pastor Sanchez Fernandez
    • 1
  • José Luis Oropeza Rodríguez
    • 1
  • Edgardo Manuel Felipe Riverón
    • 1
  1. 1.Centre of Computer ResearchNational Polytechnic InstituteMéxicoMéxico

Personalised recommendations