Using microbial fuel cell output metrics and nonlinear modeling techniques for smart biosensing

Science of the Total Environment

Volumn 449, Issue , 2013, Pages 223-228

  Feng, Yinghua ^a   Kayode, Olubanke ^a   Harper, Willie F ^b  

^a University of Pittsburgh   (United States)

^b AIR FORCE INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Biosensing; Microbial fuel cells; Neural network algorithms; Time series analysis; Water quality

Indexed keywords

DISCRETE WAVELET TRANSFORMS; HARMONIC ANALYSIS; MICROBIAL FUEL CELLS; NEURAL NETWORKS; NONLINEAR PROGRAMMING; NORMAL DISTRIBUTION; QUALITY CONTROL; TIME SERIES ANALYSIS; WATER QUALITY;

BIOSENSING; CELL-BASED; CELL/B.E; CHEMICAL-OXYGEN DEMANDS; METRIC MODEL; MODELLING TECHNIQUES; MULTI-PEAKS; NEURAL NETWORKS ALGORITHMS; NON-LINEAR MODELLING; TIME-SERIES ANALYSIS;

DATA HANDLING;

ARTIFICIAL NEURAL NETWORK; CHEMICAL OXYGEN DEMAND; DATA PROCESSING; FUEL CELL; MONITORING SYSTEM; NUMERICAL MODEL; TIME SERIES ANALYSIS; WATER QUALITY;

ANALYTIC METHOD; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOSENSING; BIOSENSOR; CHEMICAL OXYGEN DEMAND; CONCENTRATION (PARAMETERS); FIELD STUDY; MICROBIAL FUEL CELL; PRIORITY JOURNAL; SIGNAL DETECTION; STATISTICAL MODEL; TIME SERIES ANALYSIS; TREND STUDY; WATER ANALYSIS; WATER QUALITY;

EID: 84874382869     PISSN: 00489697     EISSN: 18791026     Source Type: Journal    
DOI: 10.1016/j.scitotenv.2013.01.004     Document Type: Article

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