Development of a non-invasive blood glucose monitor: application of artificial neural networks for signal processing

2002 ◽  
Author(s):  
M.B. Savage ◽  
S. Kun ◽  
H. Harjunmaa ◽  
R.A. Peura
Keyword(s):  
Neural Networks ◽  
Signal Processing ◽  
Artificial Neural Networks ◽  
Blood Glucose ◽  
Non Invasive ◽  
Artificial Neural ◽  
Blood Glucose Monitor

Employment of artificial neural networks for non-invasive estimation of leaf water status using color features: a case study in Spathiphyllum wallisii

2021 ◽  
Vol 43 (5) ◽  
Author(s):  
Amin Taheri-Garavand ◽  
Abdolhossein Rezaei Nejad ◽  
Dimitrios Fanourakis ◽  
Soodabeh Fatahi ◽  
Masoumeh Ahmadi Majd
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Status ◽  
Leaf Water ◽  
Leaf Water Status ◽  
Non Invasive ◽  
Color Features ◽  
Artificial Neural

Artificial neural networks applied to the classification of hair samples according to pigment and sex using non‐invasive analytical techniques

2020 ◽  
Vol 49 (6) ◽  
pp. 632-641
Author(s):  
Tamires Messias Berto ◽  
Mônica Cardoso Santos ◽  
Fabíola Manhas Verbi Pereira ◽  
Érica Regina Filletti
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Analytical Techniques ◽  
Non Invasive ◽  
Hair Samples ◽  
Artificial Neural

scholarly journals AN OVERVIEW OF METHODS FOR GENERATING, AUGMENTING AND EVALUATING ROOM IMPULSE RESPONSE USING ARTIFICIAL NEURAL NETWORKS

2021 ◽  
Vol 13 (0) ◽  
pp. 1-5
Author(s):  
Mantas Tamulionis
Keyword(s):  
Neural Networks ◽  
Signal Processing ◽  
Artificial Neural Networks ◽  
Speech Recognition ◽  
Impulse Response ◽  
Automatic Speech Recognition ◽  
Audio Signal ◽  
Training Data ◽  
Audio Signal Processing ◽  
Artificial Neural

Methods based on artificial neural networks (ANN) are widely used in various audio signal processing tasks. This provides opportunities to optimize processes and save resources required for calculations. One of the main objects we need to get to numerically capture the acoustics of a room is the room impulse response (RIR). Increasingly, research authors choose not to record these impulses in a real room but to generate them using ANN, as this gives them the freedom to prepare unlimited-sized training datasets. Neural networks are also used to augment the generated impulses to make them similar to the ones actually recorded. The widest use of ANN so far is observed in the evaluation of the generated results, for example, in automatic speech recognition (ASR) tasks. This review also describes datasets of recorded RIR impulses commonly found in various studies that are used as training data for neural networks.

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