Neural networks and pattern recognition techniques applied to optical fibre sensors

2000 ◽  
Vol 22 (5) ◽  
pp. 385-404 ◽  
Author(s):  
W.B. Lyons ◽  
E. Lewis
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Optical Fibre ◽  
Pattern Recognition Techniques ◽  
Optical Fibre Sensors

scholarly journals Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 509 ◽  
Author(s):  
Ivan Miguel Pires ◽  
Gonçalo Marques ◽  
Nuno M. Garcia ◽  
Francisco Flórez-Revuelta ◽  
Maria Canavarro Teixeira ◽  
...  
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Mobile Device ◽  
Daily Living ◽  
Accelerometer Data ◽  
Pattern Recognition Techniques ◽  
Multiple Sensors ◽  
Computational Resources

The application of pattern recognition techniques to data collected from accelerometers available in off-the-shelf devices, such as smartphones, allows for the automatic recognition of activities of daily living (ADLs). This data can be used later to create systems that monitor the behaviors of their users. The main contribution of this paper is to use artificial neural networks (ANN) for the recognition of ADLs with the data acquired from the sensors available in mobile devices. Firstly, before ANN training, the mobile device is used for data collection. After training, mobile devices are used to apply an ANN previously trained for the ADLs’ identification on a less restrictive computational platform. The motivation is to verify whether the overfitting problem can be solved using only the accelerometer data, which also requires less computational resources and reduces the energy expenditure of the mobile device when compared with the use of multiple sensors. This paper presents a method based on ANN for the recognition of a defined set of ADLs. It provides a comparative study of different implementations of ANN to choose the most appropriate method for ADLs identification. The results show the accuracy of 85.89% using deep neural networks (DNN).

scholarly journals Pattern recognition techniques for the identification of Activities of Daily Living using mobile device accelerometer

2018 ◽  
Author(s):  
Ivan Miguel Pires ◽  
Nuno M. Garcia ◽  
Nuno Pombo ◽  
Francisco Flórez-Revuelta ◽  
Susanna Spinsante ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Data Processing ◽  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Daily Living ◽  
Pattern Recognition Techniques ◽  
Artificial Intelligence Methods ◽  
Processing Power

This paper focuses on the recognition of Activities of Daily Living (ADL) applying pattern recognition techniques to the data acquired by the accelerometer available in the mobile devices. The recognition of ADL is composed by several stages, including data acquisition, data processing, and artificial intelligence methods. The artificial intelligence methods used are related to pattern recognition, and this study focuses on the use of Artificial Neural Networks (ANN). The data processing includes data cleaning, and the feature extraction techniques to define the inputs for the ANN. Due to the low processing power and memory of the mobile devices, they should be mainly used to acquire the data, applying an ANN previously trained for the identification of the ADL. The main purpose of this paper is to present a new method based on ANN for the identification of a defined set of ADL with a reliable accuracy. This paper also presents a comparison of different types of ANN in order to choose the type for the implementation of the final model. Results of this research probes that the best accuracies are achieved with Deep Neural Networks (DNN) with an accuracy higher than 80%. The results obtained are similar with other studies, but we compared tree types of ANN in order to discover the best method in order to obtain these results with less memory, verifying that, after the generation of the model, the DNN method, when compared with others, is also the fastest to obtain the results with better accuracy.

scholarly journals Evaluation of hyperparameters in CNN for detecting patterns in images

2018 ◽  
Vol 11 (2) ◽  
pp. 140-145
Author(s):  
Robinson Jiménez Moreno ◽  
Oscar Avilés ◽  
Diana Marcela Ovalle
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Electric Energy ◽  
Effective Solution ◽  
Current Pattern ◽  
New Techniques ◽  
Pattern Recognition Techniques ◽  
Learning Techniques ◽  
Images Processing

Deep learning techniques have emerged as an effective solution to the problems of current pattern recognition techniques, such as neural networks. Within these new techniques, the convolutional neural networks (CNN) offer an integration to the recognition of patterns in images, given by the traditional set of images processing plus neuronal networks. This article presents the analysis of the different hyper parameters that imply the training of a CNN, which allows to validate the effects on the accuracy of the network. It is used as a base the recognition of electric energy meters, obtaining a network with an accuracy of 96.32 %.

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