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http://dx.doi.org/10.9728/dcs.2017.18.6.1165

Neural networks optimization for multi-dimensional digital signal processing in IoT devices  

Choi, KwonTaeg (Division of Software Application, Kangnam University)
Publication Information
Journal of Digital Contents Society / v.18, no.6, 2017 , pp. 1165-1173 More about this Journal
Abstract
Deep learning method, which is one of the most famous machine learning algorithms, has proven its applicability in various applications and is widely used in digital signal processing. However, it is difficult to apply deep learning technology to IoT devices with limited CPU performance and memory capacity, because a large number of training samples requires a lot of memory and computation time. In particular, if the Arduino with a very small memory capacity of 2K to 8K, is used, there are many limitations in implementing the algorithm. In this paper, we propose a method to optimize the ELM algorithm, which is proved to be accurate and efficient in various fields, on Arduino board. Experiments have shown that multi-class learning is possible up to 15-dimensional data on Arduino UNO with memory capacity of 2KB and possible up to 42-dimensional data on Arduino MEGA with memory capacity of 8KB. To evaluate the experiment, we proved the effectiveness of the proposed algorithm using the data sets generated using gaussian mixture modeling and the public UCI data sets.
Keywords
Neural Network; Extreme Learning Machine; Machine Learning; Arduino;
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