Multi-layer neural network classification of tongue movement ear pressure signal for human machine interface

The use of Deep Neural Networks (DNNs) for the classification of electrochemical mechanisms based on training with simulations of the initial cycle of potential have been reported. In this paper,...

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Neural Network Classification of Stellar Spectra

Statistical Challenges in Modern Astronomy II ◽

10.1007/978-1-4612-1968-2_42 ◽

1997 ◽

pp. 445-446

Author(s):

Coryn Bailer-Jones ◽

Mike Irwin ◽

Ted von Hippel

Keyword(s):

Neural Network ◽

Stellar Spectra ◽

Neural Network Classification

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Klasifikasi Motif Batik Berbasis Kemiripan Ciri dengan Wavelet Transform dan Fuzzy Neural Network

ComTech Computer Mathematics and Engineering Applications ◽

10.21512/comtech.v5i1.2630 ◽

2014 ◽

Vol 5 (1) ◽

pp. 361

Author(s):

A Haris Rangkuti

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Fuzzy Neural Network ◽

Classification Methods ◽

Neural Network Classification ◽

Fuzzy Input ◽

Fuzzy Neural ◽

Input Variables

This paper introduces a classification of the image of the batik process, which is based on the similarity of the characteristics, by combining the method of wavelet transform Daubechies type 2 level 2, to process the characteristic texture consisting of standard deviation, mean and energy as input variables, using the method of Fuzzy Neural Network (FNN). Fuzzyfikasi process will be carried out all input values with five categories: Very Low (VL), Low (L), Medium (M), High (H) and Very High (VH). The result will be a fuzzy input in the process of neural network classification methods. The result will be a fuzzy input in the process of neural network classification methods. For the image to be processed seven types of batik motif is ceplok, kawung, lereng, parang, megamendung, tambal and nitik. The results of the classification process with FNN is rule generation, so for the new image of batik can be immediately known motif types after treatment with FNN classification. For the degree of precision of this method is 86-92%.

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Parametric Convolutional Neural Network for Radar-based Human Activity Classification Using Raw ADC Data

10.36227/techrxiv.12896108 ◽

2020 ◽

Author(s):

Thomas Stadelmayer ◽

Avik Santra

Keyword(s):

Neural Network ◽

Human Activity ◽

Recognition System ◽

Radar Data ◽

Fast Time ◽

Activity Classification ◽

Radar Sensors ◽

Equivalent State ◽

Machine Interface

Radar sensors offer a promising and effective sensing modality for human activity classification. Human activity classification enables several smart homes applications for energy saving, human-machine interface for gesture controlled appliances and elderly fall-motion recognition. Present radar-based activity recognition system exploit micro-Doppler signature by generating Doppler spectrograms or video of range-Doppler images (RDIs), followed by deep neural network or machine learning for classification. Although, deep convolutional neural networks (DCNN) have been shown to implicitly learn features from raw sensor data in other fields, such as camera and speech, yet for the case of radar DCNN preprocessing followed by feature image generation, such as video of RDI or Doppler spectrogram, is required to develop a scalable and robust classification or regression application. In this paper, we propose a parametric convolutional neural network that mimics the radar preprocessing across fast-time and slow-time radar data through 2D sinc filter or 2D wavelet filter kernels to extract features for classification of various human activities. It is demonstrated that our proposed solution shows improved results compared to equivalent state-of-art DCNN solutions that rely on Doppler spectrogram or video of RDIs as feature images.

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