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Intelligent Sensor Data Pre-processing Using Continuous Restricted Boltzmann Machine

The objective of the project is to finda solution to pre-process noisy signalfor sensors in Lab-on-a-Chip (LOC) and System-on-Chip (SOC) technologies. This solution must be able to process continuous-time, analogue sensor signals directly. It must also be amenable to hardware implementation, with...

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Main Author: Suhaimi, Emil Zaidan
Format: Final Year Project
Language:English
Published: Universiti Teknologi PETRONAS 2007
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://utpedia.utp.edu.my/9537/1/2007%20-%20Intelligent%20Sensor%20Data%20Pre-Processing%20using%20Continuous%20Restricted%20Boltzmann%20Machine.pdf
http://utpedia.utp.edu.my/9537/
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spelling my-utp-utpedia.95372017-01-25T09:45:54Z http://utpedia.utp.edu.my/9537/ Intelligent Sensor Data Pre-processing Using Continuous Restricted Boltzmann Machine Suhaimi, Emil Zaidan TK Electrical engineering. Electronics Nuclear engineering The objective of the project is to finda solution to pre-process noisy signalfor sensors in Lab-on-a-Chip (LOC) and System-on-Chip (SOC) technologies. This solution must be able to process continuous-time, analogue sensor signals directly. It must also be amenable to hardware implementation, with low power consumption. This solution is found in the Continuous Restricted Boltzmann Machine (CRBM), which is a type of Artificial Neural Network which exhibits probabilistic and stochastic behavior. CRBM utilizes continuous stochastic neurons, where Gaussian noise is applied to the inputofthe neurons. The noise inputs cause neurons to have continuous-valued, probabilistic outputs. The use ofstochastic neurons in CRBMgives it modelingflexibility that is useful with real data. The training algorithm of CRBM requires only addition c;nd multiplication, which is computationally inexpensive in hardware and software. The ability ofCRBM to model any given data set is shown by training the CRBM on various data sets reflecting real-world data. In this study, CRBM is shown to be suitable to be implemented in LOC andSOC applications aforementioned. Universiti Teknologi PETRONAS 2007-06 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/9537/1/2007%20-%20Intelligent%20Sensor%20Data%20Pre-Processing%20using%20Continuous%20Restricted%20Boltzmann%20Machine.pdf Suhaimi, Emil Zaidan (2007) Intelligent Sensor Data Pre-processing Using Continuous Restricted Boltzmann Machine. Universiti Teknologi PETRONAS. (Unpublished)
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Electronic and Digitized Intellectual Asset
url_provider http://utpedia.utp.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Suhaimi, Emil Zaidan
Intelligent Sensor Data Pre-processing Using Continuous Restricted Boltzmann Machine
description The objective of the project is to finda solution to pre-process noisy signalfor sensors in Lab-on-a-Chip (LOC) and System-on-Chip (SOC) technologies. This solution must be able to process continuous-time, analogue sensor signals directly. It must also be amenable to hardware implementation, with low power consumption. This solution is found in the Continuous Restricted Boltzmann Machine (CRBM), which is a type of Artificial Neural Network which exhibits probabilistic and stochastic behavior. CRBM utilizes continuous stochastic neurons, where Gaussian noise is applied to the inputofthe neurons. The noise inputs cause neurons to have continuous-valued, probabilistic outputs. The use ofstochastic neurons in CRBMgives it modelingflexibility that is useful with real data. The training algorithm of CRBM requires only addition c;nd multiplication, which is computationally inexpensive in hardware and software. The ability ofCRBM to model any given data set is shown by training the CRBM on various data sets reflecting real-world data. In this study, CRBM is shown to be suitable to be implemented in LOC andSOC applications aforementioned.
format Final Year Project
author Suhaimi, Emil Zaidan
author_facet Suhaimi, Emil Zaidan
author_sort Suhaimi, Emil Zaidan
title Intelligent Sensor Data Pre-processing Using Continuous Restricted Boltzmann Machine
title_short Intelligent Sensor Data Pre-processing Using Continuous Restricted Boltzmann Machine
title_full Intelligent Sensor Data Pre-processing Using Continuous Restricted Boltzmann Machine
title_fullStr Intelligent Sensor Data Pre-processing Using Continuous Restricted Boltzmann Machine
title_full_unstemmed Intelligent Sensor Data Pre-processing Using Continuous Restricted Boltzmann Machine
title_sort intelligent sensor data pre-processing using continuous restricted boltzmann machine
publisher Universiti Teknologi PETRONAS
publishDate 2007
url http://utpedia.utp.edu.my/9537/1/2007%20-%20Intelligent%20Sensor%20Data%20Pre-Processing%20using%20Continuous%20Restricted%20Boltzmann%20Machine.pdf
http://utpedia.utp.edu.my/9537/
_version_ 1739831684675665920
score 13.211869

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