Identification of Short Duration Voltage Variations Based on Short Time Fourier Transform and Artificial Neural Network

2020 ◽  
Author(s):  
Dimas Okky Anggriawan ◽  
Endro Wahjono ◽  
Indhana Sudiharto ◽  
Aji Akbar Firdaus ◽  
Dianing Novita Nurmala Putri ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fourier Transform ◽  
Short Duration ◽  
Short Time Fourier Transform ◽  
Artificial Neural ◽  
Short Time

Low-Cost Hardware Implementation of Human Blood Identification Device Using Feed-Forward Artificial Neural Network on FPGA

2018 ◽  
Author(s):  
Rizki Eka Putri ◽  
Denny Darlis
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Image Resolution ◽  
Blood Type ◽  
Feed Forward ◽  
Testing Facility ◽  
System Input ◽  
Artificial Neural ◽  
Short Time

This article was under review for ICELTICS 2018 -- In the medical world there is still service dissatisfaction caused by lack of blood type testing facility. If the number of tested blood arise, a lot of problems will occur so that electronic devices are needed to determine the blood type accurately and in short time. In this research we implemented an Artificial Neural Network on Xilinx Spartan 3S1000 Field Programable Gate Array using XSA-3S Board to identify the blood type. This research uses blood sample image as system input. VHSIC Hardware Discription Language is the language to describe the algorithm. The algorithm used is feed-forward propagation of backpropagation neural network. There are 3 layers used in design, they are input, hidden1, and output. At hidden1layer has two neurons. In this study the accuracy of detection obtained are 92%, 92%, 92%, 90% and 86% for 32x32, 48x48, 64x64, 80x80, and 96x96 pixel blood image resolution, respectively.

Non-Invasive Optical Blood Glucose Measurement based on Discrete Fourier Transform and Fast Artificial Neural Network: Fasting Normal Glucose Participants Case Study

2019 ◽  
Author(s):  
Renan Prasta Jenie ◽  
Evy Damayanthi ◽  
Irzaman Irzaman ◽  
Rimbawan Rimbawan ◽  
Dadang Sukandar ◽  
...  
Keyword(s):  
Neural Network ◽  
Clinical Trial ◽  
Artificial Neural Network ◽  
Fourier Transform ◽  
Blood Glucose ◽  
Discrete Fourier Transform ◽  
Glucose Measurement ◽  
Non Invasive ◽  
Artificial Neural

A prototype non-invasive blood glucose level measurement optical device (NI-BGL-MOD) has been developed. The NI-BGL-MOD uses a discrete Fourier transform (DFT) method and a fast artificial neural network algorithm to optimize device performance. The appropriate light-emitting diode for the sensory module was selected based on near-infrared spectrophotometry of a blood glucose model and human blood. DFT is implemented in an analog-to-digital converter module. An in vitro trial using the blood glucose model along with a clinical trial involving 110 participants were conducted to evaluate the performance of the prototype. The root-mean-square error of the prototype was 10.8 mg/dl in the in vitro trial and 3.64 mg/dl in the clinical trial, which is lower than the ISO-15197:2016 mandated value of 10 mg/dl. In each trial, consensus error grid analysis indicated that the measurement error was within the safe range. The sensitivity and specificity of the prototype were 0.83 (0.36, 1.00) and 0.90 (0.55, 1.00) in the in vitro trial and 0.81 (0.75, 0.85) and 0.83 (0.78, 0.87) in the clinical trial, respectively. In general, the proposed NI-BGL-MOD demonstrated good performance than gold-standard measurement. Key words: Non-invasive blood glucose measurement, optical device, discrete Fourier transform, multi-formulatric regression, fast artificial neural network

scholarly journals Intelligent Automatic V6 and V8 Engine Sound Detection Based on Artificial Neural Network

2019 ◽  
Vol 130 ◽  
pp. 01035 ◽  
Author(s):  
Wenny Vincent ◽  
Astuti Winda ◽  
Mahmud Iwan Solihin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Intelligent System ◽  
Recorded Sound ◽  
Sound Detection ◽  
Testing Accuracy ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The sound of V6 or V8 engines has its own cultural appeal that cannot be replaced by the modern four-cylinder naturally aspirated or turbocharged engines. The identification of the type of engine by the sound is not an easy task, even for the professionals. An intelligent system that can identify V6 to V8 engines from various cars will give an insight of the features in the engine sounds that characterized the two different engines. In this work, an Artificial Neural Network (ANN) approach is applied for identifying cylinder of the engine based on the engine sound identification is proposed. The recorded sound of the engine is then processed in order to get some features which later be used in the proposed system. The Fast Fourir Transform (FFT) is adopted as a feature and later used as input to the Artificial Neural Network (ANN) based identifier. The Experimental results confirm the effectiveness of the proposed intelligent automatic six cylinder and eight cylinder engine based on Fast Fourier Transform (FFT) and Artificial Neural Network (ANN), since it resulting the training and testing accuracy of 100 % and 100 %, respectively.

Real Time Harmonic Load Identification Based on Fast Fourier Transform and Artificial Neural Network

2021 ◽  
Vol 16 (3) ◽  
pp. 220
Author(s):  
Dimas Okky Anggriawan ◽  
Aidin Amsyar ◽  
Aji Akbar Firdaus ◽  
Endro Wahjono ◽  
Indhana Sudiharto ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Harmonic Load ◽  
Load Identification ◽  
Time Harmonic ◽  
Artificial Neural

Identification and Quantification of Industrial Grade Glycerol Adulteration in Red Wine with Fourier Transform Infrared Spectroscopy Using Chemometrics and Artificial Neural Networks

2005 ◽  
Vol 59 (12) ◽  
pp. 1553-1561 ◽  
Author(s):  
Vivechana Dixit ◽  
Jagdish C. Tewari ◽  
Byoung-Kwan Cho ◽  
Joseph M. K. Irudayaraj
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fourier Transform ◽  
Red Wine ◽  
Radial Basis Function Network ◽  
Fourier Transform Infrared ◽  
Linear Discriminant ◽  
Industrial Grade ◽  
Artificial Neural ◽  
Propagation Network

Fourier transform infrared (FT-IR) single bounce micro-attenuated total reflectance (mATR) spectroscopy, combined with multivariate and artificial neural network (ANN) data analysis, was used to determine the adulteration of industrial grade glycerol in selected red wines. Red wine samples were artificially adulterated with industrial grade glycerol over the concentration range from 0.1 to 15% and calibration models were developed and validated. Single bounce infrared spectra of glycerol adulterated wine samples were recorded in the fingerprint mid-infrared region, 900–1500 cm−1. Partial least squares (PLS) and PLS first derivatives were used for quantitative analysis ( r2 = 0.945 to 0.998), while linear discriminant analysis (LDA) and canonical variate analysis (CVA) were used for classification and discrimination. The standard error of prediction (SEP) in the validation set was between 1.44 and 2.25%. Classification of glycerol adulterants in the different brands of red wine using CVA resulted in a classification accuracy in the range between 94 and 98%. Artificial neural network analysis based on the quick back propagation network (BPN) and the radial basis function network (RBFN) algorithms had classification success rates of 93% using BPN and 100% using RBFN. The genetic algorithm network was able to predict the concentrations of glycerol in wine up to an accuracy of r2 = 0.998.

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