scholarly journals Study on the Identification ofRadix Bupleurifrom Its Unofficial Varieties Based on Discrete Wavelet Transformation Feature Extraction of ATR-FTIR Spectroscopy Combined with Probability Neural Network

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Wenying Jin ◽  
Chayan Wan ◽  
Cungui Cheng
Keyword(s):  
Neural Network ◽  
Infrared Spectra ◽  
Wavelet Transformation ◽  
Research Result ◽  
Attenuated Total Reflection ◽  
Discrete Wavelet ◽  
Peak Intensity ◽  
Different Types ◽  
Probability Neural Network ◽  
Radix Bupleuri

The attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) was employed to acquire the infrared spectra ofRadix Bupleuriand its unofficial varieties: the root ofBupleurum smithiiWolff and the root ofBupleurum bicauleHelm. The infrared spectra and spectra of Fourier self-deconvolution (FSD), discrete wavelet transform (DWT), and probability neural network (PNN) of these species were analyzed. By the method of FSD, there were conspicuous differences of the infrared absorption peak intensity of different types betweenRadix Bupleuriand its unofficial varieties. But it is hard to tell the differences between the root ofBupleurum smithiiWolff and the root ofBupleurum bicaule. The differences could be shown more clearly when the DWT was used. The research result shows that by the DWT technology it is easier to identifyRadix Bupleurifrom its unofficial varieties the root ofBupleurum smithiiWolff and the root ofBupleurum bicaule.

scholarly journals Robust and Blind Multiple Image Watermarking Using CNN and DWT in Video

2020 ◽  
Vol 6 (3) ◽  
pp. 8-13
Author(s):  
Farha Khan ◽  
M. Sarwar Raeen
Keyword(s):  
Neural Network ◽  
Wavelet Coefficient ◽  
Image Watermarking ◽  
High Capacity ◽  
Multimedia Systems ◽  
Convolution Neural Network ◽  
Discrete Wavelet ◽  
Host Image ◽  
Different Types ◽  
Result Analysis

Digital watermarking was introduced as a result of rapid advancement of networked multimedia systems. It had been developed to enforce copyright technologies for cover of copyright possession. Due to increase in growth of internet users of networks are increasing rapidly. It has been concluded that to minimize distortions and to increase capacity, techniques in frequency domain must be combined with another technique which has high capacity and strong robustness against different types of attacks. In this paper, a robust multiple watermarking which combine Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT)and Convolution Neural Network techniques on selected middle band of the video frames is used. This methodology is considered to be robust blind watermarking because it successfully fulfills the requirement of imperceptibility and provides high robustness against a number of image-processing attacks such as Mean filtering, Median filtering, Gaussian noise, salt and pepper noise, poison noise and rotation attack. The proposed method embeds watermark by decomposing the host image. Convolution neural network calculates the weight factor for each wavelet coefficient. The watermark bits are added to the selected coefficients without any perceptual degradation for host image. The simulation is performed on MATLAB platform. The result analysis is evaluated on PSNR and MSE which is used to define robustness of the watermark that means that the watermark will not be destroyed after intentional or involuntary attacks and can still be used for certification. The analysis of the results was made with different types of attacks concluded that the proposed technique is approximately 14% efficient as compared to existing work.

scholarly journals Using ATR-FTIR Spectra and Convolutional Neural Networks for Characterizing Mixed Plastic Waste

2021 ◽  
Author(s):  
Shengli Jiang ◽  
Zhuo Xu ◽  
Medhavi Kamran ◽  
Stas Zinchik ◽  
Sidike Paheding ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Convolutional Neural Network ◽  
Total Reflection ◽  
Plastic Waste ◽  
Attenuated Total Reflection ◽  
Plastic Materials ◽  
Different Types

<p>We present a convolutional neural network (CNN) framework for classifying different types of plastic materials that are commonly found in mixed plastic waste (MPW) streams. The CNN framework uses experimental ATR-FTIR (attenuated total reflection-Fourier transform infrared spectroscopy) spectra to classify ten different plastic types. We show that the approach reaches accuracies of over 87% and that some plastic types can be perfectly classified.</p>

Application of the Compound Model of BP Neural Networks and Wavelet Transform in Image Definition Identification

2012 ◽  
Vol 605-607 ◽  
pp. 2265-2269
Author(s):  
Rui Kun Gong ◽  
Ya Nan Zhang ◽  
Chong Hao Wang ◽  
Li Jing Zhao
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Bp Neural Network ◽  
Wavelet Transformation ◽  
Recognition Rate ◽  
Discrete Wavelet ◽  
Training Set ◽  
Identification Method ◽  
Compound Model ◽  
Image Definition

First, the background, significance and general implementation of the image definition identification are introduced. Then, basic theory of wavelet transform and neural network is expounded. An identification method of image definition based on the composite model of wavelet analysis and neural network is suggested．The two—dimensional discrete wavelet transformation is used to filter image signal and extract its brim character which is input into BP neural network for identification. 4 layers of BP neural network are constructed to perform image definition identification. The compound model is first trained by 90 images from the training set, and then is tested by 87 images from the testing set. The results show that this is a very effective identification method which can obtain a higher recognition rate.

Identification of Modified Starches Using Infrared Spectroscopy and Artificial Neural Network Processing

1998 ◽  
Vol 52 (3) ◽  
pp. 329-338 ◽  
Author(s):  
Ludmila Dolmatova ◽  
Cyril Ruckebusch ◽  
Nathalie Dupuy ◽  
Jean-Pierre Huvenne ◽  
Pierre Legrand
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Infrared Spectra ◽  
Corn Starch ◽  
Raw Materials ◽  
Attenuated Total Reflection ◽  
Novel Approach ◽  
Modified Starches ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The authentication of food is a very important issue both for the consumers and for the food industry with respect to all levels of the food chain from raw materials to finished products. Corn starch can be used in a wide variety of food preparation as bakery cream fillings, sauce, or dry mixes. There are many modifications of the corn starch in connection with its use in the agrofood industry. This paper describes a novel approach to the classification of modified starches and the recognition of their modifications by artificial neural network (ANN) processing of attenuated total reflection Fourier transform spectroscopy (ATR/FT-IR) spectra. Using the self-organizing artificial neural network of the Kohonen type, we can obtain natural groupings of similarly modified samples on a two-dimensional plane. Such mapping provides the expert with the possibility of analyzing the distribution of samples and predicting modifications of unknown samples by using their relative position with respect to existing clusters. On the basis of the available information in the infrared spectra, a feedforward artificial neural network, trained with the intensities of the derivative infrared spectra as input and the starch modifications as output, allows the user to identify modified starches presented as prediction samples.

scholarly journals Study on the early detection of gastric cancer based on discrete wavelet transformation feature extraction of FT-IR spectra combined with probability neural network

2011 ◽  
Vol 26 (3) ◽  
pp. 155-165 ◽  
Author(s):  
Tao Hu ◽  
Yu-Hui Lu ◽  
Cun-Gui Cheng ◽  
Xiao-Chen Sun
Keyword(s):  
Neural Network ◽  
Gastric Cancer ◽  
Feature Extraction ◽  
Ir Spectra ◽  
Wavelet Transformation ◽  
Gastric Cancer Tissue ◽  
Discrete Wavelet ◽  
Cancer Tissue ◽  
Discrete Wavelet Transformation ◽  
Ft Ir

This paper introduces a new method for the early detection of gastric cancer using a combination of feature extraction based on discrete wavelet transformation (DWT) for horizontal attenuated total reflectance–Fourier transform infrared spectroscopy (HATR–FT-IR) and classification using probability neural network (PNN). 344 FT-IR spectra were collected from 172 pairs of fresh normal and abnormal stomach tissue᾽s samples. After preprocessing, 5 features were extracted with DWT analysis. Based on the PNN classification, all FT-IR spectra were classified into three categories. The accuracy of identifying normal gastric tissue, early gastric cancer tissue and gastric cancer tissue samples were 100.00, 97.56 and 100.00%, respectively. This result indicated that FT-IR with DWT and PNN could effectively and easily diagnose gastric cancer in its early stages.

scholarly journals Using ATR-FTIR Spectra and Convolutional Neural Networks for Characterizing Mixed Plastic Waste

2021 ◽  
Author(s):  
Shengli Jiang ◽  
Zhuo Xu ◽  
Medhavi Kamran ◽  
Stas Zinchik ◽  
Sidike Paheding ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Convolutional Neural Network ◽  
Total Reflection ◽  
Plastic Waste ◽  
Attenuated Total Reflection ◽  
Plastic Materials ◽  
Different Types

<p>We present a convolutional neural network (CNN) framework for classifying different types of plastic materials that are commonly found in mixed plastic waste (MPW) streams. The CNN framework uses experimental ATR-FTIR (attenuated total reflection-Fourier transform infrared spectroscopy) spectra to classify ten different plastic types. We show that the approach reaches accuracies of over 87% and that some plastic types can be perfectly classified.</p>

scholarly journals Image Fusion Based on Wavelet Transformation

2020 ◽  
Vol 9 (5) ◽  
pp. 473-477
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
Wavelet Transformation ◽  
Discrete Wavelet ◽  
Processing Application ◽  
Pulse Coupled Neural Network ◽  
Image Processing Application

The article based totally on the MATLAB software program simulation was carried out on the image fusion; to design and develop a MATLAB based image processing application for fusing two images of the similar scene received through other modalities. The application is required to use Discrete Wavelet Transform (DWT) and Pulse Coupled Neural Network (PCNN) techniques. The comparison is to be performed on the results obtained on the above mentioned techniques.

scholarly journals Recognition of MIR Data of Semen Armeniacae Amarum and Semen Persicae Using Discrete Wavelet Transformation and BP-Artificial Neural Network

2012 ◽  
Vol 27 ◽  
pp. 253-264 ◽  
Author(s):  
Cun-Gui Cheng ◽  
Peng Yu ◽  
Chang-Shun Wu ◽  
Jia-Ni Shou
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transformation ◽  
Feature Vector ◽  
Recognition Rate ◽  
Back Propagation ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transformation ◽  
Artificial Neural ◽  
The Difference

Horizontal attenuation total reflection-Fourier transformation infrared spectroscopy (HATR-FT-IR) is used to measure the Mid-IR (MIR) of semen armeniacae amarum and its confusable varieties semen persicae. In order to extrude the difference between semen armeniacae amarum and semen persicae, discrete wavelet transformation (DWT) is used to decompose the MIR of semen armeniacae amarum and semen persicae. Two main scales are selected as the feature extracting space in the DWT domain. According to the distribution of semen armeniacae amarum and semen persicae’s MIR, five feature regions are determined at every spectra band by selecting two scales in the DWT domain. Thus, ten feature parameters form the feature vector. The feature vector is input to the back-propagation artificial neural network (BP-ANN) to train so as to accurately classify the semen armeniacae amarum and semen persicae. 100 couples of MIR are used to train and test the proposed method, where 50 couples of data are used to train samples and other 50 couples of data are used to test samples. Experimental results show that the accurate recognition rate between semen armeniacae amarum and semen persicae is averaged 99% following the proposed method.

scholarly journals Using ATR-FTIR Spectra and Convolutional Neural Networks for Characterizing Mixed Plastic Waste

2021 ◽  
Author(s):  
Shengli Jiang ◽  
Zhuo Xu ◽  
Medhavi Kamran ◽  
Stas Zinchik ◽  
Sidike Paheding ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Convolutional Neural Network ◽  
Total Reflection ◽  
Plastic Waste ◽  
Attenuated Total Reflection ◽  
Plastic Materials ◽  
Different Types

<p>We present a convolutional neural network (CNN) framework for classifying different types of plastic materials that are commonly found in mixed plastic waste (MPW) streams. The CNN framework uses experimental ATR-FTIR (attenuated total reflection-Fourier transform infrared spectroscopy) spectra to classify ten different plastic types. We show that the approach reaches accuracies of over 87% and that some plastic types can be perfectly classified.</p>

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