scholarly journals Hyperspectral image analysis using a simultaneous denoising and intrinsic order selection (DIOS) approach

2021 ◽  
Author(s):  
Masoud Farzam
Keyword(s):  
Hyperspectral Image ◽  
Signal To Noise Ratio ◽  
Data Cube ◽  
Least Square ◽  
Estimation Methods ◽  
Order Selection ◽  
Coloured Noise ◽  
Dimension Estimation ◽  
Hyperspectral Image Analysis ◽  
Simulation Results

Recent hyperspectral applications demand for higher accuracy and speed. This thesis develops a hyperspectral application analysis solution to address challenges in the different steps of denoising, order selection and unmixing of hyperspectral application data. Currently, all these steps process the data in cascade to achieve the optimum results. While in existing approaches the desired criterion is different in these steps, the proposed simultaneous Denoising and Intrinsic Order Selection (DIOS) method unifies these criteria. This property not only makes more sense for the desired optimization problem, but also leads to a faster processing algorithm. Consequently, DIOS avoids possible error propagation from the denoising stage to the dimension estimation stage, leading to more accurate results. The proposed method is based on minimizing the estimated Mean Square Error (MSE). The success rate of existing dimension estimation methods declines with the increase of image dimension and the decrease of Signal-to-Noise Ratio (SNR). The most competitive method fails to detect the correct dimension in 30% of cases around 2dB. However, in simulation results DIOS is shown to be successful with a failure rate of about 5%. The proposed unmixing method, based on a simple least square estimation, improves the speed performance least 10 times for an average-sized data cube of 2MB. Compared to some well known existing approaches, the unmixing method improves the estimated MSE up to 60% for SNR<10dB. A new whitening process for hyperspectral applications with coloured noise is also proposed. Since the proposed method avoids the inversion of large matrices, computational complexity is substantially decreased. In the presence of coloured noise, simulation results show that the proposed whitening method lowers the MSE of unmixing and outperforms the existing whitening methods particularly when the noise correction factors increase.

scholarly journals Hyperspectral image analysis using a simultaneous denoising and intrinsic order selection (DIOS) approach

2021 ◽  
Author(s):  
Masoud Farzam
Keyword(s):  
Hyperspectral Image ◽  
Signal To Noise Ratio ◽  
Data Cube ◽  
Least Square ◽  
Estimation Methods ◽  
Order Selection ◽  
Coloured Noise ◽  
Dimension Estimation ◽  
Hyperspectral Image Analysis ◽  
Simulation Results

Recent hyperspectral applications demand for higher accuracy and speed. This thesis develops a hyperspectral application analysis solution to address challenges in the different steps of denoising, order selection and unmixing of hyperspectral application data. Currently, all these steps process the data in cascade to achieve the optimum results. While in existing approaches the desired criterion is different in these steps, the proposed simultaneous Denoising and Intrinsic Order Selection (DIOS) method unifies these criteria. This property not only makes more sense for the desired optimization problem, but also leads to a faster processing algorithm. Consequently, DIOS avoids possible error propagation from the denoising stage to the dimension estimation stage, leading to more accurate results. The proposed method is based on minimizing the estimated Mean Square Error (MSE). The success rate of existing dimension estimation methods declines with the increase of image dimension and the decrease of Signal-to-Noise Ratio (SNR). The most competitive method fails to detect the correct dimension in 30% of cases around 2dB. However, in simulation results DIOS is shown to be successful with a failure rate of about 5%. The proposed unmixing method, based on a simple least square estimation, improves the speed performance least 10 times for an average-sized data cube of 2MB. Compared to some well known existing approaches, the unmixing method improves the estimated MSE up to 60% for SNR<10dB. A new whitening process for hyperspectral applications with coloured noise is also proposed. Since the proposed method avoids the inversion of large matrices, computational complexity is substantially decreased. In the presence of coloured noise, simulation results show that the proposed whitening method lowers the MSE of unmixing and outperforms the existing whitening methods particularly when the noise correction factors increase.

scholarly journals Trends in Deep Learning for Medical Hyperspectral Image Analysis

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Uzair Khan ◽  
Sidike Paheding ◽  
Colin Elkin ◽  
Vijay Devabhaktuni
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Hyperspectral Image ◽  
Hyperspectral Image Analysis

scholarly journals Cooperative Spectrum Sensing Based on Convolutional Neural Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4440
Author(s):  
Youheng Tan ◽  
Xiaojun Jing
Keyword(s):  
Neural Networks ◽  
Spectrum Sensing ◽  
Convolutional Neural Networks ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Majority Voting ◽  
Signal To Noise ◽  
Hidden Terminal ◽  
Simulation Results ◽  
Sensing Performance

Cooperative spectrum sensing (CSS) is an important topic due to its capacity to solve the issue of the hidden terminal. However, the sensing performance of CSS is still poor, especially in low signal-to-noise ratio (SNR) situations. In this paper, convolutional neural networks (CNN) are considered to extract the features of the observed signal and, as a consequence, improve the sensing performance. More specifically, a novel two-dimensional dataset of the received signal is established and three classical CNN (LeNet, AlexNet and VGG-16)-based CSS schemes are trained and analyzed on the proposed dataset. In addition, sensing performance comparisons are made between the proposed CNN-based CSS schemes and the AND, OR, majority voting-based CSS schemes. The simulation results state that the sensing accuracy of the proposed schemes is greatly improved and the network depth helps with this.

Dual-Hop Amplify-and-Forward Variable Gain Relaying over Mixture Gamma Fading Channels

2015 ◽  
Vol 719-720 ◽  
pp. 767-772
Author(s):  
Wei Jun Cheng
Keyword(s):  
Fading Channels ◽  
Signal To Noise Ratio ◽  
Symbol Error Rate ◽  
Amplify And Forward ◽  
Variable Gain ◽  
Average Symbol Error Rate ◽  
Simulation Results ◽  
End To End ◽  
The Moment ◽  
Relaying System

In this paper, we present the end-to-end performance of a dual-hop amplify-and-forward variablegain relaying system over Mixture Gamma distribution. Novel closed-form expressions for the probability density function and the moment-generation function of the end-to-end Signal-to-noise ratio (SNR) are derived. Moreover, the average symbol error rate, the average SNR and the average capacity are found based on the above new expressions, respectively. These expressions are more simple and accuracy than the previous ones obtained by using generalized-K (KG) distribution. Finally, numerical and simulation results are shown to verify the accuracy of the analytical results.

Fuzzy C-Regression Models

2013 ◽  
Vol 278-280 ◽  
pp. 1323-1326
Author(s):  
Yan Hua Yu ◽  
Li Xia Song ◽  
Kun Lun Zhang
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Linear Models ◽  
Least Squares Method ◽  
Least Square Method ◽  
Least Square ◽  
Estimation Methods ◽  
Statistical Regression ◽  
Fuzzy Linear Regression ◽  
Fuzzy Degree

Fuzzy linear regression has been extensively studied since its inception symbolized by the work of Tanaka et al. in 1982. As one of the main estimation methods, fuzzy least squares approach is appealing because it corresponds, to some extent, to the well known statistical regression analysis. In this article, a restricted least squares method is proposed to fit fuzzy linear models with crisp inputs and symmetric fuzzy output. The paper puts forward a kind of fuzzy linear regression model based on structured element, This model has precise input data and fuzzy output data, Gives the regression coefficient and the fuzzy degree function determination method by using the least square method, studies the imitation degree question between the observed value and the forecast value.

Model Optimization and Stability of Hemoglobin Analysis in Human Soluble Blood Samples by FTIR/ATR Spectroscopy

2011 ◽  
Vol 55-57 ◽  
pp. 1168-1171
Author(s):  
Tao Pan ◽  
Ai Hong Peng ◽  
Wen Jie Huang
Keyword(s):  
Signal To Noise Ratio ◽  
Attenuated Total Reflection ◽  
Partial Least Square ◽  
Least Square ◽  
Model Optimization ◽  
Optimal Model ◽  
Blood Samples ◽  
Quantification Method ◽  
Atr Spectroscopy ◽  
Pls Method

Using Fourier transform infrared spectroscopy (FTIR), attenuated total reflection (ATR) technology and partial least square (PLS) method, the rapid quantification method of hemoglobin (HGB) in human soluble blood samples was established. Based on the distribution of samples’ HGB chemical value and absorbance on 1543 cm-1 which had the highest signal to noise ratio for HGB, all samples were divided into calibration set and prediction set for 50 times. PLS models were established for all divisions, based on the average data RMSEPAve, the stable optimal model was selected, the corresponding PLS factor, RMSEPAve and RP,Ave were 2, 6.81 g/L and 0.943 respectively.

A Novel Symbol Rate Estimation Algorithm for Phase Modulating Signals in Wireless Communications

2013 ◽  
Vol 443 ◽  
pp. 392-396
Author(s):  
Peng Zhou ◽  
Chi Sheng Li
Keyword(s):  
Signal To Noise Ratio ◽  
Estimation Algorithm ◽  
Frequency Resolution ◽  
Phase Shift Keying ◽  
Phase Jump ◽  
Rate Estimation ◽  
Awgn Channel ◽  
Symbol Rate ◽  
Shift Keying ◽  
Simulation Results

In this paper, we proposed a new symbol rate estimation algorithm for phase shift keying (PSK) and qua drawtube amplitude modulation (QAM) signals in AWGN channel First we constructe a delay-multiplied signal, from which we obtaine the modulated information. Then we calculated the instantaneous autocorrelation of the delay-multiplied signal to pick out the phase jump. To eliminate the restriction of frequency resolution in fast Fourier transform, we performed a Chirp-Z transform to find out the exact spectral line which represente the symbol rate of the signal to be analyzed. Compared with the existing algorithms, it is a simple solution that has a better performance and accuracy in low signal-to-noise-ratio channel conditions. Simulation results show that the probability of relative estimating deviation below 0.1% reaches 100% and the average and standard variance of absolute estimation deviation are at the magnitude of 10-2 when SNR is over 2dB.

Sign in / Sign up

Export Citation Format

Share Document