This document discusses using ensemble empirical mode decomposition (EEMD) to improve spectral similarity measurements for hyperspectral remote sensing data classification. EEMD decomposes spectra into intrinsic mode functions (IMFs) to better extract spectral features. The study compares using EEMD and spectral angle mapper (SAM) to traditional SAM alone. Experimental results on mineral spectra show EEMD improves classification accuracy over traditional methods. The document also explores optimizing EEMD parameters like noise levels and number of IMFs used for classification. Parallel GPU processing is used to speed up computation of EEMD on large hyperspectral datasets.
DESPECKLING OF SAR IMAGES BY OPTIMIZING AVERAGED POWER SPECTRAL VALUE IN CURV...ijistjournal
The document describes a novel algorithm for despeckling synthetic aperture radar (SAR) images using particle swarm optimization (PSO) in the curvelet domain. The algorithm first identifies homogeneous regions in the speckled image using variance calculations. It then uses PSO to optimize the thresholding of curvelet coefficients, with the objective of minimizing the average power spectral value. This provides an optimized threshold to apply curvelet-based despeckling. The proposed method is tested on standard images and shown to outperform conventional filters like median and Lee filters in reducing speckle noise.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Estimation of impervious surface based on integrated analysis of classificati...grssieee
The document describes a methodology for estimating impervious surface percentages (ISP) in urban areas using support vector machines (SVM) on Landsat TM imagery. The methodology involves: (1) an SVM classification to identify impervious surface areas at the pixel level, (2) using the classified areas as inputs to an SVM regression to estimate sub-pixel ISP values, and (3) comparing ISP estimates using different spectral features as inputs. The results show that the SVM approach is suitable for large-area ISP mapping and that adding a transformed spectral feature like "greenness" can improve accuracy by adjusting values where training samples are lacking.
Comparative analysis of filters and wavelet based thresholding methods for im...csandit
This document compares different image denoising techniques including filters and wavelet-based thresholding methods. It finds that wavelet-based Bayes shrinkage outperforms other techniques in terms of peak signal-to-noise ratio and mean square error. Specifically, it applies various denoising methods to images corrupted with Gaussian and speckle noise, and evaluates the results using PSNR and MSE metrics. The simulation results show that Bayes shrinkage produces higher PSNR and lower MSE than filtering methods or other wavelet thresholding approaches.
Segmentation Based Multilevel Wide Band Compression for SAR Images Using Coif...CSCJournals
Synthetic aperture radar (SAR) data represents a significant resource of information for a large variety of researchers. Thus, there is a strong interest in developing data encoding and decoding algorithms which can obtain higher compression ratios while keeping image quality to an acceptable level. In this work, results of different wavelet-based image compression and segmentation based wavelet image compression are assessed through controlled experiments on synthetic SAR images. The effects of dissimilar wavelet functions, number of decompositions are examined in order to find optimal family for SAR images. The choice of optimal wavelets in segmentation based wavelet image compression is coiflet for low frequency and high frequency component. The results presented here is a good reference for SAR application developers to choose the wavelet families and also it concludes that wavelets transform is rapid, robust and reliable tool for SAR image compression. Numerical results confirm the potency of this approach.
Ghost free image using blur and noise estimationijcga
This paper presents an efficient image enhancement method by fusion of two different exposure images in
low-light condition. We use two degraded images with different exposures: one is a long-exposure image
that preserves the brightness but contains blur and the other is a short-exposure image that contains a lot of
noise but preserves object boundaries. The weight map used for image fusion without artifacts of blur and
noise is computed using blur and noise estimation. To get a blur map, edges in a long-exposure image are
detected at multiple scales and the amount of blur is estimated at detected edges. Also, we can get a noise
map by noise estimation using a denoised short-exposure image. Ghost effect between two successive
images is avoided according to the moving object map that is generated by a sigmoid comparison function
based on the ratio of two input images. We can get result images by fusion of two degraded images using
the weight maps. The proposed method can be extended to high dynamic range imaging without using
information of a camera response function or generating a radiance map. Experimental results with various
sets of images show the effectiveness of the proposed method in enhancing details and removing ghost
artifacts.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
ANALYSIS OF INTEREST POINTS OF CURVELET COEFFICIENTS CONTRIBUTIONS OF MICROS...sipij
This paper focuses on improved edge model based on Curvelet coefficients analysis. Curvelet transform is
a powerful tool for multiresolution representation of object with anisotropic edge. Curvelet coefficients
contributions have been analyzed using Scale Invariant Feature Transform (SIFT), commonly used to study
local structure in images. The permutation of Curvelet coefficients from original image and edges image
obtained from gradient operator is used to improve original edges. Experimental results show that this
method brings out details on edges when the decomposition scale increases.
DESPECKLING OF SAR IMAGES BY OPTIMIZING AVERAGED POWER SPECTRAL VALUE IN CURV...ijistjournal
The document describes a novel algorithm for despeckling synthetic aperture radar (SAR) images using particle swarm optimization (PSO) in the curvelet domain. The algorithm first identifies homogeneous regions in the speckled image using variance calculations. It then uses PSO to optimize the thresholding of curvelet coefficients, with the objective of minimizing the average power spectral value. This provides an optimized threshold to apply curvelet-based despeckling. The proposed method is tested on standard images and shown to outperform conventional filters like median and Lee filters in reducing speckle noise.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Estimation of impervious surface based on integrated analysis of classificati...grssieee
The document describes a methodology for estimating impervious surface percentages (ISP) in urban areas using support vector machines (SVM) on Landsat TM imagery. The methodology involves: (1) an SVM classification to identify impervious surface areas at the pixel level, (2) using the classified areas as inputs to an SVM regression to estimate sub-pixel ISP values, and (3) comparing ISP estimates using different spectral features as inputs. The results show that the SVM approach is suitable for large-area ISP mapping and that adding a transformed spectral feature like "greenness" can improve accuracy by adjusting values where training samples are lacking.
Comparative analysis of filters and wavelet based thresholding methods for im...csandit
This document compares different image denoising techniques including filters and wavelet-based thresholding methods. It finds that wavelet-based Bayes shrinkage outperforms other techniques in terms of peak signal-to-noise ratio and mean square error. Specifically, it applies various denoising methods to images corrupted with Gaussian and speckle noise, and evaluates the results using PSNR and MSE metrics. The simulation results show that Bayes shrinkage produces higher PSNR and lower MSE than filtering methods or other wavelet thresholding approaches.
Segmentation Based Multilevel Wide Band Compression for SAR Images Using Coif...CSCJournals
Synthetic aperture radar (SAR) data represents a significant resource of information for a large variety of researchers. Thus, there is a strong interest in developing data encoding and decoding algorithms which can obtain higher compression ratios while keeping image quality to an acceptable level. In this work, results of different wavelet-based image compression and segmentation based wavelet image compression are assessed through controlled experiments on synthetic SAR images. The effects of dissimilar wavelet functions, number of decompositions are examined in order to find optimal family for SAR images. The choice of optimal wavelets in segmentation based wavelet image compression is coiflet for low frequency and high frequency component. The results presented here is a good reference for SAR application developers to choose the wavelet families and also it concludes that wavelets transform is rapid, robust and reliable tool for SAR image compression. Numerical results confirm the potency of this approach.
Ghost free image using blur and noise estimationijcga
This paper presents an efficient image enhancement method by fusion of two different exposure images in
low-light condition. We use two degraded images with different exposures: one is a long-exposure image
that preserves the brightness but contains blur and the other is a short-exposure image that contains a lot of
noise but preserves object boundaries. The weight map used for image fusion without artifacts of blur and
noise is computed using blur and noise estimation. To get a blur map, edges in a long-exposure image are
detected at multiple scales and the amount of blur is estimated at detected edges. Also, we can get a noise
map by noise estimation using a denoised short-exposure image. Ghost effect between two successive
images is avoided according to the moving object map that is generated by a sigmoid comparison function
based on the ratio of two input images. We can get result images by fusion of two degraded images using
the weight maps. The proposed method can be extended to high dynamic range imaging without using
information of a camera response function or generating a radiance map. Experimental results with various
sets of images show the effectiveness of the proposed method in enhancing details and removing ghost
artifacts.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
ANALYSIS OF INTEREST POINTS OF CURVELET COEFFICIENTS CONTRIBUTIONS OF MICROS...sipij
This paper focuses on improved edge model based on Curvelet coefficients analysis. Curvelet transform is
a powerful tool for multiresolution representation of object with anisotropic edge. Curvelet coefficients
contributions have been analyzed using Scale Invariant Feature Transform (SIFT), commonly used to study
local structure in images. The permutation of Curvelet coefficients from original image and edges image
obtained from gradient operator is used to improve original edges. Experimental results show that this
method brings out details on edges when the decomposition scale increases.
This document proposes a new method for multifocus image fusion that operates based on categorizing image energy levels. It calculates the energy of gradient for input images to identify focused vs. blurred regions. The images are divided into low, mid, and high energy regions using thresholds on the average energy map. Pixels are then selected from the input images for each region using different fusion rules. Experimental results on book, clock, leaf, and wafer images show the proposed method produces clearer fused images without artifacts compared to other spatial and transform domain fusion methods.
Satellite image compression algorithm based on the fftijma
Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the
image to an unacceptable level ,the reduction in file size allows more images to be stored in a given amount
of disk or memory space, it also reduces the time required for images to be sent over the ground This paper
presents a new coding scheme for satellite images. In this study we apply the fast Fourier transform and the
scalar quantization for standard LENA image and satellite image, The results obtained after the (SQ) phase
are encoded using entropy encoding, after decompression, the results show that it is possible to achieve
higher compression ratios, more than 78%, the results are discussed in the paper.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document discusses echo cancellation using adaptive combination of normalized subband adaptive filters (NSAFs). It presents the following:
1. Fullband adaptive filters can have slow convergence due to correlated speech input and long echo path impulse responses. Subband adaptive filters (SAFs) address this by using individual adaptive filters in spectral subbands.
2. Adaptive combination of SAFs provides a way to achieve both fast convergence and small steady-state error. It independently adapts filters with different step sizes, then combines them using a mixing parameter adapted by stochastic gradient descent.
3. The proposed method adaptively combines NSAFs in subbands. It uses a large step size filter for fast convergence and a
Effect of Material Thickness on Attenuation (dB) of PTFE Using Finite Element...Abubakar Yakubu
This research article examines how the thickness of polytetrafluoroethylene (PTFE) samples affects the attenuation of electromagnetic waves at X-band frequency using finite element method (FEM) simulations. The results show that as the thickness of the PTFE samples increases, the attenuation also increases. Specifically, the 15 mm thick PTFE sample has an attenuation of -3.32 dB, the 30 mm sample has an attenuation of 0.64 dB, and the 50 mm sample has an attenuation of 1.97 dB. The study finds that increasing sample thickness leads to a decrease in electromagnetic wave transmission and an increase in attenuation.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A NOVEL ALGORITHM FOR IMAGE DENOISING USING DT-CWT sipij
This paper addresses image enhancement system consisting of image denoising technique based on Dual Tree Complex Wavelet Transform (DT-CWT) . The proposed algorithm at the outset models the noisy remote sensing image (NRSI) statistically by aptly amalgamating the structural features and textures from it. This statistical model is decomposed using DTCWT with Tap-10 or length-10 filter banks based on
Farras wavelet implementation and sub band coefficients are suitably modeled to denoise with a method which is efficiently organized by combining the clustering techniques with soft thresholding - softclustering technique. The clustering techniques classify the noisy and image pixels based on the
neighborhood connected component analysis(CCA), connected pixel analysis and inter-pixel intensity variance (IPIV) and calculate an appropriate threshold value for noise removal. This threshold value is used with soft thresholding technique to denoise the image .Experimental results shows that that the
proposed technique outperforms the conventional and state-of-the-art techniques .It is also evaluated that the denoised images using DTCWT (Dual Tree Complex Wavelet Transform) is better balance between smoothness and accuracy than the DWT.. We used the PSNR (Peak Signal to Noise Ratio) along with
RMSE to assess the quality of denoised images.
PERFORMANCE ANALYSIS OF UNSYMMETRICAL TRIMMED MEDIAN AS DETECTOR ON IMAGE NOI...ijistjournal
This Paper Analyze the performance of Unsymmetrical trimmed median, which is used as detector for the detection of impulse noise, Gaussian noise and mixed noise is proposed. The proposed algorithm uses a fixed 3x3 window for the increasing noise densities. The pixels in the current window are arranged in sorting order using a improved snake like sorting algorithm with reduced comparator. The processed pixel is checked for the occurrence of outliers, if the absolute difference between processed pixels is greater than fixed threshold. Under high noise densities the processed pixel is also noisy hence the median is checked using the above procedure. if found true then the pixel is considered as noisy hence the corrupted pixel is replaced by the median of the current processing window. If median is also noisy then replace the corrupted pixel with unsymmetrical trimmed median else if the pixel is termed uncorrupted and left unaltered. The proposed algorithm (PA) is tested on varying detail images for various noises. The proposed algorithm effectively removes the high density fixed value impulse noise, low density random valued impulse noise, low density Gaussian noise and lower proportion of mixed noise. The proposed algorithm is targeted on Xc3e5000-5fg900 FPGA using Xilinx 7.1 compiler version which requires less number of slices, optimum speed and low power when compared to the other median finding architectures.
Molecular dynamics (MD) is a very useful tool to understand various phenomena in atomistic detail. In MD, we can overcome the size- and time-scale problems by efficient parallelization. In this lecture, I’ll explain various parallelization methods of MD with some examples of GENESIS MD software optimization on Fugaku.
The fourier transform for satellite image compressioncsandit
The document presents a new method for compressing satellite images using the Fourier transform and scalar quantization. The method involves taking the Fourier transform of the image, scalar quantizing the amplitude values, and encoding the results with run-length encoding and Huffman coding. Testing on satellite images and Lena showed compression ratios over 65% while maintaining good image quality after reconstruction.
Denoising Process Based on Arbitrarily Shaped WindowsCSCJournals
Many factors, such as moving objects, introduce noise in digital images. The presence of noise affects image quality. The image denoising process works on reconstructing a noiseless image and improving its quality. When an image has an additive white Gaussian noise (AWGN) then denoising becomes a challenging process. In our research, we present an improved algorithm for image denoising in the wavelet domain. Homogenous regions for an input image are estimated using a region merging algorithm. The local variance and wavelet shrinkage algorithm are applied to denoise each image patch. Experimental results based on peak signal to noise ratio (PSNR) measurements showed that our algorithm provided better results compared with a denoising algorithm based on a minimum mean square error (MMSE) estimator.
Wavelet packet transforms were used to extract features from acoustic emission signals for tool wear monitoring. The acoustic emission signals were decomposed into different frequency bands using wavelet packet transforms. The root mean square values of the decomposed signals in each frequency band were extracted as features. Seven features were found to be most sensitive to tool wear based on analysis of experimental data. By dividing the features by cutting speed, the sensitivity of the features to changes in cutting conditions was reduced, providing effective monitoring of tool wear under different conditions using wavelet packet analysis of acoustic emission signals.
Image fusion using nsct denoising and target extraction for visual surveillanceeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Charge Sharing Suppression in Single Photon Processing Pixel Arrayijeei-iaes
This paper proposes a mechanism for suppression of charge sharing in single photon processing pixel array by introducing additional circuit. The idea of the proposed mechanism is that in each pixel only analog part will introduced, the digital part is shared between each four pixels, this leads to reduce the number of transistors (area). By having communication pixels, a decision that which one of the neighboring pixels shall collect the distributed charges is taken. The functionality, which involves analog and digital behaviors, is modeled in VHDL.
IMAGE AUTHENTICATION THROUGH ZTRANSFORM WITH LOW ENERGY AND BANDWIDTH (IAZT)IJNSA Journal
In this paper a Z-transform based image authentication technique termed as IAZT has been proposed to
authenticate gray scale images. The technique uses energy efficient and low bandwidth based invisible data
embedding with a minimal computational complexity. Near about half of the bandwidth is required
compared to the traditional Z–transform while transmitting the multimedia contents such as images with
authenticating message through network. This authenticating technique may be used for copyright
protection or ownership verification. Experimental results are computed and compared with the existing
authentication techniques like Li’s method [11], SCDFT [13], Region-Based method [14] and many more
based on Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Image Fidelity (IF), Universal
Quality Image (UQI) and Structural Similarity Index Measurement (SSIM) which shows better performance
in IAZT.
This paper presents an approach for image restoration in the presence of blur and noise. The image is divided into independent regions modeled with a Gaussian prior. Wavelet-based methods are used for image denoising, while classical Wiener filtering is used for deblurring. The algorithm finds the maximum a posteriori estimate at the intersection of convex sets generated by Wiener filtering. It provides efficient image restoration without sacrificing the simplicity of filtering, and generates a better restored image compared to previous methods.
Chaotic signals denoising using empirical mode decomposition inspired by mult...IJECEIAES
The document describes a new method for denoising chaotic signals corrupted by additive noise using empirical mode decomposition (EMD) inspired by multivariate denoising. EMD is used to decompose the noisy chaotic signal into intrinsic mode functions (IMFs), which are then thresholded using a multivariate denoising algorithm combining wavelet transforms and principal component analysis. This proposed EMD-MD method is compared to other techniques using metrics like root mean square error and signal-to-noise ratio gain. Simulation results on Lorenz, Chen and Rossler chaotic systems show the EMD-MD method achieves the best denoising performance compared to conventional methods.
Ensemble Empirical Mode Decomposition: An adaptive method for noise reductionIOSR Journals
Abstract:Empirical mode decomposition (EMD), a data analysis technique, is used to denoise non-stationary and non-linear processes. The method does not require any pre & post processing of signal and use of any specified basis functions. But EMD suffers from a problem called mode mixing. So to overcome this problem a new method known as Ensemble Empirical mode decomposition (EEMD) has been introduced. The presented paper gives the detail of EEMD and its application in various fields. EEMD is a time–space analysis method, in which the added white noise is averaged out with sufficient number of trials; and the averaging process results in only the component of the signal (original data). EEMD is a truly noise-assisted data analysis (NADA) method and represents a substantial improvement over the original EMD. Keywords –Data analysis, Empirical mode decomposition, intrinsic mode function, mode mixing, NADA,
Ensemble Empirical Mode Decomposition: An adaptive method for noise reduction IOSR Journals
This document describes ensemble empirical mode decomposition (EEMD), an adaptive method for noise reduction in signals. EEMD is an improvement over empirical mode decomposition (EMD) that can overcome the problem of mode mixing. EEMD works by decomposing the signal into intrinsic mode functions (IMFs) in the presence of added white noise, which is then averaged out. The algorithm adds white noise to the target signal multiple times, applies EMD each time, and takes the mean of the IMFs as the final result. This process separates different scales present in the signal and reduces noise. The document evaluates EEMD on electrocardiogram and other non-stationary signals, demonstrating its effectiveness in noise reduction.
An Improved Empirical Mode Decomposition Based On Particle Swarm OptimizationIJRES Journal
End effect is the main factor affecting the application of Empirical Mode Decomposition (EMD).
This paper presents an improve EMD for decomposing short signal. First, analyzing the frequency components
of signal to be decomposed, and construct the parameter equation with the amplitude and initial phase of signal
as unknowns. Second, employing particle swarm optimization (PSO) to estimate the unknown parameters, and
extending the inspected signal according to the obtained parameters. Thirdly, using EMD to decompose the
extended signal into a series of intrinsic mode functions (IMFs) and a residual. The IMFs of original signal are
extracted from these obtained IMFs. The correlation coefficients between the IMFs and the signal are calculated
to judge the pseudo-IMFs. The simulation result shows that the presented method is effective and extends the
application of EMD.
Empirical mode decomposition and normal shrink tresholding for speech denoisingijitjournal
In this paper a signal denoising scheme based on Empirical mode decomposition (EMD) is presented. the
noisy signal is decomposed in an adaptive manner by the EMD algorithm which allows to obtain intrinsic
oscillatory called Intrinsic mode functions (IMFs)component by a process called sifting process. The basic
principle of the method is to decompose a speech signal corrupted by additive white Gaussian random
noise into segments each frame is categorised as either signal-dominant or noise-dominant then
reconstruct the signal with IMFs signal dominant frame previously filtered or thresholded.It is shown, on
the basis of intensivesimulations that EMD improves the signal to noise ratio and address the problem of
signal degradation. The denoising method is applied to real signal with different noise levels and the
results compared to Winner and universal threshold of DONOHO and JOHNSTONE [11] with soft and
hard tresholding.Theeffect of level noise value on the performances of the proposed denoising is analysed.
A New Approach for Solving Inverse Scattering Problems with Overset Grid Gene...TELKOMNIKA JOURNAL
This paper presents a new approach of Forward-Backward Time-Stepping (FBTS)
utilizing Finite-Difference Time-Domain (FDTD) method with Overset Grid Generation (OGG)
method to solve the inverse scattering problems for electromagnetic (EM) waves. The proposed
FDTD method is combined with OGG method to reduce the geometrically complex problem to a
simple set of grids. The grids can be modified easily without the need to regenerate the grid
system, thus, it provide an efficient approach to integrate with the FBTS technique. Here, the
characteristics of the EM waves are analyzed. For the research mentioned in this paper, the
‘measured’ signals are syntactic data generated by FDTD simulations. While the ‘simulated’
signals are the calculated data. The accuracy of the proposed approach is validated. Good
agreements are obtained between simulation data and measured data. The proposed approach
has the potential to provide useful quantitative information of the unknown object particularly for
shape reconstruction, object detection and others.
This document proposes a new method for multifocus image fusion that operates based on categorizing image energy levels. It calculates the energy of gradient for input images to identify focused vs. blurred regions. The images are divided into low, mid, and high energy regions using thresholds on the average energy map. Pixels are then selected from the input images for each region using different fusion rules. Experimental results on book, clock, leaf, and wafer images show the proposed method produces clearer fused images without artifacts compared to other spatial and transform domain fusion methods.
Satellite image compression algorithm based on the fftijma
Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the
image to an unacceptable level ,the reduction in file size allows more images to be stored in a given amount
of disk or memory space, it also reduces the time required for images to be sent over the ground This paper
presents a new coding scheme for satellite images. In this study we apply the fast Fourier transform and the
scalar quantization for standard LENA image and satellite image, The results obtained after the (SQ) phase
are encoded using entropy encoding, after decompression, the results show that it is possible to achieve
higher compression ratios, more than 78%, the results are discussed in the paper.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document discusses echo cancellation using adaptive combination of normalized subband adaptive filters (NSAFs). It presents the following:
1. Fullband adaptive filters can have slow convergence due to correlated speech input and long echo path impulse responses. Subband adaptive filters (SAFs) address this by using individual adaptive filters in spectral subbands.
2. Adaptive combination of SAFs provides a way to achieve both fast convergence and small steady-state error. It independently adapts filters with different step sizes, then combines them using a mixing parameter adapted by stochastic gradient descent.
3. The proposed method adaptively combines NSAFs in subbands. It uses a large step size filter for fast convergence and a
Effect of Material Thickness on Attenuation (dB) of PTFE Using Finite Element...Abubakar Yakubu
This research article examines how the thickness of polytetrafluoroethylene (PTFE) samples affects the attenuation of electromagnetic waves at X-band frequency using finite element method (FEM) simulations. The results show that as the thickness of the PTFE samples increases, the attenuation also increases. Specifically, the 15 mm thick PTFE sample has an attenuation of -3.32 dB, the 30 mm sample has an attenuation of 0.64 dB, and the 50 mm sample has an attenuation of 1.97 dB. The study finds that increasing sample thickness leads to a decrease in electromagnetic wave transmission and an increase in attenuation.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A NOVEL ALGORITHM FOR IMAGE DENOISING USING DT-CWT sipij
This paper addresses image enhancement system consisting of image denoising technique based on Dual Tree Complex Wavelet Transform (DT-CWT) . The proposed algorithm at the outset models the noisy remote sensing image (NRSI) statistically by aptly amalgamating the structural features and textures from it. This statistical model is decomposed using DTCWT with Tap-10 or length-10 filter banks based on
Farras wavelet implementation and sub band coefficients are suitably modeled to denoise with a method which is efficiently organized by combining the clustering techniques with soft thresholding - softclustering technique. The clustering techniques classify the noisy and image pixels based on the
neighborhood connected component analysis(CCA), connected pixel analysis and inter-pixel intensity variance (IPIV) and calculate an appropriate threshold value for noise removal. This threshold value is used with soft thresholding technique to denoise the image .Experimental results shows that that the
proposed technique outperforms the conventional and state-of-the-art techniques .It is also evaluated that the denoised images using DTCWT (Dual Tree Complex Wavelet Transform) is better balance between smoothness and accuracy than the DWT.. We used the PSNR (Peak Signal to Noise Ratio) along with
RMSE to assess the quality of denoised images.
PERFORMANCE ANALYSIS OF UNSYMMETRICAL TRIMMED MEDIAN AS DETECTOR ON IMAGE NOI...ijistjournal
This Paper Analyze the performance of Unsymmetrical trimmed median, which is used as detector for the detection of impulse noise, Gaussian noise and mixed noise is proposed. The proposed algorithm uses a fixed 3x3 window for the increasing noise densities. The pixels in the current window are arranged in sorting order using a improved snake like sorting algorithm with reduced comparator. The processed pixel is checked for the occurrence of outliers, if the absolute difference between processed pixels is greater than fixed threshold. Under high noise densities the processed pixel is also noisy hence the median is checked using the above procedure. if found true then the pixel is considered as noisy hence the corrupted pixel is replaced by the median of the current processing window. If median is also noisy then replace the corrupted pixel with unsymmetrical trimmed median else if the pixel is termed uncorrupted and left unaltered. The proposed algorithm (PA) is tested on varying detail images for various noises. The proposed algorithm effectively removes the high density fixed value impulse noise, low density random valued impulse noise, low density Gaussian noise and lower proportion of mixed noise. The proposed algorithm is targeted on Xc3e5000-5fg900 FPGA using Xilinx 7.1 compiler version which requires less number of slices, optimum speed and low power when compared to the other median finding architectures.
Molecular dynamics (MD) is a very useful tool to understand various phenomena in atomistic detail. In MD, we can overcome the size- and time-scale problems by efficient parallelization. In this lecture, I’ll explain various parallelization methods of MD with some examples of GENESIS MD software optimization on Fugaku.
The fourier transform for satellite image compressioncsandit
The document presents a new method for compressing satellite images using the Fourier transform and scalar quantization. The method involves taking the Fourier transform of the image, scalar quantizing the amplitude values, and encoding the results with run-length encoding and Huffman coding. Testing on satellite images and Lena showed compression ratios over 65% while maintaining good image quality after reconstruction.
Denoising Process Based on Arbitrarily Shaped WindowsCSCJournals
Many factors, such as moving objects, introduce noise in digital images. The presence of noise affects image quality. The image denoising process works on reconstructing a noiseless image and improving its quality. When an image has an additive white Gaussian noise (AWGN) then denoising becomes a challenging process. In our research, we present an improved algorithm for image denoising in the wavelet domain. Homogenous regions for an input image are estimated using a region merging algorithm. The local variance and wavelet shrinkage algorithm are applied to denoise each image patch. Experimental results based on peak signal to noise ratio (PSNR) measurements showed that our algorithm provided better results compared with a denoising algorithm based on a minimum mean square error (MMSE) estimator.
Wavelet packet transforms were used to extract features from acoustic emission signals for tool wear monitoring. The acoustic emission signals were decomposed into different frequency bands using wavelet packet transforms. The root mean square values of the decomposed signals in each frequency band were extracted as features. Seven features were found to be most sensitive to tool wear based on analysis of experimental data. By dividing the features by cutting speed, the sensitivity of the features to changes in cutting conditions was reduced, providing effective monitoring of tool wear under different conditions using wavelet packet analysis of acoustic emission signals.
Image fusion using nsct denoising and target extraction for visual surveillanceeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Charge Sharing Suppression in Single Photon Processing Pixel Arrayijeei-iaes
This paper proposes a mechanism for suppression of charge sharing in single photon processing pixel array by introducing additional circuit. The idea of the proposed mechanism is that in each pixel only analog part will introduced, the digital part is shared between each four pixels, this leads to reduce the number of transistors (area). By having communication pixels, a decision that which one of the neighboring pixels shall collect the distributed charges is taken. The functionality, which involves analog and digital behaviors, is modeled in VHDL.
IMAGE AUTHENTICATION THROUGH ZTRANSFORM WITH LOW ENERGY AND BANDWIDTH (IAZT)IJNSA Journal
In this paper a Z-transform based image authentication technique termed as IAZT has been proposed to
authenticate gray scale images. The technique uses energy efficient and low bandwidth based invisible data
embedding with a minimal computational complexity. Near about half of the bandwidth is required
compared to the traditional Z–transform while transmitting the multimedia contents such as images with
authenticating message through network. This authenticating technique may be used for copyright
protection or ownership verification. Experimental results are computed and compared with the existing
authentication techniques like Li’s method [11], SCDFT [13], Region-Based method [14] and many more
based on Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Image Fidelity (IF), Universal
Quality Image (UQI) and Structural Similarity Index Measurement (SSIM) which shows better performance
in IAZT.
This paper presents an approach for image restoration in the presence of blur and noise. The image is divided into independent regions modeled with a Gaussian prior. Wavelet-based methods are used for image denoising, while classical Wiener filtering is used for deblurring. The algorithm finds the maximum a posteriori estimate at the intersection of convex sets generated by Wiener filtering. It provides efficient image restoration without sacrificing the simplicity of filtering, and generates a better restored image compared to previous methods.
Chaotic signals denoising using empirical mode decomposition inspired by mult...IJECEIAES
The document describes a new method for denoising chaotic signals corrupted by additive noise using empirical mode decomposition (EMD) inspired by multivariate denoising. EMD is used to decompose the noisy chaotic signal into intrinsic mode functions (IMFs), which are then thresholded using a multivariate denoising algorithm combining wavelet transforms and principal component analysis. This proposed EMD-MD method is compared to other techniques using metrics like root mean square error and signal-to-noise ratio gain. Simulation results on Lorenz, Chen and Rossler chaotic systems show the EMD-MD method achieves the best denoising performance compared to conventional methods.
Ensemble Empirical Mode Decomposition: An adaptive method for noise reductionIOSR Journals
Abstract:Empirical mode decomposition (EMD), a data analysis technique, is used to denoise non-stationary and non-linear processes. The method does not require any pre & post processing of signal and use of any specified basis functions. But EMD suffers from a problem called mode mixing. So to overcome this problem a new method known as Ensemble Empirical mode decomposition (EEMD) has been introduced. The presented paper gives the detail of EEMD and its application in various fields. EEMD is a time–space analysis method, in which the added white noise is averaged out with sufficient number of trials; and the averaging process results in only the component of the signal (original data). EEMD is a truly noise-assisted data analysis (NADA) method and represents a substantial improvement over the original EMD. Keywords –Data analysis, Empirical mode decomposition, intrinsic mode function, mode mixing, NADA,
Ensemble Empirical Mode Decomposition: An adaptive method for noise reduction IOSR Journals
This document describes ensemble empirical mode decomposition (EEMD), an adaptive method for noise reduction in signals. EEMD is an improvement over empirical mode decomposition (EMD) that can overcome the problem of mode mixing. EEMD works by decomposing the signal into intrinsic mode functions (IMFs) in the presence of added white noise, which is then averaged out. The algorithm adds white noise to the target signal multiple times, applies EMD each time, and takes the mean of the IMFs as the final result. This process separates different scales present in the signal and reduces noise. The document evaluates EEMD on electrocardiogram and other non-stationary signals, demonstrating its effectiveness in noise reduction.
An Improved Empirical Mode Decomposition Based On Particle Swarm OptimizationIJRES Journal
End effect is the main factor affecting the application of Empirical Mode Decomposition (EMD).
This paper presents an improve EMD for decomposing short signal. First, analyzing the frequency components
of signal to be decomposed, and construct the parameter equation with the amplitude and initial phase of signal
as unknowns. Second, employing particle swarm optimization (PSO) to estimate the unknown parameters, and
extending the inspected signal according to the obtained parameters. Thirdly, using EMD to decompose the
extended signal into a series of intrinsic mode functions (IMFs) and a residual. The IMFs of original signal are
extracted from these obtained IMFs. The correlation coefficients between the IMFs and the signal are calculated
to judge the pseudo-IMFs. The simulation result shows that the presented method is effective and extends the
application of EMD.
Empirical mode decomposition and normal shrink tresholding for speech denoisingijitjournal
In this paper a signal denoising scheme based on Empirical mode decomposition (EMD) is presented. the
noisy signal is decomposed in an adaptive manner by the EMD algorithm which allows to obtain intrinsic
oscillatory called Intrinsic mode functions (IMFs)component by a process called sifting process. The basic
principle of the method is to decompose a speech signal corrupted by additive white Gaussian random
noise into segments each frame is categorised as either signal-dominant or noise-dominant then
reconstruct the signal with IMFs signal dominant frame previously filtered or thresholded.It is shown, on
the basis of intensivesimulations that EMD improves the signal to noise ratio and address the problem of
signal degradation. The denoising method is applied to real signal with different noise levels and the
results compared to Winner and universal threshold of DONOHO and JOHNSTONE [11] with soft and
hard tresholding.Theeffect of level noise value on the performances of the proposed denoising is analysed.
A New Approach for Solving Inverse Scattering Problems with Overset Grid Gene...TELKOMNIKA JOURNAL
This paper presents a new approach of Forward-Backward Time-Stepping (FBTS)
utilizing Finite-Difference Time-Domain (FDTD) method with Overset Grid Generation (OGG)
method to solve the inverse scattering problems for electromagnetic (EM) waves. The proposed
FDTD method is combined with OGG method to reduce the geometrically complex problem to a
simple set of grids. The grids can be modified easily without the need to regenerate the grid
system, thus, it provide an efficient approach to integrate with the FBTS technique. Here, the
characteristics of the EM waves are analyzed. For the research mentioned in this paper, the
‘measured’ signals are syntactic data generated by FDTD simulations. While the ‘simulated’
signals are the calculated data. The accuracy of the proposed approach is validated. Good
agreements are obtained between simulation data and measured data. The proposed approach
has the potential to provide useful quantitative information of the unknown object particularly for
shape reconstruction, object detection and others.
Performance of enhanced lte otdoa position ing approach through nakagami-m fa...Elmourabit Ilham
This document analyzes the performance of an enhanced LTE OTDOA positioning technique called Adaptive OTDOA (A-OTDOA) through a Nakagami-m fading channel. A-OTDOA uses adaptive filters to cancel noise from received positioning reference signals before estimating time differences of arrival, improving accuracy. The document introduces A-OTDOA and the Normalized Least Mean Square adaptive algorithm used. It then discusses modeling the propagation environment, including Nakagami-m fading channels, to test A-OTDOA's performance in a worst-case scenario without line of sight.
Fault Tolerant Matrix Pencil Method for Direction of Arrival Estimationsipij
Continuing to estimate the Direction-of-arrival (DOA) of the signals impinging on the antenna array, even when a few elements of the underlying Uniform Linear Antenna Array (ULA) fail to work will be of practical interest in RADAR, SONAR and Wireless Radio Communication Systems. This paper proposes a new technique to estimate the DOAs when a few elements are malfunctioning. The technique combines Singular Value Thresholding (SVT) based Matrix Completion (MC) procedure with the Direct Data Domain (D3) based Matrix Pencil (MP) Method. When the element failure is observed, first, the MC is performed to recover the missing data from failed elements, and then the MP method is used to estimate the DOAs. We also, propose a very simple technique to detect the location of elements failed, which is required to perform MC procedure. We provide simulation studies to demonstrate the performance and usefulness of the proposed technique. The results indicate a better performance, of the proposed DOA estimation scheme under different antenna failure scenarios.
DESPECKLING OF SAR IMAGES BY OPTIMIZING AVERAGED POWER SPECTRAL VALUE IN CURV...ijistjournal
Synthetic Aperture Radar (SAR) images are inherently affected by multiplicative speckle noise, due to the coherent nature of scattering phenomena. In this paper, a novel algorithm capable of suppressing speckle noise using Particle Swarm Optimization (PSO) technique is presented. The algorithm initially identifies homogenous region from the corrupted image and uses PSO to optimize the Thresholding of curvelet coefficients to recover the original image. Average Power Spectrum Value (APSV) has been used as objective function of PSO. The Proposed algorithm removes Speckle noise effectively and the performance of the algorithm is tested and compared with Mean filter, Median filter, Lee filter, Statistic Lee filter, Kuan filter, frost filter and gamma filter., outperforming conventional filtering methods.
Maximum Radiated Emissions of Printed Circuit Board Using Analytical Methods IJECEIAES
The rapid progress of technology has imposed significant challenges on Printed Circuit Boards (PCB) designers. Once of those challenges is to satisfy the electromagnetic compatibility (EMC) compliance requirements. For that reason, EMC compliance must be considered earlier at the design stage for time and cost savings. Conventionally, full wave simulation is employed to check whether the designed PCB meets EMC standards or not. However, this method is not a suitable option since it requires intensive computational time and thus increasing the unit cost. This paper describes novel analytical models for estimating the radiated emissions (RE) of PCB. These models can be used to help the circuit designer to modify their circuit based on the maximum allowable RE comparing to the relevant EMC-RE standard limit. Although there are many RE sources on PCB, this paper focuses on the significant source of RE on PCB; namely PCB-traces. The trace geometry, termination impedance, dielectric type, etc. can be specified based on the maximum allowable emissions. The proposed models were verified by comparing the results of the proposed models with both simulation and experimental results. Good agreements were obtained between the analytically computed results and simulation/measurement results with accuracy of ±3dB.
This document discusses different techniques for modelling optical properties of nanostructures, including frequency domain and time domain methods. It provides examples of the rigorous coupled wave analysis (RCWA) frequency domain method and finite difference time domain (FDTD) time domain method. RCWA is suitable for periodic gratings and involves representing fields with Fourier expansions. FDTD discretizes Maxwell's equations in time and space using Yee's algorithm and is applicable to arbitrary structures but time consuming. Examples show using these methods to design tunable photonic crystal cavities.
Performance comparison of automatic peak detection for signal analyserjournalBEEI
The aim of this paper is to propose a new peak detection method for a portable device, which know as modified automatic threshold peak detection (M-ATPD). M-ATPD evolves out of ATPD with a focus on reducing computational time. The proposed method replaces the clustering threshold calculation in ATPD with a standard deviation threshold calculation. M-ATPD reduces computational time by 2 times faster compared to ATPD for control signal and 8.65 times faster compared to ATPD for raw biosignals. Modified ATPD also shows a slight improvement in terms of detection error, with a decrease of about 6.66% to 13.33% in peak detection of noise signals. Modified ATPD successfully fixes the error of peak detection on pulse control signals associated with ATPD. For raw biosignals, in total M-ATPD achieved 19.41% lower detection error compare to ATPD.
Research on Space Target Recognition Algorithm Based on Empirical Mode Decomp...Nooria Sukmaningtyas
The space target recognition algorithm, which is based on the time series of radar cross section
(RCS), is proposed in this paper to solve the problems of space target recognition in the active radar
system. In the algorithm, EMD method is applied for the first time to extract the eigen of RCS time series.
The normalized instantaneous frequencies of high-frequency intrinsic mode functions obtained by EMD are
used as the eigen values for the recognition, and an effective target recognition criterion is established.
The effectiveness and the stability of the algorithm are verified by both simulation data and real data. In
addition, the algorithm could reduce the estimation bias of RCS caused by inaccurate evaluation, and it is
of great significance in promoting the target recognition ability of narrow-band radar in practice.
Recovery of low frequency Signals from noisy data using Ensembled Empirical M...inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Particle Swarm Optimization for the Path Loss Reduction in Suburban and Rural...IJECEIAES
In the present work, a precise optimization method is proposed for tuning the parameters of the COST231 model to improve its accuracy in the path loss propagation prediction. The Particle Swarm Optimization is used to tune the model parameters. The predictions of the tuned model are compared with the most popular models. The performance criteria selected for the comparison of various empirical path loss models is the Root Mean Square Error (RMSE). The RMSE between the actual and predicted data are calculated for various path loss models. It turned out that the tuned COST 231 model outperforms the other studied models.
The document describes a method for de-noising electrocardiogram (ECG) signals using empirical mode decomposition (EMD) combined with higher order statistics (HOS). EMD is used to decompose ECG signals into intrinsic mode functions (IMFs). Then, HOS measures including kurtosis and bispectrum are applied to the IMFs to identify and remove Gaussian noise components. The algorithm is tested on ECG signals with different levels of signal-to-noise ratio, and signal improvement is measured using SNR improvement and percent root mean square difference. Results show the method effectively de-noises ECG signals.
De-Noising Corrupted ECG Signals By Empirical Mode Decomposition (EMD) With A...IOSR Journals
The electrocardiogram (ECG) signals which are extensively used for heart disease diagnosis and patient monitoring are usually corrupted with various sources of noise. In this paper, an algorithm is developed to de-noise ECG signals based on Empirical Mode Decomposition (EMD) with application of Higher Order Statistics (HOS). The algorithm is applied on several ECG signals for different levels of Signal to Noise Ratio (SNR). The SNR improvement (SNRimp) and Percent Root mean square Difference (PRD (%)) are analyzed. The results show that the developed algorithm is a reasonable one to de-noise ECG signals.
A New Approach for Speech Enhancement Based On Eigenvalue Spectral SubtractionCSCJournals
In this paper, a phase space reconstruction-based method is proposed for speech enhancement. The method embeds the noisy signal into a high dimensional reconstructed phase space and uses Spectral Subtraction idea. The advantages of the proposed method are fast performance, high SNR and good MOS. In order to evaluate the proposed method, ten signals of TIMIT database mixed with the white additive Gaussian noise and then the method was implemented. The efficiency of the proposed method was evaluated by using qualitative and quantitative criteria.
This document summarizes a study that uses signal processing and optimization techniques to detect faults in roller bearings. Specifically, it applies minimum entropy deconvolution (MED) and the Teager-Kaiser energy operator (TKEO) to enhance the discrimination of defect-induced signals in bearing vibration data. It also uses empirical mode decomposition (EMD) to decompose vibration signals into intrinsic mode functions (IMFs), and a genetic algorithm to optimize the weights of IMFs to further improve fault detection sensitivity as measured by kurtosis values. Experimental results on a test bearing show that the EMD-GA approach successfully improved the resolution of detectability between a roller bearing with defect and an intact bearing system.
IRJET- Performance Evaluation of DOA Estimation using MUSIC and Beamformi...IRJET Journal
This document presents a simulation study comparing the MUSIC algorithm and LMS adaptive beamforming algorithm for direction of arrival (DOA) estimation and beamforming in a smart antenna system. The MUSIC algorithm uses eigendecomposition to estimate the DOA of multiple signals and finds the position location of the desired user. The LMS algorithm then adapts the beam pattern by adjusting weights to maximize gain towards the desired user while nulling interference from other directions. The simulation results show sharp peaks in the MUSIC spectrum to accurately locate the desired user and deep nulls in the LMS beam pattern to suppress interference.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
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- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
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- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
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1. Remote Sens. 2014, 6, 2069-2083; doi:10.3390/rs6032069
remote sensing
ISSN 2072-4292
www.mdpi.com/journal/remotesensing
Article
Ensemble Empirical Mode Decomposition Parameters
Optimization for Spectral Distance Measurement in
Hyperspectral Remote Sensing Data
Hsuan Ren 1,2
, Yung-Ling Wang 2
, Min-Yu Huang 3
, Yang-Lang Chang 3,
* and Hung-Ming Kao 4
1
Center for Space and Remote Sensing Research, National Central University, No. 300,
Jhong-da Rd., Jhong-li City 320, Taiwan; E-Mail: hren@csrsr.ncu.edu.tw
2
Department of Computer Science and Information Engineering, National Central University,
No. 300, Jung-da Rd., Chung-li City 320, Taiwan; E-Mail: afrc506585@yahoo.com.tw
3
Department of Electrical Engineering, National Taipei University of Technology, No. 1, Sec. 3,
Chung-Hsiao E. Rd., Taipei City 106, Taiwan; E-Mail: s0302151@gmail.com
4
Institute of Applied Geosciences, National Taiwan Ocean University, No. 2, Pei-Ning Rd.,
Keelung 202, Taiwan; E-Mail: hartge1020@yahoo.com.tw
* Author to whom correspondence should be addressed; E-Mail: ylchang@ntut.edu.tw;
Tel.: +886-2-2771-2171 (ext. 2156); Fax: +886-2-2731-7187.
Received: 12 December 2013; in revised form: 5 February 2014 / Accepted: 10 February 2014 /
Published: 7 March 2014
Abstract: This study proposed a new approach to measure the similarity between spectra
to discriminate materials and evaluate the performance of parameter-selection procedures.
Many pure pixel vector-based similarity measurements have been developed in the past to
calculate the distance between two pixel vectors. However, those methods may not be
effective since they do not take full advantage of the spectral correlation. In this study, we
adopt Ensemble Empirical Mode Decomposition (EEMD) to decompose the spectrum into
serial components and employ these components to improve the performance of spectral
discrimination. Performance evaluation was conducted with several commonly used
measurements, and the spectral samples used for experimentation were provided by the
spectral library of United States Geological Survey (USGS). The experimental results have
demonstrated that EEMD can extract the spectral features more effectively than common
spectral similarity measurements, and it better characterizes spectral properties.
Our experimental results also suggest general rules for selecting noise standard deviation,
the number of iterations for EEMD and the collection of Intrinsic Mode Functions (IMFs)
for classification. Finally, since EEMD is a time-consuming algorithm, we also
OPEN ACCESS
2. Remote Sens. 2014, 6 2070
implement parallel processing with a Graphics Processing Unit (GPU) to increase the
processing speed.
Keywords: hyperspectral; remote sensing; ensemble empirical mode decomposition (EEMD);
spectral angle mapper; similarity measurement
1. Introduction
Hyperspectral image classification is very important for endmember discrimination in various
applications. In the past, many pure pixel vector-based similarity measurements have been proposed to
evaluate the similarity between two pixel vectors. Several popular methods, including the Euclidean
Distance (ED), Mahanalobis Distance (MD) [1–4] and Spectral Angle Mapper (SAM) [1–6],
are widely used to measure the spectral distance and provide acceptable results for pure pixel
classification. However, they also have some drawbacks, because those distance measurements do not
fully utilize the correlation between bands [4].
In this study, we adopted a signal-analysis method to analyze spectral data by Empirical Mode
Decomposition (EMD), which will generate a collection of Intrinsic Mode Functions (IMF) [7].
The decomposition procedure of EMD depends on the magnitude of the original signal with various
intrinsic time scales, i.e., it can decompose the signal into different frequency components. The EMD
has been widely used in the past for time-domain signal processing, and was also employed to
decompose the time-sequence signal to determine intrinsic information [8,9]. For EMD to be effective,
the differences in both frequencies and amplitude must be sufficient for decomposition analysis. If the
physical criteria for the differences between two signals are not met, the sifting process derives an IMF
with single tone modulated in amplitude instead of a superposition of two unimodular tones [9]. Thus,
the modulated signal would no longer encompass the characteristics of the original signals.
To overcome the problem of mode mixing, Wu and Huang proposed Ensemble Empirical Mode
Decomposition (EEMD) [10,11].
In various signal processing applications, both EMD and EEMD have been implemented for feature
extraction and noise reduction [12,13]. Especially for remote sensing images, 2D-EMD [14–16] and
MEEMD [11] have been proposed recently for the decomposition of hyperspectral image into IMFs,
but they apply to pre-selected two-dimensional image band instead of one-dimensional spectral
information. The aim of this research is to discriminate materials by extracting the unique absorption
features from the spectrum of each pixel. In this study, we propose a two-stage process for spectral
similarity measurement. It first adopts EEMD to generate a series of IMFs and accumulate a set of
IMFs for enhancing absorption features, followed by SAM as a common technique for hyperspectral
image classification.
Furthermore, due to the large amount of large-dimension data processing required, it is not efficient
to process hyperspectral images with EEMD [17]. Therefore, parallel processing with a Graphics
Processing Unit (GPU) is implemented for EEMD [18,19]. The performance analysis shows that GPU
can significantly reduce the computing time for EEMD.
3. Remote Sens. 2014, 6 2071
2. Methodology
The proposed method is a two-stage process to measure the spectral similarity of two pixel vectors.
In the first stage, EEMD is adopted to decompose a series of IMFs, and a set of IMFs is accumulated to
enhance absorption features. Secondly, SAM is utilized as the distance measure for spectral similarity.
Because of the computational complexity, parallel processing architecture is also implemented.
2.1. Ensemble Empirical Mode Decomposition (EEMD)
EEMD is a self-adaptive algorithm. In comparison, the traditional Fourier transform needs to
convert the signal by frequency-domain integral analysis, but EMD can be directly performed for
decomposition on a time-domain signal. After a special sifting process, a signal x(t) can be
decomposed to n units of hj representing IMFs, and an item rn as its trend.
1
n
j n
j
x t h r
(1)
All IMFs are orthogonal to each other, and each IMF represents a unique range of energy
and frequency. The sum of all IMFs is equal to the original data. The IMF must satisfy the following
two conditions [9]:
1. The numbers of extrema and zero-crossings of IMFs must be either equal or differ at most by one.
2. At any point, the mean of local maxima and local minima envelopes is zero.
In reality, the nature of a signal x(t) does not satisfy the definition of IMF. That is to say, a large
part of the data consists of various frequencies. To satisfy the definition of IMF, the use of EMD
incorporates the sifting process [7]. This process serves two purposes: (1) to eliminate ride waves; and
(2) make the IMF wave profiles more symmetric.
By using EMD for signal decomposition, the input signal must satisfy the following
three conditions:
1. The signal has at least two extrema; one is the maximum and the other the minimum.
2. The time-period scale is defined by the time lapse between two extrema.
3. If the data have no extrema, only the inflection point is recorded, and the extrema can then be
estimated by differentiation.
Finally, the results can be calculated by integration of these components.
The algorithm is summarized as follows:
(1) Identify all extrema of x(t)
(2) Interpolate between minima (resp. maxima) with “envelopes” emin(t) (resp. emax(t))
(3) Compute the mean envelope
2
))()((
)( minmax tete
tmk
, where k is the iteration number.
(4) Extract the detail hj = x(t)−mk(t).
(5) Repeat (1)–(4) until hj(t) meets the definition of IMF, and IMF converges.
(6) Repeat (1)–(5) to generate a residual rn(t), rn(t) = x(t)−hn(t)
4. Remote Sens. 2014, 6 2072
In practice, the above procedure has to be refined by a sifting process which repeats steps (1)–(4) on
the signal r(t), until it can be considered as having zero-mean according to the stopping criteria. Once
this is achieved, the result is considered as the effective IMF. Then step (6) is applied to generate the
corresponding residual rn(t).
To make sure the EMD decomposition process generates IMFs that meet its conditions,
Huang et al. [7] proposed that a stopping criterion in the sifting process is needed for the EMD
process. The criterion can be implemented by limiting the size of the standard deviation (SD) by twice
sifting the results as defined below:
2
( 1 )
2
0 ( 1 )
( ) ( )
( )
T
j j
t j
h t h t
S D
h t
(2)
A typical value is between 0.2 and 0.3 [7]. When the computed SD value lies in the specified range,
the sifting process is automatically stopped. Figure 1 shows the operating procedures of EMD. First, in
Figure 1(a), the signal x(t) is input and decomposed to n IMFs. Each IMF is calculated by the “k times”
sifting process until the SD is less than 0.3 as shown in Figure 1(b). The sifting process
(see Figure 1(c)) computes the difference between the signal x(t) and the mean of the maxima and
minima envelopes.
Figure 1. The procedure for Empirical Mode Decomposition (EMD). (a) Main flow;
(b) Calculation of IMF; (c) The sifting process.
(a) (b) (c)
5. Remote Sens. 2014, 6 2073
Although the use of EMD has made significant contributions in many applications, its ability to
handle signal-processing problems is still insufficient. Rilling and Flandrin [8,9] stated that EMD
decomposition capability strongly depends on the frequencies and amplitudes, and the differences in
both frequencies and amplitudes of two signals must be sufficient for EMD decomposition analysis.
If the criteria for the differences between two signals are not met, the sifting process derives an IMF
with a single tone modulated in amplitude instead of a superposition of two unimodular tones. This
phenomenon is called the beat effect. To overcome the problem of mode mixing, Wu and Huang [10]
proposed EEMD. A uniform distribution of white noise is added to signals before decomposition to
reduce the effect of the mode mixing in the EMD process [20]. As a result, the EEMD method is
capable of resolving both the issues of mode mixing and multi-dimensional computation [11].
For EEMD, the ratio of the added white noise and the number of signals in the ensemble must be
predetermined. According to the number in the ensemble, different white noise wi(t) with the same
amplitude is added N times to an original signal x(t) to generate N modified signals xi(t).
( ) ( ) ( )i ix t x t w t ,...,N,i 21 (3)
Next, the EMD decomposition is performed on each modified signal xi(t). Assume the signal is
decomposed into n units of IMF and one residue as a trend. Further, by the EEMD method, it will get
N × n IMF signals and n trends rin(t). Then, xi(t) can be rewritten as:
n
j
iniji trthtx
1
)()()( ,...,N,i 21 (4)
To reduce the mode mixing, the EEMD method averages the result of the IMF set Hj(t) and the
trend R(t) derived from EMD.
N
i
ijj th
N
tH
1
)(
1
)( ,...,n,j 21 (5)
N
i
in tr
N
tR
1
)(
1
)( (6)
The error in the decomposition caused by the added white noise is given by the following empirical
formula of Wu et al. [10] for large amounts of data:
n
N
(7)
where N is the number of ensembles, ε is the amplitude of the added noise, and εn is the final standard
deviation. According to this empirical formula, wi(t) can be obtained,
( ) ( )i
w t noise t (8)
The EEMD process is shown in Figure 2. Comparing EEMD and EMD (Figure 1), the only
difference is that EEMD needs to average N hj(t) to get each IMF, but EMD does not.
6. Remote Sens. 2014, 6 2074
Figure 2. The procedure for Ensemble Empirical Mode Decomposition (EEMD) processing.
2.2. Spectral Angle Mapper
A measurement of the similarity of pixels is normally needed for spectral mapping, and the Spectral
Angle Mapper (SAM) is a widely used as a spectral similarity metric in remote sensing [1–6]. In a
scatter plot of pixel values from two bands of a spectral image, pixel spectra and target spectra will
plot as points as shown in Figure 3. If a vector is drawn from the origin through each point, the angle
between any two vectors defines the spectral angle between those two points. The SAM computes a
spectral angle between the closest set of pixel spectra and the target spectra, si and sj.
1
1 1
1 1
2 22 2
1 1
,
( , ) cos ( )
cos ( )
[ ] [ ]
i j
i j
i j
L
il jll
L L
il jll l
s s
SAM s s
s s
s s
s s
(9)
Figure 3. Concept of Spectral Angle Mapper (SAM).
7. Remote Sens. 2014, 6 2075
2.3. Parallel Computing Implementations
In this research, the experiment adopts parallel-computing technology [18,19] to speed up the
EEMD. The EEMD method can be performed by a GPU developed by NVIDIA. The experiment
divides EEMD into two sections. The first is to assign threads for computing each individual ith
x(t),
and to record an entire result of IMF hj(t) by iterative computation. For each input spectrum x(t), N
additive Gaussian noises are randomly generated. Each thread processes one noisy spectrum and
decomposes it into IMFs. The second is to compute in a parallel manner for a vector ensemble mean of
the jth
IMF from all threads (see Figure 4).
Figure 4. The Graphics Processing Unit (GPU) architecture of EEMD.
3. Experimental Results
The experimental data were provided by the United States Geological Survey (USGS) spectra
library, where five minerals were chosen: actinolite, andradite, goethite, hematite and illite. Each
material has four to 10 spectra (Figure 5). For each mineral, at least one spectrum is quite different
from the others, which reduces the classification accuracy. The EEMD can extract the absorption
feature to improve the accuracy.
To demonstrate the effectiveness of EEMD, the SAM was applied to the original and decomposed
spectra by EMD and EEMD. Furthermore, a comparison of parameter settings for EEMD was also
conducted, including noise standard deviation, number of signals in each ensemble average and
number of accumulated IMFs.
8. Remote Sens. 2014, 6 2076
Figure 5. Spectral reflectance results for five minerals.
20 40 60 80 100 120 140 160 180 200 220
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Actinolite
Reflection
Band
20 40 60 80 100 120 140 160 180 200 220
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Andradite
Reflection
Band
20 40 60 80 100 120 140 160 180 200 220
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Goethite
Reflection
Band
20 40 60 80 100 120 140 160 180 200 220
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Hematite
Reflection
Band
20 40 60 80 100 120 140 160 180 200 220
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Illite
Reflection
Band
3.1. SAM for Original Data
To assess the accuracy of discrimination between minerals, 2000 spectra of each material were
simulated with additive white Gaussian noise having signal-to-noise ratio (SNR) 40, and spectral
similarity was measured by SAM. Table 1 shows the similarity discrimination of these five minerals in
contrast to the original spectra. The SAM values between pairs of five original spectra are
75.8%, 80%, 75%, 70.2% and 80%. The experiment examined the degree of accuracy by kappa
coefficient [21]. The test result is reliable because the kappa coefficient is 0.7025.
Table 1. Similarity discrimination rates on the experimental samples of five minerals vs.
USGS spectral library.
Actinolite Andradite Goethite Hematite Illite
Actinolite 75.8% 0% 0% 0% 0%
Andradite 0% 80% 25% 29.4% 20%
Goethite 0% 0% 75% 0% 0%
Hematite 20% 20% 0% 70.2% 0%
Illite 4.2% 0% 0% 0.4% 80%
Kappa value 0.7025
3.2. SAM for EMD Decomposed Data
The EMD algorithm was applied to decompose the spectra into seven IMFs (Figure 6), and spectral
similarity was measured by SAM for each IMF. From Table 2, the largest kappa coefficient occurs for
the first IMF (0.6564) which is slightly less than that for the original data, while the worst kappa
coefficient occurs for the sixth IMF (0.0079), which did not provide good discrimination.
9. Remote Sens. 2014, 6 2077
Figure 6. The spectra of Intrinsic Mode Functions (IMFs) by EMD for Actinolite.
0 50 100 150 200
0
0.5
1
Original
0 50 100 150 200
-0.1
0
0.1
IMF1
0 50 100 150 200
-0.1
0
0.1
IMF2
0 50 100 150 200
-0.05
0
0.05
IMF3
0 50 100 150 200
-0.1
0
0.1
IMF4
0 50 100 150 200
-0.5
0
0.5
IMF5
0 50 100 150 200
-0.01
0
0.01
IMF6
0 50 100 150 200
0
0.5
1
Residual
Band
Table 2. The similarity discrimination rate for the EMD process on the samples of five minerals.
Actinolite Andradite Goethite Hematite Illite Kappa
IMF 1 100% 60% 75% 58.3% 80% 0.65640
IMF 2 100% 60% 25% 50% 80% 0.52632
IMF 3 28.6% 80% 0% 75% 60% 0.46137
IMF 4 0% 40% 0% 75% 40% 0.34278
IMF 5 100% 20% 0% 25% 0% 0.02623
IMF 6 0% 60% 0% 16.7% 0% 0.00794
IMF 7 33.3% 40% 0% 25% 80% 0.22132
Figure 7. Spectra of accumulations of IMFs from 1–6.
0 50 100 150 200
0
0.5
1
Original
0 50 100 150 200
-0.2
0
0.2
IMF1-2
0 50 100 150 200
-0.2
0
0.2
IMF1-3
0 50 100 150 200
-0.5
0
0.5
IMF1-4
0 50 100 150 200
-0.5
0
0.5
IMF1-5
0 50 100 150 200
-0.5
0
0.5
IMF1-6
10. Remote Sens. 2014, 6 2078
Each IMF is ordered sequentially from higher frequencies to lower frequencies. Summation of all
IMFs yields the original data. Because we compared the absorption feature of each IMF with the
original spectra, the wavelength of the absorption feature can be estimated. Since this feature is
distributed through several IMFs, combining a set of IMFs should enhance the absorption features. The
accumulation of IMFs is shown in Figure 7, and the absorption features are clearly observed.
3.3. SAM for EEMD Decomposed Data
EEMD was employed to overcome the drawback of mixing modes in EMD. Several parameters
have to be determined to initialize EEMD, including noise standard deviation (Nstd), number of
signals in each ensemble average (N) and number of accumulated IMFs.
First, the accuracy of performance for each number of signals in the ensemble average was analyzed
by kappa coefficient (Table 3). The noise standard deviation was selected from 0.1–0.9, and the
numbers, N, for the ensemble averages were 10, 50, 80, 100, 500 and 1,000. When N = 1, EEMD is
identical to EMD. The experimental results show a stable kappa value when N exceeds 100 (Figure 8).
Therefore, considering the efficiency of the algorithm, N was set to be 100 for EEMD.
Table 3. Kappa value vs. N and Nstd for IMF 1 with SNR = 30.
IMF 1 1 10 25 50 80 100 500 1000
0.1 0.1795 0.1184 0.1190 0.1185 0.1101 0.1116 0.1094 0.1098
0.2 0.1259 0.1291 0.1369 0.1161 0.1129 0.1103 0.1105 0.1109
0.3 0.0918 0.1240 0.1265 0.1225 0.1198 0.1154 0.1160 0.1154
0.4 0.0405 0.1064 0.1159 0.1194 0.1146 0.1114 0.1138 0.1130
0.5 0.0850 0.1028 0.1091 0.1189 0.1100 0.1119 0.1121 0.1133
0.6 0.0760 0.0984 0.1095 0.1145 0.1101 0.1109 0.1120 0.1134
0.7 0.0519 0.1248 0.0988 0.1056 0.1058 0.1108 0.1124 0.1134
0.8 0.0458 0.0526 0.0881 0.1011 0.1018 0.1088 0.1110 0.1148
0.9 0.0259 0.0445 0.0563 0.0830 0.0936 0.0985 0.1059 0.1074
Figure 8. Kappa value vs. N and Nstd for IMF1 with SNR = 30.
1
25
5080100
500
1000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0
0.05
0.1
0.15
0.2
N
SNR=30 IMF 1
Nstd
Kappacoefficent
11. Remote Sens. 2014, 6 2079
Secondly, the noise standard deviation needs to be determined. From Table 4, N for the ensemble
average is 100, the noise standard deviation (Nstd) is from 0.1–0.9. The simulated data contain additive
white Gaussian noise with SNR = 20, 30 and 40. The results show that the kappa coefficients are over
0.7511 and 0.8688 for the third and fourth IMF, respectively, for all Nstd from 0.1–0.9 and SNR 40.
Significant improvements are obtained with the third or fourth IMF with EEMD alone compared with
the accuracy of original data (0.7025). The maximum kappa value is 0.9771 when Nstd = 0.2, SNR = 40
for the fourth IMF. Therefore, if the spectral estimate has high SNR, with noise standard deviation less
than 0.2, EEMD performs well.
Table 4. Kappa values of IMF1~7 in SNR = 20~40 vs. Nstd under N = 100.
Nstd
N = 100
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
IMF 1
SNR = 20 0.1051 0.1128 0.1131 0.1151 0.1146 0.1114 0.1110 0.1096 0.1098
SNR = 30 0.1116 0.1103 0.1154 0.1114 0.1119 0.1109 0.1108 0.1088 0.0985
SNR = 40 0.5613 0.5068 0.4920 0.4710 0.4570 0.4398 0.4284 0.4149 0.4034
IMF 2
SNR = 20 0.1935 0.2066 0.2183 0.2240 0.2236 0.2199 0.2180 0.2214 0.2145
SNR = 30 0.1588 0.1540 0.1486 0.1421 0.1370 0.1351 0.1294 0.1271 0.1293
SNR = 40 0.7173 0.6500 0.5960 0.5678 0.5336 0.5158 0.4974 0.4799 0.4515
IMF 3
SNR = 20 0.3125 0.3678 0.3850 0.3924 0.4005 0.3953 0.3848 0.3740 0.3764
SNR = 30 0.3511 0.3501 0.3381 0.3359 0.3414 0.3363 0.3371 0.3336 0.3343
SNR = 40 0.8385 0.8631 0.8505 0.8253 0.8034 0.7885 0.7708 0.7596 0.7511
IMF 4
SNR = 20 0.4814 0.5785 0.6063 0.6280 0.6235 0.6151 0.5966 0.5949 0.5863
SNR = 30 0.5061 0.4936 0.4670 0.4749 0.4513 0.4430 0.4291 0.4293 0.4109
SNR = 40 0.8791 0.9771 0.9531 0.9535 0.9220 0.9191 0.9035 0.8945 0.8688
IMF 5
SNR = 20 0.3475 0.5211 0.6199 0.6321 0.6619 0.6733 0.6636 0.6755 0.6873
SNR = 30 0.5855 0.6571 0.6681 0.6628 0.6655 0.6600 0.6530 0.6496 0.6473
SNR = 40 0.6133 0.7764 0.7995 0.8221 0.8414 0.8653 0.8696 0.8800 0.8865
IMF 6
SNR = 20 0.3429 0.3730 0.5574 0.5650 0.5858 0.6030 0.6039 0.5963 0.5866
SNR = 30 0.5319 0.5486 0.7200 0.7258 0.7438 0.7719 0.7508 0.7521 0.7618
SNR = 40 0.3021 0.4833 0.5025 0.5571 0.5835 0.6221 0.6366 0.6514 0.6636
IMF 7
SNR = 20 0.3730 0.4540 0.4581 0.4643 0.4554 0.4296 0.4196 0.4291 0.3998
SNR = 30 0.4858 0.6079 0.6413 0.6675 0.6866 0.6948 0.6731 0.6783 0.6276
SNR = 40 0.4964 0.5014 0.4991 0.5225 0.5339 0.5316 0.5186 0.5125 0.4715
Finally, the number of accumulated IMFs was analyzed. Several IMFs contain absorption features,
so that the accumulation of a set of IMFs should enhance the classification accuracy (Figure 6).
To accumulate IMFs, the absorption features must be clearly identified. In this experiment, the number
of signals in the ensemble average was set as 100 to optimize, as closely as possible, the tradeoff
between noise reduction and efficiency. EEMD was applied to various SNRs and noise standard
deviations. Table 5 shows the classification accuracy using SAM values of the accumulated IMFs:
IMF 1–2, IMF 1–3, IMF 1–4, IMF 1–5, IMF 1–6. The results show that the kappa coefficients are over
0.7556 and 0.9224 for IMF 1–3 and IMF 1–4, respectively, for all Nstd and SNR 40. They also
indicate that EEMD provides further improvements for the fourth IMF compared with the third IMF.
The highest kappa value is 0.9909 when Nstd = 0.2, SNR = 40 for IMF 1–4.
12. Remote Sens. 2014, 6 2080
Table 5. Kappa values of the accumulated IMF (1–2 to 1–6) in SNR = 20~40 vs. Nstd
when N = 100.
Nstd
N = 100
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
IMF 1–2
SNR = 20 0.2243 0.2315 0.2375 0.2348 0.2343 0.2328 0.2321 0.2281 0.2231
SNR = 30 0.4661 0.4526 0.4386 0.4285 0.4199 0.4140 0.4075 0.4009 0.3951
SNR = 40 0.7448 0.6868 0.6369 0.6208 0.5811 0.5608 0.5436 0.5276 0.5276
IMF 1–3
SNR = 20 0.4011 0.4111 0.4095 0.4016 0.3965 0.3921 0.3863 0.3808 0.3818
SNR = 30 0.7179 0.7220 0.7056 0.6946 0.6815 0.6701 0.6599 0.6466 0.6375
SNR = 40 0.8910 0.8726 0.8526 0.8335 0.8154 0.8000 0.7841 0.7668 0.7556
IMF 1–4
SNR = 20 0.6004 0.6374 0.6433 0.6419 0.6380 0.6266 0.6143 0.6085 0.5965
SNR = 30 0.8473 0.8438 0.8468 0.8558 0.8481 0.8410 0.8308 0.8256 0.8174
SNR = 40 0.9855 0.9909 0.9833 0.9844 0.9749 0.9634 0.9510 0.9401 0.9224
IMF 1–5
SNR = 20 0.7158 0.7911 0.7994 0.7988 0.8051 0.8038 0.7975 0.7939 0.7923
SNR = 30 0.9201 0.9343 0.9374 0.9440 0.9455 0.9460 0.9404 0.9465 0.9439
SNR = 40 0.9378 0.9430 0.9645 0.9754 0.9830 0.9864 0.9909 0.9924 0.9945
IMF 1–6
SNR = 20 0.8164 0.8130 0.8020 0.8108 0.8074 0.8116 0.8015 0.8028 0.8041
SNR = 30 0.8984 0.8805 0.8539 0.8738 0.8636 0.8716 0.8601 0.8789 0.8760
SNR = 40 0.9083 0.8868 0.8473 0.8869 0.8724 0.8845 0.9909 0.8875 0.8873
Furthermore, the kappa coefficient decreased when Nstd > 0.5, indicating that the classification
accuracy was also reduced. If the spectra have low SNR, both the number of IMFs and the Nstd need
to be increased to achieve an acceptable performance. On the other hand, with high SNR, no matter
how many IMFs are used, the classification performs well when Nstd < 0.5. The stable performance
appears with IMF 1–5 and Nstd = 0.5, the kappa coefficient is 0.8051, 0.9455, and 0.9830. For higher
classification accuracy and computational efficiency, it suggests EEMD with N = 100, Nstd = 0.2–0.5
and accumulation of IMF 1–4 or IMF 1–5.
3.4. EEMD Speedup by GPU
The computation environments are shown in Table 6. The proposed algorithm was developed to run
on NVIDIA Tesla C1060 GPU via CUDA, and was compared with its CPU serial code on Intel Xeon
5504 with Linux, and Intel i5-2400 with Windows 7. CUDA (Computer Unified Device Architecture)
is a parallel-computing platform and programming model created by NVIDIA and implemented by the
GPUs that they produce. Table 7 shows the performance of EEMD in four different processing
environments and computer language. For performance comparison, the numbers of test samples (N)
are chosen from 500 to 3,000. In PC environment, the computing time is approximately proportional to
the number of samples and C/C++ language is about five times faster than Matlab. Comparing the
environments using C language, the cluster architectures can further improve the performance. The
computational performances have 15 and 60 times improvement with quad-core CPU and GPU,
respectively, when N exceeds 2,000. It is worth noting that the 240-core GPU is not efficiently utilized
with a small sample size—when N = 500—with only a 30 time improvement for GPU compared to a
PC environment.
13. Remote Sens. 2014, 6 2081
Table 6. Computation Environments.
PC Cluster
CPU CPU GPU
Operating System Windows 7 SP1 Debian GNU/Linux 6.0.2
Platform Intel i5-2400
Intel Xeon 5504
(Quad-core)
Tesla c1060
(240 cores)
Clock rate 3.1 GHz 2.0 GHz 1.3 GHz
Memory DDR3 4G × 2 DDR3 2G × 6 DDR3 4G
Language
Matlab
2008a
VS2008-C/C++ Linux-C Linux-C&CUDA
Table 7. The performance of EEMD in various computational environments (values are
in seconds).
N Matlab 2008a VS2008-C/C++ Linux-C Linux-C&CUDA
500 34,127 6240 423.03 207.59
1000 66,574 13,073 846.19 283.22
1500 99,650 19,188 1268.53 345.29
2000 132,624 24,757 1692.13 408.37
2500 165,575 30,966 2112.62 561.02
3000 199,109 37,097 2548.61 684.97
4. Conclusions
Empirical mode decomposition (EMD), a fully data-driven method for decomposing signals
(Huang et al. [7]), is excellent for extracting nonlinear characteristics of signals. Additionally, EEMD
outperforms EMD by accommodating noise and avoiding the beat effect. We proposed a two-stage
process for spectral similarity measurement; first, adopt EEMD to generate a series of IMFs and
then accumulate a set of IMFs for enhancing absorption features; secondly, use the SAM technique
for hyperspectral image classification. The experimental results show that EEMD-decomposed
hyperspectral signals can enhance discrimination ability. The IMFs also indicate the absorption
features of spectra, and the accumulated IMFs can improve absorption characteristics. Our study also
overcame two drawbacks of EEMD; the algorithm is time-consuming and several parameters have to
be determined before processing. To overcome the first drawback, we propose parallel processing with
GPU architecture to decompose spectral data. The performance analysis shows that GPU can
significantly reduce the computing time for EEMD. Our insights into selecting three key parameters
(noise standard deviation, number of signals in ensemble averages, and the number of accumulated
IMFs for EEMD) assist in overcoming the second drawback.
Acknowledgments
The authors would like to express their appreciation to United States Geological Survey for
providing spectral library used in this study.
14. Remote Sens. 2014, 6 2082
Author Contributions
Hsuan Ren and Hung-Ming Kao provided the background knowledge of EMD and EEMD to
decompose 1-dimensional signal into IMFs. Hsuan Ren, Yung-Ling Wang and Hung-Ming Kao
conceived the research topic of using EMD and EEMD to decompose spectrum to improve the
classification performance. Min-Yu Huang, Yung-Ling Wang and Hung-Ming Kao implemented EMD
and EEMD in various computational environments for comparison. Min-Yu Huang and Yung-Ling
Wang conducted the data acquisition, analysis, literature review, tables, figures and preparation of
manuscript. Yang-Lang Chang, Hsuan Ren and Hung-Ming Kao not only participated in methods
selection and discussions but also in editing and revisions of the paper. Hsuan Ren, Yang-Lang Chang,
Yung-Ling Wang and Hung-Ming Kao also had provided the background knowledge and performed
editing in the revised manuscript.
Conflicts of Interest
The authors declare no conflict of interest.
References
1. Chang, C.I. Hyperspectral Imaging: Techniques for Spectral Detection and Classification;
Kluwer Academic/Plenum Publishers: New York, NY, USA, 2003.
2. Carvalho, O.A., Jr.; Guimaraes, R.F.; Gillespie, A.R.; Silva, N.C.; Gomes, R.A.T. A new
approach to change vector analysis using distance and similarity measures. Remote Sens. 2011, 3,
2473–2493.
3. Kamal, M.; Phinn, S. Hyperspectral data for mangrove species mapping: A comparison of
pixel-based and object-based approach. Remote Sens. 2011, 3, 2222–2242.
4. Keshava, N. Distance metrics and band selection in hyperspectral processing with application
to material identification and spectral libraries. IEEE Trans. Geosci. Remote Sens. 2004, 42,
1552–1565
5. Hecker, C.; van der Meijde, M.; van der Werff, H.; van der Meer, F.D. Assessing the influence of
reference spectra on synthetic SAM classification results, IEEE Trans. Geosci. Remote Sens.
2008, 46, 4162–4172.
6. Van der Linden, S.; Waske, B.; Hostert, P. Towards an Optimized Use of the Specral Angle
Space. In Proceedings of the 5th EARSeL Workshop on Imaging Spectroscopy, Bruges, Belgium,
23–25 April 2007; pp. 1–5.
7. Huang, N.E.; Shen, Z.; Long, S.R.; Wu, M.C.; Shih, H.H.; Zheng, Q.; Yen, N.C.; Tung, C.C.;
Liu, H.H. The empirical mode decomposition and the Hilbert spectrum for nonlinear and
non-stationary time series analysis. Proc. R. Soc. A 1998, 454, 903–999.
8. Flandrin, P.; Rilling, G.; Goncalvés, P. Empirical mode decomposition as a filter bank.
IEEE Signal Process. Lett. 2004, 11, 112–114.
9. Rilling, G.; Flandrin, P.; Goncalves, P. On Empirical Mode Decomposition and its Algorithm.
In Proceedings of the 6th IEEE/EURASIP Workshop on Nonlinear Signal and Image Processing
(NSIP’03), Grado, Italy, 8–11 June 2003; pp. 8–11.