Utilizing Artificial Intelligence to Collect Pavement Surface Condition Data

2020 ◽  
Vol 38 (1A) ◽  
pp. 74-82
Author(s):  
Hasan H. Joni ◽  
Imzahim A. Alwan ◽  
Ghazwan Naji
Keyword(s):  
Artificial Intelligence ◽  
Signal To Noise Ratio ◽  
Surface Condition ◽  
Discrete Wavelet ◽  
Signal To Noise ◽  
Band Frequency ◽  
Message Length ◽  
Frequency Components ◽  
Level 1 ◽  
Level 2

Recently the Discrete-Wavelet-Transform (DWT) has been represented as signal processing powerful tool to separate the signal into its band frequency components. In this paper, improvement of the steganography techniques by hiding the required message into the suitable frequency band is presented. The results show that the increase of the message length will reduce the Peak Signal to Noise Ratio (PSNR), while the PSNR increases with the increasing the  DWT levels. It should be noted that the PSNR reduction was from -13.8278 to -17.77208 when increasing the message length from 161 to 505 characters. In this context, the PSNR is increased from -13.8278 to 7.0554 and from -17.7208 to 1.7901 when the DWT increased from level (1) to level (2).

Improvement of Image Steganography Using Discrete Wavelet Transform

2020 ◽  
Vol 38 (1A) ◽  
pp. 83-87
Author(s):  
Manal K. Oudah ◽  
Rula S. Khudhair ◽  
Saad M. Kaleefah ◽  
Aqeela N. Abed
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Discrete Wavelet ◽  
Signal To Noise ◽  
Band Frequency ◽  
Message Length ◽  
Frequency Components ◽  
Level 1 ◽  
Level 2

Recently the Discrete-Wavelet-Transform (DWT) has been represented as signal processing powerful tool to separate the signal into its band frequency components. In this paper, improvement of the steganography techniques by hiding the required message into the suitable frequency band is presented. The results show that the increase of the message length will reduce the Peak Signal to Noise Ratio (PSNR), while the PSNR increases with the increasing the DWT levels. It should be noted that the PSNR reduction was from -13.8278 to -17.77208 when increasing the message length from 161 to 505 characters. In this context, the PSNR is increased from -13.8278 to 7.0554 and from -17.7208 to 1.7901 when the DWT increased from level (1) to level (2).

scholarly journals Encryption Based Image Watermarking Algorithm in 2DWT-DCT Domains

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5540
Author(s):  
Nayeem Hasan ◽  
Md Saiful Islam ◽  
Wenyu Chen ◽  
Muhammad Ashad Kabir ◽  
Saad Al-Ahmadi
Keyword(s):  
Signal To Noise Ratio ◽  
Image Watermarking ◽  
Similarity Index ◽  
Structural Similarity ◽  
Reconstructed Image ◽  
Discrete Wavelet ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Watermarking Scheme

This paper proposes an encryption-based image watermarking scheme using a combination of second-level discrete wavelet transform (2DWT) and discrete cosine transform (DCT) with an auto extraction feature. The 2DWT has been selected based on the analysis of the trade-off between imperceptibility of the watermark and embedding capacity at various levels of decomposition. DCT operation is applied to the selected area to gather the image coefficients into a single vector using a zig-zig operation. We have utilized the same random bit sequence as the watermark and seed for the embedding zone coefficient. The quality of the reconstructed image was measured according to bit correction rate, peak signal-to-noise ratio (PSNR), and similarity index. Experimental results demonstrated that the proposed scheme is highly robust under different types of image-processing attacks. Several image attacks, e.g., JPEG compression, filtering, noise addition, cropping, sharpening, and bit-plane removal, were examined on watermarked images, and the results of our proposed method outstripped existing methods, especially in terms of the bit correction ratio (100%), which is a measure of bit restoration. The results were also highly satisfactory in terms of the quality of the reconstructed image, which demonstrated high imperceptibility in terms of peak signal-to-noise ratio (PSNR ≥ 40 dB) and structural similarity (SSIM ≥ 0.9) under different image attacks.

scholarly journals Implementation of Spatial-Scale Domain Based De-Noising Techniques using Different Thresholding

2020 ◽  
Vol 9 (3) ◽  
pp. 3594-3603
Keyword(s):  
Wavelet Transform ◽  
Root Mean Square Error ◽  
Signal To Noise Ratio ◽  
Objective Evaluation ◽  
Contourlet Transform ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Stationary Wavelet Transform ◽  
Signal To Noise ◽  
Different Levels

This paper aims in presenting a thorough comparison of performance and usefulness of multi-resolution based de-noising technique. Multi-resolution based image denoising techniques overcome the limitation of Fourier, spatial, as well as, purely frequency based techniques, as it provides the information of 2-Dimensional (2-D) signal at different levels and scales, which is desirable for image de-noising. The multiresolution based de-noising techniques, namely, Contourlet Transform (CT), Non Sub-sampled Contourlet Transform (NSCT), Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform (DWT), have been selected for the de-noising of camera images. Further, the performance of different denosing techniques have been compared in terms of different noise variances, thresholding techniques and by using well defined metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Root Mean Square Error (RMSE). Analysis of result shows that shift-invariant NSCT technique outperforms the CT, SWT and DWT based de-noising techniques in terms of qualititaive and quantitative objective evaluation

scholarly journals An Image Steganography Algorithm using Fractional Discrete Wavelet Transform with Advanced Encryption System

2020 ◽  
Vol 9 (5) ◽  
pp. 541-546
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Signal To Noise ◽  
Noise Ratio

The research constitutes a distinctive technique of steganography of image. The procedure used for the study is Fractional Random Wavelet Transform (FRWT). The contrast between wavelet transform and the aforementioned FRWT is that it comprises of all the benefits and features of the wavelet transform but with additional highlights like randomness and partial fractional value put up into it. As a consequence of the fractional value and the randomness, the algorithm will give power and a rise in the surveillance layers for steganography. The stegano image will be acquired after administrating the algorithm which contains not only the coated image but also the concealed image. Despite the overlapping of two images, any diminution in the grade of the image is not perceived. Through this steganographic process, we endeavor for expansion in surveillance and magnitude as well. After running the algorithm, various variables like Mean Square Error (MSE) and Peak Signal to Noise ratio (PSNR) are deliberated. Through the intended algorithm, a rise in the power and imperceptibility is perceived and it can also support diverse modification such as scaling, translation and rotation with algorithms which previously prevailed. The irrefutable outcome demonstrated that the algorithm which is being suggested is indeed efficacious.

scholarly journals MODELLING OF PARTIAL DISCHARGE ANALYSIS SYSTEM USING WAVELET TRANSFORM DENOISING TECHNIQUE IN LABVIEW ENVIRONMENT

2019 ◽  
Vol 81 (6) ◽  
Author(s):  
A. Nazifah Abdullah ◽  
S. H. K. Hamadi ◽  
M. Isa ◽  
B. Ismail ◽  
A. N. Nanyan ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Virtual Instrument ◽  
Signal To Noise Ratio ◽  
Partial Discharge ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Mother Wavelet ◽  
Signal To Noise ◽  
Labview Software ◽  
Analysis System

Partial discharge (PD) measurement is an essential to detect and diagnose the existence of the PD. However, this measurement has faced noise disturbance in industrial environments. Thus, PD analysis system using discrete wavelet transform (DWT) denoising technique via Laboratory Virtual Instrument Engineering Workbench (LabVIEW) software is proposed to distinguish noise from the measured PD signal. In this work, the performance of denoising process is analyzed based on calculated mean square error (MSE) and signal to noise ratio (SNR). The result is manipulated based on Haar, Daubechies, Coiflets, Symlets and Biorthogonal type of mother wavelet with different decomposition levels. From the SNR results, all types of the mother wavelet are suitable to be used in denoising technique since the value of SNR is in large positive value. Therefore, further studies were conducted and found out that db14, coif3, sym5 and bior5.5 wavelets with least MSE value are considered good to be used in the denoising technique. However, bior5.5 wavelet is proposed as the most optimum mother wavelet due to consistency of producing minimum value of MSE and followed by db14.

scholarly journals Fast Compressed Sensing MRI Based on Complex Double-Density Dual-Tree Discrete Wavelet Transform

2017 ◽  
Vol 2017 ◽  
pp. 1-13
Author(s):  
Shanshan Chen ◽  
Bensheng Qiu ◽  
Feng Zhao ◽  
Chao Li ◽  
Hongwei Du
Keyword(s):  
Wavelet Transform ◽  
Compressed Sensing ◽  
Discrete Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Discrete Wavelet ◽  
Translation Invariance ◽  
Signal To Noise ◽  
Reconstruction Methods ◽  
Noise Ratio

Compressed sensing (CS) has been applied to accelerate magnetic resonance imaging (MRI) for many years. Due to the lack of translation invariance of the wavelet basis, undersampled MRI reconstruction based on discrete wavelet transform may result in serious artifacts. In this paper, we propose a CS-based reconstruction scheme, which combines complex double-density dual-tree discrete wavelet transform (CDDDT-DWT) with fast iterative shrinkage/soft thresholding algorithm (FISTA) to efficiently reduce such visual artifacts. The CDDDT-DWT has the characteristics of shift invariance, high degree, and a good directional selectivity. In addition, FISTA has an excellent convergence rate, and the design of FISTA is simple. Compared with conventional CS-based reconstruction methods, the experimental results demonstrate that this novel approach achieves higher peak signal-to-noise ratio (PSNR), larger signal-to-noise ratio (SNR), better structural similarity index (SSIM), and lower relative error.

scholarly journals A review of various image fusion types and transforms

2021 ◽  
Vol 24 (3) ◽  
pp. 1515
Author(s):  
Ayodeji Olalekan Salau ◽  
Shruti Jain ◽  
Joy Nnenna Eneh
Keyword(s):  
Image Fusion ◽  
Signal To Noise Ratio ◽  
Discrete Wavelet ◽  
Signal To Noise ◽  
Transform Method ◽  
Important Approach ◽  
Fusion Applications ◽  
Noise Ratio ◽  
Ihs Transform ◽  
Validation Of Results

Utilizing multiple views of an image is an important approach in digital photography, video editing, and medical image fusion applications. Image fusion (ImF) methods are used to improve an image's quality and remove noise from the image signal, resulting in a higher signal-to-noise ratio. A complete assessment of the literature on the different transform kinds, techniques, and rules utilized in ImF is presented in this paper. To assess the outcomes, a white flower image was fused using discrete wavelet transform (DWT) and discrete cosine transform (DCT) techniques. For validation of results, the red, green, blue (RGB) and intensity hue saturation (IHS) values of individual and fused images were evaluated. The results obtained from the fused images with the spatial IHS transform method give a remarkable performance. Furthermore, the results of the performance evaluation using DWT and DCT fusion techniques show that the same peak signal to noise ratio (PSNR) of 114.04 was achieved for both PSNR 1 and PSNR 2 for DCT, and different results were obtained for DWT. For signal to noise ratio (SNR), SNR 1 and SNR 2 achieved slightly similar values of 114.00 and 114.01 for DCT, while a SNR of 113.28 and 112.26 was achieved for SNR 1 and SNR 2 respectively.

scholarly journals Face recognition using enhancement discrete wavelet transform based on MATLAB

2021 ◽  
Vol 23 (2) ◽  
pp. 1128
Author(s):  
Asma Abdulelah Abdulrahman ◽  
Fouad Shaker Tahir
Keyword(s):  
Neural Network ◽  
Communication Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Signal To Noise ◽  
High Quality

<p>In this work, it was proposed to compress the color image after de-noise by proposing a coding for the discrete transport of new wavelets called discrete chebysheve wavelet transduction (DCHWT) and linking it to a neural network that relies on the convolutional neural network to compress the color image. The aim of this work is to find an effective method for face recognition, which is to raise the noise and compress the image in convolutional neural networks to remove the noise that caused the image while it was being transmitted in the communication network. The work results of the algorithm were calculated by calculating the peak signal to noise ratio (PSNR), mean square error (MSE), compression ratio (CR) and bit-per-pixel (BPP) of the compressed image after a color image (256×256) was entered to demonstrate the quality and efficiency of the proposed algorithm in this work. The result obtained by using a convolutional neural network with new wavelets is to provide a better CR with the ratio of PSNR to be a high value that increases the high-quality ratio of the compressed image to be ready for face recognition.</p>

scholarly journals Learning-Assisted Rain Attenuation Prediction Models

2020 ◽  
Vol 10 (17) ◽  
pp. 6017
Author(s):  
Md Abdus Samad ◽  
Dong-You Choi
Keyword(s):  
Prediction Models ◽  
Signal To Noise Ratio ◽  
Rain Attenuation ◽  
Rain Event ◽  
Signal To Noise ◽  
Research Issues ◽  
The Earth ◽  
Open Research ◽  
Frequency Components ◽  
Mitigation Techniques

Rain attenuation becomes significant to degrade the earth-space or terrestrial radio link’s signal-to-noise ratio (SNR). So, to maintain the desired SNR level, with the help of fade mitigation techniques (FMTs), it needs to control transmitted signals power considering the expected rainfall. However, since the rain event is a random phenomenon, the rain attenuation that may be experienced by a specific link is difficult to estimate. Many empirical, physical, and compound nature-based models exist in the literature to predict the expected rain attenuation. Furthermore, many optimizations and decision-making functions have become simpler since the development of the learning-assisted (LA) technique. In this work, the LA rain attenuation (LARA) model was classified based on input parameters. Besides, for comparative analysis, each of the supported frequency components of LARA models were tabulated, and an accurate contribution of each model was identified. In contrast to all the currently available LARA models, the accuracy and correlation of input-output parameters are presented. Additionally, it summarizes and discusses open research issues and challenges.

scholarly journals A Versatile Analog Electronic Interface for Piezoelectric Sensors Used for Impacts Detection and Positioning in Structural Health Monitoring (SHM) Systems

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1047
Author(s):  
Lorenzo Capineri ◽  
Andrea Bulletti
Keyword(s):  
Lamb Waves ◽  
Signal To Noise Ratio ◽  
Piezoelectric Sensors ◽  
Signal Spectrum ◽  
Signal To Noise ◽  
Front End ◽  
Analog Front End ◽  
Frequency Components ◽  
Noise Ratio ◽  
The Impact

Continuous monitoring of mechanical impacts is one of the goals of modern SHM systems using a sensor network installed on a structure. For the evaluation of the impact position, there are generally applied triangulation techniques based on the estimation of the differential time of arrival (DToA). The signals generated by impacts are multimodal, dispersive Lamb waves propagating in the plate-like structure. Symmetrical S0 and antisymmetrical A0 Lamb waves are both generated by impact events with different velocities and energies. The discrimination of these two modes is an advantage for impact positioning and characterization. The faster S0 is less influenced by multiple path signal overlapping and is also less dispersive, but its amplitude is generally 40–80 dB lower than the amplitude of the A0 mode. The latter has an amplitude related to the impact energy, while S0 amplitude is related to the impact velocity and has higher frequency spectral content. For these reasons, the analog front-end (AFE) design is crucial to preserve the information of the impact event, and at the same time, the overall signal chain must be optimized. Large dynamic range ADCs with high resolution (at least 12-bit) are generally required for processing these signals to retrieve the DToA information found in the full signal spectrum, typically from 20 kHz to 500 kHz. A solution explored in this work is the design of a versatile analog front-end capable of matching the different types of piezoelectric sensors used for impact monitoring (piezoceramic, piezocomposite or piezopolymer) in a sensor node. The analog front-end interface has a programmable attenuator and three selectable configurations with different gain and bandwidth to optimize the signal-to-noise ratio and distortion of the selected Lamb wave mode. This interface is realized as a module compatible with the I/O of a 16 channels real-time electronic system for SHM previously developed by the authors. High-frequency components up to 270 kHz and lower-frequency components of the received signals are separated by different channels and generate high signal-to-noise ratio signals that can be easily treated by digital signal processing using a single central unit board with ADC and FPGA.

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