Discrete Wavelet Transform Algorithm Research Articles

Secure medical image steganography based on Discrete Wavelet Transformation and ElGamal encryption algorithm

The latest development in computing methodologies and related steganography based applications had created a novel practice such as telemedicine where patient diagnosis images or allied information can be used diagnosis practices that are located in remote areas. In order to provide accurate and effective telemedicine the flawless or seamless biomedical information is required from patient. With respect to this the medical data may remain prone to get corrupted by hackers or might get manipulated when transmitted through an insecure channel. The cryptosystems which are available are not efficient enough to solve the above mentioned issues. Hence, in this research work a much efficient and effective image steganography technique has been proposed in order to hide the confidential and important image information. We have incorporated Discrete Wavelet Transformation (DWT) technique instead of wavelet transform techniques to embed the secret message in the required cover images. Also, to assure continuous communication through insecure channel, a new model called ElGamal encryption cryptosystem model has been implemented which contains the steganography scheme which has been proposed and developed. To maintain the content of Region of Interest (ROI), Region of Non- Interest (RONI) is used to embed the secret information. The overall performance analysis reveals that the DWT with ElGamal encryption provides more efficient and also have high embedding capacity, imperceptibility when compared to other methods like Hamming code, Syndrome-Trellis Code (STC) and Rivest-Shamir- Adleman (RSA) based methods.

An efficient implementation of one-dimensional discrete wavelet transform algorithms for GPU architectures

In this paper, the authors present several self-developed implementation variants of the Discrete Wavelet Transform (DWT) computation algorithms and compare their execution times against the commonly approved ones for representative modern Graphics Processing Units (GPUs) architectures. The proposed solutions avoid the time-consuming modulo divisions and conditional instructions used for DWT filters wrapping by proper expansion of the DWTs input data vectors. The main goal of the research is to improve the computation times for popular DWT algorithms for representative modern GPU architectures while retaining the code’s clarity and simplicity. The relations between algorithms execution time improvements for GPUs are also compared with their counterparts for traditional sequential processors. The experimental study shows that the proposed implementations, in the case of parallel realization on GPUs, are characterized by very simple kernel code and high execution time performance.

Epileptic seizure identification in EEG signals using DWT, ANN and sequential window algorithm

A patient-specific novel systematic methodology is described in this study for automatic seizure detection from raw electroencephalogram (EEG) signals. Filtering process by means of band-pass finite impulse response (FIR) filter with the frequency range of 0.5–40 Hz is implemented at the outset to eliminate different artifacts and noises mixed with raw EEG signals. As EEGs are highly non-linear and non-stationary signals in nature, discrete wavelet transform (DWT) is then used to analyze the signals in time-frequency domain. DWT with four level decomposition is performed using db6 mother wavelet for feature extraction. A new feature set, composed of eleven non-linear statistical features extracted from each sub-bands resulting from due to wavelet decomposition, is then fed to the input of artificial neural network (ANN) to classify the signal accurately. Finally, a novel algorithm named sequential window algorithm is carried out to improve the classification performance. 99.44% mean classification accuracy, 80.66% average sensitivity, 4.12 s mean latency and 0.2% average false positive rate (FPR) are achieved in this study. This study successfully reduces the latency time with more accuracy and significantly low FPR.

Multilevel Redundant Discrete Wavelet Transform (ML-RDWT) and Optimal Red Deer Algorithm (ORDA) Centred Approach to Mitigate the Effect of ICI, BER and CIR in a MIMO-OFDM System

Nowadays, there is a great demand for ultra-high data rate (UHDR) transmission on most 5th generation wireless networks. In this concern, the multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) scheme is used on a large scale to achieve UHDR transmission with reduced inter-symbol interference (ISI) and inter-carrier interference (ICI). Discrete wavelet transform-based OFDM (DWT-OFDM) provides better orthogonality due to presence of orthogonal wavelets, which mitigates the effects caused by ISI and ICI. Also, it has extended bandwidth than the traditional OFDM systems. But a major drawback in this system is that it suffers from down sampling. The down-sampling effect reduces the actual size of the input bit streams. As a result, the system performance is degraded. For solving this problem, a multilevel redundant discrete wavelet transform (ML-RDWT) is used instead of DWT to achieve improved spectral performance. Here, complex down-sampling operation is eliminated. From the simulation outcomes, it is clearly viewed that effects caused by ICI, ISI and BER are mitigated by improving the performance of CIR. The proposed method employs optimal red deer algorithm (ORDA) to locate the optimized weights for the ICI cancellation system. This algorithm enhances the spectral efficiency by achieving high CIR with reduced BER, ISI and ICI. The BER in the proposed MIMO-ML-RDWT-OFDM-ORDA method is 68%, 76%, 38% and 75%, which is very low when compared to the BER in the existing techniques like MIMO-DWT-OFDM-RDA, MIMO-RNS-OFDM-PNMA, MIMO-OFDM-BMA and MIMO-OFDM-ICIMA. The ISI in the proposed method is 94%, 91%, 95% low when compared to the ISI in the existing techniques. The ICI in the proposed work is 71%, 57%, 73% and 86% low when compared to the ICI in the existing techniques. Therefore, the general performance of the proposed MIMO-ML-RDWT-OFDM-ORDA method is improved in an efficient way with less complexity, error rate and processing delay.

The Sensitivity of The Heart Rate Detection for Atrial Electrograms Signal

Atrial Fibrillation (AF) is the most familiar example of arrhythmia that will occur health problems such as stroke, heart failure and other complications. Globally, the number of AF patients will more than triple by 2050 worldwide. Current methods involve performing large-area ablation without knowing the exact location of key parts. The reliability of the technology can be used as a target for atrial fibrillation’s catheter ablation. The factors that leading to the onset of atrial fibrillation include the triggering factors that induce arrhythmia and the substrate that maintains the arrhythmia. The project’s aim is to create a method for identifying AF that can be used as screening tool in medical practice. The primary goals for the detection method’s design are to develop a MATLAB software program that can compare the complexity of a normal ECG signal and an AF ECG signal. Currently, this can be achieved by the ECG Signal’s R peaks and RR Interval. For AF detection, there are more R peaks and RR Intervals and it is irregular. In this research, the detection of AF is based on the heart rate (RR Intervals). For the ECG preprocessing, Pan-Tompkins Algorithm and Discrete Wavelet Transform is used to detect the sensitivity on the R peaks and RR Intervals. As a result, Discrete Wavelet Transform algorithm gives 100% sensitivity for the dataset obtained from MIT-BIH Atrial Fibrillation and MIT-BIH Arrhythmia Database.

EEG Motor-Imagery BCI System Based on Maximum Overlap Discrete Wavelet Transform (MODWT) and Machine learning algorithm

The ability of the human brain to communicate with its environment has become a reality through the use of a Brain-Computer Interface (BCI)-based mechanism. Electroencephalography (EEG) has gained popularity as a non-invasive way of brain connection. Traditionally, the devices were used in clinical settings to detect various brain diseases. However, as technology advances, companies such as Emotiv and NeuroSky are developing low-cost, easily portable EEG-based consumer-grade devices that can be used in various application domains such as gaming, education. This article discusses the parts in which the EEG has been applied and how it has proven beneficial for those with severe motor disorders, rehabilitation, and as a form of communicating with the outside world. This article examines the use of the SVM, k-NN, and decision tree algorithms to classify EEG signals. To minimize the complexity of the data, maximum overlap discrete wavelet transform (MODWT) is used to extract EEG features. The mean inside each window sample is calculated using the Sliding Window Technique. The vector machine (SVM), k-Nearest Neighbor, and optimize decision tree load the feature vectors.

Robust image watermarking using the social group optimization algorithm

Watermarking images is essential for several reasons like security, validation etc. The extracted watermark should have similar characteristics as that of the watermark image embedded during the watermarking process. Also, it is important that the watermark image should not differ the features of the host image after embedding. In order conceive this features, it is important to have a technique of watermarking. In this paper, such an attempt has been made to incorporate the novel techniques of watermarking using single value decomposition (SVD), discrete wavelet transformation (DWT) and social group optimization algorithm (SGOA). The simulation-based experiment has been carried out to evaluate the performance of the technique in terms of structural similarity index (SSI). The Matlab environment is used for the simulations.

Comparison Study for Three Compression Techniques (Wavelet, Contourlet and Curvelet Transformation)

Researches and studies on compressing digital images are aiming to make it easier to deal with networks, communications and Internet by reducing the size of the multimedia files transferred, and reducing the execution time and transmission time. In this research, the lossy compression method was adopted as one of the solutions that reduce the size of the data required to compress the image, through the process of compression of digital image data using Discrete Wavelet Transform algorithms using Haar filter, and Contourlet. Using Laplace and Directional Filter, Curvelet transformation using FDCT- Wrapping Technology .The performance of the algorithms used in the proposed research is also evaluated using a Ratio Compression (RC) scale, As well as the Peak signal to noise ratio (PSNR) scale, the mean sequence error (MSE) scale, the signal to noise ratio (SNR) scale, and finally, the Normalization correlation (NC) scale. Correspondence between the original image and the recovered image after compression, in order to choose the best algorithm that achieves the best compression ratio of the image and maintains the parameters of the recovered image based on the standards (MSE, PSNR, SNR, COR and CR) used with the three algorithms, and the results showed that the Curvelet transformation algorithm achieved : best compression ratio, but at the expense of image quality.

Propagation of Random Errors by the Discrete Wavelet Transform Algorithm

The paper presents considerations concerning the transfer of random errors from the input to the output of the Discrete Wavelet Transform (DWT) algorithm. The concept of determining an uncertainty of its output data based on the probabilistic error description has been presented. The DWT is discussed as the product of the vector of input quantities and the matrix of algorithm coefficients. Calculations of the uncertainty of a single output result of the algorithm are described with assumption that the input quantities are burdened by random errors of known distributions. Theoretical considerations have been verified by simulation experiments using the Monte Carlo method. Determining the uncertainty at the DWT output is possible due to the specific properties of transferring random errors by linear and additive algorithms.

Discrete wavelet transformation and genetic algorithm – back propagation neural network applied in monitoring woodworking tool wear conditions in the milling operation spindle power signals

Tool wear conditions monitoring is an important mechanical processing system that can improve the processing quality of wood plastic composite furniture and reduce industrial energy consumption. An appropriate signal, feature extraction method, and model establishment method can effectively improve the accuracy of tool wear monitoring. In this work, an effective method based on discrete wavelet transformation (DWT) and genetic algorithm (GA) – back propagation (BP) neural network was proposed to monitor the tool wear conditions. The spindle power signals under different spindle speeds, depths of milling, and tool wear conditions were collected by power sensors connected to the machine tool control box. Based on the feature extraction method, the approximate coefficients of spindle power signal were extracted by DWT. Then, the extracted approximate coefficients, spindle speeds, depths of milling, and tool wear conditions were taken as samples to train the monitoring model. Threshold and weight of BP neural network were optimized by GA, and the accuracy of monitoring model established by the GA – BP neural network can reach 100%. Thus, the proposed monitoring method can accurately monitor tool wear conditions with different milling parameters, which can achieve the purpose of improving the processing quality of wood plastic composite furniture and reducing energy consumption.

A model development on GIS-driven data to predict temporal daily collision through integrating Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms; case study: Tehran-Qazvin freeway

The aim of this study is to develop a model to predict temporal daily collision by integrating of Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms. As a case study, the integrated model was tested on 1097 daily traffic collisions data of Karaj-Qazvin freeway from 2009 to 2013 and the results were compared with the conventional ANN prediction model. In this method, initially, the raw collision data were analyzed, normalized, and classified via Geographical Information System (GIS). Partial Autocorrelation Function (PACF) was also utilized to evaluate the temporal autocorrelation for consecutive existing daily data. The results of this study showed that the proposed integrated DWT-ANN method provided higher predictive accuracy in daily traffic collision than ANN model by increasing coefficient of determination (R2) from 0.66 to 0.82.

VME-DWT: An Efficient Algorithm for Detection and Elimination of Eye Blink From Short Segments of Single EEG Channel.

Recent advances in development of low-cost single-channel electroencephalography (EEG) headbands have opened new possibilities for applications in health monitoring and brain-computer interface (BCI) systems. These recorded EEG signals, however, are often contaminated by eye blink artifacts that can yield the fallacious interpretation of the brain activity. This paper proposes an efficient algorithm, VME-DWT, to remove eye blinks in a short segment of the single EEG channel. The proposed algorithm: (a) locates eye blink intervals using Variational Mode Extraction (VME) and (b) filters only contaminated EEG interval using an automatic Discrete Wavelet Transform (DWT) algorithm. The performance of VME-DWT is compared with an automatic Variational Mode Decomposition (AVMD) and a DWT-based algorithms, proposed for suppressing eye blinks in a short segment of the single EEG channel. The VME-DWT detects and filters 95% of the eye blinks from the contaminated EEG signals with SNR ranging from -8 to +3 dB. The VME-DWT shows superiority to the AVMD and DWT with the higher mean value of correlation coefficient (0.92 vs. 0.83, 0.58) and lower mean value of RRMSE (0.42 vs. 0.59, 0.87). The VME-DWT can be a suitable algorithm for removal of eye blinks in low-cost single-channel EEG systems as it is: (a) computationally-efficient, the contaminated EEG signal is filtered in millisecond time resolution, (b) automatic, no human intervention is required, (c) low-invasive, EEG intervals without contamination remained unaltered, and (d) low-complexity, without need to the artifact reference.

Discrete Wavelet Transformation Approach for Surface Defects Detection in Friction Stir Welded Joints

Abstract Friction Stir Welding joint quality depends on input parameters such as tool rotational speed, tool traverse speed, tool tilt angle and an axial force. Surface defects formation occurs when these input parameters are not selected properly. The main objective of the recent paper is to develop Discrete Wavelet Transform algorithm by using Python programming and further subject it to the Friction Stir Welded samples for the identification of various external surface defects present.

Online DWT algorithm for identification of aerodynamic damping in wind turbines

A new online identification method for estimating the time-varying aerodynamic damping of a wind turbine is proposed. Through a combination of measured aeroelastic response data and an aeroelastic wind turbine model, the time-varying aerodynamic damping matrix can be identified with the aid of the real-time Discrete Wavelet Transform (DWT) technique. The proposed method is validated by means of a 13-DOF aero-servo-elastic wind turbine model, which is used to mimic the in situ aeroelastic response of the NREL 5 MW wind turbine. A measurement noise sensitivity survey is conducted. Results from current DWT-based estimation method are compared with those from previously proposed Continues Wavelet Transform (CWT)-based estimation method and found to be superior in terms of accuracy and efficiency.

Bearing Fault Classification of Induction Motors Using Discrete Wavelet Transform and Ensemble Machine Learning Algorithms

Bearing fault diagnosis at early stage is very significant to ensure seamless operation of induction motors in industrial environment. The identification and classification of faults helps to undertook maintenance operation in an efficient manner. This paper presents an ensemble machine learning-based fault classification scheme for induction motors (IMs) utilizing the motor current signal that uses the discrete wavelet transform (DWT) for feature extraction. Three wavelets (db4, sym4, and Haar) are used to decompose the current signal, and several features are extracted from the decomposed coefficients. In the pre-processing stage, notch filtering is used to remove the line frequency component to improve classification performance. Finally, the two ensemble machine learning (ML) classifiers random forest (RF) and extreme gradient boosting (XGBoost) are trained and tested using the extracted feature set to classify the bearing fault condition. Both classifier models demonstrate very promising results in terms of accuracy and other accepted performance indicators. Our proposed method achieves an accuracy slightly greater than 99%, which is better than other models examined for the same dataset.

Cardioid graph based ECG biometric in varying physiological conditions using compressed QRS

This paper proposes a robust biometric identification system using compressed electrocardiogram (ECG) signal by varying physiological conditions. The ECG data were obtained by recording a total of 30 healthy subjects where they performed six regular daily activities repeatedly at a sampling frequency of 1000 Hz. Then, the QRS complexes are segmented by implementing Amplitude Based Technique (ABT) where it compares the amplitudes of ECG points to determine the R peak. The segmented QRS is then compressed for various levels by using Discrete Wavelet Transform (DWT) algorithms and first 3 Daubechies (db) wavelet are computed. Next, a Cardioid graph is generated. In order to verify the matching process, the classification is performed by using the Multilayer Perceptron (MLP) technique. The results show that by applying this method, the accuracy of the identification rate can be achieved as high as 96.4% even when the data file is compressed up to 73.3%. When the data file is compressed, the outcomes also demonstrate that the execution time is less compare to non-compressed data. Therefore, the biometric identification system can be implemented efficiently as there will be a lesser issue regarding the data storage, execution time and accuracy based on the outcome of the study.

Low Power FPGA Implementation of Multi-View Video Coding with Hybrid Compression and Decompression Algorithm

The video is one of the most useful and most appealing medium to represent some information. More usage of digital multi-media via communications media, wireless communications, intranet, internet and cellular mobile leads to the uncontrollable growth of data in media. The video compression technique is used in this research work to improve the processing speed of the entire system. In this work, Low Cost - Multi Video Coding - Hybrid Compression and Decompression (LC-MVC-HCD) method is used to reduce computation complexity. The combinational of Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) algorithms are denoted as hybrid algorithm. Based on this hybrid algorithm, the compression process is performing which improves the video coding efficiency of MVC. The LC-MVC-HCD methodwas implemented in the Matlab, Xilinx and Cadence tool. In Application Specific Integrated Circuit (ASIC) implementation, the area, power, and delay minimized by using the cadence encounter tool with 180nm and 45nm technology. In Field Programmable Gate Array (FPGA) implementation, the number of Lookup Tables (LUTs), slice, and flip-flop are minimized based on two different kinds of Virtex devices such as Virtex -6 and Virtex-7. In Matlab, Peak Signal to Noise Ratio (PSNR), computational time and bit error rate were analyzed for the LC-MVC-HCD method. The experimental outcome showed that the proposed methodology has improved performance ASIC and FPGA up to 2-3% compared to existing methods like Direct mode decision MVC and LC-MVC-DWT.

A Quantitative Assessment of Time, Frequency, and Time-frequency Algorithms for Automated Seizure Detection and Monitoring

Epilepsy is a chronic brain disorder impacting more than 65 million people worldwide (1% of the population). Its primary symptoms, seizures, can occur without warning and can be deadly. Each year, over 100,000 patients die from Sudden Unexpected Death in Epilepsy (SUDEP). A reliable seizure warning system can help patients stay safe. This work presents a comprehensive, comparative analysis of three different signal processing algorithms for automated seizure/ictal detection. The methods perform feature extraction and seizure detection on scalp electroencephalogram (EEG) signals. The first optimized mathematical model, Approximate Entropy, performed statistical time domain analysis using a new sliding window protocol. The second algorithm performed seizure-specific spectral energy binning using the Fast Fourier Transform in the frequency domain. The third method applied signal decomposition to extract ictal features by implementing a time-frequency Discrete Wavelet Transform method. Each epileptic seizure detection algorithm was successfully validated using >75 hours of recordings from the Boston Children’s Hospital’s CHB-MIT scalp EEG clinical database. Results indicated that the Discrete Wavelet Transform algorithm performed the best, achieving a seizure detection sensitivity of 92% and a specificity of 98%. The experimental results show that the proposed methods can be effective for accurate automated seizure detection and monitoring in clinical care.

Secure the Data by using Image Compression in Multi-Cloud Storage

Cloud storage providers are in ability for trust the data available and accessible that makes physical condition to be secure and running. Organizations bargain storage capacity from the providers to store user data, business, or request data. Clients desires to furnish their data to public cloud servers (PCSs). Recently got security troubles take to solve and aid more client’s improvement their records in multi-cloud Storage. The customers allow saving information in the cloud the use of services supplied by using more than one cloud storage companies (CSPs). It is a hopeful method to enlarge the degree of records availability and confidentiality, as all at once these distinctive CSPs are out of provider at the identical time or collude with each other to extract records of a user. This suggest a survey on the viable protection merits by means of creating use of multiple wonderful clouds at the identical time. In addition, this advice work as merging and splitting standards at some point of storage in cloud environment. In file access, personal keys produced the use of digital key generator. 3DES encryption algorithm generates key in a cipher text format to the users. Towards facts confidentiality, this advocate an assured facts hiding and spitting picture technique in cloud storage. In propose work, essential purpose will be photo compression with mutable statistics hiding approach whilst storing in the real cloud for data hiding & photo compression via the use of Discrete Wavelet Transform (DWT) algorithm.

Medical image DENOISING scheme using discrete wavelet transform and optimization with different noises

SummaryIn Medical Image Processing (MIP), the images are influenced by various types of noises. Hence, reducing such noises becomes a crucial issue. The main objective is to denoise the noisy images effectively with reduced computational cost. This paper proposes a denoising technique using Discrete Wavelet Transform (DWT) and Social Spider Optimization (SSO) algorithm. Initially, DWT is applied over various noises over the input medical images. Then, the wavelet coefficients are optimized by applying the SSO algorithm. Finally, the inverse of DWT (IDWT) is applied over the optimized coefficients. The denoised image is obtained, and then, the PSNR evaluation is utilized for finding the superior performance. Experimental results using MATLAB show that the proposed algorithm has better performance when compared with the existing approaches.