Discrete Wavelet Coefficients Research Articles

Evaluation of features and channels of electroencephalographic signals for biometric systems

Biometric systems are essential tools in modern society where most of our personal information lives in digital form. Although there is a significant variety of biometrics, electroencephalogram (EEG) signals are a useful technique to guarantee that the person is alive, they are universal, and not falsifiable. Nevertheless, EEG processing needs to address some challenges to become a viable technique to build production-ready biometric systems. These challenges include the adequate selection of features and channels that maximize the quality of the results and optimize resources. This work provides an analysis of which are the most important features and channels for the correct operation of a biometric system. The experimental analysis worked with two datasets and evaluated 19 features belonging to three groups, wavelet-based, spectral, and complexity. Five classifiers were trained: multilayer perceptron, AdaBoost, random forest, support vector machine, and K-nearest neighbors. The results found that the best feature for developing a biometric system is the standard deviation extracted from the coefficients of a three-level discrete wavelet transform. Additionally, the experimental results with the two datasets showed that the proposed method for channel selection can reduce the necessary number of channels while maintaining its performance. Our results, from one of the datasets, showed a reduction of 21 channels (from 32 to 11) and indicated that the best channels to develop biometric systems seem to be those located on the central area of the scalp.

Reliable human authentication using AI-based multibiometric image sensor fusion: Assessment of performance in information security

Reliable human authentication is essential for assuring the availability, confidentiality, and integrity of sensitive data and resources in the field of information security. Due to the vulnerabilities of conventional authentication techniques like passwords and PINs, interest in biometric-based authentication systems has developed. To get over this, we suggest an efficient voice and iris recognition-based multimodal biometric verification strategy for human authenticating tools. We first collect the voice and iris datasets from 150 people. Then, we used a median filter and high pass filter to preprocess the voice and iris data. The features of voice identification are extracted using the shifted delta cepstral coefficient (SDCC) and the Mel frequency discrete wavelet coefficient (MFCC), and these two coefficients are evaluated. The outcomes of the extraction of iris identification features using Local binary pattern (LBP) and Speeded robust features (SURF) are evaluated. The classifier Fine Tuned Cuckoo Search Optimized Convolutional Neural Network (FCSO-CNN) performs voice and iris recognition modalities. Voice and iris biometric systems may be combined into a single multimodal biometric system by fusing their respective feature sets and scoring algorithms. The results of the computer simulation demonstrate that for speech recognition, using the SDC and MFC coefficients produces better results, while for iris recognition, using the LBP and FCSO-CNN experiment produces superior outcomes. Additionally, the scores fusion works superior to other scenarios in the proposed multimodal biometrics system. The suggested system provides a dependable and strong solution for human identification, assisting in the improvement of security measures across a variety of industries, including financial institutions, governmental agencies, and the defense of key infrastructure.

Identifying earthquake-induced structural damage with normalized discrete wavelet coefficients

Identifying earthquake-induced structural damage with normalized discrete wavelet coefficients

Low SNR multimirror Fabry-Perot pressure sensor optic spectrum signal analysis and demodulation via SVM-KNN regressors.

We demonstrate an ensemble learning based method to solve the problem of low SNR Fabry-Perot sensor spectrum signal demodulation. Taking the eight-layer approximate coefficients of a multilevel discrete wavelet transform as input features, an ensemble model that combines multiple SVM and KNN learners is trained. Bootstrap and booting techniques are introduced for better modeling performance and stability. It is shown that the performance of the proposed ensemble model based on SVM-KNN regressors is excellent; an accuracy of 0.46%F.S. relative mean error is achieved. This method could provide insight into the construction of a large scale fiber based Fabry-Perot sensor network.

A visually meaningful image encryption algorithm based on P-tensor product compressive sensing and newly-designed 2D memristive chaotic map

The noise-like visual feature of cipher images produced by using the traditional image encryption technology explicitly reflects the presence of secret information. To overcome this issue, a visually meaningful image encryption algorithm is proposed based on a newly designed 2D memristive chaotic map, P-tensor product compressive sensing (PTP-CS) and discrete Hartley transform (DHT). For concreteness, a new two-dimensional discrete memristive chaotic map is first designed to provide highly unpredictable secret code streams while maintaining low time consumption. Second, the threshold processing and zigzag confusion operations are performed on the discrete wavelet coefficients of the plain image to meet the prerequisites for effective compression. Third, the intermediate secret information is obtained by utilizing PTP-CS in the compression layer. Information entropy and edge entropy are employed to adaptively identify the complex regions that are suitable for embedding due to inconspicuous visual degradation in the carrier image. Finally, the embedding of the secret information in the DHT domain of these regions is accomplished. Security test and performance analysis confirm that our algorithm has the advantage of a high balance between the encryption security and the decryption recovery, and exhibits excellent performance in important indicators such as visual quality, robustness and timeliness.

Gray Level Co-Occurrence Matrix, Fractal and Wavelet Analyses of Discrete Changes in Cell Nuclear Structure following Osmotic Stress: Focus on Machine Learning Methods

In this work, we demonstrate that it is possible to create supervised machine-learning models using a support vector machine and random forest algorithms to separate yeast cells exposed to hyperosmotic stress from intact cells. We performed fractal, gray level co-occurrence matrix (GLCM), and discrete wavelet transform analyses on digital micrographs of nuclear regions of interest of a total of 2000 Saccharomyces cerevisiae cells: 1000 exposed to hyperosmotic environments and 1000 control cells. For each nucleus, we calculated values for fractal dimension, angular second moment, inverse difference moment, textural contrast, correlation feature, textural variance, and discrete wavelet coefficient energy. The support vector machine achieved an acceptable classification accuracy of 71.7% in predicting whether the cell belonged to the experimental or control group. The random forest model performed better than the support vector machine, with a classification accuracy of 79.8%. These findings can serve as a starting point for developing AI-based methods that use GLCM, fractal, and wavelet data to classify damaged and healthy cells and make predictions about various physiological and pathological phenomena associated with osmotic stress.

Brain Tumor Detection Through Modified Optimization Algorithm by Region-based Image Fusion

This article is about the fusion of Brain images which are having different features. This article addresses the problems raised in pixel-level image fusion,such as blurring and artifacts caused by the unwanted addition of noise components in fused images. We extracted the regions of the brain image with the proposed tested optimal thresholding-based segmentation by optimizing thresholds with the proposed Sine function adapted improved Whale Optimization Algorithm (SiWOA) algorithm. We named the proposed fusion as SiWOA-FUSION. These optimal segmented regions and discrete wavelet coefficients of images are fused based on interval type - 2 fuzzy rules. Finally, combined image visual quality is optimized with SiWOA by assuming the amount of the correlation of differences (ACD) as an objective function. Experiments are tested on standard benchmark databases and proved better than existing methods.

The Importance of Context Awareness in Acoustics-Based Automated Beehive Monitoring

The vital role of honeybees in pollination and their high rate of mortality in the last decade have raised concern among beekeepers and researchers alike. As such, robust and remote sensing of beehives has emerged as a potential tool to help monitor the health of honeybees. Over the last decade, several monitoring systems have been proposed, including those based on in-hive acoustics. Despite its popularity, existing audio-based systems do not take context into account (e.g., environmental noise factors), and thus the performance may be severely hampered when deployed. In this paper, we investigate the effect that three different environmental noise factors (i.e., nearby train rail squealing, beekeeper speech, and rain noise) can have on three acoustic features (i.e., spectrogram, mel frequency cepstral coefficients, and discrete wavelet coefficients) used in existing automated beehive monitoring systems. To this end, audio data were collected continuously over a period of three months (August, September, and October) in 2021 from 11 urban beehives located in downtown Montréal, Québec, Canada. A system based on these features and a convolutional neural network was developed to predict beehive strength, an indicator of the size of the colony. Results show the negative impact that environmental factors can have across all tested features, resulting in an increase of up to 355% in mean absolute prediction error when heavy rain was present.

Early Detection of Parkinson’s Disease Using Fusion of Discrete Wavelet Transformation and Histograms of Oriented Gradients

Parkinson’s disease primarily affects people in their later years, and there is no cure for this disease; however, the proper medication of patients can lead to a healthy life. Appropriate care and treatment of Parkinson’s disease can be improved if the disease is detected in its early phase. Thus, there is an urgent need to develop novel methods for early illness detection. With this aim for the early detection of Parkinson’s disease, in this study, we utilized hand-drawn images by Parkinson’s disease patients to effectively reduce the clinical experimental costs for poor people. Initially, discrete wavelet coefficients were extracted for each pattern of images; thereafter, on top of that, histograms of oriented gradient features were also extracted to refine the level of features. Thereafter, the fusion approach-based features were fed to various machine learning algorithms. The proposed work was validated on two different datasets, each of which consisted of various patterns, including spiral, wave, cube, and triangle images. The main contribution of this work is the fusion of two feature extraction techniques, which are histograms of oriented gradient features and discrete wavelet transform coefficients. The extracted features were then provided as input into different machine learning algorithms. In our experiment(s) on two datasets, the results achieved an accuracy of 79.7% and 97.8%, respectively, for all four discrete wavelet transform coefficients. This work demonstrates the utilities of fusion-based features for all four discrete wavelet transformation coefficients to detect Parkinson’s disease, using image processing and machine learning techniques.

Research on the Method of Detecting the Spreading Rate of the Simultaneous Crushed Stone Sealing Layer Based on Machine Vision

Synchronous chip seal is an advanced road constructing technology, and the gravel coverage rate is an important indicator of the construction quality. The traditional method to measure the gravel coverage rate usually depends on observation by human eyes, which is rough and inefficient. In this paper, a detection method of gravel coverage based on improved wavelet algorithm is proposed. By decomposing the image with two-dimensional discrete wavelet, the high-frequency and low-frequency coefficients are extracted. The noise of the high-frequency coefficients in the image is removed by improving the threshold function, and the contrast of the gravel target in the low-frequency coefficients is improved by the multiscale Retinex algorithm, and then two-dimensional wavelet reconstruction is carried out. Finally, the gravel target is segmented by the block threshold method, and the pixel ratio of the gravel is calculated to complete the detection of the gravel coverage. The experimental results show that the proposed method can effectively segment the gravel target and reduce the influence of environmental factors on the detection accuracy. The detection accuracy error is within ±2%, which can meet the detection requirements. The improved wavelet algorithm improves the signal-to-noise ratio of the denoised image, reduces the mean square error, and achieves a relatively good denoising effect.

Wavelet based Video Compression techniques for Industrial monitoring applications

The principle of industrial machine monitoring and vehicle camera transmission system are focusing on this paper. Video compression is widely used in many industrial applications like continuous monitoring of machines which consumes more storage by capturing every motion detection in machine, hence video coding is highly recommended for video compression without any loss in the actual video. By using wavelet transform delivers the superior localization both in frequency and time domain and its results showcases the better performance while comparing with discrete cosine transform. A comparative analysis has been carried on Video compression using Haar and orthogonal (Daubechies) wavelet. Duplicate coefficients of discrete wavelet transform is reduced by using quantization technique. It aims to attain minimum error while preserving the high peak signal to noise ratio and image quality in the acceptable range. Using PSNR as measure of quality, this paper shows that Daubechies wavelet provides the better quality of video compared to Haar wavelet. Evaluation of performance is depends upon on compression ratio, PSNR, MSE, and SSIM.

A wavelet-based capsule neural network for ECG biometric identification

Electrocardiogram (ECG) signals have received a high level of attention from the biometric research community due to their unique nature for each person, which makes them suitable for developing accurate and reliable human identification systems. Although most existing ECG-based biometric recognition methods have received prominent results, several consecutive heartbeat segments are used in their approaches to achieve high accuracy, which is challenging to apply in biometric systems deployed in real-world applications. This paper proposes a new approach for human identification via ECG, based on a combination of Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT) along with a novel kind of deep learning technique known as Capsule network. The CWT is used to transform a single heartbeat signal into the time–frequency domain, and the DWT is adopted to extract spectral information of 2D frequency-time scalogram images to further improve accuracy. The discrete wavelet coefficients are then used as input to the capsule network promoting the recognition performance due to its high learning capacities. To support real-life practicality of ECG biometric identification system, the effectiveness and efficiency of our approach were evaluated over four databases that include normal and abnormal ECG records: PTB Diagnosis ECG (PTB), MIT-BIH Arrhythmia, MIT-BIH Normal Sinus Rhythm (NSRDB), and the MIT-BIH ST Change (STDB) databases. Experimental results demonstrate that our proposed method was able to achieve high identification accuracies and outperforming other state-of-the-art methods, by achieving an accuracy of 99.5%, 98.1%, 98.2%, and 100% on the PTB, MIT-BIH arrhythmia, STDB, and NSRDB respectively. Furthermore, the approach showed very good generalization ability since the training and test sets were completely different, which demonstrates the feasibility to promote the application of our approach in practice.

Optical fibre based quench detection in HTS applications using machine learning classifiers

A Mach-Zehnder Interferometer (MZI) based optical fibre sensing technique, developed and patented by EPFL, is an efficient and economical way to detect hotspots in High Temperature Superconductor (HTS) applications. Due to the MZI sensitivity being a composite of strain sensitive and temperature sensitive contributions, the MZI gives an instantaneous response to a quench (within 10 ms), because of the quick strain transfer to the optical fibre. However, the MZI output signal can also manifest the environmental noise caused by mechanical vibrations, bubbling in the cryostat and temperature variations, along with the response to the quench. This presents the problems of false alarms and indiscernible response to a quench. Discrete wavelet transform (DWT) has been proven to be a useful tool for feature extraction in different fields requiring signal categorization and hence holds the potential to enable quench recognition in the MZI output. This paper proposes an effective approach of performing DWT based feature extraction on experimental data and subsequently using the extracted features for the MZI response classification using two machine learning based classification techniques: k-nearest neighbours (KNN) and Artificial Neural Network (ANN). For this manuscript, experiments were performed using MZI for quench detection in an HTS tape. Feature extraction was then implemented on these experimental measurements using discrete wavelet coefficients extracted at different decomposition levels from the MZI output; these features were then used to train the KNN and ANN models for identifying quench in the MZI signal. This method could be a valuable supplement to the MZI technique by enabling the development of a real time application that can process the MZI output data as well as eliminate the occurrences of false alarms; thereby facilitating reliable quench detection. With this development, the MZI technique would become an even more attractive solution for the health monitoring of HTS applications.

An Efficient Method for Automated Breast Mass Segmentation and Classification in Digital Mammograms

Background: Automatic detection and classification of breast masses in mammograms are still challenging tasks. Today, computer-aided diagnosis (CAD) systems are being developed to assist radiologists in interpreting mammograms. Objectives: This study aimed to provide a novel method for automatic segmentation and classification of masses in mammograms to help radiologists make an accurate diagnosis. Materials and Methods: For an efficient mass diagnosis in mammograms, we proposed an automatic scheme to perform both mass detection and classification. First, a combination of several image enhancement algorithms, including contrast-limited adaptive histogram equalization (CLAHE), guided imaging, and median filtering, was investigated to enhance the visual features of breast area and increase the accuracy of segmentation outcomes. Second, the density of discrete wavelet coefficient density (DDWCs), based on the quincunx lifting scheme (QLS), was proposed to find suspicious mass regions or regions of interest (ROIs). Finally, mass lesions that appeared in the mammogram were classified into four categories of benign, probably benign, malignant, and probably malignant, based on the morphological shape. The proposed method was evaluated among 1593 images from the Curated Breast Imaging Subset-Digital Database for Screening Mammography (CBIS-DDSM) dataset. Results: The experimental results revealed that the suspected region localization had 100% sensitivity, with a mean of 6.4 ± 4.5 false positive (FP) detections per image. Moreover, the results showed an overall accuracy of 85.9% and an area under the curve (AUC) of 0.901 for the mass classification algorithm. Conclusion: The present results showed the comparable performance of our proposed method to that of the state-of-the-art methods.

Semi-fragile watermarking scheme based on perceptual hash function (PHF) for image tampering detection

The increasing performances of personal computers, as well as software of image processing, enable the easy manipulation of digital media content. Unfortunately, this easy manipulation makes the detection of changes in the multimedia content a very difficult task. Digital watermarking is among the most appropriate techniques to verify the integrity of multimedia content. In this paper, we propose a semi-fragile watermarking scheme for JPEG2000 image self-authentication that ensuring the robustness of the watermark against the compression attacks generated by the JPEG2000 encoder itself. The scheme is combined with the JPEG2000 encoder by embedding the generated watermark into the host image during the JPEG2000 compression process. To generate the watermark we suppose to use a perceptual hash function (PHF) operating on discrete wavelet coefficients of the host image. The proposed watermark generation process leads the system to verify the integrity of the image without the need to any file except for the watermarked image. The watermark is embedded during the JPEG2000 compression process after Discrete Wavelet Transform (DWT) step into the approximation sub-band coefficients of the five wavelet decomposition using Index Modulation Quantification (QIM), and can be extracted during image decoding. To prove the authentication of the image, the system compares the extracted watermark with the new watermark generated from the received image. Experimental results show that our proposed approach has not only an extremely high accuracy of tampering detection but also a relatively very high resistance against JPEG2000 compression attacks.

Image texture model based on energy features

In information processing and control systems based on computer vision technologies, it is necessary to provide an effective indicative representation of images for their subsequent analysis. One of the widely used approaches is based on the construction of texture models. In this paper, the description of the texture using energy features is considered. The proposed model is a set of weights of image pixels reflecting their significance from the point of view of image perception. The significance of the pixel is estimated using the energy of the coefficients of the orthogonal discrete multiresolution wavelet transform. The paper presents expressions for calculating pixel weights and shows that the resulting texture models can be used to classify images.

AE signature analysis using continuous and discrete wavelet transforms to predict grinding wheel conditions

In this study, experiments were conducted in a surface grinding operation to acquire and analyze the AE signature to establish a statistical correlation between Acoustic Emission features extracted in wavelet domain with grinding wheel conditions. Grinding wear plot was established to identify the grinding wheel conditions by monitoring the wear in the abrasive grinding wheel and workpiece. Continuous and Discrete Wavelet transforms were carried out to extract the wavelet coefficients. Decision Tree based statistical models were built using discrete and continuous wavelet coefficients. The performance of J48 Decision tree and Classification and Regression Decision Tree (CART) are compared using the classification accuracy and kappa statistics measures. In discrete wavelet transforms, wavelet coefficients are extracted using four mother wavelets namely Haar, Daubechies, Symlet, and Coiflet. In Continuous Wavelet Transforms, the Morlet wavelet is used to extract the 1D wavelet coefficients using 2D scalograms.

Improving sand flow rate measurement using the wavelet transform and ultrasonic sensors

Abstract Accurate sand flow rate measurement is needed to minimize the side effects of sand production in gas fields. There are concerns about the accuracy of sand flow measurement using the sand measuring devices available on the market. In this paper, ultrasonic sensors and discrete wavelet transform signal analysis method is used to measure the sand flow rate. It is found that the strength of the discrete wavelet coefficients in the frequency range of 15–62 kHz has a linear relationship with sand flow rate. This finding provides a new methodology to accurately measure sand flow rate. The proposed method does not need fluid velocity as a prerequisite for sand rate measurement, so it greatly simplifies the system design when flow meters are not used for fluid velocity measurement. Also, this method has a much simpler calibration procedure compared to that of the sand detectors commonly used in the industry.

High-SNR steganography for digital audio signal in the wavelet domain

Imperceptible, robust, and embedding capacity are three main requirements for the steganography of digital audio signal. To enhance them, this study presents a novel steganography for digital audio signal in the wavelet domain. Since the performance of imperceptible and robust are usually in term of signal-to-noise ratio (SNR) and bit-error-rate (BER), we propose a quantization-based optimization model to maximize SNR and reduce BER in embedding secret message. In the proposed model, quantization technique with unknow coefficients of discrete wavelet transform (DWT) is rewritten as the first constraint. The adjustment of scaling DWT coefficients is considered as the second constraint. At the same time, signal-to-noise ratio (SNR) is converted into a performance index. In solving this model, we use matrix operations and Lagrange multiplier to obtain optimal DWT coefficients and scaling factors. Moreover, the invariant feature of the scaling factors against amplitude scaling attack is proved. In extraction, secret message can be detected without original audio signal. Experimental results show that the proposed steganography has high SNR and strong robustness against many malicious attacks when comparing to some exiting methods.

Signature Recognition using 2D Discrete Wavelet Transforms

Signature recognition is one of the most secure techniques used for person identification. Wavelet transforms possess an extra time localization property over Fourier transforms, despite both are frequency localized. That is why; it is more useful to analyze the one dimensional and two dimensional signals both. First of all the features of a signature are extracted and then matching is performed. We have proposed feature extraction technique using discrete wavelet transforms level-1 and matching of signatures are performed using some statistical parameters of discrete wavelet coefficients using matching percentage, sum of absolute difference, mean square error and city block distance.