Discrete Wavelet Packet Transform Research Articles

Fault Detection and Tolerance of a Hybrid Five Level Inverter

Multilevel inverters (MLIs) have received a significant attention in literature due to advent of various topologies utilising power semiconductor switches with reduced voltage stress. However, the increased component count and susceptible nature of semiconductor components increase the chance of failure in inverter. The fault in power switches, either open circuit or short circuit, if persist for long time may result in complete system shutdown. This damage can be avoided by detecting and diagnosing the fault in less period of time to continue the healthy operation. The present paper studies an open-circuit fault detection method on conventional five-level NPC inverter topology with output voltage waveform analysis by discrete wavelet packet transform (DWPT) and classifying the fault location by artificial neural network (ANN). The strategy to tolerate both abovementioned type of failure on any location after detection is proposed by implementing a novel redundant architecture in conventional five-level inverter. This architecture has an advantage of utilising less device count to tolerate fault on single as well as multiple switch combinations. The proposed work is studied in MATLAB/Simulink and validated experimentally.

A Comparative Analysis of Time-frequency Feature Extraction Techniques for Large Scale Electroencephalogram Data

Recognition of human emotions is a fascinating research field that motivates many researchers to use various approaches, such as facial expression, speech or gesture of the body. Electroencephalogram (EEG) is another approach of recognizing human emotion through brain signals and has offered promising findings. Although EEG signals provide detail information on human emotional states, the analysis of non-linear and chaotic characteristics of EEG signals is a substantial problem. The main challenge remains in analyzing EEG signals to extract relevant features in order to achieve optimum classification performance. Various feature extraction methods have been developed by researchers, which mainly can be categorized under time, frequency or time-frequency based feature extraction methods. Yet, there are numerous setting that could affect the performance of any model. In this paper, we investigated the performance of Discrete Wavelet Transform (DWT) and Discrete Wavelet Packet Transform (DWPT), which are time-frequency domain methods using Support Vector Machine (SVM) and k-Nearest Neighbor (KNN) classification techniques. Different SVM kernel functions and distance metrics of KNN are tested in this study by using subject-dependent and subject -independent approaches. The experiment is implemented using publicly available DEAP dataset. The experimental results show that DWT is mostly suitable with weighted KNN classifier while DWPT reported better results when tested using Linear SVM classifier to accurately classify the EEG signals on subject-dependent approach. Consistent results are observed for DWT-KNN on subject-independent approach, however SVM works better in the setting of quadratic kernel functions. These results indicate that further investigation is significant to examine the impact of different setting of methods in analyzing large scale of EEG data

Recurrent Quantification Analysis-Based Emotion Classification in Stroke Using Electroencephalogram Signals

Stroke is a cerebrovascular disorder, and one of the most common effects of stroke is emotional disturbances. This present work classifies six emotions (anger, sadness, happiness, fear, disgust, and surprise) of two types of stroke (left brain damage and right brain-damage) using electroencephalogram (EEG) signals. EEG signals are collected from 19 each subject of LBD, RBD, and normal control (NC) at a sampling rate of 128 Hz. The IIR Bandpass filter and amplitude thresholding methods are used to reduce noise and artifacts' effects, respectively. Discrete Wavelet Packet Transform is used to extract five EEG frequency bands (alpha, beta, gamma, alpha to gamma, and beta to gamma). A set of nonlinear features are extracted from five different EEG frequency ranges using recurrent quantification analysis. Finally, the extracted features are mapped to six corresponding emotions using three nonlinear classifiers (K nearest neighbor, probabilistic neural network, and random forest). The experimental results indicate that LBD subjects have severe emotional impairment than RBD. The mean of diagonal line length ( ), recurrence rate, and maximum mean diagonal length (Lmax) feature give maximum classification rate of 85.24% NC, 79.54%, and 79.09% using RF classifier compared to other features. The alpha to gamma (13–49 Hz) band helps identify Stroke emotional state changes compared to other frequency bands.

WITHDRAWN: ADP efficient 4-level DWPT using approximation

In this paper, presented an efficient 4-level discrete wavelet packet transform (DWPT) using approximation where area and delay are considerably reduced. It gives lesser number of hardware arithmetic computations and lesser delay required at each computation level. Compared with the existing design, the presented design gives 16.34% less area for block size 2. The Area Delay Product (ADP) of proposed shift add register approximated design have saving of 21.7% and CPD of 4.4% lesser for block size 2 over existing design.

Bayesian wavelet-packet historical functional linear models

Historical Functional Linear Models (HFLM) quantify associations between a functional predictor and functional outcome where the predictor is an exposure variable that occurs before, or at least concurrently with, the outcome. Prior work on the HFLM has largely focused on estimation of a surface that represents a time-varying association between the functional outcome and the functional exposure. This existing work has employed frequentist and spline-based estimation methods, with little attention paid to formal inference or adjustment for multiple testing and no approaches that implement wavelet-bases. In this work, we propose a new functional regression model that estimates the time-varying, lagged association between a functional outcome and a functional exposure. Building off of recently developed function-on-function regression methods, the model employs a novel use the wavelet-packet decomposition of the exposure and outcome functions that allows us to strictly enforce the temporal ordering of exposure and outcome, which is not possible with existing wavelet-based functional models. Using a fully Bayesian approach, we conduct formal inference on the time-varying lagged association, while adjusting for multiple testing. We investigate the operating characteristics of our wavelet-packet HFLM and compare them to those of two existing estimation procedures in simulation. We also assess several inference techniques and use the model to analyze data on the impact of lagged exposure to particulate matter finer than 2.5μg, or PM2.5, on heart rate variability in a cohort of journeyman boilermakers during the morning of a typical day's shift.

DWPT vs OFDM Under a Noisy Industrial Channel

In the industrial world, a trend towards connected, robotic and intelligent factories is in constant development to face competition from countries with low production costs. The industrial revolution ‘Industry 4.0’ is considerably reducing the boundaries between the physical and digital worlds. This has given rise to interconnected factories in which people, machines and products interact with each other using smart sensors. Commonly referred to as Industrial Internet of Thing (IIoT). Except that, it requires adapted communication systems for the industrial environment. Such a propagation environment is known by its complexity that should be considered in order to propose a robust and reliable communicating system. In this paper, a performance evaluation between a pulse-modulated Discrete Wavelet Packet Transform (DWPT) system and an Orthogonal Frequency Division Multiplexing (OFDM) multicarrier modulation system over a noisy industrial channel will be presented. Thus, allowing not only to provide an alternative to conventional high-rate industrial wireless communication systems but also to know the limits of both techniques

WITHDRAWN: Enhanced key frame identification and background subtraction methods for automatic license plate recognition of motorcyclists without helmets

With the increasing number of cameras available in the cities, there is an increasing interest in recognizing license plate of two wheelers riders who do not wear helmet. This can be performed in four stages, background modeling, vehicle detection, helmet detection and automatic license plate recognition. This work is focused on the background modeling to extract foreground objects from its background. In this work, a 2-step method is proposed. The first step proposes and uses a key frame identification algorithm to extract a set of optimal frames. The background subtraction is then performed on these frames in the second step. A discrete wavelet packet transformation based method is used to separate background and foreground objects. Experimental results prove that the proposed algorithm is more effective when compared with the existing approaches.

Automatic EEG-based Alertness Classification using Sparse Representation and Dictionary Learning

Automation of human alertness identification has been widely investigated in recent decades. Many applications can benefit from automatic alertness state identification, such as driver fatigue detection, monotonous task workers' vigilance detection and sleep studies in the medical field. Many researchers have tried to exploit different types of behavioural aspects in vigilance detection, such as eye movement, head position and facial expression. On the other hand, some biomedical signals like ECG, EEG and heart rhythm are also exploited; however, there is a consensus of the superiority of EEG signal in alertness classification due to its close relation with different human vigilant states. In this paper, we propose an automatic method for vigilance detection using a single EEG channel along with sparse representation and dictionary learning. We used Discrete Wavelet Packet Transform to extract the features related to different human vigilance states. We use well-known other classifiers to compare the performance of our proposed method. Results of classification with sparse representation and dictionary learning produced better accuracy results than the other methods.

Power Measurement Using Stockwell Transform

The spread of harmonics in electric power systems causes deviations in voltage and current waveforms from the ideal sinusoidal form and, consequently, the traditional power calculation is unsuitable. One of the power theories defined for non-sinusoidal conditions is the IEEE Standard 1459-2010, based on Fourier Transform (FT). However, when applied to non-stationary signals, the FT leads to miscalculation due to spectral leakage, being unsuitable for non-stationary waveforms. In this paper, a new approach for the IEEE Standard 1459-2010 definitions is proposed using Stockwell Transform (ST). The ST provides a time-frequency resolution which retains magnitude and phase information of signals over time. The performance of the proposed method was evaluated in three scenarios including stationary waveforms, a non-linear circuit, and real measured data. The results were compared to the IEEE Standard 1459-2010 using the FT and the Stationary Discrete Wavelet Packet Transform (SDWPT) approaches. In the three scenarios studied, the proposed method presented a good estimation for each quantity defined in IEEE Standard 1459-2010 and provided more accurate results than FT-based and SDWPT-based methods.

Determining damages in ceramic plates by using discrete wavelet packet transform and support vector machine

In this study, an analysis was conducted by using discrete wavelet packet transform (DWPT) and support vector machine (SVM) methods to determine undamaged and cracked plates. The pendulum was used to land equal impacts on plates in this experimental study. Sounds, which emerge from plates as a result of the impacts applied to undamaged and cracked plates, are sound signals used in the analysis and DWPT of these sound signals were obtained with 128 decompositions for feature extraction. The first four components, reflecting the characteristics of undamaged and cracked plates within these 128 components, were selected for enhancing the performance of the classifier and energy values were used as feature vectors. In the study, the SVM model was created by selecting appropriate C and γ parameters for the classifier. Undamaged and cracked plates were seen to be successfully identified by an analysis of the training and testing phases. Undamaged and cracked statuses of the plates that are undamaged and have the analysis had identified different cracks. The biggest advantage of this analysis method used is that it is high-precision, is relatively low in cost regarding experimental equipment and requires hardware.

Memory Efficient Architecture for Lifting-Based Discrete Wavelet Packet Transform

This brief presents a novel VLSI architecture for computing discrete wavelet packet transform (DWPT) for the continuous flow of data. Each stage of the proposed multi-stage architecture consists of the bit-reordering circuit and serial wavelet filter. The conventional wavelet filter has been modified for single path serial data. The intermediate coefficients are reordered with the help of a bit reordering circuit in order to maintain a continuous flow of data from input to output end with minimum memory and minimum latency. In comparison to the recently published architectures, the proposed one not only reduces the memory requirement by more than 50% but also achieves a 100% hardware utilization ratio. Furthermore, the area and power requirements are reduced by more than 60% and 50%, respectively.

A new gearbox compound fault recognition approach based on improved double wavelet packet transform

A new approach for the diagnosis of compound faults in gearboxes is proposed in this paper. To extract characteristics in the frequency band of non-stationary raw vibration signals, a double-tree complex wavelet packet transform (DTCWPT) is used to decompose the signals. A singular value spectrum (SVP) is generated by performing singular value decomposition (SVD) on the matrix formed by all of the components. The new analysis method, DTCWPT-SVP, is used to diagnose different operating conditions of a gearbox, including single faults and compound faults, with a k-nearest neighbour (kNN) classifier. The results show that the minimum recognition accuracy using DTCWPT-SVP is 91.9% with different values of k in the kNN and that DTCWPT has better performance in signal decomposition than discrete wavelet packet transform (DWPT). Furthermore, the decomposition level used in DTCWPT is analysed in this paper. For the gearbox vibration problem, a level 2 or 3 DTCWPT can achieve good performance.

Supervised single channel dual domains speech enhancement using sparse non-negative matrix factorization

In this paper, we propose a novel single-channel speech enhancement algorithm that applies dual-domain transforms comprising of dual-tree complex wavelet transform (DTCWT) and short-time Fourier transform (STFT) with a sparse non-negative matrix factorization (SNMF). The first domain belongs to the DTCWT, which is utilized on the time domain signals to conquer the weakness of signal distortions brought about by the downsampling of the discrete wavelet packet transform (DWPT) and delivered a set of subband signals. The second domain alludes to the STFT, which is exploited to each subband signal and built a complex spectrogram. At last, we apply the SNMF to the magnitude spectrogram for extracting speech components. In short, the DTCWT decomposes the time-domain noisy signal into a set of subband signals and afterward applied STFT to each subband signal, and we get nonnegative matrices by taking the absolute value of the complex matrix. From this point forward, we apply SNMF to each nonnegative matrix and identify the speech components. Finally, the estimated signal can be achieved through a subband binary ratio mask (SBRM) by applying the inverse STFT (ISTFT) and, subsequently, the inverse DTCWT (IDTCWT). The proposed approach is assessed utilizing the GRID audio-visual and IEEE databases, and diverse kinds of noises such as stationary, non-stationary, and quasi-stationary. The exploratory outcomes demonstrate that the proposed algorithm improved objective speech quality and intelligibility altogether at all considered signal to noise ratios (SNRs), compared to the other seven speech enhancement methods of STFT-SNMF, STFT-SNMFSE, MLD-STFT-SNMF, STFT-GDL, STFT-CJSR, DTCWT-SNMF, and DWPT-STFT-SNMF.

RETRACTED: A hybrid security strategy (HS2) for reliable video streaming in fog computing

Abstract Recently, digital communication and storage over the Internet through the cloud have increased in a rapid manner. Accordingly, data security becomes a vital issue as there are many hackers who want to steal or alter others’ private data. Fog computing comes to secure and facilitate the connection to the cloud. It works as an intermediary between the cloud and the peripheral devices. Accordingly, the channel between the fog and those devices must be secured against attacks. Many efforts have been done to secure the fog through the use of steganography to embed important data into a cover digital medium and cryptography to encrypt the data into a meaningless form. As steganography and cryptography have their own advantages, combining them into a hybrid system will certainly promote the security level. Video steganography uses video as a cover media for hiding secret data as video streaming over internet is very popular and common that cannot be doubted by intruders. The advantage of using video files as a cover is that they are more secure against hacker attacks due to their relative complexity compared to other multimedia types and the data redundancy which permits to embed large amount of secret data. In this paper a new hybrid security strategy (HS2) merging the advantages of both cryptography and steganography for securing the fog has been proposed. HS2 embeds a secret image into a video as the host medium. HS2 has two main contributions. The first is a new encryption technique that depends on α-blocks of linear feedback shift registers (LFSRs) merged with an adder/subtractor to generate a strong secret key for each block. All blocks outputs are combined using a nonlinear function to construct the final key. Such generated key will be XORed with the secret image to produce the encrypted secret image. The second contribution is a new steganography methodology based on an enhanced discrete wavelet packet transform (DWPT) to embed the encrypted secret image into a cover video. Experimental results and security analysis have shown that the proposed HS2 outperforms recent security strategies and resist common attacks when applied to the fog environment.

Hybrid Deep Learning-Based Model for Wind Speed Forecasting Based on DWPT and Bidirectional LSTM Network

Accurate wind speed forecasting is essential for the reliability and security of the power system, and optimal operation and management of wind integrated smart grids. However, it is still a challenging task due to the highly uncertain and volatile nature of wind speed. Accordingly, in this work, a novel deep learning-based model integrating the discrete wavelet packet transform (DWPT) and bidirectional long short-term memory (BLSTM) is developed to precisely capture deep temporal features and learn the time-varying relationship of wind speed time series. In the proposed method, by applying the DWPT, both approximations and details parts are decomposed by passing through the filters to choose the frequency band related to the features of the original signal more adaptively. The BLSTM networks are incorporated to deal with the uncertainties more effectively as they have bidirectional memory capability (feedforward and feedback loops) to investigate both previous and future hidden layers data. To simultaneously improve the forecasting performance and decrease the learning complexity, the reconstructed state space of historical wind data is employed to reflect the evolution laws of wind speed. Two case studies using real-world wind speed datasets gathered from Flatirons campus (M2) of National Renewable Energy Laboratory (NREL) located in Colorado, USA and weather station of Edmonton, Canada are implemented to demonstrate the effectiveness and superiority of the proposed hybrid method compared to the shallow architectures and state-of-the-art deep learning models in the recent literature.

Performance of IDWPT/DWPT compared with OFDM under an Industrial Channel

Abstract In the industrial world, a trend towards connected, robotic and intelligent factories is on the rise to face competition from countries with low production costs. The industrial revolution ‘Industry 4.0’ is considerably reducing the boundaries between the physical and digital worlds. This has given rise to interconnected factories in which people, machines and products interact with each other using smart sensors. This paper presents a comparison between a pulse-modulated Discrete Wavelet Packet Transform (DWPT) system and an Orthogonal Frequency Division Multiplexing (OFDM) multicarrier modulation system in a noisy industrial propagation environment. This to provide an alternative to conventional high-rate wireless communication systems using an Industrial Internet of Thing (IIoT).

Contribution to S-EMG Signal Compression in 1D by the Combination of the Modified Discrete Wavelet Packet Transform (MDWPT) and the Discrete Cosine Transform (DCT)

A new Modified Discrete Wavelets Packets Transform (MDWPT) based method for the compression of Surface EMG signal (s-EMG) data is presented. A Modified Discrete Wavelets Packets Transform (MDWPT) is applied to the digitized s-EMG signal. A Discrete Cosine Transforms (DCT) is applied to the MDWPT coefficients (only on detail coefficients). The MDWPT+ DCT coefficients are quantized with a Uniform Scalar Dead-Zone Quantizer (USDZQ). An arithmetic coder is employed for the entropy coding of symbol streams. The proposed approach was tested on more than 35 actuals S-EMG signals divided into three categories. The proposed approach was evaluated by the following parameters: Compression Factor (CF), Signal to Noise Ratio (SNR), Percent Root mean square Difference (PRD), Mean Frequency Distortion (MFD) and the Mean Square Error (MSE). Simulation results show that the proposed coding algorithm outperforms some recently developed s-EMG compression algorithms.

Headspace Oxygen Concentration Measurement for Pharmaceutical Glass Bottles in Open-Path Optical Environment Using TDLAS/WMS

Accurate measurement of oxygen concentration for pharmaceutical glass bottles is of great significance to ensure the asepsis of medicine and stability of ingredients. With merits of high sensitivity, low cost, noncontact, and real-time response, the wavelength-modulation-based tunable diode laser absorption spectroscopy (TDLAS/WMS) technology shows great potential to achieve in-site oxygen concentration detection by the single-line spectrum measurement. This article focuses on headspace oxygen concentration measurement in the open-path optical environment, which is extremely challenging due to the short light path length and random ambient noises. First, a signal reconstruction method is established based on the discrete wavelet packet transform (DWPT), where random noise suppression is implicitly achieved. Then, oxygen concentration is inversed among the data between two adjacent valley values of the demodulated second-harmonic signal by multiple linear regression (MLR), and the linear discriminant analysis (LDA) is imported to enhance the information sparsity of second-harmonic signal matrix and to address the multicollinearity problem. Simulation results prove that our proposed method achieved considerable detection accuracy with the average absolute error of 0.05%. This article also designed a TDLAS/WMS prototype, and the experimental results aiming at glass bottles with different oxygen concentration of 0%, 5%, 10%, and 21% in open-path optical environment indicate our method has achieved an encouraging average absolute error of 0.54% and can survive well when the normalized signal-to-noise ratio (SNR) is within 0.85–1. These results promise that the proposed methodology can be widely applied in in-site automatic optic inspection (AOI) instrumentation of headspace oxygen concentration measurement for glass vials.

A hybrid security strategy (HS2) for reliable video streaming in fog computing

Recently, digital communication and storage over the Internet through the cloud have increased in a rapid manner. Accordingly, data security becomes a vital issue as there are many hackers who want to steal or alter others’ private data. Fog computing comes to secure and facilitate the connection to the cloud. It works as an intermediary between the cloud and the peripheral devices. Accordingly, the channel between the fog and those devices must be secured against attacks. Many efforts have been done to secure the fog through the use of steganography to embed important data into a cover digital medium and cryptography to encrypt the data into a meaningless form. As steganography and cryptography have their own advantages, combining them into a hybrid system will certainly promote the security level. Video steganography uses video as a cover media for hiding secret data as video streaming over internet is very popular and common that cannot be doubted by intruders. The advantage of using video files as a cover is that they are more secure against hacker attacks due to their relative complexity compared to other multimedia types and the data redundancy which permits to embed large amount of secret data. In this paper a new hybrid security strategy (HS2) merging the advantages of both cryptography and steganography for securing the fog has been proposed. HS2 embeds a secret image into a video .mp4 as the host medium. HS2 has two main contributions. The first is a new encryption technique that depends on α-blocks of linear feedback shift registers (LFSRs) merged with an adder/subtractor to generate a strong secret key for each block. All blocks outputs are combined using a nonlinear function to construct the final key. Such generated key will be XORed with the secret image to produce the encrypted secret image. The second contribution is a new steganography methodology based on an enhanced discrete wavelet packet transform (DWPT) to embed the encrypted secret image into a cover video. Experimental results and security analysis have shown that the proposed HS2 outperforms recent security strategies and resist common attacks when applied to the fog environment.

Wideband spectrum sensing in cognitive radio using discrete wavelet packet transform and principal component analysis

The cognitive radio is a wireless technology which offers so much promise in the mitigation of the problem of spectrum scarcity. To achieve this noble objective, a cognitive radio must be characterized by speed and accuracy. In this paper, we present a spectrum sensing technique using discrete wavelet packet transform (DWPT) and principal component analysis (PCA) with convex optimization. An input signal was decomposed using DWPT to generate the required data matrix whose dimension was reduced using PCA and optimized using convex optimization. This technique was extensively compared with spectrum sensing using discrete Fourier transform (DFT), which is a popular wideband scheme. It was found that the DWPT scheme significantly outperformed DFT in spectrum detection accuracy. This was attributed to the fact that the time–frequency nature of wavelet transform makes it less lossy, i.e., it preserves information content much more than the DFT. The effect of PCA on the scheme was also evaluated and it was found that data dimensionality reduction was considerable, which impacts positively on the speed of spectrum detection. Data dimensionality reduction due to PCA also translates to memory and energy savings for the CR, since less energy is spent in processing signal data. It can then be said that this technique is also energy efficient, a desirable feature for mobile devices.