Performance Of Discrete Wavelet Transform Research Articles

Improving Bit Error Rate of MIMO-OFDM System Using Discrete Wavelet Transformation

This paper has developed a discrete wavelet transform (DWT) for multicarrier modulation in MIMO-OFDM system. The proposed system used additive Gaussian white noise (AGWN) and Rayleigh flat fading channel with respect to SNR using binary phase shift keying (BPSK). The algorithms for various scenarios of MIMO-OFDM system have been developed and simulated in MATLAB environment. The performance metric of the system was based on bit error ratio (BER) variation over values of signal to noise ratio (SNR) for different transmit and receive antennas including single input multiple output (SIMO) arrangement. The simulation results showed that the performance of DWT based MIMO-OFDM outperformed DFT based MIMO-OFDM. For the 2×1, 2×2, 2×3 MISO and MIMO systems, the DWT scheme offered reduced BER of 1.803e-05, 1.502e-05, and 1.413e-05 against 0.005847, 0.005361, and 0.005325 for the DFT scheme with respect to signal to noise ratio (SNR) at 10 dB. Generally, the results showed that using DWT scheme in OFDM for wireless communication system does not require an extra subcarrier to provide cyclic prefix function that serves as additional cost burden and reduces bandwidth.

Analysis of image watermarking with a discrete wavelet transform for digital data security

One of the ways to secure digital data is image watermarking. The method for image watermarking is used with discrete wavelet transform (DWT). The problem in this article is how the performance of DWT for image watermarking. The stages of the research method are: (1) Collecting the dataset that will be used as the trial host (2) Processing data with watermarking techniques, (3) Inserting data using DWT (4) checking the quality of each method, (6) analyzing the results each method. This research has revealed the stages of data security on image watermarking with DWT and the results show that the images provided with watermarking are well secured.

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

Empirical Wavelet Transform; Stationary and Nonstationary Signals

Signal decomposition into the frequency components is one of the oldest challenges in the digital signal processing. In early nineteenth century, Fourier transform (FT) showed that any applicable signal can be decomposed by unlimited sinusoids. However, the relationship between time and frequency is lost under using FT. According to many researches for appropriate time-frequency representation, in early twentieth century, wavelet transform (WT) was proposed. WT is a well-known method which developed in order to decompose a signal into frequency components. In contrast with original WT which is not adaptive according to the input signal, empirical wavelet transform (EWT) was proposed. In this paper, the performance of discrete WT (DWT) and EWT in terms of signal decomposing into basic components are compared. For this purpose, a stationary signal including five sinusoids and ECG as biomedical and nonstationary signal are used. Due to being non-adaptive, DWT may remove signal components but EWT because of being adaptive is appropriate. EWT can also extract the baseline of ECG signal easier than DWT.

Analysis of Compressed Foetal Phono-Cardio-Graphy (PCG) Signals with Discrete Cosine Transform and Discrete Wavelet Transform

ABSTRACT In the present era, precise medical instruments are available to record the medical signals for diagnosis as well as regular monitoring of pregnant lady and foetus. These recorded signals are then transmitted to the medical expert as radio signals for clinical advice. The recorded signal if transmitted without any modification, it may consume a lot of memory space and bandwidth. To address this problem, a tunable compression technique has been implemented for Foetal Phonocardiography (PCG) signal using Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). The results show that the overall increase in compression ratio was 16.2% and 19.4%, by varying the user-defined percentage root mean square difference (UPRD) value from 0.5 to 2.0 for DCT and DWT, respectively. The performance of DWT was better as compared to DCT for Foetal PCG signals in terms of high spatial resolution in DWT.

Sensitivity Enhancement of DWT Based Algorithm for Detection of CpG Islands in DNA Sequences

CpG Islands in DNA sequences play a very important role as gene markers, these are associated with some human malignancies, and also these are useful in the identification of promoter regions. The performance of an algorithm to detect the CpG Islands may be affected by the choice of a particular numerical mapping method. Due to the importance of numerical mapping to convert the nucleotides of DNA sequence to numerical values, it is essential to analyze the performance of various mapping schemes for the detection of CpG Islands in DNA sequences. Hence, in this paper the performance of Discrete Wavelet Transform based algorithm for the detection of CpG Islands in DNA sequences has been studied using existing numerical mapping schemes and its sensitivity has also been enhanced using 24 combinations of integer mapping scheme. The experiment has been done on 30 DNA sequences which are downloaded from National Center for Biotechnology Information. The performance parameters considered in the paper for evaluation purpose are sensitivity, specificity, and accuracy. The results obtained show that the proposed approach of combination of 24 mapping scheme of integer mapping outperforms in terms of sensitivity at different thresholds.

Performance Evaluation of Wavelet-Based Optical Wireless System using On-Off Keying Modulation

Abstract The paper investigates the performance of DWT (Discrete Wavelet Transform) and CWT (Continuous Wavelet Transform) techniques based on On – Off keying modulation (OOK) for reduced BER with increase in SNR in optical wireless system. In this work, the effects of Inter-symbol Interference (ISI) and light interference on Artificial Neural Network (ANN) receiver are analyzed and reported. In proposed optical wireless system the On- Off Keying (OOK) modulation is used at receiver to analyze BER and SNR. It is further concluded that error prospect in the received signal relates to the noise level, which is given in terms of obtained SNR value.

Text dependant speaker recognition using MFCC, LPC and DWT

The objective of this work is to investigate the benefit of discrete wavelet transform combined with LPC, for speaker identification system applied for Algerian Berber language, compared to the traditional Mel frequency analysis. We’ve developed a speaker identification system for Algerian Berber language. The corpus concerns two dataset, the first one concerns eight isolated words and the second is dedicated for continuous speech repeated by Algerian native Berber. We’ve used MFCC feature, their first and second derivatives and discrete wavelet transform (DWT) followed by linear predictive coding (LPC) to ameliorate the parameterization phase. Mahalanobis distance, ascendant classification and pitch analysis were used for characterizing our speech signals. We evaluate the performance of DWT–LPC feature for clean and additive noisy speech. The multilayer perceptron classifier was used for this purpose, efficiency was improved for DWT combined with LPC feature vectors.

Performance of discrete wavelet technique in the de-noising of noisy spectrum in the mid-infrared region near 4.5 µm

We applied discrete wavelet transform (DWT) technique for de-noising of carbon monoxide (CO) spectrum which is experimentally obtained in the mid-infrared region using a difference-frequency-based spectrometer. The performance of DWT is firstly examined by de-noising of a simulated P(16) CO absorption line based on the real data associated with the experimental setup we arranged in laboratory. It is found that when the db20 function from Daubechie wavelet family is used at level 7 of composition, highest signal-to-noise ratio (SNR) ~23 can be achieved. Similar results are obtained in the experiment in which a noisy CO trace is de-noised using the same procedure as described in the simulation. Subsequently, highest SNR of ~9.2 and lowest residuals is experimentally obtained using the db20 function which confirms the merit of DWT technique in detection of low-level absorption signals.

A Fast Discrete Wavelet Transform Using Hybrid Parallelism on GPUs

Wavelet transform has been widely used in many signal and image processing applications. Due to its wide adoption for time-critical applications, such as streaming and real-time signal processing, many acceleration techniques were developed during the past decade. Recently, the graphics processing unit (GPU) has gained much attention for accelerating computationally-intensive problems and many solutions of GPU-based discrete wavelet transform (DWT) have been introduced, but most of them did not fully leverage the potential of the GPU. In this paper, we present various state-of-the-art GPU optimization strategies in DWT implementation, such as leveraging shared memory, registers, warp shuffling instructions, and thread- and instruction-level parallelism (TLP, ILP), and finally elaborate our hybrid approach to further boost up its performance. In addition, we introduce a novel mixed-band memory layout for Haar DWT, where multi-level transform can be carried out in a single fused kernel launch. As a result, unlike recent GPU DWT methods that focus mainly on maximizing ILP, we show that the optimal GPU DWT performance can be achieved by hybrid parallelism combining both TLP and ILP together in a mixed-band approach. We demonstrate the performance of our proposed method by comparison with other CPU and GPU DWT methods.

Performance Assessment of OFDM Utilizing FFT/DWT over Rician Channel Effected by CFO

Orthogonal Frequency Division Multiplexing (OFDM) is a popular kind of multi-carrier based transmission technique that has found its applications in some wireless and wireline systems. But this system suffers from several facts that degrade its performance resulting in the interference of sub-carriers. Carrier Frequency Offset (CFO) is considered as one of the factor that causes interference in the system, leading to an Inter-Carrier Interference (ICI) between the carriers that results in a degradation in the performance factor of the system. In this research work, a comparative estimation is done on the Bit Error Rate (BER) performance of Discrete Wavelet Transform (DWT) OFDM and Fourier Transform (FFT) OFDM in the presence of CFO in both of the given systems. The research focuses mainly on the improvement of the BER in the system. The complete analysis is done by considering the channel to be Rician and the systems performance is estimated for some practical values of CFO. The complete simulation is done in the MATLAB. As the CFO increases the error in the system also increases, but still in that situation, DWT shows better improvement than the conventional system. The BER performance of OFDM systems shows improvement for all the diverse PSK modulation.

Performance Evaluation for Wavelet Based Ofdm System Effected by Cfo over Rayleigh Channel

OFDM systems due to its orthogonality factor provides significant spectrum efficiency, robustness to fading and simple cost effective implementation. One of the factors that effects the OFDM system performance is the Carrier Frequency Offset (CFO) that results in the Inter-Carrier Interference (ICI) which causes a degradation of the OFDM system performance. In this paper, a discussion on the performance of Discrete Wavelet Transform (DWT) OFDM and Fourier Transform (FFT) OFDM is done by considering the presence of CFO in both the systems. A comparative study of both the systems is done on the basis of Bit Error Rate (BER) performance these systems show. The system is considered on the basis of improvement in the BER. Simulations conclude that the system that uses wavelet implementation performs better than the conventional OFDM system. The DWT performance even with CFO factors shows considerable improvement in the bit error performance than the FFT based traditional system. Further, the whole simulation is done under the Rayleigh channel, the performance calculated for five practical values of CFO from 0 to 0.2 with step difference of 0.05. The BER performance is simulated using MATLAB software. Further, the whole simulation is done under the Rayleigh channel, the performance calculated for five practical values of CFO from 0 to 0.2 with step difference of 0.05. The DWT based OFDM outperforms the FFT based OFDM for all the values of CFO.

Performance of Discrete Wavelet Transform Based on OFDM with Adaptive Modulation

In the mobile environments, the channel will be highly hostile at high data rate transmission. To combat with this problem, many designs were proposed and developed, such as, Orthogonal Frequency Division Multiplexing (OFDM) system. Adaptive modulation improves the system efficiency by matching transmission parameters to channel variations, so that, OFDM system with Adaptive modulation technique will be achieved an efficient bandwidth transmission technique in wireless fading environments. In this paper, Discrete Wavelet Transform (DWT) is used in OFDM system and illustrates the improvement of Bit Error Rate (BER) and throughput performances compared with used Discrete Fourier Transform (DFT). DWT with Zero Padding (DWT-ZP) is also used as addition technique which supports high performances for the system, all tested in frequency selective fading channel. The obtained results show that a significant improvements in BER and throughput can be achieved demonstrating the superiority of use DWT compared with use traditional DFT in Adaptive OFDM system.

Airborne hyperspectral discrimination of tree species with different ages using discrete wavelet transform

In this article, the capability of discrete wavelet transform (DWT) to discriminate tree species with different ages using airborne hyperspectral remote sensing is investigated. The performance of DWT is compared against commonly used traditional methods, i.e. original reflectance and first and second derivatives. The hyperspectral data are obtained from Thetford forest of the UK, which contains Corsican and Scots pines with different ages and broadleaved tree species. The discrimination is performed by employing three different spectral measurement techniques (SMTs) including Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and a combination of SAM and SID. Five different mother wavelets with a total of 50 different orders are tested. The wavelet detail coefficient (CD) from each decomposition level and combination of all CDs plus the approximation coefficient from the final decomposition level (C-All) are extracted from each mother wavelet. The results show the superiority of DWT against the reflectance and derivatives for all the three SMTs. In DWT, C-All provided the highest discrimination accuracy compared to other coefficients. An overall accuracy difference of about 20–30% is observed between the finest coefficient and C-All. Amongst the SMTs, SID provided the highest accuracy, while SAM showed the lowest accuracy. Using DWT in combination with SID, an overall accuracy up to around 71.4% is obtained, which is around 13.5%, 14.7%, and 27% higher than the accuracies achieved with reflectance and first and second derivatives, respectively.

Performance Analysis of Adaptive MIMO Based OFDM using FFT and DWT

In this paper we examine the performance of Discrete Wavelet Transform (DWT) based Multiple Input Multiple output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) system using adaptive modulation and compare with DWT based MIMO OFDM system and Fast Fourier Transform (FFT) based MIMO OFDM system. Wavelet based OFDM has lot of advantages compare to the FFT based OFDM like there is no need of cyclic prefix, flexibility and optimal resolution. DWT with Haar mother constructed multi carrier in addition to the predictable OFDM is less than the consequence of taking multiple antennas scheme while taking BPSK and QPSK as dual modulation schemes in additive white Gaussian noise channel (AWGN). Based on the performance of bit error rate to the signal to noise ratio, the DWT constructed multicarrier scheme was established to be higher than the predictable OFDM. While using adaptive modulation in DWT based MIMO OFDM system with BPSK results in higher BER performance than DWT based MIMO OFDM system

A wavelet-based thresholding approach to reconstructing unreliable spectrogram components

Data imputation approaches for robust automatic speech recognition reconstruct noise corrupted spectral information by exploiting prior knowledge of the relationship between target speech and background through the use of spectrographic masks. Most of these approaches are model-based techniques that can only provide accurate estimates of the underlying clean speech when the characteristics of the noise corrupted features do not deviate from those of the model. Discrete wavelet transform (DWT) based de-noising methods can also be used for re-estimating the underlying clean speech from a noise corrupted signal, but often require that the background noise is stationary and modeled by a Gaussian distribution. A novel approach is presented here for incorporating the information derived from spectrographic masks in a DWT-based de-noising method. The spectrographic masks are used for deriving thresholds for de-noising wavelet domain coefficients making DWT based de-noising more suitable for non-stationary noise conditions. The results of an experimental study are presented to demonstrate the performance of DWT based data imputation relative to other established techniques on the Aurora 2 noisy speech recognition task. It will be shown that the proposed approach reduces the impact of model mismatch associated with parametric approaches and exploits the robustness of non-parametric wavelet de-noising approach.

A novel embedding technique for lossless data hiding in medical images employing histogram shifting method

In this paper, a lossless data hiding method based on histogram shifting for MR images using Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are presented. In this method, the algorithms are validated to hide the data in wavelet coefficients of high frequency subbands. This scheme has the advantage of comparing the DCT coefficients and the DWT coefficients which permit low distortion between the watermarked image and the original image. It also shifts a part of the histogram of high frequency subbands and embeds the data by using the created histogram zero point. To prevent the overflows and underflows in the spatial domain, caused by the modification of the DCT coefficients and the DWT coefficients, the histogram modification technique is applied. Therefore, we present a validated method to evaluate and compare the performance of DWT and DCT on task, in terms of data embedding payload and the Peak Signal to Noise Ratio (PSNR) in the medical image. A careful experimental analysis validates the method showing its superiority over the existing methods.

Noise Smoothing for Structural Vibration Test Signals Using an Improved Wavelet Thresholding Technique

In structural vibration tests, one of the main factors which disturb the reliability and accuracy of the results are the noise signals encountered. To overcome this deficiency, this paper presents a discrete wavelet transform (DWT) approach to denoise the measured signals. The denoising performance of DWT is discussed by several processing parameters, including the type of wavelet, decomposition level, thresholding method, and threshold selection rules. To overcome the disadvantages of the traditional hard- and soft-thresholding methods, an improved thresholding technique called the sigmoid function-based thresholding scheme is presented. The procedure is validated by using four benchmarks signals with three degrees of degradation as well as a real measured signal obtained from a three-story reinforced concrete scale model shaking table experiment. The performance of the proposed method is evaluated by computing the signal-to-noise ratio (SNR) and the root-mean-square error (RMSE) after denoising. Results reveal that the proposed method offers superior performance than the traditional methods no matter whether the signals have heavy or light noises embedded.

Close Accord on DWT Performance and Real-Time Implementation for Protection Applications

In this paper, a simpler and faster algorithm of discrete wavelet transform (DWT) for a digital signal processing (DSP) implementation is proposed and intensively tested. Applicability of real-time implementation of this algorithm is verified. The computational frame in this algorithm is independent on the sampling rate, but it depends on the length of the mother wavelet filters. Therefore, signal with any sampling rate can be analyzed with the same computational effort. This implementation algorithm is exploited to investigate the DWT response experimentally. Impact of the mother wavelet, sampling frequency, fault inception angle and the signal transients on the resulted DWT levels is evaluated from the protection perspectives. Considering the DSP channel noise, these factors are strongly affect DWT response especially in high details spike, which the most of the protection applications depended on. Shortcomings of DWT application for protection relays are outlined and corrective recommendations are included.

DWT‐based extraction of residual currents throughout unearthed MV networks for detecting high‐impedance faults due to leaning trees

AbstractModelling of a high‐impedance arcing fault due to a leaning tree in medium voltage (MV) networks was experimentally verified and the network transients due to this fault were also investigated. Even though the tree had a very high resistance value, the initial transients were periodically caused by the arc reignitions after each zero‐crossing. In this paper, these features are extracted from residual currents using discrete wavelet transform (DWT) to localise this fault event. The DWT performance at different measuring nodes throughout an unearthed 20 kV network can be gathered at the base station using wireless sensors concept. So, the DWT is evaluated for a wide area of the network and the fault detection is confirmed by numerous DWT extractors. Due to the periodicity of arc reignitions, the initial transients are localised not only at fault starting instant but also during the fault period that will enhance the detection security. The term of locating the faulty section is determined based on ratios of the residual current amplitudes. The fault cases are simulated by ATP/EMTP and the arc model is implemented using the universal arc representation. Copyright © 2007 John Wiley & Sons, Ltd.