scholarly journals Wavelet Theory and Application in Communication and Signal Processing

2021 ◽  
Author(s):  
Nizar Al Bassam ◽  
Vidhyalavanya Ramachandran ◽  
Sumesh Eratt Parameswaran
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Communication Systems ◽  
Orthogonal Frequency Division Multiplexing ◽  
Applied Mathematics ◽  
Multimedia Systems ◽  
Biomedical Signal Processing ◽  
Discrete Wavelet

Wavelet analysis is the recent development in applied mathematics. For several applications, Fourier analysis fails to provide tangible results due to non-stationary behavior of signals. In such situation, wavelet transforms can be used as a potential alternative. The book chapter starts with the description about importance of frequency domain representation with the concept of Fourier series and Fourier transform for periodic, aperiodic signals in continuous and discrete domain followed by shortcoming of Fourier transform. Further, Short Time Fourier Transform (STFT) will be discussed to induce the concept of time frequency analysis. Explanation of Continuous Wavelet Transform (CWT) and Discrete Wavelet Transform (DWT) will be provided with the help of theoretical approach involving mathematical equations. Decomposition of 1D and 2D signals will be discussed suitable examples, leading to application concept. Wavelet based communication systems are becoming popular due to growing multimedia applications. Wavelet based Orthogonal Frequency Division Multiplexing (OFDM) technique and its merit also presented. Biomedical signal processing is an emerging field where wavelet provides considerable improvement in performance ranging from extraction of abnormal areas and improved feature extraction scheme for further processing. Advancement in multimedia systems together with the developments in wireless technologies demands effective data compression schemes. Wavelet transform along with EZW, SPIHT algorithms are discussed. The chapter will be a useful guide to undergraduate and post graduate who would like to conduct a research study that include wavelet transform and its usage.

BER Improvement in OFDM Systems Using Wavelet Transform Based on Kalman Filter

2020 ◽  
pp. 102-124
Author(s):  
Sajjan Singh
Keyword(s):  
Kalman Filter ◽  
Wavelet Transform ◽  
Fading Channels ◽  
Communication Systems ◽  
Orthogonal Frequency Division Multiplexing ◽  
High Speed ◽  
Performance Enhancement ◽  
Average Power ◽  
Discrete Wavelet ◽  
Rate Analysis

Orthogonal frequency division multiplexing (OFDM) is an efficient method of data transmission for high speed communication systems over multipath fading channels. However, the peak-to-average power ratio (PAPR) is a major drawback of multicarrier transmission systems such as OFDM is the high sensitivity of frequency offset. The bit error rate analysis (BER) of discrete wavelet transform (DWT)-OFDM system is compared with conventional fast Fourier transform (FFT)-OFDMA system in order to ensure that wavelet transform based OFDMA transmission gives better improvement to combat ICI than FFT-based OFDMA transmission and hence improvement in BER. Wavelet transform is applied together with OFDM technology in order to improve performance enhancement. In the proposed system, a Kalman filter has been used in order to improve BER by minimizing the effect of ICI and noise. The obtained results from the proposed system simulation showed acceptable BER performance at standard SNR.

CWT × DWT × DTWT × SDTWT: Clarifying terminologies and roles of different types of wavelet transforms

2020 ◽  
Vol 18 (06) ◽  
pp. 2030001 ◽  
Author(s):  
Rodrigo Capobianco Guido ◽  
Fernando Pedroso ◽  
André Furlan ◽  
Rodrigo Colnago Contreras ◽  
Luiz Gustavo Caobianco ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Time ◽  
Wavelet Transforms ◽  
Signal Analysis ◽  
Applied Mathematics ◽  
Discrete Wavelet ◽  
Frequency Signal ◽  
Time Frequency ◽  
Subsequent Investigation ◽  
Different Types

Wavelets have been placed at the forefront of scientific researches involving signal processing, applied mathematics, pattern recognition and related fields. Nevertheless, as we have observed, students and young researchers still make mistakes when referring to one of the most relevant tools for time–frequency signal analysis. Thus, this correspondence clarifies the terminologies and specific roles of four types of wavelet transforms: the continuous wavelet transform (CWT), the discrete wavelet transform (DWT), the discrete-time wavelet transform (DTWT) and the stationary discrete-time wavelet transform (SDTWT). We believe that, after reading this correspondence, readers will be able to correctly refer to, and identify, the most appropriate type of wavelet transform for a certain application, selecting relevant and accurate material for subsequent investigation.

Phase-based spectrum analysis method for identifying weak harmonics

2018 ◽  
Vol 24 (23) ◽  
pp. 5585-5596 ◽  
Author(s):  
Jingsong Xie ◽  
Wei Cheng ◽  
Yanyang Zi ◽  
Mingquan Zhang
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Characteristic Frequency ◽  
Discrete Wavelet ◽  
Accurate Identification ◽  
Processing Methods ◽  
Characteristic Frequencies ◽  
Frequency Detection ◽  
Signal Processing Methods

Fault characteristic frequency extraction is an important means for the fault diagnosis of rotating machineries. Traditional signal processing methods commonly use the amplitude information of signals to detect damages. However, when the amplitudes of characteristic frequencies are weak, the recognition effects of traditional methods may be unsatisfactory. Therefore, this paper proposes the phase-based enhanced phase waterfall plot (EPWP) method and frequency equal ratio line (FERL) method for identifying weak harmonics. Taking a cracked rotor as an example, the characteristic frequency detection performances of the EPWP and FERL methods are compared with that of the traditional signal processing methods namely fast Fourier transform, short-time Fourier transform, discrete wavelet transform, continuous wavelet transform, ensemble empirical mode decomposition, and Hilbert–Huang transform. Research results demonstrate that the effects of EPWP and FERL for the recognitions of weak harmonics which are contained in steady signals and transient signals are better than that of the traditional signal processing methods. The accurate identification of weak characteristic frequencies in the vibration signals can provide an important reference for damage detections and improve the diagnostic accuracy.

scholarly journals Transformasi Wavelet dengan Teknik Clipping Filtering untuk Mereduksi PAPR pada OFDM

2020 ◽  
Vol 12 (1) ◽  
pp. 1-8
Author(s):  
Ni Made Ary Esta Dewi Wirastuti ◽  
Ida Bagus Dharma Dhyaksa
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Orthogonal Frequency Division Multiplexing ◽  
Average Power ◽  
Simulation Method ◽  
Local Oscillator ◽  
Discrete Wavelet ◽  
Spectral Leakage ◽  
Synchronization Errors ◽  
Offset Frequency

Orthogonal Frequency Division Multiplexing (OFDM) is chosen as multiplexing techniques and broadly used in today’s radiocommunication environments to overcome spectrum insufficiency. With several superior advantages, however, OFDM is terribly affected by high peak to average power ratio (PAPR) due to offset frequency errors and local oscillator (LO) frequency synchronization errors. The high PAPR can cause nonlinear distortion, which outcomes in intermodulation and spectral leakage. This study aims to model the use of wavelet transform (discrete wavelet transform (DWT)) to replace Fourier transform (discrete Fourier transform (DFT)) that used in conventional OFDM, later in this paper is termed as DFT-OFDM. Clipping filtering techniques then applied to DWT-OFDM. The model was proposed to reduce PAPR in DFT-OFDM. The model was compared to DFT-OFDM using Matlab simulation method. The performance was evaluated using the Complementary Cumulative Distributive Function (CCDF) vs. PAPR. The results show that at PAPR 10-3for DFT-OFDM, it was produced PAPR of 10.6 dB whereas in DWT-OFDM, using Daubechies orde 7 (Daubechies7),  Symlet orde 7 (Symlet7), Coiflet orde 2 (Coiflet2), were reached PAPR 4.8 dB, PAPR 3.3 dB, PAPR 3 dB, respectively. It means Coiflet2 providing the best PAPR reduction among other orthogonal wavelets. By applied clipping filtering to wavelet Coiflet2, it was produced PAPR of 2.9 dB for classical clipping and 2.8 dB for deep clipping. It show that wavelet Coiflet2 with deep clipping provided the best PAPR.

Study of Preprocessing Methods for the Determination of Crystalline Phases in Binary Mixtures of Drug Substances by X-ray Powder Diffraction and Multivariate Calibration

2000 ◽  
Vol 54 (8) ◽  
pp. 1222-1230 ◽  
Author(s):  
Tom Artursson ◽  
Anders Hagman ◽  
Seth Björk ◽  
Johan Trygg ◽  
Svante Wold ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Powder Diffraction ◽  
Wavelet Transforms ◽  
Multivariate Calibration ◽  
Discrete Wavelet ◽  
Data Set ◽  
X Ray ◽  
Drug Substances ◽  
Orthogonal Signal Correction

In this paper, various preprocessing methods were tested on data generated by X-ray powder diffraction (XRPD) in order to enhance the partial least-squares (PLS) regression modeling performance. The preprocessing methods examined were 22 different discrete wavelet transforms, Fourier transform, Savitzky–Golay, orthogonal signal correction (OSC), and combinations of wavelet transform and OSC, and Fourier transform and OSC. Root mean square error of prediction (RMSEP) of an independent test set was used to measure the performance of the various preprocessing methods. The best PLS model was obtained with a wavelet transform (Symmlet 8), which at the same time compressed the data set by a factor of 9.5. With the use of wavelet and X-ray powder diffraction, concentrations of less than 10% of one crystal from could be detected in a binary mixture. The linear range was found to be in the range 10–70% of the crystalline form of phenacetin, although semiquantitative work could be carried out down to a level of approximately 2%. Furthermore, the wavelet-pretreated models were able to handle admixtures and deliberately added noise.

scholarly journals Study of the Discrete Wavelet Transform Based Designed System Under Different Simulation Conditions

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Amplitude Modulation ◽  
Orthogonal Frequency Division Multiplexing ◽  
Pulse Amplitude ◽  
Discrete Wavelet ◽  
Pulse Amplitude Modulation ◽  
Frequency Division Multiplexing ◽  
Frequency Division

Wavelet Transforms is an Important Part of, Systems Theory and Signal Processing and finds numerous important applications in Science and Engineering. In this paper, we investigated the performance of proposed scheme coded Discrete wavelet transform based Orthogonal frequency division multiplexing scheme over Additive white Gaussian noise channel using Pulse Amplitude Modulation in terms of Energy bits per noise ratio values. The simulation has been done using MATLAB software and results are compared with ½ rate convolution coded Discrete wavelet transform based Orthogonal frequency division multiplexing system. It is found by MATLAB simulations that the performance of proposed scheme coded Discrete wavelet transform based Orthogonal frequency division multiplexing outperforms than that of ½ rate convolution encoded Discrete wavelet transform based Orthogonal frequency division multiplexing with 16-Pulse Amplitude Modulation. Along with this, different orders of reverse biorthogonal and biorthogonal wavelets are implemented to simulate the proposed system with 16-Pulse Amplitude Modulation scheme. The performance of proposed system is compared and it is found that proposed system performs better than conventional system under all different simulation conditions. This study finds important applications in Signal Processing.

scholarly journals Time Frequency Analysis of Wavelet and Fourier Transform

2020 ◽  
Author(s):  
Karlton Wirsing
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Frequency Domain ◽  
Frequency Analysis ◽  
Discrete Wavelet ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Efficient Access ◽  
The Fourier Transform

Signal processing has long been dominated by the Fourier transform. However, there is an alternate transform that has gained popularity recently and that is the wavelet transform. The wavelet transform has a long history starting in 1910 when Alfred Haar created it as an alternative to the Fourier transform. In 1940 Norman Ricker created the first continuous wavelet and proposed the term wavelet. Work in the field has proceeded in fits and starts across many different disciplines, until the 1990’s when the discrete wavelet transform was developed by Ingrid Daubechies. While the Fourier transform creates a representation of the signal in the frequency domain, the wavelet transform creates a representation of the signal in both the time and frequency domain, thereby allowing efficient access of localized information about the signal.

scholarly journals A New Approach for Enhancing the Services of the 5G Mobile Network and IOT-Related Communication Devices Using Wavelet-OFDM and Its Applications in Healthcare

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Mordecai F. Raji ◽  
JianPing Li ◽  
Amin Ul Haq ◽  
Victor Ejianya ◽  
Jalaluddin Khan ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Communication Systems ◽  
Mobile Network ◽  
Performance Comparison ◽  
Support Network ◽  
Time Frequency ◽  
Communication Devices ◽  
The Fourier Transform

The heart of the current wireless communication systems (including 5G) is the Fourier transform-based orthogonal frequency division multiplex (OFDM). Over time, a lot of research has proposed the wavelet transform-based OFDM as a better replacement of Fourier in the physical layer solutions because of its performance and ability to support network-intensive applications such as the Internet of Things (IoT). In this paper, we weigh the wavelet transform performances against the future wireless application system requirements and propose guidelines and approaches for wavelet applications in 5G waveform design. This is followed by a detailed impact on healthcare. Using an image as the test data, a comprehensive performance comparison between Fourier transform and various wavelet transforms has been done considering the following 5G key performance indicators (KPIs): energy efficiency, modulation and demodulation complexity, reliability, latency, spectral efficiency, effect of transmission/reception under asynchronous transmission, and robustness to time-/frequency-selective channels. Finally, the guidelines for wavelet transform use are presented. The guidelines are sufficient to serve as approaches for tradeoffs and also as the guide for further developments.

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