Wavelet Theory

The wavelet is a powerful mathematical tool that plays an important role in science and technology. This book looks at some of the most creative and popular applications of wavelets including biomedical signal processing, image processing, communication signal processing, Internet of Things (IoT), acoustical signal processing, financial market data analysis, energy and power management, and COVID-19 pandemic measurements and calculations. The editor’s personal interest is the application of wavelet transform to identify time domain changes on signals and corresponding frequency components and in improving power amplifier behavior

A Review of Classification Problems and Algorithms in Renewable Energy Applications

Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field

Signal fingerprinting and machine learning framework for UAV detection and identification.

Advancement in technology has led to creative and innovative inventions. One such invention includes unmanned aerial vehicles (UAVs). UAVs (also known as drones) are now an intrinsic part of our society because their application is becoming ubiquitous in every industry ranging from transportation and logistics to environmental monitoring among others. With the numerous benign applications of UAVs, their emergence has added a new dimension to privacy and security issues. There are little or no strict regulations on the people that can purchase or own a UAV. For this reason, nefarious actors can take advantage of these aircraft to intrude into restricted or private areas. A UAV detection and identification system is one of the ways of detecting and identifying the presence of a UAV in an area. UAV detection and identification systems employ different sensing techniques such as radio frequency (RF) signals, video, sounds, and thermal imaging for detecting an intruding UAV. Because of the passive nature (stealth) of RF sensing techniques, the ability to exploit RF sensing for identification of UAV flight mode (i.e., flying, hovering, videoing, etc.), and the capability to detect a UAV at beyond visual line-of-sight (BVLOS) or marginal line-of-sight makes RF sensing techniques promising for UAV detection and identification. More so, there is constant communication between a UAV and its ground station (i.e., flight controller). The RF signals emitting from a UAV or UAV flight controller can be exploited for UAV detection and identification. Hence, in this work, an RF-based UAV detection and identification system is proposed and investigated. In RF signal fingerprinting research, the transient and steady state of the RF signals can be used to extract a unique signature. The first part of this work is to use two different wavelet analytic transforms (i.e., continuous wavelet transform and wavelet scattering transform) to investigate and analyze the characteristics or impacts of using either state for UAV detection and identification. Coefficient-based and image-based signatures are proposed for each of the wavelet analysis transforms to detect and identify a UAV. One of the challenges of using RF sensing is that a UAV\u27s communication links operate at the industrial, scientific, and medical (ISM) band. Several devices such as Bluetooth and WiFi operate at the ISM band as well, so discriminating UAVs from other ISM devices is not a trivial task. A semi-supervised anomaly detection approach is explored and proposed in this research to differentiate UAVs from Bluetooth and WiFi devices. Both time-frequency analytical approaches and unsupervised deep neural network techniques (i.e., denoising autoencoder) are used differently for feature extraction. Finally, a hierarchical classification framework for UAV identification is proposed for the identification of the type of unmanned aerial system signal (UAV or UAV controller signal), the UAV model, and the operational mode of the UAV. This is a shift from a flat classification approach. The hierarchical learning approach provides a level-by-level classification that can be useful for identifying an intruding UAV. The proposed frameworks described here can be extended to the detection of rogue RF devices in an environment

Application of Wavelet-based Denoising to Improve the Accuracy of Nanopore Sequencing Data

DNA sequencing methods in biology are divided into three generations based on their time of invention and technology used. First generation sequencing technologies introduced in the 1970s sequenced short strands of DNA, with the longest strand ranging from 300-1000 base pairs in the Sanger method. Second generation technologies improved on the first generation by being high throughput, scalable and parallel. After successful genome assemblies of small and large organisms using first and second generation sequencing methods, the last two decades brought about third generation sequencing technologies. Third generation sequencing technologies focus on sequencing single nucleotide molecules and produce real-time, high-throughput basecalls and are scalable, low cost and portable. Nanopore sequencing is a third generation sequencing technology that works by measuring the change in electric current in an ionic membrane as a DNA strand passes through a nanopore embedded in the membrane. A major limitation that has prevented mass adoption of nanopore sequencing commercially is its lower accuracy compared to second generation sequencing technologies. The aim in this project was to improve the accuracy of nanopore sequencing by reducing noise in the nanopore signal. Wavelets were used to decompose the nanopore signal, remove noise and then reconstruct the signal. The modified signal was used for training a new basecalling model. It was observed that a significant difference in basecall quality can be achieved between the default model used by Oxford Nanopore Technologies's Guppy basecaller and our custom denoised model in terms of mean percentage identity. An increase of 5.3% was achieved in mean percentage identity while maintaining the mean read quality of basecalls for Bacteriophage lambda dataset. Both mean percentage identity and mean read quality for the custom model were overall more consistent with lesser low scoring outliers. Haar wavelet was demonstrated as the most suitable wavelet candidate with level of decomposition and threshold values 4 and 0.04 respectively for denoising nanopore sequencing data. Results were validated by training and testing with and without wavelet denoising on three existing nanopore datasets

Advances in Sensors and Sensing for Technical Condition Assessment and NDT

The adequate assessment of key apparatus conditions is a hot topic in all branches of industry. Various online and offline diagnostic methods are widely applied to provide early detections of any abnormality in exploitation. Furthermore, different sensors may also be applied to capture selected physical quantities that may be used to indicate the type of potential fault. The essential steps of the signal analysis regarding the technical condition assessment process may be listed as: signal measurement (using relevant sensors), processing, modelling, and classification. In the Special Issue entitled “Advances in Sensors and Sensing for Technical Condition Assessment and NDT”, we present the latest research in various areas of technology

Optimized state feedback regulation of 3DOF helicopter system via extremum seeking

In this paper, an optimized state feedback regulation of a 3 degree of freedom (DOF) helicopter is designed via extremum seeking (ES) technique. Multi-parameter ES is applied to optimize the tracking performance via tuning State Vector Feedback with Integration of the Control Error (SVFBICE). Discrete multivariable version of ES is developed to minimize a cost function that measures the performance of the controller. The cost function is a function of the error between the actual and desired axis positions. The controller parameters are updated online as the optimization takes place. This method significantly decreases the time in obtaining optimal controller parameters. Simulations were conducted for the online optimization under both fixed and varying operating conditions. The results demonstrate the usefulness of using ES for preserving the maximum attainable performance

Seismological data acquisition and signal processing using wavelets

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This work deals with two main fields: a) The design, built, installation, test, evaluation, deployment and maintenance of Seismological Network of Crete (SNC) of the Laboratory of Geophysics and Seismology (LGS) at Technological Educational Institute (TEI) at Chania. b) The use of Wavelet Transform (WT) in several applications during the operation of the aforementioned network. SNC began its operation in 2003. It is designed and built in order to provide denser network coverage, real time data transmission to CRC, real time telemetry, use of wired ADSL lines and dedicated private satellite links, real time data processing and estimation of source parameters as well as rapid dissemination of results. All the above are implemented using commercial hardware and software which is modified and where is necessary, author designs and deploy additional software modules. Up to now (July 2008) SNC has recorded 5500 identified events (around 970 more than those reported by national bulletin the same period) and its seismic catalogue is complete for magnitudes over 3.2, instead national catalogue which was complete for magnitudes over 3.7 before the operation of SNC. During its operation, several applications at SNC used WT as a signal processing tool. These applications benefited from the adaptation of WT to non-stationary signals such as the seismic signals. These applications are: HVSR method. WT used to reveal undetectable non-stationarities in order to eliminate errors in site’s fundamental frequency estimation. Denoising. Several wavelet denoising schemes compared with the widely used in seismology band-pass filtering in order to prove the superiority of wavelet denoising and to choose the most appropriate scheme for different signal to noise ratios of seismograms. EEWS. WT used for producing magnitude prediction equations and epicentral estimations from the first 5 secs of P wave arrival. As an alternative analysis tool for detection of significant indicators in temporal patterns of seismicity. Multiresolution wavelet analysis of seismicity used to estimate (in a several years time period) the time where the maximum emitted earthquake energy was observed

Monitoring land subsidence of airport using InSAR time-series techniques with atmospheric and orbital error corrections

Land subsidence is one of the common geological hazards worldwide and mostly caused by human activities including the construction of massive infrastructures. Large infrastructure such as airport is susceptible to land subsidence due to several factors. Therefore, monitoring of the land subsidence at airport is crucial in order to prevent undesirable loss of property and life. Remote sensing technique, especially Interferometric Synthetic Aperture Radar (InSAR) has been successfully applied to measure the surface deformation over the past few decades although atmospheric artefact and orbital errors are still a concerning issue in this measurement technique. Multi-temporal InSAR, an extension of InSAR technique, uses large sets of SAR scenes to investigate the temporal evolution of surface deformation and mitigate errors found in a single interferogram. This study investigates the long-term land subsidence of the Kuala Lumpur International Airport (KLIA), Malaysia and Singapore Changi Airport (SCA), Singapore by using two multi-temporal InSAR techniques like Small Baseline Subset (SBAS) and Multiscale InSAR Time Series (MInTS). General InSAR processing was conducted to generate interferogram using ALOS PALSAR data from 2007 until 2011. Atmospheric and orbital corrections were carried out for all interferograms using weather model, namely European Centre for Medium Range Weather Forecasting (ECMWF) and Network De-Ramping technique respectively before estimating the time series land subsidence. The results show variation of subsidence with respect to corrections (atmospheric and orbital) as well as difference between multi-temporal InSAR techniques (SBAS and MInTS) used. After applying both corrections, a subsidence ranging from 2 to 17 mm/yr was found at all the selected areas at the KLIA. Meanwhile, for SCA, a subsidence of about less than 10 mm/yr was found. Furthermore, a comparison between two techniques (SBAS and MInTS) show a difference rate of subsidence of about less than 1 mm/yr for both study area. SBAS technique shows more linear result as compared to the MInTS technique which shows slightly scattering pattern but both techniques show a similar trend of surface deformation in both study sites. No drastic deformation was observed in these two study sites and slight deformation was detected which about less than 20mm/yr for both study areas probably occurred due to several reasons including conversion of the land use from agricultural land, land reclamation process and also poor construction. This study proved that InSAR time series surface deformation measurement techniques are useful as well as capable to monitor deformation of large infrastructure such as airport and as an alternative to costly conventional ground measurement for infrastructure monitoring

FFTと連続ウェーブレット変換法を用いた同期位相計測に基づく電力システムのモード検出とダンピング推定

The thesis carries out the estimation of damping as well as the frequencymode of inter area oscillations in the range of 0.1 to 1.0 Hz. This belongs underthe topic of angle stability management of power systems. Previously some otherstudies had been conducted in this area at which most of them employed themethods such as the least squares, Yule-Walker, autoregressive (AR),autoregressive moving average (ARMA), the Kalman filter and the subspacemethod. Another research also had been conducted which based on Fast FourierTransform (FFT) analysis individually, the damping ratio and frequencyoscillation were estimated from eigenvalue of the matrix associated to a SingleMachine Infinite Bus (SMIB) model. An output-only-based simplified oscillationmodel was developed to estimate the characteristic of inter-area power oscillationbased on extracted oscillation data. However, this previous method did notexplain how to calculate damping ratio without considering any simplified model.Furthermore, the behavior of the signal during certain time of analysis could notbe described.This thesis promotes a novel approach in analyzing PMU data based onFast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT)algorithm. Then proceed by demodulating the slicing signal at a particular peakand ridge of the signal using a decrement technique. The approach applied in thisthesis can be classified into the non-parametric approach, where it works directlyon the data. The damping calculation method in this thesis emphasized on theaccurate and robust damping estimations which was proved by attempting thesimulation towards various level of signal to noise ratio (SNR).To verify the outcome of this method a synthesized signal contains ofthree ringdown modes representing a real signal from PMU was analyzed. Theresults were compared to the given parameters and it was clearly shown that thismethod gave the result within an acceptable range of error. Additionally, theacceptability of this method was also verified by comparing to the result ofeigenvalue-based calculation on a standard power system model. The simulationindicated the results of the two approaches fitted each other means this FFT-CWTis workable to assess the damping ratio of a small signal oscillation in powersystem. The advantage of this method is no prior data of the system required;hence this approach is very applicable in the power system where gathering datafrom the network is not attainable.This thesis also elaborated the application of wide area signal recorded byPMU, refined by the FFT-CWT method, for controlling the oscillation damping ofpower system. The simulation showed the application of wide area signal as aninput to the damping controller has a great prospective to countermeasure the interarea oscillation in the system.九州工業大学博士学位論文 学位記番号:工博甲第413号 学位授与年月日:平成28年3月25日1. INTRODUCTION|2. SYNCHROPHASOR MEASUREMENT AND THE METHOD OF ANALYSIS|3. FAST FOURIER TRANSFORM AND CONTINUOUS WAVELET TRANSFORM APPROACH|4. APPLICATION OF THE APPROACH FOR MODE AND DAMPING CALCULATION|5. WIDE AREA SIGNAL DAMPING CONTROLLER|6. CONCLUSION AND FUTURE WORK九州工業大学平成27年