Simultaneous Amplitude and Phase Measurement for Periodic Optical Signals Using Time-Resolved Optical Filtering

Time-resolved optical filtering (TROF) measures the spectrogram or sonogram by a fast photodiode followed a tunable narrowband optical filter. For periodic signal and to match the sonogram, numerical TROF algorithm is used to find the original complex electric field or equivalently both the amplitude and phase. For phase-modulated optical signals, the TROF algorithm is initiated using the craters and ridges of the sonogram.Comment: 10 pages, 5 figure

Computational Experiment in the Problem of the Recent Traces of Oceanic Cosmic Impacts

The paper aims to fill the gap between geological practice on the problem of the recent traces of oceanic cosmic impacts and computational experiment on tsunami geology problem. Computational experiment and numerical analysis data of the oceanic comet impacts could be more effective for mega tsunami understanding and prediction than the traditional geological methods. We explored the depositional traces of large-scale water impact on the coast and modeling of mega tsunami given the steep waves and Mach reflection on the rocky shore and described chevron dunes form on the base of neural network technology to solve the inverse tsunami problem. The ocean impact craters were explored using the wavelet analysis digital data of the ocean bottom.Статья призвана заполнить пробел между геологическими изысканиями по проблеме недавних следов космических океанских воздействий и возможностями вычислительного эксперимента по проблеме геологических следов цунами. Вычислительный эксперимент и численный анализ данных о последствиях воздействий комет на океан могут быть более эффективными для понимания и прогнозирования мегацунами, чем традиционные геологические методы. Мы анализировали следы отложений от крупномасштабных воздействий воды на побережье и моделирование мегацунами с учетом крутизны волны и отражения Маха на скалистом берегу, а также описание формы дюн-шевронов на основе нейросетевой технологии для решения обратной задачи цунами. Импактные кратеры в океане были изучены с использованием вейвлет-анализа цифровых данных океанского дна

Detection Of Chipping In Ceramic Cutting Inserts From Workpiece Profile Signature During Turning Process Using Machine Vision

Ceramic tools are prone to chipping due to their low impact toughness. Tool chipping significantly decreases the surface finish quality and dimensional accuracy of the workpiece. Thus, in-process detection of chipping in ceramic tools is important especially in unattended machining. Existing in-process tool failure detection methods using sensor signals have limitations in detecting tool chipping. The monitoring of tool wear from the workpiece profile using machine vision has great potential to be applied in-process, however no attempt has been made to detect tool chipping. In this work, a vision-based approach has been developed to detect tool chipping in ceramic insert from 2-D workpiece profile signature. The profile of the workpiece surface was captured using a DSLR camera. The surface profile was extracted to sub-pixel accuracy using invariant moment method. The effect of chipping in the ceramic cutting tools on the workpiece profile was investigated using autocorrelation function (ACF) and fast Fourier transform (FFT). Detection of onset tool chipping was conducted by using the sub-window FFT and continuous wavelet transform (CWT). Chipping in the ceramic tool was found to cause the peaks of ACF of the workpiece profile to decrease rapidly as the lag distance increased and deviated significantly from one another at different workpiece rotation angles. From FFT analysis the amplitude of the fundamental feed frequency increases steadily with cutting duration during gradual wear, however, fluctuates significantly after tool has chipped. The stochastic behaviour of the cutting process after tool chipping leads to a sharp increase in the amplitude of spatial frequencies below the fundamental feed frequency. CWT method was found more effective to detect the onset of tool chipping at 16.5 s instead of 17.13 s by sub-window FFT. Root mean square of CWT coefficients for the workpiece profile at higher scale band was found to be more sensitive to chipping and thus can be used as an indicator to detect the occurrence of the tool chipping in ceramic inserts

An acoustic water tank disdrometer

Microwave engineers and geomorphologists require rainfall data with a much greater temporal resolution and a better representation of the numbers of large raindrops than is available from current commercial instruments. This thesis describes the development of an acoustic instrument that determines rain parameters from the sound of raindrops falling into a tank of water. It is known as the acoustic water tank disdrometer (AWTD).There is a direct relationship between the kinetic energy of a raindrop and the acoustic energy generated upon impact. Rain kinetic energy flux density (KE) is estimated from measurements of the sound field in the tank and these have been compared to measurements from a co-sited commercial disdrometer.Furthermore, using an array of hydrophones it is possible to determine the drop size and impact position of each raindrop falling into the tank. Accumulating the information from many impacts allows a drop size distribution (DSD) to be calculated.Eight months of data have been collected in the eastern UK. The two methods of parameter estimation are developed and analysed to show that the acoustic instrument can produce rain KE measurements with a one-second integration times and DSDs with accurate large drop-size tails

Application of signal processing methods to the Rover Environmental Monitoring Station data for the analysis of environmental processes on Mars

La presente tesis analiza los datos de dos de los sensores de la estación meteorológica REMS, localizada Mars Science Laboratory (MSL), activa en Marte desde agosto de 2012: el sensor de presión (PS) y los sensores de temperatura del aire (ATS). La información contenida en los datos de estos sensores es muy valiosa y reveladora para comprender mejor los procesos meteorológicos que tienen lugar diariamente en Marte. Es necesario conocer en profundidad el funcionamiento de ambos sensores y el flujo de procesamiento de datos que se sigue para transformar los datos digitales recibidos de Marte en información tangible y legible. El objetivo principal de esta tesis es utilizar métodos de procesado de señal que nos ayuden a extraer información útil contenida en los datos de estos sensores, que no son visible a simple vista. Se desea extraer información relacionada con procesos ambientales de Marte, contribuyendo así a una mejor comprensión del planeta rojo. Con todos estos conocimientos adquiridos, se ha realizado una investigación exhaustiva de los métodos de procesamiento de señales para encontrar los mejores que se ajusten a nuestros datos y nos ayuden a encontrar la información relacionada con procesos ambientales. Usando los datos del PS y del ATS, han utilizado wavelets para quitar ruido en los datos del ATS y el algoritmo de Análisis de Espectro Singular (SSA) para encontrar indicadores relacionados con los procesos ambientales en Marte. Se han encontrado precursores de tormentas de polvo en los datos del PS y posibles nubes de hielo en los datos ATS, ambos utilizando el algoritmo SSA

Development of an acoustic emission monitoring system for crack detection during arc welding

Condition monitoring techniques are employed to monitor the structural integrity of a structure or the performance of a process. They are used to evaluate the structural integrity including damage initiation and propagation in engineering components. Early damage detection, maintenance and repairs can prevent structural failures, reduce maintenance and replacement costs, and guarantee that the structure runs securely during its service life. Acoustic emission (AE) technology is one of the condition monitoring methods widely employed in the industry. AE is an attractive option for condition monitoring purposes, the number of industrial applications where is used is rising. AE signals are elastic stress waves created by the fast release of energy from local sources occurring inside of materials, e.g. crack initiating and propagating. The AE technique includes recording this phenomenon with piezoelectric sensors, which is mounted on the surface of a structure. The signals are subsequently analysed in order to extract useful information about the nature of the AE source. AE has a high sensitivity to crack propagation and able to locate AE activity sources. It is a passive approach. It listens to the elastic stress waves releasing from material and able to operate in real-time monitoring to detect both cracks initiating and propagating. In this study, the use of AE technology to detect and monitor the possible occurrence of cracking during the arc welding process has been investigated. Real-time monitoring of the automated welding operation can help increase productivity and reliability while reducing cost. Monitoring of welding processes using AE technology remains a challenge, especially in the field of real-time data analysis, since a large amount of data is generated during monitoring. Also, during the welding process, many interferences can occur, causing difficulties in the identifications of the signals related to cracking events. A significant issue in the practical use of the AE technique is the existence of independent sources of a signal other than those related to cracking. These spurious AE signals make the discovering of the signals from the cracking activity difficult. Therefore, it is essential to discriminate the signal to identify the signal source. The need for practical data analysis is related to the three main objectives of monitoring, which is where this study has focused on. Firstly, the assessment of the noise levels and the characteristics of the signal from different materials and processes, secondly, the identification of signals arising from cracking and thirdly, the study of the feasibility of online monitoring using the AE features acquired in the initial study. Experimental work was carried out under controlled laboratory conditions for the acquisition of AE signals during arc welding processing. AE signals have been used for the assessment of noise levels as well as to identify the characteristics of the signals arising from different materials and processes. The features of the AE signals arising from cracking and other possible signal sources from the welding process and environment have also collected under laboratory conditions and analysed. In addition to the above mentioned aspects of the study, two novel signal processing methods based on signal correlation have been developed for efficiently evaluating data acquired from AE sensors. The major contributions of this research can be summarised as follows. The study of noise levels and filtering of different arc welding processes and materials is one of the areas where the original contribution is identified with respect to current knowledge. Another key contribution of the present study is the developing of a model for achieving source discrimination. The crack-related signals and other signals arising from the background are compared with each other. Two methods that have the potential to be used in a real-time monitoring system have been considered based on cross-correlation and pattern recognition. The present thesis has contributed to the improvement of the effectiveness of the AE technique for the detection of the possible occurrence of cracking during arc welding

Single-Station Seismo-Acoustic Monitoring of Nyiragongo\u27s Lava Lake Activity (D.R. Congo)

Since its last effusive eruption in 2002, Nyiragongo has been an open-vent volcano characterized by the world\u27s largest persistent lava lake. This lava lake provides a unique opportunity to detect pressure change in the magmatic system by analyzing its level fluctuations. We demonstrate that this information is contained in the seismic and infrasound signals generated by the lava lake\u27s activity. The continuous seismo-acoustic monitoring permits quantification of lava lake dynamics, which is analyzed retrospectively to identify periods of volcanic unrest. Synchronous, high-resolution satellite SAR (Synthetic Aperture Radar) images are used to constrain lava lake level by measuring the length of the SAR shadow cast by the rim of the pit crater where the lava lake is located. Seventy-two estimations of the lava lake level were obtained with this technique between August 2016 and November 2017. These sporadic measurements allow for a better interpretation of the continuous infrasound and seismic data recorded at the closest station (~6 km from the crater). Jointly analyzed seismo-acoustic and SAR data reveal that slight changes in the spectral properties of the continuous cross-correlated low-frequency seismo-acoustic records (and not solely single events) can be used to track fluctuations of the lava lake level on a daily and hourly basis. We observe that drops of the lava lake and the appearance of significant long period (LP) “lava lake” events are a consequence of a probable deep lateral magma intrusion beneath Nyiragongo, which induces changes in its shallow plumbing system. In addition to contributing to understanding lava lake dynamics, this study highlights the potential to continuously monitor pressure fluctuations within the magmatic system using a single seismo-acoustic station located several kilometers from the vent