20,894 research outputs found
Using Acoustic Holography for Vibration Analysis
Disertační práce se zabývá bezkontaktní analýzou vibrací pomocí metod akustické holografie v blízkém poli. Akustická holografie v blízkém poli je experimentální metoda, která rekonstruuje akustické pole v těsné blízkosti povrchu vibrujícího předmětu na základě měření akustického tlaku nebo akustické rychlosti v určité vzdálenosti od zkoumaného předmětu. Konkrétní realizace této metody závisí na použitém výpočetním algoritmu. Vlastní práce je zaměřena zejména na rozbor algoritmů, které využívají k rekonstrukci zvukového pole v blízkosti vibrujícího objektu transformaci do domény vlnových čísel (prostorová transformace), kde probíhá vlastní výpočet. V úvodu práce je vysvětlena základní teorie metody akustické holografie v blízkém poli s popisem základních vlastností a dále rozborem konkrétních nejčastěji používaných algoritmům pro lokalizaci a charakterizaci zdroje zvuku a pro následnou vibrační analýzu. Stěžejní část práce se věnuje pokročilým metodám zpracování, které se snaží určitým způsobem optimalizovat přesnost predice zvukového pole v blízkosti vibrujícího předmětu v reálných podmínkách. Jde zejména o problematiku použitého měřicího systému s akustickými snímači, které nejsou ideální, a dále o možnost měření v prostorách s difúzním charakterem zvukového pole. Pro tento případ byla na základě literárního průzkumu optimalizována a ověřena metoda využívající dvouvrstvé mikrofonní pole, které umožňuje oddělení zvukových polí přicházejících z různých stran a tedy úspěšné měření v uzavřených prostorách např. kabin automobilů a letadel. Součástí práce byla také optimalizace, rozšíření a následné ověření algoritmů publikovaných v posledních letech pro měření v reálných podmínkách za použití běžně dostupných akustických snímačů.The main aim of the thesis is application of near-field acoustic holography for non-contact vibration analysis. Near-field acoustic holography is an experimental technique for reconstruction of sound field close to the surface of the vibrating object based on measurement of sound pressure or acoustic particle velocity in certain distance from the examined object. Practical realization of this method depends on used calculation procedure. The thesis is focused on analysis of acoustic holography algorithms with transformation into wavenumber domain (spatial transformation) where the reconstruction of the sound field near vibrating object is calculated. The introductory part of the thesis describes the theory of near-field acoustic holography with general characteristics and with analysis of most common algorithms used for localization and characterization of sound source and consequent vibration analysis. Principal part of the thesis deals with advanced processing methods where these methods try to optimize the accuracy of prediction of sound field near vibrating object in real environment. In this study, real measurement conditions represent the measurement system with non-ideal acoustic sensors and also areas with reverberant sound field. Based on literature study, there has been optimized and verified the new method which uses double layer microphone array to separate incoming and outgoing sound field, thus allows successful measurement in confined space e.g. cabins of cars and airplanes. Part of the thesis has been also focused on optimization, extension and successive experimental validation of selected classical algorithms published in last decade for possible measurement in real conditions and with common acoustic sensors.
Acoustical structured illumination for super-resolution ultrasound imaging.
Structured illumination microscopy is an optical method to increase the spatial resolution of wide-field fluorescence imaging beyond the diffraction limit by applying a spatially structured illumination light. Here, we extend this concept to facilitate super-resolution ultrasound imaging by manipulating the transmitted sound field to encode the high spatial frequencies into the observed image through aliasing. Post processing is applied to precisely shift the spectral components to their proper positions in k-space and effectively double the spatial resolution of the reconstructed image compared to one-way focusing. The method has broad application, including the detection of small lesions for early cancer diagnosis, improving the detection of the borders of organs and tumors, and enhancing visualization of vascular features. The method can be implemented with conventional ultrasound systems, without the need for additional components. The resulting image enhancement is demonstrated with both test objects and ex vivo rat metacarpals and phalanges
Propagating Wave Phenomena Detected in Observations and Simulations of the Lower Solar Atmosphere
We present high-cadence observations and simulations of the solar
photosphere, obtained using the Rapid Oscillations in the Solar Atmosphere
imaging system and the MuRAM magneto-hydrodynamic code, respectively. Each
dataset demonstrates a wealth of magneto-acoustic oscillatory behaviour,
visible as periodic intensity fluctuations with periods in the range 110-600 s.
Almost no propagating waves with periods less than 140s and 110s are detected
in the observational and simulated datasets, respectively. High concentrations
of power are found in highly magnetised regions, such as magnetic bright points
and intergranular lanes. Radiative diagnostics of the photospheric simulations
replicate our observational results, confirming that the current breed of
magneto-hydrodynamic simulations are able to accurately represent the lower
solar atmosphere. All observed oscillations are generated as a result of
naturally occurring magnetoconvective processes, with no specific input driver
present. Using contribution functions extracted from our numerical simulations,
we estimate minimum G-band and 4170 Angstrom continuum formation heights of 100
km and 25 km, respectively. Detected magneto-acoustic oscillations exhibit a
dominant phase delay of -8 degrees between the G-band and 4170 Angstrom
continuum observations, suggesting the presence of upwardly propagating waves.
More than 73% of MBPs (73% from observations, 96% from simulations) display
upwardly propagating wave phenomena, suggesting the abundant nature of
oscillatory behaviour detected higher in the solar atmosphere may be traced
back to magnetoconvective processes occurring in the upper layers of the Sun's
convection zone.Comment: 13 pages, 9 figures, accepted into Ap
Advanced algorithms for audio and image processing
The objective of the thesis is the development of a set of innovative algorithms around the topic of beamforming in the field of acoustic imaging, audio and image processing, aimed at significantly improving the performance of devices that exploit these computational approaches. Therefore the context is the improvement of devices (ultrasound machines and video/audio devices) already on the market or the development of new ones which, through the proposed studies, can be introduced on new the markets with the launch of innovative high-tech start-ups. This is the motivation and the leitmotiv behind the doctoral work carried out. In fact, in the first part of the work an innovative image reconstruction algorithm in the field of ultrasound biomedical imaging is presented, which is connected to the development of such equipment that exploits the computing opportunities currently offered nowadays at low cost by GPUs (Moore\u2019s law). The proposed target is to obtain a new pipeline of the reconstruction of the image abandoning the architecture of such hardware based In the first part of the thesis I faced the topic of the reconstruction of ultrasound images for applications hypothesized on a software based device through image reconstruction algorithms processed in the frequency domain. An innovative beamforming algorithm based on seismic migration is presented, in which a transformation of the RF data is carried out and the reconstruction algorithm can evaluate a masking of the k-space of the data, speeding up the reconstruction process and reducing the computational burden. The analysis and development of the algorithms responsible for carrying out the thesis has been approached from a feasibility point in an off-line context and on the Matlab platform, processing both synthetic simulated generated data and real RF data: the subsequent development of these algorithms within of the future ultrasound biomedical equipment will exploit an high-performance computing framework capable of processing customized kernel pipelines (henceforth called \u2019filters\u2019) on CPU/GPU. The type of filters implemented involved the topic of Plane Wave Imaging (PWI), an alternative method of acquiring the ultrasound image compared to the state of the art of the traditional standard B-mode which currently exploit sequential sequence of insonification of the sample under examination through focused beams transmitted by the probe channels. The PWI mode is interesting and opens up new scenarios compared to the usual signal acquisition and processing techniques, with the aim of making signal processing in general and image reconstruction in particular faster and more flexible, and increasing importantly the frame rate opens up and improves clinical applications. The innovative idea is to introduce in an offline seismic reconstruction algorithm for ultrasound imaging a further filter, named masking matrix. The masking matrices can be computed offline knowing the system parameters, since they do not depend from acquired data. Moreover, they can be pre-multiplied to propagation matrices, without affecting the overall computational load. Subsequently in the thesis, the
topic of beamforming in audio processing on super-direct linear arrays of microphones is addressed. The aim is to make an in depth analysis of two main families of data-independent approaches and algorithms present in the literature by comparing their performances and the trade-off between directivity and frequency invariance, which is not yet known at to the state-of-the-art. The goal is to validate the best algorithm that allows, from the perspective of an implementation, to experimentally verify performance, correlating it with the characteristics and error statistics. Frequency-invariant beam patterns are often required by systems using an array of sensors to process broadband signals. In some experimental conditions, the array spatial aperture is shorter than the involved wavelengths. In these conditions, superdirective beamforming is essential for an efficient system. I present a comparison between two methods that deal with a data-independent beamformer based on a filter-and-sum structure. Both
methods (the first one numerical, the second one analytic) formulate a mathematical convex minimization problem, in which the variables to be optimized are the filters coefficients or frequency responses. In the described simulations, I have chosen a geometry and a set-up of parameters that allows us to make a fair comparison between the performances of the two different design methods analyzed. In particular, I addressed a small linear array for audio capture with different purposes (hearing aids, audio surveillance system, video-conference system, multimedia device, etc.). The research activity carried out has been used for the launch of a high-tech device through an innovative start-up in the field of glasses/audio
devices (https://acoesis.com/en/). It has been proven that the proposed algorithm gives the possibility of obtaining higher performances than the state of the art of similar algorithms, additionally providing the possibility of connecting directivity or better generalized directivity to the statistics of phase errors and gain of sensors, extremely important in superdirective arrays in the case of real and industrial implementation. Therefore, the method proposed by the comparison is innovative because it quantitatively links the physical construction characteristics of the array to measurable and experimentally verifiable quantities, making the real implementation process controllable. The third topic faced is the reconstruction of the Room Impluse Response (RIR) using audio processing blind methods. Given an unknown audio source, the estimation of time differences-of-arrivals (TDOAs) can be efficiently and robustly solved using blind channel identification and exploiting the cross-correlation identity (CCI). Prior blind works have improved the estimate of TDOAs by means of different algorithmic solutions and optimization strategies, while always sticking to the case N = 2 microphones. But what if we can obtain a direct improvement in performance by just increasing N? In the fourth Chapter I tried to investigate this direction, showing that, despite the arguable simplicity, this is capable of (sharply) improving upon state-of-the-art blind channel identification methods based on CCI, without modifying the computational pipeline. Inspired by our results, we seek to warm up the community and the practitioners by paving the way (with two concrete, yet preliminary, examples) towards joint approaches in which advances in the optimization are combined with an increased number of microphones, in order to achieve further improvements. Sound source localisation applications can be tackled by inferring the time-difference-of-arrivals (TDOAs) between a sound-emitting source and a set of microphones. Among the referred applications, one can surely list room-aware sound reproduction, room geometry\u2019s estimation, speech enhancement. Despite a broad spectrum of
prior works estimate TDOAs from a known audio source, even when the signal emitted from the acoustic source is unknown, TDOAs can be inferred by comparing the signals received at two (or more) spatially separated microphones, using the notion of cross-corrlation identity (CCI). This is the key theoretical tool, not only, to make the ordering of microphones irrelevant during the acquisition stage, but also to solve the problem as blind channel identification, robustly and reliably inferring TDOAs from an unknown audio source. However, when dealing with natural environments, such \u201cmutual agreement\u201d between microphones can be tampered by a variety of audio ambiguities such as ambient noise. Furthermore, each observed signal may contain multiple distorted or delayed replicas of the emitting source due to reflections or generic boundary effects related to the (closed) environment. Thus, robustly estimating TDOAs is surely a challenging problem and CCI-based approaches cast it as single-input/multi-output blind channel identification. Such methods promote robustness in the estimate from the methodological standpoint: using either energy-based regularization, sparsity or positivity constraints, while also pre-conditioning the solution space. Last but not least, the Acoustic Imaging is an imaging modality that exploits the propagation of acoustic waves in a medium to recover the spatial distribution and intensity of sound sources in a given region. Well known and widespread acoustic imaging applications are, for example, sonar and ultrasound. There are active and passive imaging devices: in the context of this thesis I consider a passive imaging system called Dual Cam that does not emit any sound but acquires it from the environment. In an acoustic image each pixel corresponds to the sound intensity of the source, the whose position is described by a particular pair of angles and, in the case in which the beamformer can, as in our case, work in near-field, from a distance on which the system is focused. In the last part of this work I propose the use of a new modality characterized by a richer information content, namely acoustic images, for the sake of audio-visual scene understanding. Each pixel in such images is characterized by a spectral signature, associated to a specific direction in space and obtained by processing the audio signals coming from an array of microphones. By coupling such array with a video camera, we obtain spatio-temporal alignment of acoustic images and video frames. This constitutes a powerful source of self-supervision, which can be exploited in the learning pipeline we are proposing, without resorting to expensive data annotations. However, since 2D planar arrays are cumbersome and not as widespread as ordinary microphones, we propose that the richer information content of acoustic images can be distilled, through a self-supervised
learning scheme, into more powerful audio and visual feature representations. The learnt feature representations can then be employed for downstream tasks such as classification and cross-modal retrieval, without the need of a microphone array. To prove that, we introduce a novel multimodal dataset consisting in RGB videos, raw audio signals and acoustic images, aligned in space and synchronized in time. Experimental results demonstrate the validity of our hypothesis and the effectiveness of the proposed pipeline, also when tested for tasks and datasets different from those used for training. Chapter 6 closes the thesis, presenting a development activity of a new Dual Cam POC to build-up from it a spin-off, assuming to apply for an innovation project for hi-tech start- ups (such as a SME instrument H2020) for a 50Keuro grant, following the idea of the technology transfer. A deep analysis of the reference market, technologies and commercial competitors, business model and the FTO of intellectual property is then conducted. Finally, following the latest technological trends (https://www.flir.eu/products/si124/) a new version of the device (planar audio array) with reduced dimensions and improved technical characteristics is simulated, simpler and easier to use than the current one, opening up new interesting possibilities of development not only technical and scientific but also in terms of business fallout
Passive element enriched photoacoustic computed tomography (PER PACT) for simultaneous imaging of acoustic propagation properties and light absorption\ud
We present a ‘hybrid’ imaging approach which can image both light absorption properties and acoustic transmission properties of an object in a two-dimensional slice using a computed tomography (CT) photoacoustic imager. The ultrasound transmission measurement method uses a strong optical absorber of small cross-section placed in the path of the light illuminating the sample. This absorber, which we call a passive element acts as a source of ultrasound. The interaction of ultrasound with the sample can be measured in transmission, using the same ultrasound detector used for photoacoustics. Such measurements are made at various angles around the sample in a CT approach. Images of the ultrasound propagation parameters, attenuation and speed of sound, can be reconstructed by inversion of a measurement model. We validate the method on specially designed phantoms and biological specimens. The obtained images are quantitative in terms of the shape, size, location, and acoustic properties of the examined heterogeneitie
Improvement of signal analysis for the ultrasonic microscopy
This dissertation describes the improvement of signal analysis in ultrasonic microscopy for nondestructive testing. Specimens with many thin layers, like modern electronic components, pose a particular challenge for identifying and localizing defects. In this thesis, new evaluation algorithms have been developed which enable analysis of highly complex layer-stacks. This is achieved by a specific evaluation of multiple reflections, a newly developed iterative reconstruction and deconvolution algorithm, and the use of classification algorithms with a highly optimized simulation algorithm. Deep delaminations inside a 19-layer component can now not only be detected, but also localized. The new analysis methods also enable precise determination of elastic material parameters, sound velocities, thicknesses, and densities of multiple layers. The highly improved precision of determined reflections parameters with deconvolution also provides better and more conclusive results with common analysis methods.:Kurzfassung......................................................................................................................II
Abstract.............................................................................................................................V
List ob abbreviations........................................................................................................X
1 Introduction.......................................................................................................................1
1.1 Motivation.....................................................................................................................2
1.2 System theoretical description.....................................................................................3
1.3 Structure of the thesis..................................................................................................6
2 Sound field.........................................................................................................................8
2.1 Sound field measurement............................................................................................8
2.2 Sound field modeling..................................................................................................11
2.2.1 Reflection and transmission coefficients.........................................................11
2.2.2 Sound field modeling with plane waves..........................................................13
2.2.3 Generalized sound field position.....................................................................19
2.3 Receiving transducer signal.......................................................................................20
2.3.1 Calculation of the transducer signal from the sound field...............................20
2.3.2 Received signal amplitude..............................................................................21
2.3.3 Measurement of reference signals..................................................................24
3 Ultrasonic Simulation......................................................................................................27
3.1 State of the art............................................................................................................27
3.2 Simulation approach..................................................................................................28
3.2.1 Sound field measurement based simulation...................................................28
3.2.2 Reference signal based simulation.................................................................30
3.3 Determination of the impulse response.....................................................................31
3.3.1 1D ray-trace algorithm....................................................................................31
3.3.2 2D ray-trace algorithm....................................................................................33
3.3.3 Complexity reduction – optimizations.............................................................35
4 Deconvolution – Determination of reflection parameters............................................38
4.1 State of the art............................................................................................................39
4.1.1 Decomposition techniques..............................................................................39
4.1.2 Deconvolution.................................................................................................41
4.2 Analytic signal investigations for deconvolution.........................................................42
4.3 Single reference pulse deconvolution........................................................................44
4.4 Multi-pulse deconvolution..........................................................................................47
4.4.1 Homogeneous multi-pulse deconvolution.......................................................48
4.4.2 Multi-pulse deconvolution with simulated GSP profile....................................49
5 Reconstruction.................................................................................................................50
5.1 State of the art............................................................................................................50
5.2 Reconstruction approach...........................................................................................51
5.3 Direct material parameter estimation.........................................................................52
5.3.1 Sound velocities and layer thickness..............................................................52
5.3.2 Density, elastic modules and acoustic attenuation.........................................54
5.4 Iterative material parameter determination of a single layer......................................56
5.5 Reconstruction of complex specimens......................................................................60
5.5.1 Material characterization of multiple layers ....................................................60
5.5.2 Iterative simulation parameter optimization with correlation...........................62
5.5.3 Pattern recognition reconstruction of specimens with known base structure. 66
6 Applications and results.................................................................................................71
6.1 Analysis of stacked components................................................................................71
6.2 Time-of-flight and material analysis...........................................................................74
7 Conclusions and perspectives.......................................................................................78
References.......................................................................................................................82
Figures.............................................................................................................................86
Tables...............................................................................................................................88
Appendix..........................................................................................................................89
Acknowledgments.........................................................................................................100
Danksagung...................................................................................................................101Die vorgelegte Dissertation befasst sich mit der Verbesserung der Signalauswertung für die Ultraschallmikroskopie in der zerstörungsfreien Prüfung. Insbesondere bei Proben mit vielen dünnen Schichten, wie bei modernen Halbleiterbauelementen, ist das Auffinden und die Bestimmung der Lage von Fehlstellen eine große Herausforderung. In dieser Arbeit wurden neue Auswertealgorithmen entwickelt, die eine Analyse hochkomplexer Schichtabfolgen ermöglichen. Erreicht wird dies durch die gezielte Auswertung von Mehrfachreflexionen, einen neu entwickelten iterativen Rekonstruktions- und Entfaltungsalgorithmus und die Nutzung von Klassifikationsalgorithmen im Zusammenspiel mit einem hoch optimierten neu entwickelten Simulationsalgorithmus. Dadurch ist es erstmals möglich, tief liegende Delaminationen in einem 19-schichtigem Halbleiterbauelement nicht nur zu detektieren, sondern auch zu lokalisieren. Die neuen Analysemethoden ermöglichen des Weiteren eine genaue Bestimmung von elastischen Materialparametern, Schallgeschwindigkeiten, Dicken und Dichten mehrschichtiger Proben. Durch die stark verbesserte Genauigkeit der Reflexionsparameterbestimmung mittels Signalentfaltung lassen sich auch mit klassischen Analysemethoden deutlich bessere und aussagekräftigere Ergebnisse erzielen. Aus den Erkenntnissen dieser Dissertation wurde ein Ultraschall-Analyseprogramm entwickelt, das diese komplexen Funktionen auf einer gut bedienbaren Oberfläche bereitstellt und bereits praktisch genutzt wird.:Kurzfassung......................................................................................................................II
Abstract.............................................................................................................................V
List ob abbreviations........................................................................................................X
1 Introduction.......................................................................................................................1
1.1 Motivation.....................................................................................................................2
1.2 System theoretical description.....................................................................................3
1.3 Structure of the thesis..................................................................................................6
2 Sound field.........................................................................................................................8
2.1 Sound field measurement............................................................................................8
2.2 Sound field modeling..................................................................................................11
2.2.1 Reflection and transmission coefficients.........................................................11
2.2.2 Sound field modeling with plane waves..........................................................13
2.2.3 Generalized sound field position.....................................................................19
2.3 Receiving transducer signal.......................................................................................20
2.3.1 Calculation of the transducer signal from the sound field...............................20
2.3.2 Received signal amplitude..............................................................................21
2.3.3 Measurement of reference signals..................................................................24
3 Ultrasonic Simulation......................................................................................................27
3.1 State of the art............................................................................................................27
3.2 Simulation approach..................................................................................................28
3.2.1 Sound field measurement based simulation...................................................28
3.2.2 Reference signal based simulation.................................................................30
3.3 Determination of the impulse response.....................................................................31
3.3.1 1D ray-trace algorithm....................................................................................31
3.3.2 2D ray-trace algorithm....................................................................................33
3.3.3 Complexity reduction – optimizations.............................................................35
4 Deconvolution – Determination of reflection parameters............................................38
4.1 State of the art............................................................................................................39
4.1.1 Decomposition techniques..............................................................................39
4.1.2 Deconvolution.................................................................................................41
4.2 Analytic signal investigations for deconvolution.........................................................42
4.3 Single reference pulse deconvolution........................................................................44
4.4 Multi-pulse deconvolution..........................................................................................47
4.4.1 Homogeneous multi-pulse deconvolution.......................................................48
4.4.2 Multi-pulse deconvolution with simulated GSP profile....................................49
5 Reconstruction.................................................................................................................50
5.1 State of the art............................................................................................................50
5.2 Reconstruction approach...........................................................................................51
5.3 Direct material parameter estimation.........................................................................52
5.3.1 Sound velocities and layer thickness..............................................................52
5.3.2 Density, elastic modules and acoustic attenuation.........................................54
5.4 Iterative material parameter determination of a single layer......................................56
5.5 Reconstruction of complex specimens......................................................................60
5.5.1 Material characterization of multiple layers ....................................................60
5.5.2 Iterative simulation parameter optimization with correlation...........................62
5.5.3 Pattern recognition reconstruction of specimens with known base structure. 66
6 Applications and results.................................................................................................71
6.1 Analysis of stacked components................................................................................71
6.2 Time-of-flight and material analysis...........................................................................74
7 Conclusions and perspectives.......................................................................................78
References.......................................................................................................................82
Figures.............................................................................................................................86
Tables...............................................................................................................................88
Appendix..........................................................................................................................89
Acknowledgments.........................................................................................................100
Danksagung...................................................................................................................10
In-situ acoustic-based analysis system for physical and chemical properties of the lower Martian atmosphere
The Environmental Acoustic Reconnaissance and Sounding experiment (EARS), is
composed of two parts: the Environmental Acoustic Reconnaissance (EAR)
instrument and the Environmental Acoustic Sounding Experiment (EASE). They are
distinct, but have the common objective of characterizing the acoustic
environment of Mars. The principal goal of the EAR instrument is "listening" to
Mars. This could be a most significant experiment if one thinks of everyday
life experience where hearing is possibly the most important sense after sight.
Not only will this contribute to opening up this important area of planetary
exploration, which has been essentially ignored up until now, but will also
bring the general public closer in contact with our most proximate planet. EASE
is directed at characterizing acoustic propagation parameters, specifically
sound velocity and absorption, and will provide information regarding important
physical and chemical parameters of the lower Martian atmosphere; in
particular, water vapor content, specific heat capacity, heat conductivity and
shear viscosity, which will provide specific constraints in determining its
composition. This would enable one to gain a deeper understanding of Mars and
its analogues on Earth. Furthermore, the knowledge of the physical and chemical
parameters of the Martian atmosphere, which influence its circulation, will
improve the comprehension of its climate now and in the past, so as to gain
insight on the possibility of the past presence of life on Mars. These aspect
are considered strategic in the contest of its exploration, as is clearly
indicated in NASA's four main objectives on "Long Term Mars Exploration
Program" (http://marsweb.jpl.nasa.gov/mer/science).Comment: 16 pages including figure
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