Dissertations / Theses on the topic 'Noise subtraction'

Human fingerprints are rich in details called the minutiae, which can be used as identification marks for fingerprint verification. To get the details, the fingerprint capturing techniques are to be improved. Since when we the fingerprint is captured, the noise from outside adds to it. The goal of this thesis is to remove the noise present in the fingerprint image. To achieve a good quality fingerprint image, this noise has to be removed or suppressed and here it is done by using an algorithm or technique called ’Spectral Subtraction’, where the algorithm is based on subtraction of estimated noise spectrum from noisy signal spectrum. The performance of the algorithm is assessed by comparing the original fingerprint image and image obtained after spectral subtraction several parameters like PSNR, SSIM and also for different fingerprints on the database. Finally, performance matching was done using NIST matching software, and the obtained results were presented in the form of Receiver Operating Characteristics (ROC)graphs, using MATLAB, and the experimental results were presented.

"Speech enhancement in an automobile is a challenging problem because interference can come from engine noise, fans, music, wind, road noise, reverberation, echo, and passengers engaging in other conversations. Hands-free microphones make the situation worse because the strength of the desired speech signal reduces with increased distance between the microphone and talker. Automobile safety is improved when the driver can use a hands-free interface to phones and other devices instead of taking his eyes off the road. The demand for high quality hands-free communication in the automobile requires the introduction of more powerful algorithms. This thesis shows that a unique combination of five algorithms can achieve superior speech enhancement for a hands-free system when compared to beamforming or spectral subtraction alone. Several different designs were analyzed and tested before converging on the configuration that achieved the best results. Beamforming, voice activity detection, spectral subtraction, perceptual nonlinear weighting, and talker isolation via pitch tracking all work together in a complementary iterative manner to create a speech enhancement system capable of significantly enhancing real world speech signals. The following conclusions are supported by the simulation results using data recorded in a car and are in strong agreement with theory. Adaptive beamforming, like the Generalized Side-lobe Canceller (GSC), can be effectively used if the filters only adapt during silent data frames because too much of the desired speech is cancelled otherwise. Spectral subtraction removes stationary noise while perceptual weighting prevents the introduction of offensive audible noise artifacts. Talker isolation via pitch tracking can perform better when used after beamforming and spectral subtraction because of the higher accuracy obtained after initial noise removal. Iterating the algorithm once increases the accuracy of the Voice Activity Detection (VAD), which improves the overall performance of the algorithm. Placing the microphone(s) on the ceiling above the head and slightly forward of the desired talker appears to be the best location in an automobile based on the experiments performed in this thesis. Objective speech quality measures show that the algorithm removes a majority of the stationary noise in a hands-free environment of an automobile with relatively minimal speech distortion."

This thesis work fits in the Newtonian noise (NN) cancellation framework for gravitational-wave (GW) detectors of 2nd and 3rd generation. At frequencies below 20 Hz the NN affects GW detectors by generating gravity gradients that mask the GW signals that we want to measure. My work can be divided in three main tasks: the optimization of a seismic array for the NN cancellation in underground detectors, the optimization of a seismic array for Advanced Virgo + (which, respect to the former one, relied on seismic measurements and not on a seismic model) and the evaluation of the NN and the seismic field at the KAGRA site. I will briefly summarize in the following the main results of these three works. In the first work I performed a global optimization for finding the optimal locations of an array of sensors for the NN cancellation for underground detectors. Since we need to search for the optimal positions of N sensors in a 3D space, the computational efforts required are very demanding. At the present time, seismic correlations in the relevant frequency band for ET from 3Hz to 20Hz are not available. So we modelled the seismic field as isotropic and homogeneous. With this work I was able to assess the feasibility of applying active NN reduction in underground detectors and reaching a factor 10 of noise reduction with 15 sensors at 10 Hz. In 2019 this work was published. The second work I made during my PhD was conceptually similar to the previous one but very different in the approach used to solve it. Exploiting a theoretical model in Virgo was not an option given its complicated structure. I then used Virgo's seismic data to run the optimization of sensor locations. The main challenge here was that I had to perform a gaussian process regression over a 4D space, and not enough data were available for this purpose. I found a way to bypass the regression over the 4D space by exploiting the convolution theorem. This allowed me to perform the regression over a space with reduced dimension, i.e., in 2D. The global optimization algorithm was then run hundreds of times in order to statistically prove the global minimum, exactly as done in the work for the underground optimization. The results proved that with 15 seismometers we can reach a noise reduction factor of 3-7, which is enough for the aimed sensitivity of the next observing runs. The results of this work were then used to set the array that will be used to cancel the NN in Advanced Virgo +. This work has been published in 2020. This approach could also be useful in future, where it will be needed to optimize underground seismic arrays with real seismic data. Finally, in the third work I used seismic data collected in the Kamioka mine (where the gravitational-wave detector KAGRA is hosted) to investigate the seismic noise caused by the infrastructure and to calculate a NN budget. These are important aspects that need to be investigated in view of the 3rd generation GW detector Einstein Telescope. The data indicated that the infrastructure noise starts to be important well above 10 Hz, where the NN loses its impact on the detector and where the seismic isolation system is capable of killing the noise. Moreover, I used the data from three seismometers to perform a beamforming analysis and find the seismic velocities and the seismic wave main directions. The extracted values were then used as a reference for the estimation of the NN budget. For completeness, I also estimated the NN budget coming from surface Rayleigh waves. This was made by exploiting the data of the F-net network, in Japan. I then showed that the NN from surface and body waves can be neglected for KAGRA.

Large astronomical facilities usually provide data reduction pipeline designed to deliver ready-to-use scientific data, and too often as- tronomers are relying on this to avoid the most difficult part of an astronomer job Standard data reduction pipelines however are usu- ally designed and tested to have good performance on data with av- erage Signal to Noise Ratio (SNR) data, and the issues that are related with the reduction of data in the very low SNR domain are not taken int account properly. As a result, informations in data with low SNR are not optimally exploited. During the last decade our group has collected thousands of spec- tra using the GIRAFFE spectrograph at Very Large Telescope (Chile) of the European Southern Observatory (ESO) to determine the ge- ometrical distance and dynamical state of several Galactic Globular Clusters but ultimately the analysis has been hampered by system- atics in data reduction, calibration and radial velocity measurements. Moreover these data has never been exploited to get other informa- tions like temperature and metallicity of stars, because considered too noisy for these kind of analyses. In this thesis we focus our attention on data reduction and analysis of spectra with very low SNR. The dataset we analyze in this thesis comprises 7250 spectra for 2771 stars of the Globular Cluster M 4 (NGC 6121) in the wavelength region 5145 − 5360Å obtained with GIRAFFE. Stars from the upper Red Giant Branch down to the Main Sequence have been observed in very different conditions, including nights close to full moon, and reaching SNR ≃ 10 for many spectra in the dataset. We will first review the basic steps of data reduction and spec- tral extraction, adapting techniques well tested in other field (like photometry) but still under-developed in spectroscopy. We improve the wavelength dispersion solution and the correction of radial veloc- ity shift between day-time calibrations and science observations by following a completely different approach with respect to the ESO pipeline. We then analyze deeply the best way to perform sky sub- traction and continuum normalization, the most important sources respectively of noise and systematics in radial velocity determination and chemical analysis of spectra. The huge number of spectra of our dataset requires an automatic but robust approach, which we do not fail to provide. We finally determine radial velocities for the stars in the sample with unprecedented precision with respect to previous works with similar data and we recover the same stellar atmosphere parameters of other studies performed on the same cluster but on brighter stars, with higher spectral resolution and wavelength range ten times larger than our data. In the final chapter of the thesis we face a similar problem but from a completely different perspective. High resolution, high SNR data from the High Accuracy Radial Velocity Planet Searcher spectro- graph (HARPS) in La Silla (Chile) have been used to calibrate the at- mospheric stellar parameters as functions of the main characteristics of Cross-Correlation Functions, specifically built by including spec- tral lines with different sensitivity to stellar atmosphere parameters. These tools has been designed to be quick and to be easy to imple- ment in a instrument pipeline for a real-time determination, neverthe- less they provide accurate parameters even for lower SNR spectra.
Telescopi di grandi dimensioni usualmente rendono disponibili dei programmi per la riduzione dati che restituiscono all’astronomo dati già pronti per l’analisi scientifica, e sempre più spesso gli astronomi si appoggiano a questi programmi per evitare un lavoro lungo e diffi- cile. I programmi di riduzione dati standard sono però progettati per restituire buoni risultati su dati con buon Rapporto Segnale Rumore (RSR), e spesso i problemi legati alla riduzione di dati a basso RSR non sono presi in considerazione, con il risultato che le informazioni che contengono non sono adeguatamente utilizzate. Negli ultimi anni il nostro gruppo di ricerca ha collezionato migli- aia di spettri osservati con lo strumento GIRAFFE collegato al Very Large Telescope dell’Osservatorio Europeo del Sud in Cile, con lo scopo di determinare la distanza geometrica e lo stato dinamico di diversi Ammassi Globulari Galattici, ma in definitiva l’analisi è stata ostaco- lata da errori sistematici nella riduzione e calibrazione dei dati e nella misura delle velocità radiali. Inoltre questi dati non sono mai stati uti- lizzati per determinare altre informazioni come temperatura e metal- licità delle stesse, poiché considerati troppo rumorosi per questo tipo di analisi. In questa tesi concentriamo la nostra attenzione sulla riduzione dati ed analisi di spettri con bassissimo RSR. Il set di dati che analizziamo in questa tesi è composto da 7250 spettri per 2771 stelle dell’ammasso globulare M 4 (NGC 6121) ottenute con GIRAFFE nell’intervallo spet- trale 5145 − 5360Å. Stelle della parte superiore del Ramo delle Giganti Rossi fino alla Sequenza Principale sono state osservate in condizioni molto differenti, comprese notti con luna piena, fino ad raggiungere un valore limite di RSR ≃ 10 per molti spettri. La nostra analisi incomincia con i passi di base della riduzione dati ed estrazione degli spettri, adattando tecniche ben testate in altri campi (come la fotometria) ma ancora non ben sviluppate in spettroscopia. Continuiamo con il migliorare la soluzione della dispersione in lunghezza d’onda la correzione per piccoli spostamenti nelle velocità radiali di riferimento tra i dati di calibrazione presi durante il giorno e le osservazioni scientifiche seguendo un approccio completamente differente rispetto a quello ESO. Analizziamo poi la miglior maniera per effettuare la sottrazione del cielo e la normalizzazione del continuo, le due più importanti fonti rispettivamente di rumore ed errori sistematici nella misura delle velocità radiali nell’analisi chimica degli spettri. L’alto numero di spettri del nostro dataset richiede un approccio automatico ma robusto, che non manchiamo di fornire. Determiniamo infine per il nostro campione di stelle velocità radiali con una precisione mai vista in precedenza per dati di questo tipo e ritroviamo gli stessi parametri atmosferici di altri lavori svolti su stelle più brillanti, con dati a risoluzione spettrale maggiore e su intervalli di lunghezza d’onda dieci volte superiori a quello dei nostri dati. Nell’ultimo capitolo della tesi affrontiamo una problematica simile ma da una prospettiva completamente differente. Spettri ad alta risoluzione e ad alto RSR ottenuti con lo spettrografo HARPS sono stati usati per calibrare i parametri atmosferici stellari in funzione delle caratteristiche di funzioni di cross-correlazione specificatamente costruite includendo linee spettrali con diversa sensibilità ai parametri atmosferici stellari. Questi strumenti sono stati progettati per essere facilmente implementati un programma di riduzione dati, pur tuttavia senza sacrificare l’accuratezza dei parametri determinati anche per spettri a basso Rapporto Segnale Rumore.

Este trabalho tem a finalidade de apresentar e comparar técnicas de redução de ruído utilizando como critérios de avaliação a mínima distorção espectral e a redução de ruído, na reconstrução dos sinais de voz degradados por ruído. Para tanto, utilizou-se os filtros de Kalman de tempo discreto e de freqüência discreta em conjunto com a técnica de subtração espectral de potência. Os sinais utilizados foram contaminados por ruídos branco e colorido, e a avaliação do desempenho dos algoritmos foi realizada tendo-se como parâmetros a relação sinal/ruído segmentada (SNRseg) e a distância de Itakura-Saito (d(a,b)). Após o processamento, verificou-se que a técnica, proposta neste trabalho, de filtragem de Kalman no tempo em conjunto com a subtração espectral de potência, apresentou resultados um pouco melhores em relação à filtragem de Kalman na freqüência em conjunto com a subtração espectral de potência.
This work has as main objective to present and to compare techniques of noise reduction using as evaluation criterion the low spectral distortion and the noise reduction in the reconstruction of corrupted speech signals. For so much, it was used the Kalman\'s filters in the time and frequency domain together with the technique of power spectral subtraction. The used signals were corrupted by white and colored noises and the evaluation of effectiveness of the algorithms was accomplished using the segmental signal-to-noise ratio (SNRseg) and the Itakura-Saito distance (d(a,b)). After the processing, it was noticed that the Kalman filtering in the time together with power spectral subtraction presented better results than the Kalman filtering in the frequency together with power spectral subtraction.

Bioengineering
M.S.
Raman spectroscopy is an effective optical analysis of the biochemically specific characterization of tissues without contrast agents or exogenous dyes. Applications of Raman spectroscopy include analysis and biomarker investigation, disease diagnosis and surgical guidance. One major challenge in Raman spectroscopy is removing inherent fluorescence background present in samples to acquire Raman signatures. In some tissues, like liver, kidney and darkly pigment skin, the auto-fluorescence background is strong enough to overwhelm the Raman peaks in conventional Near-Infrared (NIR) Raman systems. Recent publications have shown that using Raman systems with excitation sources with wavelengths beyond 830 nm and short-wave infrared (SWIR) InGaAs Array detectors resulted in dramatically reduced auto-fluorescence. The unique characteristics of Raman signals collected from SWIR systems versus NIR Raman systems requires inspection of the suitability of spectral pre-processing techniques. This thesis focused on the development of spectral processing techniques at three different steps; 1) detector background & noise reduction; 2) Auto-fluorescence background subtraction; 3) detection of outlier measurements to assist statistical classification. Detector background and noise reduction was compared between two different techniques, and a direct subtraction method resulted in better performance to reduce fixed pattern noise unique to InGaAs arrays. For the aim 2, three different algorithms for fluorescence background removal were developed, and a modified polynomial fitting method was found to be most appropriate for the low signal-to-noise (SNR) spectra. Finally, local outlier factor(LOF), a multivariate statistical outlier metric, was implemented in a two-stage fashion, and shown to be effective at identifying raw measurement errors and Raman spectra outliers. The overall outcome of this thesis was the evaluation of spectral processing techniques for SWIR Raman spectroscopy systems, and the development of specific techniques to optimize data quality and best prepare spectra for statistical analysis.
Temple University--Theses

Orientador: Jozué Vieira Filho
Banca: Suely Cunha Amaro Mantovani
Banca: Marco Aparecido Queiroz Duarte
Resumo: A proposta deste trabalho é aprimorar a performance da técnica de redução de ruído, subtração espectral baseado na relação SNR a Priori, através da implementação de dois novos parâmetros Potência de Articulação e Não-Articulação obtidas a partir de algumas técnicas psicoacústicas. Faz-se um estudo da anatomia do sistema de audição humana e algumas limitações físicas, com o objetivo de entender o princípio básico da técnica ANIQUE, que é um sistema de avaliação objetiva de voz e têm como princípio o modelamento da percepção humana da voz. Através do modelo ANIQUE são extraídas as principais técnicas psicoacústicas para obtenção dos novos parâmetros, Potência de Articulação e Não- Articulação. Procurou-se apresentar de maneira resumida o processo de equacionamento das técnicas de redução de ruído em sinais de voz e das técnicas psicoacústicas. Posteriormente são descritos todos os processos das técnicas utilizadas que foram simuladas utilizando a linguagem de programação do MatLab®, seguido das avaliações objetivas dos sinais processados pelo software PESQ, que é um programa de avaliação objetiva de voz. Os resultados mostram que a implementação das técnicas psicoacústicas foram eficazes para melhorar a performance da técnica subtração espectral baseada na relação SNR a Priori
Abstract: The purpose of this work is to enhance the performance of noise reduction techniques based on spectral subtraction, which take in account the a priori signal-to-noise (SNR a Priori) to be estimated considering psychoacoustic criteria. in order to understand the basic principle of the ANIQUE, which is a psychoacoustic based technique used to evaluate the quality of speech signals, it was necessary to develop a study of the anatomy of the human hearing and some physical limitations, From the ANIQUE are extracted new parameters namely Articulation and Non-Articulation Powers, used to estimate the SNR_prio. As a result, it was obtained a new spectral based technique which was implemented in the MatLab® environment and evaluated using the objective quality measure for speech signal simulations namely PESQ. The results show that the implementation of psychoacoustic techniques were effective in enhance the performance of the spectral subtraction technique based on SNR a Priori
Mestre

This paper describes a MATLAB application with the main purpose of the simulation of noise components and noise elimination methods of Digital Radiography. The main parts of simulator are the model of a scene, procedures for loading the noise components to image data and methods for image processing. Various methods are employed depending on the type of noise. Subtraction techniques are used for the elimination of structural noise. The physical noise suppression is obtained using several methods of cumulation and Pixel Shift is used to reduce motion artifacts caused by the existence of moving noise. The techniques of superposition highlight the areas of interest in an image. Included are also auxiliary procedures for simulator running and presentation of final data. The model and the presented application can be used mainly for educational purposes as a powerful didactic tool.

Forensic audio recordings are usually made with a single covert microphone in non-ideal conditions. In non-ideal conditions the recordings are highly susceptible to various types of noise. The noise is usually broadband noise, co-talker interference, impulsive noise, narrow band noise or convolutional noise. There are existing speech enhancement techniques available to suppress most of the noise types mentioned, but when the noise is of a considerable level the performance of most enhancement techniques tend to decrease significantly. This thesis presents a study of speech enhancement techniques that are applicable to the enhancement of forensic audio recordings or that can be used in a forensic recording environment. It considers both pre-processing and post-processing speech enhancement techniques. This thesis investigates the improvement of some of the existing speech enhancement techniques as well as proposing some new ones. The performance of the improved and proposed speech enhancement techniques were evaluated objectively using the segmental signal-to-noise ratio (SNRseg) and subjectively using the mean opinion score (MOS). A review of the current speech enhancement techniques is presented in the thesis and is also used as a reference in some comparisons. The current speech enhancement techniques considered are those that are applicable to forensic audio recordings. The performance of the existing techniques are assessed in the comparisons with the speech enhancement techniques proposed by this thesis. Two pre-processing speech enhancement techniques are presented in this thesis. The first pre-processing speech enhancement technique is designed to improve existing broadband noise suppression techniques by the use of frequency shift keying (FSK) signals. It is based on a simple concept, which is to insert a known tone of sufficient amplitude into the silent segments of a speech signal prior to transmission. At the receiver the detection of silent or non-speech segments used in estimating the noise, becomes a simpler and more accurate task due to the inserted tone. The second pre-processing speech enhancement technique is designed to suppress a wide range of noises and it is based on zero padding. Zero padding involves inserting a zero value sample in between each speech signal sample prior to transmission. The inserted zero value samples allow accurate characterisation of the noise in the adjacent speech samples. At the receiver the noise is estimated from the sample positions allocated for the zero value samples. Several post-processing speech enhancement techniques are presented in this thesis. The first post-processing speech enhancement technique is designed for the suppression of co-talker interference and it uses a combination of dynamic time warping (DTW) and dual channel adaptive filtering. This technique is proposed for the suppression of co-talker interference, when the co-talker interference or noise reference signal is obtainable at a later instance as in the case of many covert forensic recordings. The corrupted speech signal and the noise reference signal are aligned using DTW and then the co-talker interference is suppressed using a dual channel adaptive filter. The second post-processing speech enhancement technique is designed for broadband noise suppression and is based on spectral subtraction but it incorporates the masking properties of the human auditory system for improved performance. Auditory masking is used to find the masking threshold, below which the noise is no longer perceivable. Only those noise components above the masking threshold are suppressed. This approach is taken to reduce any byproducts such as musical noise. The third post-processing speech enhancement technique is designed for broadband noise suppression and is based on spectral subtraction but it exploits the human auditory systems perception of frequency. Critical band analysis is used to group frequencies that are similarly perceived, which are then treated as a single entity by the enhancement technique.

Made available in DSpace on 2014-06-11T19:29:49Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-02-25Bitstream added on 2014-06-13T19:59:46Z : No. of bitstreams: 1 kanda_az_me_ilha.pdf: 1642888 bytes, checksum: 49ebcd86d5690e7be50fcdca35a52a48 (MD5)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
A proposta deste trabalho é aprimorar a performance da técnica de redução de ruído, subtração espectral baseado na relação SNR a Priori, através da implementação de dois novos parâmetros Potência de Articulação e Não-Articulação obtidas a partir de algumas técnicas psicoacústicas. Faz-se um estudo da anatomia do sistema de audição humana e algumas limitações físicas, com o objetivo de entender o princípio básico da técnica ANIQUE, que é um sistema de avaliação objetiva de voz e têm como princípio o modelamento da percepção humana da voz. Através do modelo ANIQUE são extraídas as principais técnicas psicoacústicas para obtenção dos novos parâmetros, Potência de Articulação e Não- Articulação. Procurou-se apresentar de maneira resumida o processo de equacionamento das técnicas de redução de ruído em sinais de voz e das técnicas psicoacústicas. Posteriormente são descritos todos os processos das técnicas utilizadas que foram simuladas utilizando a linguagem de programação do MatLab®, seguido das avaliações objetivas dos sinais processados pelo software PESQ, que é um programa de avaliação objetiva de voz. Os resultados mostram que a implementação das técnicas psicoacústicas foram eficazes para melhorar a performance da técnica subtração espectral baseada na relação SNR a Priori
The purpose of this work is to enhance the performance of noise reduction techniques based on spectral subtraction, which take in account the a priori signal-to-noise (SNR a Priori) to be estimated considering psychoacoustic criteria. in order to understand the basic principle of the ANIQUE, which is a psychoacoustic based technique used to evaluate the quality of speech signals, it was necessary to develop a study of the anatomy of the human hearing and some physical limitations, From the ANIQUE are extracted new parameters namely Articulation and Non-Articulation Powers, used to estimate the SNR_prio. As a result, it was obtained a new spectral based technique which was implemented in the MatLab® environment and evaluated using the objective quality measure for speech signal simulations namely PESQ. The results show that the implementation of psychoacoustic techniques were effective in enhance the performance of the spectral subtraction technique based on SNR a Priori

This diploma thesis deals with methods of noise suppression for speech recognition systems. In theoretical part are discussed basic terms of this topic and also methods for noise suppression. These are spectral subtraction, Wiener filtering, RASTA, mapping of spectrogram or algorithms based on noise estimation. In second part types of noise are analyzed, there is proposal and implementation of spectral subtraction method of noise suppression for speech recognition system. Also extensive testing of spectral subtractive algorithms in comparison with Wiener filter is conducted. Assessment of this testing is done with defined metrics, successfulness of recognition, recognition system score and signal to noise ratio.

Certains aspects de l'extraction du signal cosmologique à 21 cm à partir des observations radio, ainsi que le traitement des données interférométriques pour des observations depuis le sol et depuis l'espace ont été étudiés et sont présentés dans cette thèse. J'ai développé un modèle cohérent et à haute résolution du ciel en radio, qui peut fournir une carte complète et précise du ciel, dans la gamme de fréquence 10 MHz à 2,3 GHz, avec une résolution pouvant atteindre une minute d'arc. Le modèle inclut plusieurs sources de rayonnement diffus, en particulier le synchrotron Galactique, les sources radio brillantes du ciel, ainsi qu'un modèle des sources faibles. J'ai également mis au point une méthode pour extraire le signal 21 cm cosmologique, fortement contaminé par les émissions d'avant-plan et le bruit des récepteurs. La méthode utilise une cascade de deux filtres de Wiener, dans l'espace des fréquences d'abord, et ensuite, dans le domaine angulaire. Le premier filtre exploite les variations lentes des émissions d'avant-plan selon la fréquence, tandis que le second filtre exploite les corrélations angulaires du signal cosmologique pour filtrer le bruit des récepteurs, considéré non corrélé entre deux directions différentes. J'ai développé un nouvel algorithme d'imagerie pour les interféromètres en orbite lunaire. Un tel instrument serait idéal pour cartographier le ciel en dessous de 30 MHz; il ne serait en effet pas soumis aux perturbations ionosphériques et serait protégé des interférences électromagnétiques dues aux émissions terrestres. J'ai montré que l'utilisation de la précession du plan orbital du satellite permet de résoudre le problème de la symétrie miroir. La méthode exploite la relation de projection linéaire entre la carte du ciel et les visibilités mesurées, tant dans l'espace angulaire que dans l'espace des harmoniques sphériques pour reconstruire la carte du ciel. L'algorithme d'imagerie gère la complication due à l'ombre de la Lune se déplaçant avec le temps sur le champ de vue des antennes couvrant tout le ciel. Notons que ces effets ne sont pas pris en charge par les algorithmes d'imagerie existants tels que la W-Projection et la WStacking. Enfin, j'ai effectué une première analyse des données de l'interféromètre de transit PAON-4. J'ai évalué la performance globale du réseau en termes de température de bruit et de la réponse des antennes. J'ai pu étalonner avec succès les visibilités, en déterminant à la fois l'amplitude et la phase des termes de gain complexes, tout en corrigeant les décalages de pointage des antennes de PAON4. J'ai ensuite reconstruit la carte du ciel pour une bande de 10 degrés autour de la déclinaison de la source brillante Cygnus A, à partir du flot de données PAON-4 calibré et nettoyé, en appliquant l'algorithme de décomposition en mode m dans l'espace des harmoniques sphériques
Some aspects of extracting cosmological 21cm signal from radio observations, as well as processing of interferometric data for ground based or space born instruments have been studied and discussed in this dissertation. I have developed a high-resolution self-consistent radio whole sky model, which provides an accurate full sky maps in the frequency range from 10 MHz to 2.3 GHz, with angular resolution up to 1 arcmin. It includes bright and faint radio sources, Galactic synchrotron and Galactic freefree emissions. I have also developed a method to extract the faint cosmological 21-cm signal, heavily contaminated by foreground emissions and receiver noise. The method uses a cascade of two Wiener filters, in frequency domain and then, in angular domain. The first filter exploits the smoothness of the foreground emissions along the frequency, while the second filter exploits the angular correlations of the cosmological signal, due to the receiver noise is considered to be nearly uncorrelated between different directions. I have developed a studied the performance of a new imaging algorithm for lunar orbit interferometers. Such an instrument would be ideal for mapping the radio sky below 30 MHz, as it would be free from ionospheric perturbations, as well as electromagnetic interferences due to terrestrial emissions. I have shown that we make use of the precession of satellite orbital plane to solve the mirror symmetry problem, and exploit the linear mapping between the sky map and the measured visibilities, both in angular space and spherical harmonic space to reconstruct the sky map. The imaging algorithm handles the time-varying Moon's blockage over the whole sky field of view, which are not handled by existing imaging algorithms such as the WProjection and the W-Stacking. Finally, I have carried out a first analysis of the observational visibility data from the PAON-4 transit interferometer. I have evaluated the overall performance of the array in terms of system temperature and antenna response, and successfully calibrated the visibilities, determining both amplitude and phase of the complex gain terms, while correcting PAON-4 antennae pointing offsets. I have then reconstructed the sky map for a 10 degree strip around Cygnus A declination, from the cleaned calibrated PAON-4 data streams, applying the m-mode decomposition map-making algorithm in spherical harmonic space

碩士
元智大學
工業工程與管理學系
97
Automated inspection of assembled printed circuit boards (PCB) is a requirement to ensure the quality of the product and to decrease manufacturing cost. An assembled PCB comprises complex conductive paths with different electronic components such as integrated circuits (IC), resistors and capacitors, which make template matching the only possible approach for automated optical inspection. Current template-based defect detection techniques use simple image subtraction to identify defects on assembled PCBs which relies on accurate image alignment of the reference and inspection images. If this cannot be achieved, noise points are generated around the edges. Changes in illumination are another factor to consider since image difference will yield false detections. This study is aimed to develop three template-based approaches for defect detection and, especially, focuses on PCBs and light emitting diode (LED) chips. These methods are robust under small misalignments, changes in illumination and manufacturing variation. The first proposed method, named statistical process control (SPC) with multiple templates, uses statistical process control techniques. It takes a number of defect-free images to form a “mean” template, described by the gray-level mean and standard deviation of each individual pixel in the image. This method is faster than the simple image subtraction method, and is tolerant to noise due to misalignments, illumination changes and manufacturing product variation. The second proposed method is a kernel-based scheme. The gray-level difference between the template and the inspection image in a small neighborhood window can be efficiently calculated from a kernel function. The weighted kernel value can then be used for discriminating defect points from the background. The kernel method is also fast enough when compared to the simple image subtraction method and very responsive to detect low-contrast defects. The third method is called hybrid method which combines the SPC with multiple templates and the kernel-based method. The hybrid method takes a number of defect-free images but instead of calculating the “mean” template, it uses these images to calculate the values of the kernel weighting function. The hybrid method is computationally fast and very responsive to the detection of low-contrast defects. All these proposed methods are compared against a benchmark method which is based on image subtraction for defect detection. The benchmark method calculates the minimum value of the pixel intensities between a template image and an inspection image. The minimum value is calculated in a neighborhood window and is used to overcome minor misalignments. Tests are conducted with defect-free and defective PCBs and LED chips under different misalignments and illumination variation. In comparison with the benchmark method, all the proposed methods produce noise-free inspections results. They are computationally fast for on-line real-time defect detection and robust enough to overcome noise due to misalignments, changes in illumination and manufacturing product variation.

In speech communication systems such as voice-controlled systems, hands-free mobile telephones and hearing aids, the received signals are degraded by room reverberation and background noise. This degradation can reduce the perceived quality and intelligibility of the speech, and decrease the performance of speech enhancement and source localization. These problems are difficult to solve due to the colored and nonstationary nature of the speech signals, and features of the Room Impulse Response (RIR) such as its long duration and non-minimum phase. In this dissertation, we focus on two topics of speech enhancement and speaker localization in noisy reverberant environments. A two-stage speech enhancement method is presented to suppress both early and late reverberation in noisy speech using only one microphone. It is shown that this method works well even in highly reverberant rooms. Experiments under different acoustic conditions confirm that the proposed blind method is superior in terms of reducing early and late reverberation effects and noise compared to other well known single-microphone techniques in the literature. Time Difference Of Arrival (TDOA)-based methods usually provide the most accurate source localization in adverse conditions. The key issue for these methods is to accurately estimate the TDOA using the smallest number of microphones. Two robust Time Delay Estimation (TDE) methods are proposed which use the information from only two microphones. One method is based on adaptive inverse filtering which provides superior performance even in highly reverberant and moderately noisy conditions. It also has negligible failure estimation which makes it a reliable method in realistic environments. This method has high computational complexity due to the estimation in the first stage for the first microphone. As a result, it can not be applied in time-varying environments and real-time applications. Our second method improves this problem by introducing two effective preprocessing stages for the conventional Cross Correlation (CC)-based methods. The results obtained in different noisy reverberant conditions including a real and time-varying environment demonstrate that the proposed methods are superior compared to the conventional TDE methods.
Graduate
2015-04-23
0544
0984
saeed.mosayyebpour@gmail.com