The dissertation thesis focuses on objective assessment and reduction of disturbing background noise in a musical signal. In this work, a new algorithm for the assessment of background noise audibility is proposed. The listening tests performed show that this new algorithm better predicts the background noise audibility than the existing algorithms do. An advantage of this new algorithm is the fact that it can be used even in the case of a general audio signal and not only musical signal, i.e. in the case when the audibility of one sound on the background of another sound is assessed. The existing algorithms often fail in this case. The next part of the dissertation thesis deals with an adaptive segmentation scheme for the segmentation of long-term musical signals into short segments of different lengths. A new adaptive segmentation scheme is then introduced here. It has been shown that this new adaptive segmentation scheme significantly improves the subjectively perceived quality of the musical signal from the output of noise reduction systems which use this new adaptive segmentation scheme. The quality improvement is better than that achieved by other segmentation schemes tested.

This thesis concerns with processing of embedded terminals’ images and their classification. There is problematics of moire noise reduction thought filtration in frequency domain and the image normalization for further processing analyzed. Keypoints detectors and descriptors are used for image classification. Detectors FAST and Harris corner detector and descriptors SURF, BRIEF and BRISK are emphasized as well as their evaluation in terms of potential contribution to this work.

Modular construction of housing is an industry on the rise. The prefabrication of construction modules gives a greater control over material consumption, promotes standardization, and lessens the disturbance during the construction time in the field. Lindbäcks Bygg AB prefabricates housing modules in their factory at Haraholmen, Piteå. The modules are then transported to construction sites all over the country and assembled. Lindbäcks Bygg AB has made the design choice to focus on lightweight wooden constructions for their modules, which comes with its own set of challenges. The urban densification of our city regions has led to more and more residential building being planned and constructed in areas that are submitted to high noise levels from surrounding traffic. This puts a great strain on the sound reduction of the building’s façade wall construction, something that has proven to be a problem with lightweight wooden walls. The aim of this master thesis project has been to test different solutions for lightweight façade wall constructions, and to ultimately find one or more solution that meet the requirements of the Swedish standards and regulations, as well as the needs of the tenants, and that can be implemented by Lindbäcks Bygg AB for manufacturing and assembly. The project has therefore been carried out in two parts, one theoretical and one practical. The theoretical part has verified the problem with traffic noise levels in residential buildings and identified the critical areas of the current façade construction. The theoretical work led to a list of defining factors for the sound reduction, which in turn worked as a basis for the ideation process.  As a result, the theoretical part led to a number of different construction concepts for façade walls. The concepts were first tested theoretically through modelling and calculation, and verified with the acousticians at Tyréns Sverige AB and the engineers of Lindbäcks Bygg AB. The most promising concepts were sent on to be manufactured. The practical part of this master thesis project was carried out mainly as acoustical measurements and the subsequent analysis, where the manufactured façade wall constructions were mounted on a free-standing module. The result of the practical part, and of the master thesis project, were four different façade wall constructions that met the requirements for noise reduction, which were presented to Lindbäcks Bygg AB and Tyréns Sverige AB. The construction most easily implemented is a construction with an airgap between the wall panels, and this construction will now have to be structurally verified and adapted for the factory, before being utilized in future building projects. / Modulärt byggande av bostäder är en bransch på frammarsch. Prefabricering av byggnadsmoduler ger en större kontroll över materialåtgången, främjar standardisering och minskar störningarna under byggtiden i fält. Lindbäcks Bygg AB prefabricerar bostadsmoduler i sin fabrik på Haraholmen, Piteå. Modulerna transporteras sedan till byggarbetsplatser över hela landet och monteras till bostadskomplex. Lindbäcks Bygg AB har gjort designvalet att fokusera på lätta träkonstruktioner för sina moduler, vilket kommer med sin egen uppsättning utmaningar. Förtätningen av våra stadsregioner har lett till att allt mer bostadsbyggande planeras och uppförs i områden som utsätts för höga bullernivåer från omgivande trafik. Detta utsätter byggnadens fasadväggskonstruktion för en stor akustisk belastning, något som har visat sig vara ett problem med lätta träväggar. Syftet med detta examensarbete har varit att testa olika lösningar för lätta fasadväggskonstruktioner, och att i slutändan hitta en eller flera lösningar som uppfyller kraven i svenska standarder och föreskrifter samt hyresgästernas behov, och som kan implementeras av Lindbäcks Bygg AB för tillverkning och montering. Projektet har därför genomförts i två delar, en teoretisk och en praktisk. Den teoretiska delen har verifierat problemet med trafikbullernivåer i bostadshus och identifierat kritiska områden i den nuvarande fasadkonstruktionen. Det teoretiska arbetet ledde fram till en lista med kritiska faktorer för ljudreduktionen, som i sin tur fungerade som grund för designprocessen. Som ett resultat ledde den teoretiska delen till en rad olika konstruktionskoncept för fasadväggar. Koncepten testades först teoretiskt genom modellering och beräkning, och verifierades av akustikerna på Tyréns Sverige AB och ingenjörerna på Lindbäcks Bygg AB. De mest lovande koncepten skickades vidare för tillverkning. Den praktiska delen av detta examensarbete genomfördes främst som akustiska mätningar och efterföljande analys, där de tillverkade fasadväggkonstruktionerna monterades på en fristående modul. Resultatet av den praktiska delen, och av examensarbetet, blev fyra olika fasadväggskonstruktioner som uppfyller kraven för ljudreduktion, vilka presenterades för Lindbäcks Bygg AB och Tyréns Sverige AB. Den konstruktion som är enklast att implementera är en konstruktion med luftspalt mellan väggpanelerna, och denna konstruktion kommer nu att behöva verifieras med avseende på ex. hållfasthet och anpassas för fabriken, innan den kan användas i framtida byggprojekt.

Este documento describe la elaboración de un concreto poroso con fibra de polipropileno (CPF). Se propone este material como una alternativa para reducir la contaminación sonora causada por la interacción entre el neumático y calzada. La propuesta consta de dos etapas de estudio: Elaboración de un concreto poroso y evaluación acústica mediante un modelo matemático microestructural. En la primera etapa, se evaluó dieciséis diseños, los cuales de cada mezcla se realizaron doce muestras para posteriormente ser sometidas al ensayo de resistencia a la compresión. Así se obtuvieron un total de 192 probetas que fueron evaluadas a los 7, 14 y 28 días de tiempo de curado. Los diseños están compuestos con agregados recomendados por el ACI 522R, agregados de HUSO 8 y agregados de HUSO 67, de los cuales se pretende seleccionar un concreto poroso que pueda cumplir las especificaciones mínimas de un pavimento manteniendo un porcentaje de vacíos óptimo para su posterior evaluación acústica. De los diseños obtenidos se estudió la influencia de la porosidad y porcentaje de vacíos en las propiedades mecánicas del concreto. De esta manera, se encontró una dosificación de CPF adecuado para la propuesta con una relación agua cemento de 0.36, fibra tipo I, 0% de aditivo y piedra de HUSO 8, con el cual se obtuvo una resistencia a la compresión de 296.37 kg/cm2, con el cual se cumplía los requisitos mínimos de resistencia del concreto para su aplicación a un pavimento. En la segunda etapa, se pretende obtener resultados del coeficiente de absorción acústica mediante una simulación numérica basados en la teoría microestructural de Neithalath. Este modelo fue aplicado a los diseños de CPF obtenidos en la primera parte, teniendo en cuenta el tamaño, forma de los agregados y la relación de vacíos. El desarrollo de este modelo matemático y su correlación con las mediciones físicas permiten la predicción del coeficiente máximo absorción acústica de un CPF basado en las características geométricas de la estructura de poros. Los diseños óptimos sometidos a la simulación numérica obtuvieron un coeficiente de absorción acústica de 0.79 y 0.63 para agregados de HUSO 8 y HUSO 67 respectivamente. De esta manera, con la investigación y experimentación se llegó a la conclusión que el CPF permite reducir el ruido generado por la interacción del neumático y la calzada, con lo cual se verificó un nuevo beneficio del concreto poroso en la ingeniería de pavimentos. / This document describes the fabrication of a polypropylene fiber (CPF) porous concrete. This material is proposed as an alternative to reduce noise pollution caused by the interaction between the tire and road. The proposal consists of two stages of study: Preparation of a porous concrete and acoustic evaluation using a microstructural mathematical model. In the first stage, sixteen designs were evaluated, of which twelve samples were made from each mixture and subsequently subjected to the compression resistance test. Thus, a total of 192 specimens were obtained, which were evaluated at 7, 14 and 28 days of curing time. The designs are composed of aggregates recommended by ACI 522R, aggregates of HUSO 8 and aggregates of HUSO 67, of which it is intended to select a porous concrete that can meet the minimum specifications of a pavement while maintaining an optimal percentage of voids for its subsequent evaluation. acoustics. The influence of porosity and void percentage on the mechanical properties of concrete was studied from the designs obtained. In this way, a suitable CPF dosage was found for the proposal with a water-cement ratio of 0.36, type I fiber, 0% additive and HUSO 8 stone, with which a compressive strength of 296.37 kg / cm2, with which the minimum strength requirements of concrete for its application to a pavement were met. In the second stage, it is intended to obtain results of the acoustic absorption coefficient by means of a numerical simulation based on the microstructural theory of Neithalath. This model was applied to the CPF designs obtained in the first part, taking into account the size, shape of the aggregates and the ratio of voids. The development of this mathematical model and its correlation with physical measurements allow the prediction of the maximum acoustic absorption coefficient of a CPF based on the geometric characteristics of the pore structure. The optimal designs submitted to the numerical simulation obtained a sound absorption coefficient of 0.79 and 0.63 for aggregates of HUSO 8 and HUSO 67 respectively. In this way, with the investigation and experimentation, it was concluded that the CPF allows to reduce the noise generated by the interaction of the tire and the road, which verified a new benefit of porous concrete in pavement engineering. / Tesis

Die Raman-Spektroskopie ist eine optische Messtechnik, die in der Lage ist, spektroskopische Information zu liefern, welche molekülspezifisch und einzigartig in Bezug auf die Eigenschaften der untersuchten Spezies sind. Sie ist ein unverzichtbares analytisches Instrument, das Anwendung in verschiedenen Bereichen findet, wie etwa der Medizin oder der in situ Beobachtung von chemischen Prozessen. Wegen ihren Eigenschaften, wie der hohen Spezifität und der Möglichkeit von Tracer-freien Messung, hat die Raman-Spektroskopie die Tumordiagnostik stark beeinflusst. Aufgrund einer äußerst starken Beeinflussung der Raman-Spektren durch Hintergrundsignale, ist das Isolieren und Interpretieren von Raman-Spektren eine große Herausforderung. Im Rahmen dieser Arbeit wurden verschiedene Ansätze der Spektrenbearbeitung entwickelt, die benötigt werden um Raman-Spektren aus verrauschten und stark mit Hintergrundsignalen behafteten Rohspektren zu extrahieren. Diese Ansätze beinhalten im Speziellen eine auf dem Vector-Casting basierende Methode zur Rauschminimierung und eine auf dem deep neural networks basierende Methoden zur Entfernung von Rauschen und Hintergrundsignalen. Verschiedene neuronale Netze wurden mittels simulierter Spektren trainiert und an experimentell gemessenen Spektren evaluiert. Die im Rahmen dieser Arbeit vorgeschlagenen Ansätze wurden mit alternativen Methoden auf dem aktuellen Stand der Entwicklung unter Zuhilfenahme von verschiedenen Signal-Rausch-Verhältnissen, Standardabweichungen und dem Structural Similarity Index verglichen. Die hier entwickelten Ansätze zeigen gute Ergebnisse und sind bisher bekannten Methoden überlegen, vor allem für Raman-Spektren mit einem niedrigem Signal-Rausch-Verhältnis und extrem starken Fluoreszenz-Hintergrund. Zusätzlich erfordern die auf Deep Neural Networks basierten Methoden keinerlei menschliches Eingreifen. Die Motivation hinter dieser Arbeit ist die Verbesserung der Raman-Spektroskopie, vor allem der Shifted-Excitation Raman Difference Spectroscopy (SERDS) hin zu einem noch besseren Instrument in der Prozessanalytik und Tumordiagnostik. Die Integration der oben genannten Ansätze zur Spektrenbearbeitung von SERDS in Kombination mit Methoden des maschinellen Lernens ermöglichen es, physiologische Schleimhaut, nicht-maligne Läsionen und orale Plattenepithelkarzinome mit einer Genauigkeit zu unterscheiden, die bisherigen Methoden überlegen ist. Die spezifischen Merkmale in den bearbeiteten Raman-Spektren können verschiedenen chemischen Zusammensetzungen in den jeweiligen Geweben zugeordnet werden. Die Übertragbarkeit auf einen ähnlichen Ansatz zur Erkennung von Brusttumoren wurde überprüft. Die bereinigten Raman-Spektren von normalem Brustgewebe, Fibroadenoma und invasiven Mammakarzinom konnten mithilfe der spektralen Eigenschaften von Proteinen, Lipiden und Nukleinsäuren unterschieden werden. Diese Erkenntnisse lassen das Potential von SERDS in Kombination mit Ansätzen des maschinellen Lernens als universelles Werkzeug zur Tumordiagnose erkennen.:Versicherung Abstract Zusammenfassung der Ergebnisse der Dissertation Table of Contents Abbreviations and symbols 1 Introduction 2 State of the art of the purification of Raman spectra 2.1 Experimental methods for the enhancement of the signal-to-background ratio and the signal-to-noise ratio 2.2 Mathematical methods for the extraction of pure Raman spectra from raw spectra 2.3 Raman based cancer diagnostics 2.4 Neural networks for the evaluation of Raman spectra 2.5 Objective 3 Application relevant fundaments 3.1 Basics of Raman spectroscopy 3.2 Simulation of raw Raman spectra 3.3 Shifted-excitation Raman difference Spectroscopy 3.4 Raman experimental setup 3.5 Mathematical method for Raman spectra refinement 3.6 Deep neural networks 4 Summary of the published results 4.1 A shifted-excitation Raman difference spectroscopy evaluation strategy for the efficient isolation of Raman spectra from extreme fluorescence interference 4.2 Vector casting for noise reduction 4.3 Refinement of spectra using a deep neural network; fully automated removal of noise and background 4.4 Breast Tumor Analysis using Shifted Excitation Raman difference Spectroscopy 4.5 Optical diagnosis of clinically apparent lesions of oral cavity by label free Raman spectroscopy Conclusion / Raman spectroscopy is an optical measurement technique able to provide spectroscopic information that is molecule-specific and unique to the nature of the specimen under investigation. It is an invaluable analytical tool that finds application in several fields such as medicine and in situ chemical processing. Due to its high specificity and label-free features, Raman spectroscopy greatly impacted cancer diagnostics. However, retrieving and interpreting the Raman spectrum that contains the molecular information is challenging because of extreme background interference. I have developed various spectra-processing approaches required to purify Raman spectra from noisy and heavily background interfered raw Raman spectra. In detail, these are a new noise reduction method based on vector casting and new deep neural networks for the efficient removal of noise and background. Several neural network models were trained on simulated spectra and then tested with experimental spectra. The here proposed approaches were compared with the state-of-the-art techniques via different signal-to-noise ratios, standard deviation, and the structural similarity index metric. The methods presented here perform well and are superior in comparison to what has been reported before, especially at small signal-to-noise ratios, and for extreme fluorescence interfered raw Raman spectra. Furthermore, the deep neural network-based methods do not rely on any human intervention. The motivation behind this study is to make Raman spectroscopy, especially the shifted-excitation Raman difference spectroscopy (SERDS), an even better tool for process analytics and cancer diagnostics. The integration of the above-mentioned spectra-processing approaches into SERDS in combination with machine learning tools enabled the differentiation between physiological mucosa, non-malignant lesions, and oral squamous cell carcinomas with high accuracy, above the state of the art. The distinguishable features obtained in the purified Raman spectra are assignable to different chemical compositions of the respective tissues. The feasibility of a similar approach for breast tumors was also investigated. The purified Raman spectra of normal breast tissue, fibroadenoma, and invasive carcinoma were discriminable with respect to the spectral features of proteins, lipids, and nucleic acid. These findings suggest the potential of SERDS combined with machine learning techniques as a universal tool for cancer diagnostics.:Versicherung Abstract Zusammenfassung der Ergebnisse der Dissertation Table of Contents Abbreviations and symbols 1 Introduction 2 State of the art of the purification of Raman spectra 2.1 Experimental methods for the enhancement of the signal-to-background ratio and the signal-to-noise ratio 2.2 Mathematical methods for the extraction of pure Raman spectra from raw spectra 2.3 Raman based cancer diagnostics 2.4 Neural networks for the evaluation of Raman spectra 2.5 Objective 3 Application relevant fundaments 3.1 Basics of Raman spectroscopy 3.2 Simulation of raw Raman spectra 3.3 Shifted-excitation Raman difference Spectroscopy 3.4 Raman experimental setup 3.5 Mathematical method for Raman spectra refinement 3.6 Deep neural networks 4 Summary of the published results 4.1 A shifted-excitation Raman difference spectroscopy evaluation strategy for the efficient isolation of Raman spectra from extreme fluorescence interference 4.2 Vector casting for noise reduction 4.3 Refinement of spectra using a deep neural network; fully automated removal of noise and background 4.4 Breast Tumor Analysis using Shifted Excitation Raman difference Spectroscopy 4.5 Optical diagnosis of clinically apparent lesions of oral cavity by label free Raman spectroscopy Conclusion

Image classification using data from neuromorphic vision sensors is a challenging task that affects the use of dynamic vision sensor cameras in real- world environments. One impeding factor is noise in the neuromorphic event stream, which is often generated by the dynamic vision sensors themselves. This means that effective noise filtration is key to successful use of event- based data streams in real-world applications. In this paper we harness two feature representations of neuromorphic vision data in order to apply conventional frame-based image tools on the neuromorphic event stream. We use a standard noise filter to evaluate the effectiveness of noise filtration using a popular dataset converted to neuromorphic vision data. The two feature representations are the best-of-class standard Histograms of Averaged Time Surfaces (HATS) and a simpler grid matrix representation. To evaluate the effectiveness of the noise filter, we compare classification accuracies using various noise filter windows at different noise levels by adding additional artificially generated Gaussian noise to the dataset. Our performance metrics are reported as classification accuracy. Our results show that the classification accuracy using frames generated with HATS is not significantly improved by a noise filter. However, the classification accuracy of the frames generated with the more traditional grid representation is improved. These results can be refined and tuned for other datasets and may eventually contribute to on- the- fly noise reduction in neuromorphic vision sensors. / Händelsekameror är en ny typ av kamera som registrerar små ljusförändringar i kamerans synfält. Sensorn som kameran bygger på är modellerad efter näthinnan som finns i våra ögon. Näthinnan är uppbyggd av tunna lager av celler som omvandlar ljus till nervsignaler. Eftersom synsensorer efterliknar nervsystemet har de getts namnet neuromorfiska synsensorer. För att registrera små ljusförändringar måste dessa sensorer vara väldigt känsliga vilket även genererar ett elektroniskt brus. Detta brus försämrar kvalitén på signalen vilket blir en förhindrande faktor när dessa synsensorer ska användas i praktiken och ställer stora krav på att hitta effektiva metoder för brusredusering. Denna avhandling undersöker två typer av digitala framställningar som omvandlar signalen ifrån händelsekameror till något som efterliknar vanliga bilder som kan användas med traditionella metoder för bildigenkänning. Vi undersöker brusreduseringens inverkan på den övergripande noggrannhet som uppnås av en artificiell intelligens vid bildigenkänning. För att utmana AIn har vi tillfört ytterligare normalfördelat brus i signalen. De digitala framställningar som används är dels histogram av genomsnittliga tidsytor (eng. histograms of averaged time surfaces) och en matrisrepresentation. Vi visar att HATS är robust och klarar av att generera digitala framställningar som tillåter AIn att bibehålla god noggrannhet även vid höga nivåer av brus, vilket medför att brusreduseringens inverkan var försumbar. Matrisrepresentationen gynnas av brusredusering vid högre nivåer av brus.

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This thesis focuses on image processing using wavelet transform. The usage of wavelet transform is analysed especially for image compression and image noise reduction purposes. The analysis describes in detail aspects and application of the following wavelet transform methods: CWT, DWT, DTWT, 2D DWT. The thesis further explains the meaning of the mother wavelet and studies certain specific kinds of wavelets, kinds of thresholding and its purposes and also touches on the JPEG2000 standard. Mathematica and Sage software packages were used to design algorithms for image compression and image noise reduction, utilising relevant wavelet transform findings. The concluding part of the thesis compares the two software packages and results obtained using different algorithms.

We explore the applicability of local polynomial approximation of signals for noise suppression. In the context of data regression, Savitzky and Golay showed that least-squares approximation of data with a polynomial of fixed order, together with a constant window length, is identical to convolution with a finite impulse response filter, whose characteristics depend entirely on two parameters, namely, the order and window length. Schafer’s recent article in IEEE Signal Processing Magazine provides a detailed account of one-dimensional Savitzky-Golay (SG) filters. Drawing motivation from this idea, we present an elaborate study of two-dimensional SG filters and employ them for image denoising by optimizing the filter response to minimize the mean-squared error (MSE) between the original image and the filtered output. The key contribution of this thesis is a method for optimal selection of order and window length of SG filters for denoising images. First, we apply the denoising technique for images contaminated by additive Gaussian noise. Owing to the absence of ground truth in practice, direct minimization of the MSE is infeasible. However, the classical work of C. Stein provides a statistical method to overcome the hurdle. Based on Stein’s lemma, an estimate of the MSE, namely Stein’s unbiased risk estimator (SURE), is derived, and the two critical parameters of the filter are optimized to minimize the cost. The performance of the technique improves when a regularization term, which penalizes fast variations in the estimate, is added to the optimization cost. In the next three chapters, we focus on non-Gaussian noise models. In Chapter 3, image degradation in the presence of a compound noise model, where images are corrupted by mixed Poisson-Gaussian noise, is addressed. Inspired by Hudson’s identity, an estimate of MSE, namely Poisson unbiased risk estimator (PURE), which is analogous to SURE, is developed. Combining both lemmas, Poisson-Gaussian unbiased risk estimator (PGURE) minimization is performed to obtain the optimal filter parameters. We also show that SG filtering provides better lowpass approximation for a multiresolution denoising framework. In Chapter 4, we employ SG filters for reducing multiplicative noise in images. The standard SG filter frequency response can be controlled along horizontal or vertical directions. This limits its ability to capture oriented features and texture that lie at other angles. Here, we introduce the idea of steering the SG filter kernel and perform mean-squared error minimization based on the new concept of multiplicative noise unbiased risk estimation (MURE). Finally, we propose a method to robustify SG filters, robustness to deviation from Gaussian noise statistics. SG filters work on the principle of least-squares error minimization, and are hence compatible with maximum-likelihood (ML) estimation in the context of Gaussian statistics. However, for heavily-tailed noise such as the Laplacian, where ML estimation requires mean-absolute error minimization in lieu of MSE minimization, standard SG filter performance deteriorates. `1 minimization is a challenge since there is no closed-form solution. We solve the problem by inducing the `1-norm criterion using the iteratively reweighted least-squares (IRLS) method. At every iteration, we solve an l`2 problem, which is equivalent to optimizing a weighted SG filter, but, as iterations progress, the solution converges to that corresponding to `1 minimization. The results thus obtained are superior to those obtained using the standard SG filter.

Ce travail tente d'analyser la réponse d'une jet axisymétrique turbulent à une excitation fluidique stationnaire et instationnaire lorsque le contenu fréquentiel et aziumutal (!,m) de la perturbation est maîtrisé. Le dispositif de contrôle utilisé est composé de 16 microjets ronds répartis sur le bord de fuite de la tuyère. L'utilisation des microjets provoque une réduction du champ acoustique rayonné (particulièrement pour le cas de contrôle stationnaire). Le champ aérodynamique est ensuite sondé grâce à des mesures fil chaud et PIV stéréoscopique résolue en temps. L'excitation instationnaire permet d'utiliser les moyennes de phase afin d'effectuer une décomposition triple du champ de vitesse. L'étude de la composante cyclique de la “réponse du jet” montre une synchronisation spatio-temporelle importante sur une grande étendue spatiale. En d'autres mots, le forçage a une grande autorité déterministe sur l'écoulement. De plus, la comparaison de la composante cyclique de la réponse du jet avec la théorie de la stabilité linéaire indique qu'il existe des ondes d'instabilité hydrodynamique au sein du jet. L'analyse du jet contrôlé par injection fluidique stationnaire montre ensuite comment l'effet du contrôle peut être expliqué par la déformation du champ moyen conduisant à la réduction du taux de croissance des ondes d'instabilité dans le jet. Cette déformation est dûe à l'introduction d'un couple de paramètre (nombre d'onde/fréquences) pour lequel le champ moyen de l'écoulement est stable. La réponse du jet étant turbulente, cela implique que les tensions de Reynolds déforment le champ moyen de manière à ce que les modes les plus instables aient des taux de croissance plus faibles. / This work investigates the response of an axisymetric turbulent jet to steady and unsteady fluidic florcing where the azimuthal wavenumber-frequency (!,m) content of the perturbation is well known. The control setup is composed of 16 round microjets azimutally distributed around the nozzle lip. Such actuation can lead to a decrease in the acoustic energy radiated by the jet (especially for the steady case). The aerodynamic fied is investigated using hotwire measurements and time-resolved stereoscopic PIV. Using the unsteady forcing, phase-averaging is possible, and this allows the implementation of a triple decomposition of the measurements. Examination of the cyclic component of the flow response shows that a non-negligible phase-locked fluctuation is obtained over a large spatial extent, in other words, the actuation has good deterministic control authority over the flow. Furthermore, comparison of the cyclic component of the flow response with Linear Stability Theory supports the idea that the jet response comprises linear hydrodynamic instability waves. Subsequent analysis of jets controlled by steady fluidic actuation shows how the control effect can be explained by a mean-flow modification that leads to the reduction of instability-wave growth rates ; the mean flow modification is argued to be due to the introduction of azimuthal wavenumber-frequency pairs to which the mean flow is stable. The response is therefore turbulent, and involves Reynolds stresses which deform the mean-field such that the most unstable modes have lower growth rates.