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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Noise-free distortion pedal for electric guitars : Designing and constructing a unique distortion pedal for live playing guitarists

Almqvist, Tobias, Nordberg, David January 2019 (has links)
Guitarists using effect pedals to alter the way their guitar sounds often face the problem of loud noise when they want their guitars to be completely silent. This can be the result of using a distortion pedal which works well when you’re playing, but not when you have a brief pause. This project focuses on developing a complete prototype of a distortion pedal that has an adjustable mute circuit, also known as a noise gate, built into it. The project pedal consists of some functions which commercially available effect pedals have; 6.3 mm input jack, on and off switch, both a battery connector and a DC-jack, and adjustable knobs for the different effects like drive, tone and volume. The distortion circuit is inspired by the BOSS SD1 SUPER OVERDRIVE pedal and the muting function is constructed with Junction Field Effect Transistors.Two prototypes were constructed; one with hole mounted components, and one with surface mounted components. They both work largely as imagined. When the guitar is played, the desired distorted sound can be heard and adjusted with a drive, tone and volume control. When the guitar is not played, all unwanted noise is completely muted. A sensitivity adjusting knob allows for different signal strengths to be muted.The chosen method for muting the signal introduces a short and faint unwanted noise during the muting process. While it would have been better to eliminate this noise too, the prototype introduces something new and helpful for guitarists using distortion pedals. With more work, this way of integrating a mute circuit in distortion pedals could potentially set a new standard. / Gitarrister som använder effektpedaler för att ändra hur deras gitarr låter stöter ofta på problemet där oönskat brus uppstår när de vill att gitarren ska vara helt tyst. Detta kan vara resultatet av att använda en distortionspedal som fungerar väl när man spelar på gitarren, men inte när man tar en kort paus. Detta projekt fokuserar på att utveckla en fulländad prototyp av en distortionspedal som har en justerbar mutefunktion, också kallad en brusgrind. Pedalen har några funktioner som vanligtvis finns hos de kommersiellt tillgängliga pedalerna; 6.3 mm ingångsoch utgångskontakter, av-/påknapp, strömförsörjning via både ett batteri och vägguttag, och justerbara kontroller för förvrängning, ton och volym. Distortionskretsen är inspirerad av pedalen BOSS SD1 SUPER OVERDRIVE och mute-funktionen använder sig av Junction Field Effect Transistorer.Två prototyper konstruerades; en med hålmonterade komponenter, och en med ytmonterade komponenter. De både prototyperna fungerar i stort sett enligt de uppsatta målen. När gitarren spelas så hörs det önskade förvrängda ljudet som kan justeras med kontrollerna. När gitarren inte spelas dämpas allt oönskat brus till en ohörbar nivå. En känslighetskontroll tillåter olika signalstyrkor att tystas.Den valda metoden för mute-funktionen introducerar ett kort och svagt oönskat ljud som uppstår under själva dämpningsprocessen. Medan detta oljud är något som bör lösas så ger prototypen fortfarande något unikt och hjälpsamt för gitarrister. Med mer arbete skulle detta kunna förbli en ny standard inom distortionspedaler.
2

Sparse representations over learned dictionary for document analysis / Présentations parcimonieuses sur dictionnaire d'apprentissage pour l'analyse de documents

Do, Thanh Ha 04 April 2014 (has links)
Dans cette thèse, nous nous concentrons sur comment les représentations parcimonieuses peuvent aider à augmenter les performances pour réduire le bruit, extraire des régions de texte, reconnaissance des formes et localiser des symboles dans des documents graphiques. Pour ce faire, tout d'abord, nous donnons une synthèse des représentations parcimonieuses et ses applications en traitement d'images. Ensuite, nous présentons notre motivation pour l'utilisation de dictionnaires d'apprentissage avec des algorithmes efficaces pour les construire. Après avoir décrit l'idée générale des représentations parcimonieuses et du dictionnaire d'apprentissage, nous présentons nos contributions dans le domaine de la reconnaissance de symboles et du traitement des documents en les comparants aux travaux de l'état de l'art. Ces contributions s'emploient à répondre aux questions suivantes: La première question est comment nous pouvons supprimer le bruit des images où il n'existe aucune hypothèse sur le modèle de bruit sous-jacent à ces images ? La deuxième question est comment les représentations parcimonieuses sur le dictionnaire d'apprentissage peuvent être adaptées pour séparer le texte du graphique dans des documents? La troisième question est comment nous pouvons appliquer la représentation parcimonieuse à reconnaissance de symboles? Nous complétons cette thèse en proposant une approche de localisation de symboles dans les documents graphiques qui utilise les représentations parcimonieuses pour coder un vocabulaire visuel / In this thesis, we focus on how sparse representations can help to increase the performance of noise removal, text region extraction, pattern recognition and spotting symbols in graphical documents. To do that, first of all, we give a survey of sparse representations and its applications in image processing. Then, we present the motivation of building learning dictionary and efficient algorithms for constructing a learning dictionary. After describing the general idea of sparse representations and learned dictionary, we bring some contributions in the field of symbol recognition and document processing that achieve better performances compared to the state-of-the-art. These contributions begin by finding the answers to the following questions. The first question is how we can remove the noise of a document when we have no assumptions about the model of noise found in these images? The second question is how sparse representations over learned dictionary can separate the text/graphic parts in the graphical document? The third question is how we can apply the sparse representation for symbol recognition? We complete this thesis by proposing an approach of spotting symbols that use sparse representations for the coding of a visual vocabulary

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