Podcasts about wavelets

Ever wondered how hallucinogens like Salvia Divinorum affect the brain? In this episode, Neurofeedback legend Jay Gunkelman shares insights from a groundbreaking EEG study that captured 10-second, 1000-microvolt brain waves during a live altered state experiment. We also dive deep into the neurochemistry of consciousness, the science of near-death experiences, and why EEG analysis needs an upgrade. Plus, what does modern neuroscience say about autism, epileptiform content, and brain feedback loops?

Slide 1: Its the middle of the night. The Lento box is recording alone, tied to a cold, stark railing, that descends down the seawall into the water. Its an ear-witness to the nocturnal sound of this estuary place. East of Burnham-on-Crouch, facing due south, across the river, to Wallasea Island on the other side. There's a bare wind, and the tide is out. Out, but on the turn. On the turn, and rising.  Slide 2: An hour later. Still the bare wind buffeting. The water's come up fast. Is within fifteen yards of the box. Estuary birds pass at distance. Halyards of nearby yachts tink, as they sway on their moorings. All there. All subtle.  Slide 3: Two hours later. The water's still rising. Up and up the seawall. Now up the steepest stretch. Within a few yards of the box. Waves. Heard at close quarters. Heard bobbling, over the many ridged joins that make up the seawall.  Slide 4: Another hour. And no more rising. This is the high tide. Water within an arms length of the microphone box. The wind has softened. The waves are full of themselves. Full, and falling over each other.  Slide 5: Half an hour more. This high water seems always to have been. But the waves have changed. Changed into wavelets. Now chopping at the boundary of the seawall. Chopping and moving from right to left. To the left is west. It indicates the tide has turned. Mid-stream the water will be bobbly. Bouncy water that water people know means everything is not about to change, but has changed already.  Slide 6: Just ten minutes later and this world is a very different place. Different because beyond all the chopping and bobbling wavelets, is a vast body of water that has, in its entirity, changed direction. It's silently moving not from left to right of scene, but from right back to left.   Slide 7: The water, receding. The high tide, passed. Wavelets, shrunk, to the size of fingertips. Rippling fingertips, playing along the ridged surface of the seawall. And fine, tiny, sharp sounds too. Of vegetation. Popping and drying in this new air. What's opened up again is the wide soundscape of this place. this panoramic tidal place. So vast and empty. Under an ink black sky. With the warm glow of a ship's engine. Docked, far right of scene, at the terminal in Burnham-on-Crouch. Sometimes heard to the keen ear, at this distance only ever fleetingly, are the night patrolling curlews.  * We made this recording several years ago in August. A night when heavy rain and squally weather fronts were moving inland from the North Sea. This audio has waited on a hard drive to have its day. We hope you enjoy listening to these scenes of the changing tide. The scenes are taken from a four hour segment which are presented in sequence, to portray the dramatic changes in the soundscape heard from the same point on the seawall. 

It's a still summer's evening on a beach on the Isle of Wight. It's still very warm and the heat seems to affect the sea itself. Wavelets sweep lazily onto the beach and then flop and sink into the shingle. BBC Countryfile Magazine's Sound Escapes are a weekly audio postcard from the countryside to help you relax and transport you somewhere beautiful, wherever you happen to be. Recorded by Jack Bateman, presented by Fergus Collins. Email the Plodcast team – and send your sound recordings of the countryside – to: editor@countryfile.com Learn more about your ad choices. Visit podcastchoices.com/adchoices

The ferry's powerful engine rumbles as the vessel steers into the harbor. Wavelets slap the hull when the boat enters the widest part of the channel. Cool, salty breezes feel refreshing, adding to the delight of being out on the water. Gulls wheel overhead, their high-pitched cries piercing the aquamarine sky. The combination of wind, waves, and sunshine is healing - melting away stress, leaving you feeling relaxed and peaceful. ---

Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses by Aymeric Galan et al. on Wednesday 23 November Modeling the mass distribution of galaxy-scale strong gravitational lenses is a task of increasing difficulty. The high-resolution and depth of imaging data now available render simple analytical forms ineffective at capturing lens structures spanning a large range in spatial scale, mass scale, and morphology. In this work, we address the problem with a novel multiscale method based on wavelets. We tested our method on simulated Hubble Space Telescope (HST) imaging data of strong lenses containing the following different types of mass substructures making them deviate from smooth models: (1) a localized small dark matter subhalo, (2) a Gaussian random field (GRF) that mimics a nonlocalized population of subhalos along the line of sight, and (3) galaxy-scale multipoles that break elliptical symmetry. We show that wavelets are able to recover all of these structures accurately. This is made technically possible by using gradient-informed optimization based on automatic differentiation over thousands of parameters, which also allow us to sample the posterior distributions of all model parameters simultaneously. By construction, our method merges the two main modeling paradigms - analytical and pixelated - with machine-learning optimization techniques into a single modular framework. It is also well-suited for the fast modeling of large samples of lenses. All methods presented here are publicly available in our new Herculens package. arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2207.05763v2

Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses by Aymeric Galan et al. on Tuesday 22 November Modeling the mass distribution of galaxy-scale strong gravitational lenses is a task of increasing difficulty. The high-resolution and depth of imaging data now available render simple analytical forms ineffective at capturing lens structures spanning a large range in spatial scale, mass scale, and morphology. In this work, we address the problem with a novel multiscale method based on wavelets. We tested our method on simulated Hubble Space Telescope (HST) imaging data of strong lenses containing the following different types of mass substructures making them deviate from smooth models: (1) a localized small dark matter subhalo, (2) a Gaussian random field (GRF) that mimics a nonlocalized population of subhalos along the line of sight, and (3) galaxy-scale multipoles that break elliptical symmetry. We show that wavelets are able to recover all of these structures accurately. This is made technically possible by using gradient-informed optimization based on automatic differentiation over thousands of parameters, which also allow us to sample the posterior distributions of all model parameters simultaneously. By construction, our method merges the two main modeling paradigms - analytical and pixelated - with machine-learning optimization techniques into a single modular framework. It is also well-suited for the fast modeling of large samples of lenses. All methods presented here are publicly available in our new Herculens package. arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2207.05763v2

Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses by Aymeric Galan et al. on Tuesday 22 November Modeling the mass distribution of galaxy-scale strong gravitational lenses is a task of increasing difficulty. The high-resolution and depth of imaging data now available render simple analytical forms ineffective at capturing lens structures spanning a large range in spatial scale, mass scale, and morphology. In this work, we address the problem with a novel multiscale method based on wavelets. We tested our method on simulated Hubble Space Telescope (HST) imaging data of strong lenses containing the following different types of mass substructures making them deviate from smooth models: (1) a localized small dark matter subhalo, (2) a Gaussian random field (GRF) that mimics a nonlocalized population of subhalos along the line of sight, and (3) galaxy-scale multipoles that break elliptical symmetry. We show that wavelets are able to recover all of these structures accurately. This is made technically possible by using gradient-informed optimization based on automatic differentiation over thousands of parameters, which also allow us to sample the posterior distributions of all model parameters simultaneously. By construction, our method merges the two main modeling paradigms - analytical and pixelated - with machine-learning optimization techniques into a single modular framework. It is also well-suited for the fast modeling of large samples of lenses. All methods presented here are publicly available in our new Herculens package. arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2207.05763v2

Christie Mahaffey is one of the founders and partners of Foundation Brewing Company. Her science background includes biology, human ecology, data analysis, and an interdisciplinary PhD in mechanical engineering and materials science. Christie uses all of these areas in her work at Foundation Brewing. The 2022 Maine Science Festival Call for Ideas is open! (https://www.mainesciencefestival.org/call-for-ideas).Tickets are now on sale for the 2022 Headliner: The Warming Sea. (thewarmingsea.me) - March 19, 2022, 7pm, Collins Center for the Arts.~~~~~~The Maine Science Podcast is a production of the Maine Science Festival. It was recorded at Discovery Studios, at the Maine Discovery Museum, in Bangor, ME. Edited and produced by Kate Dickerson; production support by Maranda Bouchard and social media support from Next Media. The Discover Maine theme was composed and performed by Nick Parker.  If you want to support the Maine Science Podcast and/or the Maine Science Festival, you can do so at our website mainesciencefestival.org either at our donation page OR by getting some MSF merchandise through our online store.  Find us online:Website - Maine Science FestivalMaine Science Festival on social media: Facebook    Twitter     InstagramMaine Science Podcast on social media: Facebook    Twitter     InstagramMaine Science Festival Store - https://bit.ly/MSF-storecontact us: podcast@mainesciencefestival.org© 2021 Maine Science Festival

Welcome to the The Food Professor podcast episode 9, I'm Michael LeBlanc, and I'm Sylvain Charlebois!Back to School Professor!  How will your work life be different, what does university life look like for your students, and what does that mean for the foodservice industry that takes care of feeding tens of thousands of students coast-to-coast?Winter is coming. Patio season is almost over for most of Canada - what comes next for the restaurant industry: https://www.cbc.ca/news/business/patio-season-over-restaurants-pandemic-1.5712856  .  The news keeps getting worse for the sector (https://www.cdc.gov/mmwr/volumes/69/wr/mm6936a5.htm?s_cid=mm6936a5_w) talk about how the restaurant economy can be saved, or at least is worth saving and what New Brunswick is doing to help: https://www.retail-insider.com/retail-insider/2020/9/dont-want-to-save-the-restaurant-industry-fine-but-use-it-to-save-the-economySpecial Guest: Margaret Hudson, President Burnbrae Farms: https://www.linkedin.com/in/margaret-hudson-521a7a9/Nobody panic, its the second wave: assess the stability, durability of the food supply chain as we (may or may not) be heading into a second waveLets talk about the weather in California/Oregon - how will wildfires and other challenges in California impact Canadians and the food supplyWhats Poppin?!? The Popeye's Chicken sandwich is about to hit Canada and it's a big deal!  https://www.680news.com/2020/09/14/the-popeyes-chicken-sandwich-is-finally-available-at-all-canadian-restaurants/. Factoids: Popeyes sold 203 million chicken sandwiches in the past year; Texas sold the most chicken sandwiches in the last 12 months; and the most sandwiches sold by one store in one day was 3,582 sandwiches on November 3, 2019.******Thanks to Margaret Hudson from Burnbrae Farms for being our guest on this episode of The Food Professor podcast!If you liked what you heard you can subscribe on Apple iTunes , Spotify or your favourite podcast platform, please rate and review, and be sure and recommend to a friend or colleague in the grocery, foodservice,  or restaurant industry.    I'm Michael LeBlanc, producer and host of The Voice of Retail podcast and a bunch of other stuff, and I'm Sylvain Charlebois!Have a safe week everyone!

Poem Wavelets : a few lines, a few wavelets moving back and forth in time, sometimes relevant, sometimes irrelevant but never dismissive of its momentary reality. #poem , #poetry , #love , #people , #society , #culture , #art , #life , #harmony

Esta semana se ha fallado el Premio Abel, uno de los premios más importantes en el mundo de las matemáticas. El galardonado ha sido Yves Meyer, por sus contribuciones al desarrollo de la teoría de las wavelets, unas "pequeñas ondas" que han encontrado numerosas aplicaciones en análisis de señales y en codificación de imágenes. En este capítulo os explicamos cómo estas "ondillas" ayudan a convertir una imagen en un archivo que podemos leer en nuestro ordenador o en nuestro móvil. Este programa se emitió originalmente el 21 de marzo de 2017. Podéis escuchar el resto de audios de La Brújula en su canal de iVoox y en la web de Onda Cero, ondacero.es

Zahlen in Ebene (statt auf Gerade) dargestellt. und jetzt sehen wir uns wieder an, was mit der Formel oben passiert nach wievielen Durchläufen ist das Ergebnis > 4? je nach Ergebnis andere Graustufe, schöner andere Farbe Das Ergebnis im Bereich von -2 bis +2 und -2i bis +2i sieht nett aus: Das Programm dazu ist einfach context = document.getElementById('myCanvas').getContext('2d'); for(x = 0; x < 800; x++) { for(y = 0; y < 800; y++) { i = zx = zy = 0 // Diese folgenden Zahlen 2 und 200 ändern, // um in x-Richtung zu schieben und zu skalieren cx = -2 + x / 200 // Die Folgenden Zahlen 2 und 200 ändern, // um in y-Richtung zu schieben und zu skalieren cy = -2 + y / 200 while(i < 255 && (zx * zx + zy * zy) < 4) { xt = zx * zy zx = zx * zx - zy * zy + cx zy= 2 * xt + cy i++ } color = i.toString(16) context.beginPath() context.rect(x, y, 1, 1) context.fillStyle = "#" + color + color + color context.fill() } } hübscher ist es mit einem Farbverlauf und noch hübscher, wenn man die Farbstufen glättet Interessantes ergibt sich, wenn man ein Detail ansieht: wieder in Graustaufen, als Farbverlauf und als geglätteter Farbverlauf. Wir betrachten hier den Bereich -.64 bis +.64 und -.74 i bis + 0.74 i Es gibt sehr schöne Videos, die eine Kamerafahrt in das Apfelmännchen machen da das ‘nur’ Rechenaufwand bedeutet, kann man beliebig weit hinein zoomen und entdeckt dabei immer neue Strukturen The Hardest Mandelbrot Zoom Ever In 2014,10^198 : New record - 350 000 000 iterations Pinwheel of Infinity - Mandelbrot Zoom 9.336x10^341 Dieses Verhalten der einfachen Gleichung ist doch überraschend Sehr eng bei einander liegende Gegenden sehen komplett unterschiedlich aus Mathematiker bezeichnen so eine Struktur als Fraktal Fraktale - Die Faszination der verborgenen Dimension - Dokumentation/Doku Fibonacci Folge in Stefans Jugend Forschungsgebiet mit Hype zum Beispiel Hoffnung auf großartige Komprimierungsalgorithmen: FiF Erratum: Jpeg 200 (von Stefan im Podcast genannt) verwendet Wavelets zur Komprimierung und keine Fraktale. Der Hype um sie ist ebenso abgeflacht, vielleicht hat sie Stefan deshalb verwechselt ;-) . doch auch in der Öffentlichkeit bekannt: Schmetterlingseffekt -> Wettervorhersage, Herzrhythmus Weg in Spiele gefunden -> Landschaftsgenerierung mit Terragen Fraktale in der Natur: Schneeflocken, Küsten, Broccoli oder Blumenkohl Neuronale Netze Auch Eingang in die Wirtschaft gefunden -> Unternehmenskultur fraktale Organisation allerdings sind hier die Begriffe aus der Mathematik nur sinngemäß übernommen. Heute ist Chaostheorie oder Fraktale in den Medien kaum mehr zu hören.

B. Augereau and P. Carré

If you experience any technical difficulties with this video or would like to make an accessibility-related request, please send a message to digicomm@uchicago.edu. Partha Niyogi Memorial Conference: "The Computational Magic of the Ventral Stream: From Visual Development to Group Theory, Hebbian Learning, and Wavelets". This conference is in honor of Partha Niyogi, the Louis Block Professor in Computer Science and Statistics at the University of Chicago. Partha lost his battle with cancer in October of 2010, at the age of 43. Partha made fundamental contributions to a variety of fields including language evolution, statistical inference, and speech recognition. The underlying themes of learning from observations and a rigorous basis for algorithms and models permeated his work.

If you experience any technical difficulties with this video or would like to make an accessibility-related request, please send a message to digicomm@uchicago.edu. Partha Niyogi Memorial Conference: "The Computational Magic of the Ventral Stream: From Visual Development to Group Theory, Hebbian Learning, and Wavelets". This conference is in honor of Partha Niyogi, the Louis Block Professor in Computer Science and Statistics at the University of Chicago. Partha lost his battle with cancer in October of 2010, at the age of 43. Partha made fundamental contributions to a variety of fields including language evolution, statistical inference, and speech recognition. The underlying themes of learning from observations and a rigorous basis for algorithms and models permeated his work.

Lecture 02: Introduction to Fourier analysis, as well as the subject of wavelets.

Letzte Woche haben 100.000 Menschen und diejenigen, die sie eingeladen haben, einen Account bei Google Wave bekommen. Bald werden es sicherlich noch mehr werden. Zeit also für uns, eine ganze Folge zu Google Wave zu machen.Wir erklären, was Google Wave eigentlich ist und im Groben, wie es funktioniert. Wir klären auf über Waves, Wavelets, Blips, Documents, Wave Server, Wave Clients, Robots, Gadgets und embedded Waves. Oder versuchen es zumindest ;-) Ihr Browser unterstützt diesen Audio-Player nicht.Länge: 1:23h (74,1 MB), Download MP3Die Slides zum PodcastGoogle Wave Begleitmaterial zum OpenWeb-Podcast Folge 19View more documents from mrtopf.Links Die Google Wave-Hauptseite mit dem Demo Eine kürzere Videoeinführung in Google Wave Informationen von Google zum Wave-Protokoll Eine Auflistung der genutzen Protokolle und ein Diagramm dazu Beispiel-Code für einen Python-basierten Google Wave Robot O’Reilly’s Einführung zu Google Wave Informationen zu Robots Der Source-Code des Servers und eines Beispiel-Clients Microblog zum deutschen Wavecamp

Es werden weiterführende Themen zur verlustbehafteten Bildkompression besprochen, insbesondere progressive JPEG-Codierung und Wavelet-Kompression (mit JPEG2000 als voraussichtlichem Nachfolger von JPEG). Ergänzend werden Spezialtechniken der verlustfreien Kompression diskutiert, insbesondere die Basistechniken von JPEG-LS: Prädiktion, Adaptive Prädiktionskorrektur über Kontextinformation, Golomb-Rice-Entropiecodierung. Abschließend wird als Überleitung zum nächsten Thema Motion JPEG kurz angesprochen.

Es werden weiterführende Themen zur verlustbehafteten Bildkompression besprochen, insbesondere progressive JPEG-Codierung und Wavelet-Kompression (mit JPEG2000 als voraussichtlichem Nachfolger von JPEG). Ergänzend werden Spezialtechniken der verlustfreien Kompression diskutiert, insbesondere die Basistechniken von JPEG-LS: Prädiktion, Adaptive Prädiktionskorrektur über Kontextinformation, Golomb-Rice-Entropiecodierung. Abschließend wird als Überleitung zum nächsten Thema Motion JPEG kurz angesprochen.

In this paper, the space-varying coefficients model on the basis of B-splines (Heim et al., (2006)) is adapted to wavelet basis functions and re-examined using artificial and real data. For an introduction to diffusion tensor imaging refer to Heim et al. (2005, Chap. 2). First, wavelet theory is introduced and explained by means of 1d and 2d examples (Sections 1.1 { 1.3). Section 1.4 is dedicated to the most common thresholding techniques that serve as regularization concepts for wavelet based models. Prior to application of the 3d wavelet decomposition to the space-varying coe cient elds, the SVCM needs to be rewritten. The necessary steps are outlined in Section 2 together with the incorporation of the positive de niteness constraint using log-Cholesky parametrization. Section 3 provides a simulation study as well as a comparison with the results obtained through B-splines and standard kernel application. Finally, a real data example is presented and discussed. The theoretical parts are based on books of Gen cay et al. (2002, Chap. 1, 4-6), Härdle et al. (1998), Ogden (1997) and Jansen (2001) if not stated otherwise.