Concept

Arrondi (mathématiques)

Notebooks associés (7)

Exploring Quantization and Oversampling through One-Bit Music

One-Bit Music[1]¶ In this notebook we will talk about quantization and oversampling and we will do so by taking a trip down memory lane to revisit the early from monophonic square waves, introduce polyphony by way of pulse-width modulation and finish with the basics of sigma-delta quantization. pacman In [1]: import matplotlib import matplotlib.pyplot as plt import numpy as np import scipy.signal as sp from scipy.io import wavfile import IPython In [2]: import sys ! wget -q -O mus

Illustrating Band Structures in Solids: Free Electrons and Atomic Orbitals

In [1]: import numpy as np from numpy import linalg as LA import matplotlib.pyplot as plt

from future import print_function

from ipywidgets import interact, interactive, fixed, interact_manual import ipywidgets as widgets %matplotlib widget The emergence of band structures[1]¶ If you have questions about this notebook, e-mail Oleg Malanyuk (oleg.malanyuk@epfl.ch) This jupyter notebook is intended to provide a illustration of the band structures in solides, starting from the points of v

Band Structure Investigation of Monolayer and Bilayer Graphene Using the Tight Binding Model

In [1]: import matplotlib.pyplot as plt import numpy as np from mpl_toolkits import mplot3d import itertools %matplotlib widget Band Structure of Mono and Bilayer Graphene[1]¶ If you have questions about this notebook, e-mail Oleg Malanyuk (oleg.malanyuk@epfl.ch) The purpose of this notebook is to investigate the band model of Mono and Bilayer graphene modeled with tight binding model. Tight binding model for monolayer graphene[2]¶ Monolayer graphene is a sheet of carbons aranged in a he

Exploring the Karplus-Strong Algorithm for Sound Synthesis

The Karplus-Strong Algorithm[1]¶

                                      [2]Paolo Prandoni, [3]LCAV, EPFL

The Karplus-Strong algorithm is a simple method to generate synthetic sounds with arbitrary pitch. In this notebook we will explore a few variations of the method to illustrate some basic notions of discrete-time signal processing. In [1]: %matplotlib inline import matplotlib import matplotlib.pyplot as plt import numpy as np import IPython In [2]: plt.rcParams["figure.figsize"]

Advantages of Digital Transmission over Analog Transmission

[1]Paolo Prandoni, [2]EPFL Analog vs Digital Transmission In this notebook we will explore the advantages of digital transmission over analog transmission. We will model the case of transmission over a long (e.g. transoceanic) cable in which several repeaters are used to compensate for the attenuation incurred by the signal. In [1]: import matplotlib import matplotlib.pyplot as plt import numpy as np import IPython from scipy.io import wavfile plt.rcParams["figure.figsize"] = (14,4)

Powder X-Ray Diffraction Analysis: Exploring Crystal Structures.

XRay Diffraction[1]¶ The purpose of this notebook is to study the powder XRay diffraction spectra of some systems. When the crystal is hit with an XRay wave, the wave is diffracted only for specific case where the Bragg condition is satisfied for one of the lattice planes in the material. For single crystal diffraction, the condition is satisfied only for specific orientation of the crystal, such that the incident ray has the correct

\theta

value for the plane. In powder XRay data, however, any plane ori

Digital Simulation of Guitar Feedback in I Feel Fine by The Beatles

I Feel Fine, Digitally[1]¶ This notebook is a little musing inspired by the "internal mechanics" of a very famous audio snippet, namely the guitar sound at the beginning of the song "I Feel Fine", written and recorded by the Beatles in 1964. The historical significance of the record lies in the following claim by John Lennon:

 "I defy anybody to find a record... unless it is some old blues
 record from 1922... that uses feedback that way. So I claim it for
 the Beatles. Before H

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