2,236 research outputs found
Characterization of multilayer stack parameters from X-ray reflectivity data using the PPM program: measurements and comparison with TEM results
Future hard (10 -100 keV) X-ray telescopes (SIMBOL-X, Con-X, HEXIT-SAT, XEUS)
will implement focusing optics with multilayer coatings: in view of the
production of these optics we are exploring several deposition techniques for
the reflective coatings. In order to evaluate the achievable optical
performance X-Ray Reflectivity (XRR) measurements are performed, which are
powerful tools for the in-depth characterization of multilayer properties
(roughness, thickness and density distribution). An exact extraction of the
stack parameters is however difficult because the XRR scans depend on them in a
complex way. The PPM code, developed at ERSF in the past years, is able to
derive the layer-by-layer properties of multilayer structures from
semi-automatic XRR scan fittings by means of a global minimization procedure in
the parameters space. In this work we will present the PPM modeling of some
multilayer stacks (Pt/C and Ni/C) deposited by simple e-beam evaporation.
Moreover, in order to verify the predictions of PPM, the obtained results are
compared with TEM profiles taken on the same set of samples. As we will show,
PPM results are in good agreement with the TEM findings. In addition, we show
that the accurate fitting returns a physically correct evaluation of the
variation of layers thickness through the stack, whereas the thickness trend
derived from TEM profiles can be altered by the superposition of roughness
profiles in the sample image
Large Chiral Diffeomorphisms on Riemann Surfaces and W-algebras
The diffeomorphism action lifted on truncated (chiral) Taylor expansion of a
complex scalar field over a Riemann surface is presented in the paper under the
name of large diffeomorphisms. After an heuristic approach, we show how a
linear truncation in the Taylor expansion can generate an algebra of symmetry
characterized by some structure functions. Such a linear truncation is
explicitly realized by introducing the notion of Forsyth frame over the Riemann
surface with the help of a conformally covariant algebraic differential
equation. The large chiral diffeomorphism action is then implemented through a
B.R.S. formulation (for a given order of truncation) leading to a more
algebraic set up. In this context the ghost fields behave as holomorphically
covariant jets. Subsequently, the link with the so called W-algebras is made
explicit once the ghost parameters are turned from jets into tensorial ghost
ones. We give a general solution with the help of the structure functions
pertaining to all the possible truncations lower or equal to the given order.
This provides another contribution to the relationship between KdV flows and
W-diffeomorphimsComment: LaTeX file, 31 pages, no figure. Version to appear in J. Math. Phys.
Work partly supported by Region PACA and INF
Metamorph: Real-Time High-Level Sound Transformations Based On A Sinusoids Plus Noise Plus Transients Model
Spectral models provide ways to manipulate musical audio signals that can be both powerful and intuitive, but high-level control is often required in order to provide flexible real-time control over the potentially large parameter set. This paper introduces Metamorph, a new open source library for high-level sound transformation. We
describe the real-time sinusoids plus noise plus transients model that is used by Metamorph and explain the opportunities that it provides for sound manipulation
Real-time segmentation of the temporal evolution of musical sounds
Since the studies of Helmholtz, it has been known that the temporal evolution of musical sounds plays an important role
in our perception of timbre. The accurate temporal segmentation of musical sounds into regions with distinct characteristics
is therefore of interest to researchers in the field of timbre perception as well as to those working with different forms
of sound modelling and manipulation. Following recent work by Hajda (1996), Peeters (2004) and Caetano et al (2010),
this paper presents a new method for the automatic segmentation of the temporal evolution of isolated musical sounds in real-time. We define attack, sustain and release segments using cues from a combination of the amplitude envelope, the spectro- temporal evolution and a measurement of the stability of the sound that is derived from the onset detection function. We conclude with an evaluation of the method
Python for audio signal processing
This paper discusses the use of Python for developing audio signal processing applications. Overviews of Python language, NumPy, SciPy and Matplotlib are given, which together form a powerful platform for scientic computing. We then show how SciPy was used to create two audio programming libraries,
and describe ways that Python can be integrated with the SndObj library and Pure Data, two existing environments for music composition and signal processing
Python for audio signal processing
This paper discusses the use of Python for developing audio signal processing applications. Overviews of Python language, NumPy, SciPy and Matplotlib are given, which together form a powerful platform for scientic computing. We then show how SciPy was used to create two audio programming libraries,
and describe ways that Python can be integrated with the SndObj library and Pure Data, two existing environments for music composition and signal processing
SIMPL: A Python Library for Sinusoidal Modelling
This paper introduces Simpl, a new open source library for sinusoidal
modelling written in Python. The library is presented as a
resource for researchers in spectral signal processing, who might
like to access existing methods and techniques. The text provides
an overview of the design of the library, describing its data abstractions
and integration with other systems. This is complemented
by some brief examples exploring the functionality of the library
Real-Time Detection of Musical Onsets with Linear Prediction and Sinusoidal Modelling
Real-time musical note onset detection plays a vital role in many audio
analysis processes, such as score following, beat detection and various sound
synthesis by analysis methods. This paper provides a review of some of the
most commonly used techniques for real-time onset detection. We suggest
ways to improve these techniques by incorporating linear prediction, as well
as presenting a novel algorithm for real-time onset detection using sinusoidal
modelling. We provide comprehensive results for both the detection accuracy
and the computational performance of all of the described techniques,
evaluated using Modal, our new open source library for musical onset detection,
which comes with a free database of samples with hand-labelled note
onsets
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