11,835 research outputs found
A novel approach to design low-cost two-stage frequency-response masking filters
The multistage frequency-response masking (FRM) technique is widely used to reduce the complexity of a filter when the transition bandwidth is extremely small. In this brief, a real generalized two-stage FRM filter without any constraint on the subfilters or the interpolation factors was proposed. New principles and equations were deduced to determine the design parameters. The subfilters were then jointly optimized using non-linear optimization. Experiential results show that when the proposed algorithm obtains different solutions with the conventional algorithm, the solution of the proposed approach is better with less number of filter coefficients and sometimes with lower delay as well than the conventional two-stage FRM, which can lead to a reduced hardware cost in applications
Design of Multistage Decimation Filters Using Cyclotomic Polynomials: Optimization and Design Issues
This paper focuses on the design of multiplier-less decimation filters
suitable for oversampled digital signals. The aim is twofold. On one hand, it
proposes an optimization framework for the design of constituent decimation
filters in a general multistage decimation architecture. The basic building
blocks embedded in the proposed filters belong, for a simple reason, to the
class of cyclotomic polynomials (CPs): the first 104 CPs have a z-transfer
function whose coefficients are simply {-1,0,+1}. On the other hand, the paper
provides a bunch of useful techniques, most of which stemming from some key
properties of CPs, for designing the proposed filters in a variety of
architectures. Both recursive and non-recursive architectures are discussed by
focusing on a specific decimation filter obtained as a result of the
optimization algorithm.
Design guidelines are provided with the aim to simplify the design of the
constituent decimation filters in the multistage chain.Comment: Submitted to CAS-I, July 07; 11 pages, 5 figures, 3 table
Design and Implementation of Low Complexity Reconfigurable Filtered-OFDM based LDACS
L-band Digital Aeronautical Communication System (LDACS) aims to exploit
vacant spectrum in L-band via spectrum sharing, and orthogonal frequency
division multiplexing (OFDM) is the currently accepted LDACS waveform.
Recently, various works dealing with improving the spectrum utilization of
LDACS via filtering/windowing are being explored. In this direction, we propose
an improved and low complexity reconfigurable filtered OFDM (LRef-OFDM) based
LDACS using novel interpolation and masking based multi-stage digital filter.
The proposed filter is designed to meet the stringent non-uniform spectral
attenuation requirements of LDACS standard. It offers significantly lower
complexity as well as higher transmission bandwidth than state-of-the-art
approaches. We also integrate the proposed filter in our end-to-end LDACS
testbed realized using Zynq System on Chip and analyze the performance in the
presence of -band legacy user interference as well as LDACS specific
wireless channels. Via extensive experimental results, we demonstrate the
superiority of the proposed LRef-OFDM over OFDM and Filtered-OFDM based LDACS
in terms of power spectral density, bit error rate, implementation complexity,
and group delay parameters.Comment: Paper with Appendi
Channelization for Multi-Standard Software-Defined Radio Base Stations
As the number of radio standards increase and spectrum resources come under more pressure, it becomes ever less efficient to reserve bands of spectrum for exclusive use by a single radio standard. Therefore, this work focuses on channelization structures compatible with spectrum sharing among multiple wireless standards and dynamic spectrum allocation in particular. A channelizer extracts independent communication channels from a wideband signal, and is one of the most computationally expensive components in a communications receiver. This work specifically focuses on non-uniform channelizers suitable for multi-standard Software-Defined Radio (SDR) base stations in general and public mobile radio base stations in particular.
A comprehensive evaluation of non-uniform channelizers (existing and developed during the course of this work) shows that parallel and recombined variants of the Generalised Discrete Fourier Transform Modulated Filter Bank (GDFT-FB) represent the best trade-off between computational load and flexibility for dynamic spectrum allocation. Nevertheless, for base station applications (with many channels) very high filter orders may be required, making the channelizers difficult to physically implement.
To mitigate this problem, multi-stage filtering techniques are applied to the GDFT-FB. It is shown that these multi-stage designs can significantly reduce the filter orders and number of operations required by the GDFT-FB. An alternative approach, applying frequency response masking techniques to the GDFT-FB prototype filter design, leads to even bigger reductions in the number of coefficients, but computational load is only reduced for oversampled configurations and then not as much as for the multi-stage designs. Both techniques render the implementation of GDFT-FB based non-uniform channelizers more practical.
Finally, channelization solutions for some real-world spectrum sharing use cases are developed before some final physical implementation issues are considered
Bio-motivated features and deep learning for robust speech recognition
Mención Internacional en el título de doctorIn spite of the enormous leap forward that the Automatic Speech
Recognition (ASR) technologies has experienced over the last five years
their performance under hard environmental condition is still far from
that of humans preventing their adoption in several real applications.
In this thesis the challenge of robustness of modern automatic speech
recognition systems is addressed following two main research lines.
The first one focuses on modeling the human auditory system to
improve the robustness of the feature extraction stage yielding to novel
auditory motivated features. Two main contributions are produced.
On the one hand, a model of the masking behaviour of the Human
Auditory System (HAS) is introduced, based on the non-linear filtering
of a speech spectro-temporal representation applied simultaneously
to both frequency and time domains. This filtering is accomplished
by using image processing techniques, in particular mathematical
morphology operations with an specifically designed Structuring Element
(SE) that closely resembles the masking phenomena that take
place in the cochlea. On the other hand, the temporal patterns of
auditory-nerve firings are modeled. Most conventional acoustic features
are based on short-time energy per frequency band discarding
the information contained in the temporal patterns. Our contribution
is the design of several types of feature extraction schemes based on
the synchrony effect of auditory-nerve activity, showing that the modeling
of this effect can indeed improve speech recognition accuracy in
the presence of additive noise. Both models are further integrated into
the well known Power Normalized Cepstral Coefficients (PNCC).
The second research line addresses the problem of robustness in
noisy environments by means of the use of Deep Neural Networks
(DNNs)-based acoustic modeling and, in particular, of Convolutional
Neural Networks (CNNs) architectures. A deep residual network
scheme is proposed and adapted for our purposes, allowing Residual
Networks (ResNets), originally intended for image processing tasks,
to be used in speech recognition where the network input is small
in comparison with usual image dimensions. We have observed that
ResNets on their own already enhance the robustness of the whole system
against noisy conditions. Moreover, our experiments demonstrate
that their combination with the auditory motivated features devised
in this thesis provide significant improvements in recognition accuracy
in comparison to other state-of-the-art CNN-based ASR systems
under mismatched conditions, while maintaining the performance in
matched scenarios.
The proposed methods have been thoroughly tested and compared
with other state-of-the-art proposals for a variety of datasets and
conditions. The obtained results prove that our methods outperform
other state-of-the-art approaches and reveal that they are suitable for
practical applications, specially where the operating conditions are
unknown.El objetivo de esta tesis se centra en proponer soluciones al problema
del reconocimiento de habla robusto; por ello, se han llevado a cabo
dos líneas de investigación.
En la primera líınea se han propuesto esquemas de extracción de características novedosos, basados en el modelado del comportamiento
del sistema auditivo humano, modelando especialmente los fenómenos
de enmascaramiento y sincronía. En la segunda, se propone mejorar
las tasas de reconocimiento mediante el uso de técnicas de
aprendizaje profundo, en conjunto con las características propuestas.
Los métodos propuestos tienen como principal objetivo, mejorar la
precisión del sistema de reconocimiento cuando las condiciones de
operación no son conocidas, aunque el caso contrario también ha sido
abordado.
En concreto, nuestras principales propuestas son los siguientes:
Simular el sistema auditivo humano con el objetivo de mejorar
la tasa de reconocimiento en condiciones difíciles, principalmente
en situaciones de alto ruido, proponiendo esquemas de
extracción de características novedosos.
Siguiendo esta dirección, nuestras principales propuestas se detallan a continuación:
• Modelar el comportamiento de enmascaramiento del sistema
auditivo humano, usando técnicas del procesado de
imagen sobre el espectro, en concreto, llevando a cabo el
diseño de un filtro morfológico que captura este efecto.
• Modelar el efecto de la sincroní que tiene lugar en el nervio
auditivo.
• La integración de ambos modelos en los conocidos Power
Normalized Cepstral Coefficients (PNCC).
La aplicación de técnicas de aprendizaje profundo con el objetivo
de hacer el sistema más robusto frente al ruido, en particular
con el uso de redes neuronales convolucionales profundas, como
pueden ser las redes residuales.
Por último, la aplicación de las características propuestas en
combinación con las redes neuronales profundas, con el objetivo
principal de obtener mejoras significativas, cuando las condiciones
de entrenamiento y test no coinciden.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Javier Ferreiros López.- Secretario: Fernando Díaz de María.- Vocal: Rubén Solera Ureñ
Image Processing Using FPGAs
This book presents a selection of papers representing current research on using field programmable gate arrays (FPGAs) for realising image processing algorithms. These papers are reprints of papers selected for a Special Issue of the Journal of Imaging on image processing using FPGAs. A diverse range of topics is covered, including parallel soft processors, memory management, image filters, segmentation, clustering, image analysis, and image compression. Applications include traffic sign recognition for autonomous driving, cell detection for histopathology, and video compression. Collectively, they represent the current state-of-the-art on image processing using FPGAs
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