Joint physical-MAC layer design of the broadcast channel protocol in adhoc networks

Broadcast transmission mode in ad hoc networks is critical to manage multihop routing or providing medium accesscontrol (MAC)-layer fairness. In this paper, it is shown that ahigher capacity to exchange information among neighbors may beobtained through a physical-MAC cross-layer design of the broadcastprotocol exploiting signal separation principles. Coherentdetection and separation of contending nodes is possible throughtraining sequences which are selected at random from a reducedset. Guidelines for the design of this set are derived for a lowimpact on the network performance and the receiver complexity.

Heterogeneous multicore systems for signal processing

This thesis explores the capabilities of heterogeneous multi-core systems, based on multiple Graphics Processing Units (GPUs) in a standard desktop framework. Multi-GPU accelerated desk side computers are an appealing alternative to other high performance computing (HPC) systems: being composed of commodity hardware components fabricated in large quantities, their price-performance ratio is unparalleled in the world of high performance computing. Essentially bringing “supercomputing to the masses”, this opens up new possibilities for application fields where investing in HPC resources had been considered unfeasible before. One of these is the field of bioelectrical imaging, a class of medical imaging technologies that occupy a low-cost niche next to million-dollar systems like functional Magnetic Resonance Imaging (fMRI). In the scope of this work, several computational challenges encountered in bioelectrical imaging are tackled with this new kind of computing resource, striving to help these methods approach their true potential. Specifically, the following main contributions were made: Firstly, a novel dual-GPU implementation of parallel triangular matrix inversion (TMI) is presented, addressing an crucial kernel in computation of multi-mesh head models of encephalographic (EEG) source localization. This includes not only a highly efficient implementation of the routine itself achieving excellent speedups versus an optimized CPU implementation, but also a novel GPU-friendly compressed storage scheme for triangular matrices. Secondly, a scalable multi-GPU solver for non-hermitian linear systems was implemented. It is integrated into a simulation environment for electrical impedance tomography (EIT) that requires frequent solution of complex systems with millions of unknowns, a task that this solution can perform within seconds. In terms of computational throughput, it outperforms not only an highly optimized multi-CPU reference, but related GPU-based work as well. Finally, a GPU-accelerated graphical EEG real-time source localization software was implemented. Thanks to acceleration, it can meet real-time requirements in unpreceeded anatomical detail running more complex localization algorithms. Additionally, a novel implementation to extract anatomical priors from static Magnetic Resonance (MR) scansions has been included.

Noise reduction in biomedical speech signal processing based on time and frequency Kalman filtering combined with spectral subtraction

The goal of this paper is to study and propose a new technique for noise reduction used during the reconstruction of speech signals, particularly for biomedical applications. The proposed method is based on Kalman filtering in the time domain combined with spectral subtraction. Comparison with discrete Kalman filter in the frequency domain shows better performance of the proposed technique. The performance is evaluated by using the segmental signal-to-noise ratio and the Itakura-Saito`s distance. Results have shown that Kalman`s filter in time combined with spectral subtraction is more robust and efficient, improving the Itakura-Saito`s distance by up to four times. (C) 2007 Elsevier Ltd. All rights reserved.

Ultrasonic Viscosity Measurement Using the Shear-Wave Reflection Coefficient with a Novel Signal Processing Technique

Real-time viscosity measurement remains a necessity for highly automated industry. To resolve this problem, many studies have been carried out using an ultrasonic shear wave reflectance method. This method is based on the determination of the complex reflection coefficient`s magnitude and phase at the solid-liquid interface. Although magnitude is a stable quantity and its measurement is relatively simple and precise, phase measurement is a difficult task because of strong temperature dependence. A simplified method that uses only the magnitude of the reflection coefficient and that is valid under the Newtonian regimen has been proposed by some authors, but the obtained viscosity values do not match conventional viscometry measurements. In this work, a mode conversion measurement cell was used to measure glycerin viscosity as a function of temperature (15 to 25 degrees C) and corn syrup-water mixtures as a function of concentration (70 to 100 wt% of corn syrup). Tests were carried out at 1 MHz. A novel signal processing technique that calculates the reflection coefficient magnitude in a frequency band, instead of a single frequency, was studied. The effects of the bandwidth on magnitude and viscosity were analyzed and the results were compared with the values predicted by the Newtonian liquid model. The frequency band technique improved the magnitude results. The obtained viscosity values came close to those measured by the rotational viscometer with percentage errors up to 14%, whereas errors up to 96% were found for the single frequency method.

An advanced method for eliminating impacts from frequency deviations in power system signal processing

Frequency deviation is a common problem for power system signal processing. Many power system measurements are carried out in a fixed sampling rate assuming the system operates in its nominal frequency (50 or 60 Hz). However, the actual frequency may deviate from the normal value from time to time due to various reasons such as disturbances and subsequent system transients. Measurement of signals based on a fixed sampling rate may introduce errors under such situations. In order to achieve high precision signal measurement appropriate algorithms need to be employed to reduce the impact from frequency deviation in the power system data acquisition process. This paper proposes an advanced algorithm to enhance Fourier transform for power system signal processing. The algorithm is able to effectively correct frequency deviation under fixed sampling rate. Accurate measurement of power system signals is essential for the secure and reliable operation of power systems. The algorithm is readily applicable to such occasions where signal processing is affected by frequency deviation. Both mathematical proof and numerical simulation are given in this paper to illustrate robustness and effectiveness of the proposed algorithm. Crown Copyright (C) 2003 Published by Elsevier Science B.V. All rights reserved.

ECG denoising based on adaptive signal processing technique

An Electrocardiogram (ECG) monitoring system deals with several challenges related with noise sources. The main goal of this text was the study of Adaptive Signal Processing Algorithms for ECG noise reduction when applied to real signals. This document presents an adaptive ltering technique based on Least Mean Square (LMS) algorithm to remove the artefacts caused by electromyography (EMG) and power line noise into ECG signal. For this experiments it was used real noise signals, mainly to observe the di erence between real noise and simulated noise sources. It was obtained very good results due to the ability of noise removing that can be reached with this technique. A recolha de sinais electrocardiogr a cos (ECG) sofre de diversos problemas relacionados com ru dos. O objectivo deste trabalho foi o estudo de algoritmos adaptativos para processamento digital de sinal, para redu c~ao de ru do em sinais ECG reais. Este texto apresenta uma t ecnica de redu c~ao de ru do baseada no algoritmo Least Mean Square (LMS) para remo c~ao de ru dos causados quer pela actividade muscular (EMG) quer por ru dos causados pela rede de energia el ectrica. Para as experiencias foram utilizados ru dos reais, principalmente para aferir a diferen ca de performance do algoritmo entre os sinais reais e os simulados. Foram conseguidos bons resultados, essencialmente devido as excelentes caracter sticas que esta t ecnica tem para remover ru dos.

Fractional discrete-time signal processing: scale conversion and linear prediction

Nonlinear Dynamics, Vol. 29

Fractional signal processing: scale conversion

In Proceedings of the “ECCTD '01 - European Conference on Circuit Theory and Design, Espoo, Finland, August 2001

A signal processing view of differintegration

Proceedings of the European Control Conference, ECC’01, Porto, Portugal, September 2001

Analysis of Natural and Artificial Phenomena Using Signal Processing and Fractional Calculus

In this paper we study several natural and man-made complex phenomena in the perspective of dynamical systems. For each class of phenomena, the system outputs are time-series records obtained in identical conditions. The time-series are viewed as manifestations of the system behavior and are processed for analyzing the system dynamics. First, we use the Fourier transform to process the data and we approximate the amplitude spectra by means of power law functions. We interpret the power law parameters as a phenomenological signature of the system dynamics. Second, we adopt the techniques of non-hierarchical clustering and multidimensional scaling to visualize hidden relationships between the complex phenomena. Third, we propose a vector field based analogy to interpret the patterns unveiled by the PL parameters.

Parallel programming in biomedical signal processing

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Ground penetrating radar signal processing for landmine detection

Ground penetrating radar; landmine; background clutter removal, buried targets detecting

Improved cardiac gating and patient monitoring in high field magnetic resonance imaging by means of electrocardiogram signal processing

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2015

Connections between signal processing and complex analysis

We describe one of the research lines of the Grup de Teoria de Funcions de la UAB UB, which deals with sampling and interpolation problems in signal analysis and their connections with complex function theory.