Wavelets in a problem of signal processing

In this work a new method is proposed for noise reduction in speech signals in the wavelet domain. The method for signal processing makes use of a transfer function, obtained as a polynomial combination of three processings, denominated operators. The proposed method has the objective of overcoming the deficiencies of the thresholding methods and the effective processing of speech corrupted by real noises. Using the method, two speech signals are processed, contaminated by white noise and colored noises. To verify the quality of the processed signals, two evaluation measures are used: signal to noise ratio (SNR) and perceptual evaluation of speech quality (PESQ).

A current-mode based Field-Programmable Analog Array for signal processing applications

This paper presents a new approach to develop Field Programmable Analog Arrays (FPAAs),(1) which avoids excessive number of programming elements in the signal path, thus enhancing the performance. The paper also introduces a novel FPAA architecture, devoid of the conventional switching and connection modules. The proposed FPAA is based on simple current mode sub-circuits. An uncompounded methodology has been employed for the programming of the Configurable Analog Cell (CAC). Current mode approach has enabled the operation of the FPAA presented here, over almost three decades of frequency range. We have demonstrated the feasibility of the FPAA by implementing some signal processing functions.

Pattern recognition theory in nonlinear signal processing

A body of research has developed within the context of nonlinear signal and image processing that deals with the automatic, statistical design of digital window-based filters. Based on pairs of ideal and observed signals, a filter is designed in an effort to minimize the error between the ideal and filtered signals. The goodness of an optimal filter depends on the relation between the ideal and observed signals, but the goodness of a designed filter also depends on the amount of sample data from which it is designed. In order to lessen the design cost, a filter is often chosen from a given class of filters, thereby constraining the optimization and increasing the error of the optimal filter. To a great extent, the problem of filter design concerns striking the correct balance between the degree of constraint and the design cost. From a different perspective and in a different context, the problem of constraint versus sample size has been a major focus of study within the theory of pattern recognition. This paper discusses the design problem for nonlinear signal processing, shows how the issue naturally transitions into pattern recognition, and then provides a review of salient related pattern-recognition theory. In particular, it discusses classification rules, constrained classification, the Vapnik-Chervonenkis theory, and implications of that theory for morphological classifiers and neural networks. The paper closes by discussing some design approaches developed for nonlinear signal processing, and how the nature of these naturally lead to a decomposition of the error of a designed filter into a sum of the following components: the Bayes error of the unconstrained optimal filter, the cost of constraint, the cost of reducing complexity by compressing the original signal distribution, the design cost, and the contribution of prior knowledge to a decrease in the error. The main purpose of the paper is to present fundamental principles of pattern recognition theory within the framework of active research in nonlinear signal processing.

Control of thermal damage in grinding by digital signal processing of raw acoustic emission

Grinding process is usually the last finishing process of a precision component in the manufacturing industries. This process is utilized for manufacturing parts of different materials, so it demands results such as low roughness, dimensional and shape error control, optimum tool-life, with minimum cost and time. Damages on the parts are very expensive since the previous processes and the grinding itself are useless when the part is damaged in this stage. This work aims to investigate the efficiency of digital signal processing tools of acoustic emission signals in order to detect thermal damages in grinding process. To accomplish such a goal, an experimental work was carried out for 15 runs in a surface grinding machine operating with an aluminum oxide grinding wheel and ABNT 1045 e VC131 steels. The acoustic emission signals were acquired from a fixed sensor placed on the workpiece holder. A high sampling rate acquisition system at 2.5 MHz was used to collect the raw acoustic emission instead of root mean square value usually employed. In each test AE data was analyzed off-line, with results compared to inspection of each workpiece for burn and other metallurgical anomaly. A number of statistical signal processing tools have been evaluated.

A new microcontrolled structural health monitoring system based on the electromechanical impedance principle

This article presents a new method to detect damage in structures based on the electromechanical impedance principle. The system follows the variations in the output voltage of piezoelectric transducers and does not compute the impedance itself. The proposed system is portable, autonomous, versatile, and could efficiently replace commercial instruments in different structural health monitoring applications. The identification of damage is performed by simply comparing the variations of root mean square voltage from response signals of piezoelectric transducers, such as lead zirconate titanate patches bonded to the structure, obtained for different frequencies of the excitation signal. The proposed system is not limited by the sampling rate of analog-to-digital converters, dispenses Fourier transform algorithms, and does not require a computer for processing, operating autonomously. A low-cost prototype based on microcontroller and digital synthesizer was built, and experiments were carried out on an aluminum structure and excellent results have been obtained. © The Author(s) 2012.

Time-domain analysis of piezoelectric impedance-based structural health monitoring using multilevel wavelet decomposition

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Structural damage detection by fuzzy clustering

The development of strategies for structural health monitoring (SHM) has become increasingly important because of the necessity of preventing undesirable damage. This paper describes an approach to this problem using vibration data. It involves a three-stage process: reduction of the time-series data using principle component analysis (PCA), the development of a data-based model using an auto-regressive moving average (ARMA) model using data from an undamaged structure, and the classification of whether or not the structure is damaged using a fuzzy clustering approach. The approach is applied to data from a benchmark structure from Los Alamos National Laboratory, USA. Two fuzzy clustering algorithms are compared: fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms. It is shown that while both fuzzy clustering algorithms are effective, the GK algorithm marginally outperforms the FCM algorithm. (C) 2008 Elsevier Ltd. All rights reserved.

Oropharyngeal dysphagia identification using wavelets and optimum path forest

The swallowing disturbers are defined as oropharyngeal dysphagia when present specifies signals and symptoms that are characterized for alterations in any phases of swallowing. Early diagnosis is crucial for the prognosis of patients with dysphagia and the potential to diagnose dysphagia in a noninvasive manner by assessing the sounds of swallowing is a highly attractive option for the dysphagia clinician. This study proposes a new framework for oropharyngeal dysphagia identification, having two main contributions: a new set of features extract from swallowing signal by discrete wavelet transform and the dysphagia classification by a novel pattern classifier called OPF. We also employed the well known SVM algorithm in the dysphagia identification task, for comparison purposes. We performed the experiments in two sub-signals: the first was the moment of the maximal peak (MP) of the signal and the second is the swallowing apnea period (SAP). The OPF final accuracy obtained were 85.2% and 80.2% for the analyzed signals MP and SAP, respectively, outperforming the SVM results. ©2008 IEEE.

Monitoramento da integridade em estruturas aeronáuticas

Pós-graduação em Engenharia Mecânica - FEIS

Structural damage detection in an aeronautical panel using analysis of variance

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Introdução à identificação de sistemas utilizando o System Identification Toolbox do MATLAB em conjunto com o Arduino para o laboratório de controle linear

The education designed and planned in a clear and objective manner is of paramount importance for universities to prepare competent professionals for the labor market, and above all can serve the population with an efficient work. Specifically, in relation to engineering, conducting classes in the laboratories it is very important for the application of theory and development of the practical part of the student. The planning and preparation of laboratories, as well as laboratory equipment and activities should be developed in a succinct and clear way, showing to students how to apply in practice what has been learned in theory and often shows them why and where it can be used when they become engineers. This work uses the MATLAB together with the System Identification Toolbox and Arduino for the identification of linear systems in Linear Control Lab. MATLAB is a widely used program in the engineering area for numerical computation, signal processing, graphing, system identification, among other functions. Thus the introduction to MATLAB and consequently the identification of systems using the System Identification Toolbox becomes relevant in the formation of students to thereafter when necessary to identify a system the base and the concept has been seen. For this procedure the open source platform Arduino was used as a data acquisition board being the same also introduced to the student, offering them a range of software and hardware for learning, giving you every day more luggage to their training

The CMS experiment at the CERN LHC

The Compact Muon Solenoid (CMS) detector is described. The detector operates at the Large Hadron Collider (LHC) at CERN. It was conceived to study proton-proton (and lead-lead) collisions at a centre-of-mass energy of 14 TeV (5.5 TeV nucleon-nucleon) and at luminosities up to 10(34)cm(-2)s(-1) (10(27)cm(-2)s(-1)). At the core of the CMS detector sits a high-magnetic-field and large-bore superconducting solenoid surrounding an all-silicon pixel and strip tracker, a lead-tungstate scintillating-crystals electromagnetic calorimeter, and a brass-scintillator sampling hadron calorimeter. The iron yoke of the flux-return is instrumented with four stations of muon detectors covering most of the 4 pi solid angle. Forward sampling calorimeters extend the pseudo-rapidity coverage to high values (vertical bar eta vertical bar <= 5) assuring very good hermeticity. The overall dimensions of the CMS detector are a length of 21.6 m, a diameter of 14.6 m and a total weight of 12500 t.

Noise reduction in speech signals using a TMS320C31 digital signal processor

This paper describes a speech enhancement system (SES) based on a TMS320C31 digital signal processor (DSP) for real-time application. The SES algorithm is based on a modified spectral subtraction method and a new speech activity detector (SAD) is used. The system presents a medium computational load and a sampling rate up to 18 kHz can be used. The goal is load and a sampling rate up to 18 kHz can be used. The goal is to use it to reduce noise in an analog telephone line.

Common architecture for discrete wavelet transform analysis and synthesis with sequential and constant processing elements

This paper adresses the problem on processing biological data such as cardiac beats, audio and ultrasonic range, calculating wavelet coefficients in real time, with processor clock running at frequency of present ASIC's and FPGA. The Paralell Filter Architecture for DWT has been improved, calculating wavelet coefficients in real time with hardware reduced to 60%. The new architecture, which also processes IDWT, is implemented with the Radix-2 or the Booth-Wallace Constant multipliers. Including series memory register banks, one integrated circuit Signal Analyzer, ultrasonic range, is presented.

Mandible protrusion and decrease of TMJ sounds: An electrovibratographic examination

This study quantified by, electrovibratography, the amount of mandible protrusion required to decrease significantly temporomandibular joint (TMJ) vibratory energy as an aid in the diagnosis of the recapture of anteriorly displaced disk. Eighteen patients diagnosed as having anterior disk displacement with reduction and TMJ clicking were submitted to electrovibratographic examination at the first appointment and treated with a stabilizing appliance and anterior positioning appliance with 1 to 5 mm protrusion. Vibratory energy was checked in each of these positions. Baseline data were used as control. At the first appointment, the patients had vibrations with more elevated intensities at the middle and late phases of the mouth opening cycle. At only one clinical step, mandible protrusion was obtained with the anterior repositioning appliance, ranging from 1 to 5 mm protusion. At each new position, a new electrovibratographic exam was made. After the 5-mm mandibular projection, only 2 patients presented vibration, with means between 0.6 and 2.8 Hz. Data were analyzed statistically by ANOVA and Tukey's test (α=0.05). The outcomes of this study indicate that 3 mm is the minimum amount of mandible protrusion to significantly decrease the TMJ vibratory energy and to recapture the displaced articular disk.