High signal to noise ratio THz spectroscopy with ASOPS and signal processing schemes for mapping and controlling molecular and bulk relaxation processes

Asynchronous Optical Sampling has the potential to improve signal to noise ratio in THz transient sperctrometry. The design of an inexpensive control scheme for synchronising two femtosecond pulse frequency comb generators at an offset frequency of 20 kHz is discussed. The suitability of a range of signal processing schemes adopted from the Systems Identification and Control Theory community for further processing recorded THz transients in the time and frequency domain are outlined. Finally, possibilities for femtosecond pulse shaping using genetic algorithms are mentioned.

Digital signal processing of optical monitoring by means of a Kalman filter

A Kalman filter algorithm has been applied to interpret the optical reflectance excursions during vacuum deposition of infrared coatings and multilayer thin-film filters. The application has been described in detail elsewhere and this paper now reports on-line experience for estimating deposition rate and thickness. The estimation proved sufficiently reliable to firstly 'navigate' regular manufacture (as controlled by a skilled operator) and to subsequently reproduce the skill without interpretation or intervention whilst maintaining exemplary product quality. Optical control by means of this Kalman filter application is therefore considered suitable as a basis for the automated manufacture of infrared coatings and multilayer thin-film filters.

Use of the LMS adaptive signal processing technique to improve signal data rates from practical oil well logging equipment

The simulation and development work that has been undertaken to produce a signal equaliser used to improve the data rates from oil well logging instruments is presented. The instruments are lowered into the drill bore hole suspended by a cable which has poor electrical characteristics. The equaliser described in the paper corrects for the distortions that occur from the cable (dispersion and attenuation) with the result that the instrument can send data at 100 K.bits/second down its own suspension cable of 12 Km in length. The use of simulation techniques and tools were invaluable in generating a model for the distortions and proved to be a useful tool when site testing was not available.

Computer intensive methods in control and signal processing: the curse of dimensionality

This volume is based upon the 2nd IEEE European Workshop on Computer-Intensive Methods in Control and Signal Processing, held in Prague, August 1996.

Ex Vivo Accuracy of an Apex Locator Using Digital Signal Processing in Primary Teeth

The purpose of this study was to evaluate ex vivo the accuracy an electronic apex locator during root canal length determination in primary molars. Methods: One calibrated examiner determined the root canal length in 15 primary molars (total=34 root canals) with different stages of root resorption. Root canal length was measured both visually, with the placement of a K-file 1 mm short of the apical foramen or the apical resorption bevel, and electronically using an electronic apex locator (Digital Signal Processing). Data were analyzed statistically using the intraclass correlation (ICC) test. Results: Comparing the actual and electronic root canal length measurements in the primary teeth showed a high correlation (ICC=0.95) Conclusions: The Digital Signal Processing apex locator is useful and accurate for apex foramen location during root canal length measurement in primary molars. (Pediatr Dent 200937:320-2) Received April 75, 2008 vertical bar Lost Revision August 21, 2008 vertical bar Revision Accepted August 22, 2008

Electronic working length determination in primary teeth by Propex and Digital Signal Processing

The purpose of this study was to evaluate the accuracy of electronic apex locators Digital Signal Processing (DSP) and ProPex, for root canal length determination in primary teeth. Fifteen primary molars (a total of 34 root canals) were divided into two groups: Group I - without physiological resorption (n = 16); and Group II - with physiological resorption (n = 18). The length of each canal was measured by introducing a file until its tip was visible and then it was retracted 1 mm. For electronic measurement, the devices were set to 1 mm short of the apical resorption. The data were analysed statistically using the intraclass correlation coefficient (ICC). Results showed that the ICC was high for both electronic apex locators in all situations - with (ICC: DSP = 0.82 and Propex = 0.89) or without resorption (ICC: DSP = 0.92 and Propex = 0.90). Both apex locators were extremely accurate in determining the working length in primary teeth, both with or without physiological resorption.

Signal Processing and patternrecognition algorithm for monitoringParkinson’s disease.

This masters thesis describes the development of signal processing and patternrecognition in monitoring Parkison’s disease. It involves the development of a signalprocess algorithm and passing it into a pattern recogniton algorithm also. Thesealgorithms are used to determine , predict and make a conclusion on the study ofparkison’s disease. We get to understand the nature of how the parkinson’s disease isin humans.

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.

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).

Psychophysical analysis of contrast processing segregated into magnocellular and parvocellular systems in asymptomatic carriers of 11778 Leber`s hereditary optic neuropathy

We examined achromatic contrast discrimination in asymptomatic carriers of 11778 Leber`s hereditary optic neuropathy (LHON 18 controls) and 18 age-match were also tested. To evaluate magnocellular (MC) and Parvocellular (PC) contrast discrimination, we used a version of Pokorny and Smith`s (1997) Pulsed/steady-pedestal paradigms (PPP/SPP) thought to be detected via PC and MC pathways, respectively. A luminance pedestal (four 1 degrees x 1 degrees squares) was presented on a 12 cd/m(2) surround. The luminance of one of the squares (trial square, TS) was randomly incremented for either 17 or 133 ms. Observers had to detect the TS, in a forced-choice task, at each duration, for three pedestal levels: 7, 12, 19 cd/m(2). In the SPP, the pedestal was fixed, and the TS was modulated. For the PPP, all four pedestal squares pulsed for 17 or 133 ms, and the TS was simultaneously incremented or decremented. We found that contrast discrimination thresholds of LHON carriers were significantly higher than controls` in the condition with the highest luminance of both paradigms, implying impaired contrast processing with no evidence of differential sensitivity losses between the two systems. Carriers` thresholds manifested significantly longer temporal integration than controls in the SPP, consistent with slowed MC responses. The SPP and PPP paradigms can identify contrast and temporal processing deficits in asymptomatic LHON carriers, and thus provide an additional tool for early detection and characterization of the disease.

Nuevos esquemas para el procesado de señales tensoriales = New schemes for tensor signal processing

Programa doctorado: Cibernética y Telecomunicación (2002/2004)

Fault detection in rotating machines by vibration signal processing techniques

Machines with moving parts give rise to vibrations and consequently noise. The setting up and the status of each machine yield to a peculiar vibration signature. Therefore, a change in the vibration signature, due to a change in the machine state, can be used to detect incipient defects before they become critical. This is the goal of condition monitoring, in which the informations obtained from a machine signature are used in order to detect faults at an early stage. There are a large number of signal processing techniques that can be used in order to extract interesting information from a measured vibration signal. This study seeks to detect rotating machine defects using a range of techniques including synchronous time averaging, Hilbert transform-based demodulation, continuous wavelet transform, Wigner-Ville distribution and spectral correlation density function. The detection and the diagnostic capability of these techniques are discussed and compared on the basis of experimental results concerning gear tooth faults, i.e. fatigue crack at the tooth root and tooth spalls of different sizes, as well as assembly faults in diesel engine. Moreover, the sensitivity to fault severity is assessed by the application of these signal processing techniques to gear tooth faults of different sizes.