BPSK to ASK signal conversion using injection-locked oscillators-Part I: theory

This paper presents a new method and circuit for the conversion of binary phase-shift keying (BPSK) signals into amplitude shift keying signals. The basic principles of the conversion method are the superharmonic injection and locking of oscillator circuits, and interference phenomena. The first one is used to synchronize the oscillators, while the second is used to generate an amplitude interference pattern that reproduces the original phase modulation. When combined with an envelope detector, the proposed converter circuit allows the coherent demodulation of BPSK signals without need of any explicit carrier recovery system. The time response of the converter circuit to phase changes of the input signal, as well as the conversion limits, are discussed in detail.

Use of photoacoustic effect for the detection of phase transitions in liquid crystal mixtures

We report on a laser induced photoacoustic study of the nematic-to-isotropic transition in certain commercial nematic liquid crystal mixtures, namely BL001, BL002, BL032 and BL035. A simple analysis of the experimental data using the Rosencwaig–Gersho theory shows that the heat capacities of all these compounds exhibit a sharp peak as the temperature of the sample is varied across the transition region. Also, substantial differences in the photoacoustic signal amplitudes in nematic and isotropic phases have been noticed for all the mixtures. The increased light scattering property of the nematic phase may be the reason for the enhanced photoacoustic signal amplitude in this phase.

Use of photoacoustic effect for the detection of phase transitions in liquid crystal mixtures

We report on a laser induced photoacoustic study of the nematic-to-isotropic transition in certain commercial nematic liquid crystal mixtures, namely BL001, BL002, BL032 and BL035. A simple analysis of the experimental data using the Rosencwaig–Gersho theory shows that the heat capacities of all these compounds exhibit a sharp peak as the temperature of the sample is varied across the transition region. Also, substantial differences in the photoacoustic signal amplitudes in nematic and isotropic phases have been noticed for all the mixtures. The increased light scattering property of the nematic phase may be the reason for the enhanced photoacoustic signal amplitude in this phase

Use of photoacoustic effect for the detection of phase transitions in liquid crystal mixtures

We report on a laser induced photoacoustic study of the nematic-to-isotropic transition in certain commercial nematic liquid crystal mixtures, namely BL001, BL002, BL032 and BL035. A simple analysis of the experimental data using the Rosencwaig–Gersho theory shows that the heat capacities of all these compounds exhibit a sharp peak as the temperature of the sample is varied across the transition region. Also, substantial differences in the photoacoustic signal amplitudes in nematic and isotropic phases have been noticed for all the mixtures. The increased light scattering property of the nematic phase may be the reason for the enhanced photoacoustic signal amplitude in this phase.

Laser Induced Photoacoustic Technique for the Detection of Phase Transitions in Liquid Crystals

In this paper we report the use of a laser induced phoroacoustic technique for the detection of multiple phase transitions in heptyl-oxy-cyanobiphenyl (70CB) and octyl-oxy-cyanobiphenyl (80CB) liquid crystals. The observed photoacoustic signal amplitude profile carries clear signatures of two tirst order transitions in 70CB and two tirst order and a second order transitions in 80CB. Analysis of the experimental data using Rosencwaig-Gersho theory shows that the sudden decrease in the photoacoustic (PA) signal amplitude during phase transitions is due to a sharp increase in the heat capacity of the samples near the transition temperatures.

Permutation entropy based real-time chatter detection using audio signal in turning process

Machine tool chatter is an unfavorable phenomenon during metal cutting, which results in heavy vibration of cutting tool. With increase in depth of cut, the cutting regime changes from chatter-free cutting to one with chatter. In this paper, we propose the use of permutation entropy (PE), a conceptually simple and computationally fast measurement to detect the onset of chatter from the time series using sound signal recorded with a unidirectional microphone. PE can efficiently distinguish the regular and complex nature of any signal and extract information about the dynamics of the process by indicating sudden change in its value. Under situations where the data sets are huge and there is no time for preprocessing and fine-tuning, PE can effectively detect dynamical changes of the system. This makes PE an ideal choice for online detection of chatter, which is not possible with other conventional nonlinear methods. In the present study, the variation of PE under two cutting conditions is analyzed. Abrupt variation in the value of PE with increase in depth of cut indicates the onset of chatter vibrations. The results are verified using frequency spectra of the signals and the nonlinear measure, normalized coarse-grained information rate (NCIR).

Theory and generalization of Monte Carlo models of the BOLD signal source

The dependency of the blood oxygenation level dependent (BOLD) signal on underlying hemodynamics is not well understood. Building a forward biophysical model of this relationship is important for the quantitative estimation of the hemodynamic changes and neural activity underlying functional magnetic resonance imaging (fMRI) signals. We have developed a general model of the BOLD signal which can model both intra- and extravascular signals for an arbitrary tissue model across a wide range of imaging parameters. The model of the BOLD signal was instantiated as a look-up-table (LuT), and was verified against concurrent fMRI and optical imaging measurements of activation induced hemodynamics. Magn Reson Med, 2008. © 2008 Wiley-Liss, Inc.

Robust fault diagnosis in power distribution systems based on fuzzy ARTMAP neural network-aided evidence theory

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

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.

N-methyl-D-aspartate channel and consciousness: From signal coincidence detection to quantum computing

Research on Blindsight, Neglect/Extinction and Phantom limb syndromes, as well as electrical measurements of mammalian brain activity, have suggested the dependence of vivid perception on both incoming sensory information at primary sensory cortex and reentrant information from associative cortex. Coherence between incoming and reentrant signals seems to be a necessary condition for (conscious) perception. General reticular activating system and local electrical synchronization are some of the tools used by the brain to establish coarse coherence at the sensory cortex, upon which biochemical processes are coordinated. Besides electrical synchrony and chemical modulation at the synapse, a central mechanism supporting such a coherence is the N-methyl-D-aspartate channel, working as a 'coincidence detector' for an incoming signal causing the depolarization necessary to remove Mg 2+, and reentrant information releasing the glutamate that finally prompts Ca 2+ entry. We propose that a signal transduction pathway activated by Ca 2+ entry into cortical neurons is in charge of triggering a quantum computational process that accelerates inter-neuronal communication, thus solving systemic conflict and supporting the unity of consciousness. © 2001 Elsevier Science Ltd.

The use of current transformers for noise detection due to sparking in insulators strings in high voltage transmission lines

In a general way, in an electric power utility the current transformers (CT) are used to measurement and protection of transmission lines (TL) 1 The Power Line Carriers systems (PLC) are used for communication between electrical substations and transmission line protection. However, with the increasing use of optical fiber to communication (due mainly to its high data transmission rate and low signal-noise relation) this application loses potentiality. Therefore, other functions must be defined to equipments that are still in using, one of them is detecting faults (short-circuits) and transmission lines insulator strings damages 2. The purpose of this paper is to verify the possibility of using the path to the ground offered by the CTs instead of capacitive couplings / capacitive potential transformers to detect damaged insulators, since the current transformers are always present in all transmission lines (TL's) bays. To this a comparison between this new proposal and the PLC previous proposed system 2 is shown, evaluating the economical and technical points of view. ©2010 IEEE.

Detection of determinant genes and diagnostic via Item Response Theory

ABSTRACT: This work presents a method to analyze characteristics of a set of genes that can have an influence in a certain anomaly, such as a particular type of cancer. A measure is proposed with the objective of diagnosing individuals regarding the anomaly under study and some characteristics of the genes are analyzed. Maximum likelihood equations for general and particular cases are presented.

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.