Flávio Henrique Vasconcelos

Real-time wavelet transform algorithms for the processing of continuous streams of data

Two new algorithms to compute the direct and the inverse discrete wavelet transform of continuous streams of data are discussed in this paper. The algorithms are optimized to be used in uniprocessor systems, presenting as one of their main features the ability to compute over the borders of the data segments without relying on any techniques that are traditionally used for thus task, like zero padding. A modified version of the recursive pyramid algorithm was used to compute the direct transform, keeping just the features to minimize data storage but eliminating its dependency on extension techniques. The inverse transform is computed by an algorithm inspired in the RPA, the quadrature mirror filter bank and the overlap-save method for filter convolutions. To evaluate the performance the algorithms were implemented on a DSP coupled to a digitizer through its external memory bus, what allowed deterministic behavior. After a stage of optimization an analysis of data storage and computation load were made. These results and the potential applications are discussed at the end of the paper.

Maximum respiratory pressure measuring system: calibration and evaluation of uncertainty

The objective of this paper is to present a methodology for the evaluation of uncertainties in the measurements results obtained during the calibration of a digital manovacuometer prototype (DM) with a load cell sensor pressure device incorporated. Calibration curves were obtained for both pressure sensors of the DM using linear regression by weighted least squares method (WLS). Two models were built to evaluate uncertainty. One takes into account the information listed in the sensor datasheet, resulting in the maximum permissible measurement error of the manovacuometer, and the other on the WLS implemented during calibration. Considering a range of ten calibration points, it was found that calibration procedure designed using WLS modeling indicates that the range of measurement uncertainty extends from 0.2 up to 0.5 kPa. This is inside the manufacter range that extends from 1.5 up 3.5 kPa, showing adequacy for use.

A real-time processing system for denoising of partial discharge signals using the wavelet transform

The wavelet transform (WT) has been proved to be an efficient tool for partial discharge (PD) processing due its capability to stand out inhomogeneous and localized signal features. In previous work, some authors have investigated issues related to the ways to choose a mother wavelet, strategies to denoise PDs and its in field usability. This paper presents a realtime system for partial discharge signal denoising and processing using the discrete wavelet transform (DWT). Real-time processing is an important feature for PD analysis since it allows data acquisition for long periods, leading to a better statistical characterization. Moreover, real-time processing presents benefits for the investigation of new diagnostic procedures and the development of more specific PD applications, like on-line monitoring. The developed system can be used as a preprocessing unit coupled to an existing PD analyzer or a standalone unit employed both for PD acquisition and denoising. Continuous stream processing is carried out by an appropriate border treatment both during DWT processing and PD extraction. Deterministic behavior is guaranteed by a dedicated hardware architecture associated to a real-time operating system. Hardware based optimizations allowed acquisition rates comparable to commercial PD analyzers. The results were obtained with a focus on performance evaluation in terms of computational loads, storage requirements and noise removal for several wavelet filters, decomposition levels and denoising techniques.

A comparison of cycle spinning versus stationary wavelet transform for the extraction of features of partial discharge signals

This paper presents a comparison of three feature extraction methods to denoise partial discharge (PD) signals. The denoising technique employs the Stationary Wavelet Transform (SWT) associated to a spatially-adaptive selection procedure based on the coefficients propagation along decomposition levels (scales). The PD and noise related coefficients are identified and separated by an automatic data classifier using Support Vector Machines (SVM). The first and second feature extraction methods act directly on the SWT coefficients and differ only on the procedures to characterize the propagation. The third method relies on Cycle Spinning (CS) on the several translated Discrete Wavelet Transform (DWT) obtained from SWT. We conducted an empirical study using Analysis of Variance (ANOVA) to evaluate the influence of the methods on denoising performance and to guarantee the statistical significance of the tests. Afterwards, performance was evaluated considering real PD signals measured in air and in solid dielectrics, corrupted by several types of interferences, both stationary and time-varying. The results show that the three approaches allow robust signal recovering and significant noise rejection, but differ substantially on the quality of the reconstructed signals.

Estimating Vegetation Water Content with Wireless Sensor Network Communication Signals

This paper proposes a distributed measuring system based on wireless sensor networks (WSN) employed to estimate plant water-content in agricultural fields. The WSN is present in a crop field in order to measure environmental variables, like soil moisture. Water-content is obtained by measuring the attenuation of the network communication signal. The need for distributed measurements to estimate agricultural crop parameters is pointed-out and a mathematical model of the radio-wave propagation through the vegetation is developed. This model is used to estimate plant water-content. Experimental results are also presented.

Data processing system for denoising of signals in real-time using the wavelet transform

This paper describes a system able to acquire, process and eliminate noise in continuous streams of data in real-time. The signal processing algorithms were based on the discrete wavelet transform and employ a new approach to deal with border problems, allowing to process the data continuously. The system was implemented using a DSP coupled to a digitizer through its external memory bus to guarantee deterministic behavior while maintaining some degree of flexibility in its configuration. The achieved performance and potential applications are discussed at the end of the text.

A real-time system for denoising of signals in continuous streams through the wavelet transform

This paper describes a system able to acquire, process and eliminate noise in continuous streams of data in real-time. The signal processing algorithms were based on the discrete wavelet transform and employ a new approach to deal with border problems, allowing the data to be continuously processed while they are acquired. The system was implemented using a DSP coupled to a digitizer through its external memory bus to guarantee deterministic behavior while maintaining some degree of flexibility on its configuration. The achieved performance and potential applications are discussed at the end of the text.

Temperature Sensing System With Short-Range Wireless Sensor Based on Inductive Coupling

This paper presents the theory, development, and results for a temperature sensing system that employs a wireless sensor with powering and communications based on inductive coupling. The theory of inductive coupling is presented along with a methodology that has been developed to determine the system elements in order to fulfill communication range and bandwidth requirements in a certain class of applications. A miniaturized wireless sensor was built (volume 3.2 cm3), and the whole system was validated by means of an experiment where the sensor was immersed in a water bath, in which the temperature was varied during 69 h in a cycle covering a span of 30°C. Measurements had a high correlation with an accurate reference thermometer. It is shown that the interruption of the radio frequency (RF) field during the measurements is a useful method to improving the measurement quality. In the course of the validation experiment, this method reduced the standard deviation of the measures up 0.01°C.

Mining undecimated Wavelet Transform maxima lines: An effective way to denoise partial discharge signals

On-site and on-line partial discharge (PD) measurements are well recognized as difficult tasks due to the large scale and diversity of interferences usually encountered in high voltage facilities. Several techniques have been proposed in literature to improve test conditions on the field, based either on analog and digital approaches. More recently, wavelets and their derivatives have shown to be very effective for the processing of PD signals due to their potential to identify transient, time-localized signals. This paper presents the development and results of a new technique to denoise PD signals based on data mining concepts applied to Undecimated Wavelet Transform (UWT) modulus maxima lines propagation. The theory of modulus maxima lines is introduced and their use is justified as an effective way to identify and localize PD pulses. The UWT was employed as a way to increase robustness against the effects of decimation, which is a characteristic of the orthogonal Wavelet Transform that causes random losses of PD pulses. We took advantage of the improved UWT capability to recover signals by using a modified version of the cycle spinning approach. Denoising was performed by separating the maxima lines related to PDs from those related to noise, followed by reconstruction using the inverse UWT. Separation was achieved by the use of data mining tools. The performances of several algorithms were evaluated and compared, including investigations regarding statistically divergent noise types, effects of data pre-processing like normalization and cleansing, procedures for training and comparisons of supervised and unsupervised techniques. Results obtained for both simulated and measured PD signals confirm that the approach is superior when compared to standard linear filters and non-linear wavelet-based techniques. This is particularly noticeable when processing non-stationary time-localized interferences, a situation in which these techniques fail completely.

A partial discharge data acquisition system based on programmable digital oscilloscopes

This paper describes a partial discharge measurement system based on a programmable digital oscilloscope. The system enables the acquisition of partial discharge data both on phase-resolved and time-resolved basis, thus allowing these two analyses. After the acquisition of partial discharge data, statistical operators are applied over the phase distributions, as a way to identify the type of defect present on the equipment under test and its corresponding degradation level. To check for the system performance and develop an initial database on partial discharges, it was used to measure partial discharges in air and solid insulation, and their main characteristics were determined. These measurements were carried out in a measurement cell developed with great care to prevent signal deterioration, allowing the registration of almost distortion-free PD pulses.