Assessment of maturity degree of composts from domestic solid wastes by fluorescence and fourier transform infrared spectroscopies

Methods of assessment of compost maturity are needed so the application of composted materials to lands will provide optimal benefits. The aim of the present paper is to assess the maturity reached by composts from domestic solid wastes (DSW) prepared under periodic and permanent aeration systems and sampled at different composting time, by means of excitation-emission matrix (EEM) fluorescence spectroscopy and Fourier transform infrared spectroscopy (FT-IR). EEM spectra indicated the presence of two different fluorophores centered, respectively, at Ex/Em wavelength pairs of 330/425 and 280/330 nm. The fluorescence intensities of these peaks were also analyzed, showing trends related to the maturity of composts. The contour density of EEM maps appeared to be strongly reduced with composting days. After 30 and 45 days of composting, FT-IR spectra exhibited a decrease of intensity of peaks assigned to polysaccharides and in the aliphatic region. EEM and FT-IR techniques seem to produce spectra that correlate with the degree of maturity of the compost. Further refinement of these techniques should provide a relatively rapid method of assessing the suitability of the compost to land application.

A combined wavelet-element free Galerkin method for numerical calculations of electromagnetic fields

A combined wavelet-element free Galerkin (EFG) method is proposed for solving electromagnetic EM) field problems. The bridging scales are used to preserve the consistency and linear independence properties of the entire bases. A detailed description of the development of the discrete model and its numerical implementations is given to facilitate the reader to. understand the proposed algorithm. A numerical example to validate the proposed method is also reported.

DIODE-LASER AND FOURIER-TRANSFORM SPECTROSCOPY OF THE (CH3OH)-C-13 C-O STRETCHING MODE

In this work we present high resolution Doppler limited absorption spectra measurements of the C-O stretching mode of (CH3OH)-C-13, obtained from diode laser spectroscopy, and the Fourier Transform spectrum obtained at 0. 12 cm-1 resolution. By using these data and previously known spectroscopic information, we determined the frequency and the J quantum number for the multiplets of the P and R(J) branches of the C-O stretching fundamental band. Infrared transitions in coincidence with emission lines of the regular CO2 laser and some of its isotope parents are pointed out.

Oriented texture classification based on self-organizing neural network and Hough transform

This paper presents a technique for oriented texture classification which is based on the Hough transform and Kohonen's neural network model. In this technique, oriented texture features are extracted from the Hough space by means of two distinct strategies. While the first operates on a non-uniformly sampled Hough space, the second concentrates on the peaks produced in the Hough space. The described technique gives good results for the classification of oriented textures, a common phenomenon in nature underlying an important class of images. Experimental results are presented to demonstrate the performance of the new technique in comparison, with an implemented technique based on Gabor filters.

Activation function study for wavelet network

The main purpose of this paper is to investigate theoretically and experimentally the use of family of Polynomial Powers of the Sigmoid (PPS) Function Networks applied in speech signal representation and function approximation. This paper carries out practical investigations in terms of approximation fitness (LSE), time consuming (CPU Time), computational complexity (FLOP) and representation power (Number of Activation Function) for different PPS activation functions. We expected that different activation functions can provide performance variations and further investigations will guide us towards a class of mappings associating the best activation function to solve a class of problems under certain criteria.

Symmetry transform in Faddeev-Jackiw quantization of dual models

We study the presence of symmetry transformations in the Faddeev-Jackiw approach for constrained systems. Our analysis is based in the case of a particle submitted to a particular potential which depends on an arbitrary function. The method is implemented in a natural way and symmetry generators are identified. These symmetries permit us to obtain the absent elements of the sympletic matrix which complement the set of Dirac brackets of such a theory. The study developed here is applied in two different dual models. First, we discuss the case of a two-dimensional oscillator interacting with an electromagnetic potential described by a Chern-Simons term and second the Schwarz-Sen gauge theory, in order to obtain the complete set of non-null Dirac brackets and the correspondent Maxwell electromagnetic theory limit. ©1999 The American Physical Society.

Harmonics filtering and detection of disturbances using wavelets

Traditional mathematical tools, like Fourier Analysis, have proven to be efficient when analyzing steady-state distortions; however, the growing utilization of electronically controlled loads and the generation of a new dynamics in industrial environments signals have suggested the need of a powerful tool to perform the analysis of non-stationary distortions, overcoming limitations of frequency techniques. Wavelet Theory provides a new approach to harmonic analysis, focusing the decomposition of a signal into non-sinusoidal components, which are translated and scaled in time, generating a time-frequency basis. The correct choice of the waveshape to be used in decomposition is very important and discussed in this work. A brief theoretical introduction on Wavelet Transform is presented and some cases (practical and simulated) are discussed. Distortions commonly found in industrial environments, such as the current waveform of a Switched-Mode Power Supply and the input phase voltage waveform of motor fed by inverter are analyzed using Wavelet Theory. Applications such as extracting the fundamental frequency of a non-sinusoidal current signal, or using the ability of compact representation to detect non-repetitive disturbances are presented.

Multipath reduction from GPS double differences using wavelets: How far can we go?

Wavelets are being extensively used in Geodetic applications. In this paper, the Multi-Resolution Analysis (MRA) using wavelets is applied to pseudorange and carrier phase GPS double differences (DDs) in order to reduce multipath effects. The wavelets were already applied to GPS carrier phase DDs, but some questions remain: How good can be the results, and are all multipath effects reduced? The answers to these questions are discussed in this paper. Thus, the wavelet transform is used to decompose the DD signals, splitting them in lower resolution components. After the decomposition process, the wavelet shrinkage is performed by thresholding to eliminate the components relative to multipath effects. Then, the DD observation can be reconstructed. This new DD signal is used to perform the baseline processing. The daily multipath repeatability was verified. With the application of the proposed approach, the results showed that the reliability of the ambiguity resolution and accuracy of the results improved when compared with the standard procedure. Furthermore, the method showed to be very efficient computationally, because, it is not noticed, at practical level, difference in the time span between the processing with and without application of the proposed method. However, only the high frequency multipath was eliminated.

GPS ambiguity resolution and validation under multipath effects: Improvements using wavelets

Integer carrier phase ambiguity resolution is the key to rapid and high-precision global navigation satellite system (GNSS) positioning and navigation. As important as the integer ambiguity estimation, it is the validation of the solution, because, even when one uses an optimal, or close to optimal, integer ambiguity estimator, unacceptable integer solution can still be obtained. This can happen, for example, when the data are degraded by multipath effects, which affect the real-valued float ambiguity solution, conducting to an incorrect integer (fixed) ambiguity solution. Thus, it is important to use a statistic test that has a correct theoretical and probabilistic base, which has became possible by using the Ratio Test Integer Aperture (RTIA) estimator. The properties and underlying concept of this statistic test are shortly described. An experiment was performed using data with and without multipath. Reflector objects were placed surrounding the receiver antenna aiming to cause multipath. A method based on multiresolution analysis by wavelet transform is used to reduce the multipath of the GPS double difference (DDs) observations. So, the objective of this paper is to compare the ambiguity resolution and validation using data from these two situations: data with multipath and with multipath reduced by wavelets. Additionally, the accuracy of the estimated coordinates is also assessed by comparing with the ground truth coordinates, which were estimated using data without multipath effects. The success and fail probabilities of the RTIA were, in general, coherent and showed the efficiency and the reliability of this statistic test. After multipath mitigation, ambiguity resolution becomes more reliable and the coordinates more precise. © Springer-Verlag Berlin Heidelberg 2007.

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.

Spectral analysis and low-frequency multipath mitigation for kinematic applications

Low-frequency multipath is still one of the major challenges for high precision GPS relative positioning. In kinematic applications, mainly, due to geometry changes, the low-frequency multipath is difficult to be removed or modeled. Spectral analysis has a powerful technique to analyze this kind of non-stationary signals: the wavelet transform. However, some processes and specific ways of processing are necessary to work together in order to detect and efficiently mitigate low-frequency multipath. In this paper, these processes are discussed. Some experiments were carried out in a kinematic mode with a controlled and known vehicle movement. The data were collected in the presence of a reflector surface placed close to the vehicle to cause, mainly, low-frequency multipath. From theanalyses realized, the results in terms of double difference residuals and statistical tests showed that the proposed methodology is very efficient to detect and mitigate low-frequency multipath effects. © 2008 IEEE.

An effective wavelet method to detect and mitigate low-frequency multipath effects

To ensure high accuracy results from GPS relative positioning, the multipath effects have to be mitigated. Although the careful selection of antenna site and the use of especial antennas and receivers can minimize multipath, it cannot always be eliminated and frequently the residual multipath disturbance remains as the major error in GPS results. The high-frequency multipath from large delays can be attenuated by double difference (DD) denoising methods. But the low-frequency multipath from short delays is very difficult to be reduced or modeled. In this paper, it is proposed a method based on wavelet regression (WR), which can effectively detect and reduce the low-frequency multipath. The wavelet technique is firstly applied to decompose the DD residuals into the low-frequency bias and high-frequency noise components. The extracted bias components by WR are then directly applied to the DD observations to correct them from the trend. The remaining terms, largely characterized by the high-frequency measurement noise, are expected to give the best linear unbiased solutions from a least-squares (LS) adjustment. An experiment was carried out using objects placed close to the receiver antenna to cause, mainly, low-frequency multipath. The data were collected for two days to verify the multipath repeatability. The ground truth coordinates were computed with data collected in the absence of the reflector objects. The coordinates and ambiguity solution were compared with and without the multipath mitigation using WR. After mitigating the multipath, ambiguity resolution became more reliable and the coordinates were more accurate.

Análise de séries temporais de coordenadas estimadas com gps: Uma proposta metodológica para detecção, remoção e recuperação de efeitos sazonais

GPS active networks are more and more used in geodetic surveying and scientific experiments, as water vapor monitoring in the atmosphere and lithosphere plate movement. Among the methods of GPS positioning, Precise Point Positioning (PPP) has provided very good results. A characteristic of PPP is related to the modeling and / or estimation of the errors involved in this method. The accuracy obtained for the coordinates can reach few millimeters. Seasonal effects can affect such accuracy if they are not consistent treated during the data processing. Coordinates time series analyses have been realized using Fourier or Harmonics spectral analyses, wavelets, least squares estimation among others. An approach is presented in this paper aiming to investigate the seasonal effects included in the stations coordinates time series. Experiments were carried out using data from stations Manaus (NAUS) and Fortaleza (BRFT) which belong to the Brazilian Continuous GPS Network (RBMC). The coordinates of these stations were estimated daily using PPP and were analyzed through wavelets for identification of the periods of the seasonal effects (annual and semi-annual) in each time series. These effects were removed by means of a filtering process applied in the series via the least squares adjustment (LSQ) of a periodic function. The results showed that the combination of these two mathematical tools, wavelets and LSQ, is an interesting and efficient technique for removal of seasonal effects in time series.