Use Of Wavelet Filters Research Articles

EXTRACTION OF A COMPLEX TREND OF SIGNALS IN SYSTEMS OF INTELLIGENT SUPPORT OF THE MOVEMENT OF OBJECTS

The article is devoted to the study of problems of intellectualization of control systems of moving objects to ensure their reliable operation in difficult conditions in various physical environments. Intelligent traffic support systems are developed based on the integration of measurement, computing, communication and control technologies and are designed to ensure the collection, accumulation and processing of information, determination of navigation parameters, formation of control influences, visualization of the positioning and movement trajectories of objects, monitoring of thefunctional state of objects and the state of the operating environment, etc. When restoring the lost trajectory of a moving object in the conditions of its complex movement, when approximating complex algorithms in adaptive control systems, with sudden changes in the trajectory or conditions of movement, there is a need to extract a complex trend from noiselike signals. The existing filtering algorithms do not provide acceptable efficiency, especially in conditions of limited a priori information about the nature of changes in the useful signal component.The article proposes and substantiates the use of wavelet filtering to suppress interference and extract the initial signal of a complex shape. A study of the effectiveness of extracting a complex useful signal against the background of Gaussian noise and harmonic disturbances was conducted. To process the simulated signals, wave functions of various orders from the families of Dobechies, symlets, and coiflets were used. The effectiveness of wavelet filtering was evaluated by the mean square deviation value of the selected trend and the model of the useful signal. The comparative analysis of the obtained results showed the feasibility of using the wave function of the family of symlets, which ensured the minimum value of the root mean square deviation of the selected trend from the model of the useful signal.

Some Refinements on the Comparison of Areal Sampling Methods via Simulation

The design of forest inventories and development of new sampling methods useful in such inventories normally have a two-fold target of design unbiasedness and minimum variance in mind. Many considerations such as costs go into the choices of sampling method for operational and other levels of inventory. However, the variance in terms of meeting a specified level of precision is always among the most important criteria. Similarly, in designing new sampling methods, one always seeks to decrease the variance of the new method compared to existing methods. This paper provides a review of some graphical methods that may prove useful in these endeavors. In addition, in the case of the comparison of variances between new and existing methods, it introduces the use of wavelet filtering to decompose the sampling variance associated with the estimators under consideration into scale-based components of variance. This yields an analysis of variance of sorts regarding how the methods compare over different distance/area classes. The graphical tools are also shown to be applicable to the wavelet decomposition. These graphical tools may prove useful in summarizing the results for inventory design, while the wavelet results may prove helpful as we begin to look at sampling designs more in light of spatial processes for a given population of trees or downed coarse woody debris.

Estimation of reactivity and delayed neutron precursors’ concentrations using a multiscale extended Kalman filter

A wavelet based multiscale extended Kalman filtering technique for estimation of reactivity and delayed neutron precursors’ concentrations is presented in this paper. Reactivity which indicates the criticality status of the reactor core can only be measured in indirect way. Similarly delayed neutron precursors’ concentrations, the source of the delayed neutrons which play important role in reactor control cannot be measured directly. Nuclear reactor is an example of multirate nonlinear system in which different state variables evolve with widely varying dynamics. The state estimation algorithm presented here is based on and preserves merits of Extended Kalman Filtering (EKF) technique. In addition, use of wavelet filters enables multiscale decomposition of the state variables that in turn, effectively captures the multirate nature of the system. Estimation has been carried out using reactor power as the only input. In order to justify effectiveness of the proposed method, simulation results are shown for completely known power variation dataset and experimental power variation datasets collected from one of the Indian research reactors.

Reconstruction of vasculature in optical resolution photoacoustic microscopy using wavelet and Hessian based method

Photoacoustic microscopy is a high-resolution and high- contrast function modality where optical absorption is detected by ultrasonic transducer. The noise generated by the detector due to its size, sensitivity make the photoacoustic images difficult to visualize. Denoising and reconstruction of vascular structures is crucial for the early diagnosis and therapy in many medical applications. Therefore, the accurate detection of blood vessels has a great significance to assist the users including radiologists and clinicians by making them visualization of the vascular information in the images. In the proposed method, use of wavelet filtering to enhance the effect of smaller and bigger vessels in Optical Resolution Photoacoustic Microscopy (OR-PAM) is analyzed. The proposed method uses the wavelet to enhance the vasculature and then Hessian based method is applied to classify the vessel-like structures. For the evaluation, the algorithm is tested on photoacoustic images acquired non-invasively from living m...

Technique of Decomposition of Form Deviation for Freeform Surfaces

This paper presents the technique of decomposition of form deviation based on the use of wavelet filter and Fourier transform. The method allows decomposing the total deviation into systematic, random systematic (waviness) and random components. The method testing was carried out for the statistics of the deviation of the suction side of the turbine engine compressor blade. The accuracy check of the estimates of form deviations generated by the method in a series of simulated surfaces with pre-laid characteristics was carried out. The results showed that the method of decomposition allows one to estimate exactly the components of the form deviation of the simulated surfaces in their series.

Coarse-to-fine reconstruction in linear inverse problems with application to limited-angle computerized tomography

The goal of this paper is to propose and test an iterative coarse-to-fine reconstruction procedure for a certain class of linear inverse problems. This procedure is based on preconditioned iterative regularization through the conjugate gradient (CG) method, through Tikhonov preconditioning, as well as through wavelet low-pass filtering. A quadratic minimization problem associated with a linear inverse problem, can be very problematic if the quadratic form is not diagonal or nearly (block) diagonal. In the present reconstruction strategy, a nearly block diagonal representation of a quadratic form is obtained due to wavelet filtering and preconditioning. In the numerical experiments, the proposed procedure is successfully applied to limited-angle computerized tomography (limited-angle CT). The results of these experiments show that a combined use of wavelet filters and preconditioning can be effective within the present problem class.

Channel network extraction from high resolution topography using wavelets

The availability of high resolution topography from LIDAR offers new opportunities for objectively extracting the channels directly from a DEM using local topographic information, instead of inferring them indirectly based on global criteria, such as area or area‐slope threshold relationships. Here we introduce the use of wavelet filtering to delineate threshold curvatures for defining valleys and threshold slope‐direction‐change for defining probable channeled portions of the valleys. This approach exploits the topographic signatures uniquely found in high resolution topography, and reveals the fuzzy topographic transition in which local weakly convergent areas lie at the transition between hillslopes and valleys.

Target signal enhancement in near-surface controlled-source electromagnetic data

Controlled-source electromagnetic-conductivity profiles and maps were obtained on the Brazos Valley, Texas, floodplain to study the fractal statistics of geologic noise and the effects of man-made conductive metal targets. Fourier transform, discrete wavelet transforms, and variogram analyses were used. Targets tend to flatten the power-law power spectrum at small wavenumbers and shift power to higher wavenumbers. Detection and localization of targets can be achieved using wavelet spectrogram techniques. Additionally, variograms from pure background conductivity maps show a power-law trend for all lags, whereas, in the presence of targets, a short power-law trend is followed by a sill corresponding to a loss in spatial correlation. A simple preprocessing step that combines responses from two perpendicular transmitter-receiver orientations enhances the localization of targets and rejects background signals in profiles and 2D apparent-conductivity maps. Finally, a field example shows how the use of wavelet filtering is able to separate target responses from the geologic background.

On the use of wavelet filtering and correlation techniques in atmospheric condensed phase spectroscopy

The application of wavelet filtering and analysis in spectroscopy is discussed in relation to the analysis of complex atmospheric spectra, where contributions from condensed phase particles and gas phase molecules are present in the form of broad-band features and narrow lines, respectively. The broad-band contribution can be extracted as the ‘smooth signal’ component of the wavelet transform, with a large reduction in the size of the corresponding data files. This procedure is applied to an investigation of the H 2SO 4 aerosol content of a series of atmospheric spectra measured in the ATMOS missions. The sulfate content of the smooth signal is analysed by means of correlation techniques, using a set of laboratory reference spectra of varying sulfuric acid concentration and temperature. Correlation density maps and correlation curves are used to select the most appropriate spectral zones for sulfate analysis and to assess the sulfate aerosol content in the atmosphere subsequent to the eruption of the Mount Pinatubo volcano.

Frequency Filtering of Linear Features by Means of Wavelets. A Method and an Example

Cartographic generalization is one of the main difficulties when dealing with digital cartography and GIS. First works on automatic generalization of linear features were mainly devoted to algorithms of simplification, being their main target to reduce the number of co-ordinates to store. However, the use of techniques in the frequency domain provides us with a new research area. In this paper the use of wavelet filtering has shown a disposition to extract the general trend of a line, essential to reduce drastically the scale, but also to establish classifications and segmentations depending on the homogeneity and the sinuosity of the features.

Pattern discrimination using wavelet filters in a photorefractive joint-transform correlator

We propose and demonstrate the use of wavelet filters for pattern discrimination in a photorefractive (PR) joint-transform correlator. A PR BaTiO 3 crystal has been used to record the joint power spectrum in real-time. Wavelet filters are generated in the form of a filter bank and are projected serially onto the crystal, using a liquid crystal spatial light modulator (SLM). Choice of a suitable filter offers a high degree of pattern discrimination.