Optimization of Gabor Wavelets for Face Recognition

The article describes researches of a method of person recognition by face image based on Gabor wavelets. Scales of Gabor functions are determined at which the maximal percent of recognition for search of a person in a database and minimal percent of mistakes due to false alarm errors when solving an access control task is achieved. The carried out researches have shown a possibility of improvement of recognition system work parameters in the specified two modes when the volume of used data is reduced.

Segmentation of a Speech Signal with Application of Fast Wavelet Transformation

The article describes the method of preliminary segmentation of a speech signal with wavelet transformation use, consisting of two stages. At the first stage there is an allocation of sibilants and pauses, at the second – the further segmentation of the rest signal parts.

Outliers Resistant Learning Algorithm for Radial-basis-fuzzy-wavelet-neural Network in Stomach Acute Injury Diagnosis Tasks

In this paper an outliers resistant learning algorithm for the radial-basis-fuzzy-wavelet-neural network based on R. Welsh criterion is proposed. Suggested learning algorithm under consideration allows the signals processing in presence of significant noise level and outliers. The robust learning algorithm efficiency is investigated and confirmed by the number of experiments including medical applications.

Optically tunable fiber grating transmission filters

We propose and demonstrate single- and multiple-passband fiber grating transmission filters that are remotely tunable by exploitation of the optical pump-induced thermal effects in Er Yb-codoped fiber sections. A repeatable, wavelength-independent induced phase shift of 0.1p mW is obtained without hysteresis and anisotropic effects. A transmission extinction ratio of .23 dB with a 3-mW change in pump power is achieved.

Application of Wavelet Decomposition to Document Line Segmentation

ACM Computing Classification System (1998): I.7, I.7.5.

Content-Based Image Retrieval for Computer Tomography Images Using Wavelet Descriptors

An approach to building a CBIR-system for searching computer tomography images using the methods of wavelet-analysis is presented in this work. The index vectors are constructed on the basis of the local features of the image and on their positions. The purpose of the proposed system is to extract visually similar data from the individual personal records and from analogous analysis of other patients.

Fibre grating filters for suppression of near infrared OH emission lines

Here we present the design and fabrication of multi-notch optical fibre Bragg gratings for suppressing OH emission lines in the near infrared spectra of the night sky for astrophysical applications. We demonstrate a novel approach of fabricating 2, 3 and 5-notch filters using the phase mask technology, which show a good match with the model.

All-fibre Lyot filters based on 45° tilted gratings UV-inscribed in PM fibre

We report all-fibre Lyot filters formed by concatenating fibre gratings with structure tilted at 45° UV-inscribed in PM fibre. Such polarisation filters exhibit distinct transmission property for potential application in fibre lasers and sensors. © 2012 OSA.

Wavelet packet decomposition-based fault diagnosis scheme for SRM drives with a single current sensor

Power converters are a key, but vulnerable component in switched reluctance motor (SRM) drives. In this paper, a new fault diagnosis scheme for SRM converters is proposed based on the wavelet packet decomposition (WPD) with a dc-link current sensor. Open- and short-circuit faults of the power switches in an asymmetrical half-bridge converter are analyzed in details. In order to obtain the fault signature from the phase currents, two pulse-width modulation signals with phase shift are injected into the lower-switches of the converter to extract the excitation current, and the WPD algorithm is then applied to the detected currents for fault diagnosis. Moreover, a discrete degree of the wavelet packet node energy is chosen as the fault coefficient. The converter faults can be diagnosed and located directly by determining the changes in the discrete degree from the detected currents. The proposed scheme requires only one current sensor in the dc link, while conventional methods need one sensor for each phase or additional detection circuits. The experimental results on a 750-W three-phase SRM are presented to confirm the effectiveness of the proposed fault diagnosis scheme.

Az olajár és a makrogazdaság kapcsolatának elemzése folytonos wavelet transzformáció segítségével

Az empirikus makrogazdasági elemzések során rendszerint idősorok vizsgálatára szorítkozunk, ugyan-akkor egyre több tanulmány jut arra a következtetésre, hogy a frekvenciatartományok szintjén zajló folyama-tok megértése is szükséges ahhoz, hogy pontosabb képet nyerjünk a változók közötti kapcsolat irányáról, erősségéről, dinamikájáról. Jelen dolgozat célja, hogy az idő és frekvenciatérben történő elemzést biztosító folytonos wavelet transzformációk és az ezekhez kapcsolódó wavelet koherencia használatával bemutassa a svéd és a norvég gazdaság inflációs, ipari kibocsátás és GDP-mutatóinak az olajárral való együttmozgását. Az eredmények alapján a folytonos waveletekkel történő elemzés hasznos kiegészítést nyújt a szokásos idősoros technikák mellé, új, korábban nem ismert összefüggések feltárására is alkalmas lehet.

High performance shift invariant motion estimation and compensation in wavelet domain video compression

The contributions of this dissertation are in the development of two new interrelated approaches to video data compression: (1) A level-refined motion estimation and subband compensation method for the effective motion estimation and motion compensation. (2) A shift-invariant sub-decimation decomposition method in order to overcome the deficiency of the decimation process in estimating motion due to its shift-invariant property of wavelet transform. ^ The enormous data generated by digital videos call for an intense need of efficient video compression techniques to conserve storage space and minimize bandwidth utilization. The main idea of video compression is to reduce the interpixel redundancies inside and between the video frames by applying motion estimation and motion compensation (MEMO) in combination with spatial transform coding. To locate the global minimum of the matching criterion function reasonably, hierarchical motion estimation by coarse to fine resolution refinements using discrete wavelet transform is applied due to its intrinsic multiresolution and scalability natures. ^ Due to the fact that most of the energies are concentrated in the low resolution subbands while decreased in the high resolution subbands, a new approach called level-refined motion estimation and subband compensation (LRSC) method is proposed. It realizes the possible intrablocks in the subbands for lower entropy coding while keeping the low computational loads of motion estimation as the level-refined method, thus to achieve both temporal compression quality and computational simplicity. ^ Since circular convolution is applied in wavelet transform to obtain the decomposed subframes without coefficient expansion, symmetric-extended wavelet transform is designed on the finite length frame signals for more accurate motion estimation without discontinuous boundary distortions. ^ Although wavelet transformed coefficients still contain spatial domain information, motion estimation in wavelet domain is not as straightforward as in spatial domain due to the shift variance property of the decimation process of the wavelet transform. A new approach called sub-decimation decomposition method is proposed, which maintains the motion consistency between the original frame and the decomposed subframes, improving as a consequence the wavelet domain video compressions by shift invariant motion estimation and compensation. ^

Principal component and second generation wavelet analysis of Treasury yield curve evolution

Prices of U.S. Treasury securities vary over time and across maturities. When the market in Treasurys is sufficiently complete and frictionless, these prices may be modeled by a function time and maturity. A cross-section of this function for time held fixed is called the yield curve; the aggregate of these sections is the evolution of the yield curve. This dissertation studies aspects of this evolution. ^ There are two complementary approaches to the study of yield curve evolution here. The first is principal components analysis; the second is wavelet analysis. In both approaches both the time and maturity variables are discretized. In principal components analysis the vectors of yield curve shifts are viewed as observations of a multivariate normal distribution. The resulting covariance matrix is diagonalized; the resulting eigenvalues and eigenvectors (the principal components) are used to draw inferences about the yield curve evolution. ^ In wavelet analysis, the vectors of shifts are resolved into hierarchies of localized fundamental shifts (wavelets) that leave specified global properties invariant (average change and duration change). The hierarchies relate to the degree of localization with movements restricted to a single maturity at the base and general movements at the apex. Second generation wavelet techniques allow better adaptation of the model to economic observables. Statistically, the wavelet approach is inherently nonparametric while the wavelets themselves are better adapted to describing a complete market. ^ Principal components analysis provides information on the dimension of the yield curve process. While there is no clear demarkation between operative factors and noise, the top six principal components pick up 99% of total interest rate variation 95% of the time. An economically justified basis of this process is hard to find; for example a simple linear model will not suffice for the first principal component and the shape of this component is nonstationary. ^ Wavelet analysis works more directly with yield curve observations than principal components analysis. In fact the complete process from bond data to multiresolution is presented, including the dedicated Perl programs and the details of the portfolio metrics and specially adapted wavelet construction. The result is more robust statistics which provide balance to the more fragile principal components analysis. ^

Design and fabrication of two switched superconductivity microstrip hairpin filters using series micro-electro-mechanical systems (MEMS) switches

Series Micro-Electro-Mechanical System (MEMS) switches based on superconductor are utilized to switch between two bandpass hairpin filters with bandwidths of 365 MHz and nominal center frequencies of 2.1 GHz and 2.6 GHz. This was accomplished with 4 switches actuated in pairs, one pair at a time. When one pair was actuated the first bandpass filter was coupled to the input and output ports. When the other pair was actuated the second bandpass filter was coupled to the input and output ports. The device is made of a YBa2Cu 3O7 thin film deposited on a 20 mm x 20 mm LaAlO3 substrate by pulsed laser deposition. BaTiO3 deposited by RF magnetron sputtering in utilized as the insulation layer at the switching points of contact. These results obtained assured great performance showing a switchable device at 68 V with temperature of 40 K for the 2.1 GHz filter and 75 V with temperature of 30 K for the 2.6 GHz hairpin filter. ^

Electrocardiogram breathing: A study of electrocardiogram derived respiration plus multiresolution wavelet analysis of heartbeat interval variability in sleep study patients

This dissertation presents a unique research opportunity by using recordings which provide electrocardiogram (ECG) plus a reference breathing signal (RBS). ECG derived breathing (EDR) is measured and correlated against RBS. Standard deviations of multiresolution wavelet analysis coefficients (SDMW) are obtained from heart rate and classified using RBS. Prior works by others used select patients for sleep apnea scoring with EDR but no RBS. Another prior work classified select heart disease patients with SDMW but no RBS. This study used randomly chosen sleep disorder patient recordings; central and obstructive apneas, with and without heart disease.^ Implementation required creating an application because existing systems were limited in power and scope. A review survey was created to choose a development environment. The survey is presented as a learning tool and teaching resource. Development objectives were rapid development using limited resources (manpower and money). Open Source resources were used exclusively for implementation. ^ Results show: (1) Three groups of patients exist in the study. Grouping RBS correlations shows a response with either ECG interval or amplitude variation. A third group exists where neither ECG intervals nor amplitude variation correlate with breathing. (2) Previous work done by other groups analyzed SDMW. Similar results were found in this study but some subjects had higher SDMW, attributed to a large number of apneas, arousals and/or disconnects. SDMW does not need RBS to show apneic conditions exist within ECG recordings. (3) Results in this study support the assertion that autonomic nervous system variation was measured with SDMW. Measurements using RBS are not corrupted due to breathing even though respiration overlaps the same frequency band.^ Overall, this work becomes an Open Source resource which can be reused, modified and/or expanded. It might fast track additional research. In the future the system could also be used for public domain data. Prerecorded data exist in similar formats in public databases which could provide additional research opportunities. ^