Off-line signature verification using genetically optimized weighted features

This paper is concerned with off-line signature verification. Four different types of pattern representation schemes have been implemented, viz., geometric features, moment-based representations, envelope characteristics and tree-structured Wavelet features. The individual feature components in a representation are weighed by their pattern characterization capability using Genetic Algorithms. The conclusions of the four subsystems teach depending on a representation scheme) are combined to form a final decision on the validity of signature. Threshold-based classifiers (including the traditional confidence-interval classifier), neighbourhood classifiers and their combinations were studied. Benefits of using forged signatures for training purposes have been assessed. Experimental results show that combination of the Feature-based classifiers increases verification accuracy. (C) 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

Machine recognition of printed Kannada text

This paper presents the design of a full fledged OCR system for printed Kannada text. The machine recognition of Kannada characters is difficult due to similarity in the shapes of different characters, script complexity and non-uniqueness in the representation of diacritics. The document image is subject to line segmentation, word segmentation and zone detection. From the zonal information, base characters, vowel modifiers and consonant conjucts are separated. Knowledge based approach is employed for recognizing the base characters. Various features are employed for recognising the characters. These include the coefficients of the Discrete Cosine Transform, Discrete Wavelet Transform and Karhunen-Louve Transform. These features are fed to different classifiers. Structural features are used in the subsequent levels to discriminate confused characters. Use of structural features, increases recognition rate from 93% to 98%. Apart from the classical pattern classification technique of nearest neighbour, Artificial Neural Network (ANN) based classifiers like Back Propogation and Radial Basis Function (RBF) Networks have also been studied. The ANN classifiers are trained in supervised mode using the transform features. Highest recognition rate of 99% is obtained with RBF using second level approximation coefficients of Haar wavelets as the features on presegmented base characters.

Optimal feature extraction for bilingual OCR

Feature extraction in bilingual OCR is handicapped by the increase in the number of classes or characters to be handled. This is evident in the case of Indian languages whose alphabet set is large. It is expected that the complexity of the feature extraction process increases with the number of classes. Though the determination of the best set of features that could be used cannot be ascertained through any quantitative measures, the characteristics of the scripts can help decide on the feature extraction procedure. This paper describes a hierarchical feature extraction scheme for recognition of printed bilingual (Tamil and Roman) text. The scheme divides the combined alphabet set of both the scripts into subsets by the extraction of certain spatial and structural features. Three features viz geometric moments, DCT based features and Wavelet transform based features are extracted from the grouped symbols and a linear transformation is performed on them for the purpose of efficient representation in the feature space. The transformation is obtained by the maximization of certain criterion functions. Three techniques : Principal component analysis, maximization of Fisher's ratio and maximization of divergence measure have been employed to estimate the transformation matrix. It has been observed that the proposed hierarchical scheme allows for easier handling of the alphabets and there is an appreciable rise in the recognition accuracy as a result of the transformations.

Strip based coding for large images using wavelets

Thanks to advances in sensor technology, today we have many applications (space-borne imaging, medical imaging, etc.) where images of large sizes are generated. Straightforward application of wavelet techniques for above images involves certain difficulties. Embedded coders such as EZW and SPIHT require that the wavelet transform of the full image be buffered for coding. Since the transform coefficients also require storing in high precision, buffering requirements for large images become prohibitively high. In this paper, we first devise a technique for embedded coding of large images using zero trees with reduced memory requirements. A 'strip buffer' capable of holding few lines of wavelet coefficients from all the subbands belonging to the same spatial location is employed. A pipeline architecure for a line implementation of above technique is then proposed. Further, an efficient algorithm to extract an encoded bitstream corresponding to a region of interest in the image has also been developed. Finally, the paper describes a strip based non-embedded coding which uses a single pass algorithm. This is to handle high-input data rates. (C) 2002 Elsevier Science B.V. All rights reserved.

Optimal local polynomial regression of noisy time varying signals

We address the problem of local-polynomial modeling of smooth time-varying signals with unknown functional form, in the presence of additive noise. The problem formulation is in the time domain and the polynomial coefficients are estimated in the pointwise minimum mean square error (PMMSE) sense. The choice of the window length for local modeling introduces a bias-variance tradeoff, which we solve optimally by using the intersection-of-confidence-intervals (ICI) technique. The combination of the local polynomial model and the ICI technique gives rise to an adaptive signal model equipped with a time-varying PMMSE-optimal window length whose performance is superior to that obtained by using a fixed window length. We also evaluate the sensitivity of the ICI technique with respect to the confidence interval width. Simulation results on electrocardiogram (ECG) signals show that at 0dB signal-to-noise ratio (SNR), one can achieve about 12dB improvement in SNR. Monte-Carlo performance analysis shows that the performance is comparable to the basic wavelet techniques. For 0 dB SNR, the adaptive window technique yields about 2-3dB higher SNR than wavelet regression techniques and for SNRs greater than 12dB, the wavelet techniques yield about 2dB higher SNR.

Lamb wave based identification and parameter estimation of corrosion in metallic plate structure using a circular PWAS array

A circular array of Piezoelectric Wafer Active Sensor (PWAS) has been employed to detect surface damages like corrosion using lamb waves. The array consists of a number of small PWASs of 10 mm diameter and 1 mm thickness. The advantage of a circular array is its compact arrangement and large area of coverage for monitoring with small area of physical access. Growth of corrosion is monitored in a laboratory-scale set-up using the PWAS array and the nature of reflected and transmitted Lamb wave patterns due to corrosion is investigated. The wavelet time-frequency maps of the sensor signals are employed and a damage index is plotted against the damage parameters and varying frequency of the actuation signal (a windowed sine signal). The variation of wavelet coefficient for different growth of corrosion is studied. Wavelet coefficient as function of time gives an insight into the effect of corrosion in time-frequency scale. We present here a method to eliminate the time scale effect which helps in identifying easily the signature of damage in the measured signals. The proposed method becomes useful in determining the approximate location of the corrosion with respect to the location of three neighboring sensors in the circular array. A cumulative damage index is computed for varying damage sizes and the results appear promising.

Pixel based fusion using IKONOS imagery

Pixel based image fusion entails combining geometric details of a high-resolution Panchromatic (PAN) image and spectral information of a low-resolution Multispectral (MS) image to produce images with highest spatial content while preserving the spectral information. This work reviews and implements six fusion techniques – À Trous algorithm based wavelet transform (ATW), Mulitresolution Analysis based Intensity Modulation, Gram Schmidt fusion, CN Spectral, Luminance Chrominance and High pass fusion (HPF) on IKONOS imagery having 1 m PAN and 4 m MS channels. Comparative performance analysis of techniques by various methods reveals that ATW followed by HPF perform best among all the techniques.

Ultrasonic Lamb wave based monitoring of corrosion type of damage in plate using a circular array of piezoelectric transducers

In this paper we propose a concept and report experimental results based on a circular array of Piezoelectric Wafer Active Sensors (PWASs) for rapid localization and parametric identification of corrosion type damage in metallic plates. Implementation of this circular array of PWASs combines the use of ultrasonic Lamb wave propagation technique and an algorithm based on symmetry breaking in the signal pattern to locate and monitor the growth of a corrosion pit on a metallic plate. Wavelet time-frequency maps of the sensor signals are employed to obtain an insight regarding the effect of corrosion growth on the Lamb wave transmission in time-frequency scale. We present here a method to eliminate the time scale, which helps in identifying easily the signature of damage in the measured signals. The proposed method becomes useful in determining the approximate location of the damage with respect to the location of three neighboring sensors in the circular array. A cumulative damage index is computed from the wavelet coefficients for varying damage sizes and the results appear promising. Damage index is plotted against the damage parameters for frequency sweep of the excitation signal (a windowed sine signal). Results of corrosion damage are compared with circular holes of various sizes to demonstrate the applicability of present method to different types of damage. (C) 2011 Elsevier Ltd. All rights reserved.

Universality of scaling laws in correlation between velocity and shear stress in turbulent boundary layers

In this study, we analyse simultaneous measurements (at 50 Hz) of velocity at several heights and shear stress at the surface made during the Utah field campaign for the presence of ranges of scales, where distinct scale-to-scale interactions between velocity and shear stress can be identified. We find that our results are similar to those obtained in a previous study [Venugopal et al., 2003] (contrary to the claim in V2003, that the scaling relations might be dependent on Reynolds number) where wind tunnel measurements of velocity and shear stress were analysed. We use a wavelet-based scale-to-scale cross-correlation to detect three ranges of scales of interaction between velocity and shear stress, namely, (a) inertial subrange, where the correlation is negligible; (b) energy production range, where the correlation follows a logarithmic law; and (c) for scales larger than the boundary layer height, the correlation reaches a plateau.

VLSI Implementation of Spatial prediction Based Image Compression Scheme

We propose the design and implementation of hardware architecture for spatial prediction based image compression scheme, which consists of prediction phase and quantization phase. In prediction phase, the hierarchical tree structure obtained from the test image is used to predict every central pixel of an image by its four neighboring pixels. The prediction scheme generates an error image, to which the wavelet/sub-band coding algorithm can be applied to obtain efficient compression. The software model is tested for its performance in terms of entropy, standard deviation. The memory and silicon area constraints play a vital role in the realization of the hardware for hand-held devices. The hardware architecture is constructed for the proposed scheme, which involves the aspects of parallelism in instructions and data. The processor consists of pipelined functional units to obtain the maximum throughput and higher speed of operation. The hardware model is analyzed for performance in terms throughput, speed and power. The results of hardware model indicate that the proposed architecture is suitable for power constrained implementations with higher data rate

A DCT Based Filtering of Biomedical Images

Image filtering techniques have numerous potential applications in biomedical imaging and image processing. The design of filters largely depends on the a-priori knowledge about the type of noise corrupting the image and image features. This makes the standard filters to be application and image specific. The most popular filters such as average, Gaussian and Wiener reduce noisy artifacts by smoothing. However, this operation normally results in smoothing of the edges as well. On the other hand, sharpening filters enhance the high frequency details making the image non-smooth. An integrated general approach to design filters based on discrete cosine transform (DCT) is proposed in this study for optimal medical image filtering. This algorithm exploits the better energy compaction property of DCT and re-arrange these coefficients in a wavelet manner to get the better energy clustering at desired spatial locations. This algorithm performs optimal smoothing of the noisy image by preserving high and low frequency features. Evaluation results show that the proposed filter is robust under various noise distributions.

A Long Memory Process Based Parametric Modeling and Recognition of PD Signal

We address the problem of recognition and retrieval of relatively weak industrial signal such as Partial Discharges (PD) buried in excessive noise. The major bottleneck being the recognition and suppression of stochastic pulsive interference (PI) which has similar time-frequency characteristics as PD pulse. Therefore conventional frequency based DSP techniques are not useful in retrieving PD pulses. We employ statistical signal modeling based on combination of long-memory process and probabilistic principal component analysis (PPCA). An parametric analysis of the signal is exercised for extracting the features of desired pules. We incorporate a wavelet based bootstrap method for obtaining the noise training vectors from observed data. The procedure adopted in this work is completely different from the research work reported in the literature, which is generally based on deserved signal frequency and noise frequency.

Ultrasonic Guided Wave Characterization and Damage Detection in Foam-core Sandwich Panel using PWAS and LDV

Lamb wave type guided wave propagation in foam core sandwich structures and detectability of damages using spectral analysis method are reported in this paper. An experimental study supported by theoretical evaluation of the guided wave characteristics is presented here that shows the applicability of Lamb wave type guided ultrasonic wave for detection of damage in foam core sandwich structures. Sandwich beam specimens were fabricated with 10 mm thick foam core and 0.3 mm thick aluminum face sheets. Thin piezoelectric patch actuators and sensors are used to excite and sense guided wave. Group velocity dispersion curves and frequency response of sensed signal are obtained experimentally. The nature of damping present in the sandwich panel is monitored by measuring the sensor signal amplitude at various different distances measured from the center of the linear phased array. Delaminations of increasing width are created and detected experimentally by pitch-catch interrogation with guided waves and wavelet transform of the sensed signal. Signal amplitudes are analyzed for various different sizes of damages to differentiate the damage size/severity. A sandwich panel is also fabricated with a planer dimension of 600 mm x 400 mm. Release film delamination is introduced during fabrication. Non-contact Laser Doppler Vibrometer (LDV) is used to scan the panel while exciting with a surface bonded piezoelectric actuator. Presence of damage is confirmed by the reflected wave fringe pattern obtained from the LDV scan. With this approach it is possible to locate and monitor the damages by tracking the wave packets scattered from the damages.

Guided wave based detection of damage in honeycomb core sandwich structures

We report on the Lamb wave type guided wave propagation in honeycomb core sandwich structures. An experimental study supported by theoretical evaluation of the guided wave characteristics is presented that proves the potential of Lamb wave type guided wave for detection of damage in sandwich structures. A sandwich panel is fabricated with planar dimension of 600 mm x 600 mm, having a core thickness of 7 mm, cell size of 5 mm and 0.1 mm thick aluminum face sheets. Thin piezoelectric patch actuators and sensors are used to excite and sense a frequency band limited guided wave with a central frequency. A linear phased array of piezoelectric patch actuators is used to achieve higher signal strength and directivity. Group velocity dispersion curves and corresponding frequency response of sensed signal are obtained experimentally. Linearity between the excitation signal amplitude and the corresponding sensed signal amplitude is found for certain range of parameters. The nature of damping present in the sandwich panel is monitored by measuring the sensor signal amplitude at various different distances measured from the center of the linear phased array. Indentation and low velocity impact induced damages of increasing diameter covering several honeycomb cells are created. Crushing of honeycomb core with rupture of face sheet is observed while introducing the damage. The damages are then detected experimentally by pitch-catch interrogation with guided waves and wavelet transform of the sensed signal. Signal amplitudes are analyzed for various different sizes of damages to differentiate the damage size/severity. Monotonic changes in the sensor signal amplitude due to increase in the damage size has been established successfully. With this approach it is possible to locate and monitor the damages with the help of phased array and by tracking the wave packets scattered from the damages. (C) 2012 Elsevier Ltd. All rights reserved.

Space-Time Scales of Northward Propagation of Convection during Boreal Summer

This study uses precipitation estimates from the Tropical Rainfall Measuring Mission to quantify the spatial and temporal scales of northward propagation of convection over the Indian monsoon region during boreal summer. Propagating modes of convective systems in the intraseasonal time scales such as the Madden-Julian oscillation can interact with the intertropical convergence zone and bring active and break spells of the Indian summer monsoon. Wavelet analysis was used to quantify the spatial extent (scale) and center of these propagating convective bands, as well as the time period associated with different spatial scales. Results presented here suggest that during a good monsoon year the spatial scale of this oscillation is about 30 degrees centered around 10 degrees N. During weak monsoon years, the scale of propagation decreases and the center shifts farther south closer to the equator. A strong linear relationship is obtained between the center/scale of convective wave bands and intensity of monsoon precipitation over Indian land on the interannual time scale. Moreover, the spatial scale and its center during the break monsoon were found to be similar to an overall weak monsoon year. Based on this analysis, a new index is proposed to quantify the spatial scales associated with propagating convective bands. This automated wavelet-based technique developed here can be used to study meridional propagation of convection in a large volume of datasets from observations and model simulations. The information so obtained can be related to the interannual and intraseasonal variation of Indian monsoon precipitation.