Tiwari, Anil Kumar

Regular and continuous monitoring of uterine con-tractions aid in the diagnosis of critical pregnancies. Maternal and fetal complications can be timely intervened by obstetri-cians with uterine activity monitoring at homes. The aim of this paper is to reduce the number of channels for Uterine Magnetomyography (MMG) recording in order to facilitate its use in home uterine activity monitoring (HUAM). In this work, automatic channel minimization is presented by integrating Point Biserial Correlation with a comparative Merit based strategy and Local Channel Inclusion. An additional scheme of Channel Repeatability has been introduced in order to further reduce the channel set. As a result, the minimized array of 14 achieves a superior performance in comparison to the existing methods. Using Support Vector Machine (SVM) a classification accuracy, specificity and sensitivity of 78.33%, 85% and 75%, respectively, has been achieved.

This paper presents a novel algorithm for enhancement of spatial resolution of images. The proposed algorithm estimates a Least square based predictor of lower order and interpolates the coefficients of higher order predictor. We have reduced the predictor order form p to (p-1) that results into a saving of computational power. The proposed algorithm is generic that can be used with most of the LS based interpolation algorithms reported in literature. We have shown that use of interpolated prediction coefficient causes insignificant loss in subjective as well as objective (PSNR) quality of the higher resolution (HR) image as compared with the PSNR obtained by the actual prediction coefficient and there is around 40% to 50% reduction in computational complexity.

In this paper, we propose selective estimation of least square based predictor algorithm and efficient overhead management scheme for lossless compression of digital mammograms. We exploit the characteristics of mammograms that most of the mammograms contain large number of blocks with constant gray level pixels, so a block based selective least square estimation algorithm is proposed. In our proposed algorithm if all the pixels have same intensity value in any block, then we represents those blocks by a single ('1') bit otherwise the block is decorrelated using the feed forward type of autoregressive modeling. We exploit the relationship between autoregression parameters which saves around 25% overhead burden. We have also empirically found that the AR parameters of the neighboring blocks are highly correlated and to get the best decorrelation among these parameters, median edge detector (MED) is used which gives us around 40% more saving in overhead burden. So, our proposed lossless compression algorithm for digital mammograms gives better entropy and minimum overhead burden then most of the algorithms reported in literature.

In this paper we investigate the prediction scheme of Context Based Adaptive Lossless Image Coding (CALIC), the standard for lossless/near lossless image compression for continuous-tone finger-print images. We show that it is not sufficient to consider the prediction technique in a single direction for a fingerprint image as a whole for Gradient Adjusted Predictor (GAP). As a result, we propose an additional GAP scheme to achieve better speed and better prediction accuracy as and hence provide potential for further improvements in Lossless Image Compression. Experimental results indicate that the proposed scheme outperforms the existing GAP prediction for all the finger-print images tested, while the complexity of the prediction algorithm is improved by more than four times with the help of parallel implementation.

In this paper we proposed a new computationally efficient interpolation algorithm for natural images in which unknown pixels are divided into few bins. The categorization of these unknown pixels into bins is based upon the characteristics of the neighboring pixels. These characteristics are obtained by taking difference of two slopes which are in orthogonal direction and these slopes are calculated from a set of neighboring pixels. We used the Least-Squares (LS) based approach to find optimal predictors for pixels belonging to various slope bins. We also presented a simplified proposed algorithm in which we used bilinear interpolation algorithm instead of estimating LS based predictor for some bins and it results into further reduction in computational complexity without sacrificing the much performance. Our proposed algorithm gives better interpolation quality with significantly lower computational complexity as compared to recently reported interpolation algorithms.

This paper presents a new image interpolation technique for enhancement of spatial resolution of images. The proposed algorithm uses the switching of existing Soft-decision Adaptive Interpolation (SAI) algorithm and Single Pass Interpolation Algorithm (SPIA) methods. We learn the error pattern in the interpolation process of SAI method and SPIA Method after interpolating downsampled version of LR image. Then we deviced a mechanism to correct the error pattern. Emperically we found that SAI methods works better on smooth images (variation among the pixels is less) while SPIA method works better on detailed images (more variation among the pixels), because of the type of pixels used in the interpolation. So, a hybrid scheme of combining SAI method and SPIA method is proposed for best prediction of high resolution (HR) image. The proposed algorithm produces the best results in different varieties of images in terms of both PSNR measurement and subjective visual quality.

This paper presents an algorithm for classification of fetal health status using fetal heart rate variability (fHRV) analysis through phonocardiography. First, the fetal heart sound signals are acquired from the maternal abdominal surface using a specially developed Bluetooth-based wireless data recording system. Then, fetal heart rate (FHR) traces are derived from these signals. Ten numbers of linear and nonlinear features are extracted from each FHR trace. Finally, the multilayer perceptron (MLP) neural network is used to classify the health status of the fetus. Results show very promising performance toward the prediction of fetal wellbeing on the set of collected fetal heart sound signals. Finally, this work is likely to lead to an automatic screening device with additional potential of predicting fetal wellbeing. © 2011 World Scientific Publishing Company.

This paper presents a novel and generic algorithm for reduction in computational complexity associated with the estimation of LS based predictor. Many lossless compression algorithms used predictor based on Least Squares and its variance for decorrelation of images. However, computational complexity associated with estimation of such predictor is huge. So, in order to reduce the computational complexity, we proposed to estimate a LS based predictor of order p-1 and estimates the coefficients of predictor of order p. We have reduced the predictor order form p to (p - 1) that results into a saving of computational power. We have also reduced the predefined error threshold in EDP and RALP algorithm in order to negotiate the slight loss in prediction accuracy due to synthetically generated prediction coefficient. The proposed algorithm is generic that can be used with most of the LS based lossless compression algorithms reported in literature. Our proposed algorithm gives same prediction quality as compared to when we use the actual prediction coefficient and there is around 25% to 40% reduction in computational complexity. © 2011 IEEE.

Continuous foetal monitoring of physiological signals is of particular importance for early detection of complexities related to the foetus or the mother's health. The available conventional methods of monitoring mostly perform off-line analysis and restrict the mobility of subjects within a hospital or a room. Hence, the aim of this paper is to develop a foetal e-health monitoring system using mobile phones and wireless sensors for providing advanced healthcare services in the home environment. The system is tested by recording the real-time Foetal Phonocardiography (fPCG) signals from 15 subjects with different gestational periods. The performance of the developed system is compared with the existing ultrasound based Doppler shift technique, ensuring an overall accuracy of 98% of the developed system. The developed framework is non-invasive, cost-effective and simple enough to be used in home care application. It offers advanced healthcare facilities even to the pregnant women living in rural areas and avoids their unnecessary visits at the healthcare centres. Copyright © 2012 Inderscience Enterprises Ltd.

The paper presents the well-established spectral analysis approach to diagnose the physiological state of an unborn using phonocardiography. The separation of systole and diastole segments from a complete cardiac cycle of the fetus is carried out through envelope detection and threshold decision. The averaged periodogram is used for estimating both the spectral distribution and the dominant peak of the fetal heart sound signal. The current study clearly demonstrates that the fetal heart sound signals and their frequency spectra from the pathological subjects exhibit markedly different characteristics from those of healthy subjects. It can be safely concluded that the frequency spectrum and its contents of fetal cardiac sounds are specifically related to the health of the fetus. The importance of this study lies in the fact that with this approach, recognition and accurate estimation of systole and diastole periods of the fPCG signal as well as the quantification of their frequency contents can be performed very efficiently. Copyright © 2012 Inderscience Enterprises Ltd.