Tessamma Thomas

Basin-wide assessment of temperature trends in northwest and central India / Estimation par bassin versant de tendances de température au nord-ouest et au centre de l'Inde

Abstract Estimates of trends of climatic changes at basin and state scales are required for developing adaptation strategies related to planning, development and management of water resources. In the present study, seasonal and annual trends of changes in maximum temperature (T max), minimum temperature (T min), mean temperature (T mean), temperature range (T range), highest maximum temperature (H max) and lowest minimum temperature (L min) have been examined at the basin scale. The longest available records over the last century, for 43 stations covering nine river basins in northwest and central India, were used in the analysis. Of the nine river basins studied, seven showed a warming trend, whereas two showed a cooling trend. The Narmada and Sabarmati river basins experienced the maximum warming and cooling, respectively. The majority of basins in the study area show increasing trend in T range, H max and L min. Seasonal analysis of different variables shows that the greatest changes in T max and T mean were observed in the post-monsoon season, while T min experienced the greatest change in the monsoon season. This analysis provides scenarios of temperature changes which may be used for sensitivity analysis of water availability for different basins, and accordingly in planning and implementation of adaptation strategies.

Discrete wavelet transform de-noising in eukaryotic gene splicing

BackgroundThis paper compares the most common digital signal processing methods of exon prediction in eukaryotes, and also proposes a technique for noise suppression in exon prediction. The specimen used here which has relevance in medical research, has been taken from the public genomic database - GenBank.MethodsHere exon prediction has been done using the digital signal processing methods viz. binary method, EIIP (electron-ion interaction psuedopotential) method and filter methods. Under filter method two filter designs, and two approaches using these two designs have been tried. The discrete wavelet transform has been used for de-noising of the exon plots.ResultsResults of exon prediction based on the methods mentioned above, which give values closest to the ones found in the NCBI database are given here. The exon plot de-noised using discrete wavelet transform is also given.ConclusionAlterations to the proven methods as done by the authors, improves performance of exon prediction algorithms. Also it has been proven that the discrete wavelet transform is an effective tool for de-noising which can be used with exon prediction algorithms.

Applications of Fractional Fourier Transform in Sonar Signal Processing

Abstract Underwater scenario with all its complexities has been always very challenging for sonar signal processing. The reverberation and the fast-fading nature of the channel make it necessary to use chirp waveforms for sonar transmissions. The conventional techniques based on Fourier transforms often fail to fully address the issues like Doppler estimation with chirp waveforms and low signal-to-noise ratio detection due to the vagaries of the medium. Alternately, the fractional Fourier transform (henceforth shortened as FrFT) can be used in sonar signal processing for improved chirp-detection performance. However, this useful signal-processing tool is largely unknown to the sonar signal processing community. This paper demonstrates the application of FrFT in active and intercept sonar signal processing. The motivation to adopt the proposed method is the ability of FrFT to process chirp signals better than the conventional Fourier transform. FrFT is a parameterized transform with parameter a, related to the chirp rate. Many active sonar systems choose to transmit chirp signals for better detection in the presence of reverberation. FrFT if used instead of FFT in the correlation receiver has great potential as it takes advantage of the knowledge of transmitted waveform and can be therefore implemented for the optimum order. In the simulations, performance of matched filtering with FrFT has been compared with performance using conventional FFT. In the case of intercept sonar, FrFT can be used to estimate the parameters of chirps from a multi-component signal. This paper presents a novel parameter-estimation procedure by which chirp parameters are calculated from the two primary estimates, namely, optimum order and FrFT peak position. Simulation results clearly demonstrate the potential advantages of the proposed methods.

A new fast fractal modeling approach for the detection of microcalcifications in mammograms.

In this paper, a novel fast method for modeling mammograms by deterministic fractal coding approach to detect the presence of microcalcifications, which are early signs of breast cancer, is presented. The modeled mammogram obtained using fractal encoding method is visually similar to the original image containing microcalcifications, and therefore, when it is taken out from the original mammogram, the presence of microcalcifications can be enhanced. The limitation of fractal image modeling is the tremendous time required for encoding. In the present work, instead of searching for a matching domain in the entire domain pool of the image, three methods based on mean and variance, dynamic range of the image blocks, and mass center features are used. This reduced the encoding time by a factor of 3, 89, and 13, respectively, in the three methods with respect to the conventional fractal image coding method with quad tree partitioning. The mammograms obtained from The Mammographic Image Analysis Society database (ground truth available) gave a total detection score of 87.6%, 87.6%, 90.5%, and 87.6%, for the conventional and the proposed three methods, respectively.

Multiplexed Wavelet Transform Technique for Detection of Microcalcification in Digitized Mammograms

Wavelet transform (WT) is a potential tool for the detection of microcalcifications, an early sign of breast cancer. This article describes the implementation and evaluates the performance of two novel WT-based schemes for the automatic detection of clustered microcalcifications in digitized mammograms. Employing a one-dimensional WT technique that utilizes the pseudo-periodicity property of image sequences, the proposed algorithms achieve high detection efficiency and low processing memory requirements. The detection is achieved from the parent–child relationship between the zero-crossings [Marr-Hildreth (M-H) detector] /local extrema (Canny detector) of the WT coefficients at different levels of decomposition. The detected pixels are weighted before the inverse transform is computed, and they are segmented by simple global gray level thresholding. Both detectors produce 95% detection sensitivity, even though there are more false positives for the M-H detector. The M-H detector preserves the shape information and provides better detection sensitivity for mammograms containing widely distributed calcifications.

Fractal Modeling of Mammograms Based on Mean and Variance for the Detection of Microcalcifications

In this paper the breast background tissues are modeled using deterministic fractal model based on the mean and variance of the image blocks for detecting the presence of microcalcifications in mammograms are presented. Only those image blocks whose variance difference is between 0.01 and 1 are classified according to their mean value and used in the matching block searching process and therefore the time taken to model the mammograms was considerably reduced to about one third the time required to encode in the conventional fractal encoding scheme. The modeled image will be visually close to the original image and if the difference between the original and the modeled image is taken the presence of microcalcifications can be detected. The method was tested by using the mammograms obtained from MIAS database. The average correlation between the original and the modeled mammograms were obtained as 0.9740 and the average mean square error was found to be 5.939. The results show that the true positive rate is 82% with an average of 0.214 negative clusters per image for 28 mammograms were obtained.

Automatic segmentation framework for primary tumors from brain MRIs using morphological filtering techniques

This paper describes a novel framework for automatic segmentation of primary tumors and its boundary from brain MRIs using morphological filtering techniques. This method uses T2 weighted and T1 FLAIR images. This approach is very simple, more accurate and less time consuming than existing methods. This method is tested by fifty patients of different tumor types, shapes, image intensities, sizes and produced better results. The results were validated with ground truth images by the radiologist. Segmentation of the tumor and boundary detection is important because it can be used for surgical planning, treatment planning, textural analysis, 3-Dimensional modeling and volumetric analysis.

Clustering of Invariance Improved Legendre Moment Descriptor for Content Based Image Retrieval

This paper reports a k-Means clustering based technique for content based image retrieval (CBIR) using improved Legendre moment descriptor (ILMD). The ILMD is based on orthogonal Legendre moment polynomial and preprocessing steps required for invariance improvement of ILMD is discussed. A comparative study of the clustering accuracy of ILMD with popular Zernike moment descriptor (ZMD) and angular radial transformation descriptor (ARTD) is carried out The clustering accuracy of both contour shape description and region shape description were investigated. The shape databases used for evaluation were MPEG-7 approved CE-1 set B contour shape database and CE-2 set A1 region shape database. The k-Means clustering of the shape descriptors shows better accuracy for ILMD than ARTD and for ARTD than ZMD for both region and contour shape descriptor.

Automatic Optic Disc Boundary Extraction from Color Fundus Images

(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 5, No. 7, 2014

Compression of Psuedo-Periodic Signals Using 2D Wavelet Transforms

This paper attempts to utilize the pitch synchronous property of pseudo- periodic 1-dimensional signals like voiced speech, music etc, to improve the efficiency of compression retaining the distinctive characteristics of the original signal. The signal is represented in 2-dimensional form and decomposed using 2-D wavelet. The decomposed signal is compressed using various threshold parameters. Results show that higher signal to noise ratio, higher compression ratio and lower percentage distortion are obtained with the new method of 2-D compression as compared to 1-D compression. We have used a new method of pitch peak detection of voiced signals using k-means clustering algorithm.