Dynamic and Thermodynamic Structure of the Monsoon Boundary Layer over India

The objective of the study is to examine the dynamic and thermodynamic structure and the variations that occur in the surface layer during the pre-monsoon, onset and post-monsoon periods over the Indian region. The variations caused during the occurrence of micro and mesoscale systems, structure and variation in the marine boundary layer over the Indian region is also investigated. The drag coefficient computed indirectly also shows variation during various seasons. The thermodynamic structure of the atmosphere shows variation during the various seasons. The onset monsoon causes lowering of the Lifting Condensation Levels. The outcome of the study is expected to provide a better understanding of the structure and variations in the boundary layer over India, which is useful for many applications especially for numerical modeling studies.

Characteristics of ABL during active and weak phases of monsoon

Understanding of the Atmospheric Boundary Layer (ABL) is imperative in the arena of the monsoon field. Here, the features of the ABL are studied employing Conserved Variable Analysis (CVA) using equivalent potential temperature and humidity. In addition, virtual potential temperature and wind are used during active and weak phases of monsoon. The analysis is carried out utilising the radiosonde observations during the monsoon months for two stations situated in the west coast of India. All these parameters show considerable variations during active and weak monsoon phases in both the stations. The core speed and core height vary with these epochs. The core speed is found to be more than 38 knots in the active monsoon phase around 1.2 km over Trivandrum and around 2 km over Mangalore. But during weak monsoon phase the core wind speed is decreased and core height is elevated over both stations. The wind direction shows an additional along shore component during weak monsoon period. The Convective Boundary Layer (CBL) height shows increase during weak monsoon phase over both stations due to less cloudiness and subsequent insolation. The CBL height during the southwest monsoon is more over Mangalore and is attributed by the orographic lifting in the windward side of the Western Ghats while the influence of the Ghats is less over Trivandrum.

An Improved Technique for Evolving Wavelet Coefficients for Fingerprint Image Compression

In this paper, an improved technique for evolving wavelet coefficients refined for compression and reconstruction of fingerprint images is presented. The FBI fingerprint compression standard [1, 2] uses the cdf 9/7 wavelet filter coefficients. Lifting scheme is an efficient way to represent classical wavelets with fewer filter coefficients [3, 4]. Here Genetic algorithm (GA) is used to evolve better lifting filter coefficients for cdf 9/7 wavelet to compress and reconstruct fingerprint images with better quality. Since the lifting filter coefficients are few in numbers compared to the corresponding classical wavelet filter coefficients, they are evolved at a faster rate using GA. A better reconstructed image quality in terms of Peak-Signal-to-Noise-Ratio (PSNR) is achieved with the best lifting filter coefficients evolved for a compression ratio 16:1. These evolved coefficients perform well for other compression ratios also.

Faster techniques to evolve wavelet coefficients for better fingerprint image compression

In this article, techniques have been presented for faster evolution of wavelet lifting coefficients for fingerprint image compression (FIC). In addition to increasing the computational speed by 81.35%, the coefficients performed much better than the reported coefficients in literature. Generally, full-size images are used for evolving wavelet coefficients, which is time consuming. To overcome this, in this work, wavelets were evolved with resized, cropped, resized-average and cropped-average images. On comparing the peak- signal-to-noise-ratios (PSNR) offered by the evolved wavelets, it was found that the cropped images excelled the resized images and is in par with the results reported till date. Wavelet lifting coefficients evolved from an average of four 256 256 centre-cropped images took less than 1/5th the evolution time reported in literature. It produced an improvement of 1.009 dB in average PSNR. Improvement in average PSNR was observed for other compression ratios (CR) and degraded images as well. The proposed technique gave better PSNR for various bit rates, with set partitioning in hierarchical trees (SPIHT) coder. These coefficients performed well with other fingerprint databases as well.

Evolution of Better Wavelet Coefficients for Fingerprint Image Compression using cropped images

This paper explains the Genetic Algorithm (GA) evolution of optimized wavelet that surpass the cdf9/7 wavelet for fingerprint compression and reconstruction. Optimized wavelets have already been evolved in previous works in the literature, but they are highly computationally complex and time consuming. Therefore, in this work, a simple approach is made to reduce the computational complexity of the evolution algorithm. A training image set comprised of three 32x32 size cropped images performed much better than the reported coefficients in literature. An average improvement of 1.0059 dB in PSNR above the classical cdf9/7 wavelet over the 80 fingerprint images was achieved. In addition, the computational speed was increased by 90.18 %. The evolved coefficients for compression ratio (CR) 16:1 yielded better average PSNR for other CRs also. Improvement in average PSNR was experienced for degraded and noisy images as well

Raman and FTIR spectra of [Cu(H2O)6](BrO3)2 and [Al(H2O)6](BrO3)3 · 3H2O

Raman and FTIR spectra of [Cu(H2O)6](BrO3)2 and [Al(H2O)6](BrO3)3 · 3H2O are recorded and analyzed. The observed bands are assigned on the basis of BrO3 − and H2O vibrations. Additional bands obtained in the region of 3 and 1 modes in [Cu(H2O)6](BrO3)2 are due to the lifting of degeneracy of 3 modes, since the BrO3 − ion occupies a site of lower symmetry. The appearance 1 mode of BrO3 − anion at a lower wavenumber (771 cm−1) is attributed to the attachment of hydrogen to the BrO3 − anion. The presence of three inequivalent bromate groups in the [Al(H2O)6](BrO3)3 · 3H2O structure is confirmed. The lifting of degeneracy of 4 mode indicates that the symmetry of BrO3 − anion is lowered in the above crystal from C3v to C1. The appearance of additional bands in the stretching and bonding mode regions of water indicates the presence of hydrogen bonds of different strengths in both the crystals. Temperature dependent Raman spectra of single crystal [Cu(H2O)6](BrO3)2 are recorded in the range 77–523 K for various temperatures. A small structural rearrangement takes place in BrO3 − ion in the crystal at 391 K. Hydrogen bounds in the crystal are rearranging themselves leading to the loss of one water molecule at 485 K. This is preceded by the reorientation of BrO3 − ions causing a phase transition at 447 K. Changes in intensities and wavenumbers of the bands and the narrowing down of the bands at 77 K are attributed to the settling down of protons into ordered positions in the crystal

Development of Directionally Adaptive Techniques for Single Image Super-Resolution

Super Resolution problem is an inverse problem and refers to the process of producing a High resolution (HR) image, making use of one or more Low Resolution (LR) observations. It includes up sampling the image, thereby, increasing the maximum spatial frequency and removing degradations that arise during the image capture namely aliasing and blurring. The work presented in this thesis is based on learning based single image super-resolution. In learning based super-resolution algorithms, a training set or database of available HR images are used to construct the HR image of an image captured using a LR camera. In the training set, images are stored as patches or coefficients of feature representations like wavelet transform, DCT, etc. Single frame image super-resolution can be used in applications where database of HR images are available. The advantage of this method is that by skilfully creating a database of suitable training images, one can improve the quality of the super-resolved image. A new super resolution method based on wavelet transform is developed and it is better than conventional wavelet transform based methods and standard interpolation methods. Super-resolution techniques based on skewed anisotropic transform called directionlet transform are developed to convert a low resolution image which is of small size into a high resolution image of large size. Super-resolution algorithm not only increases the size, but also reduces the degradations occurred during the process of capturing image. This method outperforms the standard interpolation methods and the wavelet methods, both visually and in terms of SNR values. Artifacts like aliasing and ringing effects are also eliminated in this method. The super-resolution methods are implemented using, both critically sampled and over sampled directionlets. The conventional directionlet transform is computationally complex. Hence lifting scheme is used for implementation of directionlets. The new single image super-resolution method based on lifting scheme reduces computational complexity and thereby reduces computation time. The quality of the super resolved image depends on the type of wavelet basis used. A study is conducted to find the effect of different wavelets on the single image super-resolution method. Finally this new method implemented on grey images is extended to colour images and noisy images