P A College of Engineering

About: P A College of Engineering is a based out in . It is known for research contribution in the topics: Dihedral angle & Ring (chemistry). The organization has 298 authors who have published 594 publications receiving 4888 citations.

Protection augmentation using key replacement in acoustic steganography

Abstract: Owing to the hurried expansion in the field of the isolated computing in recent years, there is need of ordered protection for the information that is send through a set of systems. To fulfill this need, steganography is used as the one of the planned method to guard the secret message against the eavesdroppers. The implemented method provides one of the techniques of embedding the secret message bits into the audio series. The concept of the proposed method is that synonyms and antonyms of a word, replaces each and every statement of the secret message for a number of iterations and thus provides the raise of complexity. The synonyms and antonyms are again converted to number sequence and are formed into matrix. The matrix is inversed to increase the security. The principle aim is to reduce the distortion that is caused while embedding the covert message into the LSB and MSB audio bits using the replacement technique. The concept of genetic algorithm is used to reduce the distortion caused in the cover audio series and also increases the robustness of the embedded audio sequence. The robustness is provided against the intentional attacks which try to know the embedded secret message and some accidental attacks which try to add some unwanted message into the audio sequence. The main advantage of the proposed technique is to increase the difficulty and robustness of the audio sequence in audio steganography.

A second monoclinic polymorph of 4-[(E)-(4-benzyl-oxybenzyl-idene)amino]-1,5-dimethyl-2-phenyl-1H-pyrazol-3(2H)-one.

Abstract: In the title compound, C25H23N3O2, the central benzene ring makes dihedral angles of 77.14 (8) and 87.7 (2)° with the terminal benzene rings and an angle of 1.9 (1)° with the pyrazolone ring. The benzene ring and the N atom of the pyrazole ring bearing the phenyl substituent are disordered over two sets of sites with an occupancy ratio of 0.71 (2):0.29 (2). The N atoms of the pyrazole ring have a pyramidal environment, the sums of the valence angles around them being 354.6 (3) and 352.0 (6)/349.5 (15)°. In the crystal, mol­ecules are packed into layers parallel to the ac plane. The other monoclinic polymorphic form was reported recently [Dutkiewicz et al. (2012). Acta Cryst. E68, o1324].

Appearance based Techniques for Face Recognition: A Comparative Study

Abstract: In these days, appearance based approaches gain popularity in many computer vision problems, more in particular on face recognition techniques. In this context, a study on face recognition techniques based on appearance based paradigm is addressed in our work. More focus is provided to principal component analysis (PCA) based techniques where the principle of PCA is well received by pattern recognition community for most of the dimensionality reduction problems or for feature selection in a large collection of features set. We have seen several variants of PCA in the literature applied to the domain of face recognition considering variety of natural problems that would occur during face recognition. In our work, we have made an attempt to study the problem of face recognition under different situations. The study is conducted with varying dimension of features on a variety of face databases which include pose, illumination and occlusion problems. The effect of varying training samples is also addressed in our study. We have considered the standard PCA, two dimensional PCA (2D PCA) which works in row directions, alternative 2D PCA that works in column directions and bidirectional PCA for comparative analysis on many of the standard face databases such as AT&T, UMIST and IITK datasets. Extensive experimental results on each of these datasets along with computing time and their recognition accuracy under different dimension of feature vectors with varying number of training samples is reported in our work.