An Efficient Fingerprint Enhancement Technique Using Wave Atom Transform and MCS Algorithm

AbstractFingerprints are widely and successfully used for personal identification. This is mainly due to their individuality, stability through life, uniqueness among people, public acceptance and their minimum risk of intrusion. Fingerprint technology is a biometric technique utilized to identify persons based on their physical traits. The physical patterns of this technique consist of ridges and valleys that exist on the surface of fingertips. Fingerprint images are direction-oriented patterns formed by ridges and valleys. The eminence of the fingerprint image is determined by the sturdiness of a fingerprint authentication system. In order to improve the limitations of existing fingerprint image enhancement methods an efficient technique is proposed to deal with low quality fingerprint images. The proposed methodology can be divided into three modules. In the first module, the fingerprint image is subjected to denoising process where Wave atom transform is utilized. After the completion of this process the image enhancement is performed with the help of optimization algorithm. In our enhancement approach, a Modified Cuckoo Search (MCS) algorithm is used as an optimizer. This helps to look for the best gray level distribution that maximizes the objective function

An Efficient Fingerprint Identification using Neural Network and BAT Algorithm

The uniqueness, firmness, public recognition, and its minimum risk of intrusion made fingerprint is an expansively used personal authentication metrics. Fingerprint technology is a biometric technique used to distinguish persons based on their physical traits. Fingerprint based authentication schemes are becoming increasingly common and usage of these in fingerprint security schemes, made an objective to the attackers. The repute of the fingerprint image controls the sturdiness of a fingerprint authentication system. We intend for an effective method for fingerprint classification with the help of soft computing methods. The proposed classification scheme is classified into three phases. The first phase is preprocessing in which the fingerprint images are enhanced by employing median filters. After noise removal histogram equalization is achieved for augmenting the images. The second stage is the feature Extraction phase in which numerous image features such as Area, SURF, holo entropy, and SIFT features are extracted. The final phase is classification using hybrid Neural for classification of fingerprint as fake or original. The neural network is unified with BAT algorithm for optimizing the weight factor

HCV NS5B polymerase-bound conformation of a soluble sulfonamide inhibitor by 2D tranferred NOESY

HCV NS5B RNA-dependent RNA polymerase (NS5B) is essential for viral replication and is therefore considered a target for antiviral drug development. From our ongoing screening e\ufb00ort in the search for new anti-HCV agents, a novel inhibitor 1 with low lM activity against the HCV NS5B polymerase was identi\ufb01ed. SAR analysis indicated the optimal substitution pattern required for activity, for example, carboxylic acid group at 2-position of thiophene ring. We describe the steps taken to identify and solve the bioactive conformation of derivative 6 through the use of the transferred NOE method (trNOE).NRC publication: Ye