The Effect of SIFT Features as Content Descriptors in the Context of Automatic Writer Identification in Malayalam Language

The span of writer identification extends to broad domes like digital rights administration, forensic expert decisionmaking systems, and document analysis systems and so on. As the success rate of a writer identification scheme is highly dependent on the features extracted from the documents, the phase of feature extraction and therefore selection is highly significant for writer identification schemes. In this paper, the writer identification in Malayalam language is sought for by utilizing feature extraction technique such as Scale Invariant Features Transform (SIFT).The schemes are tested on a test bed of 280 writers and performance evaluated

Lead free heterogeneous multilayers with giant magneto electric coupling for microelectronics/microelectromechanical systems applications

Lead free magneto electrics with a strong sub resonant (broad frequency range) magneto electric coupling coefficient (MECC) is the goal of the day which can revolutionise the microelectronics and microelectromechanical systems (MEMS) industry. We report giant resonant MECC in lead free nanograined Barium Titanate–CoFe (Alloy)-Barium Titanate [BTO-CoFe-BTO] sandwiched thin films. The resonant MECC values obtained here are the highest values recorded in thin films/ multilayers. Sub-resonant MECC values are quite comparable to the highest MECC reported in 2-2 layered structures. MECC got enhanced by two orders at a low frequency resonance. The results show the potential of these thin films for transducer, magnetic field assisted energy harvesters, switching devices, and storage applications. Some possible device integration techniques are also discussed

Multifinger Feature Level Fusion Based Fingerprint Identification

Fingerprint based authentication systems are one of the cost-effective biometric authentication techniques employed for personal identification. As the data base population increases, fast identification/recognition algorithms are required with high accuracy. Accuracy can be increased using multimodal evidences collected by multiple biometric traits. In this work, consecutive fingerprint images are taken, global singularities are located using directional field strength and their local orientation vector is formulated with respect to the base line of the finger. Feature level fusion is carried out and a 32 element feature template is obtained. A matching score is formulated for the identification and 100% accuracy was obtained for a database of 300 persons. The polygonal feature vector helps to reduce the size of the feature database from the present 70-100 minutiae features to just 32 features and also a lower matching threshold can be fixed compared to single finger based identification