Cubic Bezier Curve Approach for Automated Offline Signature Verification with Intrusion Identification

Authentication is a process of identifying person’s rights over a system. Many authentication types are used in various systems, wherein biometrics authentication systems are of a special concern. Signature verification is a basic biometric authentication technique used widely. The signature matching algorithm uses image correlation and graph matching technique which provides false rejection or acceptance. We proposed a model to compare knowledge from signature. Intrusion in the signature repository system results in copy of the signature that leads to false acceptance. Our approach uses a Bezier curve algorithm to identify the curve points and uses the behaviors of the signature for verification. An analyzing mobile agent is used to identify the input signature parameters and compare them with reference signature repository. It identifies duplication of signature over intrusion and rejects it. Experiments are conducted on a database with thousands of signature images from various sources and the results are favorable

A Brief Introduction to Magnetoencephalography (MEG) and Its Clinical Applications

Funding Information: Acknowledgments: S.W.K.J, V.V., P.P. and B.G. acknowledge the support from Lee Kong Chian School of Medicine and Data Science and AI Research (DSAIR) Centre of NTU (Project Number ADH‐11/2017‐DSAIR and the support from the Cognitive Neuroimaging Centre (CONIC) at Nan‐ yang Technological University, Singapore. Publisher Copyright: © 2022 by the author. Licensee MDPI, Basel, Switzerland.Magnetoencephalography (MEG) plays a pivotal role in the diagnosis of brain disorders. In this review, we have investigated potential MEG applications for analysing brain disorders. The signal‐to‐noise ratio (SNRMEG =2.2 db, SNREEG <1 db) and spatial resolution (SRMEG =2–3 mm, SREEG =7–10 mm) is higher for MEG than EEG, thus MEG potentially facilitates accurate monitoring of cortical activity. We found that the direct electrophysiological MEG signals reflected the physiological status of neurological disorders and play a vital role in disease diagnosis. Single‐channel con-nectivity, as well as brain network analysis, using MEG data acquired during resting state and a given task has been used for the diagnosis of neurological disorders such as epilepsy, Alzheimer’s, Parkinsonism, autism, and schizophrenia. The workflow of MEG and its potential applications in the diagnosis of disease and therapeutic planning are also discussed. We forecast that computer-aided algorithms will play a prominent role in the diagnosis and prediction of neurological diseases in the future. The outcome of this narrative review will aid researchers to utilise MEG in diagnostics.Peer reviewe