Online and Offline Signature Verification: A Combined Approach

AbstractHandwritten signature verification is an emerging area. In this paper, an automatic signature verification system has been proposed. This work focuses on both online and offine features of handwritten signatures and aims at combining their results to verify the signature. Signatures are collected for both online and offine. Online data collected is the signing process captured using a webcam and offine data collected are the scanned signatures. Initially both data undergoes appropriate preprocessing steps. Then feature extraction is done where features based on pen tip tracking are used in case of online and gradient and projection based features are used in case of offine method. Later the online and offine method verifies the signature separately and finally their results are combined and the signature is verified using SVM. Paper also compares the results of online, offine and combined approach

Variable Step Size Maximum Correntropy Criteria Based Adaptive Filtering Algorithm

Maximum correntropy criterion (MCC) based adaptive filters are found to be robust against impulsive interference. This paper proposes a novel MCC based adaptive filter with variable step size in order to obtain improved performance in terms of both convergence rate and steady state error with robustness against impulsive interference. The optimal variable step size is obtained by minimizing the Mean Square Deviation (MSD) error from one iteration to the other. Simulation results in the context of a highly impulsive system identification scenario show that the proposed algorithm has faster convergence and lesser steady state error than the conventional MCC based adaptive filters

Association of Vitamin D Deficiency with Hypertension in Uninsured Women

Vitamin D deficiency is an epidemic in the United States. Uninsured women are at high risk due to a lower intake of vitamin D and limited sun exposure. We examined the association between vitamin D deficiency and hypertension in 96 uninsured women at a County Free Medical Clinic in urban Michigan. Questionnaires were used to obtain information about demographics, medical history including hypertension, and dietary habits. Measurements including blood pressure and serum 25(OH)D level were also collected. Prevalence of hypertension was higher in subjects with 25(OH)D less than 50nmol/l compared with others (85% vs. 27.3%, p = 0.014). For every 1 nmol/L decrease in serum 25(OH)D, there was an increase in the systolic and diastolic blood pressure by 0.20 (p =0.006) and 0.13 (p =0.003) mm of Hg respectively. These results demonstrate a high prevalence of hypertension in the vitamin D deficient, uninsured female population

ZA-APA with Adaptive Zero Attractor Controller for Variable Sparsity Environment

The zero attraction affine projection algorithm (ZA-APA) achieves better performance in terms of convergence rate and steady state error than standard APA when the system is sparse. It uses l1 norm penalty to exploit sparsity of the channel. The performance of ZA-APA depends on the value of zero attractor controller. Moreover a fixed attractor controller is not suitable for varying sparsity environment. This paper proposes an optimal adaptive zero attractor controller based on Mean Square Deviation (MSD) error to work in variable sparsity environment. Experiments were conducted to prove the suitability of the proposed algorithm for identification of unknown variable sparse system

ZA-APA with Adaptive Zero Attractor Controller for Variable Sparsity Environment

The zero attraction affine projection algorithm (ZA-APA) achieves better performance in terms of convergence rate and steady state error than standard APA when the system is sparse. It uses l1 norm penalty to exploit sparsity of the channel. The performance of ZA-APA depends on the value of zero attractor controller. Moreover a fixed attractor controller is not suitable for varying sparsity environment. This paper proposes an optimal adaptive zero attractor controller based on Mean Square Deviation (MSD) error to work in variable sparsity environment. Experiments were conducted to prove the suitability of the proposed algorithm for identification of unknown variable sparse system