No abstract

Recently, the data-selective adaptive Volterra filters have been proposed;however, up to now, there are not any theoretical analyses on its behavior rather than numerical simulations. Therefore, in this paper, we analyze the robustness (in the sense of l_2-stability) of the data-selective Volterra normalized least-mean-square (DSVNLMS) algorithm. First, we study the local robustness of this algorithm at any iteration, then we propose a global bound for the error/discrepancy in the coefficient vector. Also, we demonstrate that the DS-VNLMS algorithm improves the parameter estimation for the majority of the iterations that an update is implemented. Moreover, we also prove that if the noise bound is known, then we can set the DS-VNLMS so that it never degrades the estimate. The simulation results corroborate the validity of the executed analysis and demonstrate that the DS-VNLMS algorithm is robust against noise, no matter how its parameters are adopted.

Objectives Immobility and its physiological and psychological consequences are common problems in patients with multiple sclerosis. The aim of this study was to investigate the effect of 8 weeks of combined training on Adipsin and lipid profile and the possible relationship between these indicators and psychological function in women with multiple sclerosis. Methods In this quasi-experimental study, 40 women with multiple sclerosis were selected by purposeful sampling method and randomly divided into two equal control and exercise groups (n=20). Exercise was performed for 8 weeks (two resistance sessions and one endurance session per week). Before and after the intervention, blood samples were taken and the DASS-21 questionnaire was completed to assess anxiety, depression and stress. Data were analyzed using analysis of covariance, t-test, Bonferroni post hoc test and Pearson correlation test at a significance level of p≤0.05. Results In the exercise group, levels of Adipsin, total cholesterol, LDL, TG, weight, fat percentage, WHR, BMI, depression, anxiety and stress were significantly reduced and HDL levels were significantly increased after 8 weeks of combined exercise (p≤0.05). Also, BMI (p=0.01), fat percentage (p=0.01) and WHR (p=0.01) levels had significant positive correlation with Adipsin. There was a significant positive relationship between Total cholesterol level with depression index (p=0.04). Conclusions Performing combination exercises through improving body composition can increase the risk of obesity and cardiovascular risk factors and improve the psychological function of patients with multiple sclerosis. Specialists can use these exercises as an adjunct to drug therapy for MS patients.

Recently, the data-selective adaptive Volterra filters have been proposed; however, up to now, there are not any theoretical analyses on its behavior rather than numerical simulations. Therefore, in this paper, we analyze the robustness (in the sense of l 2 -stability) of the data-selective Volterra normalized least-mean-square (DS-VNLMS) algorithm. First, we study the local robustness of this algorithm at any iteration, then we propose a global bound for the error/discrepancy in the coefficient vector. Also, we demonstrate that the DS-VNLMS algorithm improves the parameter estimation for the majority of the iterations that an update is implemented. Moreover, we also prove that if the noise bound is known, then we can set the DS-VNLMS so that it never degrades the estimate. The simulation results corroborate the validity of the executed analysis and demonstrate that the DS-VNLMS algorithm is robust against noise, no matter how its parameters are adopted.

In this article, we give a digital signature by using Lindner–Peikert cryptosystem. The security of this digital signature is based on the assumptions about hardness of Ring-LWE and Ring-SIS problems, along with providing public key and signature of compact (1–1.5 kilobytes) size. We prove the security of our signature scheme in the Quantum Random Oracle Model. Our cryptanalysis has been done based on methods of Aggarwal et al. and Chen et al.