Portable devices such as smartphones, tablet PCs, and PDAs are a useful combination of hardware and software turned toward the mobile workers. While they present the ability to review documents, communicate via electronic mail, appointments management, meetings, etc. They usually lack a variety of essential security features. To address the security concerns of sensitive data, many individuals and organizations, knowing the associated threats mitigate them through improving authentication of users, encryption of content, protection from malware, firewalls, intrusion prevention, etc. However, no standards have been developed yet to determine whether such mobile data management systems adequately provide the fundamental security functions demanded by organizations and whether these functions have been securely developed. Therefore, this paper proposes a security framework for mobile data that combines core security mechanisms to avoid these problems and protects sensitive information without spending time and money deploying several new applications.
Abstract—The upper limb amputation exerts a significant burden on the amputee, limiting their ability to perform everyday activities, and degrading their quality of life. Amputee patients’ quality of life can be improved if they have natural control over their prosthetic hands. Among the biological signals, most commonly used to predict upper limb motor intentions, surface electromyography (sEMG), and axial acceleration sensor signals are essential components of shoulder-level upper limb prosthetic hand control systems. In this work, a pattern recognition system is proposed to create a plan for categorizing high-level upper limb prostheses in seven various types of shoulder girdle motions. Thus, combining seven feature groups, w
... Show MoreOwing to their cost-effectiveness and the natural abundance of magnesium, magnesium-ion batteries (MIBs) were introduced as encouraging alternatives to Lithium-ion batteries. Following the successful synthesis of carbon nano-tube, its B and N doped derivatives which were doped with B and N enjoyed the attention of researchers as novel anode materials (AM) for MIBs. Here, we investigated a BC2N nano-tube (BC2NNT) as an encouraging AM for MIBs. To have a deeper understanding of the electrochemical properties, cycling stability, specific capacity (SC) and the adsorption behavior of this nano-tube, first-principles density functional theory computations were performed. By performing NMR calculations, we identified two types of non-aromatic hexa
... Show MoreIn this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based on-line auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive voltage controller is to find precisely and quickly the optimal hydrogen partial pressure action to control the stack terminal voltage of the (PEMFC) model for N-step ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) implemented as a stable and robust on-line auto-tune algorithm to find the optimal weights for the proposed predictive neural network controller to improve system performance in terms of fast-tracking de
... Show MoreNitroso-R-salt is proposed as a sensitive spectrophotometric reagent for the determination of paracetamol in aqueous solution. The method is based on the reaction of paracetamol with iron(III) and subsequent reaction with nitroso-R-salt to yield a green colored complex with maximum absorption at 720 nm. Optimization of the experimental conditions was described. The calibration graph was linear in the concentration range of 0.1 – 2.0 ?g mL-1 paracetamol with a molar absorptivity of 6.9 × 104 L mol-1 cm-1. The method was successfully applied to the determination of paracetamol in pharmaceutical preparations without any interference from common excipients. The method has been statistically evaluated with British Pharmacopoeia method a
... Show MoreGenerally, radiologists analyse the Magnetic Resonance Imaging (MRI) by visual inspection to detect and identify the presence of tumour or abnormal tissue in brain MR images. The huge number of such MR images makes this visual interpretation process, not only laborious and expensive but often erroneous. Furthermore, the human eye and brain sensitivity to elucidate such images gets reduced with the increase of number of cases, especially when only some slices contain information of the affected area. Therefore, an automated system for the analysis and classification of MR images is mandatory. In this paper, we propose a new method for abnormality detection from T1-Weighted MRI of human head scans using three planes, including axial plane, co
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