SeCure Sharing Of Mobile Device Data Using Public Cloud

Alam, Mozammil; Hati, Sourav; De, Debashis; Chattopadhyay, Samiran

doi:10.1109/confluence.2014.6949344

Cited by 2 publications

(1 citation statement)

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“…Communication core which inserts, edits, deletes and displays operations of the data was designed as a middleware which has the opportunity to work with different cloud environments. The data stored in the cloud need to be protected, and providing a safe and efficient sharing of data on the cloud through mobile phones needs to have encryption algorithms [28]. In order to maintain data security in the cloud environment, we considered an encryption module in core communication with a cloud environment.…”

Section: Discussionmentioning

confidence: 99%

An Android Application for Estimating Muscle Onset Latency using Surface EMG Signal

et al. 2019

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Background: Electromyography (EMG) signal processing and Muscle Onset Latency (MOL) are widely used in rehabilitation sciences and nerve conduction studies. The majority of existing software packages provided for estimating MOL via analyzing EMG signal are computerized, desktop based and not portable; therefore, experiments and signal analyzes using them should be completed locally. Moreover, a desktop or laptop is required to complete experiments using these packages, which costs.Objective: Develop a non-expensive and portable Android application (app) for estimating MOL via analyzing surface EMG.Material and Methods: A multi-layer architecture model was designed for implementing the MOL estimation app. Several Android-based algorithms for analyzing a recorded EMG signal and estimating MOL was implemented. A graphical user interface (GUI) that simplifies analyzing a given EMG signal using the presented app was developed too.Results: Evaluation results of the developed app using 10 EMG signals showed promising performance; the MOL values estimated using the presented app are statistically equal to those estimated using a commercial Windows-based surface EMG analysis software (MegaWin 3.0). For the majority of cases relative error <10%. MOL values estimated by these two systems are linearly related, the correlation coefficient value ~ 0.93. These evaluations revealed that the presented app performed as well as MegaWin 3.0 software in estimating MOL.Conclusions: Recent advances in smart portable devices such as mobile phones have shown the great capability of facilitating and decreasing the cost of analyzing biomedical signals, particularly in academic environments. Here, we developed an Android app for estimating MOL via analyzing the surface EMG signal. Performance is promising to use the app for teaching or research purposes.

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Section: Discussionmentioning

confidence: 99%