Implementing Operations Support Systems in E-Health Based Systems

Information and communication technologies have been introduced in different dimensions of the health care. e-Health is the use of advanced communications technologies such as the Internet, portable, wireless and other sophisticated devices to support health care delivery and education. It has the potentials of improving the efficiency of health care delivery globally. With the increasing demand for information at the point of care, health care providers could explore the advances provided by mobile technologies and the increasing capabilities, compactness and pervasiveness of computing devices to adopt operations supports systems (OSS) in e-Health based systems in order to provide efficient services and enhance their performances. In this paper, we present, the development and implementation of operations supports in e- Health based systems. The system promises to deliver greater productivity for health care practitioner

Fingerprinting Smart Devices Through Embedded Acoustic Components

The widespread use of smart devices gives rise to both security and privacy concerns. Fingerprinting smart devices can assist in authenticating physical devices, but it can also jeopardize privacy by allowing remote identification without user awareness. We propose a novel fingerprinting approach that uses the microphones and speakers of smart phones to uniquely identify an individual device. During fabrication, subtle imperfections arise in device microphones and speakers which induce anomalies in produced and received sounds. We exploit this observation to fingerprint smart devices through playback and recording of audio samples. We use audio-metric tools to analyze and explore different acoustic features and analyze their ability to successfully fingerprint smart devices. Our experiments show that it is even possible to fingerprint devices that have the same vendor and model; we were able to accurately distinguish over 93% of all recorded audio clips from 15 different units of the same model. Our study identifies the prominent acoustic features capable of fingerprinting devices with high success rate and examines the effect of background noise and other variables on fingerprinting accuracy