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T1. High Performance Digital Sensors Design: How to Make it Smarter? T2. Challenges in Cloud Computing Security
DETAILED DESCRIPTION T1. High Performance Digital Sensors Design: How to Make it Smarter? This tutorial is devoted to modern developments and trends in the field of design various digital sensors and transducers: physical and chemical. Digital sensor integration is heavily driven by technology scaling. The main reason for scaling is increased system performance at reduced manufacturing cost. Below the 100 nm technology, the design of analog and mixed-signal circuits becomes perceptibly more difficult. This is particularly true for low supply voltage near 1 V or below. The result is not only an increased design effort, long development time, high risk, cost, and the need for very high volumes, but also growing power consumption. There are many reasons that analog doesn’t scale as readily. However, digital circuits scale very well with scaling standard CMOS technologies. So, the promised trend is the transition from traditional analog informative parameters such as voltage and current to the quasi-digital informative parameters of sensors outputs, for example, frequency, period, duty-cycle, PWM, pulse number. etc. It means the use of frequency (period)-to-digital converters instead of analog-to-digital converters, and implements as many sensor components as possible in the digital form. It lets get over technological limitations at scaling and gives a great opportunity to significantly improve the metrological performance of various sensor systems. Clear, the design of high performance frequency (period)-to-digital converters is not a trivial task of low cost microcontroller use. It needs novel, advanced methods for frequency-time measurements in wide frequency range. One of the aims of this tutorial is to fill-in this gap and demonstrates the easiest way to design the high performance digital sensors. This tutorial is suitable for engineers and researchers who design and investigate various digital and intelligent sensors, data acquisition, and measurement systems. It should be also useful for sensors manufacturers, graduate and post graduate students.
T2. Challenges in Cloud Computing Security Cloud computing is a distributed computing model that still faces problems. New ideas emerge to take advantage of its features and among the research challenges found in the cloud, we can highlight security concerns. This tutorial discusses the use of risk-based dynamic access control for cloud computing, presenting an access control model based on an extension of the XACML standard with three new components: the risk engine, the risk quantification web services and the risk policies. There are numerous threats and vulnerabilities that become more and more important as the use of the cloud increases, as well as concerns with stored data and its availability, confidentiality and integrity. This situation creates the need for monitoring tools and services, which provide a way for administrators to define and evaluate security metrics for their systems. This tutorial explores service level agreements for security. We provide an overview on the subject and the difficulties faced during the security metrics definition process for cloud computing. One of the great challenges in the deployment of cloud federations is identity and access management. Federated identity management is widely adopted in the cloud to provide useful features for authentication and authorization, but maintaining user privacy in those systems is still a challenge, since federation tools do not provide good features to maintain privacy. This tutorial presents a model where the cloud consumer can perform risk analysis on providers before and after contracting the service. This model establishes the responsibilities of three actors: consumer, provider and security labs. This tutorial analyzes real-time intrusion response systems in order to mitigate attacks that compromise integrity, confidentiality and availability in cloud computing platforms. This tutorial also presents an autonomic intrusion response technique enabling self-awareness, self-optimization and self-healing properties. To achieve this goal an IRAS (Intrusion Response Autonomic System) is used, considering big data techniques for data analytics and expected utility function for decision taking. |
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