Optimal discrimination between transient and permanent faults

An important practical problem in fault diagnosis is discriminating between permanent faults and transient faults. In many computer systems, the majority of errors are due to transient faults. Many heuristic methods have been used for discriminating between transient and permanent faults; however, we have found no previous work stating this decision problem in clear probabilistic terms. We present an optimal procedure for discriminating between transient and permanent faults, based on applying Bayesian inference to the observed events (correct and erroneous results). We describe how the assessed probability that a module is permanently faulty must vary with observed symptoms. We describe and demonstrate our proposed method on a simple application problem, building the appropriate equations and showing numerical examples. The method can be implemented as a run-time diagnosis algorithm at little computational cost; it can also be used to evaluate any heuristic diagnostic procedure by compariso

Towards Enhancing Traffic Sign Recognition through Sliding Windows

Automatic Traffic Sign Detection and Recognition (TSDR) provides drivers with critical information on traffic signs, and it constitutes an enabling condition for autonomous driving. Misclassifying even a single sign may constitute a severe hazard, which negatively impacts the environment, infrastructures, and human lives. Therefore, a reliable TSDR mechanism is essential to attain a safe circulation of road vehicles. Traffic Sign Recognition (TSR) techniques that use Machine Learning (ML) algorithms have been proposed, but no agreement on a preferred ML algorithm nor perfect classification capabilities were always achieved by any existing solutions. Consequently, our study employs ML-based classifiers to build a TSR system that analyzes a sliding window of frames sampled by sensors on a vehicle. Such TSR processes the most recent frame and past frames sampled by sensors through (i) Long Short-Term Memory (LSTM) networks and (ii) Stacking Meta-Learners, which allow for efficiently combining base-learning classification episodes into a unified and improved meta-level classification. Experimental results by using publicly available datasets show that Stacking Meta-Learners dramatically reduce misclassifications of signs and achieved perfect classification on all three considered datasets. This shows the potential of our novel approach based on sliding windows to be used as an efficient solution for TSR

Big Data in Critical Infrastructures Security Monitoring: Challenges and Opportunities

Critical Infrastructures (CIs), such as smart power grids, transport systems, and financial infrastructures, are more and more vulnerable to cyber threats, due to the adoption of commodity computing facilities. Despite the use of several monitoring tools, recent attacks have proven that current defensive mechanisms for CIs are not effective enough against most advanced threats. In this paper we explore the idea of a framework leveraging multiple data sources to improve protection capabilities of CIs. Challenges and opportunities are discussed along three main research directions: i) use of distinct and heterogeneous data sources, ii) monitoring with adaptive granularity, and iii) attack modeling and runtime combination of multiple data analysis techniques.Comment: EDCC-2014, BIG4CIP-201

A Monitoring Framework with Integrated Sensing Technologies for Enhanced Food Safety and Traceability

A novel and low-cost framework for food traceability, composed by commercial and proprietary sensing devices, for the remote monitoring of air, water, soil parameters and herbicide contamination during the farming process, has been developed and verified in real crop environments. It offers an integrated approach to food traceability with embedded systems supervision, approaching the problem to testify the quality of the food product. Moreover, it fills the gap of missing low-cost systems for monitoring cropping environments and pesticides contamination, satisfying the wide interest of regulatory agencies and final customers for a sustainable farming. The novelty of the proposed monitoring framework lies in the realization and the adoption of a fully automated prototype for in situ glyphosate detection. This device consists of a custom-made and automated fluidic system which, leveraging on the Molecularly Imprinted Polymer (MIP) sensing technology, permits to detect unwanted glyphosate contamination. The custom electronic mainboard, called ElectroSense, exhibits both the potentiostatic read-out of the sensor and the fluidic control to accomplish continuous unattended measurements. The complementary monitored parameters from commercial sensing devices are: temperature, relative humidity, atmospheric pressure, volumetric water content, electrical conductivity of the soil, pH of the irrigation water, total Volatile Organic Compounds (VOCs) and equivalent CO (Formula presented.). The framework has been validated during the olive farming activity in an Italian company, proving its efficacy for food traceability. Finally, the system has been adopted in a different crop field where pesticides treatments are practiced. This has been done in order to prove its capability to perform first level detection of pesticide treatments. Good correlation results between chemical sensors signals and pesticides treatments are highlighted

Analysis of Simulated Output Characteristics of Gas Sensor Based on Graphene Nanoribbon

This work presents simulated output characteristics of gas sensor transistors based on graphene nanoribbon (GNRFET). The device studied in this work is a new generation of gas sensing devices, which are easy to use, ultracompact, ultrasensitive, and highly selective. We will explain how the exposure to the gas changes the conductivity of graphene nanoribbon. The equations of the GNRFET gas sensor model include the Poisson equation in the weak nonlocality approximation with proposed sensing parameters. As we have developed this model as a platform for a gas detection sensor, we will analyze the current-voltage characteristics after exposure of the GNRFET nanosensor device to NH3 gas. A sensitivity of nearly 2.7% was indicated in our sensor device after exposure of 1 ppm of NH3. The given results make GNRFET the right candidate for use in gas sensing/measuring appliances. Thus, we will investigate the effect of the channel length on the ON- and OFF-current

The Challenge of Detection and Diagnosis of Fugacious Hardware Faults in VLSI Designs

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-38789-0_7Current integration scales are increasing the number and types of faults that embedded systems must face. Traditional approaches focus on dealing with those transient and permanent faults that impact the state or output of systems, whereas little research has targeted those faults being logically, electrically or temporally masked -which we have named fugacious. A fast detection and precise diagnosis of faults occurrence, even if the provided service is unaffected, could be of invaluable help to determine, for instance, that systems are currently under the influence of environmental disturbances like radiation, suffering from wear-out, or being affected by an intermittent fault. Upon detection, systems may react to adapt the deployed fault tolerance mechanisms to the diagnosed problem. 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