Author
S. Di Carlo
Other affiliations: Instituto Politécnico Nacional
Bio: S. Di Carlo is an academic researcher from Polytechnic University of Turin. The author has contributed to research in topics: Fault injection & Fault coverage. The author has an hindex of 20, co-authored 74 publications receiving 1106 citations. Previous affiliations of S. Di Carlo include Instituto Politécnico Nacional.
Papers published on a yearly basis
Papers
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20 Jan 2012
TL;DR: This chapter introduces the main challenges faced when developing drift correction techniques and proposes a deep overview of state-of-the-art methodologies that have been proposed in the scientific literature trying to underlying pros and cons of these techniques.
Abstract: In this chapter the authors introduce the main challenges faced when developing drift correction techniques and will propose a deep overview of state-of-the-art methodologies that have been proposed in the scientific literature trying to underlying pros and cons of these techniques and focusing on challenges still open and waiting for solutions
77 citations
07 Jul 2003
TL;DR: A watchdog processor for the MOTOROLA M68040 microprocessor is presented to protect from transient faults caused by SEUs the transmission of data between the processor and the system memory, and to ensure a correct instructions' flow, just monitoring the external bus.
Abstract: A watchdog processor for the MOTOROLA M68040 microprocessor is presented. Its main task is to protect from transient faults caused by SEUs the transmission of data between the processor and the system memory, and to ensure a correct instructions' flow, just monitoring the external bus, without modifying the internal architecture of the M68040. A description of the principal procedures is given, together with the method used for monitoring the instructions' flow.
67 citations
TL;DR: An evolutionary based adaptive drift-correction method designed to work with state-of-the-art classification systems that exploits a cutting-edge evolutionary strategy to iteratively tweak the coefficients of a linear transformation which can transparently correct raw sensors' measures thus mitigating the negative effects of the drift.
55 citations
TL;DR: Models which are both multi-level and hybrid satisfy both accuracy and capability for making a good knowledge base, making a very useful tool in computational systems biology.
Abstract: During the last decades, high-throughput techniques allowed for the extraction of a huge amount of data from biological systems, unveiling more of their underling complexity. Biological systems encompass a wide range of space and time scales, functioning according to flexible hierarchies of mechanisms making an intertwined and dynamic interplay of regulations. This becomes particularly evident in processes such as ontogenesis, where regulative assets change according to process context and timing, making structural phenotype and architectural complexities emerge from a single cell, through local interactions. The information collected from biological systems are naturally organized according to the functional levels composing the system itself. In systems biology, biological information often comes from overlapping but different scientific domains, each one having its own way of representing phenomena under study. That is, the different parts of the system to be modelled may be described with different formalisms. For a model to have improved accuracy and capability for making a good knowledge base, it is good to comprise different system levels, suitably handling the relative formalisms. Models which are both multi-level and hybrid satisfy both these requirements, making a very useful tool in computational systems biology. This paper reviews some of the main contributions in this field.
55 citations
TL;DR: The main contribution of this work lies in the possibility of applying state-of-the-art memory test algorithms to embedded cache memories without introducing any hardware or performance overheads and guaranteeing the detection of typical faults arising in nanometer CMOS technologies.
Abstract: Embedded microprocessor cache memories suffer from limited observability and controllability creating problems during in-system tests. This paper presents a procedure to transform traditional march tests into software-based self-test programs for set-associative cache memories with LRU replacement. Among all the different cache blocks in a microprocessor, testing instruction caches represents a major challenge due to limitations in two areas: 1) test patterns which must be composed of valid instruction opcodes and 2) test result observability: the results can only be observed through the results of executed instructions. For these reasons, the proposed methodology will concentrate on the implementation of test programs for instruction caches. The main contribution of this work lies in the possibility of applying state-of-the-art memory test algorithms to embedded cache memories without introducing any hardware or performance overheads and guaranteeing the detection of typical faults arising in nanometer CMOS technologies.
48 citations
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01 Jan 2009
TL;DR: This paper presents a meta-modelling framework for modeling and testing the robustness of the modeled systems and some of the techniques used in this framework have been developed and tested in the field.
Abstract: ing WS1S Systems to Verify Parameterized Networks . . . . . . . . . . . . 188 Kai Baukus, Saddek Bensalem, Yassine Lakhnech and Karsten Stahl FMona: A Tool for Expressing Validation Techniques over Infinite State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 J.-P. Bodeveix and M. Filali Transitive Closures of Regular Relations for Verifying Infinite-State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 Bengt Jonsson and Marcus Nilsson Diagnostic and Test Generation Using Static Analysis to Improve Automatic Test Generation . . . . . . . . . . . . . 235 Marius Bozga, Jean-Claude Fernandez and Lucian Ghirvu Efficient Diagnostic Generation for Boolean Equation Systems . . . . . . . . . . . . 251 Radu Mateescu Efficient Model-Checking Compositional State Space Generation with Partial Order Reductions for Asynchronous Communicating Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 Jean-Pierre Krimm and Laurent Mounier Checking for CFFD-Preorder with Tester Processes . . . . . . . . . . . . . . . . . . . . . . . 283 Juhana Helovuo and Antti Valmari Fair Bisimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Thomas A. Henzinger and Sriram K. Rajamani Integrating Low Level Symmetries into Reachability Analysis . . . . . . . . . . . . . 315 Karsten Schmidt Model-Checking Tools Model Checking Support for the ASM High-Level Language . . . . . . . . . . . . . . 331 Giuseppe Del Castillo and Kirsten Winter Table of
1,687 citations
07 Oct 2002
TL;DR: The paper defines the benchmark format and naming scheme, and presents the benchmark SOCs, and provides an overview of the research problems that can be addressed and evaluated by means of this benchmark set.
Abstract: This paper presents the ITC'02 SOC test benchmarks. The purpose of this new benchmark set is to stimulate research into new methods and tools for modular testing of SOCs and to enable the objective comparison of such methods and tools with respect to effectiveness and efficiency. The paper defines the benchmark format and naming scheme, and presents the benchmark SOCs. In addition, it provides an overview of the research problems that can be addressed and evaluated by means of this benchmark set. These research problems include the design of optimized test access infrastructures and test schedules.
310 citations
TL;DR: Experiments on the popular sensor drift data with multiple batches collected using E-nose system clearly demonstrate that the proposed DAELM significantly outperforms existing drift-compensation methods without cumbersome measures, and also bring new perspectives for ELM.
Abstract: This paper addresses an important issue known as sensor drift, which exhibits a nonlinear dynamic property in electronic nose (E-nose), from the viewpoint of machine learning. Traditional methods for drift compensation are laborious and costly owing to the frequent acquisition and labeling process for gas samples’ recalibration. Extreme learning machines (ELMs) have been confirmed to be efficient and effective learning techniques for pattern recognition and regression. However, ELMs primarily focus on the supervised, semisupervised, and unsupervised learning problems in single domain (i.e., source domain). To our best knowledge, ELM with cross-domain learning capability has never been studied. This paper proposes a unified framework called domain adaptation extreme learning machine (DAELM), which learns a robust classifier by leveraging a limited number of labeled data from target domain for drift compensation as well as gas recognition in E-nose systems, without losing the computational efficiency and learning ability of traditional ELM. In the unified framework, two algorithms called source DAELM (DAELM-S) and target DAELM (DAELM-T) are proposed in this paper. In order to perceive the differences among ELM, DAELM-S, and DAELM-T, two remarks are provided. Experiments on the popular sensor drift data with multiple batches collected using E-nose system clearly demonstrate that the proposed DAELM significantly outperforms existing drift-compensation methods without cumbersome measures, and also bring new perspectives for ELM.
283 citations
TL;DR: In the last decade, important advances have shown that proper processing can improve the robustness of the instruments against diverse perturbations, namely, environmental variables, background changes, drift, etc.
Abstract: Signal and data processing are essential elements in electronic noses as well as in most chemical sensing instruments. The multivariate responses obtained by chemical sensor arrays require signal and data processing to carry out the fundamental tasks of odor identification (classification), concentration estimation (regression), and grouping of similar odors (clustering). In the last decade, important advances have shown that proper processing can improve the robustness of the instruments against diverse perturbations, namely, environmental variables, background changes, drift, etc. This article reviews the advances made in recent years in signal and data processing for machine olfaction and chemical sensing.
281 citations