New Watermarking/Encryption Method for Medical Imaging FULL Protection in m-Health

In this paper, we present a new method for medical images security dedicated to m-Health based on a combination between a novel semi reversible watermarking approach robust to JPEG compression, a new proposed fragile watermarking and a new proposed encryption algorithm. The purpose of the combination of these three proposed algorithms (encryption, robust and fragile watermarking) is to ensure the full protection of medical image, its information and its report in terms of confidentiality and reliability (authentication and integrity). A hardware implementation to evaluate our system is done using the Texas instrument C6416 DSK card by converting m-files to C/C++ using MATLAB coder. Our m-health security system is then run on the android platform. Experimental results show that the proposed algorithm can achieve high security with good performance

A Diagnosis Study on a Train Passenger Access System using Petri Net Models

CTS 2018, 15th IFAC Symposium on Control in Transportation Systems, Savona, ITALIE, 06-/06/2018 - 08/06/2018In this paper, we conduct a diagnosis analysis of the passenger access system, while considering a high level abstraction perspective that allows for adapting discrete event models to represent the system behavior. Firstly, we establish Petri net behavioral models for the global system functions, including the nominal operating mode and various faulty behaviors. Then, based on the established Petri net models, a diagnoser-based approach is brought into play to investigate the diagnosability of the system regarding the different predetermined classes of failures

Using Model-Checking Techniques for Diagnosability Analysis of Intermittent Faults-A Railway Case-Study.

VECOS 2016 - 10th International Workshop on Verification and Evaluation of Computer and Communication Systems, Tunis, TUNISIE, 06-/10/2016 - 07/10/2016This paper addresses formal verification of intermittent fault diagnosability in Discrete Event Systems (DESs). The system is modeled by a Finite State Automaton and intermittent faults are defined as faults that can automatically recover once they have occurred. Two definitions of diagnosability, regarding the detection of fault occurrences within a finite delay and the detection of fault occurrences before their recovery, are discussed. The diagnosability is analyzed on the basis of the twin-plant structure, which is formally modeled as a Kripke structure, while diagnosability conditions are formulated using LTL temporal logic. We focus on a practical application of this approach, namely a case-study from the railway control field, will serve as a benchmark to illustrate the various developed mechanisms and to assess the scalability of the technique

Diagnosability Analysis of Intermittent Faults in Discrete Event Systems Using a Twin-plant Structure

Most research in fault diagnosis of discrete event systems has been focused on permanent failures. However, experience with monitoring of dynamic systems shows that intermittent faults are predominant, and that their diagnosis constitutes one of the most challenging tasks for surveillance activities. Among the main existing approaches to deal with permanent faults, two were widely investigated while considering different settings: the Diagnoser based approach, and the Twin-plant based approach. The latter was developed to cope with some complexity limitations of the former. In the present paper, we propose a twin-plant based approach to deal with diagnosability of intermittent faults. Firstly, we discuss various notions of diagnosability, while considering the occurrence of faults, their recovery, and the identification of the system status. Then, we establish the necessary and sufficient conditions for each notion, and develop on-the-fly algorithms to check these properties. The discussed approach is implemented in a prototype tool that is used to conduct experiments on a railway control benchmark

Fault diagnosis of discrete-event systems based on the symbolic observation graph

Fault diagnosis of discrete-event systems (DESs) has received a lot of attention in industry and academia during the last two decades. In DES based diagnosis, the two main discussed topics are offline diagnosability analysis and online diagnosis. A pioneering approach that led to the development of various techniques is based on the so-called diagnose. However, this approach suffers from the combinatorial explosion problem due to the exponential complexity of construction. To partially overcome this problem, an efficient approach to construct a symbolic diagnoser is proposed in this paper. The proposed approach consists in constructing a diagnoser based on the symbolic observation graph (SOG), which combines symbolic and enumarative representations. The construction of the diagnoser as well as the verification of diagnosability are performed simultaneously on the fly, which can considerably reduce the state space of the diagnoser and thus the overall running time. To evaluate the efficiency and the scalability of the approach, some experimental results are presented and discussed based on a DES benchmark

A semi-symbolic diagnoser for fault diagnosis of bounded labeled petri nets

In this paper, we present a diagnoser-based approach to deal with fault diagnosis of bounded labeled Petri nets. The approach consists in building a semi-symbolic diagnoser to analyze diagnosability and perform online diagnosis. The contribution of this paper is twofold: (i) from the theoretical point of view, we provide new conditions for checking diagnosability based on a novel diagnoser variant that explicitly separates the normal reachable markings from the faulty ones, in each diagnoser node. This allows us to independently keep tracking the normal and the faulty diagnoser paths more efficiently. (ii) From the practical point of view and in order to reduce the memory required to build the diagnoser efficiently, we establish a semi-symbolic encoding of the diagnoser state-space. Such a representation deploys a symbolic encoding of the diagnoser nodes content, using Binary Decision Diagrams, while it keeps an explicit encoding of the observable transitions between the nodes. In addition, we provide an on-the-fly algorithm to simultaneously construct the diagnoser and analyze diagnosability. The effectiveness of the approach is illustrated through some experimentation performed on a Petri net benchmark

DPN-SOG: A Software Tool for Fault Diagnosis of Labeled Petri Nets Using the Semi-Symbolic Diagnoser

In this paper, we present DPN-SOG, a software tool written in C++ for fault diagnosis of discrete event systems modeled by bounded labeled Petri nets. DPN-SOG (for Diagnosability analysis of Petri Nets using Symbolic Observation Graphs) implements the semi-symbolic diagnoser approach developed in [1, 2] for fault diagnosis of bounded labeled Petri nets. The implemented approach aims to cope with some limitations of the classic diagnoser-based approaches, namely the state-space explosion problem, the intermediate models and the double-checking procedure for diagnosability analysis. The key features of DPN-SOG are: (i) the on-the-fly building of the diagnoser and analysis of diagnosability, (ii) the generation of only the necessary part of the diagnoser to perform the diagnosability analysis and online diagnosis, and (iii) the evaluation of the time/memory consumption for the construction of the diagnoser and the analysis of diagnosability

Combining Enumerative and Symbolic Techniques for Diagnosis of Discrete-Event Systems

VECOS 2015 - 9th Workshop on Verification and Evaluation of Computer and Communication Systems, Bucarest, ROUMANIE, 10-/09/2015 - 11/09/2015In this paper, an efficient approach to verify diagnosability of discrete-event systems is proposed. The approach consists in constructing a hybrid diagnoser based on the symbolic observation graph (SOG), which is a technique that combines symbolic and enumerative representations in order to build a deterministic observer from a partially observed model. The construction of the diagnoser as well as the verification of diagnosability are performed simultaneously on-the-fly, which can considerably reduce the generated state space of the diagnoser and thus the overall running time. Furthermore, the proposed approach provides a heuristic strategy in order to converge fast into the necessary part, of the diagnoser, for analysing diagnosability

Medical Images Watermarking for Security in m-Health: Implementation on Smartphone Android OS

<p>Medical data (imaging or report) are personnel information which<br> are relative to the patient where this information is a medical secret.<br> Therefore, in this paper, a new security system for medical<br> imaging dedicated for m-Health is presented which is based on the<br> high capacity robust/fragile blind watermarking of audio report in<br> medical imaging. The audio signal is transformed with the<br> decomposition by wavelet transform, then, we insert the<br> normalized low coefficients of the transformed audio signal in<br> spatial space of medical imaging to watermarked. After extraction,<br> the imaging integrity is checked by analyzing the extracted audio.<br> The imaging authentication is done by the robustness of<br> watermarking against the different types of attack. The security of<br> audio signal is done using an insertion key. The proposed<br> algorithm is implemented on Smartphone android system, so,<br> activities are built using XML script and Java for input speech of<br> doctor, the insertion of this audio in medical imaging, and the<br> extraction of an audio from a watermarked imaging. The objective<br> of the paper is to concept a high capacity watermarking method<br> which can store all audio report size. with an imperceptible<br> watermark and an acceptable watermarked imaging quality and the<br> system must be available in mobile Health system.</p

Efficient diagnosability assessment via ILP optimization: a railway benchmark

IEEE ETFA 2018, 23rd International Conference on Emerging Technologies and Factory Automation, Torino, ITALIE, 04-/09/2018 - 07/09/2018Diagnosability of faults in discrete event systems modeled with Petri nets can be assessed either via graph-based techniques (also called diagnoser, verifier/twin-plant based techniques), or via the solution of optimization problems. The approaches that belong to the former class are based on the analysis of the net reachability or coverability graphs (or of a more compact version of them). The latter approach exploits the mathematical representation of the net itself to specify and solve optimization problems, which are usually expressed as integer linear programming (ILP) problems. In this paper we exploit the railway Petri net model originally proposed in [16], and extended in [14] to be used as a benchmark for diagnosability analysis, to assess the efficiency of the approach based on the solution of ILP problems proposed in [3]. In order to show the effectiveness of the proposed technique, a comparison with a graph-based approach for analyzing diagnosability is also presented