The emergency services of the hospitals are for the most part suffering, facing a constant increase in the number of patients without the financial means to follow. Part of the answer to this worrying situation lies in the optimization of existing resources, for which artificial intelligence techniques are proving promising. In this article, we evaluate this possibility in a concrete way, starting from real data and applying a comparative analysis of 4 state-of-the-art algorithms. An original way of selecting explanatory variables is applied, and also the hyperparameters of the algorithms are chosen with precision. The results show that the most powerful machine learning algorithms currently available are quite capable of making good predictions of the number of patients arriving at the emergency room, provided that they are well applied.

Firefighters are equipped with an immobility detector device also called the Personal Alert Safety System (PASS) that is integrated into the user's Self-Contained Breathing Apparatus (SCBA). If a firefighter remains motionless for a certain period of time, a loud audible alert is triggered to notify the Firefighter Assist and Search Team (FAST) deployed in the area of intervention that the wearer of the PASS device is in trouble and in need of rescue. However, this device is not reliable enough since it triggers frequently false positives which lead to developing a tolerance for sounding alarms among the crew. As a consequence, they do not seem to be concerned about it as they should and the alarms are just ignored sometimes. In this paper, we propose a PERsonal LPWAN sYstem (PERLY) prototype for state assessment and localization of Firefighters. The latter's specifications were set by personnel from the fire and emergency response department of the Doubs brigade. The aim was to make the system more reliable compared to the PASS, to add additional important functionalities, and to minimize the false positive alarms.

Several theoretical studies have clearly demonstrated that the Dual Prediction Mechanism (DPM) remains the most efficient technique for data reduction in Wireless Sensor Networks (WSNs). In real world, the deployed sensor nodes suffers from packet loss and even failures which renders the DPM unreliable, since it requires flawless synchronization between the source (sensor node) and the destination (Sink). In this paper, we introduce a Fault Tolerant Data Transmission Reduction (FTDTR) technique consisting of three main components: DPM-based transmission reduction, synchronization and packet loss detection, and finally reconstruction of missing data. Our method was evaluated on real-world data sets collected at our laboratory and compared to three recent prediction-based data reduction approaches. The results were promising in quality of the replicated measurements and transmission reduction.

There is not a proper mathematical definition of chaos, we have instead a quite big amount of definitions, each of one describes chaos in a more or less general context. Taking in account this, it is clear why it is hard to design an algorithm that produce random numbers, a kind of algorithm that could have plenty of concrete appliceautifat (anul)d bions. However we must use a finite state machine (e.g. a laptop) to produce such a sequence of random numbers, thus it is convenient, for obvious reasons, to redefine those aimed sequences as pseudorandom; also problems arise with floating point arithmetic if one wants to recover some real chaotic property (i.e. properties from functions defined on the real numbers). All this considerations are synthesized in the problem of the Pseudorandom number generators (PRNGs). A solution to these obstacles may be to post-operate on existing PRNGs to improve their performances, using the so-called chaotic iterations, i.e., specific iterations of a boolean function and a shift operator that use the inputted generator. This approach leads to a mathematical description of such PRNGs as discrete dynamical systems, on which chaos properties can be investigated using mathematical topology and measure theory. Such properties are well-formulated, and they allow us to characterize which functions improves the sensitivity to the seed, the expansivity, the ergodicity, or the topological mixing of the generator resulting from such a post-processing. Experience shows that choosing relevant boolean functions in these chaotic iterations improves the randomness of the inputted generator, for instance when considering the number of statistical tests of randomness passed successfully. If we focus on the cryptographical application of PRNGs, there are two main classical notions to be considered, namely collision and avalanche effect. In this article, we recall the chaotic properties of the proposed post-treatment and we study the collision property in families of pseudorandom sequences produced by this process.

Predicting how a genetic change affects a given character is a major challenge in biology, and being able to tackle this problem relies on our ability to develop realistic models of gene networks. However, such models are rarely tractable mathematically. In this paper, we propose a mathematical analysis of the sigmoid variant of the Wagner gene-network model. By considering the simplest case, that is, one unique self-regulating gene, we show that numerical simulations are not the only tool available to study such models: theoretical studies can be done too, by mathematical analysis of discrete dynamical systems. It is first shown that the particular sigmoid function can be theoretically investigated. Secondly, we provide an illustration of how to apply such investigations in the case of the dynamical system representing the one self-regulating gene. In this context, we focused on the composite function f a ( m . x ) where f a is the parametric sigmoid function and m is a scalar not in { 0 , 1 } and we have proven that the number of fixed-point can be deduced theoretically, according to the values of a and m.

In this paper, a novel steganographic scheme based on chaotic iterations is proposed. This research work takes place into the information hiding framework, and focus more specifically on robust steganography. Steganographic algorithms can participate in the development of a semantic web: medias being on the Internet can be enriched by information related to their contents, authors, etc., leading to better results for the search engines that can deal with such tags. As media can be modified by users for various reasons, it is preferable that these embedding tags can resist to changes resulting from some classical transformations as for example cropping, rotation, image conversion, and so on. This is why a new robust watermarking scheme for semantic search engines is proposed in this document. For the sake of completeness, the robustness of this scheme is finally compared to existing established algorithms.

The truly chaotic finite machines introduced by authors in previous research papers are presented here. A state of the art in this discipline, encompassing all previous mathematical investigations, is provided, explaining how finite state machines can behave chaotically regarding the slight alteration of their inputs. This behavior is explained using Turing machines and formalized thanks to a special family of discrete dynamical systems called chaotic iterations. An illustrative example is finally given in the field of hash functions.

Hash functions are cryptographic tools, which are notably involved in integrity checking and password storage. They are of primary importance to improve the security of exchanges through the Internet. However, as security flaws have been recently identified in the current standard in this domain, new ways to hash digital data must be investigated. In this document an original keyed hash function is evaluated. It is based on asynchronous iterations leading to functions that have been proven to be chaotic. It thus possesses various topological properties as uniformity and sensibility to its initial condition. These properties make our hash function satisfies established security requirements in this field. This claim is qualitatively proven and experimentally verified in this research work, among other things by realizing a large number of simulations.

Wireless sensor networks are often deployed in public or otherwise untrusted and even hostile environments, which prompts a number of security issues. Although security is a necessity in other types of networks, it is much more so in sensor networks due to the resource-constraint, susceptibility to physical capture, and wireless nature. In this work we emphasize two security issues: (1) secure communication infrastructure and (2) secure nodes scheduling algorithm. Due to resource constraints, specific strategies are often necessary to preserve the network's lifetime and its quality of service. For instance, to reduce communication costs nodes can go to sleep mode periodically (nodes scheduling). These strategies must be proven as secure, but protocols used to guarantee this security must be compatible with the resource preservation requirement. To achieve this goal, secure communications in such networks will be defined, together with the notions of secure scheduling. Finally, some of these security properties will be evaluated in concrete case studies.

In this paper, random forests are proposed for operating devices diagnostics in the presence of a variable number of features. In various contexts, like large or difficult-to-access monitored areas, wired sensor networks providing features to achieve diagnostics are either very costly to use or totally impossible to spread out. Using a wireless sensor network can solve this problem, but this latter is more subjected to flaws. Furthermore, the networks' topology often changes, leading to a variability in quality of coverage in the targeted area. Diagnostics at the sink level must take into consideration that both the number and the quality of the provided features are not constant, and that some politics like scheduling or data aggregation may be developed across the network. The aim of this article is ($1$) to show that random forests are relevant in this context, due to their flexibility and robustness, and ($2$) to provide first examples of use of this method for diagnostics based on data provided by a wireless sensor network.

Unattended Wireless Sensor Networks (UWSNs) are Wireless Sensor Networks characterized by sporadic sink presence and operation in hostile settings. The absence of the sink for period of time, prevents sensor nodes to offload data in real time and offer greatly increased opportunities for attacks resulting in erasure, modification, or disclosure of sensor-collected data. In this paper, we focus on UWSNs where sensor nodes collect and store data locally and try to upload all the information once the sink becomes available. One of the most relevant issues pertaining UWSNs is to guarantee a certain level of information survivability in an unreliable network and even in presence of a powerful attackers. In this paper, we first introduce an epidemic-domain inspired approach to model the information survivability in UWSN. Next, we derive a fully distributed algorithm that supports these models and give the correctness proofs.

In this paper, we aim at exploiting the power computing of a graphics processing unit (GPU) cluster for solving large sparse linear systems. We implement the parallel algorithm of the generalized minimal residual iterative method using the Compute Unified Device Architecture programming language and the MPI parallel environment. The experiments show that a GPU cluster is more efficient than a CPU cluster. In order to optimize the performances, we use a compressed storage format for the sparse vectors and the hypergraph partitioning. These solutions improve the spatial and temporal localization of the shared data between the computing nodes of the GPU cluster.

In computational biology and bioinformatics, the manner to understand evolution processes within various related organisms paid a lot of attention these last decades. However, accurate methodologies are still needed to discover genes content evolution. In a previous work, two novel approaches based on sequence similarities and genes features have been proposed. More precisely, we proposed to use genes names, sequence similarities, or both, insured either from NCBI or from DOGMA annotation tools. Dogma has the advantage to be an up-to-date accurate automatic tool specifically designed for chloroplasts, whereas NCBI possesses high quality human curated genes (together with wrongly annotated ones). The key idea of the former proposal was to take the best from these two tools. However, the first proposal was limited by name variations and spelling errors on the NCBI side, leading to core trees of low quality. In this paper, these flaws are fixed by improving the comparison of NCBI and DOGMA results, and by relaxing constraints on gene names while adding a stage of post-validation on gene sequences. The two stages of similarity measures, on names and sequences, are thus proposed for sequence clustering. This improves results that can be obtained using either NCBI or DOGMA alone. Results obtained with this "quality control test" are further investigated and compared with previously released ones, on both computational and biological aspects, considering a set of 99 chloroplastic genomes.

This paper deals with the numerical solution of financial applications, more specifically the computation of American option derivatives modeled by nonlinear boundary values problems. In such applications we have to solve large-scale algebraic systems. We concentrate on synchronous and asynchronous parallel iterative algorithms carried out on CPU and GPU networks. The properties of the operators arising in the discretized problem ensure the convergence of the parallel iterative synchronous and asynchronous algorithms. Computational experiments performed on CPU and GPU networks are presented and analyzed.

One important issue in sensor networks that has received renewed interest recently is average consensus, i.e., computing the average of n sensor measurements, where nodes iteratively exchange data with their neighbors and update their own data accordingly until reaching convergence to the right parameters estimate. In this paper, we introduce an efficient self-stabilizing algorithm to achieve/ensure the convergence of node states to the average of the initial measurements of the network. We prove that the convergence of the fusion process is finite and express an upper bound of the actual number of moves/iterations required by the algorithm. This means that our algorithm is guaranteed to reach a stable situation where no load will be sent from one sensor node to another. We also prove that the load difference between any two sensor nodes in the network is within $\frac{\varepsilon}{D}\times\left\lfloor\frac{D+1}{2}\right\rfloorε is the prescribed global equilibrium threshold (this threshold is given by the system) and D is the diameter of the network.

This paper takes place in the field of invisible chaos-based watermarking schemes. It addresses the quality study of an already pyblished algorithm by focusing on three class of properties. Its robustness is experimentally shown against classical attacks on a large set of image instances and image transformations. It correctness and completness are formally proven. Due to this main advantages, this process is fitted for practical use.

One of the main design challenges in Wireless Sensor Networks (WSN) is to prolong the system lifetime, while achieving acceptable quality of service for applications. In WSN, each sensor node is battery powered and it is not convenient to recharge or replace the batteries in many cases, especially in remote and hostile environments. Due to the limited capabilities of sensor nodes, it is usually desirable that a WSN should be deployed with high density and thus redundancy can be exploited to increase the network's lifetime. In this paper, we introduce an efficient lifetime optimization and self-stabilizing algorithm to enhance the lifetime of wireless sensor networks especially when the reliabilities of sensor nodes are expected to decrease due to use and wear-out effects. Our algorithm seeks to build resiliency by maintaining a necessary set of working nodes and replacing failed ones when needed. We provide some theoretical and simulation results, that fully demonstrate the usefulness of the proposed algorithm.

This research work presents a new class of non-blind information hiding algorithms that are stego-secure and robust. They are based on some finite domains iterations having the Devaney's topological chaos property. Thanks to a complete formalization of the approach we prove security against watermark-only attacks of a large class of steganographic algorithms. Finally a complete study of robustness is given in frequency DWT and DCT domains.

This paper presents an energy-efficient technique for data aggregation in periodic sensor networks. We investigate the problem of finding all pairs of nodes generating similar data sets such that similarity between each pair of sets is above a threshold t. We provide a frequency filtering approach to solve this problem. Our experiments demonstrate that our algorithm outperforms existing prefix filtering methods in reducing energy consumption.

This paper introduces a new notion of chaotic algorithms. These algorithms are iterative and are based on so-called chaotic iterations. Contrary to all existing studies on chaotic iterations, we are not interested in stable states of such iterations but in their possible unpredictable behaviors. By establishing a link between chaotic iterations and the notion of Devaney's topological chaos, we give conditions ensuring that these kind of algorithms produce topological chaos. This leads to algorithms that are highly unpredictable. After presenting the theoretical foundations of our approach, we are interested in its practical aspects. We show how the theoretical algorithms give rise to computer programs that produce true topological chaos, then we propose applications in the area of information security.