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International audience; Interest and need for Wireless Body Area Networks (WBANs) havesignicantly increased recently. WBAN consists of miniaturized sensors de-signed to collect and transmit data through wireless network, enabling medicalspecialists to monitor patients during their normal daily life and providing realtime opinions for medical diagnosis. Many wireless technologies have provedthemselves in WBAN applications, while others are still under investigations.The choice of the technology to adopt may depend on the disease to moni-tor and the performance requirements, i.e. reliability, latency and data rate.In addition, the suitable sensor is essential when seeking to extract the datarelated to a medical measure.This paper aims at surveying the wireless technologies used in WBAN sys-tems. In addition to a detailed survey on the existing technologies, the use ofthe emerging Low Power Wide Area Network (LPWAN) technologies, and thefuture 5G, B5G and 6G is investigated, where the suitability of these technolo-gies to WBAN applications is studied from several perspectives. Furthermore,medical applications of WBAN are discussed by presenting their methodolo-gies, the adopted wireless technologies and the used sensors. Given that eachmedical application requires the appropriate sensor to extract the data, wehighlight a wide range of the sensors used in the market for medical systems.Recent and future challenges in WBAN systems are given related to the powerconsumption, the emergence of the Internet of Things (IoT) technologies inWBAN and others.

International audience; Automation throughout history has caused profound changes in employment dynamics. With the advent of the fourth industrial revolution, a new threat may affect employability, asrobots and AI-based processes can now assume tasks considered exclusive to humans. This position paperaims to motivate the study of the effects of AI and automation on employability, extending it into a collaborative network perspective. The problem is firstly observed from a historical perspective. The collaboration aspects are considered through the analysis of two casestudies. Results suggestthata latent element of collaborative networks, complexity, may have effects in terms of employability.

International audience; In this paper we introduce a new class of Pickup and Delivery problems on circles (or rings). These problems arise in the field of public transportation systems where autonomous (i.e. driverless) vehicles travel on circular networks. We consider a set of stations arranged in a circle and a set of transportation requests. Each request asks for the transportation of a certain quantity from a pickup station to a delivery station. A fleet of capacitated vehicles is available at the depot. In the first part of the paper we propose a classification scheme for these problems. In the second part, we address the variants in which the vehicles are allowed to move in a single direction of the circle (either clockwise or counterclockwise) and the objective is to minimize the number of tours on the ring while serving all the requests. We provide a complexity analysis for this class of problems. We develop polynomial time algorithms for the variants that are polynomially solvable and proofs of NP-hardness for the variants that are NP-hard. In addition, for the latter, we provide mathematical formulations and perform computational tests that show the effectiveness of these formulations. Finally, we compare optimal solutions with those obtained using a straightforward greedy algorithm.

International audience; The Digital Twin has the ability to store historical and current data about the physical object. In IoT, the concept of general-purpose sensing is aiming at determining a set of basic sensing capabilities from which deriving, by means of Artificial Intelligence algorithms and techniques (termed Synthetic Sensing), relevant information for describing an environment. This paper explores the possible relationships and the feasibility of integration of Synthetic Sensing within a Digital Twin framework. The relevant concepts and associated technologies, the challenges, and some of the enabled scenarios that this integration can bring are presented. This approach is in its infancy and there is a strong need to validate and assess its viability, feasibility, and benefits. The paper identifies some challenges and validation steps that can lead to consolidation and broad adoption of this approach. Finally, the paper presents some future work that aiming at proving the approach.

In this paper, we are concerned with the resolution of a multiobjective complex job-shop scheduling problem stemming from semiconductor manufacturing. To produce feasible and industrially meaningful schedules, this paper extends the recently proposed batch-oblivious approach by considering unavailability periods and minimum time lags and by simultaneously optimizing multiple criteria that are relevant in the industrial context. A novel criterion on the satisfaction of production targets decided at a higher level is also proposed. Because the solution approach must be embedded in a real-time application, decision makers must express their preferences before the optimization phase. In addition, a preference model is introduced where trade-off is only allowed between some criteria. Two a priori multiobjective extensions of Simulated Annealing are proposed, which differ in how the simultaneous use of a lexicographic order and weights is handled when evaluating the fitness. A known a posteriori approach of the literature is used as a benchmark. All the metaheuristics are embedded in a Greedy Randomized Adaptive Search Procedure. The different versions of the archived GRASP approach are compared using large industrial instances. The numerical results show that the proposed approach provides good solutions regarding the preferences. Finally, the comparison of the optimized schedules with the actual factory schedules shows the significant improvements that our approach can bring.

International audience; This paper investigates the gain-scheduled observer-based consensus of linear parameter varying (LPV) multi-agent systems (MASs). The main contribution of the paper is that, differently from LPV observer-based consensus design methods previously reported in the literature, the controller and observer gains are allowed to be functions of some time-varying parameter vector, which can be measured in real-time. It is shown that, under the assumption of synchronized varying parameter trajectories, the design conditions can be described as a feasibility problem involving a finite number of linear matrix inequalities (LMIs). The obtained results are illustrated by means of a numerical example.

In this paper, we propose an image tampering localization algorithm using semi-fragile watermarking and Error-Locating codes in the DWT domain. By introducing different families of codes, we show the benefit in terms of image tampering localization and complexity of using control code error localization as an authentication function. Indeed, we first experimentally show that error localization block codes is as precise as using classical error correcting codes (Reed-Solomon and BCH codes) to locate image tampering. However, their corresponding decoding algorithms complexity is at least quadratic which make them impractical for some real time applications. To solve this problem, we introduce error-control codes called Error-Locating codes where error localization is reduced to a single syndrome computation performed with low number of binary operations (detailed later in the paper). We provide comparisons of image quality and tampering localization performances using error-detection, error-localization and error-correction approaches with different error control codes.

International audience; This paper focuses on similarity caching systems, in which a user request for an object o that is not in the cache can be (partially) satisfied by a similar stored object o , at the cost of a loss of user utility. Similarity caching systems can be effectively employed in several application areas, like multimedia retrieval, recommender systems, genome study, and machine learning training/serving. However, despite their relevance, the behavior of such systems is far from being well understood. In this paper, we provide a first comprehensive analysis of similarity caching in the offline, adversarial, and stochastic settings. We show that similarity caching raises significant new challenges, for which we propose the first dynamic policies with some optimality guarantees. We evaluate the performance of our schemes under both synthetic and real request traces.

International audience; In this paper we consider the model of single insertion-deletion systems that at each step insert or delete a single symbol in a context-free manner (i.e. at any position in the word). The corresponding operation is performed if the word contains a set of permitting (that have to be present in the word) and/or forbidding (that must not be present in the word) strings of some size. The main result of this paper states that if forbidding strings of size 2 and permitting strings of size 1 are used then computational completeness can be achieved; moreover, checking for a single permitting symbol is sufficient. We also show that in the case of systems having rules with forbidding conditions only, all regular languages can be obtained. Finally, we show the computational non-completeness in the case of systems using rules with forbidding strings of size 1 (single symbols) and permitting strings of any finite size.

International audience; Abstract Solutions of partial differential equations can exhibit multiple time scales. Standard discretization techniques are constrained to capture the finest scale to accurately predict the response of the system. In this paper, we provide an alternative route to circumvent prohibitive meshes arising from the necessity of capturing fine-scale behaviors. The proposed methodology is based on a time-separated representation within the standard Proper Generalized Decomposition, where the time coordinate is transformed into a multi-dimensional time through new separated coordinates, each representing one scale, while continuity is ensured in the scale coupling. For instance, when considering two different time scales, the governing Partial Differential Equation is commuted into a nonlinear system that iterates between the so-called microtime and macrotime, so that the time coordinate can be viewed as a 2D time. The macroscale effects are taken into account by means of a finite element-based macro-discretization, whereas the microscale effects are handled with unidimensional parent spaces that are replicated throughout the time domain. The resulting separated representation allows us a very fine time discretization without impacting the computational efficiency. The proposed formulation is explored and numerically verified on thermal and elastodynamic problems.; Solutions of partial differential equations can exhibit multiple time scales. Standard discretization techniques are constrained to capture the finest scale to accurately predict the response of the system. In this paper, we provide an alternative route to circumvent prohibitive meshes arising from the necessity of capturing fine-scale behaviors. The proposed methodology is based on a time-separated representation within the standard Proper Generalized Decomposition, where the time coordinate is transformed into a multi-dimensional time through new separated coordinates, each representing one scale, while continuity is ensured in the scale coupling. For instance, when considering two different time scales, the governing Partial Differential Equation is commuted into a nonlinear system that iterates between the so-called microtime and macrotime, so that the time coordinate can be viewed as a 2D time. The macroscale effects are taken into account by means of a finite element-based macro-discretization, whereas the microscale effects are handled with unidimensional parent spaces that are replicated throughout the time domain. The resulting separated representation allows us a very fine time discretization without impacting the computational efficiency. The proposed formulation is explored and numerically verified on thermal and elastodynamic problems.

International audience; This paper develops a state-feedback Model Predictive Control (MPC) framework for Nonlinear Parameter Varying (NLPV) systems with explicit Lipschitz nonlinearities along the input trajectory. Considering a guess for the evolution of the scheduling parameters along the prediction horizon, the proposed optimization procedure for the MPC design includes a terminal stage cost, a contracting terminal region constraint and nominal predictions scheduled with respect to this guess. The terminal region is taken so that it is monotonically decreasing to a predetermined set, which guarantees recursive feasibility of the algorithm with respect to a bound on the admissible uncertainties (introduced from the scheduling parameter guess). The terminal set is a scheduled robust control invariant set for Lipschitz nonlinear systems, computed through some proposed LMIs. The inclusion of the terminal ingredient also serves to demonstrate Input-to-State Quadratic Stability. This paper ends with a successful numerical simulation example of the technique applied to the control of a Semi-Active automotive suspension system equipped with Electro-Rheological dampers. Comparisons are given with respect to open-loop behaviour and to a Robust LQR controller.

Building renovation is a complex collaborative process requiring the interaction between planners, architects, civil engineers, energy experts, and managers of (pre-)manufacturing plants supplying building elements, components of energy supply and distribution systems “just in sequence” to densely used urban spaces – where the majority of buildings under renovation are located. Therefore, the availability of a complete, comprehensive Building Information Model, amalgamating current and future product and process models is of outstanding importance. Approaches, suggesting so-called “monolithic” building information models did not deliver the expected “value for money” since the efforts required to set up and maintain such digital models requested more resources than available. Therefore, the authors present in this paper an alternative approach to information, knowledge management, and sharing in the AECO-sector, i.e. modular ontologies. The flexible and dynamic approach to combine new and available modules of information addresses more responsively the needs of the AECO sector. Furthermore, such BIM models overcome limitations in adaptability, extensibility, etc. of current “openBIM models. Due to this the shift towards using semantic web technologies for knowledge base and semantic interoperability has been increased in the AECO industry. The work presented in the paper introduces a recently developed linked data, an ontology-based framework that harmonizes and orchestrates ontologies recently developed for the construction domain. It studies inter-model and inter ontology relationships to address concepts that are currently absent from “building ontologies”. The developed framework can be used to support collaborative environments in the engineering and manufacturing sector supporting the efficient sharing of information between architects, engineers, manufacturing plants, and assembly crews on the construction site.

International audience; The paper presents the experience collected in a case study in the construction equipment concerning the use of physical prototypes for the development of product-service systems (PSS) enabled by newdigital technologies. The paper firstly presents how a scaled physical prototype has been deployed to foster value co-creation with customers about the cross-disciplinary opportunity of the transition toward autonomous and electrical construction sites. Secondly, the paper presents the lessons learned during the empirical study.

International audience; Many applications in scientific computing process very large sparse matrices on parallel architectures. The presented work in this paper is a part of a project where our general aim is to develop an auto-tuner system for the selection of the best matrix compression format in the context of high-performance computing. The target smart system can automatically select the best compression format for a given sparse matrix, a numerical method processing this matrix, a parallel programming model and a target architecture. Hence, this paper describes the design and implementation of the proposed concept. We consider a case study consisting of a numerical method reduced to the sparse matrix vector product (SpMV), some compression formats, the data parallel as a programming model and, a distributed multi-core platform as a target architecture. This study allows extracting a set of important novel metrics and parameters which are relative to the considered programming model. Our metrics are used as input to a machine-learning algorithm to predict the best matrix compression format. An experimental study targeting a distributed multi-core platform and processing random and real-world matrices shows that our system can improve in average up to 7% the accuracy of the machine learning.

International audience; In fair resource allocation, envy freeness (EF) is one of the most interesting fairness criteria as it ensures no agent prefers the bundle of another agent. However, when considering indivisible goods, an EF allocation may not exist. In this paper, we investigate a new relaxation of EF consisting in minimizing the Ordered Weighted Average (OWA) of the envy vector. The idea is to choose the allocation that is fair in the sense of the distribution of the envy among agents. The OWA aggregator is a well-known tool to express fairness in multiagent optimization. In this paper, we focus on fair OWA operators where the weights of the OWA are decreasing. When an EF allocation exists, minimizing OWA envy will return this allocation. However, when no EF allocation exists, one may wonder how fair min OWA envy allocations are. After defining the model, we show how to formulate the computation of such a min OWA envy allocation as a Mixed Integer Program. Then, we investigate the link between the min OWA allocation and other wellknown fairness measures such as max min share and EF up to one good or to any good. Finally, we run some experiments comparing the performances of our approach with MNW (Max Nash Welfare) on several criteria such as the percentage of EF up to one good and any good.

Most public blockchain protocols, including the popular Bitcoin and Ethereum blockchains, do not formally specify the order in which miners should select transactions from the pool of pending (or uncommitted) transactions for inclusion in the blockchain. Over the years, informal conventions or "norms" for transaction ordering have, however, emerged via the use of shared software by miners, e.g., the GetBlockTemplate (GBT) mining protocol in Bitcoin Core. Today, a widely held view is that Bitcoin miners prioritize transactions based on their offered "transaction fee-per-byte." Bitcoin users are, consequently, encouraged to increase the fees to accelerate the commitment of their transactions, particularly during periods of congestion. In this paper, we audit the Bitcoin blockchain and present statistically significant evidence of mining pools deviating from the norms to accelerate the commitment of transactions for which they have (i) a selfish or vested interest, or (ii) received dark-fee payments via opaque (non-public) side-channels. As blockchains are increasingly being used as a record-keeping substrate for a variety of decentralized (financial technology) systems, our findings call for an urgent discussion on defining neutrality norms that miners must adhere to when ordering transactions in the chains. Finally, we make our data sets and scripts publicly available., This is a pre-print of our paper accepted to appear to ACM IMC 2021

International audience; Systems engineering, or engineering in general, has long been relying on document-centric approaches. Switching to model-based systems engineering, or MBSE for short, has extensively been discussed over the past three decades. Since about two decades, MBSE has been commonly associated with the modeling language SysML (Systems Modeling Language), which offers a standardized notation, not a methodology of using it. SysML needs therefore to be associated with a methodology supported by tools. In this paper, a methodology supported by the free and open-source software TTool is associated with SysML. This paper focuses discussion on methodological issues, leading the authors to share their experience in real-time systems modeling. Modeling with SysML is more than just drawing the different diagrams. Associated tools offer possibilities to analyze SysML models for specific properties. In this paper, verification addresses both safety and security properties. The TTool model checker inputs the SysML model enriched with safety properties to be verified and outputs an yes/no answer for each property. Security verification checks SysML models against confidentiality, integrity, and authenticity properties. As an illustration of the proposed approach, an aircraft cockpit door control system is modeled in SysML and verified against safety and security properties.

International audience; While the recent progress on automated generalisation helped National Mapping Agencies to derive topographic maps more and more quickly, there are still practical cartographic issues that require attention. For instance, embankments are represented with line symbols showing the slope of the embankment. This paper proposes an automated algorithm, called ReBankment that displaces the embankment lines from the roads and rivers that overlap the embankment symbol. ReBankment is based on a triangulation to identify neighbourhoods, and on a least squares adjustment to displace and distort the embankment line while preserving its shape. The paper also proposes how to handle complex cases and scaling issues. ReBankment is tested on real data from a 1:25k scale topographic map. Keywords map generalisation and least squares and displacement and topographic map and embankment

International audience; Business process optimization is a strategic activity in organizations because of its potential to increase profit margins and reduce operational costs. One of the main challenges in this activity is concerned with the problem of optimizing allocation and sharing of resources. Companies are continuously adjusting their resources to their needs following different strategies. However, the dynamic provisioning strategies are hard to compare. This paper proposes an automatic analysis technique to evaluate and compare the execution time and resource occupancy of a business process relative to a workload and a provisioning strategy. Four different strategies are presented, which are guided-respectively-by recent resource usage, recent resource request, predicted behavior, and a combination of available strategies. Such analysis is performed on models conforming to an extension of BPMN with quantitative information, including resource availability and constraints. Within this framework, the approach is fully mechanized using a formal and executable specification in the Maude rewriting logic framework, which relies on existing techniques and tools for simulating probabilistic and real-time specifications. The paper includes results on the extensive experimentation that has been carried out for validation purposes.

International audience; Widely linear (WL) receivers have been developed in the past for single antenna interference cancel- lation (SAIC) of one rectilinear (R) or quasi-rectilinear (QR) data-like multi-user interference (MUI) or co-channel interference (CCI) in particular. The SAIC technology has been implemented in global system for mobile communications (GSM) handsets in particular and has been further analyzed for voice ser- vices over adaptive multi-user channels on one slot (VAMOS) standard. It remains of great interest for several current and future applications using R or QR signals, such as anti-collisions processing in radio frequency identification (RFID) or in satellite-AIS systems and to densify 5G and Beyond 5G (B5G) net- works through one dimensional signaling or over-loaded large MU-MIMO systems. It may be required to cancel the inter-symbol interference (ISI) of control and non-payload communications (CNPC) links of unmanned aerial vehicles (UAV) and the inter-carrier interference (ICI) of filter bank multi-carrier offset quadrature amplitude modulation (FBMC-OQAM), which are now candidate for B5G mobile networks. For these challenging applications, the development of enhanced WL filtering based SAIC or Multiple Antenna Interference Cancellation (MAIC) receivers for R and QR signals may be of great interest. Such a receiver, corresponding to a three-input WL frequency shift (FRESH) receiver, has been introduced recently for QR signals. However this WL receiver is not robust to a data-like MUI having a residual frequency offset (FO), which occurs for most of the previous applications. In this context, the paper first extends, for arbitrary propagation channels, the standards (for R and QR MUI) and the enhanced (for QR MUI) SAIC/MAIC WL receivers to MUI with a non-zero FO. Then, it shows the less efficiency of the two-input WL receiver for QR MUI with a non-zero FO and the performance improvement obtained with the three-input WL receiver. Finally, it analyzes, both analytically and by simulations, for R and QR MUI, the impact of the MUI FO on the performance of the proposed receivers. The results of the paper should allow the develop- ment of new powerful WL receivers for UAV CNPC links, anti-collisions AIS systems and for FBMC-OQAM networks in particular.

International audience; Virtual steering techniques enable users to navigate in larger Virtual Environments (VEs) than the physical workspace available. Even though these techniques do not require physical movement of the users (e.g. using a joystick and the head orientation to steer towards a virtual direction), recent work observed that users might unintentionally move in the physical workspace while navigating, resulting in Unintended Positional Drift (UPD). This phenomenon can be a safety issue since users may unintentionally reach the physical boundaries of the workspace while using a steering technique. In this context, as a necessary first step to improve the design of navigation techniques minimizing the UPD, this paper aims at analyzing and modeling the UPD during a virtual navigation task. In particular, we characterize and analyze the UPD for a dataset containing the positions and orientations of eighteen users performing a virtual slalom task using virtual steering techniques. Participants wore a head-mounted display and had to follow three different sinusoidal-like trajectories (with low, medium and high curvature) using a torso-steering navigation technique. We analyzed the performed motions and proposed two UPD models: the first based on a linear regression analysis and the second based on a Gaussian Mixture Model (GMM) analysis. Then, we assessed both models through a simulation-based evaluation where we reproduced the same navigation task using virtual agents. Our results indicate the feasibility of using simulation-based evaluations to study UPD. The paper concludes with a discussion of potential applications of the results in order to gain a better understanding of UPD during steering and therefore improve the design of navigation techniques by compensating for UPD.

This paper provides a broad perspective and analysis of the work done in control of hybrid and convertible unmanned aerial vehicles (UAVs) for the main existing designs. These flying machines are capable of vertical take off and landing (VTOL) in helicopter mode and able to transition to high-speed forward flight in airplane mode and vice versa. This paper aims at helping engineers and researchers develop flight control systems for VTOL UAVs. To this end, a historical perspective first shows the technological advances in VTOL aircraft over the years. The main VTOL concepts and state-of-art flight control methods for VTOL UAVs are presented and discussed. This study shows both the common parts and the fundamental differences in the modeling, guidance, control, and control allocation for each hybrid-VTOL-UAV type. The open challenges and the current trends in the field are highlighted. These are namely: 1) augmenting or replacing classical controllers with data-driven methods such as neural networks and machine-learning-based controllers; 2) incorporating as much knowledge of the vehicle as possible into the flight controller, for example through model predictive control or model-based nonlinear controllers; 3) a trend towards finding a unified-control approach valid in all flight modes without the need to switch among flight controllers or to perform predefined-gain scheduling, and 4) the need to mitigate control complexity and available computing resources.

International audience; Production ramp-up is challenged by several hurdles such as unpredicted events to achieve targets like providing on-time service in critical situations and under budget constraints. The interrelated concepts of resilience, agility and risk management increase the ability of the system to handle the changes effectively. Therefore, in order to successfully manage production ramp-up particularly in high variety environments, resilience, risk, and agility should be considered in a holistic way. This will help address complexity and uncertainty underlying manufacturing and service operations. This paper aims to fill this gap by reviewing the literature and reporting on the basic concepts and interrelationships of resilience, agility, and risk management in the ramp-up phase. Ultimately, the paper aims to lay some foundations for further research in the crossing of these areas.

International audience; This paper reports on a research work conducted within the framework of the SUSTAIN project aiming to unleash and operationalize the potential of customer centricity and sustainability requirements. More specifically, the paper presents a Decision Support System (DSS) intended to support the alignment of aggregate production planning with customer demand during the production ramp-up phase. The DSS consists of a mathematical model and an online product configurator. This will ultimately lead to a revised product portfolio and better align it with the market requirements before moving to series production.

Cascaded binary hypothesis testing is studied in this paper with two decision centers at the relay and the receiver. All terminals have their own observations, where we assume that the observations at the transmitter, the relay, and the receiver form a Markov chain in this order. The communication occurs over two hops, from the transmitter to the relay and from the relay to the receiver. Expected rate constraints are imposed on both communication links. In this work, we characterize the optimal type-II error exponents at the two decision centers under constraints on the allowed type-I error probabilities. Our recent work characterized the optimal type-II error exponents in the special case when the two decision centers have same type-I error constraints and provided an achievability scheme for the general setup. To obtain the exact characterization for the general case, in this paper we provide a new converse proof as well as a new matching achievability scheme. Our results indicate that under unequal type-I error constraints at the relay and the receiver, a tradeoff arises between the maximum type-II error probabilities at these two terminals. Previous results showed that such a tradeoff does not exist under equal type-I error constraints or under general type-I error constraints when a maximum rate constraint is imposed on the communication links., Comment: arXiv admin note: substantial text overlap with arXiv:2105.05917

Time series are ubiquitous in data mining applications. Similar to other types of data, annotations can be challenging to acquire, thus preventing from training time series classification models. In this context, clustering methods can be an appropriate alternative as they create homogeneous groups allowing a better analysis of the data structure. Time series clustering has been investigated for many years and multiple approaches have already been proposed. Following the advent of deep learning in computer vision, researchers recently started to study the use of deep clustering to cluster time series data. The existing approaches mostly rely on representation learning (imported from computer vision), which consists of learning a representation of the data and performing the clustering task using this new representation. The goal of this paper is to provide a careful study and an experimental comparison of the existing literature on time series representation learning for deep clustering. In this paper, we went beyond the sole comparison of existing approaches and proposed to decompose deep clustering methods into three main components: (1) network architecture, (2) pretext loss, and (3) clustering loss. We evaluated all combinations of these components (totaling 300 different models) with the objective to study their relative influence on the clustering performance. We also experimentally compared the most efficient combinations we identified with existing non-deep clustering methods. Experiments were performed using the largest repository of time series datasets (the UCR/UEA archive) composed of 128 univariate and 30 multivariate datasets. Finally, we proposed an extension of the class activation maps method to the unsupervised case which allows to identify patterns providing highlights on how the network clustered the time series.

International audience; Structured safety argument based on graphical representations such as GSN (Goal Structuring Notation) are used to justify the certification of critical systems. However, such approaches do not deal with uncertainties that might affect the merits of arguments. In the recent past, some authors proposed to model the confidence in such arguments using Dempster-Shafer theory. It enables us to determine the confidence degree in conclusions for some basic GSN patterns. In this paper, we refine this approach and improve the elicitation method for expert opinions used in previous papers.

We study the properties of electronic circuits after linearization around a fixed operating point in the context of closed-loop stability analysis. When distributed elements, like transmission lines, are present in the circuit it is known that unstable circuits can be created without poles in the complex right half-plane. This undermines existing closed-loop stability analysis techniques that determine stability by looking for right half-plane poles. We observed that the problematic circuits rely on unrealistic elements with an infinite bandwidth. In this paper, we therefore define a class of realistic linearized components and show that a circuit composed of realistic elements is only unstable with poles in the complex right half-plane. Furthermore, we show that the amount of right half-plane poles in a realistic circuit is finite, even when distributed elements are present. In the second part of the paper, we provide examples of component models that are realistic and show that the class includes many existing models, including ones for passive devices, active devices and transmission lines.

Pattern mining is well established in data mining research, especially for mining binary datasets. Surprisingly, there is much less work about numerical pattern mining and this research area remains under-explored. In this paper we propose Mint, an efficient MDL-based algorithm for mining numerical datasets. The MDL principle is a robust and reliable framework widely used in pattern mining, and as well in subgroup discovery. In Mint we reuse MDL for discovering useful patterns and returning a set of non-redundant overlapping patterns with well-defined boundaries and covering meaningful groups of objects. Mint is not alone in the category of numerical pattern miners based on MDL. In the experiments presented in the paper we show that Mint outperforms competitors among which IPD, RealKrimp, and Slim.

International audience; Nowadays, many research studies and industrial investigations have allowed the integration of the Internet of Things (IoT) in current and future networking applications by deploying a diversity of wireless-enabled devices ranging from smartphones, wearables, to sensors, drones, and connected vehicles. The growing number of IoT devices, the increasing complexity of IoT systems, and the large volume of generated data have made the monitoring and management of these networks extremely difficult. Numerous research papers have applied Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) techniques to overcome these difficulties by building IoT systems with effective and dynamic decision-making mechanisms, dealing with incomplete information related to their environments. The paper first reviews pre-existing surveys covering the application of RL and DRL techniques in IoT communication technologies and networking. The paper then analyzes the research papers that apply these techniques in wireless IoT to resolve issues related to routing, scheduling, resource allocation, dynamic spectrum access, energy, mobility, and caching. Finally, a discussion of the proposed approaches and their limits is followed by the identification of open issues to establish grounds for future research directions proposal.

In this paper, we analyze the effect of an enhanced voltage flip technique on the power performance of a piezoelectric energy harvester. The enhanced voltage flip principle is based on a synchronized-switch-based architecture, and is referred to as FAR (Full Active Rectifier). It uses a tiny amount of the stored charge to boost the voltage flip. This work aims to demonstrate that, beside the enhanced flip efficiency, the FAR also contributes to improve the power efficiency of the harvester, especially under changing load constraint. Therefore, the paper proposes a thorough comparison between the FAR and its conventional counterpart, the Switch-only technique. The FAR is easy to implement and does not require any external inductor or capacitor. It only needs a reduced set of switches, an active diode and a simple control sequence, and can thus be implemented on a fully integrated circuit. The FAR can be used as a standalone voltage flip solution or in addition to further boost the flip efficiency in a state-of-the-art architecture such as SSHC for example. Tests were performed on a 0.35-µm process CMOS prototype IC. Experimental results revealed that the FAR extracts 19.1μW from an off-the-shelf piezoelectric transducer when the output voltage is regulated at 1 V with 1 V open-circuit voltage and delivers up to 20% more power than the conventional Switch-only technique under load constraint. It also shows over 11× power efficiency improvement compared to a conventional diode-based full bridge rectifier.

International audience; All systems are likely to be affected by obsolescence. A component is considered obsolete when it is no longer manufactured or requested. Obsolescence must be taken into account, analysed and resolved effectively as it can lead to temporary or permanent stoppage of a system or make it impossible to repair it. The consequences of an obsolete entity must be minimised as they may have impacts, among others, on system characteristics such as availability or maintainability. These consequences can cascade through the system architecture. Understanding the propagation mechanisms can greatly contribute to obsolescence resilient design, but also to obsolescence management enabling a more effective determination of the scope of impact of actual or predicted obsolescence. The objective of this research work is to propose models to describe such propagation and to understand its principles. In fact, even if the obsolescence or rarefaction propagation is real and commonly accepted, there are no models describing its mechanisms. This paper focuses on two main points. First, it contributes to the concepts of obsolescence and rarefaction. Second, we propose descriptive and mathematical models of obsolescence and rarefaction propagation which allow to describe the likelihood of this propagation through the architecture of the system via two types of identified links, namely dependency and jump-up/jump-downs. These proposals are then illustrated through an example of a monitoring unit for a perfume bottle packaging line. After a discussion of the results obtained, the paper ends with the definition of future research pathways to ease the use of probabilistic inference tools for the exploitation of the proposed models.

This paper addresses a project scheduling environment in which the activities are partitioned among a set of agents. The project owner is interested in completing the project as soon as possible. Therefore, she/he defines rewards and penalties to stimulate the agents to complete the project faster. The project owner offers a per-day reward for early project completion and defines intermediate project milestones to be met within specific due dates, with associated per-day penalties. Each agent can, therefore, decide the duration of her/his activities, taking into account linear activity crashing costs, the reward for early project completion, and the penalty arising from violating milestone due-dates. We consider Nash equilibria, i.e., situations in which no agent has an interest in individually changing the duration of any of her/his activities, and in particular, the problem of finding a minimum-makespan equilibrium. This problem is known to be NP-hard, and in this paper, we ( i ) propose a new and efficient exact algorithmic approach for finding the minimum-makespan equilibrium and ( i i ) through an extensive computational campaign we evaluate the role played by milestones in driving the project towards the owner’s goal.

An integrated dynamic model of natural gas pipeline networks is developed in this paper. Components for gas supply, e.g., pipelines, junctions, compressor stations, LNG terminals, regulation stations and gas storage facilities are included in the model. These components are firstly modeled with respect to their properties and functions and, then, integrated at the system level by Graph Theory. The model can be used for simulating the system response in different scenarios of operation, and evaluate the consequences from the perspectives of supply security and resilience. A case study is considered to evaluate the accuracy of the model by benchmarking its results against those from literature and the software Pipeline Studio. Finally, the model is applied on a relatively complex natural gas pipeline network and the results are analyzed in detail from the supply security and resilience points of view. The main contributions of the paper are: firstly, a novel model of a complex gas pipeline network is proposed as a dynamic state-space model at system level; a method, based on the dynamic model, is proposed to analyze the security and resilience of supply from a system perspective.

International audience; The problem of blind joint FIR-MIMO channel and data estimation is addressed in this paper. Based on a regularized DML (Deterministic Maximum Likelihood) formulation of the problem, a bilinear approach is used in order to estimate jointly the channel impulse responses and the input data. This regularization is introduced as a penalty function added to the classical DML criterion representing the a priori information about the problem in order to enhance the accuracy of the estimation. Two types of priors information are considered for the transmitted data: the finite alphabet simplicity or the sparsity. The sparsity prior was also considered for channel impulse responses. The key advantage of the proposed criteria is their convexity when optimized alternatively over the channel and the input data. The proposed approach allows to improve further the estimation accuracy of such a blind estimation problem but suffers from a relatively high computational cost. Hence, a reduced complexity implementation of the latter has been proposed at the end of the paper, in an adaptive scheme for high dimensional or streaming data situations.

This paper gives the full analytical solution of the generic set of ordinary differential equations that define one-compartment toxicokinetic models. These models describe uptake and elimination processes taking place within living organisms when exposed to chemical substances. The models solved in this paper consider living organisms as a unique compartment, into which a parent compound enters via several possible exposure routes and from which it is eliminated as well as its potential metabolites. Benefiting from generic solutions of one-compartment toxicokinetic models is particularly useful when fitting them to experimental data, facilitating the writing of the inference algorithms leading to parameter estimates. Additionally, these models are of crucial interest in environmental risk assessment for the calculation of bioaccumulation metrics as required by regulators in support of decision making when they evaluate dossiers for marketing authorisation of active substances.Abstract FigureGraphical abstract

This paper presents the software Partial Pole Placement via Delay Action, or P3$\delta$ for short. P3$\delta$ is a Python software with a friendly user interface for the design of parametric stabilizing feedback laws with time-delays, thanks to two properties of the distribution of quasipolynomials' zeros, called multiplicity-induced-dominancy and coexisting real roots-induced-dominancy. After recalling recent theoretical results on these properties and their use for the feedback stabilization of control systems operating under time delays, the paper presents the main features of the current version of P3$\delta$. We detail, in particular, the assignable admissible region (the set of allowable dominant roots and the corresponding delay), which helps the user in the choice of input information, allowing a reliable stabilizing delayed feedback. We also present the newly set online version of P3$\delta$., Comment: 16th IFAC Workshop on Time Delay Systems (IFAC TDS 2021). arXiv admin note: substantial text overlap with arXiv:2007.05809

PurposeThe article provides a conceptual replication and enrichment of the status quo bias theory in the specific context of understanding IT department user resistance and user adoption. The findings can assist technology renewals and associated change management professionals to assess and plan the adoption and active usage of human resource systems.Design/methodology/approachThe authors used survey method to gather data. All items were based on prior literature. They administrated the survey to employees of GOODTECH (name changed), information systems (IS) department members, situated in France. They obtained 103 valid responses along with usage data from the system to run their path model, in order to validate the proposed research model.FindingsThe study offers an enriched user resistance model (URM) to understand why IT-savvy employees would resist or adopt new human resource tools. Apart from providing partial validity to status quo bias theory in the French context, the enriched model uses behavioral intention to use as an intermediate variable to explain the influence of two key constructs of the original theory: switching cost and switching benefits. This research provides a better explanatory power to understand the cause of user resistance and new IT use.Research limitations/implicationsThe sample size used in the study can be considered as a limitation, although power analysis reveals that the results are significant and valid. The context of the study is also limited to one country and to a specific type of IS implementation scenario. Since the purpose of the paper was to offer contextual theory enhancement, the findings are valid for this purpose.Practical implicationsDigital project managers are offered a framework to increase technology adoption of new human resource tools and evaluate how to reduce user resistance at times of technology renewals. Self-efficacy for change and colleagues’ opinion can indirectly impact behavioral intention to use via switching cost and switching benefit perceptions and thus reducing resistance perceptions as well as increasing adoption of new IT tools in post-implementation phases.Originality/valueThe paper enriches the well-established user resistance theory in IS domain in a context of human resource post-implementation phase by studying IT-savvy end user's perceptions. The paper demonstrates the need to integrate user adoption and user resistance variables in one parsimonious framework and extends support to emerging research on dual focus perspective.

Decomposing an image through Fourier, DCT or wavelet transforms is still a common approach in digital image processing, in number of applications such as denoising. In this context, data-driven dictionaries and in particular exploiting the redundancy withing patches extracted from one or several images allowed important improvements. This paper proposes an original idea of constructing such an image-dependent basis inspired by the principles of quantum many-body physics. The similarity between two image patches is introduced in the formalism through a term akin to interaction terms in quantum mechanics. The main contribution of the paper is thus to introduce this original way of exploiting quantum many-body ideas in image processing, which opens interesting perspectives in image denoising. The potential of the proposed adaptive decomposition is illustrated through image denoising in presence of additive white Gaussian noise, but the method can be used for other types of noise such as image-dependent noise as well. Finally, the results show that our method achieves comparable or slightly better results than existing approaches., Comment: 5 pages, 4 figures

International audience; LPWANs are networks characterized by the scarcity of their radio resources and their limited payload size. To extend the efficiency of the data transmission by decreasing the traffic sent from sensors, this paper proposes a lossy compression method using known ML techniques. We embedded a pre-trained neural network directly on constrained LoRaWAN devices and we tested the trade-off between compression ratio and accuracy of the compression algorithm. This paper studies multiple aspects of the system-energy consumption, error rate due to the lossy compression, compression ratio and the impact of LSTM parameter quantization-to measure the possible strengths and weaknesses of using a dual prediction system in order to reduce transmission costs. Surprisingly, machine learning used in this context does not consume a lot of energy and it even leads to energy saving in the very constrained devices which are the sensors.

In this paper, we propose a method for coarse camera pose computation which is robust to viewing conditions and does not require a detailed model of the scene. This method meets the growing need of easy deployment of robotics or augmented reality applications in any environments, especially those for which no accurate 3D model nor huge amount of ground truth data are available. It exploits the ability of deep learning techniques to reliably detect objects regardless of viewing conditions. Previous works have also shown that abstracting the geometry of a scene of objects by an ellipsoid cloud allows to compute the camera pose accurately enough for various application needs. Though promising, these approaches use the ellipses fitted to the detection bounding boxes as an approximation of the imaged objects. In this paper, we go one step further and propose a learning-based method which detects improved elliptic approximations of objects which are coherent with the 3D ellipsoid in terms of perspective projection. Experiments prove that the accuracy of the computed pose significantly increases thanks to our method and is more robust to the variability of the boundaries of the detection boxes. This is achieved with very little effort in terms of training data acquisition -- a few hundred calibrated images of which only three need manual object annotation. Code and models are released at https://github.com/zinsmatt/3D-Aware-Ellipses-for-Visual-Localization., Comment: Presented at 3DV 2020. Code and models released at https://github.com/zinsmatt/3D-Aware-Ellipses-for-Visual-Localization

Case-based reasoning, where cases are described in terms of problem-solution pairs \(\mathtt{case}^{}=(\mathtt{x}, \mathtt{y})\), amounts to propose a solution to a new problem on the basis of past experience made of stored cases. On the one hand, the building of the solution to a new problem may be viewed as a form of belief revision of the solution of a retrieved case (whose problem part is similar to the new problem) constrained by domain knowledge. On the other hand, an extrapolation mechanism based on analogical proportions has been proposed. It exploits triplets of cases \((\mathtt{case}^{a}, \mathtt{case}^{b}, \mathtt{case}^{c})\) whose descriptions of problem parts \(\mathtt{x}^{a}\), \(\mathtt{x}^{b}\), \(\mathtt{x}^{c}\) form an analogical proportion with the new problem \(\mathtt{x}^{\mathtt{tgt}}\), in such a way that “\(\mathtt{x}^{a}\) is to \(\mathtt{x}^{b}\) as \(\mathtt{x}^{c}\) is to \(\mathtt{x}^{\mathtt{tgt}}\)”. Then, the analogical inference amounts to compute a solution \(\mathtt{y}^{\mathtt{tgt}}\) of \(\mathtt{x}^{\mathtt{tgt}}\) by solving (when possible) an equation expressing that “\(\mathtt{y}^{a}\) is to \(\mathtt{y}^{b}\) as \(\mathtt{y}^{c}\) is to \(\mathtt{y}^{\mathtt{tgt}}\)” (where \(\mathtt{y}^{a}\), \(\mathtt{y}^{b}\) and \(\mathtt{y}^{c}\) are respectively the solution parts of \(\mathtt{case}^{a}\), \(\mathtt{case}^{b}\) and \(\mathtt{case}^{c}\)). The paper investigates how the belief revision view and analogical extrapolation relate. Besides that it constitutes an unexpected bridge between areas which ignore each other, it casts some light on the adaptation mechanism in case-based reasoning. The paper is illustrated by a running example.

International audience; This paper analyses the modifications of plans exchanged between supply chain actors in a tactical planning rolling horizon process. A particular focus is on the changes of planned quantities in order to respond to fluctuating demand or to adapt to internal contingencies of the organization. They create instability and nervousness in the planning system. This paper presents a data-driven study to compare the behavior of planning decision makers in a context of certain and uncertain demand. We show through simulation and statistical analysis the effect of decision characteristics of one actor on the system nervousness and the resulting uncertainty for the other actors.

International audience; This tutorial review paper consolidates the existing applications of the power watershed (PW) optimization framework in the context of image processing. In literature, it is known that PW framework when applied to some well-known graph-based image segmentation and filtering algorithms such as random walker, isoperimetric partitioning, ratio-cut clustering, multi-cut and shortest path filters yield faster yet consistent solutions. In this paper, the intuition behind the working of PW framework i.e. exploitation of contrast invariance on image data is explained. The intuitions are illustrated with toy images and experiments on simulated astronomical images. This article is primarily aimed at researchers working on image segmentation and filtering problems in application areas such as astronomy where images typically havea huge number of pixels. Classic graph-based cost minimization methods provide good results on images with small number of pixels but do not scale well for images with large number of pixels. The ideas from the article can be adapted to a large class of graph-based cost minimization methods to obtain scalable segmentation and filtering algorithms.

International audience; In this paper, we address the temporal knapsack problem (TKP). In this generalization of the classical knapsack problem, selected items enter and leave the knapsack at fixed dates. We solve the TKP with a dynamic program of exponential size, which is solved using a method called Successive Sublimation Dynamic Programming (SSDP). This method starts by relaxing a set of constraints from the initial problem, and iteratively reintroduces them when needed. We show that a direct application of SSDP to the temporal knapsack problem does not lead to an effective method, and that several improvements are needed to compete with the best results from the literature.

International audience; The autonomous vehicle (AVs) market is expanding at a rapid pace due to the advancement of information, communication, and sensor technology applications, offering a broad range of opportunities in terms of energy efficiency and addressing climate change concerns and safety. With regard to this last point, the rate of reduction in accidents is considerable when switching safety control tasks to machines from humans, which can be noted as having significantly slower response rates. This paper explores this thematic by focusing on the safety of AVs by thorough analysis of previously collected AV crash statistics and further discusses possible solutions for achieving increased autonomous vehicle safety. To achieve this, this technical paper develops a dynamic run-time safe assessment system, using the standard autonomous drive system (ADS), which is developed and simulated in case studies further in the paper. OpenCV methods for lane detection are developed and applied as robust control frameworks, which introduces the factor of vehicle crash predictability for the ego vehicle. The developed system is made to predict possible crashes by using a combination of machine learning and neural network methods, providing useful information for response mechanisms in risk scenarios. In addition, this paper explores the operational design domain (ODD) of the AV’s system and provides possible solutions to extend the domain in order to render vehicle operationality, even in safe mode. Additionally, three case studies are explored to supplement a discussion on the implementation of algorithms aimed at increasing curved lane detection ability and introducing trajectory predictability of neighbouring vehicles for an ego vehicle, resulting in lower collisions and increasing the safety of the AV overall. This paper thus explores the technical development of autonomous vehicles and is aimed at researchers and practitioners engaging in the conceptualisation, design, and implementation of safer AV systems focusing on lane detection and expanding AV safe state domains and vehicle trajectory predictability.

More than 21 000 objects fly in outer space and are exposed to the harsh space environment. The size of space objects considerably varies. Our research focuses on nanosatellites, such as CubeSats, which have to respect time, spatial and energy constraints. To tackle this issue, this paper presents and evaluates two fault tolerant online scheduling algorithms: the algorithm scheduling all tasks as aperiodic (called OneOff ) and the algorithm placing arriving tasks as aperiodic or periodic tasks (called OneOff&Cyclic ). Based on several scenarios, we analyse how much the performance (in terms of both the rejection rate and the scheduling time) of ordering policies are influenced by the system load and the proportions of simple and double tasks to all tasks to be executed. The “Earliest Deadline” and “Earliest Arrival Time” ordering policies for OneOff or the “Minimum Slack” ordering policy for OneOff&Cyclic reject the least tasks in all tested scenarios. The paper also deals with the analysis of scheduling time to evaluate real-time performance of ordering policies and shows that OneOff requires less time to find a new schedule than OneOff&Cyclic . Finally, it was found that the studied algorithms perform well also in a harsh environment and provide the same availability as systems based on triple modular redundancy with very much less system power consumption.

The Two-Dimensional Fast Fourier Transform (2D-FFT) algorithm is used for the study of many modern systems applied for security and biometrics. The adoption of this algorithm, which is a compute intensive task, is limited due to its hardware design complexity. The first objective of this paper is to underline the effect of the hardware/software co-design (Hw/Sw co-design) for the reduction of the processing time and power consumption. Secondly, we propose an innovative architecture for the 2D-FFT algorithm tested on Zynq Soc, which requires less processing time and memory compared to the traditional algorithm. Three implementations (one software and two Hw/Sw co-designs) of the 2D-FFT algorithm using the Zynq SoC are presented in this paper. The first is based on ARM processor. A speedup of 29x is obtained compared to the original implementation thanks to many optimizations. The second is a Hw/Sw co-design solution of the traditional 2D-FFT algorithm introduced on a hybrid platform combining an ARM Cortex-A9 processor with an FPGA. The third is also a Hw/Sw co-design solution using our optimized 2D-FFT algorithm to reach the real-time contraints for high-resolution images (1920 × 1080). It provides a speedup of 1.13x, 3.31x and 96.21x faster than the Hw/Sw co-design implementation of the traditional RC algorithm, the pure software implementations with and without optimizations, respectively.

International audience; Adaptive feedforward broad-band noise compensation is currently used when a correlated measurement with the disturbance (an image of the disturbance) is available. Most of the active feedforward noise control systems feature an internal "positive" acoustical feedback between the compensation system and the disturbance measurement which has to be taken into account. Adap-tive algorithms for active feedforward noise attenuation have been developed since 1985 from a local optimization point of view. This paper presents two classes of adaptive configurations for active feedforward noise compensation developed from a stability point of view. The first class uses a standard IIR(FIR) compensator structure while the second class uses the Youla-Kučera parametrization of the feedforward noise compensator. This allows to separate the problem of stabilizing the internal positive feedback loop from the minimization of the residual noise. These adaptive schemes have been developed in the context of active vibration control but, as it will be shown, they can be used also in active noise control. The paper presents in a unified manner the available algorithms and compensator structures for adaptive feedforward noise attenuation and provides a comparative experimental evaluation on a relevant experimental test-bench (a duct silencer). Index Terms-active noise control, adaptive feedfor-ward compensation, Youla-Kučera parametrization, positive feedback coupling.

The Last decade saw significant growth in the digital terrestrial television (DTT) subscribers. The High peak-to-average power ratio (PAPR) of the transmitted signal is implied by the large fluctuations in its amplitude, which is a serious issue in the contemporary and next generation DTT broadcasting systems. These large fluctuations prevent feeding the high power amplifier (HPA) at an operating point near its non-linear (NL) saturation region, thereby lowering its power efficiency. For a better future, energy-efficient green communications is the need of hour. This paper focuses on digital predistortion (DPD), which is among the widely used techniques for HPA linearization in order to minimize the NL effects. DPD technique requires a feedback signal. This paper proposes a novel adding signal-based digital predistortion, where the output of the HPA is gradually linearized by adding a signal at the input in an iterative manner. By Taylor series approximation, the DPD problem can be posed as a convex one. We prove that a tighter upper bound exists for the convergence factor and instead of directly solving the convex problem, we pick a convergence factor value within its bounds that offers a good trade-off between convergence speed and residual error value. The simulation results show that the proposed technique can quickly linearize the HPA with good convergence.