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A Cepstrum-Based Spectrum Sensing Approach for Detecting Spread Spectrum Signals
No full textInternational audienceAbstract In this manuscript, we introduce a semi-blind spectrum sensing technique based on cepstral analysis for interweave cognitive systems. The misdetection problem of spread spectrum signals leads to erroneous sensing results, which affect the quality-of-service of a legitimate user. The simplicity and accuracy of cepstral analysis approaches make them reliable for signals detection. Therefore, we formulate the averaged autocepstrum detection technique that utilizes the strength of the autocepstral features of spread spectrum signals. The proposed technique is compared with the energy detection and eigenvalue-based detection techniques and shows reliability and efficacy in terms of detection accuracy
Characterization of spatial stability and absorbed energy of laser-induced plasma in gaseous media
No full text[Communication orale]National audienc
Future engineers’ needed competencies for achieving the SDGs: the gap between academic and industry requirements
No full textInternational audienceThere is an overarching consideration in all engineering fields that graduate engineers have to be able to face important sustainability challenges in their future professional life and they will play a key role in addressing the 17 Sustainable Development Goals (SDG) of the ‘2030 Agenda for Sustainable Development’ established by the United Nations in 2015. As a consequence, sustainable development competencies are increasingly a central consideration for graduate engineers to prepare them to meet these challenges. Despite this broadening place of sustainability development in engineering education, there is no consensus in the educational litterature about the question which are the relevant sustainability competencies for future engineers to meet SDGs. The main purpose of this paper to investigate how this question is answered by academic people and industry employers and compare their perceptions. We carried out in the context of the A-STEP 2030 European Project two exploratory focus groups study with academic people and industry employers’ representatives in France. Our findings indicate, on one side, a highly significant difference concerning the sustainability goals awareness and relevance between academic and industry participants. On the other side, a relatively good convergence about the needed engineering competencies for meeting SDGs. There were a general agreement on the fact that technical skills and knowledge are well integrated in French engineering schools curricula. However, we observed an important gap between academic and industry groups concerning the inclusion of transversal skills and competencies needed for sustainability development into engineering programs. Our findings suggest the integration of transversal sustainability competencies in a more comprehensive and transversal way into engineering curricula by an increased use of interdisciplinary project-based learning carried out in a real work context based on a close collaboration between industry and academia
Introspection into Portuguese universities attractiveness: a focus on industrial engineering and management students
No full textInternational audienceIndustrial Engineering and Management (IEM), combining management techniques with engineering background, was introduced in Portuguese universities at the beginning of '90. Currently, it occupies a high position in terms of students’ preferences, when choosing their university. The main purpose of this paper is to investigate what are the factors influencing IEM students’ choice of their universities in Portugal.A quantitative survey was conducted (n=304) with the participation of ESTIEM (European Students of Industrial Engineering and Management), at Bachelor’s and Master’s levels, from five Portuguese universities (Porto, Minho, Aveiro, Coimbra and Lisbon). We carried out preliminary statistical analysis of the data.The findings show that, when choosing a university to study at, the most important factor for students is the prestige of the institution. There are also other relevant factors, such as the city of the university, the companies’ recognition and the employability rate. IEM students seem to be future oriented, as they give the highest importance to the job opportunities offered after graduation. However, they associate lower relevance on everyday factors of their academic experience, such as the evaluation methods, the support given by professors and the teachingmethodologies utilized.Findings from this study allow universities to have a better understanding about the most valued factors in the students’ decision-making process for choosing their university, as well as to adapt their recruitment strategy to this demand to attract good students for their programmes
Bright synchrotron radiation from relativistic self-trapping of a short laser pulse in near-critical density plasma
No full textInternational audienc
Hot carrier transport limits the displacive excitation of coherent phonons in bismuth
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Learning Graph Representation with Randomized Neural Network for Dynamic Texture Classification
No full textInternational audienceDynamic textures (DTs) are pseudo periodic data on a space × time support, that can represent many natural phenomena captured from video footages. Their modeling and recognition are useful in many applications of computer vision. This paper presents an approach for DT analysis combining a graph-based description from the Complex Network framework, and a learned representation from the Randomized Neural Network (RNN) model. First, a directed space × time graph modeling with only one parameter (radius) is used to represent both the motion and the appearance of the DT. Then, instead of using classical graph measures as features, the DT descriptor is learned using a RNN, that is trained to predict the gray level of pixels from local topological measures of the graph. The weight vector of the output layer of the RNN forms the descriptor. Several structures are experimented for the RNNs, resulting in networks with final characteristics of a single hidden layer of 4, 24, or 29 neurons, and input layers 4 or 10 neurons, meaning 6 different RNNs. Experimental results on DT recognition conducted on Dyntex++ and UCLA datasets show
On structural finite element modeling strategies and their influence on the optimization of final constructability of reinforced concrete structures
No full textInternational audienceThe design of nuclear civil structures based on rules in European standards makes extensive use of the Finite Element Method (FEM). The size and complexity of the models are continuously increasing. Lately, the post-processing of the FEM results has centered the engineers' contribution on analyzing the reinforcement density produced using automated methods dealing with shell or plate models, which often leads to excessive plate use even in D-regions (discontinuity or disturbance region). This practice is particularly problematic for nuclear structures, which exhibit a large set of massive parts due to radiation protection requirements in many areas such as the reactor pit, the raft, or th
Evaluating Robustness over High Level Driving Instruction for Autonomous Driving
No full textInternational audienceIn recent years, we have witnessed increasingly high performance in the field of autonomous end-toend driving. In particular, more and more research is being done on driving in urban environments, where the car has to follow high level commands to navigate. However, few evaluations are made on the ability of these agents to react in an unexpected situation. Specifically, no evaluations are conducted on the robustness of driving agents in the event of a bad high-level command. We propose here an evaluation method, namely a benchmark that allows to assess the robustness of an agent, and to appreciate its understanding of the environment through its ability to keep a safe behavior, regardless of the instruction
