Application of machine learning techniques and empirical mode decomposition for the classification of analog modulated signals

<w:PermStart w:id="205147274" w:edGrp="everyone"/>In this article, an automatic Analog Modulation Classifier based on Empirical mode decomposition and Machine learning approaches (AMC-EM) is proposed. The AMC-EM operates without a priori information and can recognise typical analog modulation schemes: amplitude modulation, phase modulation, frequency modulation, and single sideband modulation. The AMC-EM uses Empirical Mode Decomposition (EMD) to evaluate the features of the signal for the successive classification by using Machine Learning (ML). In the design of the AMC-EM, the selection of the specific ML technique is performed by comparing, with numerical tests, the performance of the (i) Support Vector Machine (SVM), (ii) k-nearest neighbor classifier, and (iii) adaptive boosting, since they are commonly used in the field of signal classification. The tests have highlighted that the SVM, specifically the quadratic SVM, permits the best possible performance concerning classification accuracy, by considering different noise intensities superimposed on the signal. To assess the advantages of the proposal, a comparison with other classifiers available in the literature has been undertaken through numerical tests. Finally, the AMC-EM is experimentally evaluated, and the experimental results agree with those of the simulation.</p

Design and creation of different simulation architectures for hybrid and electric vehicles

PFC del programa Erasmus EPSTreball desenvolupat dins el marc del programa 'European Project Semester'.Development of electric vehicle architectures requires complex analysis and innovative designs in order to produce a highly efficient mode of personal transportation acceptable to the target demographic. Using computer-aided modeling and simulation has been proven to decrease the development time of conventional vehicles while increasing overall success of the product design. Computer-aided automotive development also allows a fast response to the testing and inclusion of developing technologies in individual systems. Therefore, it follows to use this technique in the research and development of electric vehicles for consumer markets. This paper presents a system level model development and simulation for an electric vehicle using the Matlab-Simulink platform and its associated process. The current state of the art technologies for electric and plug-in hybrid electric vehicles are given to provide an introduction into the subject. Following, the project development is briefly described, detailing the specific goals for the project and the methods by which results were achieved. Next the paper discusses the analytical and simulation models for each key component as divided by the following systems: battery, charging, and traction. Model assembly and the development of a graphic user interface follows. Finally, the testing procedures for model validation, along with results, and future project works are provided

Research challenges in Measurement for Internet of Things systems

In this paper, an overview of the research challenges in measurements for the design of Internet of Things (IoT) systems is proposed. To this end, a general architecture of an IoT system is presented, which is specialized according to two key requirements: the power supply capabilities of the infrastructure and the time delay constraints of the application. Guidelines for the design of an IoT system are summarized, and the measurement needs are highlighted. A review of the research contributions is given concerning three main measurement topics: (i) energy-aware data acquisition systems, (ii) localization of mobile IoT nodes, and (iii) precise synchronization protocols

Perpendicular switching of a single ferromagnetic layer induced by in-plane current injection

International audienceModern computing technology is based on writing, storing and retrieving information encoded as magnetic bits. Although the giant magnetoresistance effect has improved the electrical read out of memory elements, magnetic writing remains the object of major research efforts. Despite several reports of methods to reverse the polarity of nanosized magnets by means of local electric fields and currents, the simple reversal of a high-coercivity, single-layer ferromagnet remains a challenge. Materials with large coercivity and perpendicular magnetic anisotropy represent the mainstay of data storage media, owing to their ability to retain a stable magnetization state over long periods of time and their amenability to miniaturization. However, the same anisotropy properties that make a material attractive for storage also make it hard to write to. Here we demonstrate switching of a perpendicularly magnetized cobalt dot driven by in-plane current injection at room temperature. Our device is composed of a thin cobalt layer with strong perpendicular anisotropy and Rashba interaction induced by asymmetric platinum and AlOx interface layers. The effective switching field is orthogonal to the direction of the magnetization and to the Rashba field. The symmetry of the switching field is consistent with the spin accumulation induced by the Rashba interaction and the spin-dependent mobility observed in non-magnetic semiconductors as well as with the torque induced by the spin Hall effect in the platinum layer. Our measurements indicate that the switching efficiency increases with the magnetic anisotropy of the cobalt layer and the oxidation of the aluminium layer, which is uppermost, suggesting that the Rashba interaction has a key role in the reversal mechanism. To prove the potential of in-plane current switching for spintronic applications, we construct a reprogrammable magnetic switch that can be integrated into non-volatile memory and logic architectures. This device is simple, scalable and compatible with present-day magnetic recording technolog

Perpendicular spin-torque switching with a synthetic antiferromagnetic reference layer

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Analog-to-information converters in the wideband RF measurement for aerospace applications: current situation and perspectives

Finding new data acquisition methods for practical instrumentation development which can be used in case of monitoring purposes of aerospace RF spectrum are a key research area nowadays. With the increasing evolution of the signal bandwidths, the requirements for new acquisition capabilities are a necessity for modern aerospace RF measurement instrumentation systems. In particular, an overview of the current trends for wideband RF instrumentation has focused on the signal acquisition stages of the instruments. The most frequent RF signal acquisition architectures and also some research trends in designing of new systems, such as AIC based wideband RF spectrum acquisition, have been presented. In the case of AIC, it could be assumed that this unique combination of hardware and software offers a powerful method for the acquisition of wideband RF signals. Although no off-the-shelf device is available already, a lot of research is currently being carried out and some prototypes have been proposed. Issues such as: utilization of CS mathematic methods and acquisition hardware for real-time RF spectrum monitoring; reducing substantially the size, the analog RF front-end complexity and the power consumption by applying the CS framework; and providing a good RF signal spectrum estimation by using CS techniques, still remain open to the research community in the field

Current Induced Switching of the Hard Layer in Perpendicular Magnetic Nanopillars

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