Guy Lamarque

SoftGenLock: active stereo and genlock for PC cluster

In this paper, we present SoftGenLock, an open source software that enables genlock and active stereo on commodity graphics cards. SoftGenLock is implemented on top of Linux. It does not require any hardware modification of the graphics card. Rather than to gain total control on signal generation, which would make the software deeply dependent on the graphics card specification, SoftGenLock applies continuous small modifications to converge and maintain genlocked video signals. To be properly synchronized with each video retrace, SoftGenLock is executed as a real-time task. The genlock signal is propagated along the different machines using the parallel port, a low latency device present on all PCs. It results in a software that only requires access to few specific registers on a graphics card: it can be ported with minimal effort on potentially any graphics card.

Unified background noise model for Power Line Communication

In Power Line Communication, it is necessary to have a model of noise to optimize transmissions and to compare different methods in a noise controlled environment. This communication presents a new method to estimate more efficiently the background noise parameters of a model extracted from the literature. The new estimation leads to more robust model parameters evaluation regarding narrowband interference. The effort done regarding to this noise source permits to highlight the dependence of the background noise to the mains period. This allows defining a new unified model, taking into account the dependence to the absolute value of the mains voltage. This provides a new background noise function verified and useful for low and high data rate technologies and for further noise studies.

Redefining Performance Evaluation Tools for Real-Time QRS Complex Classification Systems

In a heartbeat classification procedure, the detection of QRS complex waveforms is necessary. In many studies, this heartbeat extraction function is not considered: the inputs of the classifier are assumed to be correctly identified. This communication aims to redefine classical performance evaluation tools in entire QRS complex classification systems and to evaluate the effects induced by QRS detection errors on the performance of heartbeat classification processing (normal versus abnormal). Performance statistics are given and discussed considering the MIT/BIH database records that are replayed on a real-time classification system composed of the classical detector proposed by Hamilton and Tompkins, followed by a neural-network classifier. This study shows that a classification accuracy of 96.72% falls to 94.90% when a drop of 1.78% error rate is introduced in the detector quality. This corresponds to an increase of about 50% bad classifications.

Improving the Power Line Communication Signal-to-Noise Ratio During a Resistive Load Commutation

In any cable or power line communications, impulse noise is known to be the most difficult noise to filter. In particular, non periodic asynchronous impulse noise is impossible to predict. Under such noise conditions, the OFDM1 symbol generally used in PLC2 is corrupted. To overcome this problem, the signal-to-noise ratio is generally improved by detecting and/or filtering the noise. This leads however to heavy detection and computing time in comparison with the disturbance duration. In this paper, we study the parameters of the noise generated by a load commutation and propose a new approach that consists in controlling the commutation instant of the load. This approach reduces by up to 15 dB the asynchronous impulsive noise emitted by a resistive load in a 40.96-µs typical Homeplug AV3 OFDM symbol duration. Synchronisation is integrated in electronic commutation system. Results show that the load value does not influence the magnitude of the impulse noise.

A New Concept of Virtual Patient for Real-Time ECG Analyzers

Designing real-time systems such as electrocardiogram (ECG) analyzers and evaluating their performance precisely is not an easy task. One of the difficulties is due to the general loss of performance that is frequently observed when implementing data processing algorithms on such systems and running the devices in new environments. Performance values are traditionally estimated theoretically or numerically (personal computer simulations) or are evaluated on real data but in a simulated environment. While the best performance measurements are accredited due to experiments driven in a real environment, the data at stake in this case are not entirely controlled. We propose in this paper the new concept of a virtual patient for real-time ECG analyzers. This concept enables a real environment to be created virtually by generating signals from a database composed of real acquired data. The analog signals generated will be seen by the analyzer as if they were coming from instantaneous electrical heart activities. This procedure provides a more correct assessment of the ECG analyzer performance than was previously possible. The test bench is useful for Holter manufacturers since it can deliver genuine performance values of the device.

A measurement system for creating datasets of on/off-controlled electrical loads

The change in the power grid configuration, where a consumer with renewable energy resources can become an energy source, has given rise to a need for a better monitoring of the energy flow. This can be achieved by having access to a more detailed information about the energy consumption. The Non-Intrusive Load Monitoring (NILM) is one of the approaches that can be used to get such detailed energy consumption information. Most of the proposed NILM techniques are based on the use of low sampling frequency (LSF) data (1 Hz or less). Even though NILM techniques based on high sampling frequency (HSF) data are expected to be able to improve the accuracy of the LSF NILM techniques, the lack of suitable datasets for the training and the test of such techniques delayed their development. In this paper, we address this issue and we propose a measurement system that can allow the building of a suitable dataset for the training and the test of HSF NILM techniques. The proposed measurement system has three main features that are: the high sampling frequency measurement capability (kHz), the control over the turn-on and off time instants in synchronization with the grid voltage sinusoid and the measurement with aggregation scenarios of up to six appliances.

HMM-based Transient and Steady-state Current Signals Modeling for Electrical Appliances Identification

The electrical appliances identification problem is gaining a rapidly growing interest these past few years due to the recent need of this information in the new smart grid configuration. In this work, we propose to construct an appliance identification system based on the use of Hidden Markov Models (HMM) to model transient and steady-state electrical current signals. For this purpose, we investigate the usefulness of different choices for the proposed identification system such as: the use of the transient and the steady-state current signals, the use of even and odd-order harmonics as features, and the optimal number of features to take into account. This work also discusses the choice of the Short-Time Fourier Series (STFS) coefficients as adapted features for the representation of transient and steady-state current signals.

Modeling and estimation of transient current signals

In this paper, we propose a nonstationary model for transient electrical current signals based on the physical behavior of electrical appliances during their turn-on. This model takes into account the nonstationarity of those transient signals and the special form of their envelope. We also propose an algorithm for the estimation of this models parameters and we evaluate its performance on synthetic and real signals. The measured transient current signals actually reflect the physical phenomena appearing in the electrical appliances when turning on, and therefore, the model estimates of these transient current signals are useful for characterizing electrical appliances and can be helpful for distinguishing appliances in addition to the use of their steady-state power consumption.

Improved Power Quality in an Innovative Bidirectional Inverter Topology

The management of electricity consumption still represents a major issue, and particularly in individual housing mainly to find a good balance between production and consumption. Attention must be focused on the optimization of the cost of electricity consumption and, at the same time, the peak demand. Regarding a smart home management system, the inverter has a central role. Voltage and current bidirectional features are of utmost importance, specifically when an energy transfer is required from a storage system and the AC-grid, and vice versa. This article deals with an innovative bidirectional inverter topology based on sinusoidal waveforms from the generation of sine half-waves. The main challenges are both to improve the power quality of the output signals in terms of total harmonic distortion (THD), and minimize the complexity of the whole converter. Wide band-gap semiconductor devices (SiC MOSFETs) are used to optimize the efficiency of the DC-AC converter while minimizing its size and its weight. Many experimental measurements with low output power (lower than 1 kW) point out the relevance of this kind of topology.

Electrical Appliances Identification and Clustering using Novel Turn-on Transient Features

Mohamed Nait Meziane1, Abdenour Hacine-Gharbi2, Philippe Ravier1, Guy Lamarque1, Jean-Charles Le Bunetel3 and Yves Raingeaud3 1PRISME Laboratory, University of Orléans, 12 rue de Blois, 45067 Orléans, France 2LMSE Laboratory, University of Bordj Bou Arréridj, Elanasser, 34030 Bordj Bou Arréridj, Algeria 3GREMAN Laboratory, UMR 7347 CNRS University of Tours, 20 avenue Monge, 37200 Tours, France {mohamed.nait-meziane, philippe.ravier}@univ-orleans.fr, gharbi07@yahoo.fr, {lebunetel, yves.raingeaud}@univ-tours.fr