APPLICATION DES TURBOCODES DANS UN SYSTÈME MULTI USAGERS WCDMA

Dans ce travail on va montrer que les turbocodes sont parmi les meilleurs codes correcteurs d’erreurs utilisés en codage des chaînes de transmission pour l’optimisation des systèmes radio mobile WCDMA. Redondance, diversité et parcimonie sont les mots clés du codage correcteur d’erreurs. Du côté du décodage, il s’y ajoute l’efficacité, c’est-à-dire le souci de tirer le meilleur parti de toutes les informations disponibles. Parmi les codes correcteurs d’erreurs, on va appliquer les codes de Hamming, les codes convolutifs et les turbocodes pour minimiser la probabilité d’erreur afin d’améliorer les performances du système à la réception du signal  In this work we will show that turbocodes are among the best error correcting codes used in coding the chains of transmission for the optimization of WCDMA mobile radio systems. Redundancy, diversity and parsimony are the keywords of error correction coding. Decoding side, there is also the efficiency, that is to say, the desire to take full advantage of all available information. Among the error-correcting codes, we will apply the Hamming codes, convolutional codes and turbocodes to minimize the probability of error to improve system performance at the reception

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM

Experimental diagnosis of inter-turns stator fault and unbalanced voltage supply in induction motor using MCSA and DWER

This paper presents a comparative study between two techniques of signal processing to diagnose both faults the inter-turn short circuit (ITSC) in stator windings and the unbalanced voltage supply (UVS) in induction motors. The first is considered a classical technique called Motor Current Signature Analysis (MCSA) which is based on the processing of the stator current by the Fast Fourier Transform (FFT). The second is anadvanced technique based on a Discrete Wavelet Energy Ratio (DWER) of three stator currents. The aim objective of this paper is to compare the ability and effectiveness of both techniques to detect the ITSC fault and the UVS in induction motors, and distinguishing between them. An experimental implementation tests the two diagnosis techniques.The results obtained show that the MCAS technique by the FFT analysis has a difficult to discriminate between the current harmonics due to the provide voltage unbalance and those originated by ITSC faults. Unlike the DWERtechnique, which has high sensitivity and exceptional ability to detect and distinguish between the two faults that lead to the reliability of the diagnosis system. To demonstrate that the DWER is an accurate and robust diagnosis approach are used the neural network (NN) as a tool to classify the faults (ITSC and USV) where using DWER indicators as NN input. The results obtained of combination between the DWER and NN are effective and proved its ability to detect both faults under different load conditions and distinguish between them accurately with low error (10-5)

Propagation par onde de sol d'une onde electromagnetique au-dessus d'un terrain irregulier et non homogene

SIGLEINIST T 73656 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Hardware Implementation of Various DTC Strategies Using dSpace 1104 for Induction Motor Drive

This paper presents different enhancement techniques of basic direct torque control strategy for induction motor drive. It is well-known that the conventional DTC suffers from high torque ripples and variable switching frequency due to utilizing hysteresis comparators and lookup switching table. In this paper two improved techniques are presented. The first one deal with the use of an extended switching table which divides the flux locus into twelve sectors instead of six in order to solve control ambiguity and reduce ripples. The second technique bases on replacing the switching table by space vector modulation in order to maintain a fixed switching frequency and to minimize consequently the high torque and flux ripples. The effectiveness of the presented algorithms is investigated by an experimental implementation with the aid of real-time interface (RTI) based on dSpace 1104 bored

Robust multimodal 2Dand 3D face authentication using local feature fusion

IF=1.43International audienceIn this work, we present a robust face authentication approach merging multiple descriptors and exploiting both 3D and 2D information. First, we correct the heads rotation in 3D by iterative closest point algorithm, followed by an efficient preprocessing phase. Then, we extract different features namely: multi-scale local binary patterns (MSLBP), novel statistical local features (SLF), Gabor wavelets, and scale invariant feature transform (SIFT). The principal component analysis followed by enhanced fisher linear discriminant model is used for dimensionality reduction and classification. Finally, fusion at the score level is carried out using two-class support vector machines. Extensive experiments are conducted on the CASIA 3D faces database. The evaluation of individual descriptors clearly showed the superiority of the proposed SLF features. In addition, applying the (3D+2D) multimodal score level fusion, the best result is obtained by combining the SLF with the MSLBP+SIFT descriptor yielding in an equal error rate of 0.98 % and a recognition rate of RR=97.22%

Learning multi-view deep and shallow features through new discriminative subspace for bi-subject and tri-subject kinship verification

International audienceThis paper presents the combination of deep and shallow features (multi-view features) using the proposed metric learning (SILD+WCCN/LR) approach for kinship verification. Our approach based on an automatic and more efficient two-step learning into deep/shallow information. First, five layers for deep features and five shallow features (i.e. texture and shape), representing more precisely facial features involved in kinship relations (Father-Son, Father-Daughter, Mother-Son, and Mother-Daughter) are used to train the proposed Side-Information based Linear Discriminant Analysis integrating Within Class Covariance Normalization (SILD+WCCN) method. Then, each of the features projected through the discriminative subspace of the proposed SILD+WCCN metric learning method. Finally, a Logistic Regression (LR) method is used to fuse the six scores of the projected features. To show the effectiveness of our SILD+WCNN method, we do some experiments on LFW database. In term of evaluation, the proposed automatic Facial Kinship Verification (FKV) is compared with existing ones to show its effectiveness, using two challenging kinship databases. The experimental results showed the superiority of our FKV against existing ones and reached verification rates of 86.20% and 88.59% for bi-subject matching on the KinFaceW-II and TSKinFace databases, respectively. Verification rates for tri-subject matching of 90.94% and 91.23% on the available TSKinFace database for Father-Mother-Son and Father-Mother-Daughter, respectively

Sensorless MRAS-Based Predictive Torque Control for Induction Motor Drive with Load Disturbance Observer

The predictive torque control (PTC) has a similar structure to the direct torque control (DTC). However, instead of using a look-up switching table and hysteresis comparators, this technique evaluates the torque and stator flux in cost function and includes the inverter model in the control design and without need to any modulation bloc. This paper investigates the sensorless predictive torque control. The association of sensorless algorithm can reduce the cost of the drive and improves its reliability. In this work dual observer structures will be incorporated to the main PTC scheme. The model reference adaptive system (MRAS) is proposed as a speed estimator. Then, this paper proposes an enhancement in the external speed control loop using load disturbance observer (DOB). Over than the estimation of the applied load torque, this observer can ensure an accurate speed tracking and can improve the disturbance rejection ability of the speed loop. The effectiveness of the presented algorithms is verified by a numerical simulation using MATLAB/Simulink software

Kinship verification from face images in discriminative subspaces of color components

International audienceAutomatic facial kinship verification is a challenging topic in computer vision due to its complexity and its important role in many applications such as finding missing children and forensics. This paper presents a Facial Kinship Verification (FKV) approach based on an automatic and more efficient two-step learning into color/texture information. Most of the proposed methods in automatic kinship verification from face images consider the luminance information only (i.e. gray-scale) and exclude the chrominance information (i.e. color) that can be helpful, as an additional cue, for predicting relationships. We explore the joint use of color-texture information from the chrominance and the luminance channels by extracting complementary low-level features from different color spaces. More specifically, the features are extracted from each color channel of the face image and fused to achieve better discrimination. We investigate different descriptors on the existing face kinship databases, illustrating the usefulness of color information, compared with the gray-scale counterparts, in seven various color spaces. Especially, we generate from each color space three subspaces projection matrices and then score fusion methodology to fuse three distances belonging to each test pair face images. Experiments on three benchmark databases, namely the Cornell KinFace, the KinFaceW (I & II) and the TSKinFace database, show superior results compared to the state of the art