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The monitoring of a cutting tool is needed for the prediction of impending faults and estimating its Remaining Useful Life (RUL). Implementing a robust Prognostic and Health Management (PHM) system for a high speed milling CNC cutter remains a challenge for various industries to reach improved quality, reduced downtime, increased system safety and lower production costs. The purpose of the present...
Manufacturing process performances become a key issue for reliability improvement. In order to decreasing loss of production due to machine stopping, to achieve this goal to maintain the equipments in operational condition, make products quickly, economically and with high quality. This requirement can be satisfied by implementing appropriate maintenance strategies, Condition based maintenance (CBM)...
Recently, the combination of classification systems with semi-supervised learning has attracted researchers in several fields. Usually, for tasks with high complexity such as handwriting based age prediction, individual systems, using one classifier associated with specific data features, cannot provide satisfactory performance. In this paper, we investigate the contribution of the Co-training approach,...
The multimodal biometric systems make use of two or more modalities that together achieve much higher performances to overcome the defects of the unimodal biometric system. Depending on the application context, biometrics recognition system may be used either to identification or in the verification of an individual. This paper proposes multimodal biometric system based on the face and iris operate...
Feature extraction is a key component of a Monocular Simultaneous Localization and Mapping (Monocular SLAM) system which permits to extract features and can also reliably track them over frames. In this paper, a novel approach for Monocular SLAM is proposed. This approach uses the information on the camera displacement and image saliency to adequately extract stable and suitable features, ones that...
In this paper, we investigate a new method to analyze electrocardiogram (ECG) signal, extract the features, for the real time human identification using single lead human electrocardiogram. The proposed system extracts special parts of the ECG signal starting from the P wave, the QRS complex and ending with the T wave for that we used the multiresolution wavelet analysis. Different features are selected...
In this study, we proposed an analysis method of ElectroMyoGraphic (EMG) signals in order to diagnose and to identify neuromuscular pathologies (i.e.; myopathy and neuropathy). Analysis is performed fully automatically without expert assistance and without prior segmentation of muscle contractions. The method is based on Huang-Hilbert transform (HHT) which is a data-driven algorithm that decomposes...
Cell nucleus characterization is essential for pathological image analysis. In this article, we propose a shape characterization of the cell nucleus based on some selected shape descriptors. In order to separate automatically the different cell nucleus shapes, we focus our effort on the feature extraction and classification processes. The main contribution of our work is the creation of a new shape...
In this letter, a novel no reference image quality metric is developed, a set of ten features are extracted from each distorted image, then Relevance Vector Machine algorithm (RVM) is utilized to learn the mapping between the combined features and human opinion scores, experiments are conducted on the LIVE databases. The performance of the proposed metric is compared with some existing NR metrics...
In this paper, a combining Electroencephalographic (EEG) and Electro-oculographic (EOG) approach has been developed in order to allow the control of the displacement of a wheelchair by disable peoples. NeuroSky MindeWave headset and wet superficial electrodes have been used for the measurement of EEG and EOG signals respectively. These signals have been processed using Wavelet Transform (WT) and Principal...
Vibration monitoring and analysis is a powerful and recommended tool for preventive maintenance and early detection of impending failures in rotary machine. The demand for cost efficient, reliable and safe rotating machinery requires accurate fault diagnosis, classification and prognosis systems. This work presents a study to explore the performances of bearing fault diagnosis by using wavelet neural...
This work proposes an off-line handwritten signature identification system using the Histogram of Symbolic Representation (HSR). The HSR is considered as one-class classifier which has the ability to generate a model for each writer using only its own reference signatures. This method allows also modeling the writing style of each writer by taking into account the variability of signatures. To evaluate...
This paper investigates feature selection method using filter Fast Correlation based Filter FCBF combined with Genetic Algorithm GA and particle swarm optimization PSO. In this paper two hybrid approaches based on filter method FCBF and Genetic algorithm (FCBF-GA) and filter FCBF with particle swarm (FCBF-PSO) are proposed. It has been found that the proposed method FCBF-PSO outperform the proposed...
In this paper, we present an efficient approach to investigate data of EEG-based Brain-Machine Interface (BMI) using a bagging Support Vector Machines (SVMs) for collected data classification from a P3-speller paradigm. The combination of SVMs allows to handle the problem of EEG data variability between the different sessions of the acquisition process. This variability is caused by temporal non-stationarity...
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