Search results for: Sondes Manoubi

Authors: Amine Ben Slama, Aymen Mouelhi, Sondes Manoubi, Chiraz Mbarek, Hedi Trabelsi, Mounir Sayadi, Farhat Fnaiech

Abstract:

Vertigo is a sensation of feeling off balance; the cause of this symptom is very difficult to interpret and needs a complementary exam. Generally, vertigo is caused by an ear problem. Some of the most common causes include: benign paroxysmal positional vertigo (BPPV), Meniere's disease and vestibular neuritis (VN). In clinical practice, different tests of videonystagmographic (VNG) technique are used to detect the presence of vestibular neuritis (VN). The topographical diagnosis of this disease presents a large diversity in its characteristics that confirm a mixture of problems for usual etiological analysis methods. In this study, a vestibular neuritis analysis method is proposed with videonystagmography (VNG) applications using an estimation of pupil movements in the case of an uncontrolled motion to obtain an efficient and reliable diagnosis results. First, an estimation of the pupil displacement vectors using with Hough Transform (HT) is performed to approximate the location of pupil region. Then, temporal and frequency features are computed from the rotation angle variation of the pupil motion. Finally, optimized features are selected using Fisher criterion evaluation for discrimination and classification of the VN disease.Experimental results are analyzed using two categories: normal and pathologic. By classifying the reduced features using the Support Vector Machine (SVM), 94% is achieved as classification accuracy. Compared to recent studies, the proposed expert system is extremely helpful and highly effective to resolve the problem of VNG analysis and provide an accurate diagnostic for medical devices.

Keywords: nystagmus, vestibular neuritis, videonystagmographic system, VNG, Fisher criterion, support vector machine, SVM

Procedia PDF Downloads 113

Authors: Salim Ouchtati, Aissa Belmeguenai, Mouldi Bedda

Abstract:

In this study, we propose an offline system for the recognition of the fragmented handwritten numeric chains. Firstly, we realized a recognition system of the isolated handwritten digits, in this part; the study is based mainly on the evaluation of neural network performances, trained with the gradient backpropagation algorithm. The used parameters to form the input vector of the neural network are extracted from the binary images of the isolated handwritten digit by several methods: the distribution sequence, sondes application, the Barr features, and the centered moments of the different projections and profiles. Secondly, the study is extended for the reading of the fragmented handwritten numeric chains constituted of a variable number of digits. The vertical projection was used to segment the numeric chain at isolated digits and every digit (or segment) was presented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits).

Keywords: features extraction, handwritten numeric chains, image processing, neural networks

Procedia PDF Downloads 236

Authors: Juan Cuesta

Abstract:

Satellite observation is a fundamental component of air pollution monitoring systems, such as the large-scale Copernicus Programme. Next-generation satellite sensors, in orbit or programmed in the future, offer great potential to observe major air pollutants, such as tropospheric ozone, with unprecedented spatial and temporal coverage. However, satellite approaches developed for remote sensing of tropospheric ozone are based solely on measurements from a single instrument in a specific spectral range, either thermal infrared or ultraviolet. These methods offer sensitivity to tropospheric ozone located at the lowest at 3 or 4 km altitude above the surface, thus limiting their applications for ozone pollution analysis. Indeed, no current observation of a single spectral domain provides enough information to accurately measure ozone in the atmospheric boundary layer. To overcome this limitation, we have developed a multispectral synergism approach, called "IASI+GOME2", at the Laboratoire Interuniversitaire des Systèmes Atmosphériques (LISA) laboratory. This method is based on the synergy of thermal infrared and ultraviolet observations of respectively the Infrared Atmospheric Sounding Interferometer (IASI) and the Global Ozone Monitoring Experiment-2 (GOME-2) sensors embedded in MetOp satellites that have been in orbit since 2007. IASI+GOME2 allowed the first satellite observation of ozone plumes located between the surface and 3 km of altitude (what we call the lowermost troposphere), as it offers significant sensitivity in this layer. This represents a major advance for the observation of ozone in the lowermost troposphere and its application to air quality analysis. The ozone abundance derived by IASI+GOME2 shows a good agreement with respect to independent observations of ozone based on ozone sondes (a low mean bias, a linear correlation larger than 0.8 and a mean precision of about 16 %) around the world during all seasons. Using IASI+GOME2, lowermost tropospheric ozone pollution plumes are quantified both in terms of concentrations and also in the amounts of ozone photo-chemically produced along transport and also enabling the characterization of the ozone pollution, such as what occurred during the lockdowns linked to the COVID-19 pandemic. The current paper will show the IASI+GOME2 multispectral approach to observe the lowermost tropospheric ozone from space and an overview of several applications on different continents and at a global scale.

Keywords: ozone pollution, multispectral synergism, satellite, air quality

Procedia PDF Downloads 48