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Цель работы. Проведение анализа результатов стендовых испытаний скользящих электрических контактов электротранспорта с целью определения показателей теплового режима скользящего контакта. Методы исследований. Исходные данные получены во время натурных экспериментов на специализированном стенде в лабораторных условиях, которые проводились с целью определения износа контактного провода вставками разных типов для пантографов электроподвижного состава железных дорог. Для определения температуры контактного провода в месте скользящего контакта применялся неразрушающий бесконтактный метод контроля с помощью тепловизора. Полученные результаты. В ходе экспериментальной части исследований были получены тепловизорные фотографии, на которых зафиксированы температуры в месте скользящего контакта между вставкой пантографа электроподвижного состава и контактным проводом, установленным на испытательном диске. Эти температурные значения фиксировались с определенным временным интервалом, позволяющим построить зависимости изменения теплового режима во времени, определить устойчивое время нагрева системы «вставка-контактный провод» испытательного стенда и получить конечное значение температуры в конце испытаний. Принимая образец вставки при котором получена минимальная температура в зоне скользящего контакта в качестве эталонного, становится возможным по значению температуры контактного провода в зоне скользящего контакта прогнозировать конечный результат испытаний других типов накладок.. Научная новизна. Показателями температурных режимов скользящего контакта электротранспорта во время стендовых испытаний получены впервые, предложенный относительный температурный показатель позволяет по результатам стендовых испытаний прогнозировать качественные показатели скользящего контакта электротранспорта. Практическая ценность. Стендовые испытания вставок токоприемников электротранспорта являются длительной процедурой, нормативный показатель количества проходов токоприемника на стенде равен 500 тысяч проходов, что требует значительных затрат времени, энергии, человеческих ресурсов и т.д. Предложенные по результатам испытаний показатели, а именно постоянная время нагрева и относительный температурный показатель, позволяет реализовать двухэтапные испытания. На первом этапе, продолжительностью 10 тысяч оборотов диска испытательного стенда определяются предложенные показатели и сравниваются с соответствующими эталонными, в случае их соответствия становится возможным прогнозировать конечный результат испытаний как успешный (износ контактного провода меньше нормы) или не успешный. В последнем случае необходимость проведения второго этапа испытаний до 500 тыс. оборотов становится под вопросом, поскольку конечный результат будет отрицательным, а ресурсы на испытания будут задействованы значительные, а потому эти испытания нецелесообразны.

International audience; Several computing techniques and sensor technologies have been proposed in the last two decades to provide indoor localization systems for personal in-home staying. This field known as personal localization system (PLS) is quite challenging due to some faulty sensor measurements as well as people random movements. This paper describes the ongoing work of in-home PLS using data fusion between an inertial measurement unit (IMU) and a load pressure sensing floor. In addition, a fault-tolerant fusion method is proposed using a purely informational formalism: information filter on the one hand, and information theory tools on the other hand. Residues based on the Kullback-Leibler divergence are used. Using an appropriate threshold, these residues lead to the detection and exclusion of sensors faults. The experimental results show that the proposed approach is very promising, a fault-tolerant PLS that localizes and tracks people in their home with a high accuracy.

Automatic segmentation of contractions in multichannel uterine EMG signals is the main issue of interest in many recent studies. Most of them are faced with the problem of the detecting other events than contractions. Wavelets have the great advantage of being able to separate the fine details in a signal. Many studies applied wavelets for sake of detecting and analyzing abrupt changes in non-stationary signals. This study is another step of our project focused on the automatic contractions segmentation of uterine EMG signals. Indeed, we will focus on the application of dynamic cumulative sum method (DCS) with over-segmentation elimination techniques on details after wavelet decomposition of bipolar uterine EMG records. DCS will be applied in mono- and multi-dimensional. Detected events are then compared to contractions identified by expert using Margin validation test. Regarding the obtained sensitivity and other events rate of methods we find that DCS with multidimensional application give the highest sensitivity 100% and lowest other events rate 49.6%.

International audience; The objective of this work is to investigate the ability of a 2D super resolution (SR) technique in 3D restoration and enhancement of brain magnetic resonance images to facilitate the study of cerebral aging bio-markers. The SR method exploits the joint properties of the system point spread function and sub-sampling operators to derive a fast algorithm. Brain images of the common marmoset, Callithrix jacchus, acquired at different ages are used in this study. The evaluation of the final outcome of our method is done by computing the intracranial volume from the segmentation of the brain compartments: gray matter, white matter and cerebrospinal fluid. Results show that the deblurring of the images improves the segmentation process with respect to the ground truth. However, super resolution leads to the best quantification of the intracranial volume when compared to the deblurred and the original images. Therefore, despite its sub-optimality, the 2D SR method provides reliable results for improving the quality of the images used in the study of aging in terms of precision of reconstruction and computational time.

Preterm labor is the primary driver of infant mortality. These days, ponders manage the electrical sign of the uterus which can be utilized by investigation strategies so as to concentrate highlights to describe compression and their job in labor. In this investigation, a few non-straight strategies are utilized to concentrate highlights from the sign so as to portray pregnancy and work flag and afterward to arrange them utilizing diverse sort of order apparatuses.All information was recorded in France and Iceland during pregnancy and labor by utilizing of 4x4 matrix of electrodes. Highlights of this sign was extricated utilizing nonlinear techniques, LZC (Lempel-Ziv Complexity), FD (Fractal Dimension), Hjorth parameter (mobility, complexity). At that point these highlights were nourished into classifiers, for example, support vector machine, artificial neural network (single layer perceptron and multilayer perceptron (MLP)); MLP gives the best grouping (rate) utilizing the extracted features and in this manner encourages us to separate among pregnancy and labor contraction signals.

International audience

In the last ten years the ratio of preterm labor mortality increases. Many studies were done about the cause of gestation as well as preterm labor. Nowadays, studies are dealing with the signal detection of uterus contraction which can be used by analysis methods in order to determine its features and its role in labor. In this study, many Non-linear methods are used to extract features from the signal in order to differentiate between pregnancy and labor signals and to monitor pregnancy of women for each week before labor (WBL): Lempel-Ziv complexity (lzc), Fractal Dimension (FD), Hjorth parameter. All data recorded between Lebanon and France from 12 WBL until 1 WBL and labor using a 4×4 matrix of electrodes. Methods were tested first on synthetic signals to test their sensitivity to nonlinearity change then they were applied on real signals. Results show the implementation of the nonlinear analysis methods of signal processing to differentiate between the contraction signals, which appears as the variation and sensitivity of pregnancy and labor signals with respect to methods that can help to detect normal and preterm labor.

This study evaluates the ability of distributed source localization methods to accurately estimate the location of the sources as well as their sensitivity to the spatial extent of the sources when using EHG data. We use here realistic simulations of uterine electrical activity based on a realistically uterus shape and take into account 16 electrodes for simulated recorde EHG signal, A Data Driven Parcellization (DDP) method was used to segment the uterus surface into non-overlapping regions. Our results showed that the localization is sensitive to the spatial extents of the sources (range from 11 cm2 to 29 cm2, and also to the number and size of the regions defining the model. Our analysis of the evolution of the real sources during contraction showed a non linear propagation of uterine electrical activity.

Electrohysterogram source imaging, i.e., moving from the electrode/sensor space to the source space using EHG signals, provide an estimate of spatial distributions of uterine activity at millisecond scale. This paper aims to study the ability of different distributed source localization methods to recover uterine electrical activity sources. Performance was quantified using a detection accuracy index. Our result suggests that the variation based method is able to reconstruct extended uterus sources with the overall high accuracy, where the increasing of the electrodes numbers and the decreasing of the fat thickness induce a better accuracy in localization.

International audience; Fall detection for elderly and patient is a very important service that has the potential of increasing autonomy of elders while minimizing the risks of living alone. It has been an active research topic due to the fact that health care industry has a big demand for products and technology of fall detection systems. Owing to the recent rapid advancement in sensing and wireless communication technologies, fall detection systems have become possible. They allow detecting fall events for the elderly, monitoring them, and consequently providing necessary help whenever needed. This paper describes the ongoing work of detecting falls in independent living senior apartments using force sensors and 3-axis accelerometers concealed under intelligent tiles. The force sensors permit detecting elders' falls, locating, tracking and recognizing human activities (walking, standing, sitting, lying down, falling, and the transitions between them). However, the detection accuracy on real data contains false alarms coming from falling and lying postures. To solve this issue, we propose the fusion between the force sensor measurements and the accelerometer sensor decisions. As a consequence, the system accuracy is satisfactory and the results show that the proposed methods are efficient, and they can be easily used in a real elder tracking and fall detection system

International audience; Automatic fall detection is a major issue in taking care of the health of elderly people and has the potentialof increasing autonomy and independence while minimizing the risks of living alone. It has been an active researcharea due to the large demand of the healthcare association for fall detection goods. Fortunately, due to the recentfast progression in sensing technologies, fall detection system becomes prospective. It permits to monitor elders anddetect their falls, and consequently provides emergency support whenever needed. This paper describes the currentwork of detecting falls in independent living apartments using accelerometer concealed under tiles. We present theup-to-date advancement of data collection, feature extraction, feature selection, and signal changing detection, whichare essential phases of this work.

International audience; In a recent past, several techniques have been developed to analyze the effect of electrode grid alignment with the muscle fibers. But, they focused on pattern matching of Motor Unit (MU) electrical signatures and not on the interference signal. In this study, we use a method which is widely applied in connectivity and directionality analysis of stochastic and complex signals, namely, the nonlinear correlation coefficient (h²) on HD-sEMG signal matrices. The approach is first applied on simulated data from recent generation model through an (8x8) simulated electrode matrix, then on real signals using the same grid specifications on the Biceps Brachii. Both simulated and real data were evaluated with three angles of grid alignment with respect to muscle fibers. For this purpose, five parameters were extracted from obtained h² correlation matrices and tested. According to the obtained results, a relationship between h² values and the electrode matrix alignment seems to exist. However, further efforts are needed to design parameters more sensitive to grid misalignment with respect to muscle fibers.

International audience; In-house monitoring of elders and automatic fall detection using intelligent sensors is a very desirable service that has the potential of increasing autonomy and independence while minimizing the risks of living alone. The efforts of building such systems have been spanning for decades, but there still is a lot of room for improvement. This paper proposes a novel approach to make a successful monitoring and assistive services for elderly. Moreover, we present our current progress of data collection, parameters extraction and parameters selection that are essential phases of our project. Our results on the data demonstrate that the proposed system methods are efficient and accurate and can be easily used in a real fall detection system.

Among the complex systems of systems of the human body, the uterus is one of the least understood. The sequence of contraction of the myometrium results from numerous interconnected control systems (electric, hormonal, mechanical). In this paper we try to understand what the electrical activity of the uterus can tell us about uterine synchronization. This work is the first attempt to locate uterine EMG sources by means of source localization tools. These tools are tested on signals simulated by a multiscale model of the uterine EMG. As a preliminary result, we present the source localization for one pregnancy and one labor contraction recorded on the same woman. The localized sources do not demonstrate any “propagation like” behavior. This supports the hypothesis that the global organ-level communication is probably not done via action potential propagation, but may be via a coupling between the electrical and the mechanical systems controlling the uterus.

International audience; Premature labor is one of the most serious health problems in the developed world. One of the main reasons for this is that no good way exists to distinguish true labor from normal pregnancy contractions. The aim of this paper is to investigate if the application of graph theory techniques to multi-electrode uterine EMG signals can improve the discrimination between pregnancy contractions and labor. To test our methods we first applied them to synthetic graphs where we detected some differences in the parameters results and changes in the graph model from pregnancy-like graphs to labor-like graphs. Then, we applied the same methods to real signals. We obtained the best differentiation between pregnancy and labor through the same parameters. Major improvements in differentiating between pregnancy and labor were obtained using a low pass windowing preprocessing step. Results show that real graphs generally became more organized when moving from pregnancy, where the graph showed random characteristics, to labor where the graph became a more small-world like graph

International audience; In this paper, we propose a new framework to characterize the electrohysterographic (EHG) signals recorded during pregnancy and labor. The approach is based on the analysis of the propagation of the uterine electrical activity. The processing pipeline includes i) the estimation of the statistical dependencies between the different recorded EHG signals, ii) the characterization of the obtained connectivity matrices using network measures and iii) the use of these measures in clinical application: the classification between pregnancy and labor. Due to its robustness to volume conductor, we used the imaginary part of coherence in order to produce the connectivity matrix which is then transformed into a graph. We evaluate the performance of several graph measures. We also compare the results with the parameter mostly used in the literature: the peak frequency combined with the propagation velocity (PV + PF). Our results show that the use of the network measures is a promising tool to classify labor and pregnancy contractions with a small superiority of the graph strength over PV+PF

International audience; Nonlinear time series analysis can provide useful information regarding nonlinear features of biological signals. The effect of filtering on the performance of nonlinear methods is not well-understood. In this work, we investigate the effects of signal filtering on the sensitivity of four nonlinear methods: Time reversibility, Sample Entropy, Lyapunov Exponents and Delay Vector Variance. These methods were applied to uterine EMG signals with the aim of using them to discriminate between pregnancy and labor contractions. The signals were filtered using three different band-pass filters before the application of the methods. Results showed that the sensitivity of some methods such as sample entropy was significantly improved with filtering. On the other hand, filtering had little effect on some other methods such as time reversibility. This study concludes that while filtering increases computation time, it may be necessary for some nonlinear methods particularly those with low sensitivity

This article proposes a selection method that can be applied to choose the best parameters to classify contractions in the uterine electrohysterography (EHG) signal for the detection of preterm labor. Several types of parameters have historically been extracted from the electrohysterogram. These can be divided into three classes: linear parameters, nonlinear parameters and parameters related to the electrohyterogram propagation. Frequency band enhancement EHG characterization has also been extensively studied. Our work is divided in two parts. The first part is to implement and compute all the parameters already extracted from the EHG that have been published in the literature. These parameters were computed both on the original EHG and on different frequency bands obtained using wavelet packet decomposition. In the second part, we will use a new parameters selection method to eliminate all parameters that are not efficient and pertinent for classification. Our results indicate a set of 13 linear parameters, 3 nonlinear parameters and 2 propagation parameters that are potentially most useful to discriminate between pregnancy and labor contractions, either on different frequency bands or directly on original EHG.

International audience; Several measures have been proposed to detect nonlinear characteristics in time series. Results on time series, multiple surrogates and their z-score are used to statistically test for the presence or absence of non-linearity. The z-score itself has sometimes been used as a measure of nonlinearity. The sensitivity of nonlinear methods to the nonlinearity level and their robustness to noise have rarely been evaluated in the past. While surrogates are important tools to rigorously detect nonlinearity, their usefulness for evaluating the level of nonlinearity is not clear. In this paper we investigate the performance of four methods arising from three families that are widely used in non-linearity detection: statistics (time reversibility), predictability (sample entropy, delay vector variance) and chaos theory (Lyapunov exponents). We used sensitivity to increasing complexity and the mean square error (MSE) of Monte Carlo instances for quantitative comparison of their performances. These methods were applied to a Henon nonlinear synthetic model in which we can vary the complexity degree (CD). This was done first by applying the methods directly to the signal and then using the z-score (surrogates) with and without added noise. The methods were then applied to real uterine EMG signals and used to distinguish between pregnancy and labor contraction bursts. The discrimination performances were compared to linear frequency based methods classically used for the same purpose such as mean power frequency (MPF), peak frequency (PF) and median frequency (MF). The results show noticeable difference between different methods, with a clear superiority of some of the nonlinear methods (time reversibility, Lyapunov exponents) over the linear methods. Applying the methods directly to the signals gave better results than using the z-score, except for sample entropy.

International audience; Recently, much attention has been paid to the use of non-linear analysis techniques for the characterization of biological signals. Several measures have been proposed to detect non-linear characteristics in time series. The effect of sampling frequency on the performance of these non-linear methods has rarely been evaluated. In this paper, we present a preliminary study of this effect for four methods that are widely used in non-linearity detection: Time reversibility, Sample Entropy, Lyapunov Exponents and Delay Vector Variance. These methods have been applied to real uterine EMG signals with the aim of distinguishing between pregnancy and labor contractions. The signals were used to classify contractions before and after decimating them by a factor 10. The results show that decimation improves the performance for sample entropy. It reduces it considerably for Delay Vector Variance and only slightly for Time reversibility and Lyapunov exponents. Time reversibility still gives the highest classification rate. The methods were much less computationally expensive after down sampling.

International audience; Understanding the direction and quantity of information flowing in a complex system is a fundamental task in signal processing. Several measures have been proposed to detect the quantity of synchronization and the directionality between time series and in physiological data. In this paper we use two methods that are widely used in synchronization and directionality analysis: Nonlinear correlation coefficient (h(2)) and the general synchronization (H). The performances of both methods were tested on four dimensional coupled synthetic nonlinear Rössler models. They were then applied to a single real labor contraction uterine EMG burst with the aim of using them to detect synchronization and to plot the map of direction of information flow between the whole signal channels. The results on synthetic signal show a slight superiority of H over h(2). The results obtained on a single contraction are encouraging for the future use of these tools for resolving the open question of the directionality of uterine contractions and may provide a way of finding their source loci.

ÖZ EKUVARYANT./-GRUPLARININ HESAPLANMASINDA LOKALİZASYON TEKNİKLERİ VE STIEFEL MANİFOLDLARININ EKUVARYANT KESİTLERİ Obiedat, Mohammad Doktora, Matematik Bölümü Tez Yöneticisi: Prof. Dr. Turgut Önder Eylül 1998, 97 sayfa. G sonlu bir grup ve X de G-bağlantılı bir (7-CW kompleksi olsun. Bu tezin ana amacı ekuvaryant J-grupları JOa(Xy in hesaplanması ve Stiefel manifoldlarının ekuvaryant kesitlerinin varlık problemine çözüm bulmaktır. JOg{X)' in hesabı için değişik bir yöntem bulduk. Hesaplarımızı X' in serbest, aşikar, tek nokta G-uzayı olduğu durumlar için yaptık. Çeşitli pro jektif uzaylar için JOa(FPk)' nın elementlerinin mertebesini hesapladık ve bunları Stiefel manifoldlarının ekuvaryant kesitlerinin varlığı problemine kısmi bir çözüm bulmak için kullandık. JO(X)' in hesaplanması için Atiyah-Segal tamlama teoremine dayanmayan iki yöntem bulduk. Bu yöntemleri kullanarak önce JO(X)' in elemanlarının mertebesini, sonra da JO(X)' in nasıl hesaplanacağını gösterdik. Bunları örnek uzay olarak seçtiğimiz kompleks projektif uzay CPk için hesapladık. Anahtar Sözcükler: Lokalizasyon, Ekuvaryant J-grupları, Stiefel manifoldları, Kesitler, Adams operasyonları, Bott sınıfları. ABSTRACT LOCALIZATION TECHNIQUES IN COMPUTATION OF EQUIVARIANT./-GROUPS AND EQUIVARIANT CROSS SECTIONS OF STIEFEL MANIFOLDS Obiedat, Mohammad Ph.D., Department of Mathematics Supervisor: Prof. Dr. Turgut Önder September 1998, 97 pages. Let G be a finite group and X be a finite G-connected G-CW complex. The main purpose of this dissertation is to find means for computing the equivariant J-groups, JOq(X), and then to obtain solutions for the equivariant cross section problem of Stiefel manifolds. We give an alternative method for computing JOg{X). We do our com putations for the cases: X is a free G-space, A` is a trivial G-space, and X is a one point set. We find the orders of elements of J0g(FPk) for various projective spaces, and then we use the results to obtain a partial solution for the equivariant cross section problem of Stiefel manifolds. Without using Atiyah-Segal completion theorem, we prove two methods for computing JO(X). Then we show how to use these methods to find the orders of elements of JO(X), and also to find JO(X). Our illustrative example is CPk, the complex projective space. Key words: Localization, Equivariant J-groups, Stiefel manifolds, Cross sec tions, Adams operations, Bott classes. 97