Search results for "Detection"

Robust fault detection for switched systems with time-varying delay using delta operator approach

2014

Détection robuste de mouvement par histogrammes quasi‐continus

2012

National audience; Dans cet article, nous proposons d'utiliser la représentation de distributions de valeurs par histogrammes quasi-continus pour réaliser une détection temps réel de mouvement dans une séquence d'images. Nous comparons les résultats de cette détection à deux méthodes de référence de la littérature.

Temporal Denoising of Kinect Depth Data

2012

The release of the Microsoft Kinect has attracted the attention of researchers in a variety of computer science domains. Even though this device is still relatively new, its recent applications have shown some promising results in terms of replacing current conventional methods like the stereo-camera for robotics navigation, multi-camera system for motion detection and laser scanner for 3D reconstruction. While most work around the Kinect is on how to take full advantage of its capabilities, so far only a few studies have been carried out on the limitations of this device and fewer that provide solutions to enhance the precision of its measurements. In this paper, we review and analyse curr…

Identification of microbial taxa involved in cultural heritage deterioration and able to produce health hazardous substances by molecular techniques.

2008

Fungi and bacterial, wide-spread in biosphere environments, are the main microorganisms related to the deterioration of cultural assets but, moreover, complex microbial communities may emit mixed aerosol into indoor air. In this study the microbial colonization is investigated from to point of view, conservation of cultural heritage and related potential illness to visitors or professionals. The sampling was performed by non-destructive procedures on works of art surfaces, and by the gelatin membrane filter method (Sartorius) for aerosol. The identification of microbial taxa was performed by molecular analyses based on PCR, sequencing, sequence comparison techniques and, particularly for fu…

First real–time detection of solar pp neutrinos by Borexino

2014

International audience; Solar neutrinos have been pivotal to the discovery of neutrino flavour oscillations and are a unique tool to probe the reactions that keep the Sun shine. Although most of solar neutrino components have been directly measured, the neutrinos emitted by the keystone pp reaction, in which two protons fuse to make a deuteron, have so far eluded direct detection. The Borexino experiment, an ultra-pure liquid scintillator detector running at the Laboratori Nazionali del Gran Sasso in Italy, has now filled the gap, providing the first direct real time measurement of pp neutrinos and of the solar neutrino luminosity.

Somatosensory Deviance Detection ERPs and Their Relationship to Analogous Auditory ERPs and Interoceptive Accuracy

2022

Abstract. Automatic deviance detection has been widely explored in terms of mismatch responses (mismatch negativity or mismatch response) and P3a components of event-related potentials (ERPs) under a predictive coding framework; however, the somatosensory mismatch response has been investigated less often regarding the different types of changes than its auditory counterpart. It is not known whether the deviance detection responses from different modalities correlate, reflecting a general prediction error mechanism of the central nervous system. Furthermore, interoceptive functions have been associated with predictive coding theory, but whether interoceptive accuracy correlates with devian…

Time-Frequency Characteristics of In-Home Radio Channels Influenced by Activities of the Home Occupant

2019

While aging is a serious global concern, in-home healthcare monitoring solutions are limited to context-aware systems and wearable sensors, which may easily be forgotten or ignored for privacy and comfort reasons. An emerging non-wearable fall detection approach is based on processing radio waves reflected off the body, who has no active interaction with the system. This paper reports on an indoor radio channel measurement campaign at 5.9 GHz, which has been conducted to study the impact of fall incidents and some daily life activities on the temporal and spectral properties of the indoor channel under both line-of-sight (LOS) and obstructed-LOS (OLOS) propagation conditions. The time-frequ…

Balance Sheet of Screening for Diabetic Retinopathy With a Mobile Non-Mydriatic Digital Camera in Burgundy, France

2009

Purpose: To assess the economic balance-sheet of screening for diabetic retinopathy (DR) with a mobile non-mydriatic digital camera in Burgundy, France, in a public health and economic perspective.Methods: The 72 lowest medicalised areas of Burgundy were visited. Retinal images were obtained with a non-mydriatic camera. To construct an economic model, we used available data on the prevalence of diabetes in Burgundy, the efficiency of the screening, the number of screened DR, blindness probability according to DR grading, the efficiency of laser therapy, the diabetic characteristics (gender, age, income, professional status and life expectancy) and medical costs induced by our campaign.Resul…

Dimensionality reduction framework for detecting anomalies from network logs

2012

Dynamic web services are vulnerable to multitude of intrusions that could be previously unknown. Server logs contain vast amounts of information about network traffic, and finding attacks from these logs improves the security of the services. In this research features are extracted from HTTP query parameters using 2-grams. We propose a framework that uses dimensionality reduction and clustering to identify anomalous behavior. The framework detects intrusions from log data gathered from a real network service. This approach is adaptive, works on the application layer and reduces the number of log lines that needs to be inspected. Furthermore, the traffic can be visualized. peerReviewed

Using affinity perturbations to detect web traffic anomalies

2013

The initial training phase of machine learning algorithms is usually computationally expensive as it involves the processing of huge matrices. Evolving datasets are challenging from this point of view because changing behavior requires updating the training. We propose a method for updating the training profile efficiently and a sliding window algorithm for online processing of the data in smaller fractions. This assumes the data is modeled by a kernel method that includes spectral decomposition. We demonstrate the algorithm with a web server request log where an actual intrusion attack is known to happen. Updating the kernel dynamically using a sliding window technique, prevents the proble…