Grenoble Institute of Technology

About: Grenoble Institute of Technology is a education organization based out in Grenoble, France. It is known for research contribution in the topics: Hyperspectral imaging & Geology. The organization has 3427 authors who have published 5345 publications receiving 137158 citations. The organization is also known as: Grenoble INP.

Comparison of electroactive polymers for energy scavenging applications

Abstract: Electroactive polymers, generally used as actuators, offer very promising levels of performance in sensor mode, particularly in scavenging ambient mechanical energy applications. New, innovative, high performance, flexible generators can be developed to supply low consumption systems. This technology has the potential to be an alternative to traditional solutions based on electromagnetism, electrostatic or piezoelectricity, which are rigid solutions, mostly used in the high frequency range. This paper reports an extensive investigation of electroactive polymers for energy scavenging applications. The operating principle, properties and analytic models of six polymers are presented and compared in terms of scavenging energy density, operating area, advantages and inconveniences.

Estimation of respiratory waveform using an accelerometer

Abstract: The cardiorespiratory signal is a fundamental vital sign to assess a person's health. Additionally, the cardio-respiratory signal gives a great deal of information to healthcare providers wishing to monitor healthy individuals. This paper proposes a method to detect the respiratory waveform from an accelerometer strapped onto the chest. A system was designed and several experiments were conducted on volunteers. The acquisition is performed in different status: normal, apnea, deep breathing and also in different postures: vertical (sitting, standing) or horizontal (lying down). This method could therefore be suitable for automatic identification of some respiratory malfunction, for example during the obstructive apnea.

A classifier ensemble based on fusion of support vector machines for classifying hyperspectral data

Abstract: Classification of hyperspectral data using a classifier ensemble that is based on support vector machines (SVMs) are addressed. First, the hyperspectral data set is decomposed into a few data sources according to the similarity of the spectral bands. Then, each source is processed separately by performing classification based on SVM. Finally, all outputs are used as input for final decision fusion performed by an additional SVM classifier. Results of the experiments underline how the proposed SVM fusion ensemble outperforms a standard SVM classifier in terms of overall and class accuracies, the improvement being irrespective of the size of the training sample set. The definition of the data sources resulting from the original data set is also studied.