Christophe Gisler

HES-SO University of Applied Sciences and Arts Western Switzerland · iCoSys — Institut des systèmes complexes

Simulation or emulation of mobile ad hoc networks (MANET) is used to predict or analyze the performance of MANETs under various scenarios. One challenge is to emulate realistically the MANET's radio performance. Running the Extendable Mobile Ad Hoc Network Emulator (EMANE) framework, we show how to reproduce measured characteristics, namely through...

Purpose: To test the hypothesis that contact lens sensor (CLS)-based 24-hour profiles of ocular volume changes contain information complementary to intraocular pressure (IOP) to discriminate between primary open angle glaucoma (POAG) and healthy (H) eyes. Design: Development and evaluation of a diagnostic test with machine learning. Subjects:...

More than 80 million people worldwide suffer from glaucoma, an asymptomatic and irreversible disease of the optic nerve leading to blindness unless intra-ocular pressure (IOP) is controlled. IOP is the only controllable risk factor to stabilize patients, and various therapeutic options exist to reduce IOP. IOP follows, however, individual nycthemer...

The automatic identification of appliances through the analysis of their electricity consumption has several purposes in Smart Buildings including better understanding of the energy consumption, appliance maintenance and indirect observation of human activities. Electric signatures are typically acquired with IoT smart plugs integrated or added to...

We asset about the analysis of electrical appliance consumption signatures for the identification task. We apply Hidden Markov Models to appliance signatures for the identification of their category and of the most probable sequence of states. The electrical signatures are measured at low frequency (10-1 Hz) and are sourced from a specific database...

To detect and quantify eye blinks during 24-hour intraocular pressure (IOP) monitoring with a contact lens sensor (CLS). A total of 249 recordings of 24-hour IOP patterns from 202 participants using a CLS were included. Software was developed to automatically detect eye blinks, and wake and sleep periods. The blink detection method was based on det...

We present ACS-F2, a new electric consumption signature database acquired from domestic appliances. The scenario of use is appliance identification with emerging applications such as domestic electricity consumption understanding, load shedding management and indirect human activity moni-toring. The novelty of our work is to use low-end electricity...

Diagnosing the glaucoma is a very difficult task for healthcare professionals. High intraocular pressure (IOP) remains the main treatable symptom of this degenerative disease which leads to blindness. Nowadays, new types of wearable sensors, such as the contact lens sensor Triggerfish®, provide an automated recording of 24-hour profile of ocular di...

Electricity load monitoring of appliances has become an important task considering the recent economic and ecological trends. In this game, machine learning has an important part to play, allowing for energy consumption understanding, critical equipment monitoring and even human activity recognition. This paper provides a survey of current research...

We assess the feasibility of unseen appliance recognition through the analysis of their electrical signatures recorded using low-cost smart plugs. By unseen, we stress that our approach focuses on the identification of appliances that are of different brands or models than the one in training phase. We follow a strictly defined protocol in order to...

We report on the evaluation of signal processing and classification algorithms to automatically recognize electric appliances. The system is based on low-cost smart-plugs measuring periodically the electricity values and producing time series of measurements that are specific to the appliance consumptions. In a similar way as for biometric applicat...

We report on the creation of a database of appliance consumption signatures and two test protocols to be used for appliance recognition tasks. By means of plug-based low-end sensors measuring the electrical consumption at low frequency, typically every 10 seconds, we made two acquisition sessions of one hour on about 100 home appliances divided int...

We report on the development of an innovative system which can automatically recognize home appliances based on their electric consumption profiles. The purpose of our system is to apply adequate rules to control electric appliance in order to save energy and money. The novelty of our approach is in the use of plug-based low-end sensors that measur...

We report on the development of a wireless lamp dedicated to the feedback of energy consumption. The principle is to provide a simple and intuitive feedback to residents through color variations of the lamp depending on the amount of energy consumed in a house. Our system is demonstrated on the basis of inexpensive components piloted by a gateway s...