System for supporting collaborative work

[Pending Patent US no. 60/851,304 ] Cheriet M, four colleagues from UQAM, TELUQ, ETS. System for supporting collaborative work. Vol. CA no. 2,563,866.; 2006.

Pending Patent US no. 60/851,304 | CA no. 2,563,866, October 13, 2006

M. Cheriet and four colleagues from UQÀM, TELUQ, ETS: System for supporting collaborative work

2018/05/17 L'excellence numérique en vie intelligente au cœur de Montréal

JOURNAL DE MONTREAL Jeudi, 17 mai 2018 07:00. En collaboration avec Videotron.

http://www.journaldemontreal.com/2018/05/17/un-terrain-de-jeu-au-coeur-de-la-ville-pour-developper-la-vie-intelligente-avec-videotron

En plein centre-ville montréalais se niche le QG du LabVI, un laboratoire urbain d'expérimentation de calibre international.

2 days 18 hours ago

Payless Monitoring Service for Tenants in Cloud with Traffic and Energy-aware Function Deployment

[1115] Shameli-Sendi A, Louafi H, Cheriet M. "Payless Monitoring Service for Tenants in Cloud with Traffic and Energy-aware Function Deployment.". In: 9th IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2017). Hong-Kong, China: IEEE; 2017.

Time Series-Based GHG Emissions Prediction for Smart Homes

[1114] Riekstin AC, Langevin A, Dandres T, Cheriet M. "Time Series-Based GHG Emissions Prediction for Smart Homes." IEEE Transactions on Sustainable Computing. Submitted.

Smart homes play a crucial role in reducing the residential sector electricity consumption and Greenhouse Gases (GHG) emissions. In this work, we present a time series approach to predict GHG emissions to be integrated in smart home management systems. More specifically, we used Long Short-Term Memory (LSTM), a variant of Recurrent Neural Networks. The prediction results get very close to the real values when the region under study has an energy matrix mostly based on fossil fuels, less intermittent than regions in which more renewable sources are present.

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