Abstract: In this paper, we want to determine whether the accurate prediction of nicotine concentration can be obtained by using a network of smart objects and an e-cigarette. The approach consists of, first, the recognition of factors influencing smoking cessation such as physical activity recognition and participant’s behaviors (using both smartphone and smartwatch), then the prediction of the configuration of the e-cigarette (in terms of nicotine concentration, power, and resistance of e-cigarette). The study uses a network of commonly connected objects; a smartwatch, a smartphone, and an e-cigarette transported by the participants during an uncontrolled experiment. The data obtained from sensors carried in the three devices were trained by a Long short-term memory algorithm (LSTM). Results show that our LSTM-based model allows predicting the configuration of the e-cigarette in terms of nicotine concentration, power, and resistance with a root mean square error percentage of 12.9%, 9.15%, and 11.84%, respectively. This study can help to better control consumption of nicotine and offer an intelligent configuration of the e-cigarette to users.
Abstract: This paper presents an efficient fusion algorithm for
iris images to generate stable feature for recognition in unconstrained
environment. Recently, iris recognition systems are focused on real
scenarios in our daily life without the subject’s cooperation. Under
large variation in the environment, the objective of this paper is to
combine information from multiple images of the same iris. The
result of image fusion is a new image which is more stable for further
iris recognition than each original noise iris image. A wavelet-based
approach for multi-resolution image fusion is applied in the fusion
process. The detection of the iris image is based on Adaboost
algorithm and then local binary pattern (LBP) histogram is then
applied to texture classification with the weighting scheme.
Experiment showed that the generated features from the proposed
fusion algorithm can improve the performance for verification system
through iris recognition.