Conventional sensors have been designed to minimize noise effects. Any sensor that is designed to detect a certain physical variable is influenced to a certain degree by other physical variables. This suggests that any sensor is potentially tap able of detecting multiple physical variables. In the present study, we consider sensing devices that are easily influenced by several physical variables and make full use of their multi-sensing characteristics through statistical signal processing and machine learning techniques with a wide variety of prior information. The proposed sensor design approach is completely different from the conventional approach with respect to system design and has advantages in terms of cost and system simplification compared to existing approaches. This new idea can be realized by developing a novel multiple-input/single-output sensor that can detect various variables such as pressure, acceleration, temperature and light emission by a single device. The sensor is applied to monitor the symptoms of tire, earthquake and break-in for the purpose of home security. The proposed security system consists of the following three steps: (1) detection of disaster by a probabilistic outlier detection procedure using an auto-regressive model, (2) disaster feature extraction by Kalman filter on a state space model, and (3) disaster classification by multiclass support vector machine.
[1]
T. Higuchi.
Applications of quasi-periodic oscillation models to seasonal small count time series
,
1999
.
[2]
S. Sathiya Keerthi,et al.
Improvements to Platt's SMO Algorithm for SVM Classifier Design
,
2001,
Neural Computation.
[3]
Tomoyuki Higuchi.
Processing of Time Series Data Obtained by Satellites
,
1999
.
[4]
Sin-Min Tsai,et al.
A service of home security system on intelligent network
,
1997
.
[5]
Kajiro Watanabe,et al.
Noncontact method for sleep stage estimation
,
2004,
IEEE Transactions on Biomedical Engineering.