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Wu, H. (2017). New perspectives in machine sensing by convergence of processing, learning and computing. PHILICA.COM Observation number 161.

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New perspectives in machine sensing by convergence of processing, learning and computing

Huang Wuunconfirmed user (Beijing Polytechnic University)

Published in socio.philica.com

Observation
Smart environments and objects are gradually becoming able to perceive surroundings in new sensing modalities. Such modalities go far beyond the human sensory abilities. For some years now, as an example, smart objects are equipped with increasing numbers of sensors and actuators in order to both improve efficiency (Boukabache, 2014), comfort (Chan et al., 2008) and even help and support people in special cases (Weimar et al., 2009). A variety of sensors have been used up to now: temperature sensors, humidity sensors, illumination sensors, humidity sensors, pressure/force sensors, gas and chemical sensors, inertial sensors (accelerometers, compass, gyros, etc.), visible/infrared/range cameras, radars, etc.
The usage of new all these sensing modalities, beyond human sensory perception, together with new sensing capabilities offered by advanced signal processing techniques (Donoho, 2006), machine learning (LeCun et al., 2009) and new computing modalities (Nickolls et al., 2010), are opening a new interesting scenarios. For the first time in history, human beings overcome limitations to knowledge and calculus imposed by Mother Nature, but they go further even their senses. The synergy of these human-capability improvements will produce much more progress and knowledge advancement that never.

Observation circumstances
My current studies on machine sensing.

References
Boukabache, H., Escriba, C., & Fourniols, J. Y. (2014). Toward smart aerospace structures: Design of a piezoelectric sensor and its analog interface for flaw detection. Sensors, 14(11), 20543-20561.
Chan, M., Estève, D., Escriba, C., & Campo, E. (2008). A review of smart homes—Present state and future challenges. Computer methods and programs in biomedicine, 91(1), 55-81.
Distante, C., Diraco, G., & Leone, A. (2010). Active range imaging dataset for indoor surveillance. Annals of the BMVA, London, 3, 1-16.
Donoho, D. L. (2006). Compressed sensing. IEEE Transactions on information theory, 52(4), 1289-1306.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Nickolls, J., & Dally, W. J. (2010). The GPU computing era. IEEE micro, 30(2).
Weimar, U., Simpson, R., Barsan, N., Heine, T., Simmendinger, W., Malfatti, M., Margesin, B., Gonzo, L., Grassi, M., Lombardi, A., Malcovati, P., Leone, A., Diraco, G., Siciliano, P., Sicard, OV., Pohle, R., Fleischer, M., Redaelli, A., Giacosi, A., & Bonassi, C. (2009). Microsystem Technology for Ambient Assisted Living (AAL). Procedia Chemistry, 1(1), 710-713.

Information about this Observation
This Observation has not yet been peer-reviewed
This Observation was published on 31st March, 2017 at 17:12:35 and has been viewed 795 times.

Creative Commons License
This work is licensed under a Creative Commons Attribution 2.5 License.
The full citation for this Observation is:
Wu, H. (2017). New perspectives in machine sensing by convergence of processing, learning and computing. PHILICA.COM Observation number 161.


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