STFNets: Learning Sensing Signals from the Time-Frequency Perspective with Short-Time Fourier Neural NetworksEI
Recent advances in deep learning motivate the use of deep neural networks in Internet-of-Things (IoT) applications. These networks are modelled after signal processing in the human brain, thereby leading to significant advantages at perceptual tasks such as vision and speech recognition. IoT applications, however, often measure physical phenomena, where the underlying physics (such as inertia, wireless signal propagation, or the natural frequency of oscillation) are fundamentally a function of signal frequencies, offering better featu...更多
- 1Smith, O Julius. Mathematics of the discrete fourier transform (DFT) : with audio applications., 2007.
- 3Allan Stisen, Henrik Blunck, Sourav Bhattacharya, Thor Siiger Prentow, Mikkel Baun Kjærgaard, Anind K. Dey, Tobias Sonne, Mads Møller Jensen. Smart Devices are Different: Assessing and MitigatingMobile Sensing Heterogeneities for Activity Recognition.Conference On Embedded Networked Sensor Systems, 2015.
- 4Wei Wang, Alex X. Liu, Muhammad Shahzad, Kang Ling, Sanglu Lu. Understanding and Modeling of WiFi Signal Based Human Activity Recognition.ACM International Conference on Mobile Computing and Networking, 2015.
- 6D. H. Hubel, T. Wiesel. Receptive fields and functional architecture of monkey striate cortex.The Journal of Physiology, 1968.
- 8Sidhant Gupta, Daniel Morris, Shwetak Patel, Desney Tan. SoundWave: using the doppler effect to sense gestures.CHI, pp. 1911-1914, 2012.
- 9Bryan Perozzi, Rami Al-Rfou', Steven Skiena. DeepWalk: online learning of social representations.KDD, pp. 701-710, 2014.
- 11Fisher Yu, Vladlen Koltun. Multi-Scale Context Aggregation by Dilated Convolutions.CoRR, 2015.
- 13Thomas N. Kipf, Max Welling. Semi-Supervised Classification with Graph Convolutional Networks.CoRR, 2016.
pp. 2192-2202, 2019.