Ahmed Selim

TL;DR: In this paper, the authors consider the problem of human activity recognition using triaxial accelerometers and deep learning paradigms and show that deep activity recognition models can provide better recognition accuracy of human activities, avoid the expensive design of handcrafted features in existing systems, and utilize the massive unlabeled acceleration samples for unsupervised feature extraction.

TL;DR: A deep Convolutional Neural Network model is proposed that enables Measurement Capable Devices (MCDs) to identify the presence of radar signals in the radio spectrum, even when these signals are overlapped with other sources of interference, such as commercial Long-Term Evolution (LTE) and Wireless Local Area Network (WLAN).

TL;DR: A novel (spectrogram) representation called the Quarter-spectrogram (Q-Spectrogram) that squeezes temporal and frequency information for input to CNN models and a simple WiFi classification scheme that buffers several WiFi Q-spectrograms and then makes a decision about WiFi’s presence and also gives a quantified measure of WiFi traffic density.

TL;DR: This work presents an SBD technique based on spatio-temporal Convolutional Neural Networks, and performs the largest evaluation to date for one SBD algorithm, on real and synthetic data, containing more than 4.85 million frames.