Characterization and selection of WiFi channel state information features for human activity detection in a smart public transportation system

ABSTRACT: Robust methods are needed to detect how people are moving in smart public transportation systems. This paper proposes and characterizes effective means to accurately detect passengers. We analyze a public WiFi-based activity recognition (WiAR) dataset to extract human activity features from Channel State Information (CSI) data. To do so, CSI power changes caused by nearby human activity are analyzed. Our method first extracts multi-dimensional features using a Short-Time Fourier Transform (STFT) of CSI data to capture the relevant signal features. Since the environment of a transportation system changes dynamically and non-deterministically, we propose analyzing these changes with a heuristic algorithm that leverages a decision tree to automate a decision-making solution for feature selection. Principal Component Analysis (PCA) is performed before the decision tree algorithm. Reported results are compared with those obtained from the existing methods. Based on these results, we explore the effectiveness of various features such as the chirp rate, delta band power, spectral flux, and frequency of movement. This allows identifying and recommending the most effective features for the explored detection task according to observed variability, information gain, and correlation between features. The reported classification results show that using only the chirp rate estimated from CSI information as a feature, we achieve precision = 83%, True Positive (TP)=94% , True Negative (TN)=91% and F1-score = 87%. Considering delta band power as an additional feature adds more information and allows getting higher performance with precision = 100%, TP=97% , TN=95% and F1-score = 95%

Live streaming of uncompressed HD and 4K videos using terahertz wireless links

RÉSUMÉ: Taming the Terahertz waves (100 GHz-10 THz) is considered the next frontier in wireless communications. While components for the ultra-high bandwidth Terahertz wireless communications were in rapid development over the past several years, however, their commercial availability is still lacking. Nevertheless, as we demonstrate in this paper, due to recent advances in the microwave and infrared photonics hardware, it is now possible to assemble a high-performance hybrid THz communication system for real-life applications. As an example, in this paper, we present the design and performance evaluation of the photonics-based Terahertz wireless communication system for the transmission of uncompressed 4K video feed that is built using all commercially available system components. In particular, two independent tunable lasers operating in the infrared C-band are used as a source for generating the Terahertz carrier wave using frequency difference generation in a photomixer. One of the IR laser beams carries the data which is intensity modulated using the LiNbO 3 electro-optic modulator. A zero bias Schottky diode is used as the detector and demodulator of the data stream followed by the high-gain and low-noise pre-amplifier. The Terahertz carrier frequency is fixed at 138 GHz and the system is characterized by measuring the bit error rate for the pseudo random bit sequences at 5.5 Gbps. By optimizing the link geometry and decision parameters, an error-free (BER <; 10 -10 ) transmission at a link distance of 1 m is achieved. Finally, we detail the integration of a professional 4K camera into the THz communication link and demonstrate live streaming of the uncompressed HD and 4K video followed by the analysis of link quality