scholarly journals Timestamp Estimation in P802.15.4z Amendment

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5422
Author(s):  
Ioan Domuta ◽  
Tudor Petru Palade ◽  
Emanuel Puschita ◽  
Andra Pastrav
Keyword(s):  
Multipath Propagation ◽  
Pulse Amplitude ◽  
Impulse Radio ◽  
Ultra Wideband ◽  
Selective Fading ◽  
Frequency Selective Fading ◽  
Power Spectral ◽  
Localization Precision ◽  
Frequency Selective ◽  
The Time Domain

Due to the known issue that the ranging in the 802.15.4™-2015 standard is prone to external attacks, the enhanced impulse radio (EiR), a new amendment still under development, advances the secure ranging protocol by encryption of physical layer (PHY) timestamp sequence using the AES-128 encryption algorithm. This new amendment brings many changes and enhancements which affect the impulse-radio ultra-wideband (IR-UWB) ranging procedures. The timestamp detection is the base factor in the accuracy of range estimation and inherently in the localization precision. This paper analyses the key parts of PHY which have a great contribution in timestamp estimation precision, particularly: UWB pulse, channel sounding and timestamp estimation using ciphered sequence and frequency selective fading. Unlike EiR, where the UWB pulse is defined in the time domain, in this article, the UWB pulse is synthesized from the power spectral density mask, and it is shown that the use of the entire allocated spectrum results in a decrease in risetime, an increase in pulse amplitude, and an attenuation of lateral lobes. The paper proposes a random spreading of the scrambled timestamp sequence (STS), resulting in an improvement in timestamp estimation by the attenuation lateral lobes of the correlation. The timestamp estimation in the noisy channels with non-line-of-sight and multipath propagation is achieved by cross-correlation of the received STS with the locally generated replica of STS. The propagation in the UWB channel with frequency selective fading results in small errors in the timestamp detection.

scholarly journals Single-RF Diversity Receiver for OFDM System Using ESPAR Antenna with Alternate Direction

1970 ◽  
Vol 6 (1) ◽  
pp. 89-93 ◽  
Author(s):  
Satoshi Tsukamoto ◽  
Minoru Okada
Keyword(s):  
Communication Systems ◽  
Orthogonal Frequency Division Multiplexing ◽  
Multipath Propagation ◽  
Maximal Ratio Combining ◽  
Diversity Gain ◽  
Efficient Technique ◽  
Selective Fading ◽  
Frequency Selective Fading ◽  
Frequency Selective ◽  
Diversity Scheme

This paper presents a single-RF diversity scheme for orthogonal frequency division multiplexing (OFDM) receiver using Electronically Steerable Passive Array Radiator (ESPAR) antenna whose direction changes alternately at the OFDM symbol rate. OFDM is widely used for mobile communication systems because of its broadband wireless transmission capability in a severe time dispersive multipath propagation channel. OFDM is, however, not efficient for mitigating the performance degradation due to fading. Diversity is an efficient technique for solving this problem. Although maximal ratio combining diversity is the most efficient technique, it requires the same number of RF front-end circuitry and analog-todigital converters (ADC) as antennas. Although ESPAR antenna-based diversity technique requires only a single-RF and ADC, the convergence is not fast enough to track fast variation of the channel state. Furthermore, it is not efficient in a frequency selective channel. In this paper, we propose a new OFDM diversity scheme using ESPAR antenna. The proposed scheme is capable of obtaining the diversity gain in a frequency selective fading environment and avoids the slow convergence rate problem in the conventional technique using ESPAR antenna. Computer simulation results show that the proposed scheme gives diversity gain in a frequency selective fading channel.

Acoustic-based Upper Facial Action Recognition for Smart Eyewear

2021 ◽  
Vol 5 (2) ◽  
pp. 1-28
Author(s):  
Wentao Xie ◽  
Qian Zhang ◽  
Jin Zhang
Keyword(s):  
Frequency Domain ◽  
Orthogonal Frequency Division Multiplexing ◽  
Interaction Mechanism ◽  
Phase Changes ◽  
Experimental Result ◽  
Selective Fading ◽  
Time Frequency ◽  
Facial Action ◽  
Frequency Selective Fading ◽  
Frequency Selective

Smart eyewear (e.g., AR glasses) is considered to be the next big breakthrough for wearable devices. The interaction of state-of-the-art smart eyewear mostly relies on the touchpad which is obtrusive and not user-friendly. In this work, we propose a novel acoustic-based upper facial action (UFA) recognition system that serves as a hands-free interaction mechanism for smart eyewear. The proposed system is a glass-mounted acoustic sensing system with several pairs of commercial speakers and microphones to sense UFAs. There are two main challenges in designing the system. The first challenge is that the system is in a severe multipath environment and the received signal could have large attenuation due to the frequency-selective fading which will degrade the system's performance. To overcome this challenge, we design an Orthogonal Frequency Division Multiplexing (OFDM)-based channel state information (CSI) estimation scheme that is able to measure the phase changes caused by a facial action while mitigating the frequency-selective fading. The second challenge is that because the skin deformation caused by a facial action is tiny, the received signal has very small variations. Thus, it is hard to derive useful information directly from the received signal. To resolve this challenge, we apply a time-frequency analysis to derive the time-frequency domain signal from the CSI. We show that the derived time-frequency domain signal contains distinct patterns for different UFAs. Furthermore, we design a Convolutional Neural Network (CNN) to extract high-level features from the time-frequency patterns and classify the features into six UFAs, namely, cheek-raiser, brow-raiser, brow-lower, wink, blink and neutral. We evaluate the performance of our system through experiments on data collected from 26 subjects. The experimental result shows that our system can recognize the six UFAs with an average F1-score of 0.92.

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