Design of a high-resolution separable-kernel quadratic TFD for improving newborn health outcomes using fetal movement detection

Prior to birth, fetus health can be monitored by the variety and scale of its movements. In addition, at birth, EEG signals are recorded from at-risk newborns. Studies have shown that both fetal movements and newborn EEGs are non-stationary signals. This paper aims to represent both newborn EEG and fetal movement signals in a time-frequency domain using a specifically designed time-frequency distribution (TFD) that is well adapted to these types of data for the purpose of analysis, detection and classification. The approach to design the quadratic TFDS is based on relating separable-kernel TFDS to DSP spectral window and digital filter design. To reach this goal, we compared recently proposed TFDs such as the Modified B distribution, a separable Gaussian distribution and the B distribution. Then, an extension of the modified B distribution(MBD) is proposed, referred to as the extended separable-kernel MBD. This new TFD uses a separable kernel based on an extension of the modified B kernel in both time and frequency domain with different windows for each domain. Simulation results are provided to compare the proposed Method with different TFDs and to assess its performance. The new TFD is then first applied to real fetal movement data recorded using accelerometers

Pre-equalization for IR-UWB system

International audienc

Pre-equalization for IR-UWB system

International audienc

Time-frequency features for pattern recognition using high-resolution TFDs: a tutorial review

This paper presents a tutorial review of recent advances in the field of time–frequency (t,f)(t,f) signal processing with focus on exploiting (t,f)(t,f) image feature information using pattern recognition techniques for detection and classification applications. This is achieved by (1) revisiting and streamlining the design of high-resolution quadratic time frequency distributions (TFDs) so as to produce adequate (t,f)(t,f) images, (2) using image enhancement techniques to improve the resolution of TFDs, and (3) defining new (t,f)(t,f) features such as (t,f)(t,f) flatness and (t,f)(t,f) entropy by extending time-domain or frequency-domain features. Comparative results indicate that the new (t,f)(t,f) features give better performance as compared to time-only or frequency-only features for the detection of abnormalities in newborn EEG signals. Defining high-resolution TFDs for the extraction of new (t,f)(t,f) features further improves performance. The findings are corroborated by new experimental results, theoretical derivations and conceptual insights

Enhancing passive UHF RFID backscatter energy using chirp spread spectrum signals and channel shortening

Performance of passive ultrahigh frequency radio-frequency identification (UHF RFID) systems depends on the energy level of the backscattered signal from the RF tag to the RFID reader. In the indoor environment, the transmitted signals are corrupted by the multi-path channels fading that reduce the energy of the backscatter signal and modify the waveform of the continuous wave (CW) that result a short read range. Chirp spread spectrum (CSS) signals and channel shortening (CS) techniques are used separately to combat the effect of the multi-path channel fading in different contexts. In this paper, we propose two methods that use jointly CSS and CS in the multiple-input single-output (MISO) passive UHF RFID system that employs multiple antennas at the reader and one antenna at the tag First method increases significantly the received energy on the tag. The harvest energy ensures the backscatter communication from tag to reader. Second method offers a good trade-off between maintaining of high received energy and preserving the main characteristics of the waveform of the CSS, on the tag. This method is required to ensure a secured communication between reader and tag.Scopu