Fractional Order Butterworth Filter for Fetal Electrocardiographic Signal Feature Extraction

The non-invasive Fetal Electrocardiogram (FECG) signal has become a significant method for monitoring the fetus's physiological conditions, extracted from the Abdominal Electrocardiogram (AECG) during pregnancy. The current techniques are limited during delivery for detecting and analyzing fECG. The non - intrusive fECG recorded from the mother's abdomen is contaminated by a variety of noise sources, can be a more challenging task for removing the maternal ECG. These contaminated noises have become a major challenge during the extraction of fetal ECG is managed by uni-modal technique. In this research, a new method based on the combination of Wavelet Transform (WT) and Fast Independent Component Analysis (FICA) algorithm approach to extract fECG from AECG recordings of the pregnant woman is proposed. Initially, preprocessing of a signal is done by applying a Fractional Order Butterworth Filter (FBWF). To select the Direct ECG signal which is characterized as a reference signal and the abdominal signal which is characterized as an input signal to the WT, the cross-correlation technique is used to find the signal with greater similarity among the available four abdominal signals. The model performance of the proposed method shows the most frequent similarity of fetal heartbeat rate present in the database can be evaluated through MAE and MAPE is 0.6 and 0.041209 respectively. Thus the proposed methodology of de-noising and separation of fECG signals will act as the predominant one and assist in understanding the nature of the delivery on further analysis.

Dynamic Adjustment of Weighted GCC-PHAT for Position Estimation in an Ultrasonic Local Positioning System

Ultrasonic local positioning systems (ULPS) have been brought to the attention of researchers as one of the possibilities that can be used for indoor localization. Acoustic systems combine a suitable trade-off between precision, ease of development, and cost. This work proposes a method for measuring the time of arrival of encoded emissions from a set of ultrasonic beacons, which are used to implement an accurate ULPS. This method uses the generalized cross-correlation technique with PHAT filter and weighting factor β (GCC-PHAT-β). To improve the performance of the GCC-PHAT-β in encoded emission detection, the employment is proposed of mixed-medium multiple-access techniques, based on code division and time division multiplexing of beacon emissions (CDMA and TDMA respectively), and to dynamically adjust the PHAT filter weighting factor. The receiver position is obtained by hyperbolic multilateration from the time differences of arrival (TDoA) between a reference beacon and the rest, thus avoiding the need for receiver synchronization. The results show how the dynamic adaptation of the weighting factor significantly reduces positioning errors from 20 cm to 2 cm in 80% of measurements. The simulated and real experiments prove that the proposed algorithms improve the performance of the ULPS in situations with lower signal-to-noise ratios (SNR) than 0 dB and in environments where the multipath effect makes it difficult to correctly detect the encoded ultrasonic emissions.

Development of Time Delay Estimation Algorithm Using Fuzzy Based Optimized Iterative Unscented Kalman Filter

In radar-based applications, Time Delay Estimation (TDE) is an essential criterion. Because of non-stationary behaviour, estimating the time delay between two turbulent signals is difficult. Existing delay estimation methods such as the cross correlation technique are restricted to stationary signals. The non-stationary signals are either fractal or periodic signal. The accuracy of this method is more reliable for fractal signals than for periodic signals. With a cost function at hand it is sensible to check whether the state correction results in a cost decrease in the first place, new parameter is optimized using Fuzzy Elephant Herding Optimization (FEHO). Further this paper incorporates ADAM based neural network (ADAM-NN) model for efficient time delay estimation. The study resulted in significant improvement upto 21.5% in estimating the time delay when compared with conventional methods.

ASSAS: An automatic smart students attendance system based on normalized cross-correlation

A smart student attendance system (SSAS) is presented in this paper. The system is divided into two phases: hardware and software. The Hardware phase is implemented based on Arduino's camera while the software phase is achieved by using image processing with face recognition depended on the cross-correlation technique. In comparison with traditional attendance systems, roll call, and sign-in sheet, the proposed system is faster and more reliable (because there is no action needed by a human being who by its nature makes mistakes). At the same time, it is cheaper when compared with other automatic attendance systems. The proposed system provides a faster, cheaper and reachable system for an automatic smart student attendance that monitors and generates attendance report automatically.

Atmospheric Rossiter–McLaughlin effect and transmission spectroscopy of WASP-121b with ESPRESSO

Context. Ultra-hot Jupiters are excellent laboratories for the study of exoplanetary atmospheres. WASP-121b is one of the most studied; many recent analyses of its atmosphere report interesting features at different wavelength ranges. Aims. In this paper we analyze one transit of WASP-121b acquired with the high-resolution spectrograph ESPRESSO at VLT in one-telescope mode, and one partial transit taken during the commissioning of the instrument in four-telescope mode. Methods. We take advantage of the very high S/N data and of the extreme stability of the spectrograph to investigate the anomalous in-transit radial velocity curve and study the transmission spectrum of the planet. We pay particular attention to the removal of instrumental effects, and stellar and telluric contamination. The transmission spectrum is investigated through single-line absorption and cross-correlation with theoretical model templates. Results. By analyzing the in-transit radial velocities we were able to infer the presence of the atmospheric Rossiter–McLaughlin effect. We measured the height of the planetary atmospheric layer that correlates with the stellar mask (mainly Fe) to be 1.052 ± 0.015 Rp and we also confirmed the blueshift of the planetary atmosphere. By examining the planetary absorption signal on the stellar cross-correlation functions we confirmed the presence of a temporal variation of its blueshift during transit, which could be investigated spectrum-by-spectrum thanks to the quality of our ESPRESSO data. We detected significant absorption in the transmission spectrum for Na, H, K, Li, Ca II, and Mg, and we certified their planetary nature by using the 2D tomographic technique. Particularly remarkable is the detection of Li, with a line contrast of ~0.2% detected at the 6σ level. With the cross-correlation technique we confirmed the presence of Fe I, Fe II, Cr I, and V I. Hα and Ca II are present up to very high altitudes in the atmosphere (~1.44 Rp and ~2 Rp, respectively), and also extend beyond the transit-equivalent Roche lobe radius of the planet. These layers of the atmosphere have a large line broadening that is not compatible with being caused by the tidally locked rotation of the planet alone, and could arise from vertical winds or high-altitude jets in the evaporating atmosphere.