Workshop on gravitational waves

In this article we summarise the proceedings of the Workshop on Gravitational Waves held during ICGC-95. In the first part we present the discussions on 3PN calculations (L. Blanchet, P. Jaranowski), black hole perturbation theory (M. Sasaki, J. Pullin), numerical relativity (E. Seidel), data analysis (B.S. Sathyaprakash), detection of gravitational waves from pulsars (S. Dhurandhar), and the limit on rotation of relativistic stars (J. Friedman). In the second part we briefly discuss the contributed papers which were mainly on detectors and detection techniques of gravitational waves.Comment: 18 pages, kluwer.sty, no figure

Phasor Estimation for Grid Power Monitoring: Least Square vs. Linear Kalman Filter

International audienc

Measuring gravitational waves from binary black hole coalescences: I. Signal to noise for inspiral, merger, and ringdown

We estimate the expected signal-to-noise ratios (SNRs) from the three phases (inspiral,merger,ringdown) of coalescing binary black holes (BBHs) for initial and advanced ground-based interferometers (LIGO/VIRGO) and for space-based interferometers (LISA). LIGO/VIRGO can do moderate SNR (a few tens), moderate accuracy studies of BBH coalescences in the mass range of a few to about 2000 solar masses; LISA can do high SNR (of order 10^4) high accuracy studies in the mass range of about 10^5 to 10^8 solar masses. BBHs might well be the first sources detected by LIGO/VIRGO: they are visible to much larger distances (up to 500 Mpc by initial interferometers) than coalescing neutron star binaries (heretofore regarded as the "bread and butter" workhorse source for LIGO/VIRGO, visible to about 30 Mpc by initial interferometers). Low-mass BBHs (up to 50 solar masses for initial LIGO interferometers; 100 for advanced; 10^6 for LISA) are best searched for via their well-understood inspiral waves; higher mass BBHs must be searched for via their poorly understood merger waves and/or their well-understood ringdown waves. A matched filtering search for massive BBHs based on ringdown waves should be capable of finding BBHs in the mass range of about 100 to 700 solar masses out to 200 Mpc (initial LIGO interferometers), and 200 to 3000 solar masses out to about z=1 (advanced interferometers). The required number of templates is of order 6000 or less. Searches based on merger waves could increase the number of detected massive BBHs by a factor of order 10 or more over those found from inspiral and ringdown waves, without detailed knowledge of the waveform shapes, using a "noise monitoring" search algorithm. A full set of merger templates from numerical relativity could further increase the number of detected BBHs by an additional factor of up to 4.Comment: 40 pages, Revtex, psfig.tex, seven figures, submitted to Phys Rev

Generative Adversarial Network for Photoplethysmography Reconstruction

Photoplethysmography (PPG) is an optical measurement method for blood pulse wave monitoring. The method has been widely applied in both clinical and wearable devices to collect physiological parameters, such as heart rate (HR) and heart rate variability (HRV). Unfortunately, the PPG signals are very vulnerable to motion artifacts, caused by inevitable movements of human users. To obtain reliable results from PPG-based monitoring, methods to denoise the PPG signals are necessary. Methods proposed in the literature, including signal decomposition, time-series analysis, and deep-learning based methods, reduce the effect of noise in PPG signals. However, their performance is insufficient for low signal-to-noise ratio PPG signals, or limited to noise from certain types of activities. Therefore, the aim of this study is to develop a method to remove the motion artifacts and reconstruct noisy PPG signals without any prior knowledge about the noise. In this thesis, a deep convolutional generative adversarial network (DC-GAN) based method is proposed to reconstruct the PPG signals corrupted by real-world motion artifacts. The proposed method leverages the temporal information from the distorted signal and its preceding data points to obtain the clean PPG signal. A GAN-based model is trained to generate succeeding clean PPG signals by previous data points. A sliding window moving at a fixed step on the noisy signal is used to select and update the input for the trained model by the information within the noisy signal. A PPG dataset collected by smartwatches in a health monitoring study is used to train, validate, and test the method in this study. A noisy dataset generated with real-world motion artifacts of different noise levels and lengths is used to evaluate the proposed and baseline methods. Three state-of-the-art PPG reconstruction methods are compared with our method. Two metrics, including maximum peak-to-peak error and RMSSD error, are extracted from the original and reconstructed signals to estimate the reconstruction error for HR and HRV. Our method outperforms state-of-the-art methods with the lowest values of the two evaluation matrices at all noise levels and lengths. The proposed method achieves 0.689, 1.352 and 1.821 seconds of maximum peak-to-peak errors for 5-second, 10-second, and 15-second noise at the highest noise level, respectively, and achieves 0.021, 0.048 and 0.067 seconds of RMSSD errors for the same noise cases. Consequently, our method performs the best in reconstructing distorted PPG signals and provides reliable estimation for both HR and HRV