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As imaging surveys progress in exploring the large-scale structure of the Universe through the use of weak gravitational lensing, achieving subpercent accuracy in estimating shape distortions caused by lensing, or shear, is imperative for…
Spectral envelope is one of the most important features that characterize the timbre of an instrument sound. However, it is difficult to use spectral information in the framework of conventional spectrogram decomposition methods. We…
Time series anomaly detection is widely used in IoT and cyber-physical systems, yet its evaluation remains challenging due to diverse application objectives and heterogeneous metric assumptions. This study introduces a problem-oriented…
Detecting a stochastic gravitational wave background requires that we first understand and model any astrophysical foregrounds. In the millihertz frequency band, the predominate foreground signal will be from unresolved white dwarf binaries…
Wide-area synchrophasor ambient measurements provide a valuable data source for real-time oscillation mode monitoring and analysis. This paper introduces a novel method for identifying inter-area oscillation modes using wide-area ambient…
Time-frequency scattering is a mathematical transformation of sound waves. Its core purpose is to mimick the way the human auditory system extracts information from its environment. In the context of improving the artificial intelligence of…
We present a method designed to estimate the noise power spectrum in the time domain for CMB experiments. The noise power spectrum is extracted from the time ordered data avoiding the contamination coming from sky signal and accounting the…
The effectiveness of fingerprint-based authentication systems on good quality fingerprints is established long back. However, the performance of standard fingerprint matching systems on noisy and poor quality fingerprints is far from…
Recently, deep neural network (DNN) based time-frequency (T-F) mask estimation has shown remarkable effectiveness for speech enhancement. Typically, a single T-F mask is first estimated based on DNN and then used to mask the spectrogram of…
For the last fifteen years, the limiting noise source at the low frequency end of the sensitivity window for space gravitational wave detectors has been expected to be the confusion background of overlapping galactic binary stars. Here, we…
Deep neural networks (DNNs) have greatly benefited direction of arrival (DoA) estimation methods for speech source localization in noisy environments. However, their localization accuracy is still far from satisfactory due to the…
We demonstrate high-fidelity detection of the orbital angular momentum (OAM) of light using a compact and practical OAM spectrometer that maps the OAM spectrum to time. The spectrometer consists of a single optical delay loop to achieve…
Understanding and dealing with inference biases in gravitational-wave (GW) parameter estimation when a plethora of signals are present in the data is one of the key challenges for the analysis of data from future GW detectors. Working…
Characterizing temporally correlated (``non-Markovian'') noise is a key prerequisite for achieving noise-tailored error mitigation and optimal device performance. Quantum noise spectroscopy can afford quantitative estimation of the noise…
To probe cosmological fields beyond the Gaussian level, three-point statistics can be used, all of which are related to the bispectrum. Hence, measurements of CMB anisotropies, galaxy clustering, and weak gravitational lensing alike have to…
White noise is a fundamental and fairly well understood stochastic process that conforms the conceptual basis for many other processes, as well as for the modeling of time series. Here we push a fresh perspective toward white noise that,…
Estimating a time-varying spatial covariance matrix for a beamforming algorithm is a challenging task, especially for wearable devices, as the algorithm must compensate for time-varying signal statistics due to rapid pose-changes. In this…
Parameter estimation of binary-black-hole merger events in gravitational-wave data relies on matched-filtering techniques, which, in turn, depend on accurate model waveforms. Here we characterize the systematic biases introduced in…
A novel tool, based on Bayesian filtering framework and expectation maximization algorithm, is numerically and experimentally demonstrated for accurate frequency comb noise characterization. The tool is statistically optimum in a…
Near-cloaks based on passive coatings can strongly suppress scattered-field energy in a narrow frequency band, yet an observer's ability to infer object parameters from noisy measurements need not decrease proportionally. We develop a fully…