Related papers: Maximizing NMR Sensitivity: A Guide to Receiver Ga…
We study estimation and clustering in Gaussian mixture models under variance misspecification. Observations are generated with true variance $\sigma^2$, while the component means are estimated using a likelihood with variance $\tau^2$,…
Microwave electric (MW) field measurements utilizing Rydberg atoms have witnessed significant advancements, achieving remarkable sensitivity, albeit limited to discrete MW frequencies resonant with Rydberg states. Recently, various…
We present a method that allows us for the first time to estimate the signal-to-noise ratio (SNR) of the harmonic-space galaxy bispectrum induced by gravity, a complementary probe to already well established Fourier-space clustering…
Nuclear magnetic resonance (NMR) spectroscopy is, in principle, a promising candidate to study the intracellular chemistry of single microscopic living entities. However, due to sensitivity limitations, NMR experiments were reported only on…
We review instrumentation for nuclear magnetic resonance (NMR) in zero and ultra-low magnetic field (ZULF, below 0.1 $\mu$T) where detection is based on a low-cost, non-cryogenic, spin-exchange relaxation free (SERF) $^{87}$Rb atomic…
Nuclear Magnetic Resonance (NMR) spectroscopy is a widely-used technique in the fields of bio-medicine, chemistry, and biology for the analysis of chemicals and proteins. The signals from NMR spectroscopy often have low signal-to-noise…
Of all sensor performance parameters, the conversion gain is arguably the most fundamental as it describes the conversion of photoelectrons at the sensor input into digital numbers at the output. Due in part to the emergence of deep…
Many multichannel systems use a linear filter to retrieve a signal of interest corrupted by noise whose statistics are partly unknown. The optimal filter in Gaussian noise requires knowledge of the noise covariance matrix $\Sigma$ and in…
Here we report a theoretical model based on Green's functions and averaging techniques that gives ana- lytical estimates to the signal to noise ratio (SNR) near the first parametric instability zone in parametrically- driven oscillators in…
In this work, we propose a novel energy-efficient spiking neural network (SNN)-based receiver for 5G-NR OFDM system, called neuromorphic receiver (NeuromorphicRx), replacing the channel estimation, equalization and symbol demapping blocks.…
This paper describes the most accurate analytical frequentist assessment to date of the uncertainties in the estimation of physical parameters from gravitational waves generated by non spinning binary systems and Earth-based networks of…
Spectrum sensing is one of the enabling functionalities for cognitive radio (CR) systems to operate in the spectrum white space. To protect the primary incumbent users from interference, the CR is required to detect incumbent signals at…
In this article, we provide a model to estimate a real-valued measure of the intelligibility of individual speech segments. We trained regression models based on Convolutional Neural Networks (CNN) for stop consonants…
Location-awareness is essential in various wireless applications. The capability of performing precise ranging is substantial in achieving high-accuracy localization. Due to the notorious ambiguity phenomenon, optimal ranging waveforms…
Dual functional radar and communication (DFRC) systems are a viable approach to extend the services of future communication systems. Most studies designing DFRC systems assume that the target direction is known. In our paper, we address a…
Compared to passive intelligent reflecting surface (IRS), active IRS is viewed as a more efficient promising technique to combat the double-fading impact in IRS-aided wireless network. In this paper, in order to boost the achievable rate of…
An accurate objective speech intelligibility prediction algorithms is of great interest for many applications such as speech enhancement for hearing aids. Most algorithms measures the signal-to-noise ratios or correlations between the…
In resource-constrained environments, one can employ spatial multiplexing cameras to acquire a small number of measurements of a scene, and perform effective reconstruction or high-level inference using purely data-driven neural networks.…
Diffusion Probabilistic Models have demonstrated remarkable performance across a wide range of generative tasks. However, we have observed that these models often suffer from a Signal-to-Noise Ratio-timestep (SNR-t) bias. This bias refers…
Abundances of Mo isotopes predicted by stellar models of the s process are, except for 95Mo, in good agreement with data from single grains of mainstream presolar SiC. Because the meteorite data seemed sound and no reasonable modification…