Results 61 to 70 of about 380,497 (248)
Linear amplifiers with phase-sensitive noise [PDF]
We present a model for a linear amplifier which adds phase-dependent noise to the input signal. This is achieved by preparing the internal modes of the amplifier in a squeezed vacuum. Such a scheme could be used to amplify a squeezed-signal quadrature with reduced added noise compared with conventional schemes.
Milburn, G. J. +2 more
openaire +4 more sources
Time‐resolved X‐ray solution scattering captures how proteins change shape in real time under near‐native conditions. This article presents a practical workflow for light‐triggered TR‐XSS experiments, from data collection to structural refinement. Using a calcium‐transporting membrane protein as an example, the approach can be broadly applied to study ...
Fatemeh Sabzian‐Molaei +3 more
wiley +1 more source
Noise sensitivity is a common behaviour problem in dogs. In humans, there is a well-established relationship between painful conditions and the development of fear-related avoidance responses.
Ana Luisa Lopes Fagundes +4 more
doaj +1 more source
On the Noise Sensitivity of the Randomized SVD
The randomized singular value decomposition (R-SVD) is a popular sketching-based algorithm for efficiently computing the partial SVD of a large matrix. When the matrix is low-rank, the R-SVD produces its partial SVD exactly; but when the rank is large, it only yields an approximation.
openaire +2 more sources
Single‐molecule DNA flow‐stretch assays for high‐throughput DNA–protein interaction studies
We describe an optimised single‐molecule DNA flow‐stretch assay that visualises DNA–protein interactions in real time. Linear DNA fragments are tethered to a surface and stretched by buffer flow for fluorescence imaging. Using λ and φX174 DNA, this protocol enhances reproducibility and accessibility, providing a versatile approach for studying diverse ...
Ayush Kumar Ganguli +8 more
wiley +1 more source
Physics-informed neural networks coupled with a residual-driven dynamic weighted Huber loss function
Physics-informed neural networks (PINNs) commonly use the mean squared error (MSE) as the loss function. However, this MSE is sensitive to high-residual regions and noise, often causing nonconvergence, overfitting, and loss imbalance during training.
Xia-Ting Jing +3 more
doaj +1 more source
Statistical analysis based on a two-staged SEM-SVM approach for environmental noise annoyance prediction and identification of influencing factors [PDF]
BACKGROUND AND OBJECTIVES: Noise pollution is an environmental stressor that is mainly caused due to heavy transportation in urban scenarios. Traffic noise is a growing concern in urban environments, impacting public health and well-being.
M. Rehman, M. Kamal, S. Tiwari
doaj +1 more source
Noise maps for acoustically sensitive navigation [PDF]
More and more robotic applications are equipping robots with microphones to improve the sensory information available to them. However, in most applications the auditory task is very low-level, only processing data and providing auditory event information to higher-level navigation routines.
Martinson, Eric, Arkin, Ronald C.
openaire +1 more source
This protocol paper outlines methods to establish the success of a time‐resolved serial crystallographic experiment, by means of statistical analysis of timepoint data in reciprocal space and models in real space. We show how to amplify the signal from excited states to visualise structural changes in successful experiments.
Jake Hill +4 more
wiley +1 more source
Design of Offset–Free Predictive Control with State–Space Models: Equivalences and New Results
Design of offset-free model predictive control (MPC) with a linear state-space process model is discussed in the paper, for deterministic constant or asymptotically constant external and internal (modeling errors) disturbances.
Tatjewski Piotr
doaj +1 more source

