Presence Of Detector Noise Research Articles

Multi-Scale Convolutional Neural Networks for Space Infrared Point Objects Discrimination

Object discrimination plays an important role in an infrared (IR) imaging system. However, at a long observing distance, the presence of detector noise and the absence of robust features make space objects' discrimination difficult to tackle with. In this paper, a multi-scale convolutional neural network (MCNN) is proposed for feature learning and classification. It consists of three parts: transformation, local convolution, and full convolution. Different from previous objects' classification methods, the MCNN can automatically extract features of objects at multi-timescales and multi-frequencies. Low-level features are combined with high-level features to simultaneously capture long-term tendency and short-term fluctuations of the time sequences of IR radiation intensity. Training data are generated from IR radiation models considering micro-motion dynamics and inherent properties of space point objects under different scenarios. The simulation results indicate that our method not only promotes the performance but is also robust to the detector noise. The classification accuracy can reach 96% at a strong noise level (signal-to-noise ratio is 10 dB) in a simulation scenario.

Exo-atmospheric infrared objects classification using recurrence-plots-based convolutional neural networks.

Object discrimination plays an important role in infrared (IR) imaging systems. However, at long observing distance, the presence of detector noise and absence of robust features make exo-atmospheric object classification difficult to tackle. In this paper, a recurrence-plots-based convolutional neural network (RP-CNN) is proposed for feature learning and classification. First, it uses recurrence plots (RPs) to transform time sequences of IR radiation into two-dimensional texture images. Then, a CNN model is adopted for classification. Different from previous object classification methods, RP representation has well-defined visual texture patterns, and their graphical nature exposes hidden patterns and structural changes in time sequences of IR signatures. In addition, it can process IR signatures of objects without the limitation of fixed length. Training data are generated from IR irradiation models considering micro-motion dynamics and geometrical shape of exo-atmospheric objects. Results based on time-evolving IR radiation data indicate that our method achieves significant improvement in accuracy and robustness of the exo-atmospheric IR objects classification.

Robot Classification of Human Interruptibility and a Study of Its Effects

As robots become increasingly prevalent in human environments, there will inevitably be times when the robot needs to interrupt a human to initiate an interaction. Our work introduces the first interruptibility-aware mobile-robot system, which uses social and contextual cues online to accurately determine when to interrupt a person. We evaluate multiple non-temporal and temporal models on the interruptibility classification task, and show that a variant of Conditional Random Fields (CRFs), the Latent-Dynamic CRF, is the most robust, accurate, and appropriate model for use on our system. Additionally, we evaluate different classification features and show that the observed demeanor of a person can help in interruptibility classification; but in the presence of detection noise, robust detection of object labels as a visual cue to the interruption context can improve interruptibility estimates. Finally, we deploy our system in a large-scale user study to understand the effects of interruptibility-awareness on human-task performance, robot-task performance, and on human interpretation of the robot’s social aptitude. Our results show that while participants are able to maintain task performance, even in the presence of interruptions, interruptibility-awareness improves the robot’s task performance and improves participant social perceptions of the robot.

Comparison of methods for the detection of gravitational waves from unknown neutron stars

Rapidly rotating neutron stars are promising sources of continuous gravitational wave radiation for the LIGO and Virgo interferometers. The majority of neutron stars in our galaxy have not been identified with electromagnetic observations. All-sky searches for isolated neutron stars offer the potential to detect gravitational waves from these unidentified sources. The parameter space of these blind all-sky searches, which also cover a large range of frequencies and frequency derivatives, presents a significant computational challenge. Different methods have been designed to perform these searches within acceptable computational limits. Here we describe the first benchmark in a project to compare the search methods currently available for the detection of unknown isolated neutron stars. We employ a mock data challenge to compare the ability of each search method to recover signals simulated assuming a standard signal model. We find similar performance among the short duration search methods, while the long duration search method achieves up to a factor of two higher sensitivity. We find the absence of second derivative frequency in the search parameter space does not degrade search sensivity for signals with physically plausible second derivative frequencies. We also report on the parameter estimation accuracy of each search method, and the stability of the sensitivity in frequency, frequency derivative and in the presence of detector noise.

Approaching the Heisenberg Limit without Single-Particle Detection.

We propose an approach to quantum phase estimation that can attain precision near the Heisenberg limit without requiring single-particle-resolved state detection. We show that the "one-axis twisting" interaction, well known for generating spin squeezing in atomic ensembles, can also amplify the output signal of an entanglement-enhanced interferometer to facilitate readout. Applying this interaction-based readout to oversqueezed, non-Gaussian states yields a Heisenberg scaling in phase sensitivity, which persists in the presence of detection noise as large as the quantum projection noise of an unentangled ensemble. Even in dissipative implementations-e.g., employing light-mediated interactions in an optical cavity or Rydberg dressing-the method significantly relaxes the detection resolution required for spectroscopy beyond the standard quantum limit.

Comparison of gravitational wave detector network sky localization approximations

Gravitational waves emitted during compact binary coalescences are a promising source for gravitational-wave detector networks. The accuracy with which the location of the source on the sky can be inferred from gravitational wave data is a limiting factor for several potential scientific goals of gravitational-wave astronomy, including multi-messenger observations. Various methods have been used to estimate the ability of a proposed network to localize sources. Here we compare two techniques for predicting the uncertainty of sky localization -- timing triangulation and the Fisher information matrix approximations -- with Bayesian inference on the full, coherent data set. We find that timing triangulation alone tends to over-estimate the uncertainty in sky localization by a median factor of $4$ for a set of signals from non-spinning compact object binaries ranging up to a total mass of $20 M_\odot$, and the over-estimation increases with the mass of the system. We find that average predictions can be brought to better agreement by the inclusion of phase consistency information in timing-triangulation techniques. However, even after corrections, these techniques can yield significantly different results to the full analysis on specific mock signals. Thus, while the approximate techniques may be useful in providing rapid, large scale estimates of network localization capability, the fully coherent Bayesian analysis gives more robust results for individual signals, particularly in the presence of detector noise.

Influence of reflector temperature gradients on bit-error rate in periscope laser communication systems in the presence of detector noise and platform vibrations

We consider the influence of thermal deformations of elliptic reflectors in periscope laser communication terminals on the bit-error rate (BER) taking into account the satellite-platform vibrations and the detector noise. We study the relationship between the average BER and temperature gradients of elliptic reflectors in inter-satellite laser communication systems at different values of the light beam wavelength and truncation rate, and different vibration amplitudes. The average BER increases with increase in the vibration amplitude, and it is noteworthy that the average BER increases with increase in the beam truncation rate, which contradicts the result obtained in [1] without taking noise into account. We find that for some temperature gradients there is an optimum communication wavelength that provides the minimum average BER. We use the back-fixing method for fixing the elliptic reflectors, which has proved to be much less sensitive to temperature gradients compared to the traditional around-fixing method by a press board.

Comparison of parametric and linear mass detection in the presence of detection noise

We experimentally investigate the performance of a nonlinear parametrically driven mass sensor in the presence of detection noise. Mass detection is achieved by measuring the amount of methanol vapor adsorption on the sensor. To demonstrate the advantage of parametric sensing in counteracting the influence of detection noise, we operate the sensor in both the parametric and harmonic resonance mode. Comparison of the results shows that in contrast to conventional linear harmonic sensing, the detection sensitivity does not deteriorate for the parametric case when a tenfold increase in detection noise is introduced. Furthermore, we demonstrate additional functionality of the parametric sensor by utilizing it as a threshold detector, whose performance remains the same despite the added detection noise. Taken together, these results suggest that for mass detection in the presence of detection noise, a parametrically operated sensor may offer better performance over one operated harmonically in the linear regime.

Real space estimator for the weak lensing convergence from the CMB

We propose an estimator defined in real space for the reconstruction of the weak lensing potential due to the intervening large scale structure from high resolution maps of the cosmic microwave background. This estimator was motivated as an alternative to the quadratic estimator in harmonic space to surpass the difficulties of the analysis of maps containing galactic cuts and point source excisions. Using maps synthesised by pixel remapping, we implement the estimator for two experiments, namely one in the absence and one in the presence of detector noise, and compare the reconstruction of the convergence field with that obtained with the quadratic estimator defined in harmonic space. We find good agreement between the input and the reconstructed power spectra using the proposed real space estimator. We discuss interesting features of the real space estimator and future extensions of this work.

Fast Light and the Speed of Information Transfer in the Presence of Detector Noise

The phenomenon of fast-light pulse propagation has seen renewed interest in recent years, but its relationship to the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">speed of information transfer</i> is still being debated. In this paper, we define the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">speed of information transfer</i> as the propagation speed of a point of constant signal-to-noise ratio (SNR) on the leading edge of a signal pulse. We use standard telecommunication analysis to include the effects of noise so that the bit-error rate (BER) can be calculated as a function of a variable decision time. We introduce the concept of a time-dependent Q-factor so that pulse arrival times can be compared at equivalent BERs. We show that when receiver noise is included in a gain-assisted fast-light system, the measured <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">speed of information transfer</i> can exceed the speed of light for an equivalent pulse travelling through a vacuum.

Counting of obscure events: A Bayesian approach

A general method for counting of obscure events in presence of detection noise is presented. The method combines the measurement data with additional information about the system under investigation to improve the measurement accuracy with the help of probability theory. As an example, we apply the method to the case of counting molecules in a microfluidic device and show in simulations that the accuracy is always better than for a thresholding approach. As a second example, we show how it can be used for photon counting at high count rates with an electron multiplying CCD (EMCCD) camera.

Performance limitations of a four-channel polarimeter in the presence of detection noise

The performance limitations of an imperfectly calibrated four- channel polarimeter in the presence of photon-counting detection noise is examined for arbitrary light levels. The theory described here provides a framework for developing polarimeter designs with minimal noise sen- sitivity and to evaluate the noise performance of existing designs. The polarimeter decomposes the input field into four channels, each of which is detected by the photon-counting sensor. We theoretically describe the propagation of detection noise through the polarimeter calibration matrix. The variances of both the mutual phase delay and the orthogonal inten- sities are given for photon-counting noise following Poisson statistics, and additive Gaussian detection noise. The variances of these param- eters depend on the average number of photons incident on the polar- imeter, the detector read noise, and errors in the polarimeter calibration matrix. A particular polarimeter design, whose calibration matrix is known exactly, is examined for moderate, low, and very low light levels. Theo- retical performance curves are shown for various sensor parameters and light levels, and are compared to simulated results. Very good agree- ment between theory and simulation is shown. The simulation validates the use of the Gaussian probability density function for the parallel (in- phase) and normal components of the phase fluctuations, and provides an accurate theoretical prediction of phase delay fluctuations for arbitrary light levels. The phase-delay noise cloud is illustrated for several cases.

Deblurring random blur

We introduce a modified Backus-Gilbert technique for restoring an image that has been distorted by a linear system whose impulse response function is itself random, and in the presence of detection noise. The restoration is based on a weighted superposition of a small number of shifted versions of the distorted image. Optimum weights are determined to minimize a measure of the average width of the impulse response functions of the overall system and the noise variance.