Search for: All records

Previous work showed that thermal light with a blackbody spectrum cannot be decomposed into a mixture of independent localized pulses. However, we find that in the weak-source limit and under the assumption of a flat spectrum, the first nonvacuum term in the state expansion does form a mixture of such pulses. This decomposition is essential for quantum-enhanced astronomical interferometry, which typically operates on localized pulses even though stellar light is inherently continuous-wave. We present a quantum derivation of the van Cittert–Zernike theorem that incorporates finite bandwidth, thereby justifying the operations on localized pulses while processing continuous-wave thermal light. For general spectra in the weak-source limit, we establish a criterion under which correlations between pulses can be safely neglected. When this criterion is not met, we provide a corrected strategy that accurately accounts for both the spectral profile and the detector-defined pulse shape. 

We propose a method to build an astronomical interferometer using continuous-variable quantum teleportation to overcome transmission loss between distant telescopes. The scheme relies on two-mode squeezed states shared by distant telescopes as entanglement resources, which are distributed using continuous-variable quantum repeaters. We find the optimal measurement on the teleported states, which uses beam splitters and photon-number-resolved detection. Compared to prior proposals relying on discrete states, our scheme has the advantages of using linear optics to implement it without wasting stellar photons, and making use of multiphoton events, which are regarded as noise in previous discrete schemes. We also outline the parameter regimes in which our scheme outperforms the direct detection method, schemes utilizing distributed discrete-variable entangled states, and local heterodyne techniques. 

In cold climates, ice formation on wind turbines causes power reduction produced by a wind farm. This paper introduces a framework to predict icing at the farm level based on our recently developed Temporal Convolutional Network prediction model for a single turbine using SCADA data.First, a cross-validation study is carried out to evaluate the extent predictors trained on a single turbine of a wind farm can be used to predict icing on the other turbines of a wind farm. This fusion approach combines multiple turbines, thereby providing predictions at the wind farm level. This study shows that such a fusion approach improves prediction accuracy and decreases fluctuations across different prediction horizons when compared with single-turbine prediction. Two approaches are considered to conduct farm-level icing prediction: decision fusion and feature fusion. In decision fusion, icing prediction decisions from individual turbines are combined in a majority voting manner. In feature fusion, features of individual turbines are averaged first before conducting prediction. The results obtained indicate that both the decision fusion and feature fusion approaches generate farm-level icing prediction accuracies that are 7% higher with lower standard deviations or fluctuations across different prediction horizons when compared with predictions for a single turbine. 

Icing on the blades of wind turbines during winter seasons causes a reduction in power and revenue losses. The prediction of icing before it occurs has the potential to enable mitigating actions to reduce ice accumulation. This paper presents a framework for the prediction of icing on wind turbines based on Supervisory Control and Data Acquisition (SCADA) data without requiring the installation of any additional icing sensors on the turbines. A Temporal Convolutional Network is considered as the model to predict icing from the SCADA data time series. All aspects of the icing prediction framework are described, including the necessary data preprocessing, the labeling of SCADA data for icing conditions, the selection of informative icing features or variables in SCADA data, and the design of a Temporal Convolutional Network as the prediction model. Two performance metrics to evaluate the prediction outcome are presented. Using SCADA data from an actual wind turbine, the model achieves an average prediction accuracy of 77.6% for future times of up to 48 h. 

We experimentally demonstrate that a probe beam at one wavelength, although exhibiting a weak nonlinear response on its own, can be modulated and controlled by a pump beam at another wavelength in plasmonic nanosuspensions, leading to ring-shaped pattern generation. In particular, we show that the probe and pump wavelengths can be interchanged, but the hollow beam patterns appear only in the probe beam, thanks to the gold nanosuspensions that exhibit a strong nonlinear response to pump beam illumination at the plasmonic resonant frequencies. Colloidal suspensions consisting of either gold nanospheres or gold nanorods are employed as nonlinear media, which give rise to refractive index changes and cross-phase modulation between the two beams. We perform a series of experiments to examine the dynamics of hollow beam generation at a fixed probe power as the pump power is varied and find that nonlinear beam shaping has a different power threshold in different nanosuspensions. Our results will enhance the understanding of nonlinear light–matter interactions in plasmonic nanosuspensions, which may be useful for applications in controlling light by light and in optical limiting. 

We present FusionFS, a direct-access firmware-level in-storage filesystem that exploits the near-storage computational capability for fast I/O and data processing, consequently reducing I/O bottlenecks. In FusionFS, we introduce a new abstraction, CISCOps, that combines multiple I/O and data processing operations into one fused operation and offloaded for near-storage processing. By offloading, CISCOps significantly reduces dominant I/O overheads such as system calls, data movement, communication, and other software overheads. Further, to enhance the use of CISCOps, we introduce MicroTx for fine-grained crash consistency and fast (automatic) recovery of I/O and data processing operations. We also explore scheduling techniques to ensure fair and efficient use of in-storage compute and memory resources across tenants. Evaluation of FusionFS against the state-of-the-art user-level, kernel-level, and firmware-level file systems using microbenchmarks, macrobenchmarks, and real-world applications shows up to 6.12X, 5.09X and 2.07X performance gains, and 2.65X faster recovery for applications. 

The purpose of this tutorial paper is to present a broad overview of photon-pair generation through the spontaneous four wave mixing (SFWM) process in optical fibers. Progress in optical fiber technology means that today we have at our disposal a wide variety of types of fiber, which, together with the fact that SFWM uses two pump fields, implies a truly remarkable versatility in the resulting possible photon-pair properties. We discuss how the interplay of frequency, transverse mode, and polarization degrees of freedom—the first linked to the latter two through fiber dispersion—leads to interesting entanglement properties both in individual degrees of freedom and also permitting hybrid and hyper entanglement in combinations of degrees of freedom. This tutorial covers methods for photon-pair factorability, frequency tunability, and SFWM bandwidth control, the effect of frequency non-degenerate and counterpropagating pumps, as well as methods for characterizing photon pairs generated in optical fibers.