Diffractionless image propagation and frequency conversion via four-wave mixing exploiting the thermal motion of atoms

A setup to frequency-convert an arbitrary image encoded in the spatial profile of a probe field onto a signal field using four-wave mixing in a thermal atom vapor is proposed. The atomic motion is exploited to cancel diffraction of both signal and probe fields simultaneously. We show that an incoherent probe field can be used to enhance the transverse momentum bandwidth which can be propagated without diffraction, such that smaller structures with higher spatial resolution can be transmitted. It furthermore compensate linear absorption with non-linear gain, to improve the four-wave mixing performance since the propagation dynamics of the various field intensities is favorably modified.Comment: 12 pages, 7 figure

Flexible Sensor Network Reprogramming for Logistics

Besides the currently realized applications, Wireless Sensor Networks can be put to use in logistics processes. However, doing so requires a level of flexibility and safety not provided by the current WSN software platforms. This paper discusses a logistics scenario, and presents SensorScheme, a runtime environment used to realize this scenario, based on semantics of the Scheme programming language. SensorScheme is a general purpose WSN platform, providing dynamic reprogramming, memory safety (sandboxing), blocking I/O, marshalled communication, compact code transport. It improves on the state of the art by making better use of the little available memory, thereby providing greater capability in terms of program size and complexity. We illustrate the use of our platform with some application examples, and provide experimental results to show its compactness, speed of operation and energy efficiency

The Deelen infrasound array for recording sonic booms and events of CTBT interest

The Seismology Division of the Royal Netherlands Meteorological Institute (KNMI) has build up expertise in infrasound measurements by investigating low frequency events in order to distinguish between seismic and sonic events. KNMI operates, amongst others, a sixteen element microbarometer array with an aperture of 1.5 km, the Deelen Infrasound Array (DIA). Sonic booms and events of Comprehensive Test Ban Treaty (CTBT) interest are recorded within the frequency range of 100 seconds and 40 Hertz. Recently, KNMI and Microflown Technologies B.V. started a collaboration concerning infrasound measurements. This paper reports the use of a novel sensor. The so-called Microflown [1] is an acoustic sensor, sensitive for frequencies from 0Hz up to 1kHz. The Microflown is developed at the University of Twente and commercialised by Microflown Technologies B.V [3]

Analysis of the effects of baffles on combustion instability

An analytical model has been developed for predicting the effects of baffles on combustion instability. This model has been developed by coupling an acoustic analysis of the wave motion within baffled chambers with a model for the oscillatory combustion response of a propellant droplet developed by Heidmann. A computer program was developed for numerical solution of the resultant coupled equations. Diagnostic calculations were made to determine the reasons for the improper prediction. These calculations showed that the chosen method of representing the combustion response was a very poor approximation. At the end of the program, attempts were made to minimize this effect but the model still improperly predicts the stability trends. Therefore, it is recommended that additional analysis be done with an improved approximation

Control of beam propagation in optically written waveguides beyond the paraxial approximation

Beam propagation beyond the paraxial approximation is studied in an optically written waveguide structure. The waveguide structure that leads to diffractionless light propagation, is imprinted on a medium consisting of a five-level atomic vapor driven by an incoherent pump and two coherent spatially dependent control and plane-wave fields. We first study propagation in a single optically written waveguide, and find that the paraxial approximation does not provide an accurate description of the probe propagation. We then employ coherent control fields such that two parallel and one tilted Gaussian beams produce a branched waveguide structure. The tilted beam allows selective steering of the probe beam into different branches of the waveguide structure. The transmission of the probe beam for a particular branch can be improved by changing the width of the titled Gaussian control beam as well as the intensity of the spatially dependent incoherent pump field.Comment: 10 pages, 9 figure

Locally Adaptive Tree-based Thresholding Using the treethresh Package in R

This paper introduces the treethresh package offering accurate estimation, via thresholding, of potentially sparse heterogeneous signals and the denoising of images using wavelets. It gives considerably improved performance over other estimation methods if the underlying signal or image is not homogeneous throughout but instead has distinct regions with differing sparsity or strength characteristics. It aims to identify these different regions and perform separate estimation in each accordingly. The base algorithm offers code which can be applied directly to any one-dimensional potentially sparse sequence observed subject to noise. Also included are functions which allow two-dimensional images to be denoised following transformation to the wavelet domain. In addition to reconstructing the underlying signal or image, the package provides information on the believed partitioning of the signal or image into its differing regions

Data compression and regression based on local principal curves.

Frequently the predictor space of a multivariate regression problem of the type y = m(x_1, …, x_p ) + ε is intrinsically one-dimensional, or at least of far lower dimension than p. Usual modeling attempts such as the additive model y = m_1(x_1) + … + m_p (x_p ) + ε, which try to reduce the complexity of the regression problem by making additional structural assumptions, are then inefficient as they ignore the inherent structure of the predictor space and involve complicated model and variable selection stages. In a fundamentally different approach, one may consider first approximating the predictor space by a (usually nonlinear) curve passing through it, and then regressing the response only against the one-dimensional projections onto this curve. This entails the reduction from a p- to a one-dimensional regression problem. As a tool for the compression of the predictor space we apply local principal curves. Taking things on from the results presented in Einbeck et al. (Classification – The Ubiquitous Challenge. Springer, Heidelberg, 2005, pp. 256–263), we show how local principal curves can be parametrized and how the projections are obtained. The regression step can then be carried out using any nonparametric smoother. We illustrate the technique using data from the physical sciences

Efficient and automatic methods for flexible regression on spatiotemporal data, with applications to groundwater monitoring

Fitting statistical models to spatiotemporal data requires finding the right balance between imposing smoothness and following the data. In the context of P-splines, we propose a Bayesian framework for choosing the smoothing parameter which allows the construction of fully-automatic data-driven methods for fitting flexible models to spatiotemporal data. An implementation, which is highly computationally efficient and which exploits the sparsity of the design and penalty matrices, is proposed. The findings are illustrated using a simulation study and two examples, all concerned with the modelling of contaminants in groundwater. This suggests that the proposed strategy is more stable that competing methods based on the use of criteria such as GCV and AIC