Phase-synchronous undersampling in nonlinear spectroscopy

We introduce the concept of phase-synchronous undersampling in nonlinear spectroscopy. The respective theory is presented and validated experimentally in a phase-modulated quantum beat experiment by sampling high phase modulation frequencies with low laser repetition rates. The advantage of undersampling in terms of signal quality and reduced acquisition time is demonstrated and breakdown conditions are identified. The presented method is particularly beneficial for experimental setups with limited signal/detection rates.Comment: 4 pages, 5 figure

Quantifying admissible undersampling for sparsity-exploiting iterative image reconstruction in X-ray CT

Iterative image reconstruction (IIR) with sparsity-exploiting methods, such as total variation (TV) minimization, investigated in compressive sensing (CS) claim potentially large reductions in sampling requirements. Quantifying this claim for computed tomography (CT) is non-trivial, because both full sampling in the discrete-to-discrete imaging model and the reduction in sampling admitted by sparsity-exploiting methods are ill-defined. The present article proposes definitions of full sampling by introducing four sufficient-sampling conditions (SSCs). The SSCs are based on the condition number of the system matrix of a linear imaging model and address invertibility and stability. In the example application of breast CT, the SSCs are used as reference points of full sampling for quantifying the undersampling admitted by reconstruction through TV-minimization. In numerical simulations, factors affecting admissible undersampling are studied. Differences between few-view and few-detector bin reconstruction as well as a relation between object sparsity and admitted undersampling are quantified.Comment: Revised version that was submitted to IEEE Transactions on Medical Imaging on 8/16/201

Sparse Sensing with Semi-Coprime Arrays

A semi-coprime array (SCA) interleaves two undersampled uniform linear arrays (ULAs) and a $Q$ element standard ULA. The undersampling factors of the first two arrays are $QM$ and $QN$ respectively where $M$ and $N$ are coprime. The resulting non-uniform linear array is highly sparse. Taking the minimum of the absolute values of the conventional beampatterns of the three arrays results in a beampattern free of grating lobes. The SCA offers more savings in the number of sensors than other popular sparse arrays like coprime arrays, nested arrays, and minimum redundant arrays. Also, the SCA exhibits better side lobe patterns than other sparse arrays. An example of direction of arrival estimation with the SCA illustrates SCA's promising potential in reducing number of sensors, decreasing system cost and complexity in various signal sensing and processing applications

Quantifying galaxy shapes: Sersiclets and beyond

Parametrising galaxy morphologies is a challenging task, e.g., in shear measurements of weak lensing or investigations of galaxy evolution. The huge variety of morphologies requires an approach that is highly flexible, e.g., accounting for azimuthal structure. We revisit the method of sersiclets, where galaxy morphologies are decomposed into basis functions based on the Sersic profile. This approach is justified by the fact that the Sersic profile is the first-order Taylor expansion of any real light profile. We show that sersiclets overcome the modelling failures of shapelets. However, sersiclets implicate an unphysical relation between the steepness of the light profile and the spatial scale of azimuthal structures, which is not obeyed by real galaxy morphologies and can therefore give rise to modelling failures. Moreover, we demonstrate that sersiclets are prone to undersampling, which restricts sersiclet modelling to highly resolved galaxy images. Analysing data from the Great08 challenge, we demonstrate that sersiclets should not be used in weak-lensing studies. We conclude that although the sersiclet approach appears very promising at first glance, it suffers from conceptual and practical problems that severly limit its usefulness. The Sersic profile can be enhanced by higher-order terms in the Taylor expansion, which can drastically improve model reconstructions of galaxy images. If orthonormalised, these higher-order profiles can overcome the problems of sersiclets while preserving their mathematical justification.Comment: 14 pages, 12 figures, 2 tables; accepted by MNRA

Predictive Modeling of ICU Healthcare-Associated Infections from Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling Approach

Early detection of patients vulnerable to infections acquired in the hospital environment is a challenge in current health systems given the impact that such infections have on patient mortality and healthcare costs. This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units by means of machine-learning methods. The aim is to support decision making addressed at reducing the incidence rate of infections. In this field, it is necessary to deal with the problem of building reliable classifiers from imbalanced datasets. We propose a clustering-based undersampling strategy to be used in combination with ensemble classifiers. A comparative study with data from 4616 patients was conducted in order to validate our proposal. We applied several single and ensemble classifiers both to the original dataset and to data preprocessed by means of different resampling methods. The results were analyzed by means of classic and recent metrics specifically designed for imbalanced data classification. They revealed that the proposal is more efficient in comparison with other approaches