Showing papers by "Stephen McLaughlin published in 2016"

Adaptive multimode signal reconstruction from time-frequency representations.

Abstract: This paper discusses methods for the adaptive reconstruction of the modes of multicomponent AM-FM signals by their time-frequency (TF) representation derived from their short-time Fourier transform (STFT). The STFT of an AM-FM component or mode spreads the information relative to that mode in the TF plane around curves commonly called ridges. An alternative view is to consider a mode as a particular TF domain termed a basin of attraction. Here we discuss two new approaches to mode reconstruction. The first determines the ridge associated with a mode by considering the location where the direction of the reassignment vector sharply changes, the technique used to determine the basin of attraction being directly derived from that used for ridge extraction. A second uses the fact that the STFT of a signal is fully characterized by its zeros (and then the particular distribution of these zeros for Gaussian noise) to deduce an algorithm to compute the mode domains. For both techniques, mode reconstruction is then carried out by simply integrating the information inside these basins of attraction or domains.

Assessment of the Cost and Environmental Impact of Residential Demand-Side Management

Abstract: A detailed study of the potential impact of low-voltage (LV) residential demand-side management (DSM) on the cost and greenhouse gas (GHG) emissions is presented. The proposed optimization algorithm is used to shift noncritical residential loads, with the wet load category used as a case study, in order to minimize the total daily cost and emissions due to generation. This study shows that it is possible to reshape the total power demand and reduce the corresponding cost and emissions to some extent. It is also shown that, when the baseload generating mix is dominated by coal-fired generation, the daily profiles of GHG emissions and cost conflict, such that further optimization of the cost leads to an increase in emissions.

Assessing the utility of autofluorescence-based pulmonary optical endomicroscopy to predict the malignant potential of solitary pulmonary nodules in humans.

Abstract: Solitary pulmonary nodules are common, often incidental findings on chest CT scans. The investigation of pulmonary nodules is time-consuming and often leads to protracted follow-up with ongoing radiological surveillance, however, clinical calculators that assess the risk of the nodule being malignant exist to help in the stratification of patients. Furthermore recent advances in interventional pulmonology include the ability to both navigate to nodules and also to perform autofluorescence endomicroscopy. In this study we assessed the efficacy of incorporating additional information from label-free fibre-based optical endomicrosopy of the nodule on assessing risk of malignancy. Using image analysis and machine learning approaches, we find that this information does not yield any gain in predictive performance in a cohort of patients. Further advances with pulmonary endomicroscopy will require the addition of molecular tracers to improve information from this procedure.

Depth imaging in highly scattering underwater environments using time-correlated single-photon counting

Abstract: This paper presents an optical depth imaging system optimized for highly scattering environments such as underwater. The system is based on the time-correlated single-photon counting (TCSPC) technique and the time-of-flight approach. Laboratory-based measurements demonstrate the potential of underwater depth imaging, with specific attention given to environments with a high level of scattering. The optical system comprised a monostatic transceiver unit, a fiber-coupled supercontinuum laser source with a wavelength tunable acousto-optic filter (AOTF), and a fiber-coupled single-element silicon single-photon avalanche diode (SPAD) detector. In the optical system, the transmit and receive channels in the transceiver unit were overlapped in a coaxial optical configuration. The targets were placed in a 1.75 meter long tank, and raster scanned using two galvo-mirrors. Laboratory-based experiments demonstrate depth profiling performed with up to nine attenuation lengths between the transceiver and target. All of the measurements were taken with an average laser power of less than 1mW. Initially, the data was processed using a straightforward pixel-wise cross-correlation of the return timing signal with the system instrumental timing response. More advanced algorithms were then used to process these cross-correlation results. These results illustrate the potential for the reconstruction of images in highly scattering environments, and to permit the investigation of much shorter acquisition time scans. These algorithms take advantage of the data sparseness under the Discrete Cosine Transform (DCT) and the correlation between adjacent pixels, to restore the depth and reflectivity images.

SERS as a tool for in vitro toxicology

Abstract: Measuring markers of stress such as pH and redox potential are important when studying toxicology in in vitro models because they are markers of oxidative stress, apoptosis and viability. While surface enhanced Raman spectroscopy is ideally suited to the measurement of redox potential and pH in live cells, the time-intensive nature and perceived difficulty in signal analysis and interpretation can be a barrier to its broad uptake by the biological community. In this paper we detail the development of signal processing and analysis algorithms that allow SERS spectra to be automatically processed so that the output of the processing is a pH or redox potential value. By automating signal processing we were able to carry out a comparative evaluation of the toxicology of silver and zinc oxide nanoparticles and correlate our findings with qPCR analysis. The combination of these two analytical techniques sheds light on the differences in toxicology between these two materials from the perspective of oxidative stress.

Invited review--image registration in veterinary radiation oncology: indications, implications, and future advances.

Abstract: The field of veterinary radiation therapy (RT) has gained substantial momentum in recent decades with significant advances in conformal treatment planning, image-guided radiation therapy (IGRT), and intensity-modulated (IMRT) techniques. At the root of these advancements lie improvements in tumor imaging, image alignment (registration), target volume delineation, and identification of critical structures. Image registration has been widely used to combine information from multimodality images such as computerized tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) to improve the accuracy of radiation delivery and reliably identify tumor-bearing areas. Many different techniques have been applied in image registration. This review provides an overview of medical image registration in RT and its applications in veterinary oncology. A summary of the most commonly used approaches in human and veterinary medicine is presented along with their current use in IGRT and adaptive radiation therapy (ART). It is important to realize that registration does not guarantee that target volumes, such as the gross tumor volume (GTV), are correctly identified on the image being registered, as limitations unique to registration algorithms exist. Research involving novel registration frameworks for automatic segmentation of tumor volumes is ongoing and comparative oncology programs offer a unique opportunity to test the efficacy of proposed algorithms.

Real-Time Tracking of Hidden Objects with Single-Pixel Detectors

Abstract: We demonstrate tracking of an object hidden from the direct line-of-sight. We reconstruct its position in real-time using a single laser spot and three individual single pixel single-photon detectors.

Robust spectral unmixing of multispectral Lidar waveforms

Abstract: This paper presents a new Bayesian spectral unmixing algorithm to analyse remote scenes sensed via multispectral Lidar measurements. To a first approximation, each Lidar waveform consists of the temporal signature of the observed target, which depends on the wavelength of the laser source considered and which is corrupted by Poisson noise. When the number of spectral bands is large enough, it becomes possible to identify and quantify the main materials in the scene, on top of the estimation of classical Lidar-based range profiles. Thanks to its anomaly detection capability, the proposed hierarchical Bayesian model, coupled with an efficient Markov chain Monte Carlo algorithm, allows robust estimation of depth images together with abundance and outlier maps associated with the observed 3D scene. The proposed methodology is illustrated via experiments conducted with real multispectral Lidar data.

A Bayesian framework for the multifractal analysis of images using data augmentation and a whittle approximation

Abstract: Texture analysis is an image processing task that can be conducted using the mathematical framework of multifractal analysis to study the regularity fluctuations of image intensity and the practical tools for their assessment, such as (wavelet) leaders. A recently introduced statistical model for leaders enables the Bayesian estimation of multifractal parameters. It significantly improves performance over standard (linear regression based) estimation. However, the computational cost induced by the associated nonstandard posterior distributions limits its application. The present work proposes an alternative Bayesian model for multifractal analysis that leads to more efficient algorithms. It relies on three original contributions: A novel generative model for the Fourier coefficients of log-leaders; an appropriate reparametrization for handling its inherent constraints; a data-augmented Bayesian model yielding standard conditional posterior distributions that can be sampled exactly. Numerical simulations using synthetic multifractal images demonstrate the excellent performance of the proposed algorithm, both in terms of estimation quality and computational cost.

Bayesian joint estimation of the multifractality parameter of image patches using gamma Markov Random Field priors

Abstract: Texture analysis can be embedded in the mathematical framework of multifractal (MF) analysis, enabling the study of the fluctuations in regularity of image intensity and providing practical tools for their assessment, wavelet leaders. A statistical model for leaders was proposed permitting Bayesian estimation of MF parameters for images yielding improved estimation quality over linear regression based estimation. This present work proposes an extension of this Bayesian model for patch-wise MF analysis of images. Classical MF analysis assumes space homogeneity of the MF properties whereas here we assume MF properties may change between texture elements and we do not know where the changes are located. This paper proposes a joint Bayesian model for patches formulated using spatially smoothing gamma Markov Random Field priors to counterbalance the increased statistical variability of estimates caused by small patch sizes. Numerical simulations based on synthetic multi-fractal images demonstrate that the proposed algorithm outperforms previous formulations and standard estimators.

Bayesian estimation for the local assessment of the multifractality parameter of multivariate time series

Abstract: Multifractal analysis (MF) is a widely used signal processing tool that enables the study of scale invariance models. Classical MF assumes homogeneous MF properties, which cannot always be guaranteed in practice. Yet, the local estimation of MF parameters has barely been considered due to the challenging statistical nature of MF processes (non-Gaussian, intricate dependence), requiring large sample sizes. This present work addresses this limitation and proposes a Bayesian estimator for local MF parameters of multivariate time series. The proposed Bayesian model builds on a recently introduced statistical model for leaders (i.e., specific multiresolution quantities designed for MF analysis purposes) that enabled the Bayesian estimation of MF parameters and extends it to multivariate non-overlapping time windows. It is formulated using spatially smoothing gamma Markov random field priors that counteract the large statistical variability of estimates for short time windows. Numerical simulations demonstrate that the proposed algorithm significantly outperforms current state-of-the-art estimators.

Bayesian multifractal analysis of multi-temporal images using smooth priors

Abstract: Texture analysis can be conducted within the mathematical framework of multifractal analysis (MFA) via the study of the regularity fluctuations of image amplitudes. Successfully used in various applications, however MFA remains limited to the independent analysis of single images while, in an increasing number of applications, data are multi-temporal. The present contribution addresses this limitation and introduces a Bayesian framework that enables the joint estimation of multifractal parameters for multi-temporal images. It builds on a recently proposed Gaussian model for wavelet leaders parameterized by the multifractal attributes of interest. A joint Bayesian model is formulated by assigning a Gaussian prior to the second derivatives of time evolution of the multifractal attributes associated with multi-temporal images. This Gaussian prior ensures that the multifractal parameters have a smooth temporal evolution. The associated Bayesian estimators are then approximated using a Hamiltonian Monte-Carlo algorithm. The benefits of the proposed procedure are illustrated on synthetic data.

A Bayesian approach for the multifractal analysis of spatio-temporal data

Abstract: Multifractal (MF) analysis enables the theoretical study of scale invariance models and their practical assessment via wavelet leaders. Yet, the accurate estimation of MF parameters remains a challenging task. For a range of applications, notably biomedical, the performance can potentially be improved by taking advantage of the multivariate nature of data. However, this has barely been considered in the context of MF analysis. This paper proposes a Bayesian model that enables the joint estimation of MF parameters for multivariate time series. It builds on a recently introduced statistical model for leaders and is formulated using a 3D gamma Markov random field joint prior for the MF parameters of the voxels of spatio-temporal data, represented as a multivariate time series, that counteracts the statistical variability induced by small sample size. Numerical simulations indicate that the proposed Bayesian estimator significantly outperforms current state-of-the-art algorithms.