Oliver James

A typical time series in functional magnetic resonance imaging (fMRI) exhibits autocorrelation, that is, the samples of the time series are dependent. In addition, temporal filtering, one of the crucial steps in preprocessing of functional magnetic resonance images, induces its own autocorrelation. While performing connectivity analysis in fMRI, th...

Conventional functional connectivity analysis using functional magnetic resonance imaging (fMRI) measures the correlation of temporally synchronized brain activities between brain regions. Lag structure analysis relaxes the synchronicity constraint of fMRI signals, and thus, this approach might be better at explaining functional connectivity. Howev...

In the mammalian nervous system, myelin provides electrical insulation for the neural circuit by forming a highly organized, multilayered thin film around the axon fibers. Here, we investigate the spectral reflectance from this subcellular nanostructure and devise a new label-free technique based on a spectroscopic analysis of reflected light, enab...

Autism spectrum disorder (ASD) is a neurodevelopmental disability with global implication. Altered brain connectivity in the language network has frequently been reported in ASD patients using task-based functional magnetic resonance imaging (fMRI) compared to typically developing (TD) participants. Most of these studies have focused on a specific...

In this paper, we aim to revisit the restricted isometry property of real and complex Gaussian sensing matrices. We do this reconsideration via the recently introduced restricted isometry random variable (RIV) framework for the real Gaussian sensing matrices. We first generalize the RIV framework to the complex settings and illustrate that the rest...

In this paper, we aim to revisit the restricted isometry property (RIP) of real and complex Gaussian sensing matrices. We do this reconsideration via the recently introduced restricted isometry random variable (RIV) framework for the real Gaussian sensing matrices. We first generalize the RIV framework to the complex settings and illustrate that th...

In this paper, we aim to generalize the notion of restricted isometry constant (RIC) in compressive sensing (CS) to restricted isometry random variable (RIV). Associated with a deterministic encoder there are two RICs, namely, the left and the right RIC. We show that these RICs can be generalized to a left RIV and a right RIV for an ensemble of ran...

We consider the problem of analyzing compressive sensing (CS) systems using the eigenvalues of Wishart matrices. Towards this goal, we first aim to derive new eigenvalue distributions for Wishart matrices. We next discuss the usefulness of the distributions in the context of sparse signal recovery in CS systems. Sparse signal recovery necessitates...

In this paper, we introduce filters with random transmittance to improve the resolution of miniature spectrometers. We show that a sparse signal spectrum sensed by such filters when processed by an L1 algorithm aids in improving the resolution. We also show a way to design the filters with random transmittance using optical thin-film technology. We...

In this paper, we aim to evaluate the performance of the digital signal processing (DSP) algorithm used in [8] in order to improve the resolution of spectrometers with fixed number of low-cost, non-ideal filters. In such spectrometers, the resolution is limited by the number of filters. We aim to demonstrate via new experiments that the resolution...

In this paper, we introduce a method for improving the resolution of miniature spectrometers. Our method is based on using filters with random transmittance. Such filters sense fine details of an input signal spectrum, which, when combined with a signal processing algorithm, aid in improving resolution. We also propose an approach for designing fil...

In this paper, we introduce an implementation of filters with random transmittance for miniature spectrometers with limited number of CCD elements. We also present a method for estimating the random transmittances, which are needed for recovering the signal spectrum.

The authors consider the design of pilot sequences for channel estimation in the presence of carrier frequency offset (CFO) in systems that employ orthogonal frequency division multiplexing (OFDM). The CFO introduces intercarrier interference (ICI) which degrades the accuracy of the channel estimation. In order to minimise this effect, the authors...

In this paper, we present a signal processing approach to improve the resolution of a spectrometer with a fixed number of low-cost, non-ideal filters. We aim to show that the resolution can be improved beyond the limit set by the number of filters by exploiting the sparse nature of a signal spectrum. We consider an underdetermined system of linear...

We consider the design of pilot sequences for channel estimation in the presence of carrier frequency offset (CFO) in systems that employ orthogonal frequency division multiplexing (OFDM). The CFO introduces intercarrier interference (ICI) which degrades the accuracy of the channel estimation. To minimize this effect, we solve for the pilot sequenc...

We investigate a low-rank minimum mean-square error (MMSE) channel estimator in orthogonal frequency division multiplexing (OFDM) systems. The proposed estimator is derived by using the multi-stage nested Wiener filter (MSNWF) identified in the literature as a Krylov subspace approach for rank reduction. We describe the low-rank MMSE expressions fo...

We consider the problem of pilot-aided accurate least squares (LS) channel estimation in the presence of carrier frequency offset (CFO). We first formulate the mean square error (MSE) of the LS channel estimator in the presence of CFO and then show how to select the pilot sequence (with element values constrained to plus or minus one) that minimize...

A threshold-based procedure to estimate sparse channels in an orthogonal frequency division multiplexing (OFDM) system is proposed. An optimal threshold is derived by maximising the probability of correct detection between significant and zero-valued taps estimated by the least squares (LS) estimator. Improved LS estimates are obtained by pruning t...

In this paper, two types of wavelet based schemes to improve the spectral efficiency (SE) of a digital communication system are presented. First one is wavelet based pulse shaping and the other is wavelet based digital modulation called wavelet shift keying (WSK). In pulse shaping scheme, orthonormal wavelets and their translates are used as base b...

In this paper, we propose a new compressive sensing framework for sensor networks. Unlike the conventional approaches, we consider the design of sensing matrix with the prior knowledge of the channel between the signals and the sensors. We determine that full or partial knowledge of the channel at sensors enables effective sensing matrix design and...