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The majority of functional magnetic resonance imaging (fMRI) studies obtain functional information using statistical tests based on the magnitude image reconstructions. Recently, a complex correlation (CC) test was proposed based on the complex image data in order to take advantage of phase information in the signal. However, the CC test ignores additional(More)
Internet background radiation, the fundamentally unproductive traffic that arises from misconfigurations and malicious activities, is pervasive and has complex characteristics. Understanding the network locations of hosts that generate background radiation can be helpful in the development of new techniques aimed at reducing this unwanted traffic. This(More)
The nonparametric density estimation method proposed in this paper is computationally fast, capable of detecting density discontinuities and singularities at a very high resolution, spatially adaptive, and offers near minimax convergence rates for broad classes of densities including Besov spaces. At the heart of this new method lie multi-scale signal(More)
We present an image reconstruction method for diffuse optical tomography (DOT) by using the sparsity regularization and expectation-maximization (EM) algorithm. Typical image reconstruction approaches in DOT employ Tikhonov-type regularization, which imposes restrictions on the L 2 norm of the optical properties (absorption/scattering coefficients). It(More)
This paper addresses the problem of reconstructing an image from 1-bit-quantized measurements, considering a simple but non-conventional optical acquisition model. Following a compressed-sensing design, a known pseudo-random phase-shifting mask is introduced at the aperture of the optical system. The associated reconstruction algorithm is tailored to this(More)
In this paper, we present an incomplete variables truncated conjugate gradient (IVTCG) method for bioluminescence tomography (BLT). Considering the sparse characteristic of the light source and insufficient surface measurement in the BLT scenarios, we combine a sparseness-inducing ( 1 norm) regularization term with a quadratic error term in the IVTCG-based(More)
An approach is presented for representing spatially extended cortical activity using a basis function expansion. The bases are designed to represent patches on the cortical surface. The basis function expansion coefficients are estimated for each patch by scanning modified linearly constrained minimum variance (LCMV) spatial filters over the entire surface.(More)
This paper presents the Static Computational Optical Un-dersampled Tracker (SCOUT), an architecture for compressive motion tracking systems. The architecture uses compressive sensing techniques to track moving targets at significantly higher resolution than the detector array, allowing for low cost, low weight design and a significant reduction in data(More)
P300 is a manifestation of activity in a limited capacity system "whose use in the service of different tasks is under relative control by instruction". It is accepted as an objective correlate of mental processing involved in the allocation of attentional resources when immediate memory is engaged. The aim of this study was to evaluate cognitive function(More)
The majority of fMRI studies obtain functional information using statistical tests based on the magnitude image reconstructions. Recently, a complex correlation (CC) test was proposed based on the complex image data in order to take advantage of phase information in the signal. However, the CC test ignores additional phase information in the baseline(More)