Mohammadreza Soltani

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In this paper, we propose an iterative algorithm based on hard thresholding for demixing a pair of signals from nonlinear observations of their superposition. We focus on the under-determined case where the number of available observations is far less than the ambient dimension of the signals. We derive nearly-tight upper bounds on the sample complexity of(More)
We study the problem of demixing a pair of sparse signals from nonlinear observations of their superposition. Mathematically, we consider the observation model y = f (Ax), where y ∈ R n represents the observations, f is a nonlinear link function, A ∈ R m×n is a measurement operator, and x ∈ R n is the superposition of the signals. Further, we assume that x(More)
We study the problem of <italic>demixing</italic> a pair of sparse signals from noisy, nonlinear observations of their superposition. Mathematically, we consider a nonlinear signal observation model, <inline-formula> <tex-math notation="LaTeX">$y_i = g(a_i^Tx) + e_i, \ i=1,\ldots,m$</tex-math></inline-formula>, where <inline-formula> <tex-math(More)
In large-scale Wireless Sensor Networks (WSNs), one of the most important challenges is manageability of the network. With the increase in sensor nodes, data forwarding, route selection, network reliability and data accuracy are vital characteristics of WSNs that suffer from the growth in scale. In this paper, we propose a data fusion based approach to(More)
Random sinusoidal features are a popular approach for speeding up kernel-based inference in large datasets. Prior to the inference stage, the approach suggests performing dimensionality reduction by first multiplying each data vector by a random Gaussian matrix, and then computing an element-wise sinusoid. Theoretical analysis shows that collecting a(More)
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