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- Mohammadreza Soltani, Chinmay Hegde
- 2016 IEEE Global Conference on Signal and…
- 2016

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)

- Mohammadreza Soltani, Chinmay Hegde
- ACSSC
- 2016

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)

- Mohammadreza Soltani, Chinmay Hegde
- IEEE Transactions on Signal Processing
- 2017

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)

- Mohammadreza Soltani, Michael Hempel, Hamid Sharif
- 2015 International Wireless Communications and…
- 2015

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)

- Mohammadreza Soltani, Chinmay Hegde
- 2017 IEEE International Conference on Acoustics…
- 2017

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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