3 research outputs found
Signal Reconstruction from Modulo Observations
We consider the problem of reconstructing a signal from under-determined
modulo observations (or measurements). This observation model is inspired by a
(relatively) less well-known imaging mechanism called modulo imaging, which can
be used to extend the dynamic range of imaging systems; variations of this
model have also been studied under the category of phase unwrapping. Signal
reconstruction in the under-determined regime with modulo observations is a
challenging ill-posed problem, and existing reconstruction methods cannot be
used directly. In this paper, we propose a novel approach to solving the
inverse problem limited to two modulo periods, inspired by recent advances in
algorithms for phase retrieval under sparsity constraints. We show that given a
sufficient number of measurements, our algorithm perfectly recovers the
underlying signal and provides improved performance over other existing
algorithms. We also provide experiments validating our approach on both
synthetic and real data to depict its superior performance
Folded Graph Signals: Sensing with Unlimited Dynamic Range
Self-reset analog-to-digital converters (ADCs) are used to sample high
dynamic range signals resulting in modulo-operation based folded signal
samples. We consider the case where each vertex of a graph (e.g., sensors in a
network) is equipped with a self-reset ADC and senses a time series. Graph
sampling allows the graph time series to be represented by the signals at a
subset of sampled vertices and time instances. We investigate the problem of
recovering bandlimited continuous-time graph signals from folded signal
samples. We derive sufficient conditions to achieve successful recovery of the
graph signal from the folded signal samples, which can be achieved via integer
programming. To resolve the scalability issue of integer programming, we
propose a sparse optimization recovery method for graph signals satisfying
certain technical conditions. Such an approach requires a novel graph sampling
scheme that selects vertices with small signal variation. The proposed
algorithm exploits the inherent relationship among the graph vertices in both
the vertex and time domains to recover the graph signal. Simulations and
experiments on images validate the feasibility of our proposed approach
Blind Unwrapping of Modulo Reduced Gaussian Vectors: Recovering MSBs from LSBs
We consider the problem of recovering i.i.d samples from a zero mean
multivariate Gaussian distribution with an unknown covariance matrix, from
their modulo wrapped measurements, i.e., measurement where each coordinate is
reduced modulo , for some . For this setup, which is
motivated by quantization and analog-to-digital conversion, we develop a
low-complexity iterative decoding algorithm. We show that if a benchmark
informed decoder that knows the covariance matrix can recover each sample with
small error probability, and is large enough, the performance of the
proposed blind recovery algorithm closely follows that of the informed one. We
complement the analysis with numeric results that show that the algorithm
performs well even in non-asymptotic conditions