2 research outputs found
Hardware-irrelevant parallel processing system
Parallel processing technology has been a primary tool for achieving
high-speed, high-accuracy, and broadband processing for many years across
modern information systems and data processing such as optical and radar,
synthetic aperture radar imaging, digital beam forming, and digital filtering
systems. However, hardware deviations in a parallel processing system (PPS)
severely degrade system performance and pose an urgent challenge. We propose a
hardware-irrelevant PPS of which the performance is unaffected by hardware
deviations. In this system, an embedded convolutional recurrent autoencoder
(CRAE), which learns inherent system patterns as well as acquires and removes
adverse effects brought by hardware deviations, is adopted. We implement a
hardware-irrelevant PPS into a parallel photonic sampling system to accomplish
a high-performance analog-to-digital conversion for microwave signals with high
frequency and broad bandwidth. Under one system state, a category of signals
with two different mismatch degrees is utilized to train the CRAE, which can
then compensate for mismatches in various categories of signals with multiple
mismatch degrees under random system states. Our approach is extensively
applicable to achieving hardware-irrelevant PPSs which are either discrete or
integrated in photonic, electric, and other fields
Stable Separation and Super-Resolution of Mixture Models
We consider simultaneously identifying the membership and locations of point
sources that are convolved with different band-limited point spread functions,
from the observation of their superpositions. This problem arises in
three-dimensional super-resolution single-molecule imaging, neural spike
sorting, multi-user channel identification, among other applications. We
propose a novel algorithm, based on convex programming, and establish its
near-optimal performance guarantee for exact recovery in the noise-free setting
by exploiting the spectral sparsity of the point source models as well as the
incoherence between point spread functions. Furthermore, robustness of the
recovery algorithm in the presence of bounded noise is also established.
Numerical examples are provided to demonstrate the effectiveness of the
proposed approach.Comment: conference version appeared at ISIT 2015 and SAMPTA 2015. arXiv admin
note: text overlap with arXiv:1504.0601