10,579 research outputs found
The Velocity of the Propagating Wave for General Coupled Scalar Systems
We consider spatially coupled systems governed by a set of scalar density
evolution equations. Such equations track the behavior of message-passing
algorithms used, for example, in coding, sparse sensing, or
constraint-satisfaction problems. Assuming that the "profile" describing the
average state of the algorithm exhibits a solitonic wave-like behavior after
initial transient iterations, we derive a formula for the propagation velocity
of the wave. We illustrate the formula with two applications, namely
Generalized LDPC codes and compressive sensing.Comment: 5 pages, 5 figures, submitted to the Information Theory Workshop
(ITW) 2016 in Cambridge, U
Compressive Wavefront Sensing with Weak Values
We demonstrate a wavefront sensor based on the compressive sensing,
single-pixel camera. Using a high-resolution spatial light modulator (SLM) as a
variable waveplate, we weakly couple an optical field's transverse-position and
polarization degrees of freedom. By placing random, binary patterns on the SLM,
polarization serves as a meter for directly measuring random projections of the
real and imaginary components of the wavefront. Compressive sensing techniques
can then recover the wavefront. We acquire high quality, 256x256 pixel images
of the wavefront from only 10,000 projections. Photon-counting detectors give
sub-picowatt sensitivity
Data based identification and prediction of nonlinear and complex dynamical systems
We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin
A Computational Study Of The Role Of Spatial Receptive Field Structure In Processing Natural And Non-Natural Scenes
The center-surround receptive field structure, ubiquitous in the visual system, is hypothesized to be evolutionarily advantageous in image processing tasks. We address the potential functional benefits and shortcomings of spatial localization and center-surround antagonism in the context of an integrate-and-fire neuronal network model with image-based forcing. Utilizing the sparsity of natural scenes, we derive a compressive-sensing framework for input image reconstruction utilizing evoked neuronal firing rates. We investigate how the accuracy of input encoding depends on the receptive field architecture, and demonstrate that spatial localization in visual stimulus sampling facilitates marked improvements in natural scene processing beyond uniformly-random excitatory connectivity. However, for specific classes of images, we show that spatial localization inherent in physiological receptive fields combined with information loss through nonlinear neuronal network dynamics may underlie common optical illusions, giving a novel explanation for their manifestation. In the context of signal processing, we expect this work may suggest new sampling protocols useful for extending conventional compressive sensing theory
Large Electronic Anisotropy and Enhanced Chemical Activity of Highly Rippled Phosphorene
We investigate the electronic structure and chemical activity of rippled
phosphorene induced by large compressive strains via first-principles
calculation. It is found that phosphorene is extraordinarily bendable, enabling
the accommodation of ripples with large curvatures. Such highly rippled
phosphorene shows a strong anisotropy in electronic properties. For ripples
along the armchair direction, the band gap changes from 0.84 to 0.51 eV for the
compressive strain up to -20% and further compression shows no significant
effect, for ripples along the zigzag direction, semiconductor to metal
transition occurs. Within the rippled phosphorene, the local electronic
properties, such as the modulated band gap and the alignments of frontier
orbitals, are found to be highly spatially dependent, which may be used for
modulating the injection and confinement of carriers for optical and
photovoltaic applications. The examination of the interaction of a physisorbed
NO molecule with the rippled phosphorene under different compressive strains
shows that the chemical activities of the phosphorene are significantly
enhanced at the top and bottom peaks of the ripples, indicated by the enhanced
adsorption and charge transfer between them. All these features can be ascribed
to the effect of curvatures, which modifies the orbital coupling between atoms
at the ripple peaks
Properties of spatial coupling in compressed sensing
In this paper we address a series of open questions about the construction of
spatially coupled measurement matrices in compressed sensing. For hardware
implementations one is forced to depart from the limiting regime of parameters
in which the proofs of the so-called threshold saturation work. We investigate
quantitatively the behavior under finite coupling range, the dependence on the
shape of the coupling interaction, and optimization of the so-called seed to
minimize distance from optimality. Our analysis explains some of the properties
observed empirically in previous works and provides new insight on spatially
coupled compressed sensing.Comment: 5 pages, 6 figure
Compressed Sensing of Approximately-Sparse Signals: Phase Transitions and Optimal Reconstruction
Compressed sensing is designed to measure sparse signals directly in a
compressed form. However, most signals of interest are only "approximately
sparse", i.e. even though the signal contains only a small fraction of relevant
(large) components the other components are not strictly equal to zero, but are
only close to zero. In this paper we model the approximately sparse signal with
a Gaussian distribution of small components, and we study its compressed
sensing with dense random matrices. We use replica calculations to determine
the mean-squared error of the Bayes-optimal reconstruction for such signals, as
a function of the variance of the small components, the density of large
components and the measurement rate. We then use the G-AMP algorithm and we
quantify the region of parameters for which this algorithm achieves optimality
(for large systems). Finally, we show that in the region where the GAMP for the
homogeneous measurement matrices is not optimal, a special "seeding" design of
a spatially-coupled measurement matrix allows to restore optimality.Comment: 8 pages, 10 figure
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