12,548 research outputs found
Digital phase-locked loops tracked by a relay sensor
An optimal algorithm is presented for tracking the
phase of a slowly modulating signal by means of digital sampling of its sign. Error bounds and a numerical illustration are given
Highly corrupted image inpainting through hypoelliptic diffusion
We present a new image inpainting algorithm, the Averaging and Hypoelliptic
Evolution (AHE) algorithm, inspired by the one presented in [SIAM J. Imaging
Sci., vol. 7, no. 2, pp. 669--695, 2014] and based upon a semi-discrete
variation of the Citti-Petitot-Sarti model of the primary visual cortex V1. The
AHE algorithm is based on a suitable combination of sub-Riemannian hypoelliptic
diffusion and ad-hoc local averaging techniques. In particular, we focus on
reconstructing highly corrupted images (i.e. where more than the 80% of the
image is missing), for which we obtain reconstructions comparable with the
state-of-the-art.Comment: 15 pages, 10 figure
Vector-exponential time-series modeling for polynomial J-spectral factorization
An iterative algorithm to perform the J-spectral factorization of a para-Hermitian matrix is presented. The algorithm proceeds by computing a special kernel representation of an interpolant for a sequence of points and associated directions determined from the spectral zeroes of the to-be factored matrix
Near-optimal small-depth lower bounds for small distance connectivity
We show that any depth- circuit for determining whether an -node graph
has an -to- path of length at most must have size
. The previous best circuit size lower bounds for this
problem were (due to Beame, Impagliazzo, and Pitassi
[BIP98]) and (following from a recent formula size
lower bound of Rossman [Ros14]). Our lower bound is quite close to optimal,
since a simple construction gives depth- circuits of size
for this problem (and strengthening our bound even to
would require proving that undirected connectivity is not in )
Our proof is by reduction to a new lower bound on the size of small-depth
circuits computing a skewed variant of the "Sipser functions" that have played
an important role in classical circuit lower bounds [Sip83, Yao85, H{\aa}s86].
A key ingredient in our proof of the required lower bound for these Sipser-like
functions is the use of \emph{random projections}, an extension of random
restrictions which were recently employed in [RST15]. Random projections allow
us to obtain sharper quantitative bounds while employing simpler arguments,
both conceptually and technically, than in the previous works [Ajt89, BPU92,
BIP98, Ros14]
- âŠ