53,298 research outputs found
Constrained Phase Noise Estimation in OFDM Using Scattered Pilots Without Decision Feedback
In this paper, we consider an OFDM radio link corrupted by oscillator phase
noise in the receiver, namely the problem of estimating and compensating for
the impairment. To lessen the computational burden and delay incurred onto the
receiver, we estimate phase noise using only scattered pilot subcarriers, i.e.,
no tentative symbol decisions are used in obtaining and improving the phase
noise estimate. In particular, the phase noise estimation problem is posed as
an unconstrained optimization problem whose minimizer suffers from the
so-called amplitude and phase estimation error. These errors arise due to
receiver noise, estimation from limited scattered pilot subcarriers and
estimation using a dimensionality reduction model. It is empirically shown
that, at high signal-to-noise-ratios, the phase estimation error is small. To
reduce the amplitude estimation error, we restrict the minimizer to be drawn
from the so-called phase noise geometry set when minimizing the cost function.
The resulting optimization problem is a non-convex program. However, using the
S-procedure for quadratic equalities, we show that the optimal solution can be
obtained by solving the convex dual problem. We also consider a less complex
heuristic scheme that achieves the same objective of restricting the minimizer
to the phase noise geometry set. Through simulations, we demonstrate improved
coded bit-error-rate and phase noise estimation error performance when
enforcing the phase noise geometry. For example, at high
signal-to-noise-ratios, the probability density function of the phase noise
estimation error exhibits thinner tails which results in lower bit-error-rate
Principles of Neuromorphic Photonics
In an age overrun with information, the ability to process reams of data has
become crucial. The demand for data will continue to grow as smart gadgets
multiply and become increasingly integrated into our daily lives.
Next-generation industries in artificial intelligence services and
high-performance computing are so far supported by microelectronic platforms.
These data-intensive enterprises rely on continual improvements in hardware.
Their prospects are running up against a stark reality: conventional
one-size-fits-all solutions offered by digital electronics can no longer
satisfy this need, as Moore's law (exponential hardware scaling),
interconnection density, and the von Neumann architecture reach their limits.
With its superior speed and reconfigurability, analog photonics can provide
some relief to these problems; however, complex applications of analog
photonics have remained largely unexplored due to the absence of a robust
photonic integration industry. Recently, the landscape for
commercially-manufacturable photonic chips has been changing rapidly and now
promises to achieve economies of scale previously enjoyed solely by
microelectronics.
The scientific community has set out to build bridges between the domains of
photonic device physics and neural networks, giving rise to the field of
\emph{neuromorphic photonics}. This article reviews the recent progress in
integrated neuromorphic photonics. We provide an overview of neuromorphic
computing, discuss the associated technology (microelectronic and photonic)
platforms and compare their metric performance. We discuss photonic neural
network approaches and challenges for integrated neuromorphic photonic
processors while providing an in-depth description of photonic neurons and a
candidate interconnection architecture. We conclude with a future outlook of
neuro-inspired photonic processing.Comment: 28 pages, 19 figure
Detection of variable frequency signals using a fast chirp transform
The detection of signals with varying frequency is important in many areas of
physics and astrophysics. The current work was motivated by a desire to detect
gravitational waves from the binary inspiral of neutron stars and black holes,
a topic of significant interest for the new generation of interferometric
gravitational wave detectors such as LIGO. However, this work has significant
generality beyond gravitational wave signal detection.
We define a Fast Chirp Transform (FCT) analogous to the Fast Fourier
Transform (FFT). Use of the FCT provides a simple and powerful formalism for
detection of signals with variable frequency just as Fourier transform
techniques provide a formalism for the detection of signals of constant
frequency. In particular, use of the FCT can alleviate the requirement of
generating complicated families of filter functions typically required in the
conventional matched filtering process. We briefly discuss the application of
the FCT to several signal detection problems of current interest
Local Visual Microphones: Improved Sound Extraction from Silent Video
Sound waves cause small vibrations in nearby objects. A few techniques exist
in the literature that can extract sound from video. In this paper we study
local vibration patterns at different image locations. We show that different
locations in the image vibrate differently. We carefully aggregate local
vibrations and produce a sound quality that improves state-of-the-art. We show
that local vibrations could have a time delay because sound waves take time to
travel through the air. We use this phenomenon to estimate sound direction. We
also present a novel algorithm that speeds up sound extraction by two to three
orders of magnitude and reaches real-time performance in a 20KHz video.Comment: Accepted to BMVC 201
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Wyner-Ziv side information generation using a higher order piecewise trajectory temporal interpolation algorithm
Distributed video coding (DVC) reverses the traditional coding paradigm of complex encoders allied with basic decoding, to one where the computational cost is largely incurred by the decoder. This enables low-cost, resource-poor sensors to be used at the transmitter in various applications including multi-sensor surveillance. A key constraint governing DVC performance is the quality of side information (SI), a coarse representation of original video frames which are not available at the decoder. Techniques to generate SI have generally been based on linear temporal interpolation, though these do not always produce satisfactory SI quality especially in sequences exhibiting asymmetric (non-linear) motion. This paper presents a higher-order piecewise trajectory temporal interpolation (HOPTTI) algorithm for SI generation that quantitatively and perceptually affords better SI quality in comparison to existing temporal interpolation-based approaches
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