74,735 research outputs found
Optimal Simultaneous Detection and Signal and Noise Power Estimation
Simultaneous detection and estimation is important in many engineering
applications. In particular, there are many applications where it is important
to perform signal detection and Signal-to-Noise-Ratio (SNR) estimation jointly.
Application of existing frameworks in the literature that handle simultaneous
detection and estimation is not straightforward for this class of application.
This paper therefore aims at bridging the gap between an existing framework,
specifically the work by Middleton et al., and the mentioned application class
by presenting a jointly optimal detector and signal and noise power estimators.
The detector and estimators are given for the Gaussian observation model with
appropriate conjugate priors on the signal and noise power. Simulation results
affirm the superior performance of the optimal solution compared to the
separate detection and estimation approaches.Comment: appears in 2014 IEEE International Symposium on Information Theory
(ISIT
Sequential joint signal detection and signal-to-noise ratio estimation
The sequential analysis of the problem of joint signal detection and
signal-to-noise ratio (SNR) estimation for a linear Gaussian observation model
is considered. The problem is posed as an optimization setup where the goal is
to minimize the number of samples required to achieve the desired (i) type I
and type II error probabilities and (ii) mean squared error performance. This
optimization problem is reduced to a more tractable formulation by transforming
the observed signal and noise sequences to a single sequence of Bernoulli
random variables; joint detection and estimation is then performed on the
Bernoulli sequence. This transformation renders the problem easily solvable,
and results in a computationally simpler sufficient statistic compared to the
one based on the (untransformed) observation sequences. Experimental results
demonstrate the advantages of the proposed method, making it feasible for
applications having strict constraints on data storage and computation.Comment: 5 pages, Proceedings of IEEE International Conference on Acoustics,
Speech, and Signal Processing (ICASSP), 201
The optimal search for an astrophysical gravitational-wave background
Roughly every 2-10 minutes, a pair of stellar mass black holes merge
somewhere in the Universe. A small fraction of these mergers are detected as
individually resolvable gravitational-wave events by advanced detectors such as
LIGO and Virgo. The rest contribute to a stochastic background. We derive the
statistically optimal search strategy for a background of unresolved binaries.
Our method applies Bayesian parameter estimation to all available data. Using
Monte Carlo simulations, we demonstrate that the search is both "safe" and
effective: it is not fooled by instrumental artefacts such as glitches, and it
recovers simulated stochastic signals without bias. Given realistic
assumptions, we estimate that the search can detect the binary black hole
background with about one day of design sensitivity data versus
months using the traditional cross-correlation search. This framework
independently constrains the merger rate and black hole mass distribution,
breaking a degeneracy present in the cross-correlation approach. The search
provides a unified framework for population studies of compact binaries, which
is cast in terms of hyper-parameter estimation. We discuss a number of
extensions and generalizations including: application to other sources (such as
binary neutron stars and continuous-wave sources), simultaneous estimation of a
continuous Gaussian background, and applications to pulsar timing.Comment: 16 pages, 9 figure
Throughput Optimization for Massive MIMO Systems Powered by Wireless Energy Transfer
This paper studies a wireless-energy-transfer (WET) enabled massive
multiple-input-multiple-output (MIMO) system (MM) consisting of a hybrid
data-and-energy access point (H-AP) and multiple single-antenna users. In the
WET-MM system, the H-AP is equipped with a large number of antennas and
functions like a conventional AP in receiving data from users, but additionally
supplies wireless power to the users. We consider frame-based transmissions.
Each frame is divided into three phases: the uplink channel estimation (CE)
phase, the downlink WET phase, as well as the uplink wireless information
transmission (WIT) phase. Firstly, users use a fraction of the previously
harvested energy to send pilots, while the H-AP estimates the uplink channels
and obtains the downlink channels by exploiting channel reciprocity. Next, the
H-AP utilizes the channel estimates just obtained to transfer wireless energy
to all users in the downlink via energy beamforming. Finally, the users use a
portion of the harvested energy to send data to the H-AP simultaneously in the
uplink (reserving some harvested energy for sending pilots in the next frame).
To optimize the throughput and ensure rate fairness, we consider the problem of
maximizing the minimum rate among all users. In the large- regime, we obtain
the asymptotically optimal solutions and some interesting insights for the
optimal design of WET-MM system. We define a metric, namely, the massive MIMO
degree-of-rate-gain (MM-DoRG), as the asymptotic UL rate normalized by
. We show that the proposed WET-MM system is optimal in terms of
MM-DoRG, i.e., it achieves the same MM-DoRG as the case with ideal CE.Comment: 15 double-column pages, 6 figures, 1 table, to appear in IEEE JSAC in
February 2015, special issue on wireless communications powered by energy
harvesting and wireless energy transfe
Multimode laser cooling and ultra-high sensitivity force sensing with nanowires
Photo-induced forces can be used to manipulate and cool the mechanical motion
of oscillators. When the oscillator is used as a force sensor, such as in
atomic force microscopy, active feedback is an enticing route to enhancing
measurement performance. Here, we show broadband multimode cooling of dB
down to a temperature of ~K in the stationary regime. Through the use
of periodic quiescence feedback cooling, we show improved signal-to-noise
ratios for the measurement of transient signals. We compare the performance of
real feedback to numerical post-processing of data and show that both methods
produce similar improvements to the signal-to-noise ratio of force
measurements. We achieved a room temperature force measurement sensitivity of
N with integration time of less than ms. The high
precision and fast force microscopy results presented will potentially benefit
applications in biosensing, molecular metrology, subsurface imaging and
accelerometry.Comment: 16 pages and 3 figures for the main text, 14 pages and 5 figures for
the supplementary informatio
VLSI implementation of an energy-aware wake-up detector for an acoustic surveillance sensor network
We present a low-power VLSI wake-up detector for a sensor network that uses acoustic signals to localize ground-base vehicles. The detection criterion is the degree of low-frequency periodicity in the acoustic signal, and the periodicity is computed from the "bumpiness" of the autocorrelation of a one-bit version of the signal. We then describe a CMOS ASIC that implements the periodicity estimation algorithm. The ASIC is functional and its core consumes 835 nanowatts. It was integrated into an acoustic enclosure and deployed in field tests with synthesized sounds and ground-based vehicles.Fil: Goldberg, David H.. Johns Hopkins University; Estados UnidosFil: Andreou, Andreas. Johns Hopkins University; Estados UnidosFil: Julian, Pedro Marcelo. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; Argentina. Universidad Nacional del Sur. Departamento de IngenierĂa ElĂ©ctrica y de Computadoras; ArgentinaFil: Pouliquen, Philippe O.. Johns Hopkins University; Estados UnidosFil: Riddle, Laurence. Signal Systems Corporation; Estados UnidosFil: Rosasco, Rich. Signal Systems Corporation; Estados Unido
- âŠ