5,982 research outputs found
Quality-Aware Broadcasting Strategies for Position Estimation in VANETs
The dissemination of vehicle position data all over the network is a
fundamental task in Vehicular Ad Hoc Network (VANET) operations, as
applications often need to know the position of other vehicles over a large
area. In such cases, inter-vehicular communications should be exploited to
satisfy application requirements, although congestion control mechanisms are
required to minimize the packet collision probability. In this work, we face
the issue of achieving accurate vehicle position estimation and prediction in a
VANET scenario. State of the art solutions to the problem try to broadcast the
positioning information periodically, so that vehicles can ensure that the
information their neighbors have about them is never older than the
inter-transmission period. However, the rate of decay of the information is not
deterministic in complex urban scenarios: the movements and maneuvers of
vehicles can often be erratic and unpredictable, making old positioning
information inaccurate or downright misleading. To address this problem, we
propose to use the Quality of Information (QoI) as the decision factor for
broadcasting. We implement a threshold-based strategy to distribute position
information whenever the positioning error passes a reference value, thereby
shifting the objective of the network to limiting the actual positioning error
and guaranteeing quality across the VANET. The threshold-based strategy can
reduce the network load by avoiding the transmission of redundant messages, as
well as improving the overall positioning accuracy by more than 20% in
realistic urban scenarios.Comment: 8 pages, 7 figures, 2 tables, accepted for presentation at European
Wireless 201
Stereo Computation for a Single Mixture Image
This paper proposes an original problem of \emph{stereo computation from a
single mixture image}-- a challenging problem that had not been researched
before. The goal is to separate (\ie, unmix) a single mixture image into two
constitute image layers, such that the two layers form a left-right stereo
image pair, from which a valid disparity map can be recovered. This is a
severely illposed problem, from one input image one effectively aims to recover
three (\ie, left image, right image and a disparity map). In this work we give
a novel deep-learning based solution, by jointly solving the two subtasks of
image layer separation as well as stereo matching. Training our deep net is a
simple task, as it does not need to have disparity maps. Extensive experiments
demonstrate the efficacy of our method.Comment: Accepted by European Conference on Computer Vision (ECCV) 201
A Neural Model of How the Brain Computes Heading from Optic Flow in Realistic Scenes
Animals avoid obstacles and approach goals in novel cluttered environments using visual information, notably optic flow, to compute heading, or direction of travel, with respect to objects in the environment. We present a neural model of how heading is computed that describes interactions among neurons in several visual areas of the primate magnocellular pathway, from retina through V1, MT+, and MSTd. The model produces outputs which are qualitatively and quantitatively similar to human heading estimation data in response to complex natural scenes. The model estimates heading to within 1.5° in random dot or photo-realistically rendered scenes and within 3° in video streams from driving in real-world environments. Simulated rotations of less than 1 degree per second do not affect model performance, but faster simulated rotation rates deteriorate performance, as in humans. The model is part of a larger navigational system that identifies and tracks objects while navigating in cluttered environments.National Science Foundation (SBE-0354378, BCS-0235398); Office of Naval Research (N00014-01-1-0624); National-Geospatial Intelligence Agency (NMA201-01-1-2016
Statistical Analysis of Geometric Algorithms in Vehicular Visible Light Positioning
Vehicular visible light positioning (VLP) methods find relative locations of
vehicles by estimating the positions of intensity-modulated head/tail lights of
one vehicle (target) with respect to another (ego). Estimation is done in two
steps: 1) relative bearing or range of the transmitter-receiver link is
measured over the received signal on the ego side, and 2) target position is
estimated based on those measurements using a geometric algorithm that
expresses position coordinates in terms of the bearing-range parameters. The
primary source of statistical error for these non-linear algorithms is the
channel noise on the received signals that contaminates parameter measurements
with varying levels of sensitivity. In this paper, we present two such
geometric vehicular VLP algorithms that were previously unexplored, compare
their performance with state-of-the-art algorithms over simulations, and
analyze theoretical performance of all algorithms against statistical channel
noise by deriving the respective Cramer-Rao lower bounds. The two newly
explored algorithms do not outperform existing state-of-the-art, but we present
them alongside the statistical analyses for the sake of completeness and to
motivate further research in vehicular VLP. Our main finding is that direct
bearing-based algorithms provide higher accuracy against noise for estimating
lateral position coordinates, and range-based algorithms provide higher
accuracy in the longitudinal axis due to the non-linearity of the respective
geometric algorithms.Comment: Technical report. 7 pages, 4 figure
Speech enhancement using ego-noise references with a microphone array embedded in an unmanned aerial vehicle
A method is proposed for performing speech enhancement using ego-noise
references with a microphone array embedded in an unmanned aerial vehicle
(UAV). The ego-noise reference signals are captured with microphones located
near the UAV's propellers and used in the prior knowledge multichannel Wiener
filter (PK-MWF) to obtain the speech correlation matrix estimate. Speech
presence probability (SPP) can be estimated for detecting speech activity from
an external microphone near the speech source, providing a performance
benchmark, or from one of the embedded microphones, assuming a more realistic
scenario. Experimental measurements are performed in a semi-anechoic chamber,
with a UAV mounted on a stand and a loudspeaker playing a speech signal, while
setting three distinct and fixed propeller rotation speeds, resulting in three
different signal-to-noise ratios (SNRs). The recordings obtained and made
available online are used to compare the proposed method to the use of the
standard multichannel Wiener filter (MWF) estimated with and without the
propellers' microphones being used in its formulation. Results show that
compared to those, the use of PK-MWF achieves higher levels of improvement in
speech intelligibility and quality, measured by STOI and PESQ, while the SNR
improvement is similar
Partially Adaptive Multichannel Joint Reduction of Ego-noise and Environmental Noise
Human-robot interaction relies on a noise-robust audio processing module
capable of estimating target speech from audio recordings impacted by
environmental noise, as well as self-induced noise, so-called ego-noise. While
external ambient noise sources vary from environment to environment, ego-noise
is mainly caused by the internal motors and joints of a robot. Ego-noise and
environmental noise reduction are often decoupled, i.e., ego-noise reduction is
performed without considering environmental noise. Recently, a variational
autoencoder (VAE)-based speech model has been combined with a fully adaptive
non-negative matrix factorization (NMF) noise model to recover clean speech
under different environmental noise disturbances. However, its enhancement
performance is limited in adverse acoustic scenarios involving, e.g. ego-noise.
In this paper, we propose a multichannel partially adaptive scheme to jointly
model ego-noise and environmental noise utilizing the VAE-NMF framework, where
we take advantage of spatially and spectrally structured characteristics of
ego-noise by pre-training the ego-noise model, while retaining the ability to
adapt to unknown environmental noise. Experimental results show that our
proposed approach outperforms the methods based on a completely fixed scheme
and a fully adaptive scheme when ego-noise and environmental noise are present
simultaneously.Comment: Accepted to the 2023 IEEE International Conference on Acoustics,
Speech, and Signal Processing (ICASSP 2023
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