19,803 research outputs found
ModDrop: adaptive multi-modal gesture recognition
We present a method for gesture detection and localisation based on
multi-scale and multi-modal deep learning. Each visual modality captures
spatial information at a particular spatial scale (such as motion of the upper
body or a hand), and the whole system operates at three temporal scales. Key to
our technique is a training strategy which exploits: i) careful initialization
of individual modalities; and ii) gradual fusion involving random dropping of
separate channels (dubbed ModDrop) for learning cross-modality correlations
while preserving uniqueness of each modality-specific representation. We
present experiments on the ChaLearn 2014 Looking at People Challenge gesture
recognition track, in which we placed first out of 17 teams. Fusing multiple
modalities at several spatial and temporal scales leads to a significant
increase in recognition rates, allowing the model to compensate for errors of
the individual classifiers as well as noise in the separate channels.
Futhermore, the proposed ModDrop training technique ensures robustness of the
classifier to missing signals in one or several channels to produce meaningful
predictions from any number of available modalities. In addition, we
demonstrate the applicability of the proposed fusion scheme to modalities of
arbitrary nature by experiments on the same dataset augmented with audio.Comment: 14 pages, 7 figure
Reciprocity Calibration for Massive MIMO: Proposal, Modeling and Validation
This paper presents a mutual coupling based calibration method for
time-division-duplex massive MIMO systems, which enables downlink precoding
based on uplink channel estimates. The entire calibration procedure is carried
out solely at the base station (BS) side by sounding all BS antenna pairs. An
Expectation-Maximization (EM) algorithm is derived, which processes the
measured channels in order to estimate calibration coefficients. The EM
algorithm outperforms current state-of-the-art narrow-band calibration schemes
in a mean squared error (MSE) and sum-rate capacity sense. Like its
predecessors, the EM algorithm is general in the sense that it is not only
suitable to calibrate a co-located massive MIMO BS, but also very suitable for
calibrating multiple BSs in distributed MIMO systems.
The proposed method is validated with experimental evidence obtained from a
massive MIMO testbed. In addition, we address the estimated narrow-band
calibration coefficients as a stochastic process across frequency, and study
the subspace of this process based on measurement data. With the insights of
this study, we propose an estimator which exploits the structure of the process
in order to reduce the calibration error across frequency. A model for the
calibration error is also proposed based on the asymptotic properties of the
estimator, and is validated with measurement results.Comment: Submitted to IEEE Transactions on Wireless Communications,
21/Feb/201
- …