15,394 research outputs found
Robust spatial coherence 5m from a room-temperature atom chip
We study spatial coherence near a classical environment by loading a
Bose-Einstein condensate into a magnetic lattice potential and observing
diffraction. Even very close to a surface (5m), and even when the
surface is at room temperature, spatial coherence persists for a relatively
long time (500ms). In addition, the observed spatial coherence extends
over several lattice sites, a significantly greater distance than the
atom-surface separation. This opens the door for atomic circuits, and may help
elucidate the interplay between spatial dephasing, inter-atomic interactions,
and external noise.Comment: 15 pages, 14 figures, revised for final publication. This manuscript
includes in-depth analysis of the data presented in arXiv:1502.0160
Characterization of causes of signal phase and frequency instability Final report
Characteristic instabilities in phase and frequency errors of reference oscillator
Data-driven multivariate and multiscale methods for brain computer interface
This thesis focuses on the development of data-driven multivariate and multiscale methods
for brain computer interface (BCI) systems. The electroencephalogram (EEG), the
most convenient means to measure neurophysiological activity due to its noninvasive nature,
is mainly considered. The nonlinearity and nonstationarity inherent in EEG and its
multichannel recording nature require a new set of data-driven multivariate techniques to
estimate more accurately features for enhanced BCI operation. Also, a long term goal
is to enable an alternative EEG recording strategy for achieving long-term and portable
monitoring.
Empirical mode decomposition (EMD) and local mean decomposition (LMD), fully
data-driven adaptive tools, are considered to decompose the nonlinear and nonstationary
EEG signal into a set of components which are highly localised in time and frequency. It
is shown that the complex and multivariate extensions of EMD, which can exploit common
oscillatory modes within multivariate (multichannel) data, can be used to accurately
estimate and compare the amplitude and phase information among multiple sources, a
key for the feature extraction of BCI system. A complex extension of local mean decomposition
is also introduced and its operation is illustrated on two channel neuronal
spike streams. Common spatial pattern (CSP), a standard feature extraction technique
for BCI application, is also extended to complex domain using the augmented complex
statistics. Depending on the circularity/noncircularity of a complex signal, one of the
complex CSP algorithms can be chosen to produce the best classification performance
between two different EEG classes.
Using these complex and multivariate algorithms, two cognitive brain studies are
investigated for more natural and intuitive design of advanced BCI systems. Firstly, a Yarbus-style auditory selective attention experiment is introduced to measure the user
attention to a sound source among a mixture of sound stimuli, which is aimed at improving
the usefulness of hearing instruments such as hearing aid. Secondly, emotion experiments
elicited by taste and taste recall are examined to determine the pleasure and displeasure
of a food for the implementation of affective computing. The separation between two
emotional responses is examined using real and complex-valued common spatial pattern
methods.
Finally, we introduce a novel approach to brain monitoring based on EEG recordings
from within the ear canal, embedded on a custom made hearing aid earplug. The new
platform promises the possibility of both short- and long-term continuous use for standard
brain monitoring and interfacing applications
Computational Dynamic Market Risk Measures in Discrete Time Setting
Different approaches to defining dynamic market risk measures are available
in the literature. Most are focused or derived from probability theory,
economic behavior or dynamic programming. Here, we propose an approach to
define and implement dynamic market risk measures based on recursion and state
economy representation. The proposed approach is to be implementable and to
inherit properties from static market risk measures.Comment: 16 pages, 3 figure
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