2,570 research outputs found
Explicit Mapping of Acoustic Regimes For Wind Instruments
This paper proposes a methodology to map the various acoustic regimes of wind
instruments. The maps can be generated in a multi-dimensional space consisting
of design, control parameters, and initial conditions. The bound- aries of the
maps are obtained explicitly in terms of the parameters using a support vector
machine (SVM) classifier as well as a dedicated adaptive sam- pling scheme. The
approach is demonstrated on a simplified clarinet model for which several maps
are generated based on different criteria. Examples of computation of the
probability of occurrence of a specific acoustic regime are also provided. In
addition, the approach is demonstrated on a design optimization example for
optimal intonation
Wavelet Shrinkage and Thresholding based Robust Classification for Brain Computer Interface
A macaque monkey is trained to perform two different kinds of tasks, memory
aided and visually aided. In each task, the monkey saccades to eight possible
target locations. A classifier is proposed for direction decoding and task
decoding based on local field potentials (LFP) collected from the prefrontal
cortex. The LFP time-series data is modeled in a nonparametric regression
framework, as a function corrupted by Gaussian noise. It is shown that if the
function belongs to Besov bodies, then using the proposed wavelet shrinkage and
thresholding based classifier is robust and consistent. The classifier is then
applied to the LFP data to achieve high decoding performance. The proposed
classifier is also quite general and can be applied for the classification of
other types of time-series data as well, not necessarily brain data
Sequential Design for Ranking Response Surfaces
We propose and analyze sequential design methods for the problem of ranking
several response surfaces. Namely, given response surfaces over a
continuous input space , the aim is to efficiently find the index of
the minimal response across the entire . The response surfaces are not
known and have to be noisily sampled one-at-a-time. This setting is motivated
by stochastic control applications and requires joint experimental design both
in space and response-index dimensions. To generate sequential design
heuristics we investigate stepwise uncertainty reduction approaches, as well as
sampling based on posterior classification complexity. We also make connections
between our continuous-input formulation and the discrete framework of pure
regret in multi-armed bandits. To model the response surfaces we utilize
kriging surrogates. Several numerical examples using both synthetic data and an
epidemics control problem are provided to illustrate our approach and the
efficacy of respective adaptive designs.Comment: 26 pages, 7 figures (updated several sections and figures
Optimal design of an unsupervised adaptive classifier with unknown priors
An adaptive detection scheme for M hypotheses was analyzed. It was assumed that the probability density function under each hypothesis was known, and that the prior probabilities of the M hypotheses were unknown and sequentially estimated. Each observation vector was classified using the current estimate of the prior probabilities. Using a set of nonlinear transformations, and applying stochastic approximation theory, an optimally converging adaptive detection and estimation scheme was designed. The optimality of the scheme lies in the fact that convergence to the true prior probabilities is ensured, and that the asymptotic error variance is minimum, for the class of nonlinear transformations considered. An expression for the asymptotic mean square error variance of the scheme was also obtained
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