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Optimal Excitation Signal Design for Frequency Domain System Identification Using Semidefinite Programming
Optimal Excitation Signal Design Frequency Domain System Identification Semidefinite Programming
2015/7/13
The paper discusses two methods of optimal excitation signal design for identification with Maximum Likelihood parameter estimation: The ‘classical’, dispersion function based method, and a new, semid...
A Minimax Theorem with Applications to Machine Learning, Signal Processing, and Finance
convex optimization minimax theorem robust optimization
2015/7/9
This paper concerns a fractional function of the form x^Ta/sqrt{x^TBx}, where B is positive definite. We consider the game of choosing x from a convex set, to maximize the function, and choosing (a,B)...
Real-Time Convex Optimization in Signal Processing
Real-Time Convex Optimization Signal Processing
2015/7/9
Convex optimization has been used in signal processing for a long time, to choose coefficients for use in fast (linear) algorithms, such as in filter or array design; more recently, it has been used t...
EigenPrism:Inference for High-Dimensional Signal-to-Noise Ratios
EigenPrism High-Dimensional Signal Noise Ratios
2015/6/17
Consider the following three important problems in statistical inference, namely, constructing confidence intervals for (1) the error of a high-dimensional (p > n) regression estimator, (2) the linear...
Estimation of frequency modulations on wideband signals; applications to audio signal analysis
Estimation frequency modulations wideband signals applications audio signal analysis
2013/6/14
The problem of joint estimation of power spectrum and modulation from realizations of frequency modulated stationary wideband signals is considered. The study is motivated by some specific signal clas...
Identification of Signal, Noise, and Indistinguishable Subsets in High-Dimensional Data Analysis
Two-Level Thresholding Signal detection False positive control False negative control Multiple testing Variable screening
2013/6/13
Motivated by applications in high-dimensional data analysis where strong signals often stand out easily and weak ones may be indistinguishable from the noise, we develop a statistical framework to pro...
Multi-dimensional sparse structured signal approximation using split Bregman iterations
Sparse approximation Regularization Fused-LASSO Split Bregman Multidimensional signals
2013/5/2
The paper focuses on the sparse approximation of signals using overcomplete representations, such that it preserves the (prior) structure of multi-dimensional signals. The underlying optimization prob...
Adding a systematic uncertainty to the signal estimation in the on/off-zone measurements
Adding a systematic uncertainty the signal estimation in the on/off-zone measurements
2013/4/28
The measurements with the background estimation from an off-zone are widely used in astrophysics, accelerator physics and other areas. Usually, the expected number of the background events in the off-...
Gaussian Processes for Nonlinear Signal Processing
Gaussian Processes Nonlinear Signal Processing
2013/5/2
Gaussian processes (GPs) are versatile tools that have been successfully employed to solve nonlinear estimation problems in machine learning, but that are rarely used in signal processing. In this tut...
Bandlimited Signal Reconstruction From the Distribution of Unknown Sampling Locations
Bandlimited Signal Reconstruction the Distribution Unknown Sampling Locations
2013/4/28
We study the reconstruction of bandlimited fields from samples taken at unknown but statistically distributed sampling locations. The setup is motivated by distributed sampling where precise knowledge...
Inverse Signal Classification for Financial Instruments
time-series classification signal analysis decision tree learning
2013/4/28
The paper presents new machine learning methods: signal composition, which classifies time-series regardless of length, type, and quantity; and self-labeling, a supervised-learning enhancement. The pa...
Signal Recovery in Unions of Subspaces with Applications to Compressive Imaging
Union of Subspaces Group Sparsity Convex Optimization Structured Sparsity Compressed Sensing
2012/11/22
In applications ranging from communications to genetics, signals can be modeled as lying in a union of subspaces. Under this model, signal coefficients that lie in certain subspaces are active or inac...
Residual variance and the signal-to-noise ratio in high-dimensional linear models
Asymptoticnormality,high-dimensionaldataanalysis Poincar!a inequality randommatrices residualvariance signal-to-noiseratio
2012/11/21
Residual variance and the signal-to-noise ratio are important quantities in many statistical models and model fitting procedures. They play an important role in regression diagnostics, in determining ...
Re-Weighted l_1 Dynamic Filtering for Time-Varying Sparse Signal Estimation
Re-Weighted Dynamic Filtering Time-Varying Signal Estimation
2012/9/17
Signal estimation from incomplete observations improves as more signal structure can be exploited in the inference process. Classic algorithms (e.g., Kalman filtering) have exploited strong dynamic st...
Exploiting Correlation in Sparse Signal Recovery Problems: Multiple Measurement Vectors, Block Sparsity, and Time-Varying Sparsity
Multiple Measurement Vectors Block Sparsity Time-Varying Sparsity
2011/6/16
A trend in compressed sensing (CS) is to exploit struc-
ture for improved reconstruction performance. In the
basic CS model (i.e. the single measurement vec-
tor model), exploiting the clustering s...