搜索结果: 1-12 共查到“应用数学 signal”相关记录12条 . 查询时间(0.169 秒)
GENERALIZED SPARSE SIGNAL MIXING MODEL AND APPLICATION TO NOISY BLIND SOURCE SEPARATION
GENERALIZED SPARSE SIGNAL MIXING MODEL NOISY BLIND SOURCE SEPARATION
2015/9/29
Sparse constraints on signal decompositions are justified bytypical sensor data used in a variety of signal processing fields such as acoustics, medical imaging, or wireless, but moreover can lead to ...
Comparison of Wavelet and FFT Based Single Channel Speech Signal Noise Reduction Techniques
DWT DWPT wavelet wavelet packet FFT noise control speech enhancement noise cancellation filter
2015/9/29
This paper compares wavelet and short time Fourier transform based techniques for single channel speechsignal noise reduction. Despite success of wavelet denoising of images, it has not yet been widel...
Statistical Signal Processing for Novelty Detection
Support Vector Machines Health condition monitoring Novelty detection and Machine learning methods
2015/9/29
The goal of this article is to investigate and suggest techniques for health condition monitoring and diagnosis using machine learning from sensor data. In particular, this article overview and discus...
Fast Algorithms for Signal Reconstruction without Phase
Frames mutually unbiased bases equiangular frames projective 2-designs discrete chirps
2015/9/29
We derive fast algorithms for doing signal reconstruction without phase. This type of problem is important insignal processing, especially speech recognition technology, and has relevance for state to...
Extensions of No-Go Theorems to Many Signal Systems.
On signal reconstruction without phase
Frame Signal reconstruction Phase Speech recognition
2015/9/29
We will construct new classes of Parseval frames for a Hilbert space which allow signal reconstruction from theabsolute value of the frame coefficients. As a consequence, signal reconstruction can be ...
Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information
Random matrices free probability sparsity trigonometric expansions uncertainty principle convex optimization duality in optimization total-variation minimization image reconstruction linear programming
2015/6/17
This paper considers the model problem of reconstructing an object from incomplete frequency samples. Consider a discrete-time signal f ∈ CN and a randomly chosen set of frequencies Ω. Is it pos...
Noncoherent Interference Alignment: Trade Signal Power for Diversity Towards Multiplexing
Noncoherent Interference Alignment Trade Signal Power Diversity Multiplexing
2010/12/13
This paper proposes the first known universal interference alignment scheme for general (1 × 1)K interference networks, either Gaussian or deterministic, with only 2 symbol extension.
A* Orthogonal Matching Pursuit: Best-First Search for Compressed Sensing Signal Recovery
compressed sensing best-first search A* search matching pursuit sparse representations sparse signal
2010/11/29
Compressed sensing is a recently developing area which is interested in reconstruction of sparse signals acquired in reduced dimensions. Acquiring the data with a small number of samples makes the rec...
Identification of a two-dimensional autoregressive oving average model for image signal processing
Two-dimensional ARMA Model Identification
2010/9/14
A two-dimensional (2D) autoregressive-moving average (ARMA) model for image processing has been developed. An optimum estimation and prediction approach based on this modelling has been simulated. The...
Note on sparsity in signal recovery and in matrix identification
Sparse signal recovery compressed sensing Basis Pursuit time-frequency shifts
2008/11/10
We describe a connection between the identi cation problem for matrices with sparse representations in given matrix dictionaries and the problem of sparse signal recovery. This allows the application ...
New performance bounds in signal parameter estimation under mismatch
discretization multihypothesis Gaussian process parameter
2010/9/15
In this paper we develop a new upper bound for the mean square estimation error of a parameter that takes values on a bounded interval. The bound is based on the discretization of the region into a fi...