Search results for "wavelet."
showing 7 items of 327 documents
The Radon-Wigner Transform and Its Application to First-order Optical Systems
2009
The Radon-Wigner transform is presented as a tool for the description of 1st-order optical systems. The input/output relationships for this phase-space representation are obtained and their application in analysis and design tasks is pointed out.
Computing variations of entropy and redundancy under nonlinear mappings not preserving the signal dimension: quantifying the efficiency of V1 cortex
2021
In computational neuroscience, the Efficient Coding Hypothesis argues that the neural organization comes from the optimization of information-theoretic goals [Barlow Proc.Nat.Phys.Lab.59]. A way to confirm this requires the analysis of the statistical performance of biological systems that have not been statistically optimized [Renart et al. Science10, Malo&Laparra Neur.Comp.10, Foster JOSA18, Gomez-Villa&Malo J.Neurophysiol.19]. However, when analyzing the information-theoretic performance, cortical magnification in the retina-cortex pathway poses a theoretical problem. Cortical magnification stands for the increase the signal dimensionality [Cowey&Rolls Exp. Brain Res.74]. Conventional mo…
Acoustic detection and classification of river boats
2011
We present a robust algorithm to detect the arrival of a boat of a certain type when other background noises are present. It is done via the analysis of its acoustic signature against an existing database of recorded and processed acoustic signals. We characterize the signals by the distribution of their energies among blocks of wavelet packet coefficients. To derive the acoustic signature of the boat of interest, we use the Best Discriminant Basis method. The decision is made by combining the answers from the Linear Discriminant Analysis (LDA) classifier and from the Classification and Regression Trees (CART) that is also accompanied with an additional unit, called Aisles, that reduces fal…
Curvelet-based method for orientation estimation of particles
2013
A method based on the curvelet transform is introduced for estimating from two-dimensional images the orientation distribution of small anisotropic particles. Orientation of fibers in paper is considered as a particular application of the method. Theoretical aspects of the suitability of this method are discussed and its efficiency is demonstrated with simulated and real images of fibrous systems. Comparison is made with two traditionally used methods of orientation analysis, and the new curvelet-based method is shown to perform clearly better than these traditional methods.
Multi-domain Features of the Non-phase-locked Component of Interest Extracted from ERP Data by Tensor Decomposition
2020
The waveform in the time domain, spectrum in the frequency domain, and topography in the space domain of component(s) of interest are the fundamental indices in neuroscience research. Despite the application of time–frequency analysis (TFA) to extract the temporal and spectral characteristics of non-phase-locked component (NPLC) of interest simultaneously, the statistical results are not always expectedly satisfying, in that the spatial information is not considered. Complex Morlet wavelet transform is widely applied to TFA of event-related-potential (ERP) data, and mother wavelet (which should be firstly defined by center frequency and bandwidth (CFBW) before using the method to TFA of ERP…
A new approach for geological faults detection
2004
This paper will introduce a new approach, mainly based on wavelets transform and mathematical morphology, for the detection of geological faults in satellite and aerial photos. This can be useful for territorial analysis, digital cartography and the use of geographical information systems. The proposed method is fast enough and unsupervised. Different techniques will be compared and some examples on real data will be presented to indicate the applicability of these methods.
Anomaly detection using one-class SVM with wavelet packet decomposition
2011
Anomaly detection has become a popular research topic in the field of machine learning. Support vector machine is one anomaly detection technique and it is coming one the most widely used. In this research, anomaly detection is applied to road condition monitoring, especially pothole detection, using accelerometer data. The proposed concept includes data preprocessing, feature extraction, feature selection and classification. Accelerometer data was first filtered and segmented, after which features were extracted with frequency- and time-domain functions, with genetic programming and with wavelet packet decomposition. A classification model was built using support vector machine and the cal…