Ten lectures on wavelets
Adapted wave form analysis, wavelet-packets and applications
ICIAM 91 Proceedings of the second international conference on Industrial and applied mathematics
Adapted wavelet analysis from theory to software
Adapted wavelet analysis from theory to software
Analysis of Acoustic Signatures from Moving Vehicles UsingTime-Varying Autoregressive Models
Multidimensional Systems and Signal Processing
A Wavelet Packet Algorithm for Classification and Detectionof Moving Vehicles
Multidimensional Systems and Signal Processing
Extensions of compressed sensing
Signal Processing - Sparse approximations in signal and image processing
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
A diffusion framework for detection of moving vehicles
Digital Signal Processing
Block Based Deconvolution Algorithm Using Spline Wavelet Packets
Journal of Mathematical Imaging and Vision
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We propose a robust algorithm to detect the arrival of a vehicle of arbitrary type when other noises are present. It is done via analysis of its acoustic signature against an existing database of recorded and processed acoustic signals to detect the arrival of a vehicle of arbitrary type when other noises are present. To achieve it with minimum number of false alarms, we combine a construction of a training database of acoustic signatures signals emitted by vehicles using the distribution of the energies among blocks of wavelet packet coefficients with a procedure of random search for a near-optimal footprint. The number of false alarms in the detection is minimized even under severe conditions such as: the signals emitted by vehicles of different types differ from each other, whereas the set of non-vehicle recordings (the training database) contains signals emitted by planes, helicopters, wind, speech, steps, etc. The proposed algorithm is robust even when the tested conditions are completely different from the conditions where the training signals were recorded. The proposed technique has many algorithmic variations. For example, it can be used to distinguish among different types of vehicles. The proposed algorithm is a generic solution for process control that is based on a learning phase (training) followed by an automatic real-time detection.