Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
165,477
result(s) for
"Signal processing"
Sort by:
A Wavelet Tour of Signal Processing
Mallat's book is the undisputed reference in this field - it is the only one that covers the essential material in such breadth and depth. - Laurent Demanet, Stanford UniversityThe new edition of this classic book gives all the major concepts, techniques and applications of sparse representation, reflecting the key role the subject plays in today's signal processing. The book clearly presents the standard representations with Fourier, wavelet and time-frequency transforms, and the construction of orthogonal bases with fast algorithms. The central concept of sparsity is explained and applied to signal compression, noise reduction, and inverse problems, while coverage is given to sparse representations in redundant dictionaries, super-resolution and compressive sensing applications.Features:* Balances presentation of the mathematics with applications to signal processing* Algorithms and numerical examples are implemented in WaveLab, a MATLAB toolbox* Companion website for instructors and selected solutions and code available for studentsNew in this edition* Sparse signal representations in dictionaries* Compressive sensing, super-resolution and source separation* Geometric image processing with curvelets and bandlets* Wavelets for computer graphics with lifting on surfaces* Time-frequency audio processing and denoising* Image compression with JPEG-2000* New and updated exercisesA Wavelet Tour of Signal Processing: The Sparse Way, third edition, is an invaluable resource for researchers and R&D engineers wishing to apply the theory in fields such as image processing, video processing and compression, bio-sensing, medical imaging, machine vision and communications engineering.Stephane Mallat is Professor in Applied Mathematics at École Polytechnique, Paris, France. From 1986 to 1996 he was a Professor at the Courant Institute of Mathematical Sciences at New York University, and between 2001 and 2007, he co-founded and became CEO of an image processing semiconductor company.Companion website: A Numerical Tour of Signal Processing
Includes all the latest developments since the book was published in 1999, including itsapplication to JPEG 2000 and MPEG-4Algorithms and numerical examples are implemented in Wavelab, a MATLAB toolboxBalances presentation of the mathematics with applications to signal processing
eBook
A survey of machine learning for big data processing
There is no doubt that big data are now rapidly expanding in all science and engineering domains. While the potential of these massive data is undoubtedly significant, fully making sense of them requires new ways of thinking and novel learning techniques to address the various challenges. In this paper, we present a literature survey of the latest advances in researches on machine learning for big data processing. First, we review the machine learning techniques and highlight some promising learning methods in recent studies, such as representation learning, deep learning, distributed and parallel learning, transfer learning, active learning, and kernel-based learning. Next, we focus on the analysis and discussions about the challenges and possible solutions of machine learning for big data. Following that, we investigate the close connections of machine learning with signal processing techniques for big data processing. Finally, we outline several open issues and research trends.
Journal Article
Smart Antennas and Electromagnetic Signal Processing in Advanced Wireless Technology
The book addresses the current demand for a scientific approach to advanced wireless technology and its future developments, including the current move from 4G to 5G wireless systems (2020), and the future to 6G wireless systems (2030). It gives a clear and in-depth presentation of both antennas and the adaptive signal processing that makesantennas powerful, maneuverable, and necessary for advanced wirelesstechnology.
eBook
High-coherence mid-infrared dual-comb spectroscopy spanning 2.6 to 5.2 μm
Mid-infrared dual-comb spectroscopy has the potential to supplant conventional Fourier-transform spectroscopy in applications requiring high resolution, accuracy, signal-to-noise ratio and speed. Until now, mid-infrared dual-comb spectroscopy has been limited to narrow optical bandwidths or low signal-to-noise ratios. Using digital signal processing and broadband frequency conversion in waveguides, we demonstrate a mid-infrared dual-comb spectrometer covering 2.6 to 5.2 µm with comb-tooth resolution, sub-MHz frequency precision and accuracy, and a spectral signal-to-noise ratio as high as 6,500. As a demonstration, we measure the highly structured, broadband cross-section of propane from 2,840 to 3,040 cm−1, the complex phase/amplitude spectra of carbonyl sulfide from 2,000 to 2,100 cm−1, and of a methane, acetylene and ethane mixture from 2,860 to 3,400 cm−1. The combination of broad bandwidth, comb-mode resolution and high brightness will enable accurate mid-infrared spectroscopy in precision laboratory experiments and non-laboratory applications including open-path atmospheric gas sensing, process monitoring and combustion.
Journal Article
A survey of Monte Carlo methods for parameter estimation
Statistical signal processing applications usually require the estimation of some parameters of interest given a set of observed data. These estimates are typically obtained either by solving a multi-variate optimization problem, as in the maximum likelihood (ML) or maximum a posteriori (MAP) estimators, or by performing a multi-dimensional integration, as in the minimum mean squared error (MMSE) estimators. Unfortunately, analytical expressions for these estimators cannot be found in most real-world applications, and the Monte Carlo (MC) methodology is one feasible approach. MC methods proceed by drawing random samples, either from the desired distribution or from a simpler one, and using them to compute consistent estimators. The most important families of MC algorithms are the Markov chain MC (MCMC) and importance sampling (IS). On the one hand, MCMC methods draw samples from a proposal density, building then an ergodic Markov chain whose stationary distribution is the desired distribution by accepting or rejecting those candidate samples as the new state of the chain. On the other hand, IS techniques draw samples from a simple proposal density and then assign them suitable weights that measure their quality in some appropriate way. In this paper, we perform a thorough review of MC methods for the estimation of static parameters in signal processing applications. A historical note on the development of MC schemes is also provided, followed by the basic MC method and a brief description of the rejection sampling (RS) algorithm, as well as three sections describing many of the most relevant MCMC and IS algorithms, and their combined use. Finally, five numerical examples (including the estimation of the parameters of a chaotic system, a localization problem in wireless sensor networks and a spectral analysis application) are provided in order to demonstrate the performance of the described approaches.
Journal Article