Signal

About: Signal is a research topic. Over the lifetime, 674211 publications have been published within this topic receiving 4570168 citations. The topic is also known as: electric signal.

Statistical Digital Signal Processing and Modeling

Abstract: From the Publisher: The main thrust is to provide students with a solid understanding of a number of important and related advanced topics in digital signal processing such as Wiener filters, power spectrum estimation, signal modeling and adaptive filtering. Scores of worked examples illustrate fine points, compare techniques and algorithms and facilitate comprehension of fundamental concepts. Also features an abundance of interesting and challenging problems at the end of every chapter.

Time-Frequency Analysis

Abstract: Conventionally, time series have been studied either in the time domain or the frequency domain. The representation of a signal in the time domain is localized in time, i.e. the value of the signal at each instant in time is well defined. However, the time representation of a signal is poorly localized in frequency, i.e. little information about the frequency content of the signal at a certain frequency can be known by looking at the signal in the time domain. On the other hand, the representation of a signal in the frequency domain is well localized in frequency, but is poorly localized in time, and as a consequence it is impossible to tell when certain events occurred in time.

Theory of the photoacoustic effect with solids

Abstract: When chopped light impinges on a solid in an enclosed cell, an acoustic signal is produced within the cell. This effect is the basis of a new spectroscopic technique for the study of solid and semisolid matter. A quantitative derivation is presented for the acoustic signal in a photoacoustic cell in terms of the optical, thermal, and geometric parameters of the system. The theory predicts the dependence of the signal on the absorption coefficient of the solid, thereby giving a theoretical foundation for the technique of photoacoustic spectroscopy. In particular, the theory accounts for the experimental observation that with this technique optical absorption spectra can be obtained for materials that are optically opaque.

Simultaneous acquisition of spatial harmonics (SMASH): ultra-fast imaging with radiofrequency coil arrays

Abstract: A magnetic resonance (MR) imaging apparatus and technique exploits spatial information inherent in a surface coil array to increase MR image acquisition speed, resolution and/or field of view. Partial signals are acquired simultaneously in the component coils of the array and formed into two or more signals corresponding to orthogonal spatial representations. In a Fourier embodiment, lines of the k-space matrix required for image production are formed using a set of separate, preferably linear combinations of the component coil signals to substitute for spatial modulations normally produced by phase encoding gradients. The signal combining may proceed in a parallel or flow-through fashion, or as post-processing, which in either case reduces the need for time-consuming gradient switching and expensive fast magnet arrangements. In the post-processing approach, stored signals are combined after the fact to yield the full data matrix. In the flow-through approach, a plug-in unit consisting of a coil array with an on board processor outputs two or more sets of combined spatial signals for each spin conditioning cycle, each directly corresponding to a distinct line in k-space. This partially parallel imaging strategy, dubbed SiMultaneous Acquisition of Spatial Harmonics (SMASH), is readily integrated with many existing fast imaging sequences, yielding multiplicative time savings without a significant sacrifice in spatial resolution or signal-to-noise ratio. An experimental system achieved two-fold improvement in image acquisition time with a prototype three-coil array, and larger factors are achievable with ther coil arrangements.

Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing

Abstract: This textbook introduces sparse and redundant representations with a focus on applications in signal and image processing The theoretical and numerical foundations are tackled before the applications are discussed Mathematical modeling for signal sources is discussed along with how to use the proper model for tasks such as denoising, restoration, separation, interpolation and extrapolation, compression, sampling, analysis and synthesis, detection, recognition, and more The presentation is elegant and engaging Sparse and Redundant Representations is intended for graduate students in applied mathematics and electrical engineering, as well as applied mathematicians, engineers, and researchers who are active in the fields of signal and image processing