Topic
Root-raised-cosine filter
About: Root-raised-cosine filter is a research topic. Over the lifetime, 7947 publications have been published within this topic receiving 118689 citations.
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TL;DR: In this paper, a maximum likelihood sequence estimator for a digital pulse-amplitude-modulated sequence in the presence of finite intersymbol interference and white Gaussian noise is developed, which comprises a sampled linear filter, called a whitened matched filter, and a recursive nonlinear processor, called the Viterbi algorithm.
Abstract: A maximum-likelihood sequence estimator for a digital pulse-amplitude-modulated sequence in the presence of finite intersymbol interference and white Gaussian noise is developed, The structure comprises a sampled linear filter, called a whitened matched filter, and a recursive nonlinear processor, called the Viterbi algorithm. The outputs of the whitened matched filter, sampled once for each input symbol, are shown to form a set of sufficient statistics for estimation of the input sequence, a fact that makes obvious some earlier results on optimum linear processors. The Viterbi algorithm is easier to implement than earlier optimum nonlinear processors and its performance can be straightforwardly and accurately estimated. It is shown that performance (by whatever criterion) is effectively as good as could be attained by any receiver structure and in many cases is as good as if intersymbol interference were absent. Finally, a simplified but effectively optimum algorithm suitable for the most popular partial-response schemes is described.
2,667 citations
TL;DR: Modifications to the fitting procedure are described which allow more accurate derivations of filter shapes derived from data where the notch is always placed symmetrically about the signal frequency and when the underlying filter is markedly asymmetric.
2,456 citations
05 Sep 2010
TL;DR: The guided filter is demonstrated that it is both effective and efficient in a great variety of computer vision and computer graphics applications including noise reduction, detail smoothing/enhancement, HDR compression, image matting/feathering, haze removal, and joint upsampling.
Abstract: In this paper, we propose a novel type of explicit image filter - guided filter. Derived from a local linear model, the guided filter generates the filtering output by considering the content of a guidance image, which can be the input image itself or another different image. The guided filter can perform as an edge-preserving smoothing operator like the popular bilateral filter [1], but has better behavior near the edges. It also has a theoretical connection with the matting Laplacian matrix [2], so is a more generic concept than a smoothing operator and can better utilize the structures in the guidance image. Moreover, the guided filter has a fast and non-approximate linear-time algorithm, whose computational complexity is independent of the filtering kernel size. We demonstrate that the guided filter is both effective and efficient in a great variety of computer vision and computer graphics applications including noise reduction, detail smoothing/enhancement, HDR compression, image matting/feathering, haze removal, and joint upsampling.
2,215 citations
TL;DR: In this paper, it is shown that modulo arithmetic may be used in the inverse filter to eliminate completely the possibility of instability, and a very simple automatic or adaptive equalisation system is presented.
Abstract: The limitations of present automatic and adaptive equalisers stem from the use of feedforward transversal filters. These drawbacks may be obviated by using a feedback transversal filter, the inverse filter, but this is only suitable for limited use since it can be an unstable circuit. It is shown that modulo arithmetic may be used in the inverse filter to eliminate completely the possibility of instability, and a very simple automatic or adaptive equalisation system is presented. Some interesting properties of the modulo inverse filter are included.
1,035 citations