Xem 1-20 trên 161 kết quả Signals transformation
  • Suitable for a one- or two-semester undergraduate-level electrical engineering, computer engineering, and computer science course in Discrete Systems and Digital Signal Processing. Assumes some prior knowledge of advanced calculus, linear systems for continuous-time signals, and Fourier series and transforms. Giving students a sound balance of theory and practical application, this no-nonsense text presents the fundamental concepts and techniques of modern digital signal processing with related algorithms and applications.

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  • The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octavescale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The DWT algorithms were initially based on the compactly supported conjugate quadrature filters (CQFs). However, a drawback in CQFs is due to the nonlinear phase effects such as spatial dislocations in multi-scale analysis.

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  • Discrete wavelet transform (DWT) algorithms have become standard tools for discrete-time signal and image processing in several areas in research and industry. As DWT provides both frequency and location information of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Theory and Applications describes the latest progress in DWT analysis in non-stationary signal processing, multi-scale image enhancement as well as in biomedical and industrial applications....

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  • Digital Signal Processing (DSP) is formally defined as a digital operation performed on an input sequence of numbers (including feedback from the result of the digital operation). The sequence of numbers can represent anything from digitised human speech to stock price data, processed to detect hidden periodicities or pattern

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  • The publication of the Cooley-Tukey fast Fourier transform (FFT) algorithm in 1965 has opened a new area in digital signal processing by reducing the order of complexity of some crucial computational tasks like Fourier transform and convultion from N 2 to N log 2 , where N is the problem size. The development of the major algorithms (Cooley-Tukey and split-radix FFT, prime factor algorithm and Winograd fast Fourier transform) is reviewed. Then, an attempt is made to indicate the state of the art on the subject, showin the standing of researh, open problems and implementations....

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  • The Fourier transform and its extensions have historically been the prime vehi- cle for signal analysis and representation. Since the early 1970s, block transforms with real basis functions, particularly the discrete cosine transform (DCT), have been studied extensively for transform coding applications. The availability of simple fast transform algorithms and good signal coding performance made the DCT the standard signal decomposition technique, particularly for image and video. The international standard image-video coding algorithms, i.e., CCITT...

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  • This book aims to provide information about Fourier transform to those needing to use infrared spectroscopy, by explaining the fundamental aspects of the Fourier transform, and techniques for analyzing infrared data obtained for a wide number of materials. It summarizes the theory, instrumentation, methodology, techniques and application of FTIR spectroscopy, and improves the performance and quality of FTIR spectrophotometers.

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  • Cells respond to environmental cues through a complex and dynamic network of signaling pathways that normally maintain a critical balance between cellular proliferation, differentiation, senescence, and death. One current research challenge is to identify those aberrations in signal transduction that directly contribute to a loss of this division-limited equilibrium and the progression to malignant transformation. The study of cell-signaling molecules in this context is a central component of cancer research.

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  • Discrete Wavelet Transform is a wavelet (DWT) transform that is widely used in numerical and functional analysis. Its key advantage over more traditional transforms, such as the Fourier transform, lies in its ability to offer temporal resolution, i.e. it captures both frequency and location (or time) information.

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  • (BQ) Ebook Signals, Systems, and Transforms is intended to be used primarily as a text for junior-level students in engineering curricula and for self-study by practicing engineers. It assumed that the reader has had some introduction to signal models, system models, and differ-ential equations (as in, for example, circuits courses and courses in mathematics), and some laboratory work with physical systems.

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  • The reader is provided with information on how to choose between the techniques and how to design a system that takes advantage of the best features of each of them. Imminently practical in approach, the book covers sampled data systems, choosing A-to-D and D-to-A converters for DSP applications, fast Fourier transforms, digital filters, selecting DSP hardware, interfacing to DSP chips, and hardware design techniques. It contains a number of application designs with thorough explanations.

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  • After studying this chapter you will be able to: Understand how to convert the analog to digital signal, have a thorough grasp of signal processing in linear time-invariant systems, understand the z-transform and Fourier transforms in analyzing the signal and systems, be able to design and implement FIR and IIR filters.

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  • Chapter 5 introduce z-Transform. In this chapter, you learned to: The z-transform, properties of the z-transform, causality and stability, inverse z-transform.

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  • After studying this chapter students will be able to understand frequency analysis of signals and systems. This chapter includes content: Discrete time fourier transform DTFT, discrete fourier transform DFT, fast fourier transform FFT.

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  • Lecture Digital signal processing - Lecture 1 introduction, discrete-time signals and systems. After completing this chapter, students will be able: To make the students able to apply digital filters according to known filter specifications, to provide the knowledge about the principles behind the discrete Fourier transform (DFT) and its fast computation,...

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  • In this lecture you will learn: System impulse response, linear constant-coefficient difference equations, fourier transforms and frequency response. Inviting you refer.

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  • Chapter 3 introduce the z-transform. This chapter presents the following content: z-transform, properties of the ROC, inverse z-transform, properties of z-transform.

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  • Lecture Digital signal processing - Lecture 7 presents the following content: Filter design, IIR filter design, analog filter design, IIR filter design by impulse invariance, IIR filter design by bilinear transformation.

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  • Lecture Digital signal processing - Lecture 4 introduce the discrete fourier transform. This lesson presents the following content: The discrete Fourier series, the Fourier transform of periodic signals, sampling the Fourier transform, the discrete Fourier transform, properties of the DFT, linear convolution using the DFT.

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  • The main contents of this chapter include all of the following: Direct computation of the DFT, decimation-in-time FFT algorithms, decimation-in-frequency FFT algorithms, fourier analysis of signals using the DFT.

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