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Independent Component Analysis - Chapter 20: Other Extensions
In this chapter, we present some additional extensions of the basic independent component analysis (ICA) model. First, we discuss the use of prior information on the mixing matrix, especially on its sparseness. Second, we present models that somewhat relax the assumption of the independence of the components.
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Independent Component Analysis - Chapter 21: Feature Extraction by ICA
A fundamental approach in signal processing is to design a statistical generative model of the observed signals. The components in the generative model then give a representation of the data. Such a representation can then be used in such tasks as compression, denoising, and pattern recognition. This approach is also useful from a neuroscientific viewpoint, for modeling the properties of neurons in primary sensory areas. In this chapter, we consider a certain class of widely used signals, which we call natural images....
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Independent Component Analysis - Chapter 22: Brain Imaging Applications
With the advent of new anatomical and functional brain imaging methods, it is now possible to collect vast amounts of data from the living human brain. It has thus become very important to extract the essential features from the data to allow an easier representation or interpretation of their properties. This is a very promising area of application for independent component analysis (ICA). Not only is this an area of rapid growth and great importance; some kinds of brain imaging data also seem to be quite well described by the ICA model. ...
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Independent Component Analysis - Chapter 23: Telecommunications
This chapter deals with applications of independent component analysis (ICA) and blind source separation (BSS) methods to telecommunications. In the following, we concentrate on code division multiple access (CDMA) techniques, because this specific branch of telecommunications provides several possibilities for applying ICA and BSS in a meaningful way.
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Independent Component Analysis - Chapter 24: Other Applications
It is tempting to try ICA on financial data. There are many situations in which parallel financial time series are available, such as currency exchange rates or daily returns of stocks, that may have some common underlying factors. ICA might reveal some driving mechanisms that otherwise remain hidden. In a study of a stock portfolio [22], it was found that ICA is a complementary tool to principal component analysis (PCA), allowing the underlying structure of the data to be more readily observed....
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Independent Component Analysis - Contents
Tham khảo sách 'independent component analysis - contents', kỹ thuật - công nghệ, kĩ thuật viễn thông phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả
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Real-Time Digital Signal Processing - Appendix B: Introduction of MATLAB for DSP Applications
MATLAB (MATrix LABoratory) is an interactive technical computing environment for scientific and engineering applications. It integrates numerical analysis, matrix computation, signal processing, and graphics in an easy-to-use environment. By using its relatively simple programming capability, MATLAB can be easily extended to create new functions. MATLAB is further enhanced by numerous toolboxes such as the Signal Processing Toolbox.
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Real-Time Digital Signal Processing - Appendix D: About the Software
Tham khảo sách 'real-time digital signal processing - appendix d: about the software', kỹ thuật - công nghệ, kĩ thuật viễn thông phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả
8/29/2018 5:39:35 PM +00:00
Real-Time Digital Signal Processing - Appendix A: Some Useful Formulas
Tham khảo sách 'real-time digital signal processing - appendix a: some useful formulas', kỹ thuật - công nghệ, kĩ thuật viễn thông phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả
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Real-Time Digital Signal Processing - Chapter 1: Introduction to Real-Time Digital Signal Processing
Signals can be divided into three categories ± continuous-time (analog) signals, discrete-time signals, and digital signals. The signals that we encounter daily are mostly analog signals. These signals are defined continuously in time, have an infinite range of amplitude values, and can be processed using electrical devices containing both active and passive circuit elements. Discrete-time signals are defined only at a particular set of time instances. Therefore they can be represented as a sequence of numbers that have a continuous range of values. ...
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Real-Time Digital Signal Processing - Chapter 2: Introduction to TMS320C55x Digital Signal Processor
Digital signal processors with architecture and instructions specifically designed for DSP applications have been launched by Texas Instruments, Motorola, Lucent Technologies, Analog Devices, and many other companies. DSP processors are widely used in areas such as communications, speech processing, image processing, biomedical devices and equipment, power electronics, automotive, industrial electronics, digital instruments, consumer electronics, multimedia systems, and home appliances.
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Real-Time Digital Signal Processing - Chapter 3: DSP Fundamentals and Implementation Considerations
The derivation of discrete-time systems is based on the assumption that the signal and system parameters have infinite precision. However, most digital systems, filters, and algorithms are implemented on digital hardware with finite wordlength. Therefore DSP implementation with fixed-point hardware requires special attention because of the potential quantization and arithmetic errors, as well as the possibility of overflow.
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Real-Time Digital Signal Processing - Chapter 4: Frequency Analysis
Frequency analysis of any given signal involves the transformation of a time-domain signal into its frequency components. The need for describing a signal in the frequency domain exists because signal processing is generally accomplished using systems that are described in terms of frequency response. Converting the time-domain signals and systems into the frequency domain is extremely helpful in understanding the characteristics of both signals and systems. In Section 4.1, the Fourier series and Fourier transform will be introduced....
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Real-Time Digital Signal Processing - Chapter 5: Design and Implementation of FIR Filters
A filter is a system that is designed to alter the spectral content of input signals in a specified manner. Common filtering objectives include improving signal quality, extracting information from signals, or separating signal components that have been previously combined. A digital filter is a mathematical algorithm implemented in hardware, firmware, and/or software that operates on a digital input signal to produce a digital output signal for achieving filtering objectives.
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Real-Time Digital Signal Processing - Chapter 6: Design and Implementation of IIR Filters
We have discussed the design and implementation of digital FIR filters in the previous chapter. In this chapter, our attention will be focused on the design, realization, and implementation of digital IIR filters. The design of IIR filters is to determine the transfer function H(z) that satisfies the given specifications. We will discuss the basic characteristics of digital IIR filters, and familiarize ourselves with the fundamental techniques used for the design and implementation of these filters....
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Real-Time Digital Signal Processing - Chapter 7: Fast Fourier Transform and Its Applications
Frequency analysis of digital signals and systems was discussed in Chapter 4. To perform frequency analysis on a discrete-time signal, we converted the time-domain sequence into the frequency-domain representation using the z-transform, the discrete-time Fourier transform (DTFT), or the discrete Fourier transform (DFT). The widespread application of the DFT to spectral analysis, fast convolution, and data transmission is due to the development of the fast Fourier transform (FFT) algorithm for its computation....
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Real-Time Digital Signal Processing - Chapter 8: Adaptive Filtering
As discussed in previous chapters, filtering refers to the linear process designed to alter the spectral content of an input signal in a specified manner. In Chapters 5 and 6, we introduced techniques for designing and implementing FIR and IIR filters for given specifications. Conventional FIR and IIR filters are time-invariant. They perform linear operations on an input signal to generate an output signal based on the fixed coefficients.
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Real-Time Digital Signal Processing - Chapter 9: Practical DSP Applications in Communications
There are many DSP applications that are used in our daily lives, some of which have been introduced in previous chapters. DSP algorithms, such as random number generation, tone generation and detection, echo cancellation, channel equalization, noise reduction, speech and image coding, and many others can be found in a variety of communication systems. In this chapter, we will introduce some selected DSP applications in communications that played an important role in the realization of the systems. ...
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Real-Time Digital Signal Processing
Adaptive filters are time varying, filter characteristics such as bandwidth and frequency response change with time. Thus the filter coefficients cannot be determined when the filter is implemented. The coefficients of the adaptive filter are adjusted automatically by an adaptive algorithm based on incoming signals. This has the important effect of enabling adaptive filters
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Technology White Paper
This paper presents issues to be considered when designing multi-carrier WCDMA basestations. Two topics will be the main focus of this discussion; the power amplifier linearization and the peak-to-average power reduction of a multi-carrier WCDMA signal, both of which are important for efficient operation of wideband power amplifiers and cost-effective design of the overall base station. WCDMA signal characterization, technology selection, linearization, and peak reduction methods are discussed.
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Pocket Guide for Fundamentals and GSM Testing
Nowadays, when we speak of GSM, we usually mean ªoriginalº GSM ± also known as GSM900 since 900 MHz was the original frequency band. To provide additional capacity and enable higher subscriber densities, two other systems were added later: GSM1800 (also DCS1800) and GSM1900 (also PCS 900).
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Kalman Filtering and Neural Networks - Chapter 1: KALMAN FILTERS
The celebrated Kalman filter, rooted in the state-space formulation of linear dynamical systems, provides a recursive solution to the linear optimal filtering problem. It applies to stationary as well as nonstationary environments. The solution is recursive in that each updated estimate of the state is computed from the previous estimate and the new input data, so only the previous estimate requires storage.
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Kalman Filtering and Neural Networks - Chapter 2: PARAMETER-BASED KALMAN FILTER TRAINING: THEORY AND IMPLEMENTATION
Although the rediscovery in the mid 1980s of the backpropagation algorithm by Rumelhart, Hinton, and Williams [1] has long been viewed as a landmark event in the history of neural network computing and has led to a sustained resurgence of activity, the relative ineffectiveness of this simple gradient method has motivated many researchers to develop enhanced training procedures. In fact, the neural network literature has been inundated with papers proposing alternative training Kalman Filtering and Neural Networks...
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Kalman Filtering and Neural Networks - Chapter 3: LEARNING SHAPE AND MOTION FROM IMAGE SEQUENCES
In Chapter 2, Puskorius and Feldkamp described a procedure for the supervised training of a recurrent multilayer perceptron – the nodedecoupled extended Kalman filter (NDEKF) algorithm. We now use this model to deal with high-dimensional signals: moving visual images. Many complexities arise in visual processing that are not present in onedimensional prediction problems: the scene may be cluttered with backKalman Filtering and Neural Network
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Kalman Filtering and Neural Networks - Chapter 4: CHAOTIC DYNAMICS
In this chapter, we consider another application of the extended Kalman filter recurrent multilayer perceptron (EKF-RMLP) scheme: the modeling of a chaotic time series or one that could be potentially chaotic. The generation of a chaotic process is governed by a coupled set of nonlinear differential or difference equations.
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Kalman Filtering and Neural Networks - Chapter 5: DUAL EXTENDED KALMAN FILTER METHODS
The Extended Kalman Filter (EKF) provides an efficient method for generating approximate maximum-likelihood estimates of the state of a discrete-time nonlinear dynamical system (see Chapter 1). The filter involves a recursive procedure to optimally combine noisy observations with predictions from the known dynamic model. A second use of the EKF involves estimating the parameters of a model (e.g., neural network) given clean training data of input and output data (see Chapter 2).
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Kalman Filtering and Neural Networks - Chapter 6: LEARNING NONLINEAR DYNAMICAL SYSTEMS USING THE EXPECTATION– MAXIMIZATION ALGORITHM
LEARNING STOCHASTIC NONLINEAR DYNAMICS Since the advent of cybernetics, dynamical systems have been an important modeling tool in fields ranging from engineering to the physical and social sciences. Most realistic dynamical systems models have two essential features. First, they are stochastic – the observed outputs are a noisy function of the inputs, and the dynamics itself may be driven by some unobserved noise process.
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Kalman Filtering and Neural Networks - Chapter VII: THE UNSCENTED KALMAN FILTER
In this book, the extended Kalman filter (EKF) has been used as the standard technique for performing recursive nonlinear estimation. The EKF algorithm, however, provides only an approximation to optimal nonlinear estimation. In this chapter, we point out the underlying assumptions and flaws in the EKF, and present an alternative filter with performance superior to that of the EKF. This algorithm, referred to as the unscented Kalman filter (UKF), was first proposed by Julier et al. [1–3], and further developed by Wan and van der Merwe [4–7]....
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Kalman Filtering and Neural Networks - Contents
Designations used by companies to distinguish their products are often claimed as trademarks. In all instances where John Wiley & Sons, Inc., is aware of a claim, the product names appear in initial capital or ALL CAPITAL LETTERS. Readers, however, should contact the appropriate companies for more complete information regarding trademarks and registration. Copyright 2001 by John Wiley & Sons, Inc.. All rights reserved.
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Kỹ thuật số - Hệ tổ hợp
Các phần tử logic AND, OR, NOR, NAND là các phần tử logic cơ bản còn được gọi là hệ tổ hợp đơn giản. Như vậy, ta có các hệ thống tổ hợp mà ngõ ra là các thay đổi trạng thái thì lập tức làm cho ngõ ra thay đổi trạng thái ngay (bỏ qua thời gian trễ của các phần tử logic)
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