Filtering algorithm

The first objective of Kalman filtering With a radar tracking implementation is to give deep enough insight into the mathematics of the Kalman filter algorithm to be able to choose the correct type of algorithm and to set all the parameters correctly in a basic application. This description also includes several examples of different approaches to derive and to explain the Kalman filter algorithm.
48p tieuluanhoangduy 15062015 27 6 Download

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article MeanSquare Performance Analysis of the Family of Selective Partial Update NLMS and Afﬁne Projection Adaptive Filter Algorithms in Nonstationary Environment
11p dauphong14 11022012 21 6 Download

Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí hóa hoc quốc tế đề tài : A family of variable stepsize affine projection adaptive filter algorithms using statistics of channel impulse response
15p dauphong11 06022012 18 3 Download

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Particle Filtering Algorithms for Tracking a Maneuvering Target Using a Network of Wireless Dynamic Sensors
16p dauphong20 10032012 14 2 Download

Suitable for a one or twosemester undergraduatelevel 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 continuoustime signals, and Fourier series and transforms. Giving students a sound balance of theory and practical application, this nononsense text presents the fundamental concepts and techniques of modern digital signal processing with related algorithms and applications.
1033p vantoan90 29052012 214 91 Download

The field of Digital Signal Processing has developed so fast in the last 3 decades that it can be found in the graduate and undergraduate programs of most universities. This development is related to the increasingly available technologies for implementing digital signal processing algorithms. The tremendous growth of development in the digital signal processing area has turned some of its specialized areas into fields themselves.
659p nguyenthai_thinh 14032013 36 11 Download

Adaptive filtering can be used to characterize unknown systems in timevariant environments. The main objective of this approach is to meet a difficult comprise: maximum convergence speed with maximum accuracy. Each application requires a certain approach which determines the filter structure, the cost function to minimize the estimation error, the adaptive algorithm, and other parameters; and each selection involves certain cost in computational terms, that in any case should consume less time than the time required by the application working in realtime....
162p japet75 25022013 31 7 Download

We present a novel extension to a recently proposed incremental learning algorithm for the word segmentation problem originally introduced in Goldwater (2006). By adding rejuvenation to a particle ﬁlter, we are able to considerably improve its performance, both in terms of ﬁnding higher probability and higher accuracy solutions.
5p nghetay_1 07042013 18 2 Download

Numerous crosslingual applications, including stateoftheart machine translation systems, require parallel texts aligned at the sentence level. However, collections of such texts are often polluted by pairs of texts that are comparable but not parallel. Bitext maps can help to discriminate between parallel and comparable texts. Bitext mapping algorithms use a larger set of document features than competing approaches to this task, resulting in higher accuracy. In addition, good bitext mapping algorithms are not limited to documents with structural markup such as web pages. ...
4p bunbo_1 17042013 18 2 Download

We explore learning prepositionalphrase attachment in Dutch, to use it as a filter in prosodic phrasing. From a syntactic treebank of spoken Dutch we extract instances of the attachment of prepositional phrases to either a governing verb or noun. Using crossvalidated parameter and feature selection, we train two learning algorithms, TB I and RIPPER, 011 making this distinction, based on unigram and bigram lexical features and a cooccurrence feature derived from WWW counts.
8p bunthai_1 06052013 19 1 Download

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 multiscale analysis.
308p lulanphuong 17032012 59 19 Download

The fact is your brain craves novelty. It's constantly searching, scanning, waiting for something unusual to happen. After all, that's the way it was built to help you stay alive. It takes all the routine, ordinary, dull stuff and filters it to the background so it won't interfere with your brain's real workrecording things that matter. How does your brain know what matters? It's like the creators of the Head First approach say, suppose you're out for a hike and a tiger jumps in front of you, what happens in your brain? Neurons fire. Emotions crank up. Chemicals surge. That's...
722p rose_12 06122012 93 24 Download

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
503p nhan4321 29102009 54 15 Download

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.
59p duongph05 08062010 61 14 Download

A threestage method for compressing bilevel linedrawing images is proposed. In the first stage, the raster image is vectorized using a combination of skeletonizing and line tracing algorithm. A feature image is then reconstructed from the extracted vector elements. In the second stage, the original image is processed by a featurebased filter for removing noise near the borders of the extracted line elements. This improves the image quality and results in more compressible raster image. In the final stage, the filtered raster image is compressed using the baseline JBIG algorithm....
6p kienk6e 31032011 55 14 Download

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...
45p duongph05 07062010 80 13 Download

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 highdimensional 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
13p duongph05 07062010 46 13 Download

MORE ON VOLTAGEPROCESSING TECHNIQUES 14.1 COMPARISON OF DIFFERENT VOLTAGE LEASTSQUARES ALGORITHM TECHNIQUES Table 14.11 gives a comparison for the computer requirements for the different voltage techniques discussed in the previous chapter. The comparison includes the computer requirements needed when using the normal equations given by (4.130) with the optimum leastsquares weight W given by (4.132). Table 14.
15p khinhkha 30072010 51 13 Download

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]....
60p duongph05 07062010 53 12 Download

BAYES ALGORITHM WITH ITERATIVE DIFFERENTIAL CORRECTION FOR NONLINEAR SYSTEMS 17.1 DETERMINATION OF UPDATED ESTIMATES We are now in a position to obtain the updated estimate for the nonlinear observation and target dynamic model cases [5, pp. 424–443]. We shall use the example of the ballistic projectile traveling through the atmosphere for deﬁniteness in our discussion. Assume that the past measurements have " permitted us to obtain the state vector estimate Xðt À Þ at the time t À , the last time observations were made on the target. As done at the end of Section 16.
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