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.
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 Mean-Square Performance Analysis of the Family of Selective Partial Update NLMS and Afﬁne Projection Adaptive Filter Algorithms in Nonstationary Environment
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 step-size affine projection adaptive filter algorithms using statistics of channel impulse response
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
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.
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.
Adaptive filtering can be used to characterize unknown systems in time-variant 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 real-time....
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.
Numerous cross-lingual applications, including state-of-the-art 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 mark-up such as web pages. ...
Hardware-address filtering presents about Privacy, Receive address filtering, Our new ‘nic.c’ module, The ‘sendto’ algorithm, Notes on library functions, Driver’s ‘ioctl()’ function, A change in memory-usage, Receive-filter Array.
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 cross-validated 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.
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 work--recording 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...
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
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
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.
A three-stage method for compressing bi-level line-drawing 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 feature-based 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....
Although the rediscovery in the mid 1980s of the backpropagation algorithm by Rumelhart, Hinton, and Williams  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...
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
MORE ON VOLTAGE-PROCESSING TECHNIQUES
14.1 COMPARISON OF DIFFERENT VOLTAGE LEAST-SQUARES ALGORITHM TECHNIQUES Table 14.1-1 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 (4.1-30) with the optimum least-squares weight W given by (4.1-32). Table 14.
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]....