Some algorithms of variable step size LMS adaptive filtering are studied．The VS—LMS algorithm is improved．
Another new non-Linear function between肛and e(／ t)is established．The theoretic analysis and computer
simulation results show that this algorithm converges more quickly than the origina1．Furthermore，better antinoise
property is exhibited under Low—SNR environment than the original one．
In 1960, R.E. Kalman published his famous paper describing a recursive solution
to the discrete-data Linear filtering problem. Since that time, due in large part to advances
in digital computing, the Kalman filter has been the subject of extensive research
and application, particularly in the area of autonomous or assisted
navigation.
In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discretedata
Linear filtering problem [Kalman60]. Since that time, due in large part to advances in digital
computing, the
Kalman filter
has been the subject of extensive research and application,
particularly in the area of autonomous or assisted navigation. A very “friendly” introduction to the
general idea of the Kalman filter can be found in Chapter 1 of [Maybeck79], while a more complete
introductory discussion can be found in [Sorenson70], which also contains some interesting
historical narrative.
his paper provides a tutorial and survey of methods for parameterizing
surfaces with a view to applications in geometric modelling and computer graphics.
We gather various concepts from di® erential geometry which are relevant to surface
mapping and use them to understand the strengths and weaknesses of the many
methods for parameterizing piecewise Linear surfaces and their relationship to one
another.
A one-dimensional calibration object consists of three or more colLinear points with known relative positions.
It is generally believed that a camera can be calibrated only when a 1D calibration object is in planar motion or rotates
around a ¯ xed point. In this paper, it is proved that when a multi-camera is observing a 1D object undergoing general
rigid motions synchronously, the camera set can be Linearly calibrated. A Linear algorithm for the camera set calibration
is proposed，and then the Linear estimation is further re¯ ned using the maximum likelihood criteria. The simulated and
real image experiments show that the proposed algorithm is valid and robust.
ITU-T G.729語音壓縮算法。
description:
Fixed-point description of commendation G.729 with ANNEX B Coding of Speech at 8 kbit/s using Conjugate-Structure Algebraic-Code-Excited Linear-Prediction (CS-ACELP) with Voice Activity Decision(VAD), Discontinuous Transmission(DTX), and Comfort Noise Generation(CNG).
This paper examines the asymptotic (large sample) performance
of a family of non-data aided feedforward (NDA FF) nonLinear
least-squares (NLS) type carrier frequency estimators for burst-mode
phase shift keying (PSK) modulations transmitted through AWGN and
flat Ricean-fading channels. The asymptotic performance of these estimators
is established in closed-form expression and compared with the
modified Cram`er-Rao bound (MCRB). A best Linear unbiased estimator
(BLUE), which exhibits the lowest asymptotic variance within the family
of NDA FF NLS-type estimators, is also proposed.
The need for accurate monitoring and analysis of sequential data arises in many scientic, industrial
and nancial problems. Although the Kalman lter is effective in the Linear-Gaussian
case, new methods of dealing with sequential data are required with non-standard models.
Recently, there has been renewed interest in simulation-based techniques. The basic idea behind
these techniques is that the current state of knowledge is encapsulated in a representative
sample from the appropriate posterior distribution. As time goes on, the sample evolves and
adapts recursively in accordance with newly acquired data. We give a critical review of recent
developments, by reference to oil well monitoring, ion channel monitoring and tracking
problems, and propose some alternative algorithms that avoid the weaknesses of the current
methods.
Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a Linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). See http://en.wikipedia.org/wiki/AdaBoost and the papers by Y. Freund and R. Schapire for more details [1]. This approach is one of most efficient and simple to combine continuous and nominal values. Our implementation is aimed at allowing training from millions of examples by hundreds of features in a reasonable time/memory.
