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....
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 Frequency-Domain Adaptive Algorithm for Network Echo Cancellation in VoIP
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 Time-Domain Convolutive Blind Source Separation Employing Selective-Tap Adaptive Algorithms
Qiongfeng Pan and Tyseer Aboulnasr
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
This chapter provides background information and problem descriptions
of the applications treated in this book . Most of the applications include
real data and many of them are used as case studies examined throughout the
book with different algorithms
This chapter surveys off-line formulations of single and multiple change point
estimation . Although the problem formulation yields algorithms that process
data batch.wise, many important algorithms have natural on-line implementations
and recursive approximations . This chapter is basically a projection of
the more general results in Chapter 7 to the case of signal estimation . There
are, however. some dedicated algorithms for estimating one change point offline
that apply to the current case of a scalar signal model . In the literature
of mathematical statistics.
This chapter addresses the most general problem formulation of detection in
linear systems. Basically, all problem formulations that have been discussed
so far are included in the framework considered. The main purpose is to
survey multiple model algorithms, and a secondary purpose is to overview
and compare the state of the art in different application areas for reducing
complexity, where similar algorithms have been developed independently.
In Chapter 2 the equalizers operated under the assumption of perfect channel estimation, where the receiver always had perfect knowledge of the CIR. However, the CIR is typically time variant and consequently the receiver has to estimate the CIR or the coefficients of the equalizer, in order to compensate the IS1 induced by the channel. for Algorithms have been developed in order to automatically adapt the coefficients of the equalizer directly [ l 181 or by utilizing the estimated CIR [124,125]....
The CIME-EMS Summer School in applied mathematics on “Multiscale and
Adaptivity:Modeling, Numerics and Applications” was held in Cetraro (Italy) from
July 6 to 11, 2009. This course has focused on mathematical methods for systems
that involve multiple length/time scales and multiple physics. The complexity of
the structure of these systems requires suitable mathematical and computational
tools. In addition, mathematics provides an effective approach toward devising
computational strategies for handling multiple scales and multiple physics.
We consider the problem of correcting errors made by English as a Second Language (ESL) writers and address two issues that are essential to making progress in ESL error correction - algorithm selection and model adaptation to the ﬁrst language of the ESL learner. A variety of learning algorithms have been applied to correct ESL mistakes, but often comparisons were made between incomparable data sets. We conduct an extensive, fair comparison of four popular learning methods for the task, reversing conclusions from earlier evaluations. ...
Current Referring Expression Generation algorithms rely on domain dependent preferences for both content selection and linguistic realization. We present two experiments showing that human speakers may opt for dispreferred properties and dispreferred modiﬁer orderings when these were salient in a preceding interaction (without speakers being consciously aware of this). We discuss the impact of these ﬁndings for current generation algorithms.
We propose to directly measure the importance of queries in the source domain to the target domain where no rank labels of documents are available, which is referred to as query weighting. Query weighting is a key step in ranking model adaptation. As the learning object of ranking algorithms is divided by query instances, we argue that it’s more reasonable to conduct importance weighting at query level than document level.
While labeled data is expensive to prepare, ever increasing amounts of unlabeled linguistic data are becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains.
Automatic sentiment classiﬁcation has been extensively studied and applied in recent years. However, sentiment is expressed differently in different domains, and annotating corpora for every possible domain of interest is impractical. We investigate domain adaptation for sentiment classiﬁers, focusing on online reviews for different types of products.
This paper compares different measures of graphemic similarity applied to the task of bilingual lexicon induction between a Swiss German dialect and Standard German. The measures have been adapted to this particular language pair by training stochastic transducers with the ExpectationMaximisation algorithm or by using handmade transduction rules. These adaptive metrics show up to 11% F-measure improvement over a static metric like Levenshtein distance.
We show how global constraints such as transitivity can be treated intensionally in a Zero-One Integer Linear Programming (ILP) framework which is geared to ﬁnd the optimal and coherent partition of coreference sets given a number of candidate pairs and their weights delivered by a pairwise classiﬁer (used as reliable clustering seed pairs). In order to ﬁnd out whether ILP optimization, which is NPcomplete, actually is the best we can do, we compared the ﬁrst consistent solution generated by our adaptation of an efﬁcient Zero-One algorithm with the optimal solution. ...