7,891 research outputs found
A study of digital techniques for signal processing
Analysis and definition of digital techniques for signal processin
XQuery Streaming by Forest Transducers
Streaming of XML transformations is a challenging task and only very few
systems support streaming. Research approaches generally define custom
fragments of XQuery and XPath that are amenable to streaming, and then design
custom algorithms for each fragment. These languages have several shortcomings.
Here we take a more principles approach to the problem of streaming
XQuery-based transformations. We start with an elegant transducer model for
which many static analysis problems are well-understood: the Macro Forest
Transducer (MFT). We show that a large fragment of XQuery can be translated
into MFTs --- indeed, a fragment of XQuery, that can express important features
that are missing from other XQuery stream engines, such as GCX: our fragment of
XQuery supports XPath predicates and let-statements. We then rely on a
streaming execution engine for MFTs, one which uses a well-founded set of
optimizations from functional programming, such as strictness analysis and
deforestation. Our prototype achieves time and memory efficiency comparable to
the fastest known engine for XQuery streaming, GCX. This is surprising because
our engine relies on the OCaml built in garbage collector and does not use any
specialized buffer management, while GCX's efficiency is due to clever and
explicit buffer management.Comment: Full version of the paper in the Proceedings of the 30th IEEE
International Conference on Data Engineering (ICDE 2014
Low-complexity RLS algorithms using dichotomous coordinate descent iterations
In this paper, we derive low-complexity recursive least squares (RLS) adaptive filtering algorithms. We express the RLS problem in terms of auxiliary normal equations with respect to increments of the filter weights and apply this approach to the exponentially weighted and sliding window cases to derive new RLS techniques. For solving the auxiliary equations, line search methods are used. We first consider conjugate gradient iterations with a complexity of O(N-2) operations per sample; N being the number of the filter weights. To reduce the complexity and make the algorithms more suitable for finite precision implementation, we propose a new dichotomous coordinate descent (DCD) algorithm and apply it to the auxiliary equations. This results in a transversal RLS adaptive filter with complexity as low as 3N multiplications per sample, which is only slightly higher than the complexity of the least mean squares (LMS) algorithm (2N multiplications). Simulations are used to compare the performance of the proposed algorithms against the classical RLS and known advanced adaptive algorithms. Fixed-point FPGA implementation of the proposed DCD-based RLS algorithm is also discussed and results of such implementation are presented
Volterra and general polynomial related filtering
Journal ArticleThis paper presents a review of polynomial filtering and, in particular, of tlie truncated Volterra filters. Following the introduction of the general properties of such filters, issues such as eficieiit realizations, design, adaptive algoritlims and stability are discussed
Optimal Estimation via Nonanticipative Rate Distortion Function and Applications to Time-Varying Gauss-Markov Processes
In this paper, we develop {finite-time horizon} causal filters using the
nonanticipative rate distortion theory. We apply the {developed} theory to
{design optimal filters for} time-varying multidimensional Gauss-Markov
processes, subject to a mean square error fidelity constraint. We show that
such filters are equivalent to the design of an optimal \texttt{\{encoder,
channel, decoder\}}, which ensures that the error satisfies {a} fidelity
constraint. Moreover, we derive a universal lower bound on the mean square
error of any estimator of time-varying multidimensional Gauss-Markov processes
in terms of conditional mutual information. Unlike classical Kalman filters,
the filter developed is characterized by a reverse-waterfilling algorithm,
which ensures {that} the fidelity constraint is satisfied. The theoretical
results are demonstrated via illustrative examples.Comment: 35 pages, 6 figures, submitted for publication in SIAM Journal on
Control and Optimization (SICON
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