23,377 research outputs found
Pairwise Quantization
We consider the task of lossy compression of high-dimensional vectors through
quantization. We propose the approach that learns quantization parameters by
minimizing the distortion of scalar products and squared distances between
pairs of points. This is in contrast to previous works that obtain these
parameters through the minimization of the reconstruction error of individual
points. The proposed approach proceeds by finding a linear transformation of
the data that effectively reduces the minimization of the pairwise distortions
to the minimization of individual reconstruction errors. After such
transformation, any of the previously-proposed quantization approaches can be
used. Despite the simplicity of this transformation, the experiments
demonstrate that it achieves considerable reduction of the pairwise distortions
compared to applying quantization directly to the untransformed data
Quasiconvex Programming
We define quasiconvex programming, a form of generalized linear programming
in which one seeks the point minimizing the pointwise maximum of a collection
of quasiconvex functions. We survey algorithms for solving quasiconvex programs
either numerically or via generalizations of the dual simplex method from
linear programming, and describe varied applications of this geometric
optimization technique in meshing, scientific computation, information
visualization, automated algorithm analysis, and robust statistics.Comment: 33 pages, 14 figure
Sensing motion using spectral and spatial analysis of WLAN RSSI
In this paper we present how motion sensing can be obtained just by observing the WLAN radio signal strength and its fluctuations. The temporal, spectral and spatial characteristics of WLAN signal are analyzed. Our analysis
confirms our claim that âsignal strength from access points appear to jump around more vigorously when the device is moving compared to when it is still and the number of detectable access points vary considerably while the user is on the moveâ. Using this observation, we present a novel motion detection algorithm, Spectrally Spread Motion Detection (SpecSMD) based on the spectral analysis of
WLAN signalâs RSSI. To benchmark the proposed algorithm, we used Spatially Spread Motion Detection (SpatSMD), which is inspired by the recent work of Sohn et al. Both algorithms were evaluated by carrying out extensive measurements
in a diverse set of conditions (indoors in different buildings and outdoors - city center, parking lot, university campus etc.,) and tested against the same
data sets. The 94% average classification accuracy of the proposed SpecSMD is outperforming the accuracy of SpatSMD (accuracy 87%). The motion detection algorithms presented in this paper provide ubiquitous methods for deriving the
state of the user. The algorithms can be implemented and run on a commodity device with WLAN capability without the need of any additional hardware support
On Geometric Alignment in Low Doubling Dimension
In real-world, many problems can be formulated as the alignment between two
geometric patterns. Previously, a great amount of research focus on the
alignment of 2D or 3D patterns, especially in the field of computer vision.
Recently, the alignment of geometric patterns in high dimension finds several
novel applications, and has attracted more and more attentions. However, the
research is still rather limited in terms of algorithms. To the best of our
knowledge, most existing approaches for high dimensional alignment are just
simple extensions of their counterparts for 2D and 3D cases, and often suffer
from the issues such as high complexities. In this paper, we propose an
effective framework to compress the high dimensional geometric patterns and
approximately preserve the alignment quality. As a consequence, existing
alignment approach can be applied to the compressed geometric patterns and thus
the time complexity is significantly reduced. Our idea is inspired by the
observation that high dimensional data often has a low intrinsic dimension. We
adopt the widely used notion "doubling dimension" to measure the extents of our
compression and the resulting approximation. Finally, we test our method on
both random and real datasets, the experimental results reveal that running the
alignment algorithm on compressed patterns can achieve similar qualities,
comparing with the results on the original patterns, but the running times
(including the times cost for compression) are substantially lower
Efficient Irregular Wavefront Propagation Algorithms on Hybrid CPU-GPU Machines
In this paper, we address the problem of efficient execution of a computation
pattern, referred to here as the irregular wavefront propagation pattern
(IWPP), on hybrid systems with multiple CPUs and GPUs. The IWPP is common in
several image processing operations. In the IWPP, data elements in the
wavefront propagate waves to their neighboring elements on a grid if a
propagation condition is satisfied. Elements receiving the propagated waves
become part of the wavefront. This pattern results in irregular data accesses
and computations. We develop and evaluate strategies for efficient computation
and propagation of wavefronts using a multi-level queue structure. This queue
structure improves the utilization of fast memories in a GPU and reduces
synchronization overheads. We also develop a tile-based parallelization
strategy to support execution on multiple CPUs and GPUs. We evaluate our
approaches on a state-of-the-art GPU accelerated machine (equipped with 3 GPUs
and 2 multicore CPUs) using the IWPP implementations of two widely used image
processing operations: morphological reconstruction and euclidean distance
transform. Our results show significant performance improvements on GPUs. The
use of multiple CPUs and GPUs cooperatively attains speedups of 50x and 85x
with respect to single core CPU executions for morphological reconstruction and
euclidean distance transform, respectively.Comment: 37 pages, 16 figure
Generic Subsequence Matching Framework: Modularity, Flexibility, Efficiency
Subsequence matching has appeared to be an ideal approach for solving many
problems related to the fields of data mining and similarity retrieval. It has
been shown that almost any data class (audio, image, biometrics, signals) is or
can be represented by some kind of time series or string of symbols, which can
be seen as an input for various subsequence matching approaches. The variety of
data types, specific tasks and their partial or full solutions is so wide that
the choice, implementation and parametrization of a suitable solution for a
given task might be complicated and time-consuming; a possibly fruitful
combination of fragments from different research areas may not be obvious nor
easy to realize. The leading authors of this field also mention the
implementation bias that makes difficult a proper comparison of competing
approaches. Therefore we present a new generic Subsequence Matching Framework
(SMF) that tries to overcome the aforementioned problems by a uniform frame
that simplifies and speeds up the design, development and evaluation of
subsequence matching related systems. We identify several relatively separate
subtasks solved differently over the literature and SMF enables to combine them
in straightforward manner achieving new quality and efficiency. This framework
can be used in many application domains and its components can be reused
effectively. Its strictly modular architecture and openness enables also
involvement of efficient solutions from different fields, for instance
efficient metric-based indexes. This is an extended version of a paper
published on DEXA 2012.Comment: This is an extended version of a paper published on DEXA 201
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