From commodity computers to high-performance environments: scalability analysis using self-similarity, large deviations and heavy-tails

2009 ◽  
pp. n/a-n/a
Author(s):  
Raul V. Ramirez-Velarde ◽  
Ramón M. Rodríguez-Dagnino
Keyword(s):  
Large Deviations ◽  
High Performance ◽  
Heavy Tails ◽  
Self Similarity ◽  
Scalability Analysis ◽  
Commodity Computers

scholarly journals Image mixed gaussian and impulse noise elimination based on sparse representation model

2021 ◽  
Vol 23 (3) ◽  
pp. 1440
Author(s):  
Ahmed Abdulqader Hussein ◽  
Sabahaldin A. Hussain ◽  
Ahmed Hameed Reja
Keyword(s):  
Sparse Representation ◽  
Image Denoising ◽  
Impulse Noise ◽  
Heavy Tails ◽  
Noise Distribution ◽  
Self Similarity ◽  
Noise Elimination ◽  
Variational Framework ◽  
Mixed Noise ◽  
State Of Art

<p>A modified mixed Gaussian plus impulse image denoising algorithm based on weighted encoding with image sparsity and nonlocal self-similarity priors regularization is proposed in this paper. The encoding weights and the priors imposed on the images are incorporated into a variational framework to treat more complex mixed noise distribution. Such noise is characterized by heavy tails caused by impulse noise which needs to be eliminated through proper weighting of encoding residual. The outliers caused by the impulse noise has a significant effect on the encoding weights. Hence a more accurate residual encoding error initialization plays the important role in overall denoising performance, especially at high impulse noise rates. In this paper, outliers free initialization image, and an easier to implement a parameter-free procedure for updating encoding weights have been proposed. Experimental results demonstrate the capability of the proposed strategy to recover images highly corrupted by mixed Gaussian plus impulse noise as compared with the state of art denoising algorithm. The achieved results motivate us to implement the proposed algorithm in practice.</p>

PATH PROPERTIES OF THE LINEAR MULTIFRACTIONAL STABLE MOTION

Fractals ◽  
2005 ◽  
Vol 13 (02) ◽  
pp. 157-178 ◽  
Author(s):  
STILIAN STOEV ◽  
MURAD S. TAQQU
Keyword(s):  
Heavy Tails ◽  
Hölder Regularity ◽  
Self Similarity ◽  
Similarity Parameter ◽  
Stable Motion ◽  
Multifractional Brownian Motion ◽  
Sample Paths ◽  
Hölder Exponents ◽  
Fractional Stable Motion ◽  
Holder Regularity

The linear multifractional stable motion (LMSM) processes Y = {Y(t)}t∈ℝ is an α-stable (0 < α < 2) stochastic process, which exhibits local self-similarity, has heavy tails and can have skewed distributions. The process Y is obtained from the well-known class of linear fractional stable motion (LFSM) processes by replacing their self-similarity parameter H by a function of time H(t). We show that the paths of Y(t) are bounded on bounded intervals only if 1/α ≤ H(t) < 1, t ∈ ℝ. In particular, if 0 < α ≤ 1, then Y has everywhere discontinuous paths, with probability one. On the other hand, Y has a version with continuous paths if H(t) is sufficiently regular and 1/α < H(t), t ∈ ℝ. We study the Hölder regularity of the sample paths when these are continuous and establish almost sure bounds on the pointwise and uniform pointwise Hölder exponents of the (random) function Y(t,ω), t ∈ ℝ, in terms of the function H(t) and its corresponding Hölder exponents. The Gaussian multifractional Brownian motion (MBM) processes are LMSM processes when α = 2. We obtain some new results on the Hölder regularity of their paths.

High performance pedestrian detector using local segmentation self-similarity in complex scenes

2014 ◽  
Vol 24 (1) ◽  
pp. 93-101 ◽  
Author(s):  
Hongbo Gao ◽  
Hongyu Wang ◽  
Xiaokai Liu ◽  
Xiaorui Ma
Keyword(s):  
High Performance ◽  
Self Similarity ◽  
Complex Scenes

PARAMETER ESTIMATION FOR LINEAR FRACTIONAL STABLE NOISE PROCESS

2005 ◽  
Vol 14 (02) ◽  
pp. 233-247 ◽  
Author(s):  
ZHIPIN YE ◽  
CHUANGYIN DANG
Keyword(s):  
Heavy Tails ◽  
Linear Process ◽  
Self Similarity ◽  
Unknown Parameters ◽  
Heavy Tailed Distributions ◽  
Stable Random Variables ◽  
Heavy Tailed ◽  
Two Parameters ◽  
Weight Coefficients ◽  
Stable Noise

Over the past few years, scaling phenomena involving self-similarity and heavy-tailed distributions have attracted the interest of various researchers in telecommunications and networks. In this paper, we study the linear fractional stable noise (LFSN) which exhibits both long-range dependence and heavy tails property. LFSN can be represented as a linear process with weight coefficients and α-stable random variables. The coefficients of the linear process are determined by a kernel function and depend on five parameters. This paper focuses on estimating two unknown parameters a and b. Based on minimizing square errors, several methods for estimating these two parameters are presented. Detailed tables and graphs have been included in extensive simulations which show the methods are good estimates.

High-Performance Tool for the Test of Long-Memory and Self-Similarity

2014 ◽  
pp. 93-114 ◽  
Author(s):  
Julio Ramírez-Pacheco ◽  
Deni Torres-Román ◽  
Homero Toral-Cruz ◽  
Leopoldo Vargas
Keyword(s):  
Long Memory ◽  
High Performance ◽  
Self Similarity

scholarly journals Focusing on Detail: Deep Hashing Based on Multiple Region Details (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13991-13992
Author(s):  
Quan Zhou ◽  
Xiushan Nie ◽  
Yang Shi ◽  
Xingbo Liu ◽  
Yilong Yin
Keyword(s):  
High Performance ◽  
Original Data ◽  
Multimedia Data ◽  
Self Similarity ◽  
Imbalance Problem ◽  
Retrieval Efficiency ◽  
Loss Term ◽  
Image Details ◽  
Hash Codes ◽  
Local Image

Fast retrieval efficiency and high performance hashing, which aims to convert multimedia data into a set of short binary codes while preserving the similarity of the original data, has been widely studied in recent years. Majority of the existing deep supervised hashing methods only utilize the semantics of a whole image in learning hash codes, but ignore the local image details, which are important in hash learning. To fully utilize the detailed information, we propose a novel deep multi-region hashing (DMRH), which learns hash codes from local regions, and in which the final hash codes of the image are obtained by fusing the local hash codes corresponding to local regions. In addition, we propose a self-similarity loss term to address the imbalance problem (i.e., the number of dissimilar pairs is significantly more than that of the similar ones) of methods based on pairwise similarity.

scholarly journals Large Deviations for Point Processes Based on Stationary Sequences with Heavy Tails

2010 ◽  
Vol 47 (1) ◽  
pp. 1-40 ◽  
Author(s):  
Henrik Hult ◽  
Gennady Samorodnitsky
Keyword(s):  
Large Deviations ◽  
Point Processes ◽  
Heavy Tails ◽  
Extreme Values ◽  
Stationary Sequence ◽  
Random Coefficients ◽  
Ruin Probabilities ◽  
Stationary Sequences ◽  
Linear Processes ◽  
Infinite Linear

In this paper we propose a framework that facilitates the study of large deviations for point processes based on stationary sequences with regularly varying tails. This framework allows us to keep track both of the magnitude of the extreme values of a process and the order in which these extreme values appear. Particular emphasis is put on (infinite) linear processes with random coefficients. The proposed framework provides a fairly complete description of the joint asymptotic behavior of the large values of the stationary sequence. We apply the general result on large deviations for point processes to derive the asymptotic decay of certain probabilities related to partial sum processes as well as ruin probabilities.

scholarly journals On the 5G Communications: Fractal-Shaped Antennas for PPDR Applications

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Mihai-Virgil Nichita ◽  
Maria-Alexandra Paun ◽  
Vladimir-Alexandru Paun ◽  
Viorel-Puiu Paun
Keyword(s):  
Standing Wave ◽  
High Performance ◽  
Disaster Relief ◽  
Dipole Antenna ◽  
Voltage Standing Wave Ratio ◽  
Antenna Design ◽  
Dipole Antennas ◽  
Self Similarity ◽  
Optimum Operation ◽  
Fractal Antennas

In this study, one method of using antennas based on fractals to cover few kinds of public protection and disaster relief (PPDR) communications was presented. Dedicated antenna forms, necessary for antenna design by 5G implementation, were enhanced to suit the requirements of specific applications. Employing fractal-shaped antennas have allowed us to accomplish all these actions, which request compact, conformal, and broadband high performance devices. Antennas derived from Koch’s curve fractals are studied. In order to implement PPDR communications in 5G technology, frequency bandwidths of importance have been carefully selected and properly included in the antenna developments under MATLAB environment. Important information necessary for antenna designers, such as 360 degrees directivity at various frequencies, the impedance (resistance and reactance) along the bandwidth of interest, as well as voltage standing wave ratio (VSWR) along the bandwidth of interest for dipole, one-iteration, and two-iteration Koch’s curves, respectively, have been obtained. The characteristic of directivity at selected frequencies is also highlighted. In order to maximize antenna parameters, this study has successfully proposed using fractal antennas, objects that use self-similarity property of fractals for optimum operation in several frequency ranges. For the studied antennas, we have obtained the following results regarding the maximum gains in dBi (which is the unit of the ratio between the gains of the antenna compared to the gain of an isotropic antenna). For the dipole antennas, the gains are 2.73 dBi and 4.76 dBi at 460 MHz and 770 MHz, respectively. The gains for one-iteration fractal Koch antenna are 6.91 dBi and 4.51 dBi at 460 MHz and 770 MHz, respectively, and finally, for two-iteration fractal Koch antenna, the gains are 4.91 dBi and 3.28 dBi at 460 MHz and 770 MHz, respectively. Moreover, the impedance along the bandwidth is approximately 360 Ohms for two-iteration fractal Koch antenna, 180 Ohms for one-iteration fractal Koch antenna, and 140 Ohms for dipole antenna, respectively.

High performance SIMD marching cubes isosurface extraction on commodity computers

2004 ◽  
Vol 28 (2) ◽  
pp. 213-233 ◽  
Author(s):  
Timothy S Newman ◽  
J.Brad Byrd ◽  
Pavan Emani ◽  
Amit Narayanan ◽  
Abouzar Dastmalchi
Keyword(s):  
High Performance ◽  
Marching Cubes ◽  
Isosurface Extraction ◽  
Commodity Computers
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