[Lithology feature extraction of CASI hyperspectral data based on fractal signal algorithm].

PubMed

Tang, Chao; Chen, Jian-Ping; Cui, Jing; Wen, Bo-Tao

2014-05-01

Hyperspectral data is characterized by combination of image and spectrum and large data volume dimension reduction is the main research direction. Band selection and feature extraction is the primary method used for this objective. In the present article, the authors tested methods applied for the lithology feature extraction from hyperspectral data. Based on the self-similarity of hyperspectral data, the authors explored the application of fractal algorithm to lithology feature extraction from CASI hyperspectral data. The "carpet method" was corrected and then applied to calculate the fractal value of every pixel in the hyperspectral data. The results show that fractal information highlights the exposed bedrock lithology better than the original hyperspectral data The fractal signal and characterized scale are influenced by the spectral curve shape, the initial scale selection and iteration step. At present, research on the fractal signal of spectral curve is rare, implying the necessity of further quantitative analysis and investigation of its physical implications.

Fractal analysis of MRI data for the characterization of patients with schizophrenia and bipolar disorder.

PubMed

Squarcina, Letizia; De Luca, Alberto; Bellani, Marcella; Brambilla, Paolo; Turkheimer, Federico E; Bertoldo, Alessandra

2015-02-21

Fractal geometry can be used to analyze shape and patterns in brain images. With this study we use fractals to analyze T1 data of patients affected by schizophrenia or bipolar disorder, with the aim of distinguishing between healthy and pathological brains using the complexity of brain structure, in particular of grey matter, as a marker of disease. 39 healthy volunteers, 25 subjects affected by schizophrenia and 11 patients affected by bipolar disorder underwent an MRI session. We evaluated fractal dimension of the brain cortex and its substructures, calculated with an algorithm based on the box-count algorithm. We modified this algorithm, with the aim of avoiding the segmentation processing step and using all the information stored in the image grey levels. Moreover, to increase sensitivity to local structural changes, we computed a value of fractal dimension for each slice of the brain or of the particular structure. To have reference values in comparing healthy subjects with patients, we built a template by averaging fractal dimension values of the healthy volunteers data. Standard deviation was evaluated and used to create a confidence interval. We also performed a slice by slice t-test to assess the difference at slice level between the three groups. Consistent average fractal dimension values were found across all the structures in healthy controls, while in the pathological groups we found consistent differences, indicating a change in brain and structures complexity induced by these disorders.

Fractal analysis of MRI data for the characterization of patients with schizophrenia and bipolar disorder

NASA Astrophysics Data System (ADS)

Squarcina, Letizia; De Luca, Alberto; Bellani, Marcella; Brambilla, Paolo; Turkheimer, Federico E.; Bertoldo, Alessandra

2015-02-01

Fractal geometry can be used to analyze shape and patterns in brain images. With this study we use fractals to analyze T1 data of patients affected by schizophrenia or bipolar disorder, with the aim of distinguishing between healthy and pathological brains using the complexity of brain structure, in particular of grey matter, as a marker of disease. 39 healthy volunteers, 25 subjects affected by schizophrenia and 11 patients affected by bipolar disorder underwent an MRI session. We evaluated fractal dimension of the brain cortex and its substructures, calculated with an algorithm based on the box-count algorithm. We modified this algorithm, with the aim of avoiding the segmentation processing step and using all the information stored in the image grey levels. Moreover, to increase sensitivity to local structural changes, we computed a value of fractal dimension for each slice of the brain or of the particular structure. To have reference values in comparing healthy subjects with patients, we built a template by averaging fractal dimension values of the healthy volunteers data. Standard deviation was evaluated and used to create a confidence interval. We also performed a slice by slice t-test to assess the difference at slice level between the three groups. Consistent average fractal dimension values were found across all the structures in healthy controls, while in the pathological groups we found consistent differences, indicating a change in brain and structures complexity induced by these disorders.

Detection and classification of Breast Cancer in Wavelet Sub-bands of Fractal Segmented Cancerous Zones.

PubMed

Shirazinodeh, Alireza; Noubari, Hossein Ahmadi; Rabbani, Hossein; Dehnavi, Alireza Mehri

2015-01-01

Recent studies on wavelet transform and fractal modeling applied on mammograms for the detection of cancerous tissues indicate that microcalcifications and masses can be utilized for the study of the morphology and diagnosis of cancerous cases. It is shown that the use of fractal modeling, as applied to a given image, can clearly discern cancerous zones from noncancerous areas. In this paper, for fractal modeling, the original image is first segmented into appropriate fractal boxes followed by identifying the fractal dimension of each windowed section using a computationally efficient two-dimensional box-counting algorithm. Furthermore, using appropriate wavelet sub-bands and image Reconstruction based on modified wavelet coefficients, it is shown that it is possible to arrive at enhanced features for detection of cancerous zones. In this paper, we have attempted to benefit from the advantages of both fractals and wavelets by introducing a new algorithm. By using a new algorithm named F1W2, the original image is first segmented into appropriate fractal boxes, and the fractal dimension of each windowed section is extracted. Following from that, by applying a maximum level threshold on fractal dimensions matrix, the best-segmented boxes are selected. In the next step, the segmented Cancerous zones which are candidates are then decomposed by utilizing standard orthogonal wavelet transform and db2 wavelet in three different resolution levels, and after nullifying wavelet coefficients of the image at the first scale and low frequency band of the third scale, the modified reconstructed image is successfully utilized for detection of breast cancer regions by applying an appropriate threshold. For detection of cancerous zones, our simulations indicate the accuracy of 90.9% for masses and 88.99% for microcalcifications detection results using the F1W2 method. For classification of detected mictocalcification into benign and malignant cases, eight features are identified and utilized in radial basis function neural network. Our simulation results indicate the accuracy of 92% classification using F1W2 method.

Feature selection method based on multi-fractal dimension and harmony search algorithm and its application

NASA Astrophysics Data System (ADS)

Zhang, Chen; Ni, Zhiwei; Ni, Liping; Tang, Na

2016-10-01

Feature selection is an important method of data preprocessing in data mining. In this paper, a novel feature selection method based on multi-fractal dimension and harmony search algorithm is proposed. Multi-fractal dimension is adopted as the evaluation criterion of feature subset, which can determine the number of selected features. An improved harmony search algorithm is used as the search strategy to improve the efficiency of feature selection. The performance of the proposed method is compared with that of other feature selection algorithms on UCI data-sets. Besides, the proposed method is also used to predict the daily average concentration of PM2.5 in China. Experimental results show that the proposed method can obtain competitive results in terms of both prediction accuracy and the number of selected features.

Fractal-Based Image Compression, II

DTIC Science & Technology

1990-06-01

data for figure 3 ----------------------------------- 10 iv 1. INTRODUCTION The need for data compression is not new. With humble beginnings such as...the use of acronyms and abbreviations in spoken and written word, the methods for data compression became more advanced as the need for information...grew. The Morse code, developed because of the need for faster telegraphy, was an early example of a data compression technique. Largely because of the

Roughness Perception of Haptically Displayed Fractal Surfaces

NASA Technical Reports Server (NTRS)

Costa, Michael A.; Cutkosky, Mark R.; Lau, Sonie (Technical Monitor)

2000-01-01

Surface profiles were generated by a fractal algorithm and haptically rendered on a force feedback joystick, Subjects were asked to use the joystick to explore pairs of surfaces and report to the experimenter which of the surfaces they felt was rougher. Surfaces were characterized by their root mean square (RMS) amplitude and their fractal dimension. The most important factor affecting the perceived roughness of the fractal surfaces was the RMS amplitude of the surface. When comparing surfaces of fractal dimension 1.2-1.35 it was found that the fractal dimension was negatively correlated with perceived roughness.

Evolving random fractal Cantor superlattices for the infrared using a genetic algorithm

PubMed Central

Bossard, Jeremy A.; Lin, Lan; Werner, Douglas H.

2016-01-01

Ordered and chaotic superlattices have been identified in Nature that give rise to a variety of colours reflected by the skin of various organisms. In particular, organisms such as silvery fish possess superlattices that reflect a broad range of light from the visible to the UV. Such superlattices have previously been identified as ‘chaotic’, but we propose that apparent ‘chaotic’ natural structures, which have been previously modelled as completely random structures, should have an underlying fractal geometry. Fractal geometry, often described as the geometry of Nature, can be used to mimic structures found in Nature, but deterministic fractals produce structures that are too ‘perfect’ to appear natural. Introducing variability into fractals produces structures that appear more natural. We suggest that the ‘chaotic’ (purely random) superlattices identified in Nature are more accurately modelled by multi-generator fractals. Furthermore, we introduce fractal random Cantor bars as a candidate for generating both ordered and ‘chaotic’ superlattices, such as the ones found in silvery fish. A genetic algorithm is used to evolve optimal fractal random Cantor bars with multiple generators targeting several desired optical functions in the mid-infrared and the near-infrared. We present optimized superlattices demonstrating broadband reflection as well as single and multiple pass bands in the near-infrared regime. PMID:26763335

A robust algorithm for optimisation and customisation of fractal dimensions of time series modified by nonlinearly scaling their time derivatives: mathematical theory and practical applications.

PubMed

Fuss, Franz Konstantin

2013-01-01

Standard methods for computing the fractal dimensions of time series are usually tested with continuous nowhere differentiable functions, but not benchmarked with actual signals. Therefore they can produce opposite results in extreme signals. These methods also use different scaling methods, that is, different amplitude multipliers, which makes it difficult to compare fractal dimensions obtained from different methods. The purpose of this research was to develop an optimisation method that computes the fractal dimension of a normalised (dimensionless) and modified time series signal with a robust algorithm and a running average method, and that maximises the difference between two fractal dimensions, for example, a minimum and a maximum one. The signal is modified by transforming its amplitude by a multiplier, which has a non-linear effect on the signal's time derivative. The optimisation method identifies the optimal multiplier of the normalised amplitude for targeted decision making based on fractal dimensions. The optimisation method provides an additional filter effect and makes the fractal dimensions less noisy. The method is exemplified by, and explained with, different signals, such as human movement, EEG, and acoustic signals.

A Robust Algorithm for Optimisation and Customisation of Fractal Dimensions of Time Series Modified by Nonlinearly Scaling Their Time Derivatives: Mathematical Theory and Practical Applications

PubMed Central

2013-01-01

Standard methods for computing the fractal dimensions of time series are usually tested with continuous nowhere differentiable functions, but not benchmarked with actual signals. Therefore they can produce opposite results in extreme signals. These methods also use different scaling methods, that is, different amplitude multipliers, which makes it difficult to compare fractal dimensions obtained from different methods. The purpose of this research was to develop an optimisation method that computes the fractal dimension of a normalised (dimensionless) and modified time series signal with a robust algorithm and a running average method, and that maximises the difference between two fractal dimensions, for example, a minimum and a maximum one. The signal is modified by transforming its amplitude by a multiplier, which has a non-linear effect on the signal's time derivative. The optimisation method identifies the optimal multiplier of the normalised amplitude for targeted decision making based on fractal dimensions. The optimisation method provides an additional filter effect and makes the fractal dimensions less noisy. The method is exemplified by, and explained with, different signals, such as human movement, EEG, and acoustic signals. PMID:24151522

a New Method for Calculating Fractal Dimensions of Porous Media Based on Pore Size Distribution

NASA Astrophysics Data System (ADS)

Xia, Yuxuan; Cai, Jianchao; Wei, Wei; Hu, Xiangyun; Wang, Xin; Ge, Xinmin

Fractal theory has been widely used in petrophysical properties of porous rocks over several decades and determination of fractal dimensions is always the focus of researches and applications by means of fractal-based methods. In this work, a new method for calculating pore space fractal dimension and tortuosity fractal dimension of porous media is derived based on fractal capillary model assumption. The presented work establishes relationship between fractal dimensions and pore size distribution, which can be directly used to calculate the fractal dimensions. The published pore size distribution data for eight sandstone samples are used to calculate the fractal dimensions and simultaneously compared with prediction results from analytical expression. In addition, the proposed fractal dimension method is also tested through Micro-CT images of three sandstone cores, and are compared with fractal dimensions by box-counting algorithm. The test results also prove a self-similar fractal range in sandstone when excluding smaller pores.

Fractal Music: The Mathematics Behind "Techno" Music

ERIC Educational Resources Information Center

Padula, Janice

2005-01-01

This article describes sound waves, their basis in the sine curve, Fourier's theorem of infinite series, the fractal equation and its application to the composition of music, together with algorithms (such as those employed by meteorologist Edward Lorenz in his discovery of chaos theory) that are now being used to compose fractal music on…

Power dissipation in fractal AC circuits

NASA Astrophysics Data System (ADS)

Chen, Joe P.; Rogers, Luke G.; Anderson, Loren; Andrews, Ulysses; Brzoska, Antoni; Coffey, Aubrey; Davis, Hannah; Fisher, Lee; Hansalik, Madeline; Loew, Stephen; Teplyaev, Alexander

2017-08-01

We extend Feynman’s analysis of an infinite ladder circuit to fractal circuits, providing examples in which fractal circuits constructed with purely imaginary impedances can have characteristic impedances with positive real part. Using (weak) self-similarity of our fractal structures, we provide algorithms for studying the equilibrium distribution of energy on these circuits. This extends the analysis of self-similar resistance networks introduced by Fukushima, Kigami, Kusuoka, and more recently studied by Strichartz et al.

The Correlation Fractal Dimension of Complex Networks

NASA Astrophysics Data System (ADS)

Wang, Xingyuan; Liu, Zhenzhen; Wang, Mogei

2013-05-01

The fractality of complex networks is studied by estimating the correlation dimensions of the networks. Comparing with the previous algorithms of estimating the box dimension, our algorithm achieves a significant reduction in time complexity. For four benchmark cases tested, that is, the Escherichia coli (E. Coli) metabolic network, the Homo sapiens protein interaction network (H. Sapiens PIN), the Saccharomyces cerevisiae protein interaction network (S. Cerevisiae PIN) and the World Wide Web (WWW), experiments are provided to demonstrate the validity of our algorithm.

Recent advances in coding theory for near error-free communications

NASA Technical Reports Server (NTRS)

Cheung, K.-M.; Deutsch, L. J.; Dolinar, S. J.; Mceliece, R. J.; Pollara, F.; Shahshahani, M.; Swanson, L.

1991-01-01

Channel and source coding theories are discussed. The following subject areas are covered: large constraint length convolutional codes (the Galileo code); decoder design (the big Viterbi decoder); Voyager's and Galileo's data compression scheme; current research in data compression for images; neural networks for soft decoding; neural networks for source decoding; finite-state codes; and fractals for data compression.

Improved visibility graph fractality with application for the diagnosis of Autism Spectrum Disorder

NASA Astrophysics Data System (ADS)

Ahmadlou, Mehran; Adeli, Hojjat; Adeli, Amir

2012-10-01

Recently, the visibility graph (VG) algorithm was proposed for mapping a time series to a graph to study complexity and fractality of the time series through investigation of the complexity of its graph. The visibility graph algorithm converts a fractal time series to a scale-free graph. VG has been used for the investigation of fractality in the dynamic behavior of both artificial and natural complex systems. However, robustness and performance of the power of scale-freeness of VG (PSVG) as an effective method for measuring fractality has not been investigated. Since noise is unavoidable in real life time series, the robustness of a fractality measure is of paramount importance. To improve the accuracy and robustness of PSVG to noise for measurement of fractality of time series in biological time-series, an improved PSVG is presented in this paper. The proposed method is evaluated using two examples: a synthetic benchmark time series and a complicated real life Electroencephalograms (EEG)-based diagnostic problem, that is distinguishing autistic children from non-autistic children. It is shown that the proposed improved PSVG is less sensitive to noise and therefore more robust compared with PSVG. Further, it is shown that using improved PSVG in the wavelet-chaos neural network model of Adeli and c-workers in place of the Katz fractality dimension results in a more accurate diagnosis of autism, a complicated neurological and psychiatric disorder.

Unification of two fractal families

NASA Astrophysics Data System (ADS)

Liu, Ying

1995-06-01

Barnsley and Hurd classify the fractal images into two families: iterated function system fractals (IFS fractals) and fractal transform fractals, or local iterated function system fractals (LIFS fractals). We will call IFS fractals, class 2 fractals and LIFS fractals, class 3 fractals. In this paper, we will unify these two approaches plus another family of fractals, the class 5 fractals. The basic idea is given as follows: a dynamical system can be represented by a digraph, the nodes in a digraph can be divided into two parts: transient states and persistent states. For bilevel images, a persistent node is a black pixel. A transient node is a white pixel. For images with more than two gray levels, a stochastic digraph is used. A transient node is a pixel with the intensity of 0. The intensity of a persistent node is determined by a relative frequency. In this way, the two families of fractals can be generated in a similar way. In this paper, we will first present a classification of dynamical systems and introduce the transformation based on digraphs, then we will unify the two approaches for fractal binary images. We will compare the decoding algorithms of the two families. Finally, we will generalize the discussion to continuous-tone images.

Micro and MACRO Fractals Generated by Multi-Valued Dynamical Systems

NASA Astrophysics Data System (ADS)

Banakh, T.; Novosad, N.

2014-08-01

Given a multi-valued function Φ : X \\mumap X on a topological space X we study the properties of its fixed fractal \\malteseΦ, which is defined as the closure of the orbit Φω(*Φ) = ⋃n∈ωΦn(*Φ) of the set *Φ = {x ∈ X : x ∈ Φ(x)} of fixed points of Φ. A special attention is paid to the duality between micro-fractals and macro-fractals, which are fixed fractals \\maltese Φ and \\maltese {Φ -1} for a contracting compact-valued function Φ : X \\mumap X on a complete metric space X. With help of algorithms (described in this paper) we generate various images of macro-fractals which are dual to some well-known micro-fractals like the fractal cross, the Sierpiński triangle, Sierpiński carpet, the Koch curve, or the fractal snowflakes. The obtained images show that macro-fractals have a large-scale fractal structure, which becomes clearly visible after a suitable zooming.

Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem.

PubMed

Yue, Yi-xiang; Zhang, Tong; Yue, Qun-xing

2015-01-01

Vehicle Routing Problem (VRP) is one of the key issues in optimization of modern logistics system. In this paper, a modified VRP model with hard time window is established and a Hybrid Optimization Algorithm (HOA) based on Fractal Space Filling Curves (SFC) method and Genetic Algorithm (GA) is introduced. By incorporating the proposed algorithm, SFC method can find an initial and feasible solution very fast; GA is used to improve the initial solution. Thereafter, experimental software was developed and a large number of experimental computations from Solomon's benchmark have been studied. The experimental results demonstrate the feasibility and effectiveness of the HOA.

Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem

PubMed Central

Yue, Yi-xiang; Zhang, Tong; Yue, Qun-xing

2015-01-01

Vehicle Routing Problem (VRP) is one of the key issues in optimization of modern logistics system. In this paper, a modified VRP model with hard time window is established and a Hybrid Optimization Algorithm (HOA) based on Fractal Space Filling Curves (SFC) method and Genetic Algorithm (GA) is introduced. By incorporating the proposed algorithm, SFC method can find an initial and feasible solution very fast; GA is used to improve the initial solution. Thereafter, experimental software was developed and a large number of experimental computations from Solomon's benchmark have been studied. The experimental results demonstrate the feasibility and effectiveness of the HOA. PMID:26167171

Small-angle scattering from 3D Sierpinski tetrahedron generated using chaos game

NASA Astrophysics Data System (ADS)

Slyamov, Azat

2017-12-01

We approximate a three dimensional version of deterministic Sierpinski gasket (SG), also known as Sierpinski tetrahedron (ST), by using the chaos game representation (CGR). Structural properties of the fractal, generated by both deterministic and CGR algorithms are determined using small-angle scattering (SAS) technique. We calculate the corresponding monodisperse structure factor of ST, using an optimized Debye formula. We show that scattering from CGR of ST recovers basic fractal properties, such as fractal dimension, iteration number, scaling factor, overall size of the system and the number of units composing the fractal.

Hyper-Fractal Analysis: A visual tool for estimating the fractal dimension of 4D objects

NASA Astrophysics Data System (ADS)

Grossu, I. V.; Grossu, I.; Felea, D.; Besliu, C.; Jipa, Al.; Esanu, T.; Bordeianu, C. C.; Stan, E.

2013-04-01

This work presents a new version of a Visual Basic 6.0 application for estimating the fractal dimension of images and 3D objects (Grossu et al. (2010) [1]). The program was extended for working with four-dimensional objects stored in comma separated values files. This might be of interest in biomedicine, for analyzing the evolution in time of three-dimensional images. New version program summaryProgram title: Hyper-Fractal Analysis (Fractal Analysis v03) Catalogue identifier: AEEG_v3_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEEG_v3_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: Standard CPC license, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 745761 No. of bytes in distributed program, including test data, etc.: 12544491 Distribution format: tar.gz Programming language: MS Visual Basic 6.0 Computer: PC Operating system: MS Windows 98 or later RAM: 100M Classification: 14 Catalogue identifier of previous version: AEEG_v2_0 Journal reference of previous version: Comput. Phys. Comm. 181 (2010) 831-832 Does the new version supersede the previous version? Yes Nature of problem: Estimating the fractal dimension of 4D images. Solution method: Optimized implementation of the 4D box-counting algorithm. Reasons for new version: Inspired by existing applications of 3D fractals in biomedicine [3], we extended the optimized version of the box-counting algorithm [1, 2] to the four-dimensional case. This might be of interest in analyzing the evolution in time of 3D images. The box-counting algorithm was extended in order to support 4D objects, stored in comma separated values files. A new form was added for generating 2D, 3D, and 4D test data. The application was tested on 4D objects with known dimension, e.g. the Sierpinski hypertetrahedron gasket, Df=ln(5)/ln(2) (Fig. 1). The algorithm could be extended, with minimum effort, to higher number of dimensions. Easy integration with other applications by using the very simple comma separated values file format for storing multi-dimensional images. Implementation of χ2 test as a criterion for deciding whether an object is fractal or not. User friendly graphical interface. Hyper-Fractal Analysis-Test on the Sierpinski hypertetrahedron 4D gasket (Df=ln(5)/ln(2)≅2.32). Running time: In a first approximation, the algorithm is linear [2]. References: [1] V. Grossu, D. Felea, C. Besliu, Al. Jipa, C.C. Bordeianu, E. Stan, T. Esanu, Computer Physics Communications, 181 (2010) 831-832. [2] I.V. Grossu, C. Besliu, M.V. Rusu, Al. Jipa, C. C. Bordeianu, D. Felea, Computer Physics Communications, 180 (2009) 1999-2001. [3] J. Ruiz de Miras, J. Navas, P. Villoslada, F.J. Esteban, Computer Methods and Programs in Biomedicine, 104 Issue 3 (2011) 452-460.

Fractal and multifractal analyses of bipartite networks

NASA Astrophysics Data System (ADS)

Liu, Jin-Long; Wang, Jian; Yu, Zu-Guo; Xie, Xian-Hua

2017-03-01

Bipartite networks have attracted considerable interest in various fields. Fractality and multifractality of unipartite (classical) networks have been studied in recent years, but there is no work to study these properties of bipartite networks. In this paper, we try to unfold the self-similarity structure of bipartite networks by performing the fractal and multifractal analyses for a variety of real-world bipartite network data sets and models. First, we find the fractality in some bipartite networks, including the CiteULike, Netflix, MovieLens (ml-20m), Delicious data sets and (u, v)-flower model. Meanwhile, we observe the shifted power-law or exponential behavior in other several networks. We then focus on the multifractal properties of bipartite networks. Our results indicate that the multifractality exists in those bipartite networks possessing fractality. To capture the inherent attribute of bipartite network with two types different nodes, we give the different weights for the nodes of different classes, and show the existence of multifractality in these node-weighted bipartite networks. In addition, for the data sets with ratings, we modify the two existing algorithms for fractal and multifractal analyses of edge-weighted unipartite networks to study the self-similarity of the corresponding edge-weighted bipartite networks. The results show that our modified algorithms are feasible and can effectively uncover the self-similarity structure of these edge-weighted bipartite networks and their corresponding node-weighted versions.

Fractal and multifractal analyses of bipartite networks.

PubMed

Liu, Jin-Long; Wang, Jian; Yu, Zu-Guo; Xie, Xian-Hua

2017-03-31

Bipartite networks have attracted considerable interest in various fields. Fractality and multifractality of unipartite (classical) networks have been studied in recent years, but there is no work to study these properties of bipartite networks. In this paper, we try to unfold the self-similarity structure of bipartite networks by performing the fractal and multifractal analyses for a variety of real-world bipartite network data sets and models. First, we find the fractality in some bipartite networks, including the CiteULike, Netflix, MovieLens (ml-20m), Delicious data sets and (u, v)-flower model. Meanwhile, we observe the shifted power-law or exponential behavior in other several networks. We then focus on the multifractal properties of bipartite networks. Our results indicate that the multifractality exists in those bipartite networks possessing fractality. To capture the inherent attribute of bipartite network with two types different nodes, we give the different weights for the nodes of different classes, and show the existence of multifractality in these node-weighted bipartite networks. In addition, for the data sets with ratings, we modify the two existing algorithms for fractal and multifractal analyses of edge-weighted unipartite networks to study the self-similarity of the corresponding edge-weighted bipartite networks. The results show that our modified algorithms are feasible and can effectively uncover the self-similarity structure of these edge-weighted bipartite networks and their corresponding node-weighted versions.

Fractal and multifractal analyses of bipartite networks

PubMed Central

Liu, Jin-Long; Wang, Jian; Yu, Zu-Guo; Xie, Xian-Hua

2017-01-01

Bipartite networks have attracted considerable interest in various fields. Fractality and multifractality of unipartite (classical) networks have been studied in recent years, but there is no work to study these properties of bipartite networks. In this paper, we try to unfold the self-similarity structure of bipartite networks by performing the fractal and multifractal analyses for a variety of real-world bipartite network data sets and models. First, we find the fractality in some bipartite networks, including the CiteULike, Netflix, MovieLens (ml-20m), Delicious data sets and (u, v)-flower model. Meanwhile, we observe the shifted power-law or exponential behavior in other several networks. We then focus on the multifractal properties of bipartite networks. Our results indicate that the multifractality exists in those bipartite networks possessing fractality. To capture the inherent attribute of bipartite network with two types different nodes, we give the different weights for the nodes of different classes, and show the existence of multifractality in these node-weighted bipartite networks. In addition, for the data sets with ratings, we modify the two existing algorithms for fractal and multifractal analyses of edge-weighted unipartite networks to study the self-similarity of the corresponding edge-weighted bipartite networks. The results show that our modified algorithms are feasible and can effectively uncover the self-similarity structure of these edge-weighted bipartite networks and their corresponding node-weighted versions. PMID:28361962

Research on the fractal structure in the Chinese stock market

NASA Astrophysics Data System (ADS)

Zhuang, Xin-tian; Huang, Xiao-yuan; Sha, Yan-li

2004-02-01

Applying fractal theory, this paper probes and discusses self-similarity and scale invariance of the Chinese stock market. It analyses three kinds of scale indexes, i.e., autocorrelation index, Hurst index and the scale index on the basis of detrended fluctuation analysis (DFA) algorithm and promotes DFA into a recursive algorithm. Using the three kinds of scale indexes, we conduct empirical research on the Chinese Shanghai and Shenzhen stock markets. The results indicate that the rate of returns of the two stock markets does not obey the normal distribution. A correlation exists between the stock price indexes over time scales. The stock price indexes exhibit fractal time series. It indicates that the policy guide hidden at the back influences the characteristic of the Chinese stock market.

Feature extraction algorithm for space targets based on fractal theory

NASA Astrophysics Data System (ADS)

Tian, Balin; Yuan, Jianping; Yue, Xiaokui; Ning, Xin

2007-11-01

In order to offer a potential for extending the life of satellites and reducing the launch and operating costs, satellite servicing including conducting repairs, upgrading and refueling spacecraft on-orbit become much more frequently. Future space operations can be more economically and reliably executed using machine vision systems, which can meet real time and tracking reliability requirements for image tracking of space surveillance system. Machine vision was applied to the research of relative pose for spacecrafts, the feature extraction algorithm was the basis of relative pose. In this paper fractal geometry based edge extraction algorithm which can be used in determining and tracking the relative pose of an observed satellite during proximity operations in machine vision system was presented. The method gets the gray-level image distributed by fractal dimension used the Differential Box-Counting (DBC) approach of the fractal theory to restrain the noise. After this, we detect the consecutive edge using Mathematical Morphology. The validity of the proposed method is examined by processing and analyzing images of space targets. The edge extraction method not only extracts the outline of the target, but also keeps the inner details. Meanwhile, edge extraction is only processed in moving area to reduce computation greatly. Simulation results compared edge detection using the method which presented by us with other detection methods. The results indicate that the presented algorithm is a valid method to solve the problems of relative pose for spacecrafts.

Edge detection of optical subaperture image based on improved differential box-counting method

NASA Astrophysics Data System (ADS)

Li, Yi; Hui, Mei; Liu, Ming; Dong, Liquan; Kong, Lingqin; Zhao, Yuejin

2018-01-01

Optical synthetic aperture imaging technology is an effective approach to improve imaging resolution. Compared with monolithic mirror system, the image of optical synthetic aperture system is often more complex at the edge, and as a result of the existence of gap between segments, which makes stitching becomes a difficult problem. So it is necessary to extract the edge of subaperture image for achieving effective stitching. Fractal dimension as a measure feature can describe image surface texture characteristics, which provides a new approach for edge detection. In our research, an improved differential box-counting method is used to calculate fractal dimension of image, then the obtained fractal dimension is mapped to grayscale image to detect edges. Compared with original differential box-counting method, this method has two improvements as follows: by modifying the box-counting mechanism, a box with a fixed height is replaced by a box with adaptive height, which solves the problem of over-counting the number of boxes covering image intensity surface; an image reconstruction method based on super-resolution convolutional neural network is used to enlarge small size image, which can solve the problem that fractal dimension can't be calculated accurately under the small size image, and this method may well maintain scale invariability of fractal dimension. The experimental results show that the proposed algorithm can effectively eliminate noise and has a lower false detection rate compared with the traditional edge detection algorithms. In addition, this algorithm can maintain the integrity and continuity of image edge in the case of retaining important edge information.

Visual tool for estimating the fractal dimension of images

NASA Astrophysics Data System (ADS)

Grossu, I. V.; Besliu, C.; Rusu, M. V.; Jipa, Al.; Bordeianu, C. C.; Felea, D.

2009-10-01

This work presents a new Visual Basic 6.0 application for estimating the fractal dimension of images, based on an optimized version of the box-counting algorithm. Following the attempt to separate the real information from "noise", we considered also the family of all band-pass filters with the same band-width (specified as parameter). The fractal dimension can be thus represented as a function of the pixel color code. The program was used for the study of paintings cracks, as an additional tool which can help the critic to decide if an artistic work is original or not. Program summaryProgram title: Fractal Analysis v01 Catalogue identifier: AEEG_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEEG_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 29 690 No. of bytes in distributed program, including test data, etc.: 4 967 319 Distribution format: tar.gz Programming language: MS Visual Basic 6.0 Computer: PC Operating system: MS Windows 98 or later RAM: 30M Classification: 14 Nature of problem: Estimating the fractal dimension of images. Solution method: Optimized implementation of the box-counting algorithm. Use of a band-pass filter for separating the real information from "noise". User friendly graphical interface. Restrictions: Although various file-types can be used, the application was mainly conceived for the 8-bit grayscale, windows bitmap file format. Running time: In a first approximation, the algorithm is linear.

Advanced Models and Algorithms for Self-Similar IP Network Traffic Simulation and Performance Analysis

NASA Astrophysics Data System (ADS)

Radev, Dimitar; Lokshina, Izabella

2010-11-01

The paper examines self-similar (or fractal) properties of real communication network traffic data over a wide range of time scales. These self-similar properties are very different from the properties of traditional models based on Poisson and Markov-modulated Poisson processes. Advanced fractal models of sequentional generators and fixed-length sequence generators, and efficient algorithms that are used to simulate self-similar behavior of IP network traffic data are developed and applied. Numerical examples are provided; and simulation results are obtained and analyzed.

Effect of deformation on the thermal conductivity of granular porous media with rough grain surface

NASA Astrophysics Data System (ADS)

Askari, Roohollah; Hejazi, S. Hossein; Sahimi, Muhammad

2017-08-01

Heat transfer in granular porous media is an important phenomenon that is relevant to a wide variety of problems, including geothermal reservoirs and enhanced oil recovery by thermal methods. Resistance to flow of heat in the contact area between the grains strongly influences the effective thermal conductivity of such porous media. Extensive experiments have indicated that the roughness of the grains' surface follows self-affine fractal stochastic functions, and thus, the contact resistance cannot be accounted for by models based on smooth surfaces. Despite the significance of rough contact area, the resistance has been accounted for by a fitting parameter in the models of heat transfer. In this Letter we report on a study of conduction in a packing of particles that contains a fluid of a given conductivity, with each grain having a rough self-affine surface, and is under an external compressive pressure. The deformation of the contact area depends on the fractal dimension that characterizes the grains' rough surface, as well as their Young's modulus. Excellent qualitative agreement is obtained with experimental data. Deformation of granular porous media with grains that have rough self-affine fractal surface is simulated. Thermal contact resistance between grains with rough surfaces is incorporated into the numerical simulation of heat conduction under compressive pressure. By increasing compressive pressure, thermal conductivity is enhanced more in the grains with smoother surfaces and lower Young's modulus. Excellent qualitative agreement is obtained with the experimental data.

Archean Earth Atmosphere Fractal Haze Aggregates: Light Scattering Calculations and the Faint Young Sun Paradox

NASA Astrophysics Data System (ADS)

Boness, D. A.; Terrell-Martinez, B.

2010-12-01

As part of an ongoing undergraduate research project of light scattering calculations involving fractal carbonaceous soot aggregates relevant to current anthropogenic and natural sources in Earth's atmosphere, we have read with interest a recent paper [E.T. Wolf and O.B Toon,Science 328, 1266 (2010)] claiming that the Faint Young Sun paradox discussed four decades ago by Carl Sagan and others can be resolved without invoking heavy CO2 concentrations as a greenhouse gas warming the early Earth enough to sustain liquid water and hence allow the origin of life. Wolf and Toon report that a Titan-like Archean Earth haze, with a fractal haze aggregate nature due to nitrogen-methane photochemistry at high altitudes, should block enough UV light to protect the warming greenhouse gas NH3 while allowing enough visible light to reach the surface of the Earth. To test this hypothesis, we have employed a rigorous T-Matrix arbitrary-particle light scattering technique, to avoid the simplifications inherent in Mie-sphere scattering, on haze fractal aggregates at UV and visible wavelenths of incident light. We generate these model aggregates using diffusion-limited cluster aggregation (DLCA) algorithms, which much more closely fit actual haze fractal aggregates than do diffusion-limited aggregation (DLA) algorithms.

Small-angle scattering from the Cantor surface fractal on the plane and the Koch snowflake

NASA Astrophysics Data System (ADS)

Cherny, Alexander Yu.; Anitas, Eugen M.; Osipov, Vladimir A.; Kuklin, Alexander I.

The small-angle scattering (SAS) from the Cantor surface fractal on the plane and Koch snowflake is considered. We develop the construction algorithm for the Koch snowflake, which makes possible the recurrence relation for the scattering amplitude. The surface fractals can be decomposed into a sum of surface mass fractals for arbitrary fractal iteration, which enables various approximations for the scattering intensity. It is shown that for the Cantor fractal, one can neglect with a good accuracy the correlations between the mass fractal amplitudes, while for the Koch snowflake, these correlations are important. It is shown that nevertheless, the correlations can be build in the mass fractal amplitudes, which explains the decay of the scattering intensity $I(q)\\sim q^{D_{\\mathrm{s}}-4}$ with $1 < D_{\\mathrm{s}} < 2$ being the fractal dimension of the perimeter. The curve $I(q)q^{4-D_{\\mathrm{s}}}$ is found to be log-periodic in the fractal region with the period equal to the scaling factor of the fractal. The log-periodicity arises from the self-similarity of sizes of basic structural units rather than from correlations between their distances. A recurrence relation is obtained for the radius of gyration of Koch snowflake, which is solved in the limit of infinite iterations. The present analysis allows us to obtain additional information from SAS data, such as the edges of the fractal regions, the fractal iteration number and the scaling factor.

DNABIT Compress - Genome compression algorithm.

PubMed

Rajarajeswari, Pothuraju; Apparao, Allam

2011-01-22

Data compression is concerned with how information is organized in data. Efficient storage means removal of redundancy from the data being stored in the DNA molecule. Data compression algorithms remove redundancy and are used to understand biologically important molecules. We present a compression algorithm, "DNABIT Compress" for DNA sequences based on a novel algorithm of assigning binary bits for smaller segments of DNA bases to compress both repetitive and non repetitive DNA sequence. Our proposed algorithm achieves the best compression ratio for DNA sequences for larger genome. Significantly better compression results show that "DNABIT Compress" algorithm is the best among the remaining compression algorithms. While achieving the best compression ratios for DNA sequences (Genomes),our new DNABIT Compress algorithm significantly improves the running time of all previous DNA compression programs. Assigning binary bits (Unique BIT CODE) for (Exact Repeats, Reverse Repeats) fragments of DNA sequence is also a unique concept introduced in this algorithm for the first time in DNA compression. This proposed new algorithm could achieve the best compression ratio as much as 1.58 bits/bases where the existing best methods could not achieve a ratio less than 1.72 bits/bases.

Multispectral image fusion based on fractal features

NASA Astrophysics Data System (ADS)

Tian, Jie; Chen, Jie; Zhang, Chunhua

2004-01-01

Imagery sensors have been one indispensable part of the detection and recognition systems. They are widely used to the field of surveillance, navigation, control and guide, et. However, different imagery sensors depend on diverse imaging mechanisms, and work within diverse range of spectrum. They also perform diverse functions and have diverse circumstance requires. So it is unpractical to accomplish the task of detection or recognition with a single imagery sensor under the conditions of different circumstances, different backgrounds and different targets. Fortunately, the multi-sensor image fusion technique emerged as important route to solve this problem. So image fusion has been one of the main technical routines used to detect and recognize objects from images. While, loss of information is unavoidable during fusion process, so it is always a very important content of image fusion how to preserve the useful information to the utmost. That is to say, it should be taken into account before designing the fusion schemes how to avoid the loss of useful information or how to preserve the features helpful to the detection. In consideration of these issues and the fact that most detection problems are actually to distinguish man-made objects from natural background, a fractal-based multi-spectral fusion algorithm has been proposed in this paper aiming at the recognition of battlefield targets in the complicated backgrounds. According to this algorithm, source images are firstly orthogonally decomposed according to wavelet transform theories, and then fractal-based detection is held to each decomposed image. At this step, natural background and man-made targets are distinguished by use of fractal models that can well imitate natural objects. Special fusion operators are employed during the fusion of area that contains man-made targets so that useful information could be preserved and features of targets could be extruded. The final fused image is reconstructed from the composition of source pyramid images. So this fusion scheme is a multi-resolution analysis. The wavelet decomposition of image can be actually considered as special pyramid decomposition. According to wavelet decomposition theories, the approximation of image (formula available in paper) at resolution 2j+1 equal to its orthogonal projection in space , that is, where Ajf is the low-frequency approximation of image f(x, y) at resolution 2j and , , represent the vertical, horizontal and diagonal wavelet coefficients respectively at resolution 2j. These coefficients describe the high-frequency information of image at direction of vertical, horizontal and diagonal respectively. Ajf, , and are independent and can be considered as images. In this paper J is set to be 1, so the source image is decomposed to produce the son-images Af, D1f, D2f and D3f. To solve the problem of detecting artifacts, the concepts of vertical fractal dimension FD1, horizontal fractal dimension FD2 and diagonal fractal dimension FD3 are proposed in this paper. The vertical fractal dimension FD1 corresponds to the vertical wavelet coefficients image after the wavelet decomposition of source image, the horizontal fractal dimension FD2 corresponds to the horizontal wavelet coefficients and the diagonal fractal dimension FD3 the diagonal one. These definitions enrich the illustration of source images. Therefore they are helpful to classify the targets. Then the detection of artifacts in the decomposed images is a problem of pattern recognition in 4-D space. The combination of FD0, FD1, FD2 and FD3 make a vector of (FD0, FD1, FD2, FD3), which can be considered as a united feature vector of the studied image. All the parts of the images are classified in the 4-D pattern space created by the vector of (FD0, FD1, FD2, FD3) so that the area that contains man-made objects could be detected. This detection can be considered as a coarse recognition, and then the significant areas in each son-images are signed so that they can be dealt with special rules. There has been various fusion rules developed with each one aiming at a special problem. These rules have different performance, so it is very important to select an appropriate rule during the design of an image fusion system. Recent research denotes that the rule should be adjustable so that it is always suitable to extrude the features of targets and to preserve the pixels of useful information. In this paper, owing to the consideration that fractal dimension is one of the main features to distinguish man-made targets from natural objects, the fusion rule was defined that if the studied region of image contains man-made target, the pixels of the source image whose fractal dimension is minimal are saved to be the pixels of the fused image, otherwise, a weighted average operator is adopted to avoid loss of information. The main idea of this rule is to store the pixels with low fractal dimensions, so it can be named Minimal Fractal dimensions (MFD) fusion rule. This fractal-based algorithm is compared with a common weighted average fusion algorithm. An objective assessment is taken to the two fusion results. The criteria of Entropy, Cross-Entropy, Peak Signal-to-Noise Ratio (PSNR) and Standard Gray Scale Difference are defined in this paper. Reversely to the idea of constructing an ideal image as the assessing reference, the source images are selected to be the reference in this paper. It can be deemed that this assessment is to calculate how much the image quality has been enhanced and the quantity of information has been increased when the fused image is compared with the source images. The experimental results imply that the fractal-based multi-spectral fusion algorithm can effectively preserve the information of man-made objects with a high contrast. It is proved that this algorithm could well preserve features of military targets because that battlefield targets are most man-made objects and in common their images differ from fractal models obviously. Furthermore, the fractal features are not sensitive to the imaging conditions and the movement of targets, so this fractal-based algorithm may be very practical.

Fractal Complexity-Based Feature Extraction Algorithm of Communication Signals

NASA Astrophysics Data System (ADS)

Wang, Hui; Li, Jingchao; Guo, Lili; Dou, Zheng; Lin, Yun; Zhou, Ruolin

How to analyze and identify the characteristics of radiation sources and estimate the threat level by means of detecting, intercepting and locating has been the central issue of electronic support in the electronic warfare, and communication signal recognition is one of the key points to solve this issue. Aiming at accurately extracting the individual characteristics of the radiation source for the increasingly complex communication electromagnetic environment, a novel feature extraction algorithm for individual characteristics of the communication radiation source based on the fractal complexity of the signal is proposed. According to the complexity of the received signal and the situation of environmental noise, use the fractal dimension characteristics of different complexity to depict the subtle characteristics of the signal to establish the characteristic database, and then identify different broadcasting station by gray relation theory system. The simulation results demonstrate that the algorithm can achieve recognition rate of 94% even in the environment with SNR of -10dB, and this provides an important theoretical basis for the accurate identification of the subtle features of the signal at low SNR in the field of information confrontation.

Effect of Fractal Dimension on the Strain Behavior of Particulate Media

NASA Astrophysics Data System (ADS)

Altun, Selim; Sezer, Alper; Goktepe, A. Burak

2016-12-01

In this study, the influence of several fractal identifiers of granular materials on dynamic behavior of a flexible pavement structure as a particulate stratum is considered. Using experimental results and numerical methods as well, 15 different grain-shaped sands obtained from 5 different sources were analyzed as pavement base course materials. Image analyses were carried out by use of a stereomicroscope on 15 different samples to obtain quantitative particle shape information. Furthermore, triaxial compression tests were conducted to determine stress-strain and shear strength parameters of sands. Additionally, the dynamic response of the particulate media to standard traffic loads was computed using finite element modeling (FEM) technique. Using area-perimeter, line divider and box counting methods, over a hundred grains for each sand type were subjected to fractal analysis. Relationships among fractal dimension descriptors and dynamic strain levels were established for assessment of importance of shape descriptors of sands at various scales on the dynamic behavior. In this context, the advantage of fractal geometry concept to describe irregular and fractured shapes was used to characterize the sands used as base course materials. Results indicated that fractal identifiers can be preferred to analyze the effect of shape properties of sands on dynamic behavior of pavement base layers.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ren, Jingli; Chen, Cun; Wang, Gang

This study explores the temporal scaling behavior induced shear-branching structure in response to variant temperatures and strain rates during plastic deformation of Zr-based bulk metallic glass (BMG). The data analysis based on the compression tests suggests that there are two states of shear-branching structures: the fractal structure with a long-range order at an intermediate temperature of 223 K and a larger strain rate of 2.5 × 10 –2 s –1; the disordered structure dominated at other temperature and strain rate. It can be deduced from the percolation theory that the compressive ductility, ec, can reach the maximum value at themore » intermediate temperature. Furthermore, a dynamical model involving temperature is given for depicting the shear-sliding process, reflecting the plastic deformation has fractal structure at the temperature of 223 K and strain rate of 2.5 × 10 –2 s –1.« less

Predicting beauty: fractal dimension and visual complexity in art.

PubMed

Forsythe, A; Nadal, M; Sheehy, N; Cela-Conde, C J; Sawey, M

2011-02-01

Visual complexity has been known to be a significant predictor of preference for artistic works for some time. The first study reported here examines the extent to which perceived visual complexity in art can be successfully predicted using automated measures of complexity. Contrary to previous findings the most successful predictor of visual complexity was Gif compression. The second study examined the extent to which fractal dimension could account for judgments of perceived beauty. The fractal dimension measure accounts for more of the variance in judgments of perceived beauty in visual art than measures of visual complexity alone, particularly for abstract and natural images. Results also suggest that when colour is removed from an artistic image observers are unable to make meaningful judgments as to its beauty. ©2010 The British Psychological Society.

Complex Patterns in Financial Time Series Through HIGUCHI’S Fractal Dimension

NASA Astrophysics Data System (ADS)

Grace Elizabeth Rani, T. G.; Jayalalitha, G.

2016-11-01

This paper analyzes the complexity of stock exchanges through fractal theory. Closing price indices of four stock exchanges with different industry sectors are selected. Degree of complexity is assessed through Higuchi’s fractal dimension. Various window sizes are considered in evaluating the fractal dimension. It is inferred that the data considered as a whole represents random walk for all the four indices. Analysis of financial data through windowing procedure exhibits multi-fractality. Attempts to apply moving averages to reduce noise in the data revealed lower estimates of fractal dimension, which was verified using fractional Brownian motion. A change in the normalization factor in Higuchi’s algorithm did improve the results. It is quintessential to focus on rural development to realize a standard and steady growth of economy. Tools must be devised to settle the issues in this regard. Micro level institutions are necessary for the economic growth of a country like India, which would induce a sporadic development in the present global economical scenario.

DNABIT Compress – Genome compression algorithm

PubMed Central

Rajarajeswari, Pothuraju; Apparao, Allam

2011-01-01

Data compression is concerned with how information is organized in data. Efficient storage means removal of redundancy from the data being stored in the DNA molecule. Data compression algorithms remove redundancy and are used to understand biologically important molecules. We present a compression algorithm, “DNABIT Compress” for DNA sequences based on a novel algorithm of assigning binary bits for smaller segments of DNA bases to compress both repetitive and non repetitive DNA sequence. Our proposed algorithm achieves the best compression ratio for DNA sequences for larger genome. Significantly better compression results show that “DNABIT Compress” algorithm is the best among the remaining compression algorithms. While achieving the best compression ratios for DNA sequences (Genomes),our new DNABIT Compress algorithm significantly improves the running time of all previous DNA compression programs. Assigning binary bits (Unique BIT CODE) for (Exact Repeats, Reverse Repeats) fragments of DNA sequence is also a unique concept introduced in this algorithm for the first time in DNA compression. This proposed new algorithm could achieve the best compression ratio as much as 1.58 bits/bases where the existing best methods could not achieve a ratio less than 1.72 bits/bases. PMID:21383923

Characterization of Atrophic Changes in the Cerebral Cortex Using Fractal Dimensional Analysis

PubMed Central

George, Anuh T.; Jeon, Tina; Hynan, Linda S.; Youn, Teddy S.; Kennedy, David N.; Dickerson, Bradford

2010-01-01

The purpose of this project is to apply a modified fractal analysis technique to high-resolution T1 weighted magnetic resonance images in order to quantify the alterations in the shape of the cerebral cortex that occur in patients with Alzheimer’s disease. Images were selected from the Alzheimer’s Disease Neuroimaging Initiative database (Control N=15, Mild-Moderate AD N=15). The images were segmented using a semi-automated analysis program. Four coronal and three axial profiles of the cerebral cortical ribbon were created. The fractal dimensions (Df) of the cortical ribbons were then computed using a box-counting algorithm. The mean Df of the cortical ribbons from AD patients were lower than age-matched controls on six of seven profiles. The fractal measure has regional variability which reflects local differences in brain structure. Fractal dimension is complementary to volumetric measures and may assist in identifying disease state or disease progression. PMID:20740072

Characteristics of Crushing Energy and Fractal of Magnetite Ore under Uniaxial Compression

NASA Astrophysics Data System (ADS)

Gao, F.; Gan, D. Q.; Zhang, Y. B.

2018-03-01

The crushing mechanism of magnetite ore is a critical theoretical problem on the controlling of energy dissipation and machine crushing quality in ore material processing. Uniaxial crushing tests were carried out to research the deformation mechanism and the laws of the energy evolution, based on which the crushing mechanism of magnetite ore was explored. The compaction stage and plasticity and damage stage are two main compression deformation stages, the main transitional forms from inner damage to fracture are plastic deformation and stick-slip. In the process of crushing, plasticity and damage stage is the key link on energy absorption for that the specimen tends to saturate energy state approaching to the peak stress. The characteristics of specimen deformation and energy dissipation can synthetically reply the state of existed defects inner raw magnetite ore and the damage process during loading period. The fast releasing of elastic energy and the work done by the press machine commonly make raw magnetite ore thoroughly broken after peak stress. Magnetite ore fragments have statistical self-similarity and size threshold of fractal characteristics under uniaxial squeezing crushing. The larger ratio of releasable elastic energy and dissipation energy and the faster energy change rate is the better fractal properties and crushing quality magnetite ore has under uniaxial crushing.

Fractal aspects of the flow and shear behaviour of free-flowable particle size fractions of pharmaceutical directly compressible excipient sorbitol.

PubMed

Hurychová, Hana; Lebedová, Václava; Šklubalová, Zdenka; Dzámová, Pavlína; Svěrák, Tomáš; Stoniš, Jan

Flowability of powder excipients is directly influenced by their size and shape although the granulometric influence of the flow and shear behaviour of particulate matter is not studied frequently. In this work, the influence of particle size on the mass flow rate through the orifice of a conical hopper, and the cohesion and flow function was studied for four free-flowable size fractions of sorbitol for direct compression in the range of 0.080-0.400 mm. The particles were granulometricaly characterized using an optical microscopy; a boundary fractal dimension of 1.066 was estimated for regular sorbitol particles. In the particle size range studied, a non-linear relationship between the mean particle size and the mass flow rate Q10 (g/s) was detected having amaximum at the 0.245mm fraction. The best flow properties of this fraction were verified with aJenike shear tester due to the highest value of flow function and the lowest value of the cohesion. The results of this work show the importance of the right choice of the excipient particle size to achieve the best flow behaviour of particulate material.Key words: flowability size fraction sorbitol for direct compaction Jenike shear tester fractal dimension.

Single-Image Super-Resolution Based on Rational Fractal Interpolation.

PubMed

Zhang, Yunfeng; Fan, Qinglan; Bao, Fangxun; Liu, Yifang; Zhang, Caiming

2018-08-01

This paper presents a novel single-image super-resolution (SR) procedure, which upscales a given low-resolution (LR) input image to a high-resolution image while preserving the textural and structural information. First, we construct a new type of bivariate rational fractal interpolation model and investigate its analytical properties. This model has different forms of expression with various values of the scaling factors and shape parameters; thus, it can be employed to better describe image features than current interpolation schemes. Furthermore, this model combines the advantages of rational interpolation and fractal interpolation, and its effectiveness is validated through theoretical analysis. Second, we develop a single-image SR algorithm based on the proposed model. The LR input image is divided into texture and non-texture regions, and then, the image is interpolated according to the characteristics of the local structure. Specifically, in the texture region, the scaling factor calculation is the critical step. We present a method to accurately calculate scaling factors based on local fractal analysis. Extensive experiments and comparisons with the other state-of-the-art methods show that our algorithm achieves competitive performance, with finer details and sharper edges.

New methodology for evaluating osteoclastic activity induced by orthodontic load

PubMed Central

ARAÚJO, Adriele Silveira; FERNANDES, Alline Birra Nolasco; MACIEL, José Vinicius Bolognesi; NETTO, Juliana de Noronha Santos; BOLOGNESE, Ana Maria

2015-01-01

Orthodontic tooth movement (OTM) is a dynamic process of bone modeling involving osteoclast-driven resorption on the compression side. Consequently, to estimate the influence of various situations on tooth movement, experimental studies need to analyze this cell. Objectives The aim of this study was to test and validate a new method for evaluating osteoclastic activity stimulated by mechanical loading based on the fractal analysis of the periodontal ligament (PDL)-bone interface. Material and Methods The mandibular right first molars of 14 rabbits were tipped mesially by a coil spring exerting a constant force of 85 cN. To evaluate the actual influence of osteoclasts on fractal dimension of bone surface, alendronate (3 mg/Kg) was injected weekly in seven of those rabbits. After 21 days, the animals were killed and their jaws were processed for histological evaluation. Osteoclast counts and fractal analysis (by the box counting method) of the PDL-bone interface were performed in histological sections of the right and left sides of the mandible. Results An increase in the number of osteoclasts and in fractal dimension after OTM only happened when alendronate was not administered. Strong correlation was found between the number of osteoclasts and fractal dimension. Conclusions Our results suggest that osteoclastic activity leads to an increase in bone surface irregularity, which can be quantified by its fractal dimension. This makes fractal analysis by the box counting method a potential tool for the assessment of osteoclastic activity on bone surfaces in microscopic examination. PMID:25760264

Unsteady Solution of Non-Linear Differential Equations Using Walsh Function Series

NASA Technical Reports Server (NTRS)

Gnoffo, Peter A.

2015-01-01

Walsh functions form an orthonormal basis set consisting of square waves. The discontinuous nature of square waves make the system well suited for representing functions with discontinuities. The product of any two Walsh functions is another Walsh function - a feature that can radically change an algorithm for solving non-linear partial differential equations (PDEs). The solution algorithm of non-linear differential equations using Walsh function series is unique in that integrals and derivatives may be computed using simple matrix multiplication of series representations of functions. Solutions to PDEs are derived as functions of wave component amplitude. Three sample problems are presented to illustrate the Walsh function series approach to solving unsteady PDEs. These include an advection equation, a Burgers equation, and a Riemann problem. The sample problems demonstrate the use of the Walsh function solution algorithms, exploiting Fast Walsh Transforms in multi-dimensions (O(Nlog(N))). Details of a Fast Walsh Reciprocal, defined here for the first time, enable inversion of aWalsh Symmetric Matrix in O(Nlog(N)) operations. Walsh functions have been derived using a fractal recursion algorithm and these fractal patterns are observed in the progression of pairs of wave number amplitudes in the solutions. These patterns are most easily observed in a remapping defined as a fractal fingerprint (FFP). A prolongation of existing solutions to the next highest order exploits these patterns. The algorithms presented here are considered a work in progress that provide new alternatives and new insights into the solution of non-linear PDEs.

Locally adaptive vector quantization: Data compression with feature preservation

NASA Technical Reports Server (NTRS)

Cheung, K. M.; Sayano, M.

1992-01-01

A study of a locally adaptive vector quantization (LAVQ) algorithm for data compression is presented. This algorithm provides high-speed one-pass compression and is fully adaptable to any data source and does not require a priori knowledge of the source statistics. Therefore, LAVQ is a universal data compression algorithm. The basic algorithm and several modifications to improve performance are discussed. These modifications are nonlinear quantization, coarse quantization of the codebook, and lossless compression of the output. Performance of LAVQ on various images using irreversible (lossy) coding is comparable to that of the Linde-Buzo-Gray algorithm, but LAVQ has a much higher speed; thus this algorithm has potential for real-time video compression. Unlike most other image compression algorithms, LAVQ preserves fine detail in images. LAVQ's performance as a lossless data compression algorithm is comparable to that of Lempel-Ziv-based algorithms, but LAVQ uses far less memory during the coding process.

Transition of temporal scaling behavior in percolation assisted shear-branching structure during plastic deformation

DOE PAGES

Ren, Jingli; Chen, Cun; Wang, Gang; ...

2017-03-22

This study explores the temporal scaling behavior induced shear-branching structure in response to variant temperatures and strain rates during plastic deformation of Zr-based bulk metallic glass (BMG). The data analysis based on the compression tests suggests that there are two states of shear-branching structures: the fractal structure with a long-range order at an intermediate temperature of 223 K and a larger strain rate of 2.5 × 10 –2 s –1; the disordered structure dominated at other temperature and strain rate. It can be deduced from the percolation theory that the compressive ductility, ec, can reach the maximum value at themore » intermediate temperature. Furthermore, a dynamical model involving temperature is given for depicting the shear-sliding process, reflecting the plastic deformation has fractal structure at the temperature of 223 K and strain rate of 2.5 × 10 –2 s –1.« less

Correlation of Fractal Dimension Values with Implant Insertion Torque and Resonance Frequency Values at Implant Recipient Sites.

PubMed

Suer, Berkay Tolga; Yaman, Zekai; Buyuksarac, Bora

2016-01-01

Fractal analysis is a mathematical method used to describe the internal architecture of complex structures such as trabecular bone. Fractal analysis of panoramic radiographs of implant recipient sites could help to predict the quality of the bone prior to implant placement. This study investigated the correlations between the fractal dimension values obtained from panoramic radiographs and the insertion torque and resonance frequency values of mandibular implants. Thirty patients who received a total of 55 implants of the same brand, diameter, and length in the mandibular premolar and molar regions were included in the study. The same surgical procedures were applied to each patient, and the insertion torque and resonance frequency values were recorded for each implant at the time of placement. The radiographic fractal dimensions of the alveolar bone in the implant recipient area were calculated from preoperative panoramic radiographs using a box-counting algorithm. The insertion torque and resonance frequency values were compared with the fractal dimension values using the Spearman test. All implants were successful, and none were lost during the follow-up period. Linear correlations were observed between the fractal dimension and resonance frequency, between the fractal dimension and insertion torque, and between resonance frequency and insertion torque. These results suggest that the noninvasive measurement of the fractal dimension from panoramic radiographs might help to predict the bone quality, and thus the primary stability of dental implants, before implant surgery.

Understanding soft glassy materials using an energy landscape approach

NASA Astrophysics Data System (ADS)

Hwang, Hyun Joo; Riggleman, Robert A.; Crocker, John C.

2016-09-01

Many seemingly different soft materials--such as soap foams, mayonnaise, toothpaste and living cells--display strikingly similar viscoelastic behaviour. A fundamental physical understanding of such soft glassy rheology and how it can manifest in such diverse materials, however, remains unknown. Here, by using a model soap foam consisting of compressible spherical bubbles, whose sizes slowly evolve and whose collective motion is simply dictated by energy minimization, we study the foam's dynamics as it corresponds to downhill motion on an energy landscape function spanning a high-dimensional configuration space. We find that these downhill paths, when viewed in this configuration space, are, surprisingly, fractal. The complex behaviour of our model, including power-law rheology and non-diffusive bubble motion and avalanches, stems directly from the fractal dimension and energy function of these paths. Our results suggest that ubiquitous soft glassy rheology may be a consequence of emergent fractal geometry in the energy landscapes of many complex fluids.

A new surface fractal dimension for displacement mode shape-based damage identification of plate-type structures

NASA Astrophysics Data System (ADS)

Shi, Binkai; Qiao, Pizhong

2018-03-01

Vibration-based nondestructive testing is an area of growing interest and worthy of exploring new and innovative approaches. The displacement mode shape is often chosen to identify damage due to its local detailed characteristic and less sensitivity to surrounding noise. Requirement for baseline mode shape in most vibration-based damage identification limits application of such a strategy. In this study, a new surface fractal dimension called edge perimeter dimension (EPD) is formulated, from which an EPD-based window dimension locus (EPD-WDL) algorithm for irregularity or damage identification of plate-type structures is established. An analytical notch-type damage model of simply-supported plates is proposed to evaluate notch effect on plate vibration performance; while a sub-domain of notch cases with less effect is selected to investigate robustness of the proposed damage identification algorithm. Then, fundamental aspects of EPD-WDL algorithm in term of notch localization, notch quantification, and noise immunity are assessed. A mathematical solution called isomorphism is implemented to remove false peaks caused by inflexions of mode shapes when applying the EPD-WDL algorithm to higher mode shapes. The effectiveness and practicability of the EPD-WDL algorithm are demonstrated by an experimental procedure on damage identification of an artificially-induced notched aluminum cantilever plate using a measurement system of piezoelectric lead-zirconate (PZT) actuator and scanning laser Doppler vibrometer (SLDV). As demonstrated in both the analytical and experimental evaluations, the new surface fractal dimension technique developed is capable of effectively identifying damage in plate-type structures.

Graphics processing unit-assisted lossless decompression

DOEpatents

Loughry, Thomas A.

2016-04-12

Systems and methods for decompressing compressed data that has been compressed by way of a lossless compression algorithm are described herein. In a general embodiment, a graphics processing unit (GPU) is programmed to receive compressed data packets and decompress such packets in parallel. The compressed data packets are compressed representations of an image, and the lossless compression algorithm is a Rice compression algorithm.

Spatial compression algorithm for the analysis of very large multivariate images

DOEpatents

Keenan, Michael R [Albuquerque, NM

2008-07-15

A method for spatially compressing data sets enables the efficient analysis of very large multivariate images. The spatial compression algorithms use a wavelet transformation to map an image into a compressed image containing a smaller number of pixels that retain the original image's information content. Image analysis can then be performed on a compressed data matrix consisting of a reduced number of significant wavelet coefficients. Furthermore, a block algorithm can be used for performing common operations more efficiently. The spatial compression algorithms can be combined with spectral compression algorithms to provide further computational efficiencies.

Assessment of disintegrant efficacy with fractal dimensions from real-time MRI.

PubMed

Quodbach, Julian; Moussavi, Amir; Tammer, Roland; Frahm, Jens; Kleinebudde, Peter

2014-11-20

An efficient disintegrant is capable of breaking up a tablet in the smallest possible particles in the shortest time. Until now, comparative data on the efficacy of different disintegrants is based on dissolution studies or the disintegration time. Extending these approaches, this study introduces a method, which defines the evolution of fractal dimensions of tablets as surrogate parameter for the available surface area. Fractal dimensions are a measure for the tortuosity of a line, in this case the upper surface of a disintegrating tablet. High-resolution real-time MRI was used to record videos of disintegrating tablets. The acquired video images were processed to depict the upper surface of the tablets and a box-counting algorithm was used to estimate the fractal dimensions. The influence of six different disintegrants, of different relative tablet density, and increasing disintegrant concentration was investigated to evaluate the performance of the novel method. Changing relative densities hardly affect the progression of fractal dimensions, whereas an increase in disintegrant concentration causes increasing fractal dimensions during disintegration, which are also reached quicker. Different disintegrants display only minor differences in the maximal fractal dimension, yet the kinetic in which the maximum is reached allows a differentiation and classification of disintegrants. Copyright © 2014 Elsevier B.V. All rights reserved.

SeqCompress: an algorithm for biological sequence compression.

PubMed

Sardaraz, Muhammad; Tahir, Muhammad; Ikram, Ataul Aziz; Bajwa, Hassan

2014-10-01

The growth of Next Generation Sequencing technologies presents significant research challenges, specifically to design bioinformatics tools that handle massive amount of data efficiently. Biological sequence data storage cost has become a noticeable proportion of total cost in the generation and analysis. Particularly increase in DNA sequencing rate is significantly outstripping the rate of increase in disk storage capacity, which may go beyond the limit of storage capacity. It is essential to develop algorithms that handle large data sets via better memory management. This article presents a DNA sequence compression algorithm SeqCompress that copes with the space complexity of biological sequences. The algorithm is based on lossless data compression and uses statistical model as well as arithmetic coding to compress DNA sequences. The proposed algorithm is compared with recent specialized compression tools for biological sequences. Experimental results show that proposed algorithm has better compression gain as compared to other existing algorithms. Copyright © 2014 Elsevier Inc. All rights reserved.

Wavelet compression of multichannel ECG data by enhanced set partitioning in hierarchical trees algorithm.

PubMed

Sharifahmadian, Ershad

2006-01-01

The set partitioning in hierarchical trees (SPIHT) algorithm is very effective and computationally simple technique for image and signal compression. Here the author modified the algorithm which provides even better performance than the SPIHT algorithm. The enhanced set partitioning in hierarchical trees (ESPIHT) algorithm has performance faster than the SPIHT algorithm. In addition, the proposed algorithm reduces the number of bits in a bit stream which is stored or transmitted. I applied it to compression of multichannel ECG data. Also, I presented a specific procedure based on the modified algorithm for more efficient compression of multichannel ECG data. This method employed on selected records from the MIT-BIH arrhythmia database. According to experiments, the proposed method attained the significant results regarding compression of multichannel ECG data. Furthermore, in order to compress one signal which is stored for a long time, the proposed multichannel compression method can be utilized efficiently.

A Simple, Low Overhead Data Compression Algorithm for Converting Lossy Compression Processes to Lossless

DTIC Science & Technology

1993-12-01

0~0 S* NAVAL POSTGRADUATE SCHOOL Monterey, California DTIC ELECTE THESIS S APR 11 1994DU A SIMPLE, LOW OVERHEAD DATA COMPRESSION ALGORITHM FOR...A SIMPLE. LOW OVERHEAD DATA COMPRESSION ALGORITHM FOR CONVERTING LOSSY COMPRESSION PROCESSES TO LOSSLESS. 6. AUTHOR(S) Abbott, Walter D., III 7...Approved for public release; distribution is unlimited. A Simple, Low Overhead Data Compression Algorithm for Converting Lossy Processes to Lossless by

Fast Lossless Compression of Multispectral-Image Data

NASA Technical Reports Server (NTRS)

Klimesh, Matthew

2006-01-01

An algorithm that effects fast lossless compression of multispectral-image data is based on low-complexity, proven adaptive-filtering algorithms. This algorithm is intended for use in compressing multispectral-image data aboard spacecraft for transmission to Earth stations. Variants of this algorithm could be useful for lossless compression of three-dimensional medical imagery and, perhaps, for compressing image data in general.

The Calculation of Fractal Dimension in the Presence of Non-Fractal Clutter

NASA Technical Reports Server (NTRS)

Herren, Kenneth A.; Gregory, Don A.

1999-01-01

The area of information processing has grown dramatically over the last 50 years. In the areas of image processing and information storage the technology requirements have far outpaced the ability of the community to meet demands. The need for faster recognition algorithms and more efficient storage of large quantities of data has forced the user to accept less than lossless retrieval of that data for analysis. In addition to clutter that is not the object of interest in the data set, often the throughput requirements forces the user to accept "noisy" data and to tolerate the clutter inherent in that data. It has been shown that some of this clutter, both the intentional clutter (clouds, trees, etc) as well as the noise introduced on the data by processing requirements can be modeled as fractal or fractal-like. Traditional methods using Fourier deconvolution on these sources of noise in frequency space leads to loss of signal and can, in many cases, completely eliminate the target of interest. The parameters that characterize fractal-like noise (predominately the fractal dimension) have been investigated and a technique to reduce or eliminate noise from real scenes has been developed. Examples of clutter reduced images are presented.

Fractal dimension of microbead assemblies used for protein detection.

PubMed

Hecht, Ariel; Commiskey, Patrick; Lazaridis, Filippos; Argyrakis, Panos; Kopelman, Raoul

2014-11-10

We use fractal analysis to calculate the protein concentration in a rotating magnetic assembly of microbeads of size 1 μm, which has optimized parameters of sedimentation, binding sites and magnetic volume. We utilize the original Forrest-Witten method, but due to the relatively small number of bead particles, which is of the order of 500, we use a large number of origins and also a large number of algorithm iterations. We find a value of the fractal dimension in the range 1.70-1.90, as a function of the thrombin concentration, which plays the role of binding the microbeads together. This is in good agreement with previous results from magnetorotation studies. The calculation of the fractal dimension using multiple points of reference can be used for any assembly with a relatively small number of particles. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Fractal analysis on human dynamics of library loans

NASA Astrophysics Data System (ADS)

Fan, Chao; Guo, Jin-Li; Zha, Yi-Long

2012-12-01

In this paper, the fractal characteristic of human behaviors is investigated from the perspective of time series constructed with the amount of library loans. The values of the Hurst exponent and length of non-periodic cycle calculated through rescaled range analysis indicate that the time series of human behaviors and their sub-series are fractal with self-similarity and long-range dependence. Then the time series are converted into complex networks by the visibility algorithm. The topological properties of the networks such as scale-free property and small-world effect imply that there is a close relationship among the numbers of repetitious behaviors performed by people during certain periods of time. Our work implies that there is intrinsic regularity in the human collective repetitious behaviors. The conclusions may be helpful to develop some new approaches to investigate the fractal feature and mechanism of human dynamics, and provide some references for the management and forecast of human collective behaviors.

Data Compression Techniques for Maps

DTIC Science & Technology

1989-01-01

Lempel - Ziv compression is applied to the classified and unclassified images as also to the output of the compression algorithms . The algorithms ...resulted in a compression of 7:1. The output of the quadtree coding algorithm was then compressed using Lempel - Ziv coding. The compression ratio achieved...using Lempel - Ziv coding. The unclassified image gave a compression ratio of only 1.4:1. The K means classified image

Beyond maximum entropy: Fractal Pixon-based image reconstruction

NASA Technical Reports Server (NTRS)

Puetter, Richard C.; Pina, R. K.

1994-01-01

We have developed a new Bayesian image reconstruction method that has been shown to be superior to the best implementations of other competing methods, including Goodness-of-Fit methods such as Least-Squares fitting and Lucy-Richardson reconstruction, as well as Maximum Entropy (ME) methods such as those embodied in the MEMSYS algorithms. Our new method is based on the concept of the pixon, the fundamental, indivisible unit of picture information. Use of the pixon concept provides an improved image model, resulting in an image prior which is superior to that of standard ME. Our past work has shown how uniform information content pixons can be used to develop a 'Super-ME' method in which entropy is maximized exactly. Recently, however, we have developed a superior pixon basis for the image, the Fractal Pixon Basis (FPB). Unlike the Uniform Pixon Basis (UPB) of our 'Super-ME' method, the FPB basis is selected by employing fractal dimensional concepts to assess the inherent structure in the image. The Fractal Pixon Basis results in the best image reconstructions to date, superior to both UPB and the best ME reconstructions. In this paper, we review the theory of the UPB and FPB pixon and apply our methodology to the reconstruction of far-infrared imaging of the galaxy M51. The results of our reconstruction are compared to published reconstructions of the same data using the Lucy-Richardson algorithm, the Maximum Correlation Method developed at IPAC, and the MEMSYS ME algorithms. The results show that our reconstructed image has a spatial resolution a factor of two better than best previous methods (and a factor of 20 finer than the width of the point response function), and detects sources two orders of magnitude fainter than other methods.

A Novel Range Compression Algorithm for Resolution Enhancement in GNSS-SARs.

PubMed

Zheng, Yu; Yang, Yang; Chen, Wu

2017-06-25

In this paper, a novel range compression algorithm for enhancing range resolutions of a passive Global Navigation Satellite System-based Synthetic Aperture Radar (GNSS-SAR) is proposed. In the proposed algorithm, within each azimuth bin, firstly range compression is carried out by correlating a reflected GNSS intermediate frequency (IF) signal with a synchronized direct GNSS base-band signal in the range domain. Thereafter, spectrum equalization is applied to the compressed results for suppressing side lobes to obtain a final range-compressed signal. Both theoretical analysis and simulation results have demonstrated that significant range resolution improvement in GNSS-SAR images can be achieved by the proposed range compression algorithm, compared to the conventional range compression algorithm.

EEG-based "serious" games and monitoring tools for pain management.

PubMed

Sourina, Olga; Wang, Qiang; Nguyen, Minh Khoa

2011-01-01

EEG-based "serious games" for medical applications attracted recently more attention from the research community and industry as wireless EEG reading devices became easily available on the market. EEG-based technology has been applied in anesthesiology, psychology, etc. In this paper, we proposed and developed EEG-based "serious" games and doctor's monitoring tools that could be used for pain management. As EEG signal is considered to have a fractal nature, we proposed and develop a novel spatio-temporal fractal based algorithm for brain state quantification. The algorithm is implemented with blobby visualization tools for patient monitoring and in EEG-based "serious" games. Such games could be used by patient even at home convenience for pain management as an alternative to traditional drug treatment.

Image encryption based on fractal-structured phase mask in fractional Fourier transform domain

NASA Astrophysics Data System (ADS)

Zhao, Meng-Dan; Gao, Xu-Zhen; Pan, Yue; Zhang, Guan-Lin; Tu, Chenghou; Li, Yongnan; Wang, Hui-Tian

2018-04-01

We present an optical encryption approach based on the combination of fractal Fresnel lens (FFL) and fractional Fourier transform (FrFT). Our encryption approach is in fact a four-fold encryption scheme, including the random phase encoding produced by the Gerchberg–Saxton algorithm, a FFL, and two FrFTs. A FFL is composed of a Sierpinski carpet fractal plate and a Fresnel zone plate. In our encryption approach, the security is enhanced due to the more expandable key spaces and the use of FFL overcomes the alignment problem of the optical axis in optical system. Only using the perfectly matched parameters of the FFL and the FrFT, the plaintext can be recovered well. We present an image encryption algorithm that from the ciphertext we can get two original images by the FrFT with two different phase distribution keys, obtained by performing 100 iterations between the two plaintext and ciphertext, respectively. We test the sensitivity of our approach to various parameters such as the wavelength of light, the focal length of FFL, and the fractional orders of FrFT. Our approach can resist various attacks.

Mixed raster content (MRC) model for compound image compression

NASA Astrophysics Data System (ADS)

de Queiroz, Ricardo L.; Buckley, Robert R.; Xu, Ming

1998-12-01

This paper will describe the Mixed Raster Content (MRC) method for compressing compound images, containing both binary test and continuous-tone images. A single compression algorithm that simultaneously meets the requirements for both text and image compression has been elusive. MRC takes a different approach. Rather than using a single algorithm, MRC uses a multi-layered imaging model for representing the results of multiple compression algorithms, including ones developed specifically for text and for images. As a result, MRC can combine the best of existing or new compression algorithms and offer different quality-compression ratio tradeoffs. The algorithms used by MRC set the lower bound on its compression performance. Compared to existing algorithms, MRC has some image-processing overhead to manage multiple algorithms and the imaging model. This paper will develop the rationale for the MRC approach by describing the multi-layered imaging model in light of a rate-distortion trade-off. Results will be presented comparing images compressed using MRC, JPEG and state-of-the-art wavelet algorithms such as SPIHT. MRC has been approved or proposed as an architectural model for several standards, including ITU Color Fax, IETF Internet Fax, and JPEG 2000.

Evaluation of a Text Compression Algorithm Against Computer-Aided Instruction (CAI) Material.

ERIC Educational Resources Information Center

Knight, Joseph M., Jr.

This report describes the initial evaluation of a text compression algorithm against computer assisted instruction (CAI) material. A review of some concepts related to statistical text compression is followed by a detailed description of a practical text compression algorithm. A simulation of the algorithm was programed and used to obtain…

Analyzing gene expression time-courses based on multi-resolution shape mixture model.

PubMed

Li, Ying; He, Ye; Zhang, Yu

2016-11-01

Biological processes actually are a dynamic molecular process over time. Time course gene expression experiments provide opportunities to explore patterns of gene expression change over a time and understand the dynamic behavior of gene expression, which is crucial for study on development and progression of biology and disease. Analysis of the gene expression time-course profiles has not been fully exploited so far. It is still a challenge problem. We propose a novel shape-based mixture model clustering method for gene expression time-course profiles to explore the significant gene groups. Based on multi-resolution fractal features and mixture clustering model, we proposed a multi-resolution shape mixture model algorithm. Multi-resolution fractal features is computed by wavelet decomposition, which explore patterns of change over time of gene expression at different resolution. Our proposed multi-resolution shape mixture model algorithm is a probabilistic framework which offers a more natural and robust way of clustering time-course gene expression. We assessed the performance of our proposed algorithm using yeast time-course gene expression profiles compared with several popular clustering methods for gene expression profiles. The grouped genes identified by different methods are evaluated by enrichment analysis of biological pathways and known protein-protein interactions from experiment evidence. The grouped genes identified by our proposed algorithm have more strong biological significance. A novel multi-resolution shape mixture model algorithm based on multi-resolution fractal features is proposed. Our proposed model provides a novel horizons and an alternative tool for visualization and analysis of time-course gene expression profiles. The R and Matlab program is available upon the request. Copyright © 2016 Elsevier Inc. All rights reserved.

The importance of robust error control in data compression applications

NASA Technical Reports Server (NTRS)

Woolley, S. I.

1993-01-01

Data compression has become an increasingly popular option as advances in information technology have placed further demands on data storage capabilities. With compression ratios as high as 100:1 the benefits are clear; however, the inherent intolerance of many compression formats to error events should be given careful consideration. If we consider that efficiently compressed data will ideally contain no redundancy, then the introduction of a channel error must result in a change of understanding from that of the original source. While the prefix property of codes such as Huffman enables resynchronisation, this is not sufficient to arrest propagating errors in an adaptive environment. Arithmetic, Lempel-Ziv, discrete cosine transform (DCT) and fractal methods are similarly prone to error propagating behaviors. It is, therefore, essential that compression implementations provide sufficient combatant error control in order to maintain data integrity. Ideally, this control should be derived from a full understanding of the prevailing error mechanisms and their interaction with both the system configuration and the compression schemes in use.

Data Compression for Maskless Lithography Systems: Architecture, Algorithms and Implementation

DTIC Science & Technology

2008-05-19

Data Compression for Maskless Lithography Systems: Architecture, Algorithms and Implementation Vito Dai Electrical Engineering and Computer Sciences...servers or to redistribute to lists, requires prior specific permission. Data Compression for Maskless Lithography Systems: Architecture, Algorithms and...for Maskless Lithography Systems: Architecture, Algorithms and Implementation Copyright 2008 by Vito Dai 1 Abstract Data Compression for Maskless

Spectral compression algorithms for the analysis of very large multivariate images

DOEpatents

Keenan, Michael R.

2007-10-16

A method for spectrally compressing data sets enables the efficient analysis of very large multivariate images. The spectral compression algorithm uses a factored representation of the data that can be obtained from Principal Components Analysis or other factorization technique. Furthermore, a block algorithm can be used for performing common operations more efficiently. An image analysis can be performed on the factored representation of the data, using only the most significant factors. The spectral compression algorithm can be combined with a spatial compression algorithm to provide further computational efficiencies.

A Novel Range Compression Algorithm for Resolution Enhancement in GNSS-SARs

PubMed Central

Zheng, Yu; Yang, Yang; Chen, Wu

2017-01-01

In this paper, a novel range compression algorithm for enhancing range resolutions of a passive Global Navigation Satellite System-based Synthetic Aperture Radar (GNSS-SAR) is proposed. In the proposed algorithm, within each azimuth bin, firstly range compression is carried out by correlating a reflected GNSS intermediate frequency (IF) signal with a synchronized direct GNSS base-band signal in the range domain. Thereafter, spectrum equalization is applied to the compressed results for suppressing side lobes to obtain a final range-compressed signal. Both theoretical analysis and simulation results have demonstrated that significant range resolution improvement in GNSS-SAR images can be achieved by the proposed range compression algorithm, compared to the conventional range compression algorithm. PMID:28672830

Compression of electromyographic signals using image compression techniques.

PubMed

Costa, Marcus Vinícius Chaffim; Berger, Pedro de Azevedo; da Rocha, Adson Ferreira; de Carvalho, João Luiz Azevedo; Nascimento, Francisco Assis de Oliveira

2008-01-01

Despite the growing interest in the transmission and storage of electromyographic signals for long periods of time, few studies have addressed the compression of such signals. In this article we present an algorithm for compression of electromyographic signals based on the JPEG2000 coding system. Although the JPEG2000 codec was originally designed for compression of still images, we show that it can also be used to compress EMG signals for both isotonic and isometric contractions. For EMG signals acquired during isometric contractions, the proposed algorithm provided compression factors ranging from 75 to 90%, with an average PRD ranging from 3.75% to 13.7%. For isotonic EMG signals, the algorithm provided compression factors ranging from 75 to 90%, with an average PRD ranging from 3.4% to 7%. The compression results using the JPEG2000 algorithm were compared to those using other algorithms based on the wavelet transform.

Small angle x-ray scattering study on the conformation of polystyrene in toluene during adding anti-solvent CO2

NASA Astrophysics Data System (ADS)

Liu, Yi; Chen, Dong-Feng; Wang, Hong-Li; Chen, Na; Li, Dan; Han, Bu-Xing; Rong, Li-Xia; Zhao, Hui; Wang, Jun; Dong, Bao-Zhong

2002-10-01

The conformation of polystyrene in the anti-solvent process of supercritical fluids (compressed CO2 + polystyrene + toluene) has been studied by small angle x-ray scattering with synchrotron radiation as an x-ray source. Coil-to-globule transformation of the polystyrene chain was observed with the increase of the anti-solvent CO2 pressure; i.e. polystyrene coiled at a pressure lower than the cloud point pressure (Pc) and turned into a globule with a uniform density at pressures higher than Pc. Fractal behaviour was also found in the chain contraction and the mass fractal dimension increased with increasing CO2 pressure.

Texture segmentation of non-cooperative spacecrafts images based on wavelet and fractal dimension

NASA Astrophysics Data System (ADS)

Wu, Kanzhi; Yue, Xiaokui

2011-06-01

With the increase of on-orbit manipulations and space conflictions, missions such as tracking and capturing the target spacecrafts are aroused. Unlike cooperative spacecrafts, fixing beacons or any other marks on the targets is impossible. Due to the unknown shape and geometry features of non-cooperative spacecraft, in order to localize the target and obtain the latitude, we need to segment the target image and recognize the target from the background. The data and errors during the following procedures such as feature extraction and matching can also be reduced. Multi-resolution analysis of wavelet theory reflects human beings' recognition towards images from low resolution to high resolution. In addition, spacecraft is the only man-made object in the image compared to the natural background and the differences will be certainly observed between the fractal dimensions of target and background. Combined wavelet transform and fractal dimension, in this paper, we proposed a new segmentation algorithm for the images which contains complicated background such as the universe and planet surfaces. At first, Daubechies wavelet basis is applied to decompose the image in both x axis and y axis, thus obtain four sub-images. Then, calculate the fractal dimensions in four sub-images using different methods; after analyzed the results of fractal dimensions in sub-images, we choose Differential Box Counting in low resolution image as the principle to segment the texture which has the greatest divergences between different sub-images. This paper also presents the results of experiments by using the algorithm above. It is demonstrated that an accurate texture segmentation result can be obtained using the proposed technique.

Biological sequence compression algorithms.

PubMed

Matsumoto, T; Sadakane, K; Imai, H

2000-01-01

Today, more and more DNA sequences are becoming available. The information about DNA sequences are stored in molecular biology databases. The size and importance of these databases will be bigger and bigger in the future, therefore this information must be stored or communicated efficiently. Furthermore, sequence compression can be used to define similarities between biological sequences. The standard compression algorithms such as gzip or compress cannot compress DNA sequences, but only expand them in size. On the other hand, CTW (Context Tree Weighting Method) can compress DNA sequences less than two bits per symbol. These algorithms do not use special structures of biological sequences. Two characteristic structures of DNA sequences are known. One is called palindromes or reverse complements and the other structure is approximate repeats. Several specific algorithms for DNA sequences that use these structures can compress them less than two bits per symbol. In this paper, we improve the CTW so that characteristic structures of DNA sequences are available. Before encoding the next symbol, the algorithm searches an approximate repeat and palindrome using hash and dynamic programming. If there is a palindrome or an approximate repeat with enough length then our algorithm represents it with length and distance. By using this preprocessing, a new program achieves a little higher compression ratio than that of existing DNA-oriented compression algorithms. We also describe new compression algorithm for protein sequences.

ERGC: an efficient referential genome compression algorithm

PubMed Central

Saha, Subrata; Rajasekaran, Sanguthevar

2015-01-01

Motivation: Genome sequencing has become faster and more affordable. Consequently, the number of available complete genomic sequences is increasing rapidly. As a result, the cost to store, process, analyze and transmit the data is becoming a bottleneck for research and future medical applications. So, the need for devising efficient data compression and data reduction techniques for biological sequencing data is growing by the day. Although there exists a number of standard data compression algorithms, they are not efficient in compressing biological data. These generic algorithms do not exploit some inherent properties of the sequencing data while compressing. To exploit statistical and information-theoretic properties of genomic sequences, we need specialized compression algorithms. Five different next-generation sequencing data compression problems have been identified and studied in the literature. We propose a novel algorithm for one of these problems known as reference-based genome compression. Results: We have done extensive experiments using five real sequencing datasets. The results on real genomes show that our proposed algorithm is indeed competitive and performs better than the best known algorithms for this problem. It achieves compression ratios that are better than those of the currently best performing algorithms. The time to compress and decompress the whole genome is also very promising. Availability and implementation: The implementations are freely available for non-commercial purposes. They can be downloaded from http://engr.uconn.edu/∼rajasek/ERGC.zip. Contact: rajasek@engr.uconn.edu PMID:26139636

Density distribution function of a self-gravitating isothermal compressible turbulent fluid in the context of molecular clouds ensembles

NASA Astrophysics Data System (ADS)

Donkov, Sava; Stefanov, Ivan Z.

2018-03-01

We have set ourselves the task of obtaining the probability distribution function of the mass density of a self-gravitating isothermal compressible turbulent fluid from its physics. We have done this in the context of a new notion: the molecular clouds ensemble. We have applied a new approach that takes into account the fractal nature of the fluid. Using the medium equations, under the assumption of steady state, we show that the total energy per unit mass is an invariant with respect to the fractal scales. As a next step we obtain a non-linear integral equation for the dimensionless scale Q which is the third root of the integral of the probability distribution function. It is solved approximately up to the leading-order term in the series expansion. We obtain two solutions. They are power-law distributions with different slopes: the first one is -1.5 at low densities, corresponding to an equilibrium between all energies at a given scale, and the second one is -2 at high densities, corresponding to a free fall at small scales.

Oxidation-Mediated Fingering in Liquid Metals

NASA Astrophysics Data System (ADS)

Eaker, Collin B.; Hight, David C.; O'Regan, John D.; Dickey, Michael D.; Daniels, Karen E.

2017-10-01

We identify and characterize a new class of fingering instabilities in liquid metals; these instabilities are unexpected due to the large interfacial tension of metals. Electrochemical oxidation lowers the effective interfacial tension of a gallium-based liquid metal alloy to values approaching zero, thereby inducing drastic shape changes, including the formation of fractals. The measured fractal dimension (D =1.3 ±0.05 ) places the instability in a different universality class than other fingering instabilities. By characterizing changes in morphology and dynamics as a function of droplet volume and applied electric potential, we identify the three main forces involved in this process: interfacial tension, gravity, and oxidative stress. Importantly, we find that electrochemical oxidation can generate compressive interfacial forces that oppose the tensile forces at a liquid interface. The surface oxide layer ultimately provides a physical and electrochemical barrier that halts the instabilities at larger positive potentials. Controlling the competition between interfacial tension and oxidative (compressive) stresses at the interface is important for the development of reconfigurable electronic, electromagnetic, and optical devices that take advantage of the metallic properties of liquid metals.

Higuchi Dimension of Digital Images

PubMed Central

Ahammer, Helmut

2011-01-01

There exist several methods for calculating the fractal dimension of objects represented as 2D digital images. For example, Box counting, Minkowski dilation or Fourier analysis can be employed. However, there appear to be some limitations. It is not possible to calculate only the fractal dimension of an irregular region of interest in an image or to perform the calculations in a particular direction along a line on an arbitrary angle through the image. The calculations must be made for the whole image. In this paper, a new method to overcome these limitations is proposed. 2D images are appropriately prepared in order to apply 1D signal analyses, originally developed to investigate nonlinear time series. The Higuchi dimension of these 1D signals is calculated using Higuchi's algorithm, and it is shown that both regions of interests and directional dependencies can be evaluated independently of the whole picture. A thorough validation of the proposed technique and a comparison of the new method to the Fourier dimension, a common two dimensional method for digital images, are given. The main result is that Higuchi's algorithm allows a direction dependent as well as direction independent analysis. Actual values for the fractal dimensions are reliable and an effective treatment of regions of interests is possible. Moreover, the proposed method is not restricted to Higuchi's algorithm, as any 1D method of analysis, can be applied. PMID:21931854

Fractal analysis and its impact factors on pore structure of artificial cores based on the images obtained using magnetic resonance imaging

NASA Astrophysics Data System (ADS)

Wang, Heming; Liu, Yu; Song, Yongchen; Zhao, Yuechao; Zhao, Jiafei; Wang, Dayong

2012-11-01

Pore structure is one of important factors affecting the properties of porous media, but it is difficult to describe the complexity of pore structure exactly. Fractal theory is an effective and available method for quantifying the complex and irregular pore structure. In this paper, the fractal dimension calculated by box-counting method based on fractal theory was applied to characterize the pore structure of artificial cores. The microstructure or pore distribution in the porous material was obtained using the nuclear magnetic resonance imaging (MRI). Three classical fractals and one sand packed bed model were selected as the experimental material to investigate the influence of box sizes, threshold value, and the image resolution when performing fractal analysis. To avoid the influence of box sizes, a sequence of divisors of the image was proposed and compared with other two algorithms (geometric sequence and arithmetic sequence) with its performance of partitioning the image completely and bringing the least fitted error. Threshold value selected manually and automatically showed that it plays an important role during the image binary processing and the minimum-error method can be used to obtain an appropriate or reasonable one. Images obtained under different pixel matrices in MRI were used to analyze the influence of image resolution. Higher image resolution can detect more quantity of pore structure and increase its irregularity. With benefits of those influence factors, fractal analysis on four kinds of artificial cores showed the fractal dimension can be used to distinguish the different kinds of artificial cores and the relationship between fractal dimension and porosity or permeability can be expressed by the model of D = a - bln(x + c).

Monte Carlo Sampling in Fractal Landscapes

NASA Astrophysics Data System (ADS)

Leitão, Jorge C.; Lopes, J. M. Viana Parente; Altmann, Eduardo G.

2013-05-01

We design a random walk to explore fractal landscapes such as those describing chaotic transients in dynamical systems. We show that the random walk moves efficiently only when its step length depends on the height of the landscape via the largest Lyapunov exponent of the chaotic system. We propose a generalization of the Wang-Landau algorithm which constructs not only the density of states (transient time distribution) but also the correct step length. As a result, we obtain a flat-histogram Monte Carlo method which samples fractal landscapes in polynomial time, a dramatic improvement over the exponential scaling of traditional uniform-sampling methods. Our results are not limited by the dimensionality of the landscape and are confirmed numerically in chaotic systems with up to 30 dimensions.

An improved method of continuous LOD based on fractal theory in terrain rendering

NASA Astrophysics Data System (ADS)

Lin, Lan; Li, Lijun

2007-11-01

With the improvement of computer graphic hardware capability, the algorithm of 3D terrain rendering is going into the hot topic of real-time visualization. In order to solve conflict between the rendering speed and reality of rendering, this paper gives an improved method of terrain rendering which improves the traditional continuous level of detail technique based on fractal theory. This method proposes that the program needn't to operate the memory repeatedly to obtain different resolution terrain model, instead, obtains the fractal characteristic parameters of different region according to the movement of the viewpoint. Experimental results show that the method guarantees the authenticity of landscape, and increases the real-time 3D terrain rendering speed.

Physiological Signals and Their Fractal Response to Stress Conditions, Environmental Changes and Neurodegenerative Diseases

DTIC Science & Technology

2006-11-01

exponent H=(β+1)/2 and from the fractal dimension D=2- H. The algorithms used to estimate the Hurst exponent directly are usually quite simple and...yields a curve of the type D(τ)=cτH, where c is an opportune constant and H is the Hurst exponent [Scafetta and Grigolini, 2002]. 1 Report Documentation...memory of past events. It is largely expected that the Hurst exponent , which measures the strength of this memory, evolves as a response

Near-lossless multichannel EEG compression based on matrix and tensor decompositions.

PubMed

Dauwels, Justin; Srinivasan, K; Reddy, M Ramasubba; Cichocki, Andrzej

2013-05-01

A novel near-lossless compression algorithm for multichannel electroencephalogram (MC-EEG) is proposed based on matrix/tensor decomposition models. MC-EEG is represented in suitable multiway (multidimensional) forms to efficiently exploit temporal and spatial correlations simultaneously. Several matrix/tensor decomposition models are analyzed in view of efficient decorrelation of the multiway forms of MC-EEG. A compression algorithm is built based on the principle of “lossy plus residual coding,” consisting of a matrix/tensor decomposition-based coder in the lossy layer followed by arithmetic coding in the residual layer. This approach guarantees a specifiable maximum absolute error between original and reconstructed signals. The compression algorithm is applied to three different scalp EEG datasets and an intracranial EEG dataset, each with different sampling rate and resolution. The proposed algorithm achieves attractive compression ratios compared to compressing individual channels separately. For similar compression ratios, the proposed algorithm achieves nearly fivefold lower average error compared to a similar wavelet-based volumetric MC-EEG compression algorithm.

Psychophysical Comparisons in Image Compression Algorithms.

DTIC Science & Technology

1999-03-01

Leister, M., "Lossy Lempel - Ziv Algorithm for Large Alphabet Sources and Applications to Image Compression ," IEEE Proceedings, v.I, pp. 225-228, September...1623-1642, September 1990. Sanford, M.A., An Analysis of Data Compression Algorithms used in the Transmission of Imagery, Master’s Thesis, Naval...NAVAL POSTGRADUATE SCHOOL Monterey, California THESIS PSYCHOPHYSICAL COMPARISONS IN IMAGE COMPRESSION ALGORITHMS by % Christopher J. Bodine • March

Compression of next-generation sequencing quality scores using memetic algorithm

PubMed Central

2014-01-01

Background The exponential growth of next-generation sequencing (NGS) derived DNA data poses great challenges to data storage and transmission. Although many compression algorithms have been proposed for DNA reads in NGS data, few methods are designed specifically to handle the quality scores. Results In this paper we present a memetic algorithm (MA) based NGS quality score data compressor, namely MMQSC. The algorithm extracts raw quality score sequences from FASTQ formatted files, and designs compression codebook using MA based multimodal optimization. The input data is then compressed in a substitutional manner. Experimental results on five representative NGS data sets show that MMQSC obtains higher compression ratio than the other state-of-the-art methods. Particularly, MMQSC is a lossless reference-free compression algorithm, yet obtains an average compression ratio of 22.82% on the experimental data sets. Conclusions The proposed MMQSC compresses NGS quality score data effectively. It can be utilized to improve the overall compression ratio on FASTQ formatted files. PMID:25474747

ERGC: an efficient referential genome compression algorithm.

PubMed

Saha, Subrata; Rajasekaran, Sanguthevar

2015-11-01

Genome sequencing has become faster and more affordable. Consequently, the number of available complete genomic sequences is increasing rapidly. As a result, the cost to store, process, analyze and transmit the data is becoming a bottleneck for research and future medical applications. So, the need for devising efficient data compression and data reduction techniques for biological sequencing data is growing by the day. Although there exists a number of standard data compression algorithms, they are not efficient in compressing biological data. These generic algorithms do not exploit some inherent properties of the sequencing data while compressing. To exploit statistical and information-theoretic properties of genomic sequences, we need specialized compression algorithms. Five different next-generation sequencing data compression problems have been identified and studied in the literature. We propose a novel algorithm for one of these problems known as reference-based genome compression. We have done extensive experiments using five real sequencing datasets. The results on real genomes show that our proposed algorithm is indeed competitive and performs better than the best known algorithms for this problem. It achieves compression ratios that are better than those of the currently best performing algorithms. The time to compress and decompress the whole genome is also very promising. The implementations are freely available for non-commercial purposes. They can be downloaded from http://engr.uconn.edu/∼rajasek/ERGC.zip. rajasek@engr.uconn.edu. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Efficient image compression algorithm for computer-animated images

NASA Astrophysics Data System (ADS)

Yfantis, Evangelos A.; Au, Matthew Y.; Miel, G.

1992-10-01

An image compression algorithm is described. The algorithm is an extension of the run-length image compression algorithm and its implementation is relatively easy. This algorithm was implemented and compared with other existing popular compression algorithms and with the Lempel-Ziv (LZ) coding. The Lempel-Ziv algorithm is available as a utility in the UNIX operating system and is also referred to as the UNIX uncompress. Sometimes our algorithm is best in terms of saving memory space, and sometimes one of the competing algorithms is best. The algorithm is lossless, and the intent is for the algorithm to be used in computer graphics animated images. Comparisons made with the LZ algorithm indicate that the decompression time using our algorithm is faster than that using the LZ algorithm. Once the data are in memory, a relatively simple and fast transformation is applied to uncompress the file.

Signal-Noise Identification of Magnetotelluric Signals Using Fractal-Entropy and Clustering Algorithm for Targeted De-Noising

NASA Astrophysics Data System (ADS)

Li, Jin; Zhang, Xian; Gong, Jinzhe; Tang, Jingtian; Ren, Zhengyong; Li, Guang; Deng, Yanli; Cai, Jin

A new technique is proposed for signal-noise identification and targeted de-noising of Magnetotelluric (MT) signals. This method is based on fractal-entropy and clustering algorithm, which automatically identifies signal sections corrupted by common interference (square, triangle and pulse waves), enabling targeted de-noising and preventing the loss of useful information in filtering. To implement the technique, four characteristic parameters — fractal box dimension (FBD), higuchi fractal dimension (HFD), fuzzy entropy (FuEn) and approximate entropy (ApEn) — are extracted from MT time-series. The fuzzy c-means (FCM) clustering technique is used to analyze the characteristic parameters and automatically distinguish signals with strong interference from the rest. The wavelet threshold (WT) de-noising method is used only to suppress the identified strong interference in selected signal sections. The technique is validated through signal samples with known interference, before being applied to a set of field measured MT/Audio Magnetotelluric (AMT) data. Compared with the conventional de-noising strategy that blindly applies the filter to the overall dataset, the proposed method can automatically identify and purposefully suppress the intermittent interference in the MT/AMT signal. The resulted apparent resistivity-phase curve is more continuous and smooth, and the slow-change trend in the low-frequency range is more precisely reserved. Moreover, the characteristic of the target-filtered MT/AMT signal is close to the essential characteristic of the natural field, and the result more accurately reflects the inherent electrical structure information of the measured site.

A New Challenge for Compression Algorithms: Genetic Sequences.

ERIC Educational Resources Information Center

Grumbach, Stephane; Tahi, Fariza

1994-01-01

Analyzes the properties of genetic sequences that cause the failure of classical algorithms used for data compression. A lossless algorithm, which compresses the information contained in DNA and RNA sequences by detecting regularities such as palindromes, is presented. This algorithm combines substitutional and statistical methods and appears to…

Composeable Chat over Low-Bandwidth Intermittent Communication Links

DTIC Science & Technology

2007-04-01

Compression (STC), introduced in this report, is a data compression algorithm intended to compress alphanumeric... Ziv - Lempel coding, the grandfather of most modern general-purpose file compression programs, watches for input symbol sequences that have previously... data . This section applies these techniques to create a new compression algorithm called Small Text Compression . Various sequence compression

The Basic Principles and Methods of the System Approach to Compression of Telemetry Data

NASA Astrophysics Data System (ADS)

Levenets, A. V.

2018-01-01

The task of data compressing of measurement data is still urgent for information-measurement systems. In paper the basic principles necessary for designing of highly effective systems of compression of telemetric information are offered. A basis of the offered principles is representation of a telemetric frame as whole information space where we can find of existing correlation. The methods of data transformation and compressing algorithms realizing the offered principles are described. The compression ratio for offered compression algorithm is about 1.8 times higher, than for a classic algorithm. Thus, results of a research of methods and algorithms showing their good perspectives.

Image compression/decompression based on mathematical transform, reduction/expansion, and image sharpening

DOEpatents

Fu, Chi-Yung; Petrich, Loren I.

1997-01-01

An image represented in a first image array of pixels is first decimated in two dimensions before being compressed by a predefined compression algorithm such as JPEG. Another possible predefined compression algorithm can involve a wavelet technique. The compressed, reduced image is then transmitted over the limited bandwidth transmission medium, and the transmitted image is decompressed using an algorithm which is an inverse of the predefined compression algorithm (such as reverse JPEG). The decompressed, reduced image is then interpolated back to its original array size. Edges (contours) in the image are then sharpened to enhance the perceptual quality of the reconstructed image. Specific sharpening techniques are described.

A Comparison of Some of the Most Current Methods of Image Compression

DTIC Science & Technology

1993-06-01

found FrameMaker (available on the Sun) to be very flexible with imported files. It requires them to be in Raster format. 51 5. Access a. Fractal...account manager must set up a Sun account for access to FrameMaker , Sunvision, etc. Be sure to specify the utilities needed when signing up for an

Segmentation of time series with long-range fractal correlations.

PubMed

Bernaola-Galván, P; Oliver, J L; Hackenberg, M; Coronado, A V; Ivanov, P Ch; Carpena, P

2012-06-01

Segmentation is a standard method of data analysis to identify change-points dividing a nonstationary time series into homogeneous segments. However, for long-range fractal correlated series, most of the segmentation techniques detect spurious change-points which are simply due to the heterogeneities induced by the correlations and not to real nonstationarities. To avoid this oversegmentation, we present a segmentation algorithm which takes as a reference for homogeneity, instead of a random i.i.d. series, a correlated series modeled by a fractional noise with the same degree of correlations as the series to be segmented. We apply our algorithm to artificial series with long-range correlations and show that it systematically detects only the change-points produced by real nonstationarities and not those created by the correlations of the signal. Further, we apply the method to the sequence of the long arm of human chromosome 21, which is known to have long-range fractal correlations. We obtain only three segments that clearly correspond to the three regions of different G + C composition revealed by means of a multi-scale wavelet plot. Similar results have been obtained when segmenting all human chromosome sequences, showing the existence of previously unknown huge compositional superstructures in the human genome.

Scan-Line Methods in Spatial Data Systems

DTIC Science & Technology

1990-09-04

algorithms in detail to show some of the implementation issues. Data Compression Storage and transmission times can be reduced by using compression ...goes through the data . Luckily, there are good one-directional compression algorithms , such as run-length coding 13 in which each scan line can be...independently compressed . These are the algorithms to use in a parallel scan-line system. Data compression is usually only used for long-term storage of

An Implementation Of Elias Delta Code And ElGamal Algorithm In Image Compression And Security

NASA Astrophysics Data System (ADS)

Rachmawati, Dian; Andri Budiman, Mohammad; Saffiera, Cut Amalia

2018-01-01

In data transmission such as transferring an image, confidentiality, integrity, and efficiency of data storage aspects are highly needed. To maintain the confidentiality and integrity of data, one of the techniques used is ElGamal. The strength of this algorithm is found on the difficulty of calculating discrete logs in a large prime modulus. ElGamal belongs to the class of Asymmetric Key Algorithm and resulted in enlargement of the file size, therefore data compression is required. Elias Delta Code is one of the compression algorithms that use delta code table. The image was first compressed using Elias Delta Code Algorithm, then the result of the compression was encrypted by using ElGamal algorithm. Prime test was implemented using Agrawal Biswas Algorithm. The result showed that ElGamal method could maintain the confidentiality and integrity of data with MSE and PSNR values 0 and infinity. The Elias Delta Code method generated compression ratio and space-saving each with average values of 62.49%, and 37.51%.

Dual Fractal Dimension and Long-Range Correlation of Chinese Stock Prices

NASA Astrophysics Data System (ADS)

Chen, Chaoshi; Wang, Lei

2012-03-01

The recently developed modified inverse random midpoint displacement (mIRMD) and conventional detrended fluctuation analysis (DFA) algorithms are used to analyze the tick-by-tick high-frequency time series of Chinese A-share stock prices and indexes. A dual-fractal structure with a crossover at about 10 min is observed. The majority of the selected time series show visible persistence within this time threshold, but approach a random walk on a longer time scale. The phenomenon is found to be industry-dependent, i.e., the crossover is much more prominent for stocks belonging to cyclical industries than for those belonging to noncyclical (defensive) industries. We have also shown that the sign series show a similar dual-fractal structure, while like generally found, the magnitude series show a much longer time persistence.

Effect of angle of deposition on the Fractal properties of ZnO thin film surface

NASA Astrophysics Data System (ADS)

Yadav, R. P.; Agarwal, D. C.; Kumar, Manvendra; Rajput, Parasmani; Tomar, D. S.; Pandey, S. N.; Priya, P. K.; Mittal, A. K.

2017-09-01

Zinc oxide (ZnO) thin films were prepared by atom beam sputtering at various deposition angles in the range of 20-75°. The deposited thin films were examined by glancing angle X-ray diffraction and atomic force microscopy (AFM). Scaling law analysis was performed on AFM images to show that the thin film surfaces are self-affine. Fractal dimension of each of the 256 vertical sections along the fast scan direction of a discretized surface, obtained from the AFM height data, was estimated using the Higuchi's algorithm. Hurst exponent was computed from the fractal dimension. The grain sizes, as determined by applying self-correlation function on AFM micrographs, varied with the deposition angle in the same manner as the Hurst exponent.

A comparison of select image-compression algorithms for an electronic still camera

NASA Technical Reports Server (NTRS)

Nerheim, Rosalee

1989-01-01

This effort is a study of image-compression algorithms for an electronic still camera. An electronic still camera can record and transmit high-quality images without the use of film, because images are stored digitally in computer memory. However, high-resolution images contain an enormous amount of information, and will strain the camera's data-storage system. Image compression will allow more images to be stored in the camera's memory. For the electronic still camera, a compression algorithm that produces a reconstructed image of high fidelity is most important. Efficiency of the algorithm is the second priority. High fidelity and efficiency are more important than a high compression ratio. Several algorithms were chosen for this study and judged on fidelity, efficiency and compression ratio. The transform method appears to be the best choice. At present, the method is compressing images to a ratio of 5.3:1 and producing high-fidelity reconstructed images.

Context-Sensitive Grammar Transform: Compression and Pattern Matching

NASA Astrophysics Data System (ADS)

Maruyama, Shirou; Tanaka, Youhei; Sakamoto, Hiroshi; Takeda, Masayuki

A framework of context-sensitive grammar transform for speeding-up compressed pattern matching (CPM) is proposed. A greedy compression algorithm with the transform model is presented as well as a Knuth-Morris-Pratt (KMP)-type compressed pattern matching algorithm. The compression ratio is a match for gzip and Re-Pair, and the search speed of our CPM algorithm is almost twice faster than the KMP-type CPM algorithm on Byte-Pair-Encoding by Shibata et al.[18], and in the case of short patterns, faster than the Boyer-Moore-Horspool algorithm with the stopper encoding by Rautio et al.[14], which is regarded as one of the best combinations that allows a practically fast search.

Robust estimation of fractal measures for characterizing the structural complexity of the human brain: optimization and reproducibility

PubMed Central

Goñi, Joaquín; Sporns, Olaf; Cheng, Hu; Aznárez-Sanado, Maite; Wang, Yang; Josa, Santiago; Arrondo, Gonzalo; Mathews, Vincent P; Hummer, Tom A; Kronenberger, William G; Avena-Koenigsberger, Andrea; Saykin, Andrew J.; Pastor, María A.

2013-01-01

High-resolution isotropic three-dimensional reconstructions of human brain gray and white matter structures can be characterized to quantify aspects of their shape, volume and topological complexity. In particular, methods based on fractal analysis have been applied in neuroimaging studies to quantify the structural complexity of the brain in both healthy and impaired conditions. The usefulness of such measures for characterizing individual differences in brain structure critically depends on their within-subject reproducibility in order to allow the robust detection of between-subject differences. This study analyzes key analytic parameters of three fractal-based methods that rely on the box-counting algorithm with the aim to maximize within-subject reproducibility of the fractal characterizations of different brain objects, including the pial surface, the cortical ribbon volume, the white matter volume and the grey matter/white matter boundary. Two separate datasets originating from different imaging centers were analyzed, comprising, 50 subjects with three and 24 subjects with four successive scanning sessions per subject, respectively. The reproducibility of fractal measures was statistically assessed by computing their intra-class correlations. Results reveal differences between different fractal estimators and allow the identification of several parameters that are critical for high reproducibility. Highest reproducibility with intra-class correlations in the range of 0.9–0.95 is achieved with the correlation dimension. Further analyses of the fractal dimensions of parcellated cortical and subcortical gray matter regions suggest robustly estimated and region-specific patterns of individual variability. These results are valuable for defining appropriate parameter configurations when studying changes in fractal descriptors of human brain structure, for instance in studies of neurological diseases that do not allow repeated measurements or for disease-course longitudinal studies. PMID:23831414

On the fractal morphology of combustion-generated soot aggregates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Koylu, U.O.

1995-12-31

The fractal properties of soot aggregates were investigated using ex-situ and in-situ experimental methods as well as computer simulations. Ex-situ experiments involved thermophoretic sampling and analysis by transmission electron microscopy (TEM), while in-situ measurements employed angular static light scattering and data inversion based on Rayleigh-Debye-Gans (RDG) approximation. Computer simulations used a sequential algorithm which mimics mass fractal-like structures. So from a variety of hydrocarbon-fueled laminar and turbulent nonpremixed flame environments were considered in the present study. The TEM analysis of projected soot images sampled from fuel-rich conditions of buoyant and weakly-buoyant laminar flames indicated that the fractal dimension of sootmore » was relatively independent of position in flames, fuel type and flame condition. These measurements yielded an average fractal dimension of 1.8, although other structure parameters such as the primary particle diameters and number of primary particles in aggregates had wide range of values. Fractal prefactor (lacunarity) was also measured for soot sampled from the fuel-lean conditions of turbulent flames, considering the actual morphology by tilting the samples during TEM analysis. These measurements yielded a fractal dimension of 1.65 and a lacunarity of 8.5, with experimental uncertainties (95% confidence) of 0.08 and 0.5, respectively. Relationships between the actual and projected structure properties of soot were also developed by combining TEM observations with numerical simulations. Practical approximate formulae were suggested to find radius of gyration of an aggregate from its maximum dimension, and number of primary particles in an aggregate from projected area. Finally, the fractal dimension and lacunarity of soot were obtained using light scattering for the same conditions of the above TEM measurements.« less

The Application of Fractal and Multifractal Theory in Hydraulic-Flow-Unit Characterization and Permeability Estimation

NASA Astrophysics Data System (ADS)

Chen, X.; Yao, G.; Cai, J.

2017-12-01

Pore structure characteristics are important factors in influencing the fluid transport behavior of porous media, such as pore-throat ratio, pore connectivity and size distribution, moreover, wettability. To accurately characterize the diversity of pore structure among HFUs, five samples selected from different HFUs (porosities are approximately equal, however permeability varies widely) were chosen to conduct micro-computerized tomography test to acquire direct 3D images of pore geometries and to perform mercury injection experiments to obtain the pore volume-radii distribution. To characterize complex and high nonlinear pore structure of all samples, three classic fractal geometry models were applied. Results showed that each HFU has similar box-counting fractal dimension and generalized fractal dimension in the number-area model, but there are significant differences in multifractal spectrums. In the radius-volume model, there are three obvious linear segments, corresponding to three fractal dimension values, and the middle one is proved as the actual fractal dimension according to the maximum radius. In the number-radius model, the spherical-pore size distribution extracted by maximum ball algorithm exist a decrease in the number of small pores compared with the fractal power rate rather than the traditional linear law. Among the three models, only multifractal analysis can classify the HFUs accurately. Additionally, due to the tightness and low-permeability in reservoir rocks, connate water film existing in the inner surface of pore channels commonly forms bound water. The conventional model which is known as Yu-Cheng's model has been proved to be typically not applicable. Considering the effect of irreducible water saturation, an improved fractal permeability model was also deduced theoretically. The comparison results showed that the improved model can be applied to calculate permeability directly and accurately in such unconventional rocks.

Image compression-encryption algorithms by combining hyper-chaotic system with discrete fractional random transform

NASA Astrophysics Data System (ADS)

Gong, Lihua; Deng, Chengzhi; Pan, Shumin; Zhou, Nanrun

2018-07-01

Based on hyper-chaotic system and discrete fractional random transform, an image compression-encryption algorithm is designed. The original image is first transformed into a spectrum by the discrete cosine transform and the resulting spectrum is compressed according to the method of spectrum cutting. The random matrix of the discrete fractional random transform is controlled by a chaotic sequence originated from the high dimensional hyper-chaotic system. Then the compressed spectrum is encrypted by the discrete fractional random transform. The order of DFrRT and the parameters of the hyper-chaotic system are the main keys of this image compression and encryption algorithm. The proposed algorithm can compress and encrypt image signal, especially can encrypt multiple images once. To achieve the compression of multiple images, the images are transformed into spectra by the discrete cosine transform, and then the spectra are incised and spliced into a composite spectrum by Zigzag scanning. Simulation results demonstrate that the proposed image compression and encryption algorithm is of high security and good compression performance.

A real-time ECG data compression and transmission algorithm for an e-health device.

PubMed

Lee, SangJoon; Kim, Jungkuk; Lee, Myoungho

2011-09-01

This paper introduces a real-time data compression and transmission algorithm between e-health terminals for a periodic ECGsignal. The proposed algorithm consists of five compression procedures and four reconstruction procedures. In order to evaluate the performance of the proposed algorithm, the algorithm was applied to all 48 recordings of MIT-BIH arrhythmia database, and the compress ratio (CR), percent root mean square difference (PRD), percent root mean square difference normalized (PRDN), rms, SNR, and quality score (QS) values were obtained. The result showed that the CR was 27.9:1 and the PRD was 2.93 on average for all 48 data instances with a 15% window size. In addition, the performance of the algorithm was compared to those of similar algorithms introduced recently by others. It was found that the proposed algorithm showed clearly superior performance in all 48 data instances at a compression ratio lower than 15:1, whereas it showed similar or slightly inferior PRD performance for a data compression ratio higher than 20:1. In light of the fact that the similarity with the original data becomes meaningless when the PRD is higher than 2, the proposed algorithm shows significantly better performance compared to the performance levels of other algorithms. Moreover, because the algorithm can compress and transmit data in real time, it can be served as an optimal biosignal data transmission method for limited bandwidth communication between e-health devices.

Two-dimensional compression of surface electromyographic signals using column-correlation sorting and image encoders.

PubMed

Costa, Marcus V C; Carvalho, Joao L A; Berger, Pedro A; Zaghetto, Alexandre; da Rocha, Adson F; Nascimento, Francisco A O

2009-01-01

We present a new preprocessing technique for two-dimensional compression of surface electromyographic (S-EMG) signals, based on correlation sorting. We show that the JPEG2000 coding system (originally designed for compression of still images) and the H.264/AVC encoder (video compression algorithm operating in intraframe mode) can be used for compression of S-EMG signals. We compare the performance of these two off-the-shelf image compression algorithms for S-EMG compression, with and without the proposed preprocessing step. Compression of both isotonic and isometric contraction S-EMG signals is evaluated. The proposed methods were compared with other S-EMG compression algorithms from the literature.

Image compression/decompression based on mathematical transform, reduction/expansion, and image sharpening

DOEpatents

Fu, C.Y.; Petrich, L.I.

1997-12-30

An image represented in a first image array of pixels is first decimated in two dimensions before being compressed by a predefined compression algorithm such as JPEG. Another possible predefined compression algorithm can involve a wavelet technique. The compressed, reduced image is then transmitted over the limited bandwidth transmission medium, and the transmitted image is decompressed using an algorithm which is an inverse of the predefined compression algorithm (such as reverse JPEG). The decompressed, reduced image is then interpolated back to its original array size. Edges (contours) in the image are then sharpened to enhance the perceptual quality of the reconstructed image. Specific sharpening techniques are described. 22 figs.

Geometry-driven distributed compression of the plenoptic function: performance bounds and constructive algorithms.

PubMed

Gehrig, Nicolas; Dragotti, Pier Luigi

2009-03-01

In this paper, we study the sampling and the distributed compression of the data acquired by a camera sensor network. The effective design of these sampling and compression schemes requires, however, the understanding of the structure of the acquired data. To this end, we show that the a priori knowledge of the configuration of the camera sensor network can lead to an effective estimation of such structure and to the design of effective distributed compression algorithms. For idealized scenarios, we derive the fundamental performance bounds of a camera sensor network and clarify the connection between sampling and distributed compression. We then present a distributed compression algorithm that takes advantage of the structure of the data and that outperforms independent compression algorithms on real multiview images.

[Lossless ECG compression algorithm with anti- electromagnetic interference].

PubMed

Guan, Shu-An

2005-03-01

Based on the study of ECG signal features, a new lossless ECG compression algorithm is put forward here. We apply second-order difference operation with anti- electromagnetic interference to original ECG signals and then, compress the result by the escape-based coding model. In spite of serious 50Hz-interference, the algorithm is still capable of obtaining a high compression ratio.

An Optimal Seed Based Compression Algorithm for DNA Sequences

PubMed Central

Gopalakrishnan, Gopakumar; Karunakaran, Muralikrishnan

2016-01-01

This paper proposes a seed based lossless compression algorithm to compress a DNA sequence which uses a substitution method that is similar to the LempelZiv compression scheme. The proposed method exploits the repetition structures that are inherent in DNA sequences by creating an offline dictionary which contains all such repeats along with the details of mismatches. By ensuring that only promising mismatches are allowed, the method achieves a compression ratio that is at par or better than the existing lossless DNA sequence compression algorithms. PMID:27555868

An effective and efficient compression algorithm for ECG signals with irregular periods.

PubMed

Chou, Hsiao-Hsuan; Chen, Ying-Jui; Shiau, Yu-Chien; Kuo, Te-Son

2006-06-01

This paper presents an effective and efficient preprocessing algorithm for two-dimensional (2-D) electrocardiogram (ECG) compression to better compress irregular ECG signals by exploiting their inter- and intra-beat correlations. To better reveal the correlation structure, we first convert the ECG signal into a proper 2-D representation, or image. This involves a few steps including QRS detection and alignment, period sorting, and length equalization. The resulting 2-D ECG representation is then ready to be compressed by an appropriate image compression algorithm. We choose the state-of-the-art JPEG2000 for its high efficiency and flexibility. In this way, the proposed algorithm is shown to outperform some existing arts in the literature by simultaneously achieving high compression ratio (CR), low percent root mean squared difference (PRD), low maximum error (MaxErr), and low standard derivation of errors (StdErr). In particular, because the proposed period sorting method rearranges the detected heartbeats into a smoother image that is easier to compress, this algorithm is insensitive to irregular ECG periods. Thus either the irregular ECG signals or the QRS false-detection cases can be better compressed. This is a significant improvement over existing 2-D ECG compression methods. Moreover, this algorithm is not tied exclusively to JPEG2000. It can also be combined with other 2-D preprocessing methods or appropriate codecs to enhance the compression performance in irregular ECG cases.

A low computation cost method for seizure prediction.

PubMed

Zhang, Yanli; Zhou, Weidong; Yuan, Qi; Wu, Qi

2014-10-01

The dynamic changes of electroencephalograph (EEG) signals in the period prior to epileptic seizures play a major role in the seizure prediction. This paper proposes a low computation seizure prediction algorithm that combines a fractal dimension with a machine learning algorithm. The presented seizure prediction algorithm extracts the Higuchi fractal dimension (HFD) of EEG signals as features to classify the patient's preictal or interictal state with Bayesian linear discriminant analysis (BLDA) as a classifier. The outputs of BLDA are smoothed by a Kalman filter for reducing possible sporadic and isolated false alarms and then the final prediction results are produced using a thresholding procedure. The algorithm was evaluated on the intracranial EEG recordings of 21 patients in the Freiburg EEG database. For seizure occurrence period of 30 min and 50 min, our algorithm obtained an average sensitivity of 86.95% and 89.33%, an average false prediction rate of 0.20/h, and an average prediction time of 24.47 min and 39.39 min, respectively. The results confirm that the changes of HFD can serve as a precursor of ictal activities and be used for distinguishing between interictal and preictal epochs. Both HFD and BLDA classifier have a low computational complexity. All of these make the proposed algorithm suitable for real-time seizure prediction. Copyright © 2014 Elsevier B.V. All rights reserved.

Study on data compression algorithm and its implementation in portable electronic device for Internet of Things applications

NASA Astrophysics Data System (ADS)

Asilah Khairi, Nor; Bahari Jambek, Asral

2017-11-01

An Internet of Things (IoT) device is usually powered by a small battery, which does not last long. As a result, saving energy in IoT devices has become an important issue when it comes to this subject. Since power consumption is the primary cause of radio communication, some researchers have proposed several compression algorithms with the purpose of overcoming this particular problem. Several data compression algorithms from previous reference papers are discussed in this paper. The description of the compression algorithm in the reference papers was collected and summarized in a table form. From the analysis, MAS compression algorithm was selected as a project prototype due to its high potential for meeting the project requirements. Besides that, it also produced better performance regarding energy-saving, better memory usage, and data transmission efficiency. This method is also suitable to be implemented in WSN. MAS compression algorithm will be prototyped and applied in portable electronic devices for Internet of Things applications.

Competitive Parallel Processing For Compression Of Data

NASA Technical Reports Server (NTRS)

Diner, Daniel B.; Fender, Antony R. H.

1990-01-01

Momentarily-best compression algorithm selected. Proposed competitive-parallel-processing system compresses data for transmission in channel of limited band-width. Likely application for compression lies in high-resolution, stereoscopic color-television broadcasting. Data from information-rich source like color-television camera compressed by several processors, each operating with different algorithm. Referee processor selects momentarily-best compressed output.

Simultaneous compression and encryption for secure real-time secure transmission of sensitive video transmission

NASA Astrophysics Data System (ADS)

Al-Hayani, Nazar; Al-Jawad, Naseer; Jassim, Sabah A.

2014-05-01

Video compression and encryption became very essential in a secured real time video transmission. Applying both techniques simultaneously is one of the challenges where the size and the quality are important in multimedia transmission. In this paper we proposed a new technique for video compression and encryption. Both encryption and compression are based on edges extracted from the high frequency sub-bands of wavelet decomposition. The compression algorithm based on hybrid of: discrete wavelet transforms, discrete cosine transform, vector quantization, wavelet based edge detection, and phase sensing. The compression encoding algorithm treats the video reference and non-reference frames in two different ways. The encryption algorithm utilized A5 cipher combined with chaotic logistic map to encrypt the significant parameters and wavelet coefficients. Both algorithms can be applied simultaneously after applying the discrete wavelet transform on each individual frame. Experimental results show that the proposed algorithms have the following features: high compression, acceptable quality, and resistance to the statistical and bruteforce attack with low computational processing.

Hyper-spectral image compression algorithm based on mixing transform of wave band grouping to eliminate redundancy

NASA Astrophysics Data System (ADS)

Xie, ChengJun; Xu, Lin

2008-03-01

This paper presents an algorithm based on mixing transform of wave band grouping to eliminate spectral redundancy, the algorithm adapts to the relativity difference between different frequency spectrum images, and still it works well when the band number is not the power of 2. Using non-boundary extension CDF(2,2)DWT and subtraction mixing transform to eliminate spectral redundancy, employing CDF(2,2)DWT to eliminate spatial redundancy and SPIHT+CABAC for compression coding, the experiment shows that a satisfied lossless compression result can be achieved. Using hyper-spectral image Canal of American JPL laboratory as the data set for lossless compression test, when the band number is not the power of 2, lossless compression result of this compression algorithm is much better than the results acquired by JPEG-LS, WinZip, ARJ, DPCM, the research achievements of a research team of Chinese Academy of Sciences, Minimum Spanning Tree and Near Minimum Spanning Tree, on the average the compression ratio of this algorithm exceeds the above algorithms by 41%,37%,35%,29%,16%,10%,8% respectively; when the band number is the power of 2, for 128 frames of the image Canal, taking 8, 16 and 32 respectively as the number of one group for groupings based on different numbers, considering factors like compression storage complexity, the type of wave band and the compression effect, we suggest using 8 as the number of bands included in one group to achieve a better compression effect. The algorithm of this paper has priority in operation speed and hardware realization convenience.

Nonlinear Multiscale Transformations: From Synchronization to Error Control

DTIC Science & Technology

2001-07-01

transformation (plus the quantization step) has taken place, a lossless Lempel - Ziv compression algorithm is applied to reduce the size of the transformed... compressed data are all very close, however the visual quality of the reconstructed image is significantly better for the EC compression algorithm ...used in recent times in the first step of transform coding algorithms for image compression . Ideally, a multiscale transformation allows for an

Optimisation algorithms for ECG data compression.

PubMed

Haugland, D; Heber, J G; Husøy, J H

1997-07-01

The use of exact optimisation algorithms for compressing digital electrocardiograms (ECGs) is demonstrated. As opposed to traditional time-domain methods, which use heuristics to select a small subset of representative signal samples, the problem of selecting the subset is formulated in rigorous mathematical terms. This approach makes it possible to derive algorithms guaranteeing the smallest possible reconstruction error when a bounded selection of signal samples is interpolated. The proposed model resembles well-known network models and is solved by a cubic dynamic programming algorithm. When applied to standard test problems, the algorithm produces a compressed representation for which the distortion is about one-half of that obtained by traditional time-domain compression techniques at reasonable compression ratios. This illustrates that, in terms of the accuracy of decoded signals, existing time-domain heuristics for ECG compression may be far from what is theoretically achievable. The paper is an attempt to bridge this gap.

Minimal spanning trees at the percolation threshold: A numerical calculation

NASA Astrophysics Data System (ADS)

Sweeney, Sean M.; Middleton, A. Alan

2013-09-01

The fractal dimension of minimal spanning trees on percolation clusters is estimated for dimensions d up to d=5. A robust analysis technique is developed for correlated data, as seen in such trees. This should be a robust method suitable for analyzing a wide array of randomly generated fractal structures. The trees analyzed using these techniques are built using a combination of Prim's and Kruskal's algorithms for finding minimal spanning trees. This combination reduces memory usage and allows for simulation of larger systems than would otherwise be possible. The path length fractal dimension ds of MSTs on critical percolation clusters is found to be compatible with the predictions of the perturbation expansion developed by T. S. Jackson and N. Read [Phys. Rev. EPLEEE81539-375510.1103/PhysRevE.81.021131 81, 021131 (2010)].

Multifractal analysis of mobile social networks

NASA Astrophysics Data System (ADS)

Zheng, Wei; Zhang, Zifeng; Deng, Yufan

2017-09-01

As Wireless Fidelity (Wi-Fi)-enabled handheld devices have been widely used, the mobile social networks (MSNs) has been attracting extensive attention. Fractal approaches have also been widely applied to characterierize natural networks as useful tools to depict their spatial distribution and scaling properties. Moreover, when the complexity of the spatial distribution of MSNs cannot be properly charaterized by single fractal dimension, multifractal analysis is required. For further research, we introduced a multifractal analysis method based on box-covering algorithm to describe the structure of MSNs. Using this method, we find that the networks are multifractal at different time interval. The simulation results demonstrate that the proposed method is efficient for analyzing the multifractal characteristic of MSNs, which provides a distribution of singularities adequately describing both the heterogeneity of fractal patterns and the statistics of measurements across spatial scales in MSNs.

Symmetric encryption algorithms using chaotic and non-chaotic generators: A review

PubMed Central

Radwan, Ahmed G.; AbdElHaleem, Sherif H.; Abd-El-Hafiz, Salwa K.

2015-01-01

This paper summarizes the symmetric image encryption results of 27 different algorithms, which include substitution-only, permutation-only or both phases. The cores of these algorithms are based on several discrete chaotic maps (Arnold’s cat map and a combination of three generalized maps), one continuous chaotic system (Lorenz) and two non-chaotic generators (fractals and chess-based algorithms). Each algorithm has been analyzed by the correlation coefficients between pixels (horizontal, vertical and diagonal), differential attack measures, Mean Square Error (MSE), entropy, sensitivity analyses and the 15 standard tests of the National Institute of Standards and Technology (NIST) SP-800-22 statistical suite. The analyzed algorithms include a set of new image encryption algorithms based on non-chaotic generators, either using substitution only (using fractals) and permutation only (chess-based) or both. Moreover, two different permutation scenarios are presented where the permutation-phase has or does not have a relationship with the input image through an ON/OFF switch. Different encryption-key lengths and complexities are provided from short to long key to persist brute-force attacks. In addition, sensitivities of those different techniques to a one bit change in the input parameters of the substitution key as well as the permutation key are assessed. Finally, a comparative discussion of this work versus many recent research with respect to the used generators, type of encryption, and analyses is presented to highlight the strengths and added contribution of this paper. PMID:26966561

Research on compressive sensing reconstruction algorithm based on total variation model

NASA Astrophysics Data System (ADS)

Gao, Yu-xuan; Sun, Huayan; Zhang, Tinghua; Du, Lin

2017-12-01

Compressed sensing for breakthrough Nyquist sampling theorem provides a strong theoretical , making compressive sampling for image signals be carried out simultaneously. In traditional imaging procedures using compressed sensing theory, not only can it reduces the storage space, but also can reduce the demand for detector resolution greatly. Using the sparsity of image signal, by solving the mathematical model of inverse reconfiguration, realize the super-resolution imaging. Reconstruction algorithm is the most critical part of compression perception, to a large extent determine the accuracy of the reconstruction of the image.The reconstruction algorithm based on the total variation (TV) model is more suitable for the compression reconstruction of the two-dimensional image, and the better edge information can be obtained. In order to verify the performance of the algorithm, Simulation Analysis the reconstruction result in different coding mode of the reconstruction algorithm based on the TV reconstruction algorithm. The reconstruction effect of the reconfigurable algorithm based on TV based on the different coding methods is analyzed to verify the stability of the algorithm. This paper compares and analyzes the typical reconstruction algorithm in the same coding mode. On the basis of the minimum total variation algorithm, the Augmented Lagrangian function term is added and the optimal value is solved by the alternating direction method.Experimental results show that the reconstruction algorithm is compared with the traditional classical algorithm based on TV has great advantages, under the low measurement rate can be quickly and accurately recovers target image.

Digital compression algorithms for HDTV transmission

NASA Technical Reports Server (NTRS)

Adkins, Kenneth C.; Shalkhauser, Mary JO; Bibyk, Steven B.

1990-01-01

Digital compression of video images is a possible avenue for high definition television (HDTV) transmission. Compression needs to be optimized while picture quality remains high. Two techniques for compression the digital images are explained and comparisons are drawn between the human vision system and artificial compression techniques. Suggestions for improving compression algorithms through the use of neural and analog circuitry are given.

SAR correlation technique - An algorithm for processing data with large range walk

NASA Technical Reports Server (NTRS)

Jin, M.; Wu, C.

1983-01-01

This paper presents an algorithm for synthetic aperture radar (SAR) azimuth correlation with extraneously large range migration effect which can not be accommodated by the existing frequency domain interpolation approach used in current SEASAT SAR processing. A mathematical model is first provided for the SAR point-target response in both the space (or time) and the frequency domain. A simple and efficient processing algorithm derived from the hybrid algorithm is then given. This processing algorithm enables azimuth correlation by two steps. The first step is a secondary range compression to handle the dispersion of the spectra of the azimuth response along range. The second step is the well-known frequency domain range migration correction approach for the azimuth compression. This secondary range compression can be processed simultaneously with range pulse compression. Simulation results provided here indicate that this processing algorithm yields a satisfactory compressed impulse response for SAR data with large range migration.

Fractal and Gray Level Cooccurrence Matrix Computational Analysis of Primary Osteosarcoma Magnetic Resonance Images Predicts the Chemotherapy Response.

PubMed

Djuričić, Goran J; Radulovic, Marko; Sopta, Jelena P; Nikitović, Marina; Milošević, Nebojša T

2017-01-01

The prediction of induction chemotherapy response at the time of diagnosis may improve outcomes in osteosarcoma by allowing for personalized tailoring of therapy. The aim of this study was thus to investigate the predictive potential of the so far unexploited computational analysis of osteosarcoma magnetic resonance (MR) images. Fractal and gray level cooccurrence matrix (GLCM) algorithms were employed in retrospective analysis of MR images of primary osteosarcoma localized in distal femur prior to the OsteoSa induction chemotherapy. The predicted and actual chemotherapy response outcomes were then compared by means of receiver operating characteristic (ROC) analysis and accuracy calculation. Dbin, Λ, and SCN were the standard fractal and GLCM features which significantly associated with the chemotherapy outcome, but only in one of the analyzed planes. Our newly developed normalized fractal dimension, called the space-filling ratio (SFR) exerted an independent and much better predictive value with the prediction significance accomplished in two of the three imaging planes, with accuracy of 82% and area under the ROC curve of 0.20 (95% confidence interval 0-0.41). In conclusion, SFR as the newly designed fractal coefficient provided superior predictive performance in comparison to standard image analysis features, presumably by compensating for the tumor size variation in MR images.

Fractal dimension values of cerebral and cerebellar activity in rats loaded with aluminium.

PubMed

Kekovic, Goran; Culic, Milka; Martac, Ljiljana; Stojadinovic, Gordana; Capo, Ivan; Lalosevic, Dusan; Sekulic, Slobodan

2010-07-01

Aluminium interferes with a variety of cellular metabolic processes in the mammalian nervous system and its intake might increase a risk of developing Alzheimer's disease (AD). While cerebral involvement even at the early stages of intoxication is well known, the role of cerebellum is underestimated. Our aim was to investigate cerebral and cerebellar electrocortical activity in adult male rats exposed to chronic aluminium treatment by nonlinear analytic tools. The adult rats in an aluminium-treated group were injected by AlCl(3), intraperitoneally (2 mg Al/kg, daily for 4 weeks). Fractal analysis of brain activity was performed off-line using Higuchi's algorithm. The average fractal dimension of electrocortical activity in aluminium-treated animals was lower than the average fractal dimension of electrocortical activity in the control rats, at cerebral but not at cerebellar level. The changes in the stationary and nonlinear properties of time series were more expressed in cerebral electrocortical activity than in cerebellar activity. This can be useful for developing effective diagnostic and therapeutic strategies in neurodegenerative diseases.

Complexity Theory

USGS Publications Warehouse

Lee, William H K.

2016-01-01

A complex system consists of many interacting parts, generates new collective behavior through self organization, and adaptively evolves through time. Many theories have been developed to study complex systems, including chaos, fractals, cellular automata, self organization, stochastic processes, turbulence, and genetic algorithms.

Efficient fractal-based mutation in evolutionary algorithms from iterated function systems

NASA Astrophysics Data System (ADS)

Salcedo-Sanz, S.; Aybar-Ruíz, A.; Camacho-Gómez, C.; Pereira, E.

2018-03-01

In this paper we present a new mutation procedure for Evolutionary Programming (EP) approaches, based on Iterated Function Systems (IFSs). The new mutation procedure proposed consists of considering a set of IFS which are able to generate fractal structures in a two-dimensional phase space, and use them to modify a current individual of the EP algorithm, instead of using random numbers from different probability density functions. We test this new proposal in a set of benchmark functions for continuous optimization problems. In this case, we compare the proposed mutation against classical Evolutionary Programming approaches, with mutations based on Gaussian, Cauchy and chaotic maps. We also include a discussion on the IFS-based mutation in a real application of Tuned Mass Dumper (TMD) location and optimization for vibration cancellation in buildings. In both practical cases, the proposed EP with the IFS-based mutation obtained extremely competitive results compared to alternative classical mutation operators.

Automatic localization of cerebral cortical malformations using fractal analysis.

PubMed

De Luca, A; Arrigoni, F; Romaniello, R; Triulzi, F M; Peruzzo, D; Bertoldo, A

2016-08-21

Malformations of cortical development (MCDs) encompass a variety of brain disorders affecting the normal development and organization of the brain cortex. The relatively low incidence and the extreme heterogeneity of these disorders hamper the application of classical group level approaches for the detection of lesions. Here, we present a geometrical descriptor for a voxel level analysis based on fractal geometry, then define two similarity measures to detect the lesions at single subject level. The pipeline was applied to 15 normal children and nine pediatric patients affected by MCDs following two criteria, maximum accuracy (WACC) and minimization of false positives (FPR), and proved that our lesion detection algorithm is able to detect and locate abnormalities of the brain cortex with high specificity (WACC  =  85%, FPR  =  96%), sensitivity (WACC  =  83%, FPR  =  63%) and accuracy (WACC  =  85%, FPR  =  90%). The combination of global and local features proves to be effective, making the algorithm suitable for the detection of both focal and diffused malformations. Compared to other existing algorithms, this method shows higher accuracy and sensitivity.

Automatic localization of cerebral cortical malformations using fractal analysis

NASA Astrophysics Data System (ADS)

De Luca, A.; Arrigoni, F.; Romaniello, R.; Triulzi, F. M.; Peruzzo, D.; Bertoldo, A.

2016-08-01

Malformations of cortical development (MCDs) encompass a variety of brain disorders affecting the normal development and organization of the brain cortex. The relatively low incidence and the extreme heterogeneity of these disorders hamper the application of classical group level approaches for the detection of lesions. Here, we present a geometrical descriptor for a voxel level analysis based on fractal geometry, then define two similarity measures to detect the lesions at single subject level. The pipeline was applied to 15 normal children and nine pediatric patients affected by MCDs following two criteria, maximum accuracy (WACC) and minimization of false positives (FPR), and proved that our lesion detection algorithm is able to detect and locate abnormalities of the brain cortex with high specificity (WACC  =  85%, FPR  =  96%), sensitivity (WACC  =  83%, FPR  =  63%) and accuracy (WACC  =  85%, FPR  =  90%). The combination of global and local features proves to be effective, making the algorithm suitable for the detection of both focal and diffused malformations. Compared to other existing algorithms, this method shows higher accuracy and sensitivity.

Design of Restoration Method Based on Compressed Sensing and TwIST Algorithm

NASA Astrophysics Data System (ADS)

Zhang, Fei; Piao, Yan

2018-04-01

In order to improve the subjective and objective quality of degraded images at low sampling rates effectively,save storage space and reduce computational complexity at the same time, this paper proposes a joint restoration algorithm of compressed sensing and two step iterative threshold shrinkage (TwIST). The algorithm applies the TwIST algorithm which used in image restoration to the compressed sensing theory. Then, a small amount of sparse high-frequency information is obtained in frequency domain. The TwIST algorithm based on compressed sensing theory is used to accurately reconstruct the high frequency image. The experimental results show that the proposed algorithm achieves better subjective visual effects and objective quality of degraded images while accurately restoring degraded images.

Multifractal modeling, segmentation, prediction, and statistical validation of posterior fossa tumors

NASA Astrophysics Data System (ADS)

Islam, Atiq; Iftekharuddin, Khan M.; Ogg, Robert J.; Laningham, Fred H.; Sivakumar, Bhuvaneswari

2008-03-01

In this paper, we characterize the tumor texture in pediatric brain magnetic resonance images (MRIs) and exploit these features for automatic segmentation of posterior fossa (PF) tumors. We focus on PF tumor because of the prevalence of such tumor in pediatric patients. Due to varying appearance in MRI, we propose to model the tumor texture with a multi-fractal process, such as a multi-fractional Brownian motion (mBm). In mBm, the time-varying Holder exponent provides flexibility in modeling irregular tumor texture. We develop a detailed mathematical framework for mBm in two-dimension and propose a novel algorithm to estimate the multi-fractal structure of tissue texture in brain MRI based on wavelet coefficients. This wavelet based multi-fractal feature along with MR image intensity and a regular fractal feature obtained using our existing piecewise-triangular-prism-surface-area (PTPSA) method, are fused in segmenting PF tumor and non-tumor regions in brain T1, T2, and FLAIR MR images respectively. We also demonstrate a non-patient-specific automated tumor prediction scheme based on these image features. We experimentally show the tumor discriminating power of our novel multi-fractal texture along with intensity and fractal features in automated tumor segmentation and statistical prediction. To evaluate the performance of our tumor prediction scheme, we obtain ROCs and demonstrate how sharply the curves reach the specificity of 1.0 sacrificing minimal sensitivity. Experimental results show the effectiveness of our proposed techniques in automatic detection of PF tumors in pediatric MRIs.

Segmentation of time series with long-range fractal correlations

PubMed Central

Bernaola-Galván, P.; Oliver, J.L.; Hackenberg, M.; Coronado, A.V.; Ivanov, P.Ch.; Carpena, P.

2012-01-01

Segmentation is a standard method of data analysis to identify change-points dividing a nonstationary time series into homogeneous segments. However, for long-range fractal correlated series, most of the segmentation techniques detect spurious change-points which are simply due to the heterogeneities induced by the correlations and not to real nonstationarities. To avoid this oversegmentation, we present a segmentation algorithm which takes as a reference for homogeneity, instead of a random i.i.d. series, a correlated series modeled by a fractional noise with the same degree of correlations as the series to be segmented. We apply our algorithm to artificial series with long-range correlations and show that it systematically detects only the change-points produced by real nonstationarities and not those created by the correlations of the signal. Further, we apply the method to the sequence of the long arm of human chromosome 21, which is known to have long-range fractal correlations. We obtain only three segments that clearly correspond to the three regions of different G + C composition revealed by means of a multi-scale wavelet plot. Similar results have been obtained when segmenting all human chromosome sequences, showing the existence of previously unknown huge compositional superstructures in the human genome. PMID:23645997

Handling the data management needs of high-throughput sequencing data: SpeedGene, a compression algorithm for the efficient storage of genetic data

PubMed Central

2012-01-01

Background As Next-Generation Sequencing data becomes available, existing hardware environments do not provide sufficient storage space and computational power to store and process the data due to their enormous size. This is and will be a frequent problem that is encountered everyday by researchers who are working on genetic data. There are some options available for compressing and storing such data, such as general-purpose compression software, PBAT/PLINK binary format, etc. However, these currently available methods either do not offer sufficient compression rates, or require a great amount of CPU time for decompression and loading every time the data is accessed. Results Here, we propose a novel and simple algorithm for storing such sequencing data. We show that, the compression factor of the algorithm ranges from 16 to several hundreds, which potentially allows SNP data of hundreds of Gigabytes to be stored in hundreds of Megabytes. We provide a C++ implementation of the algorithm, which supports direct loading and parallel loading of the compressed format without requiring extra time for decompression. By applying the algorithm to simulated and real datasets, we show that the algorithm gives greater compression rate than the commonly used compression methods, and the data-loading process takes less time. Also, The C++ library provides direct-data-retrieving functions, which allows the compressed information to be easily accessed by other C++ programs. Conclusions The SpeedGene algorithm enables the storage and the analysis of next generation sequencing data in current hardware environment, making system upgrades unnecessary. PMID:22591016

Experimental scheme and restoration algorithm of block compression sensing

NASA Astrophysics Data System (ADS)

Zhang, Linxia; Zhou, Qun; Ke, Jun

2018-01-01

Compressed Sensing (CS) can use the sparseness of a target to obtain its image with much less data than that defined by the Nyquist sampling theorem. In this paper, we study the hardware implementation of a block compression sensing system and its reconstruction algorithms. Different block sizes are used. Two algorithms, the orthogonal matching algorithm (OMP) and the full variation minimum algorithm (TV) are used to obtain good reconstructions. The influence of block size on reconstruction is also discussed.

Parallel image compression

NASA Technical Reports Server (NTRS)

Reif, John H.

1987-01-01

A parallel compression algorithm for the 16,384 processor MPP machine was developed. The serial version of the algorithm can be viewed as a combination of on-line dynamic lossless test compression techniques (which employ simple learning strategies) and vector quantization. These concepts are described. How these concepts are combined to form a new strategy for performing dynamic on-line lossy compression is discussed. Finally, the implementation of this algorithm in a massively parallel fashion on the MPP is discussed.

A Novel Image Compression Algorithm for High Resolution 3D Reconstruction

NASA Astrophysics Data System (ADS)

Siddeq, M. M.; Rodrigues, M. A.

2014-06-01

This research presents a novel algorithm to compress high-resolution images for accurate structured light 3D reconstruction. Structured light images contain a pattern of light and shadows projected on the surface of the object, which are captured by the sensor at very high resolutions. Our algorithm is concerned with compressing such images to a high degree with minimum loss without adversely affecting 3D reconstruction. The Compression Algorithm starts with a single level discrete wavelet transform (DWT) for decomposing an image into four sub-bands. The sub-band LL is transformed by DCT yielding a DC-matrix and an AC-matrix. The Minimize-Matrix-Size Algorithm is used to compress the AC-matrix while a DWT is applied again to the DC-matrix resulting in LL2, HL2, LH2 and HH2 sub-bands. The LL2 sub-band is transformed by DCT, while the Minimize-Matrix-Size Algorithm is applied to the other sub-bands. The proposed algorithm has been tested with images of different sizes within a 3D reconstruction scenario. The algorithm is demonstrated to be more effective than JPEG2000 and JPEG concerning higher compression rates with equivalent perceived quality and the ability to more accurately reconstruct the 3D models.

Image-Data Compression Using Edge-Optimizing Algorithm for WFA Inference.

ERIC Educational Resources Information Center

Culik, Karel II; Kari, Jarkko

1994-01-01

Presents an inference algorithm that produces a weighted finite automata (WFA), in particular, the grayness functions of graytone images. Image-data compression results based on the new inference algorithm produces a WFA with a relatively small number of edges. Image-data compression results alone and in combination with wavelets are discussed.…

An efficient coding algorithm for the compression of ECG signals using the wavelet transform.

PubMed

Rajoub, Bashar A

2002-04-01

A wavelet-based electrocardiogram (ECG) data compression algorithm is proposed in this paper. The ECG signal is first preprocessed, the discrete wavelet transform (DWT) is then applied to the preprocessed signal. Preprocessing guarantees that the magnitudes of the wavelet coefficients be less than one, and reduces the reconstruction errors near both ends of the compressed signal. The DWT coefficients are divided into three groups, each group is thresholded using a threshold based on a desired energy packing efficiency. A binary significance map is then generated by scanning the wavelet decomposition coefficients and outputting a binary one if the scanned coefficient is significant, and a binary zero if it is insignificant. Compression is achieved by 1) using a variable length code based on run length encoding to compress the significance map and 2) using direct binary representation for representing the significant coefficients. The ability of the coding algorithm to compress ECG signals is investigated, the results were obtained by compressing and decompressing the test signals. The proposed algorithm is compared with direct-based and wavelet-based compression algorithms and showed superior performance. A compression ratio of 24:1 was achieved for MIT-BIH record 117 with a percent root mean square difference as low as 1.08%.

A Novel 2D Image Compression Algorithm Based on Two Levels DWT and DCT Transforms with Enhanced Minimize-Matrix-Size Algorithm for High Resolution Structured Light 3D Surface Reconstruction

NASA Astrophysics Data System (ADS)

Siddeq, M. M.; Rodrigues, M. A.

2015-09-01

Image compression techniques are widely used on 2D image 2D video 3D images and 3D video. There are many types of compression techniques and among the most popular are JPEG and JPEG2000. In this research, we introduce a new compression method based on applying a two level discrete cosine transform (DCT) and a two level discrete wavelet transform (DWT) in connection with novel compression steps for high-resolution images. The proposed image compression algorithm consists of four steps. (1) Transform an image by a two level DWT followed by a DCT to produce two matrices: DC- and AC-Matrix, or low and high frequency matrix, respectively, (2) apply a second level DCT on the DC-Matrix to generate two arrays, namely nonzero-array and zero-array, (3) apply the Minimize-Matrix-Size algorithm to the AC-Matrix and to the other high-frequencies generated by the second level DWT, (4) apply arithmetic coding to the output of previous steps. A novel decompression algorithm, Fast-Match-Search algorithm (FMS), is used to reconstruct all high-frequency matrices. The FMS-algorithm computes all compressed data probabilities by using a table of data, and then using a binary search algorithm for finding decompressed data inside the table. Thereafter, all decoded DC-values with the decoded AC-coefficients are combined in one matrix followed by inverse two levels DCT with two levels DWT. The technique is tested by compression and reconstruction of 3D surface patches. Additionally, this technique is compared with JPEG and JPEG2000 algorithm through 2D and 3D root-mean-square-error following reconstruction. The results demonstrate that the proposed compression method has better visual properties than JPEG and JPEG2000 and is able to more accurately reconstruct surface patches in 3D.

Wearable EEG via lossless compression.

PubMed

Dufort, Guillermo; Favaro, Federico; Lecumberry, Federico; Martin, Alvaro; Oliver, Juan P; Oreggioni, Julian; Ramirez, Ignacio; Seroussi, Gadiel; Steinfeld, Leonardo

2016-08-01

This work presents a wearable multi-channel EEG recording system featuring a lossless compression algorithm. The algorithm, based in a previously reported algorithm by the authors, exploits the existing temporal correlation between samples at different sampling times, and the spatial correlation between different electrodes across the scalp. The low-power platform is able to compress, by a factor between 2.3 and 3.6, up to 300sps from 64 channels with a power consumption of 176μW/ch. The performance of the algorithm compares favorably with the best compression rates reported up to date in the literature.

The New CCSDS Image Compression Recommendation

NASA Technical Reports Server (NTRS)

Yeh, Pen-Shu; Armbruster, Philippe; Kiely, Aaron; Masschelein, Bart; Moury, Gilles; Schaefer, Christoph

2005-01-01

The Consultative Committee for Space Data Systems (CCSDS) data compression working group has recently adopted a recommendation for image data compression, with a final release expected in 2005. The algorithm adopted in the recommendation consists of a two-dimensional discrete wavelet transform of the image, followed by progressive bit-plane coding of the transformed data. The algorithm can provide both lossless and lossy compression, and allows a user to directly control the compressed data volume or the fidelity with which the wavelet-transformed data can be reconstructed. The algorithm is suitable for both frame-based image data and scan-based sensor data, and has applications for near-Earth and deep-space missions. The standard will be accompanied by free software sources on a future web site. An Application-Specific Integrated Circuit (ASIC) implementation of the compressor is currently under development. This paper describes the compression algorithm along with the requirements that drove the selection of the algorithm. Performance results and comparisons with other compressors are given for a test set of space images.

Lossless medical image compression using geometry-adaptive partitioning and least square-based prediction.

PubMed

Song, Xiaoying; Huang, Qijun; Chang, Sheng; He, Jin; Wang, Hao

2018-06-01

To improve the compression rates for lossless compression of medical images, an efficient algorithm, based on irregular segmentation and region-based prediction, is proposed in this paper. Considering that the first step of a region-based compression algorithm is segmentation, this paper proposes a hybrid method by combining geometry-adaptive partitioning and quadtree partitioning to achieve adaptive irregular segmentation for medical images. Then, least square (LS)-based predictors are adaptively designed for each region (regular subblock or irregular subregion). The proposed adaptive algorithm not only exploits spatial correlation between pixels but it utilizes local structure similarity, resulting in efficient compression performance. Experimental results show that the average compression performance of the proposed algorithm is 10.48, 4.86, 3.58, and 0.10% better than that of JPEG 2000, CALIC, EDP, and JPEG-LS, respectively. Graphical abstract ᅟ.

Characterizing Tityus discrepans scorpion venom from a fractal perspective: Venom complexity, effects of captivity, sexual dimorphism, differences among species.

PubMed

D'Suze, Gina; Sandoval, Moisés; Sevcik, Carlos

2015-12-15

A characteristic of venom elution patterns, shared with many other complex systems, is that many their features cannot be properly described with statistical or euclidean concepts. The understanding of such systems became possible with Mandelbrot's fractal analysis. Venom elution patterns were produced using the reversed phase high performance liquid chromatography (HPLC) with 1 mg of venom. One reason for the lack of quantitative analyses of the sources of venom variability is parametrizing the venom chromatograms' complexity. We quantize this complexity by means of an algorithm which estimates the contortedness (Q) of a waveform. Fractal analysis was used to compare venoms and to measure inter- and intra-specific venom variability. We studied variations in venom complexity derived from gender, seasonal and environmental factors, duration of captivity in the laboratory, technique used to milk venom. Copyright © 2015 Elsevier Ltd. All rights reserved.

Making Better Use of Bandwidth: Data Compression and Network Management Technologies

DTIC Science & Technology

2005-01-01

data , the compression would not be a success. A key feature of the Lempel - Ziv family of algorithms is that the...citeseer.nj.nec.com/yu02motion.html. Ziv , J., and A. Lempel , “A Universal Algorithm for Sequential Data Compression ,” IEEE Transac- tions on Information Theory, Vol. 23, 1977, pp. 337–342. ...probability models – Lempel - Ziv – Prediction by partial matching The central component of a lossless compression algorithm

Algorithm for Compressing Time-Series Data

NASA Technical Reports Server (NTRS)

Hawkins, S. Edward, III; Darlington, Edward Hugo

2012-01-01

An algorithm based on Chebyshev polynomials effects lossy compression of time-series data or other one-dimensional data streams (e.g., spectral data) that are arranged in blocks for sequential transmission. The algorithm was developed for use in transmitting data from spacecraft scientific instruments to Earth stations. In spite of its lossy nature, the algorithm preserves the information needed for scientific analysis. The algorithm is computationally simple, yet compresses data streams by factors much greater than two. The algorithm is not restricted to spacecraft or scientific uses: it is applicable to time-series data in general. The algorithm can also be applied to general multidimensional data that have been converted to time-series data, a typical example being image data acquired by raster scanning. However, unlike most prior image-data-compression algorithms, this algorithm neither depends on nor exploits the two-dimensional spatial correlations that are generally present in images. In order to understand the essence of this compression algorithm, it is necessary to understand that the net effect of this algorithm and the associated decompression algorithm is to approximate the original stream of data as a sequence of finite series of Chebyshev polynomials. For the purpose of this algorithm, a block of data or interval of time for which a Chebyshev polynomial series is fitted to the original data is denoted a fitting interval. Chebyshev approximation has two properties that make it particularly effective for compressing serial data streams with minimal loss of scientific information: The errors associated with a Chebyshev approximation are nearly uniformly distributed over the fitting interval (this is known in the art as the "equal error property"); and the maximum deviations of the fitted Chebyshev polynomial from the original data have the smallest possible values (this is known in the art as the "min-max property").

An accurate algorithm to calculate the Hurst exponent of self-similar processes

NASA Astrophysics Data System (ADS)

Fernández-Martínez, M.; Sánchez-Granero, M. A.; Trinidad Segovia, J. E.; Román-Sánchez, I. M.

2014-06-01

In this paper, we introduce a new approach which generalizes the GM2 algorithm (introduced in Sánchez-Granero et al. (2008) [52]) as well as fractal dimension algorithms (FD1, FD2 and FD3) (first appeared in Sánchez-Granero et al. (2012) [51]), providing an accurate algorithm to calculate the Hurst exponent of self-similar processes. We prove that this algorithm performs properly in the case of short time series when fractional Brownian motions and Lévy stable motions are considered. We conclude the paper with a dynamic study of the Hurst exponent evolution in the S&P500 index stocks.

NRGC: a novel referential genome compression algorithm.

PubMed

Saha, Subrata; Rajasekaran, Sanguthevar

2016-11-15

Next-generation sequencing techniques produce millions to billions of short reads. The procedure is not only very cost effective but also can be done in laboratory environment. The state-of-the-art sequence assemblers then construct the whole genomic sequence from these reads. Current cutting edge computing technology makes it possible to build genomic sequences from the billions of reads within a minimal cost and time. As a consequence, we see an explosion of biological sequences in recent times. In turn, the cost of storing the sequences in physical memory or transmitting them over the internet is becoming a major bottleneck for research and future medical applications. Data compression techniques are one of the most important remedies in this context. We are in need of suitable data compression algorithms that can exploit the inherent structure of biological sequences. Although standard data compression algorithms are prevalent, they are not suitable to compress biological sequencing data effectively. In this article, we propose a novel referential genome compression algorithm (NRGC) to effectively and efficiently compress the genomic sequences. We have done rigorous experiments to evaluate NRGC by taking a set of real human genomes. The simulation results show that our algorithm is indeed an effective genome compression algorithm that performs better than the best-known algorithms in most of the cases. Compression and decompression times are also very impressive. The implementations are freely available for non-commercial purposes. They can be downloaded from: http://www.engr.uconn.edu/~rajasek/NRGC.zip CONTACT: rajasek@engr.uconn.edu. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Fpack and Funpack Utilities for FITS Image Compression and Uncompression

NASA Technical Reports Server (NTRS)

Pence, W.

2008-01-01

Fpack is a utility program for optimally compressing images in the FITS (Flexible Image Transport System) data format (see http://fits.gsfc.nasa.gov). The associated funpack program restores the compressed image file back to its original state (as long as a lossless compression algorithm is used). These programs may be run from the host operating system command line and are analogous to the gzip and gunzip utility programs except that they are optimized for FITS format images and offer a wider choice of compression algorithms. Fpack stores the compressed image using the FITS tiled image compression convention (see http://fits.gsfc.nasa.gov/fits_registry.html). Under this convention, the image is first divided into a user-configurable grid of rectangular tiles, and then each tile is individually compressed and stored in a variable-length array column in a FITS binary table. By default, fpack usually adopts a row-by-row tiling pattern. The FITS image header keywords remain uncompressed for fast access by FITS reading and writing software. The tiled image compression convention can in principle support any number of different compression algorithms. The fpack and funpack utilities call on routines in the CFITSIO library (http://hesarc.gsfc.nasa.gov/fitsio) to perform the actual compression and uncompression of the FITS images, which currently supports the GZIP, Rice, H-compress, and PLIO IRAF pixel list compression algorithms.

Computer Recreations.

ERIC Educational Resources Information Center

Dewdney, A. K.

1989-01-01

Discussed are three examples of computer graphics including biomorphs, Truchet tilings, and fractal popcorn. The graphics are shown and the basic algorithm using multiple iteration of a particular function or mathematical operation is described. An illustration of a snail shell created by computer graphics is presented. (YP)

PACE: Power-Aware Computing Engines

DTIC Science & Technology

2005-02-01

more costly than compu- tation on our test platform, and it is memory access that dominates most lossless data compression algorithms . In fact, even...Performance and implementation concerns A compression algorithm may be implemented with many different, yet reasonable, data structures (including...Related work This section discusses data compression for low- bandwidth devices and optimizing algorithms for low energy. Though much work has gone

Resource efficient data compression algorithms for demanding, WSN based biomedical applications.

PubMed

Antonopoulos, Christos P; Voros, Nikolaos S

2016-02-01

During the last few years, medical research areas of critical importance such as Epilepsy monitoring and study, increasingly utilize wireless sensor network technologies in order to achieve better understanding and significant breakthroughs. However, the limited memory and communication bandwidth offered by WSN platforms comprise a significant shortcoming to such demanding application scenarios. Although, data compression can mitigate such deficiencies there is a lack of objective and comprehensive evaluation of relative approaches and even more on specialized approaches targeting specific demanding applications. The research work presented in this paper focuses on implementing and offering an in-depth experimental study regarding prominent, already existing as well as novel proposed compression algorithms. All algorithms have been implemented in a common Matlab framework. A major contribution of this paper, that differentiates it from similar research efforts, is the employment of real world Electroencephalography (EEG) and Electrocardiography (ECG) datasets comprising the two most demanding Epilepsy modalities. Emphasis is put on WSN applications, thus the respective metrics focus on compression rate and execution latency for the selected datasets. The evaluation results reveal significant performance and behavioral characteristics of the algorithms related to their complexity and the relative negative effect on compression latency as opposed to the increased compression rate. It is noted that the proposed schemes managed to offer considerable advantage especially aiming to achieve the optimum tradeoff between compression rate-latency. Specifically, proposed algorithm managed to combine highly completive level of compression while ensuring minimum latency thus exhibiting real-time capabilities. Additionally, one of the proposed schemes is compared against state-of-the-art general-purpose compression algorithms also exhibiting considerable advantages as far as the compression rate is concerned. Copyright © 2015 Elsevier Inc. All rights reserved.

An optimized compression algorithm for real-time ECG data transmission in wireless network of medical information systems.

PubMed

Cho, Gyoun-Yon; Lee, Seo-Joon; Lee, Tae-Ro

2015-01-01

Recent medical information systems are striving towards real-time monitoring models to care patients anytime and anywhere through ECG signals. However, there are several limitations such as data distortion and limited bandwidth in wireless communications. In order to overcome such limitations, this research focuses on compression. Few researches have been made to develop a specialized compression algorithm for ECG data transmission in real-time monitoring wireless network. Not only that, recent researches' algorithm is not appropriate for ECG signals. Therefore this paper presents a more developed algorithm EDLZW for efficient ECG data transmission. Results actually showed that the EDLZW compression ratio was 8.66, which was a performance that was 4 times better than any other recent compression method widely used today.

Lossless compression of image data products on th e FIFE CD-ROM series

NASA Technical Reports Server (NTRS)

Newcomer, Jeffrey A.; Strebel, Donald E.

1993-01-01

How do you store enough of the key data sets, from a total of 120 gigabytes of data collected for a scientific experiment, on a collection of CD-ROM's, small enough to distribute to a broad scientific community? In such an application where information loss in unacceptable, lossless compression algorithms are the only choice. Although lossy compression algorithms can provide an order of magnitude improvement in compression ratios over lossless algorithms the information that is lost is often part of the key scientific precision of the data. Therefore, lossless compression algorithms are and will continue to be extremely important in minimizing archiving storage requirements and distribution of large earth and space (ESS) data sets while preserving the essential scientific precision of the data.

Compressive Sensing of Foot Gait Signals and Its Application for the Estimation of Clinically Relevant Time Series.

PubMed

Pant, Jeevan K; Krishnan, Sridhar

2016-07-01

A new signal reconstruction algorithm for compressive sensing based on the minimization of a pseudonorm which promotes block-sparse structure on the first-order difference of the signal is proposed. Involved optimization is carried out by using a sequential version of Fletcher-Reeves' conjugate-gradient algorithm, and the line search is based on Banach's fixed-point theorem. The algorithm is suitable for the reconstruction of foot gait signals which admit block-sparse structure on the first-order difference. An additional algorithm for the estimation of stride-interval, swing-interval, and stance-interval time series from the reconstructed foot gait signals is also proposed. This algorithm is based on finding zero crossing indices of the foot gait signal and using the resulting indices for the computation of time series. Extensive simulation results demonstrate that the proposed signal reconstruction algorithm yields improved signal-to-noise ratio and requires significantly reduced computational effort relative to several competing algorithms over a wide range of compression ratio. For a compression ratio in the range from 88% to 94%, the proposed algorithm is found to offer improved accuracy for the estimation of clinically relevant time-series parameters, namely, the mean value, variance, and spectral index of stride-interval, stance-interval, and swing-interval time series, relative to its nearest competitor algorithm. The improvement in performance for compression ratio as high as 94% indicates that the proposed algorithms would be useful for designing compressive sensing-based systems for long-term telemonitoring of human gait signals.

Context Modeler for Wavelet Compression of Spectral Hyperspectral Images

NASA Technical Reports Server (NTRS)

Kiely, Aaron; Xie, Hua; Klimesh, matthew; Aranki, Nazeeh

2010-01-01

A context-modeling sub-algorithm has been developed as part of an algorithm that effects three-dimensional (3D) wavelet-based compression of hyperspectral image data. The context-modeling subalgorithm, hereafter denoted the context modeler, provides estimates of probability distributions of wavelet-transformed data being encoded. These estimates are utilized by an entropy coding subalgorithm that is another major component of the compression algorithm. The estimates make it possible to compress the image data more effectively than would otherwise be possible. The following background discussion is prerequisite to a meaningful summary of the context modeler. This discussion is presented relative to ICER-3D, which is the name attached to a particular compression algorithm and the software that implements it. The ICER-3D software is summarized briefly in the preceding article, ICER-3D Hyperspectral Image Compression Software (NPO-43238). Some aspects of this algorithm were previously described, in a slightly more general context than the ICER-3D software, in "Improving 3D Wavelet-Based Compression of Hyperspectral Images" (NPO-41381), NASA Tech Briefs, Vol. 33, No. 3 (March 2009), page 7a. In turn, ICER-3D is a product of generalization of ICER, another previously reported algorithm and computer program that can perform both lossless and lossy wavelet-based compression and decompression of gray-scale-image data. In ICER-3D, hyperspectral image data are decomposed using a 3D discrete wavelet transform (DWT). Following wavelet decomposition, mean values are subtracted from spatial planes of spatially low-pass subbands prior to encoding. The resulting data are converted to sign-magnitude form and compressed. In ICER-3D, compression is progressive, in that compressed information is ordered so that as more of the compressed data stream is received, successive reconstructions of the hyperspectral image data are of successively higher overall fidelity.

Fractal-Based Image Compression

DTIC Science & Technology

1989-09-01

6. A Mercedes Benz symbol generated using an IFS code ................. 21 7. (a) U-A fern and (b) A-0 fern generated with RIFS codes...22 8. Construction of the Mercedes - Benz symbol using RIFS ................ 23 9. The regenerated perfect image of the Mercedes - Benz symbol using R IF...quite often, it cannot be done with a reasonable number of transforms. As an example, the Mercedes Benz symbol generated using an IFS code is illustrated

A novel fractal image compression scheme with block classification and sorting based on Pearson's correlation coefficient.

PubMed

Wang, Jianji; Zheng, Nanning

2013-09-01

Fractal image compression (FIC) is an image coding technology based on the local similarity of image structure. It is widely used in many fields such as image retrieval, image denoising, image authentication, and encryption. FIC, however, suffers from the high computational complexity in encoding. Although many schemes are published to speed up encoding, they do not easily satisfy the encoding time or the reconstructed image quality requirements. In this paper, a new FIC scheme is proposed based on the fact that the affine similarity between two blocks in FIC is equivalent to the absolute value of Pearson's correlation coefficient (APCC) between them. First, all blocks in the range and domain pools are chosen and classified using an APCC-based block classification method to increase the matching probability. Second, by sorting the domain blocks with respect to APCCs between these domain blocks and a preset block in each class, the matching domain block for a range block can be searched in the selected domain set in which these APCCs are closer to APCC between the range block and the preset block. Experimental results show that the proposed scheme can significantly speed up the encoding process in FIC while preserving the reconstructed image quality well.

Comparison of Open Source Compression Algorithms on Vhr Remote Sensing Images for Efficient Storage Hierarchy

NASA Astrophysics Data System (ADS)

Akoguz, A.; Bozkurt, S.; Gozutok, A. A.; Alp, G.; Turan, E. G.; Bogaz, M.; Kent, S.

2016-06-01

High resolution level in satellite imagery came with its fundamental problem as big amount of telemetry data which is to be stored after the downlink operation. Moreover, later the post-processing and image enhancement steps after the image is acquired, the file sizes increase even more and then it gets a lot harder to store and consume much more time to transmit the data from one source to another; hence, it should be taken into account that to save even more space with file compression of the raw and various levels of processed data is a necessity for archiving stations to save more space. Lossless data compression algorithms that will be examined in this study aim to provide compression without any loss of data holding spectral information. Within this objective, well-known open source programs supporting related compression algorithms have been implemented on processed GeoTIFF images of Airbus Defence & Spaces SPOT 6 & 7 satellites having 1.5 m. of GSD, which were acquired and stored by ITU Center for Satellite Communications and Remote Sensing (ITU CSCRS), with the algorithms Lempel-Ziv-Welch (LZW), Lempel-Ziv-Markov chain Algorithm (LZMA & LZMA2), Lempel-Ziv-Oberhumer (LZO), Deflate & Deflate 64, Prediction by Partial Matching (PPMd or PPM2), Burrows-Wheeler Transform (BWT) in order to observe compression performances of these algorithms over sample datasets in terms of how much of the image data can be compressed by ensuring lossless compression.

Minimal spanning trees at the percolation threshold: a numerical calculation

NASA Astrophysics Data System (ADS)

Sweeney, Sean; Middleton, A. Alan

2013-03-01

Through computer simulations on a hypercubic lattice, we grow minimal spanning trees (MSTs) in up to five dimensions and examine their fractal dimensions. Understanding MSTs is imporant for studying systems with quenched disorder such as spin glasses. We implement a combination of Prim's and Kruskal's algorithms for finding MSTs in order to reduce memory usage and allow for simulation of larger systems than would otherwise be possible. These fractal objects are analyzed in an attempt to numerically verify predictions of the perturbation expansion developed by T. S. Jackson and N. Read for the pathlength fractal dimension ds of MSTs on percolation clusters at criticality [T. S. Jackson and N. Read, Phys. Rev. E 81, 021131 (2010)]. Examining these trees also sparked the development of an analysis technique for dealing with correlated data that could be easily generalized to other systems and should be a robust method for analyzing a wide array of randomly generated fractal structures. This work was made possible in part by NSF Grant No. DMR-1006731 and by the Syracuse University Gravitation and Relativity computing cluster, which is supported in part by NSF Grant No. PHY-0600953.

Retinal vasculature classification using novel multifractal features

NASA Astrophysics Data System (ADS)

Ding, Y.; Ward, W. O. C.; Duan, Jinming; Auer, D. P.; Gowland, Penny; Bai, L.

2015-11-01

Retinal blood vessels have been implicated in a large number of diseases including diabetic retinopathy and cardiovascular diseases, which cause damages to retinal blood vessels. The availability of retinal vessel imaging provides an excellent opportunity for monitoring and diagnosis of retinal diseases, and automatic analysis of retinal vessels will help with the processes. However, state of the art vascular analysis methods such as counting the number of branches or measuring the curvature and diameter of individual vessels are unsuitable for the microvasculature. There has been published research using fractal analysis to calculate fractal dimensions of retinal blood vessels, but so far there has been no systematic research extracting discriminant features from retinal vessels for classifications. This paper introduces new methods for feature extraction from multifractal spectra of retinal vessels for classification. Two publicly available retinal vascular image databases are used for the experiments, and the proposed methods have produced accuracies of 85.5% and 77% for classification of healthy and diabetic retinal vasculatures. Experiments show that classification with multiple fractal features produces better rates compared with methods using a single fractal dimension value. In addition to this, experiments also show that classification accuracy can be affected by the accuracy of vessel segmentation algorithms.

A 3D model for rain-induced landslides based on molecular dynamics with fractal and fractional water diffusion

NASA Astrophysics Data System (ADS)

Martelloni, Gianluca; Bagnoli, Franco; Guarino, Alessio

2017-09-01

We present a three-dimensional model of rain-induced landslides, based on cohesive spherical particles. The rainwater infiltration into the soil follows either the fractional or the fractal diffusion equations. We analytically solve the fractal partial differential equation (PDE) for diffusion with particular boundary conditions to simulate a rainfall event. We developed a numerical integration scheme for the PDE, compared with the analytical solution. We adapt the fractal diffusion equation obtaining the gravimetric water content that we use as input of a triggering scheme based on Mohr-Coulomb limit-equilibrium criterion. This triggering is then complemented by a standard molecular dynamics algorithm, with an interaction force inspired by the Lennard-Jones potential, to update the positions and velocities of particles. We present our results for homogeneous and heterogeneous systems, i.e., systems composed by particles with same or different radius, respectively. Interestingly, in the heterogeneous case, we observe segregation effects due to the different volume of the particles. Finally, we analyze the parameter sensibility both for the triggering and the propagation phases. Our simulations confirm the results of a previous two-dimensional model and therefore the feasible applicability to real cases.

Predicting DNA binding proteins using support vector machine with hybrid fractal features.

PubMed

Niu, Xiao-Hui; Hu, Xue-Hai; Shi, Feng; Xia, Jing-Bo

2014-02-21

DNA-binding proteins play a vitally important role in many biological processes. Prediction of DNA-binding proteins from amino acid sequence is a significant but not fairly resolved scientific problem. Chaos game representation (CGR) investigates the patterns hidden in protein sequences, and visually reveals previously unknown structure. Fractal dimensions (FD) are good tools to measure sizes of complex, highly irregular geometric objects. In order to extract the intrinsic correlation with DNA-binding property from protein sequences, CGR algorithm, fractal dimension and amino acid composition are applied to formulate the numerical features of protein samples in this paper. Seven groups of features are extracted, which can be computed directly from the primary sequence, and each group is evaluated by the 10-fold cross-validation test and Jackknife test. Comparing the results of numerical experiments, the group of amino acid composition and fractal dimension (21-dimension vector) gets the best result, the average accuracy is 81.82% and average Matthew's correlation coefficient (MCC) is 0.6017. This resulting predictor is also compared with existing method DNA-Prot and shows better performances. © 2013 The Authors. Published by Elsevier Ltd All rights reserved.

Data Compression Using the Dictionary Approach Algorithm

DTIC Science & Technology

1990-12-01

Compression Technique The LZ77 is an OPM/L data compression scheme suggested by Ziv and Lempel . A slightly modified...June 1984. 12. Witten H. I., Neal M. R. and Cleary G. J., Arithmetic Coding For Data Compression , Communication ACM June 1987. 13. Ziv I. and Lempel A...AD-A242 539 NAVAL POSTGRADUATE SCHOOL Monterey, California DTIC NOV 181991 0 THESIS DATA COMPRESSION USING THE DICTIONARY APPROACH ALGORITHM

A new version of Visual tool for estimating the fractal dimension of images

NASA Astrophysics Data System (ADS)

Grossu, I. V.; Felea, D.; Besliu, C.; Jipa, Al.; Bordeianu, C. C.; Stan, E.; Esanu, T.

2010-04-01

This work presents a new version of a Visual Basic 6.0 application for estimating the fractal dimension of images (Grossu et al., 2009 [1]). The earlier version was limited to bi-dimensional sets of points, stored in bitmap files. The application was extended for working also with comma separated values files and three-dimensional images. New version program summaryProgram title: Fractal Analysis v02 Catalogue identifier: AEEG_v2_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEEG_v2_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 9999 No. of bytes in distributed program, including test data, etc.: 4 366 783 Distribution format: tar.gz Programming language: MS Visual Basic 6.0 Computer: PC Operating system: MS Windows 98 or later RAM: 30 M Classification: 14 Catalogue identifier of previous version: AEEG_v1_0 Journal reference of previous version: Comput. Phys. Comm. 180 (2009) 1999 Does the new version supersede the previous version?: Yes Nature of problem: Estimating the fractal dimension of 2D and 3D images. Solution method: Optimized implementation of the box-counting algorithm. Reasons for new version:The previous version was limited to bitmap image files. The new application was extended in order to work with objects stored in comma separated values (csv) files. The main advantages are: Easier integration with other applications (csv is a widely used, simple text file format); Less resources consumed and improved performance (only the information of interest, the "black points", are stored); Higher resolution (the points coordinates are loaded into Visual Basic double variables [2]); Possibility of storing three-dimensional objects (e.g. the 3D Sierpinski gasket). In this version the optimized box-counting algorithm [1] was extended to the three-dimensional case. Summary of revisions:The application interface was changed from SDI (single document interface) to MDI (multi-document interface). One form was added in order to provide a graphical user interface for the new functionalities (fractal analysis of 2D and 3D images stored in csv files). Additional comments: User friendly graphical interface; Easy deployment mechanism. Running time: In the first approximation, the algorithm is linear. References:[1] I.V. Grossu, C. Besliu, M.V. Rusu, Al. Jipa, C.C. Bordeianu, D. Felea, Comput. Phys. Comm. 180 (2009) 1999-2001.[2] F. Balena, Programming Microsoft Visual Basic 6.0, Microsoft Press, US, 1999.

Optimized multilayered wideband absorbers with graded fractal FSS

NASA Astrophysics Data System (ADS)

Vinoy, K. J.; Jose, K. A.; Varadan, Vijay K.; Varadan, Vasundara V.

2001-08-01

Various approaches have been followed for the reduction of radar cross section (RCS), especially of aircraft and missiles. In this paper we present the use of multiple layers of FSS-like fractal geometries printed on dielectric substrates for the same goal. The experimental results shown here indicate 15 dB reduction in the reflection of a flat surface, by the use of this configuration with low loss dielectrics. An extensive optimization scheme is required for extending the angle coverage as well as the bandwidth of the absorber. A brief investigation of such a scheme involving genetic algorithm for this purpose is also presented here.

Squish: Near-Optimal Compression for Archival of Relational Datasets

PubMed Central

Gao, Yihan; Parameswaran, Aditya

2017-01-01

Relational datasets are being generated at an alarmingly rapid rate across organizations and industries. Compressing these datasets could significantly reduce storage and archival costs. Traditional compression algorithms, e.g., gzip, are suboptimal for compressing relational datasets since they ignore the table structure and relationships between attributes. We study compression algorithms that leverage the relational structure to compress datasets to a much greater extent. We develop Squish, a system that uses a combination of Bayesian Networks and Arithmetic Coding to capture multiple kinds of dependencies among attributes and achieve near-entropy compression rate. Squish also supports user-defined attributes: users can instantiate new data types by simply implementing five functions for a new class interface. We prove the asymptotic optimality of our compression algorithm and conduct experiments to show the effectiveness of our system: Squish achieves a reduction of over 50% in storage size relative to systems developed in prior work on a variety of real datasets. PMID:28180028

Novel image compression-encryption hybrid algorithm based on key-controlled measurement matrix in compressive sensing

NASA Astrophysics Data System (ADS)

Zhou, Nanrun; Zhang, Aidi; Zheng, Fen; Gong, Lihua

2014-10-01

The existing ways to encrypt images based on compressive sensing usually treat the whole measurement matrix as the key, which renders the key too large to distribute and memorize or store. To solve this problem, a new image compression-encryption hybrid algorithm is proposed to realize compression and encryption simultaneously, where the key is easily distributed, stored or memorized. The input image is divided into 4 blocks to compress and encrypt, then the pixels of the two adjacent blocks are exchanged randomly by random matrices. The measurement matrices in compressive sensing are constructed by utilizing the circulant matrices and controlling the original row vectors of the circulant matrices with logistic map. And the random matrices used in random pixel exchanging are bound with the measurement matrices. Simulation results verify the effectiveness, security of the proposed algorithm and the acceptable compression performance.

Fundamental study of compression for movie files of coronary angiography

NASA Astrophysics Data System (ADS)

Ando, Takekazu; Tsuchiya, Yuichiro; Kodera, Yoshie

2005-04-01

When network distribution of movie files was considered as reference, it could be useful that the lossy compression movie files which has small file size. We chouse three kinds of coronary stricture movies with different moving speed as an examination object; heart rate of slow, normal and fast movies. The movies of MPEG-1, DivX5.11, WMV9 (Windows Media Video 9), and WMV9-VCM (Windows Media Video 9-Video Compression Manager) were made from three kinds of AVI format movies with different moving speeds. Five kinds of movies that are four kinds of compression movies and non-compression AVI instead of the DICOM format were evaluated by Thurstone's method. The Evaluation factors of movies were determined as "sharpness, granularity, contrast, and comprehensive evaluation." In the virtual bradycardia movie, AVI was the best evaluation at all evaluation factors except the granularity. In the virtual normal movie, an excellent compression technique is different in all evaluation factors. In the virtual tachycardia movie, MPEG-1 was the best evaluation at all evaluation factors expects the contrast. There is a good compression form depending on the speed of movies because of the difference of compression algorithm. It is thought that it is an influence by the difference of the compression between frames. The compression algorithm for movie has the compression between the frames and the intra-frame compression. As the compression algorithm give the different influence to image by each compression method, it is necessary to examine the relation of the compression algorithm and our results.

Surface Modeling to Support Small-Body Spacecraft Exploration and Proximity Operations

NASA Technical Reports Server (NTRS)

Riedel, Joseph E.; Mastrodemos, Nickolaos; Gaskell, Robert W.

2011-01-01

In order to simulate physically plausible surfaces that represent geologically evolved surfaces, demonstrating demanding surface-relative guidance navigation and control (GN&C) actions, such surfaces must be made to mimic the geological processes themselves. A report describes how, using software and algorithms to model body surfaces as a series of digital terrain maps, a series of processes was put in place that evolve the surface from some assumed nominal starting condition. The physical processes modeled in this algorithmic technique include fractal regolith substrate texturing, fractally textured rocks (of empirically derived size and distribution power laws), cratering, and regolith migration under potential energy gradient. Starting with a global model that may be determined observationally or created ad hoc, the surface evolution is begun. First, material of some assumed strength is layered on the global model in a fractally random pattern. Then, rocks are distributed according to power laws measured on the Moon. Cratering then takes place in a temporal fashion, including modeling of ejecta blankets and taking into account the gravity of the object (which determines how much of the ejecta blanket falls back to the surface), and causing the observed phenomena of older craters being progressively buried by the ejecta of earlier impacts. Finally, regolith migration occurs which stratifies finer materials from coarser, as the fine material progressively migrates to regions of lower potential energy.

Low-Complexity Lossless and Near-Lossless Data Compression Technique for Multispectral Imagery

NASA Technical Reports Server (NTRS)

Xie, Hua; Klimesh, Matthew A.

2009-01-01

This work extends the lossless data compression technique described in Fast Lossless Compression of Multispectral- Image Data, (NPO-42517) NASA Tech Briefs, Vol. 30, No. 8 (August 2006), page 26. The original technique was extended to include a near-lossless compression option, allowing substantially smaller compressed file sizes when a small amount of distortion can be tolerated. Near-lossless compression is obtained by including a quantization step prior to encoding of prediction residuals. The original technique uses lossless predictive compression and is designed for use on multispectral imagery. A lossless predictive data compression algorithm compresses a digitized signal one sample at a time as follows: First, a sample value is predicted from previously encoded samples. The difference between the actual sample value and the prediction is called the prediction residual. The prediction residual is encoded into the compressed file. The decompressor can form the same predicted sample and can decode the prediction residual from the compressed file, and so can reconstruct the original sample. A lossless predictive compression algorithm can generally be converted to a near-lossless compression algorithm by quantizing the prediction residuals prior to encoding them. In this case, since the reconstructed sample values will not be identical to the original sample values, the encoder must determine the values that will be reconstructed and use these values for predicting later sample values. The technique described here uses this method, starting with the original technique, to allow near-lossless compression. The extension to allow near-lossless compression adds the ability to achieve much more compression when small amounts of distortion are tolerable, while retaining the low complexity and good overall compression effectiveness of the original algorithm.

DELIMINATE--a fast and efficient method for loss-less compression of genomic sequences: sequence analysis.

PubMed

Mohammed, Monzoorul Haque; Dutta, Anirban; Bose, Tungadri; Chadaram, Sudha; Mande, Sharmila S

2012-10-01

An unprecedented quantity of genome sequence data is currently being generated using next-generation sequencing platforms. This has necessitated the development of novel bioinformatics approaches and algorithms that not only facilitate a meaningful analysis of these data but also aid in efficient compression, storage, retrieval and transmission of huge volumes of the generated data. We present a novel compression algorithm (DELIMINATE) that can rapidly compress genomic sequence data in a loss-less fashion. Validation results indicate relatively higher compression efficiency of DELIMINATE when compared with popular general purpose compression algorithms, namely, gzip, bzip2 and lzma. Linux, Windows and Mac implementations (both 32 and 64-bit) of DELIMINATE are freely available for download at: http://metagenomics.atc.tcs.com/compression/DELIMINATE. sharmila@atc.tcs.com Supplementary data are available at Bioinformatics online.

LFQC: a lossless compression algorithm for FASTQ files

PubMed Central

Nicolae, Marius; Pathak, Sudipta; Rajasekaran, Sanguthevar

2015-01-01

Motivation: Next Generation Sequencing (NGS) technologies have revolutionized genomic research by reducing the cost of whole genome sequencing. One of the biggest challenges posed by modern sequencing technology is economic storage of NGS data. Storing raw data is infeasible because of its enormous size and high redundancy. In this article, we address the problem of storage and transmission of large FASTQ files using innovative compression techniques. Results: We introduce a new lossless non-reference based FASTQ compression algorithm named Lossless FASTQ Compressor. We have compared our algorithm with other state of the art big data compression algorithms namely gzip, bzip2, fastqz (Bonfield and Mahoney, 2013), fqzcomp (Bonfield and Mahoney, 2013), Quip (Jones et al., 2012), DSRC2 (Roguski and Deorowicz, 2014). This comparison reveals that our algorithm achieves better compression ratios on LS454 and SOLiD datasets. Availability and implementation: The implementations are freely available for non-commercial purposes. They can be downloaded from http://engr.uconn.edu/rajasek/lfqc-v1.1.zip. Contact: rajasek@engr.uconn.edu PMID:26093148

High-grade video compression of echocardiographic studies: a multicenter validation study of selected motion pictures expert groups (MPEG)-4 algorithms.

PubMed

Barbier, Paolo; Alimento, Marina; Berna, Giovanni; Celeste, Fabrizio; Gentile, Francesco; Mantero, Antonio; Montericcio, Vincenzo; Muratori, Manuela

2007-05-01

Large files produced by standard compression algorithms slow down spread of digital and tele-echocardiography. We validated echocardiographic video high-grade compression with the new Motion Pictures Expert Groups (MPEG)-4 algorithms with a multicenter study. Seven expert cardiologists blindly scored (5-point scale) 165 uncompressed and compressed 2-dimensional and color Doppler video clips, based on combined diagnostic content and image quality (uncompressed files as references). One digital video and 3 MPEG-4 algorithms (WM9, MV2, and DivX) were used, the latter at 3 compression levels (0%, 35%, and 60%). Compressed file sizes decreased from 12 to 83 MB to 0.03 to 2.3 MB (1:1051-1:26 reduction ratios). Mean SD of differences was 0.81 for intraobserver variability (uncompressed and digital video files). Compared with uncompressed files, only the DivX mean score at 35% (P = .04) and 60% (P = .001) compression was significantly reduced. At subcategory analysis, these differences were still significant for gray-scale and fundamental imaging but not for color or second harmonic tissue imaging. Original image quality, session sequence, compression grade, and bitrate were all independent determinants of mean score. Our study supports use of MPEG-4 algorithms to greatly reduce echocardiographic file sizes, thus facilitating archiving and transmission. Quality evaluation studies should account for the many independent variables that affect image quality grading.

RNACompress: Grammar-based compression and informational complexity measurement of RNA secondary structure.

PubMed

Liu, Qi; Yang, Yu; Chen, Chun; Bu, Jiajun; Zhang, Yin; Ye, Xiuzi

2008-03-31

With the rapid emergence of RNA databases and newly identified non-coding RNAs, an efficient compression algorithm for RNA sequence and structural information is needed for the storage and analysis of such data. Although several algorithms for compressing DNA sequences have been proposed, none of them are suitable for the compression of RNA sequences with their secondary structures simultaneously. This kind of compression not only facilitates the maintenance of RNA data, but also supplies a novel way to measure the informational complexity of RNA structural data, raising the possibility of studying the relationship between the functional activities of RNA structures and their complexities, as well as various structural properties of RNA based on compression. RNACompress employs an efficient grammar-based model to compress RNA sequences and their secondary structures. The main goals of this algorithm are two fold: (1) present a robust and effective way for RNA structural data compression; (2) design a suitable model to represent RNA secondary structure as well as derive the informational complexity of the structural data based on compression. Our extensive tests have shown that RNACompress achieves a universally better compression ratio compared with other sequence-specific or common text-specific compression algorithms, such as Gencompress, winrar and gzip. Moreover, a test of the activities of distinct GTP-binding RNAs (aptamers) compared with their structural complexity shows that our defined informational complexity can be used to describe how complexity varies with activity. These results lead to an objective means of comparing the functional properties of heteropolymers from the information perspective. A universal algorithm for the compression of RNA secondary structure as well as the evaluation of its informational complexity is discussed in this paper. We have developed RNACompress, as a useful tool for academic users. Extensive tests have shown that RNACompress is a universally efficient algorithm for the compression of RNA sequences with their secondary structures. RNACompress also serves as a good measurement of the informational complexity of RNA secondary structure, which can be used to study the functional activities of RNA molecules.

RNACompress: Grammar-based compression and informational complexity measurement of RNA secondary structure

PubMed Central

Liu, Qi; Yang, Yu; Chen, Chun; Bu, Jiajun; Zhang, Yin; Ye, Xiuzi

2008-01-01

Background With the rapid emergence of RNA databases and newly identified non-coding RNAs, an efficient compression algorithm for RNA sequence and structural information is needed for the storage and analysis of such data. Although several algorithms for compressing DNA sequences have been proposed, none of them are suitable for the compression of RNA sequences with their secondary structures simultaneously. This kind of compression not only facilitates the maintenance of RNA data, but also supplies a novel way to measure the informational complexity of RNA structural data, raising the possibility of studying the relationship between the functional activities of RNA structures and their complexities, as well as various structural properties of RNA based on compression. Results RNACompress employs an efficient grammar-based model to compress RNA sequences and their secondary structures. The main goals of this algorithm are two fold: (1) present a robust and effective way for RNA structural data compression; (2) design a suitable model to represent RNA secondary structure as well as derive the informational complexity of the structural data based on compression. Our extensive tests have shown that RNACompress achieves a universally better compression ratio compared with other sequence-specific or common text-specific compression algorithms, such as Gencompress, winrar and gzip. Moreover, a test of the activities of distinct GTP-binding RNAs (aptamers) compared with their structural complexity shows that our defined informational complexity can be used to describe how complexity varies with activity. These results lead to an objective means of comparing the functional properties of heteropolymers from the information perspective. Conclusion A universal algorithm for the compression of RNA secondary structure as well as the evaluation of its informational complexity is discussed in this paper. We have developed RNACompress, as a useful tool for academic users. Extensive tests have shown that RNACompress is a universally efficient algorithm for the compression of RNA sequences with their secondary structures. RNACompress also serves as a good measurement of the informational complexity of RNA secondary structure, which can be used to study the functional activities of RNA molecules. PMID:18373878

Compact Encoding of Robot-Generated 3D Maps for Efficient Wireless Transmission

DTIC Science & Technology

2003-01-01

Lempel - Ziv -Welch (LZW) and Ziv - Lempel (LZ77) respectively. Image based compression can also be based on dic- tionaries... compression of the data , without actually displaying a 3D model, printing statistical results for comparison of the different algorithms . 1http... compression algorithms , and wavelet algorithms tuned to the specific nature of the raw laser data . For most such applications, the usage of lossless

Multispectral Image Compression Based on DSC Combined with CCSDS-IDC

PubMed Central

Li, Jin; Xing, Fei; Sun, Ting; You, Zheng

2014-01-01

Remote sensing multispectral image compression encoder requires low complexity, high robust, and high performance because it usually works on the satellite where the resources, such as power, memory, and processing capacity, are limited. For multispectral images, the compression algorithms based on 3D transform (like 3D DWT, 3D DCT) are too complex to be implemented in space mission. In this paper, we proposed a compression algorithm based on distributed source coding (DSC) combined with image data compression (IDC) approach recommended by CCSDS for multispectral images, which has low complexity, high robust, and high performance. First, each band is sparsely represented by DWT to obtain wavelet coefficients. Then, the wavelet coefficients are encoded by bit plane encoder (BPE). Finally, the BPE is merged to the DSC strategy of Slepian-Wolf (SW) based on QC-LDPC by deep coupling way to remove the residual redundancy between the adjacent bands. A series of multispectral images is used to test our algorithm. Experimental results show that the proposed DSC combined with the CCSDS-IDC (DSC-CCSDS)-based algorithm has better compression performance than the traditional compression approaches. PMID:25110741

Multispectral image compression based on DSC combined with CCSDS-IDC.

PubMed

Li, Jin; Xing, Fei; Sun, Ting; You, Zheng

2014-01-01

Remote sensing multispectral image compression encoder requires low complexity, high robust, and high performance because it usually works on the satellite where the resources, such as power, memory, and processing capacity, are limited. For multispectral images, the compression algorithms based on 3D transform (like 3D DWT, 3D DCT) are too complex to be implemented in space mission. In this paper, we proposed a compression algorithm based on distributed source coding (DSC) combined with image data compression (IDC) approach recommended by CCSDS for multispectral images, which has low complexity, high robust, and high performance. First, each band is sparsely represented by DWT to obtain wavelet coefficients. Then, the wavelet coefficients are encoded by bit plane encoder (BPE). Finally, the BPE is merged to the DSC strategy of Slepian-Wolf (SW) based on QC-LDPC by deep coupling way to remove the residual redundancy between the adjacent bands. A series of multispectral images is used to test our algorithm. Experimental results show that the proposed DSC combined with the CCSDS-IDC (DSC-CCSDS)-based algorithm has better compression performance than the traditional compression approaches.

JPEG2000 still image coding quality.

PubMed

Chen, Tzong-Jer; Lin, Sheng-Chieh; Lin, You-Chen; Cheng, Ren-Gui; Lin, Li-Hui; Wu, Wei

2013-10-01

This work demonstrates the image qualities between two popular JPEG2000 programs. Two medical image compression algorithms are both coded using JPEG2000, but they are different regarding the interface, convenience, speed of computation, and their characteristic options influenced by the encoder, quantization, tiling, etc. The differences in image quality and compression ratio are also affected by the modality and compression algorithm implementation. Do they provide the same quality? The qualities of compressed medical images from two image compression programs named Apollo and JJ2000 were evaluated extensively using objective metrics. These algorithms were applied to three medical image modalities at various compression ratios ranging from 10:1 to 100:1. Following that, the quality of the reconstructed images was evaluated using five objective metrics. The Spearman rank correlation coefficients were measured under every metric in the two programs. We found that JJ2000 and Apollo exhibited indistinguishable image quality for all images evaluated using the above five metrics (r > 0.98, p < 0.001). It can be concluded that the image quality of the JJ2000 and Apollo algorithms is statistically equivalent for medical image compression.

Scale-dependent intrinsic entropies of complex time series.

PubMed

Yeh, Jia-Rong; Peng, Chung-Kang; Huang, Norden E

2016-04-13

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal's complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease. © 2016 The Author(s).

Adaptable Binary Programs

DTIC Science & Technology

1994-04-01

a variation of Ziv - Lempel compression [ZL77]. We found that using a standard compression algorithm rather than semantic compression allowed simplified...mentation. In Proceedings of the Conference on Programming Language Design and Implementation, 1993. (ZL77] J. Ziv and A. Lempel . A universal algorithm ...required by adaptable binaries. Our ABS stores adaptable binary information using the conventional binary symbol table and compresses this data using

Highly Efficient Compression Algorithms for Multichannel EEG.

PubMed

Shaw, Laxmi; Rahman, Daleef; Routray, Aurobinda

2018-05-01

The difficulty associated with processing and understanding the high dimensionality of electroencephalogram (EEG) data requires developing efficient and robust compression algorithms. In this paper, different lossless compression techniques of single and multichannel EEG data, including Huffman coding, arithmetic coding, Markov predictor, linear predictor, context-based error modeling, multivariate autoregression (MVAR), and a low complexity bivariate model have been examined and their performances have been compared. Furthermore, a high compression algorithm named general MVAR and a modified context-based error modeling for multichannel EEG have been proposed. The resulting compression algorithm produces a higher relative compression ratio of 70.64% on average compared with the existing methods, and in some cases, it goes up to 83.06%. The proposed methods are designed to compress a large amount of multichannel EEG data efficiently so that the data storage and transmission bandwidth can be effectively used. These methods have been validated using several experimental multichannel EEG recordings of different subjects and publicly available standard databases. The satisfactory parametric measures of these methods, namely percent-root-mean square distortion, peak signal-to-noise ratio, root-mean-square error, and cross correlation, show their superiority over the state-of-the-art compression methods.

Halftoning processing on a JPEG-compressed image

NASA Astrophysics Data System (ADS)

Sibade, Cedric; Barizien, Stephane; Akil, Mohamed; Perroton, Laurent

2003-12-01

Digital image processing algorithms are usually designed for the raw format, that is on an uncompressed representation of the image. Therefore prior to transforming or processing a compressed format, decompression is applied; then, the result of the processing application is finally re-compressed for further transfer or storage. The change of data representation is resource-consuming in terms of computation, time and memory usage. In the wide format printing industry, this problem becomes an important issue: e.g. a 1 m2 input color image, scanned at 600 dpi exceeds 1.6 GB in its raw representation. However, some image processing algorithms can be performed in the compressed-domain, by applying an equivalent operation on the compressed format. This paper is presenting an innovative application of the halftoning processing operation by screening, to be applied on JPEG-compressed image. This compressed-domain transform is performed by computing the threshold operation of the screening algorithm in the DCT domain. This algorithm is illustrated by examples for different halftone masks. A pre-sharpening operation, applied on a JPEG-compressed low quality image is also described; it allows to de-noise and to enhance the contours of this image.

Evaluation of registration, compression and classification algorithms. Volume 1: Results

NASA Technical Reports Server (NTRS)

Jayroe, R.; Atkinson, R.; Callas, L.; Hodges, J.; Gaggini, B.; Peterson, J.

1979-01-01

The registration, compression, and classification algorithms were selected on the basis that such a group would include most of the different and commonly used approaches. The results of the investigation indicate clearcut, cost effective choices for registering, compressing, and classifying multispectral imagery.

Freeing Space for NASA: Incorporating a Lossless Compression Algorithm into NASA's FOSS System

NASA Technical Reports Server (NTRS)

Fiechtner, Kaitlyn; Parker, Allen

2011-01-01

NASA's Fiber Optic Strain Sensing (FOSS) system can gather and store up to 1,536,000 bytes (1.46 megabytes) per second. Since the FOSS system typically acquires hours - or even days - of data, the system can gather hundreds of gigabytes of data for a given test event. To store such large quantities of data more effectively, NASA is modifying a Lempel-Ziv-Oberhumer (LZO) lossless data compression program to compress data as it is being acquired in real time. After proving that the algorithm is capable of compressing the data from the FOSS system, the LZO program will be modified and incorporated into the FOSS system. Implementing an LZO compression algorithm will instantly free up memory space without compromising any data obtained. With the availability of memory space, the FOSS system can be used more efficiently on test specimens, such as Unmanned Aerial Vehicles (UAVs) that can be in flight for days. By integrating the compression algorithm, the FOSS system can continue gathering data, even on longer flights.

Compression and fast retrieval of SNP data.

PubMed

Sambo, Francesco; Di Camillo, Barbara; Toffolo, Gianna; Cobelli, Claudio

2014-11-01

The increasing interest in rare genetic variants and epistatic genetic effects on complex phenotypic traits is currently pushing genome-wide association study design towards datasets of increasing size, both in the number of studied subjects and in the number of genotyped single nucleotide polymorphisms (SNPs). This, in turn, is leading to a compelling need for new methods for compression and fast retrieval of SNP data. We present a novel algorithm and file format for compressing and retrieving SNP data, specifically designed for large-scale association studies. Our algorithm is based on two main ideas: (i) compress linkage disequilibrium blocks in terms of differences with a reference SNP and (ii) compress reference SNPs exploiting information on their call rate and minor allele frequency. Tested on two SNP datasets and compared with several state-of-the-art software tools, our compression algorithm is shown to be competitive in terms of compression rate and to outperform all tools in terms of time to load compressed data. Our compression and decompression algorithms are implemented in a C++ library, are released under the GNU General Public License and are freely downloadable from http://www.dei.unipd.it/~sambofra/snpack.html. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Compression and fast retrieval of SNP data

PubMed Central

Sambo, Francesco; Di Camillo, Barbara; Toffolo, Gianna; Cobelli, Claudio

2014-01-01

Motivation: The increasing interest in rare genetic variants and epistatic genetic effects on complex phenotypic traits is currently pushing genome-wide association study design towards datasets of increasing size, both in the number of studied subjects and in the number of genotyped single nucleotide polymorphisms (SNPs). This, in turn, is leading to a compelling need for new methods for compression and fast retrieval of SNP data. Results: We present a novel algorithm and file format for compressing and retrieving SNP data, specifically designed for large-scale association studies. Our algorithm is based on two main ideas: (i) compress linkage disequilibrium blocks in terms of differences with a reference SNP and (ii) compress reference SNPs exploiting information on their call rate and minor allele frequency. Tested on two SNP datasets and compared with several state-of-the-art software tools, our compression algorithm is shown to be competitive in terms of compression rate and to outperform all tools in terms of time to load compressed data. Availability and implementation: Our compression and decompression algorithms are implemented in a C++ library, are released under the GNU General Public License and are freely downloadable from http://www.dei.unipd.it/~sambofra/snpack.html. Contact: sambofra@dei.unipd.it or cobelli@dei.unipd.it. PMID:25064564

SCALCE: boosting sequence compression algorithms using locally consistent encoding.

PubMed

Hach, Faraz; Numanagic, Ibrahim; Alkan, Can; Sahinalp, S Cenk

2012-12-01

The high throughput sequencing (HTS) platforms generate unprecedented amounts of data that introduce challenges for the computational infrastructure. Data management, storage and analysis have become major logistical obstacles for those adopting the new platforms. The requirement for large investment for this purpose almost signalled the end of the Sequence Read Archive hosted at the National Center for Biotechnology Information (NCBI), which holds most of the sequence data generated world wide. Currently, most HTS data are compressed through general purpose algorithms such as gzip. These algorithms are not designed for compressing data generated by the HTS platforms; for example, they do not take advantage of the specific nature of genomic sequence data, that is, limited alphabet size and high similarity among reads. Fast and efficient compression algorithms designed specifically for HTS data should be able to address some of the issues in data management, storage and communication. Such algorithms would also help with analysis provided they offer additional capabilities such as random access to any read and indexing for efficient sequence similarity search. Here we present SCALCE, a 'boosting' scheme based on Locally Consistent Parsing technique, which reorganizes the reads in a way that results in a higher compression speed and compression rate, independent of the compression algorithm in use and without using a reference genome. Our tests indicate that SCALCE can improve the compression rate achieved through gzip by a factor of 4.19-when the goal is to compress the reads alone. In fact, on SCALCE reordered reads, gzip running time can improve by a factor of 15.06 on a standard PC with a single core and 6 GB memory. Interestingly even the running time of SCALCE + gzip improves that of gzip alone by a factor of 2.09. When compared with the recently published BEETL, which aims to sort the (inverted) reads in lexicographic order for improving bzip2, SCALCE + gzip provides up to 2.01 times better compression while improving the running time by a factor of 5.17. SCALCE also provides the option to compress the quality scores as well as the read names, in addition to the reads themselves. This is achieved by compressing the quality scores through order-3 Arithmetic Coding (AC) and the read names through gzip through the reordering SCALCE provides on the reads. This way, in comparison with gzip compression of the unordered FASTQ files (including reads, read names and quality scores), SCALCE (together with gzip and arithmetic encoding) can provide up to 3.34 improvement in the compression rate and 1.26 improvement in running time. Our algorithm, SCALCE (Sequence Compression Algorithm using Locally Consistent Encoding), is implemented in C++ with both gzip and bzip2 compression options. It also supports multithreading when gzip option is selected, and the pigz binary is available. It is available at http://scalce.sourceforge.net. fhach@cs.sfu.ca or cenk@cs.sfu.ca Supplementary data are available at Bioinformatics online.

Function representation with circle inversion map systems

NASA Astrophysics Data System (ADS)

Boreland, Bryson; Kunze, Herb

2017-01-01

The fractals literature develops the now well-known concept of local iterated function systems (using affine maps) with grey-level maps (LIFSM) as an approach to function representation in terms of the associated fixed point of the so-called fractal transform. While originally explored as a method to achieve signal (and 2-D image) compression, more recent work has explored various aspects of signal and image processing using this machinery. In this paper, we develop a similar framework for function representation using circle inversion map systems. Given a circle C with centre õ and radius r, inversion with respect to C transforms the point p˜ to the point p˜', such that p˜ and p˜' lie on the same radial half-line from õ and d(õ, p˜)d(õ, p˜') = r2, where d is Euclidean distance. We demonstrate the results with an example.

Adaptive Integration of the Compressed Algorithm of CS and NPC for the ECG Signal Compressed Algorithm in VLSI Implementation

PubMed Central

Tseng, Yun-Hua; Lu, Chih-Wen

2017-01-01

Compressed sensing (CS) is a promising approach to the compression and reconstruction of electrocardiogram (ECG) signals. It has been shown that following reconstruction, most of the changes between the original and reconstructed signals are distributed in the Q, R, and S waves (QRS) region. Furthermore, any increase in the compression ratio tends to increase the magnitude of the change. This paper presents a novel approach integrating the near-precise compressed (NPC) and CS algorithms. The simulation results presented notable improvements in signal-to-noise ratio (SNR) and compression ratio (CR). The efficacy of this approach was verified by fabricating a highly efficient low-cost chip using the Taiwan Semiconductor Manufacturing Company’s (TSMC) 0.18-μm Complementary Metal-Oxide-Semiconductor (CMOS) technology. The proposed core has an operating frequency of 60 MHz and gate counts of 2.69 K. PMID:28991216

New algorithm for lossless hyper-spectral image compression with mixing transform to eliminate redundancy

NASA Astrophysics Data System (ADS)

Xie, ChengJun; Xu, Lin

2008-03-01

This paper presents a new algorithm based on mixing transform to eliminate redundancy, SHIRCT and subtraction mixing transform is used to eliminate spectral redundancy, 2D-CDF(2,2)DWT to eliminate spatial redundancy, This transform has priority in hardware realization convenience, since it can be fully implemented by add and shift operation. Its redundancy elimination effect is better than (1D+2D)CDF(2,2)DWT. Here improved SPIHT+CABAC mixing compression coding algorithm is used to implement compression coding. The experiment results show that in lossless image compression applications the effect of this method is a little better than the result acquired using (1D+2D)CDF(2,2)DWT+improved SPIHT+CABAC, still it is much better than the results acquired by JPEG-LS, WinZip, ARJ, DPCM, the research achievements of a research team of Chinese Academy of Sciences, NMST and MST. Using hyper-spectral image Canal of American JPL laboratory as the data set for lossless compression test, on the average the compression ratio of this algorithm exceeds the above algorithms by 42%,37%,35%,30%,16%,13%,11% respectively.

Universal data compression

NASA Astrophysics Data System (ADS)

Lindsay, R. A.; Cox, B. V.

Universal and adaptive data compression techniques have the capability to globally compress all types of data without loss of information but have the disadvantage of complexity and computation speed. Advances in hardware speed and the reduction of computational costs have made universal data compression feasible. Implementations of the Adaptive Huffman and Lempel-Ziv compression algorithms are evaluated for performance. Compression ratios versus run times for different size data files are graphically presented and discussed in the paper. Required adjustments needed for optimum performance of the algorithms relative to theoretical achievable limits will be outlined.

National Information Systems Security Conference (19th) held in Baltimore, Maryland on October 22-25, 1996. Volume 1

DTIC Science & Technology

1996-10-25

been demonstrated that steganography is ineffective 195 when images are stored using this compression algorithm [2]. Difficulty in designing a general...Despite the relative ease of employing steganography to covertly transport data in an uncompressed 24-bit image , lossy compression algorithms based on... image , the security threat that steganography poses cannot be completely eliminated by application of a transform-based lossy compression algorithm

An image compression survey and algorithm switching based on scene activity

NASA Technical Reports Server (NTRS)

Hart, M. M.

1985-01-01

Data compression techniques are presented. A description of these techniques is provided along with a performance evaluation. The complexity of the hardware resulting from their implementation is also addressed. The compression effect on channel distortion and the applicability of these algorithms to real-time processing are presented. Also included is a proposed new direction for an adaptive compression technique for real-time processing.

Mache: No-Loss Trace Compaction

DTIC Science & Technology

1988-09-15

Data Compression . IEEE Computer 176 (June 1984), 8-19. 10. ZIV , J. AND LEMPEL , A. A Universal Algorithm for Sequential Data Com- pression. IEEE... compression scheme which takes ad- vantage of repeating patterns in the sequence of bytes. I have used the Lempel - Ziv compression algorithm [9,10,11...Transactions on Information Theory 23 (1976), 75-81. 11. ZIV , J. AND LEMPEL , A. Compression of Individual Sequences via Variable-

Compression of multispectral Landsat imagery using the Embedded Zerotree Wavelet (EZW) algorithm

NASA Technical Reports Server (NTRS)

Shapiro, Jerome M.; Martucci, Stephen A.; Czigler, Martin

1994-01-01

The Embedded Zerotree Wavelet (EZW) algorithm has proven to be an extremely efficient and flexible compression algorithm for low bit rate image coding. The embedding algorithm attempts to order the bits in the bit stream in numerical importance and thus a given code contains all lower rate encodings of the same algorithm. Therefore, precise bit rate control is achievable and a target rate or distortion metric can be met exactly. Furthermore, the technique is fully image adaptive. An algorithm for multispectral image compression which combines the spectral redundancy removal properties of the image-dependent Karhunen-Loeve Transform (KLT) with the efficiency, controllability, and adaptivity of the embedded zerotree wavelet algorithm is presented. Results are shown which illustrate the advantage of jointly encoding spectral components using the KLT and EZW.

Using Fractal and Local Binary Pattern Features for Classification of ECOG Motor Imagery Tasks Obtained from the Right Brain Hemisphere.

PubMed

Xu, Fangzhou; Zhou, Weidong; Zhen, Yilin; Yuan, Qi; Wu, Qi

2016-09-01

The feature extraction and classification of brain signal is very significant in brain-computer interface (BCI). In this study, we describe an algorithm for motor imagery (MI) classification of electrocorticogram (ECoG)-based BCI. The proposed approach employs multi-resolution fractal measures and local binary pattern (LBP) operators to form a combined feature for characterizing an ECoG epoch recording from the right hemisphere of the brain. A classifier is trained by using the gradient boosting in conjunction with ordinary least squares (OLS) method. The fractal intercept, lacunarity and LBP features are extracted to classify imagined movements of either the left small finger or the tongue. Experimental results on dataset I of BCI competition III demonstrate the superior performance of our method. The cross-validation accuracy and accuracy is 90.6% and 95%, respectively. Furthermore, the low computational burden of this method makes it a promising candidate for real-time BCI systems.

Introduction to the fractality principle of consciousness and the sentyon postulate

PubMed Central

Bieberich, Erhard

2013-01-01

Recently, consciousness research has gained much attention. Indeed, the question at stake is significant: why is the brain not just a computing device, but generates a perception from within? Ambitious endeavors trying to simulate the entire human brain assume that the algorithm will do the trick: as soon as we assemble the brain in a computer and increase the number of operations per time, consciousness will emerge by itself. I disagree with this simplistic representation. My argument emerges from the “atomism paradox”: the irreducible space of the consciously perceived world, the endospace is incompatible with the reducible and decomposable architecture of the brain or a computer. I will first discuss the fundamental challenges in current consciousness models and then propose a new model based on the fractality principle: “the whole is in each of its parts”. This new model copes with the atomism paradox by implementing an iterative mapping of information from higher order brain structures to smaller scales on the cellular and molecular level, which I will refer to as “fractalization”. This information fractalization gives rise to a new form of matter that is conscious (“bright matter”). Bright matter is composed of conscious particles or units named “sentyons”. The internal fractality of these sentyons will close a loop (the “psychic loop”) in a recurrent fractal neural network (RFNN) that allows for continuous and complete information transformation and sharing between higher order brain structures and the endpoint substrate of consciousness at the molecular level. PMID:23950765

a Predictive Model of Permeability for Fractal-Based Rough Rock Fractures during Shear

NASA Astrophysics Data System (ADS)

Huang, Na; Jiang, Yujing; Liu, Richeng; Li, Bo; Zhang, Zhenyu

This study investigates the roles of fracture roughness, normal stress and shear displacement on the fluid flow characteristics through three-dimensional (3D) self-affine fractal rock fractures, whose surfaces are generated using the modified successive random additions (SRA) algorithm. A series of numerical shear-flow tests under different normal stresses were conducted on rough rock fractures to calculate the evolutions of fracture aperture and permeability. The results show that the rough surfaces of fractal-based fractures can be described using the scaling parameter Hurst exponent (H), in which H = 3 - Df, where Df is the fractal dimension of 3D single fractures. The joint roughness coefficient (JRC) distribution of fracture profiles follows a Gauss function with a negative linear relationship between H and average JRC. The frequency curves of aperture distributions change from sharp to flat with increasing shear displacement, indicating a more anisotropic and heterogeneous flow pattern. Both the mean aperture and permeability of fracture increase with the increment of surface roughness and decrement of normal stress. At the beginning of shear, the permeability increases remarkably and then gradually becomes steady. A predictive model of permeability using the mean mechanical aperture is proposed and the validity is verified by comparisons with the experimental results reported in literature. The proposed model provides a simple method to approximate permeability of fractal-based rough rock fractures during shear using fracture aperture distribution that can be easily obtained from digitized fracture surface information.

Hardware Implementation of Lossless Adaptive Compression of Data From a Hyperspectral Imager

NASA Technical Reports Server (NTRS)

Keymeulen, Didlier; Aranki, Nazeeh I.; Klimesh, Matthew A.; Bakhshi, Alireza

2012-01-01

Efficient onboard data compression can reduce the data volume from hyperspectral imagers on NASA and DoD spacecraft in order to return as much imagery as possible through constrained downlink channels. Lossless compression is important for signature extraction, object recognition, and feature classification capabilities. To provide onboard data compression, a hardware implementation of a lossless hyperspectral compression algorithm was developed using a field programmable gate array (FPGA). The underlying algorithm is the Fast Lossless (FL) compression algorithm reported in Fast Lossless Compression of Multispectral- Image Data (NPO-42517), NASA Tech Briefs, Vol. 30, No. 8 (August 2006), p. 26 with the modification reported in Lossless, Multi-Spectral Data Comressor for Improved Compression for Pushbroom-Type Instruments (NPO-45473), NASA Tech Briefs, Vol. 32, No. 7 (July 2008) p. 63, which provides improved compression performance for data from pushbroom-type imagers. An FPGA implementation of the unmodified FL algorithm was previously developed and reported in Fast and Adaptive Lossless Onboard Hyperspectral Data Compression System (NPO-46867), NASA Tech Briefs, Vol. 36, No. 5 (May 2012) p. 42. The essence of the FL algorithm is adaptive linear predictive compression using the sign algorithm for filter adaption. The FL compressor achieves a combination of low complexity and compression effectiveness that exceeds that of stateof- the-art techniques currently in use. The modification changes the predictor structure to tolerate differences in sensitivity of different detector elements, as occurs in pushbroom-type imagers, which are suitable for spacecraft use. The FPGA implementation offers a low-cost, flexible solution compared to traditional ASIC (application specific integrated circuit) and can be integrated as an intellectual property (IP) for part of, e.g., a design that manages the instrument interface. The FPGA implementation was benchmarked on the Xilinx Virtex IV LX25 device, and ported to a Xilinx prototype board. The current implementation has a critical path of 29.5 ns, which dictated a clock speed of 33 MHz. The critical path delay is end-to-end measurement between the uncompressed input data and the output compression data stream. The implementation compresses one sample every clock cycle, which results in a speed of 33 Msample/s. The implementation has a rather low device use of the Xilinx Virtex IV LX25, making the total power consumption of the implementation about 1.27 W.

Symmetry compression method for discovering network motifs.

PubMed

Wang, Jianxin; Huang, Yuannan; Wu, Fang-Xiang; Pan, Yi

2012-01-01

Discovering network motifs could provide a significant insight into systems biology. Interestingly, many biological networks have been found to have a high degree of symmetry (automorphism), which is inherent in biological network topologies. The symmetry due to the large number of basic symmetric subgraphs (BSSs) causes a certain redundant calculation in discovering network motifs. Therefore, we compress all basic symmetric subgraphs before extracting compressed subgraphs and propose an efficient decompression algorithm to decompress all compressed subgraphs without loss of any information. In contrast to previous approaches, the novel Symmetry Compression method for Motif Detection, named as SCMD, eliminates most redundant calculations caused by widespread symmetry of biological networks. We use SCMD to improve three notable exact algorithms and two efficient sampling algorithms. Results of all exact algorithms with SCMD are the same as those of the original algorithms, since SCMD is a lossless method. The sampling results show that the use of SCMD almost does not affect the quality of sampling results. For highly symmetric networks, we find that SCMD used in both exact and sampling algorithms can help get a remarkable speedup. Furthermore, SCMD enables us to find larger motifs in biological networks with notable symmetry than previously possible.

The Polygon-Ellipse Method of Data Compression of Weather Maps

DTIC Science & Technology

1994-03-28

Report No. DOT’•FAAJRD-9416 Pr•oject Report AD-A278 958 ATC-213 The Polygon-Ellipse Method of Data Compression of Weather Maps ELDCT E J.L. GerIz 28...a o means must he- found to Compress this image. The l’olygion.Ellip.e (PE.) encoding algorithm develop.ed in this report rt-premrnt. weather regions...severely compress the image. For example, Mode S would require approximately a 10-fold compression . In addition, the algorithms used to perform the

A comparison of spectral decorrelation techniques and performance evaluation metrics for a wavelet-based, multispectral data compression algorithm

NASA Technical Reports Server (NTRS)

Matic, Roy M.; Mosley, Judith I.

1994-01-01

Future space-based, remote sensing systems will have data transmission requirements that exceed available downlinks necessitating the use of lossy compression techniques for multispectral data. In this paper, we describe several algorithms for lossy compression of multispectral data which combine spectral decorrelation techniques with an adaptive, wavelet-based, image compression algorithm to exploit both spectral and spatial correlation. We compare the performance of several different spectral decorrelation techniques including wavelet transformation in the spectral dimension. The performance of each technique is evaluated at compression ratios ranging from 4:1 to 16:1. Performance measures used are visual examination, conventional distortion measures, and multispectral classification results. We also introduce a family of distortion metrics that are designed to quantify and predict the effect of compression artifacts on multi spectral classification of the reconstructed data.

Fast and accurate face recognition based on image compression

NASA Astrophysics Data System (ADS)

Zheng, Yufeng; Blasch, Erik

2017-05-01

Image compression is desired for many image-related applications especially for network-based applications with bandwidth and storage constraints. The face recognition community typical reports concentrate on the maximal compression rate that would not decrease the recognition accuracy. In general, the wavelet-based face recognition methods such as EBGM (elastic bunch graph matching) and FPB (face pattern byte) are of high performance but run slowly due to their high computation demands. The PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) algorithms run fast but perform poorly in face recognition. In this paper, we propose a novel face recognition method based on standard image compression algorithm, which is termed as compression-based (CPB) face recognition. First, all gallery images are compressed by the selected compression algorithm. Second, a mixed image is formed with the probe and gallery images and then compressed. Third, a composite compression ratio (CCR) is computed with three compression ratios calculated from: probe, gallery and mixed images. Finally, the CCR values are compared and the largest CCR corresponds to the matched face. The time cost of each face matching is about the time of compressing the mixed face image. We tested the proposed CPB method on the "ASUMSS face database" (visible and thermal images) from 105 subjects. The face recognition accuracy with visible images is 94.76% when using JPEG compression. On the same face dataset, the accuracy of FPB algorithm was reported as 91.43%. The JPEG-compressionbased (JPEG-CPB) face recognition is standard and fast, which may be integrated into a real-time imaging device.

Fractal analysis of multiscale spatial autocorrelation among point data

USGS Publications Warehouse

De Cola, L.

1991-01-01

The analysis of spatial autocorrelation among point-data quadrats is a well-developed technique that has made limited but intriguing use of the multiscale aspects of pattern. In this paper are presented theoretical and algorithmic approaches to the analysis of aggregations of quadrats at or above a given density, in which these sets are treated as multifractal regions whose fractal dimension, D, may vary with phenomenon intensity, scale, and location. The technique is illustrated with Matui's quadrat house-count data, which yield measurements consistent with a nonautocorrelated simulated Poisson process but not with an orthogonal unit-step random walk. The paper concludes with a discussion of the implications of such analysis for multiscale geographic analysis systems. -Author

IFSM fractal image compression with entropy and sparsity constraints: A sequential quadratic programming approach

NASA Astrophysics Data System (ADS)

Kunze, Herb; La Torre, Davide; Lin, Jianyi

2017-01-01

We consider the inverse problem associated with IFSM: Given a target function f , find an IFSM, such that its fixed point f ¯ is sufficiently close to f in the Lp distance. Forte and Vrscay [1] showed how to reduce this problem to a quadratic optimization model. In this paper, we extend the collage-based method developed by Kunze, La Torre and Vrscay ([2][3][4]), by proposing the minimization of the 1-norm instead of the 0-norm. In fact, optimization problems involving the 0-norm are combinatorial in nature, and hence in general NP-hard. To overcome these difficulties, we introduce the 1-norm and propose a Sequential Quadratic Programming algorithm to solve the corresponding inverse problem. As in Kunze, La Torre and Vrscay [3] in our formulation, the minimization of collage error is treated as a multi-criteria problem that includes three different and conflicting criteria i.e., collage error, entropy and sparsity. This multi-criteria program is solved by means of a scalarization technique which reduces the model to a single-criterion program by combining all objective functions with different trade-off weights. The results of some numerical computations are presented.

Evaluation of Algorithms for Compressing Hyperspectral Data

NASA Technical Reports Server (NTRS)

Cook, Sid; Harsanyi, Joseph; Faber, Vance

2003-01-01

With EO-1 Hyperion in orbit NASA is showing their continued commitment to hyperspectral imaging (HSI). As HSI sensor technology continues to mature, the ever-increasing amounts of sensor data generated will result in a need for more cost effective communication and data handling systems. Lockheed Martin, with considerable experience in spacecraft design and developing special purpose onboard processors, has teamed with Applied Signal & Image Technology (ASIT), who has an extensive heritage in HSI spectral compression and Mapping Science (MSI) for JPEG 2000 spatial compression expertise, to develop a real-time and intelligent onboard processing (OBP) system to reduce HSI sensor downlink requirements. Our goal is to reduce the downlink requirement by a factor > 100, while retaining the necessary spectral and spatial fidelity of the sensor data needed to satisfy the many science, military, and intelligence goals of these systems. Our compression algorithms leverage commercial-off-the-shelf (COTS) spectral and spatial exploitation algorithms. We are currently in the process of evaluating these compression algorithms using statistical analysis and NASA scientists. We are also developing special purpose processors for executing these algorithms onboard a spacecraft.

StirMark Benchmark: audio watermarking attacks based on lossy compression

NASA Astrophysics Data System (ADS)

Steinebach, Martin; Lang, Andreas; Dittmann, Jana

2002-04-01

StirMark Benchmark is a well-known evaluation tool for watermarking robustness. Additional attacks are added to it continuously. To enable application based evaluation, in our paper we address attacks against audio watermarks based on lossy audio compression algorithms to be included in the test environment. We discuss the effect of different lossy compression algorithms like MPEG-2 audio Layer 3, Ogg or VQF on a selection of audio test data. Our focus is on changes regarding the basic characteristics of the audio data like spectrum or average power and on removal of embedded watermarks. Furthermore we compare results of different watermarking algorithms and show that lossy compression is still a challenge for most of them. There are two strategies for adding evaluation of robustness against lossy compression to StirMark Benchmark: (a) use of existing free compression algorithms (b) implementation of a generic lossy compression simulation. We discuss how such a model can be implemented based on the results of our tests. This method is less complex, as no real psycho acoustic model has to be applied. Our model can be used for audio watermarking evaluation of numerous application fields. As an example, we describe its importance for e-commerce applications with watermarking security.

KungFQ: a simple and powerful approach to compress fastq files.

PubMed

Grassi, Elena; Di Gregorio, Federico; Molineris, Ivan

2012-01-01

Nowadays storing data derived from deep sequencing experiments has become pivotal and standard compression algorithms do not exploit in a satisfying manner their structure. A number of reference-based compression algorithms have been developed but they are less adequate when approaching new species without fully sequenced genomes or nongenomic data. We developed a tool that takes advantages of fastq characteristics and encodes them in a binary format optimized in order to be further compressed with standard tools (such as gzip or lzma). The algorithm is straightforward and does not need any external reference file, it scans the fastq only once and has a constant memory requirement. Moreover, we added the possibility to perform lossy compression, losing some of the original information (IDs and/or qualities) but resulting in smaller files; it is also possible to define a quality cutoff under which corresponding base calls are converted to N. We achieve 2.82 to 7.77 compression ratios on various fastq files without losing information and 5.37 to 8.77 losing IDs, which are often not used in common analysis pipelines. In this paper, we compare the algorithm performance with known tools, usually obtaining higher compression levels.

Vehicle-triggered video compression/decompression for fast and efficient searching in large video databases

NASA Astrophysics Data System (ADS)

Bulan, Orhan; Bernal, Edgar A.; Loce, Robert P.; Wu, Wencheng

2013-03-01

Video cameras are widely deployed along city streets, interstate highways, traffic lights, stop signs and toll booths by entities that perform traffic monitoring and law enforcement. The videos captured by these cameras are typically compressed and stored in large databases. Performing a rapid search for a specific vehicle within a large database of compressed videos is often required and can be a time-critical life or death situation. In this paper, we propose video compression and decompression algorithms that enable fast and efficient vehicle or, more generally, event searches in large video databases. The proposed algorithm selects reference frames (i.e., I-frames) based on a vehicle having been detected at a specified position within the scene being monitored while compressing a video sequence. A search for a specific vehicle in the compressed video stream is performed across the reference frames only, which does not require decompression of the full video sequence as in traditional search algorithms. Our experimental results on videos captured in a local road show that the proposed algorithm significantly reduces the search space (thus reducing time and computational resources) in vehicle search tasks within compressed video streams, particularly those captured in light traffic volume conditions.

CoGI: Towards Compressing Genomes as an Image.

PubMed

Xie, Xiaojing; Zhou, Shuigeng; Guan, Jihong

2015-01-01

Genomic science is now facing an explosive increase of data thanks to the fast development of sequencing technology. This situation poses serious challenges to genomic data storage and transferring. It is desirable to compress data to reduce storage and transferring cost, and thus to boost data distribution and utilization efficiency. Up to now, a number of algorithms / tools have been developed for compressing genomic sequences. Unlike the existing algorithms, most of which treat genomes as one-dimensional text strings and compress them based on dictionaries or probability models, this paper proposes a novel approach called CoGI (the abbreviation of Compressing Genomes as an Image) for genome compression, which transforms the genomic sequences to a two-dimensional binary image (or bitmap), then applies a rectangular partition coding algorithm to compress the binary image. CoGI can be used as either a reference-based compressor or a reference-free compressor. For the former, we develop two entropy-based algorithms to select a proper reference genome. Performance evaluation is conducted on various genomes. Experimental results show that the reference-based CoGI significantly outperforms two state-of-the-art reference-based genome compressors GReEn and RLZ-opt in both compression ratio and compression efficiency. It also achieves comparable compression ratio but two orders of magnitude higher compression efficiency in comparison with XM--one state-of-the-art reference-free genome compressor. Furthermore, our approach performs much better than Gzip--a general-purpose and widely-used compressor, in both compression speed and compression ratio. So, CoGI can serve as an effective and practical genome compressor. The source code and other related documents of CoGI are available at: http://admis.fudan.edu.cn/projects/cogi.htm.

The use of fractal dimension analysis in estimation of blood vessels shape in transplantable mammary adenocarcinoma in Wistar rats after photodynamic therapy combined with cysteine protease inhibitors.

PubMed

Jurczyszyn, Kamil; Osiecka, Beata J; Ziółkowski, Piotr

2012-01-01

Fractal dimension analysis (FDA) is modern mathematical method widely used to describing of complex and chaotic shapes when classic methods fail. The main aim of this study was evaluating the influence of photodynamic therapy (PDT) with cystein proteases inhibitors (CPI) on the number and morphology of blood vessels inside tumor and on increase of effectiveness of combined therapy in contrast to PDT and CPI used separately. Animals were divided into four groups: control, treated using only PDT, treated using only CPI and treated using combined therapy, PDT and CPI. Results showed that time of animal survival and depth of necrosis inside tumor were significantly higher in CPI+PDT group in contrast to other groups. The higher value of fractal dimension (FD) was observed in control group, while the lowest value was found in the group which was treated by cystein protease inhibitors. The differences between FD were observed in CPI group and PDT+CPI group in comparison to control group. Our results revealed that fractal dimension analysis is a very useful tool in estimating differences between irregular shapes like blood vessels in PDT treated tumors. Thus, the implementation of FDA algorithms could be useful method in evaluating the efficacy of PDT.

The Use of Fractal Dimension Analysis in Estimation of Blood Vessels Shape in Transplantable Mammary Adenocarcinoma in Wistar Rats after Photodynamic Therapy Combined with Cysteine Protease Inhibitors

PubMed Central

Jurczyszyn, Kamil; Osiecka, Beata J.; Ziółkowski, Piotr

2012-01-01

Fractal dimension analysis (FDA) is modern mathematical method widely used to describing of complex and chaotic shapes when classic methods fail. The main aim of this study was evaluating the influence of photodynamic therapy (PDT) with cystein proteases inhibitors (CPI) on the number and morphology of blood vessels inside tumor and on increase of effectiveness of combined therapy in contrast to PDT and CPI used separately. Animals were divided into four groups: control, treated using only PDT, treated using only CPI and treated using combined therapy, PDT and CPI. Results showed that time of animal survival and depth of necrosis inside tumor were significantly higher in CPI+PDT group in contrast to other groups. The higher value of fractal dimension (FD) was observed in control group, while the lowest value was found in the group which was treated by cystein protease inhibitors. The differences between FD were observed in CPI group and PDT+CPI group in comparison to control group. Our results revealed that fractal dimension analysis is a very useful tool in estimating differences between irregular shapes like blood vessels in PDT treated tumors. Thus, the implementation of FDA algorithms could be useful method in evaluating the efficacy of PDT. PMID:22991578

VLSI chip-set for data compression using the Rice algorithm

NASA Technical Reports Server (NTRS)

Venbrux, J.; Liu, N.

1990-01-01

A full custom VLSI implementation of a data compression encoder and decoder which implements the lossless Rice data compression algorithm is discussed in this paper. The encoder and decoder reside on single chips. The data rates are to be 5 and 10 Mega-samples-per-second for the decoder and encoder respectively.

Filtered gradient reconstruction algorithm for compressive spectral imaging

NASA Astrophysics Data System (ADS)

Mejia, Yuri; Arguello, Henry

2017-04-01

Compressive sensing matrices are traditionally based on random Gaussian and Bernoulli entries. Nevertheless, they are subject to physical constraints, and their structure unusually follows a dense matrix distribution, such as the case of the matrix related to compressive spectral imaging (CSI). The CSI matrix represents the integration of coded and shifted versions of the spectral bands. A spectral image can be recovered from CSI measurements by using iterative algorithms for linear inverse problems that minimize an objective function including a quadratic error term combined with a sparsity regularization term. However, current algorithms are slow because they do not exploit the structure and sparse characteristics of the CSI matrices. A gradient-based CSI reconstruction algorithm, which introduces a filtering step in each iteration of a conventional CSI reconstruction algorithm that yields improved image quality, is proposed. Motivated by the structure of the CSI matrix, Φ, this algorithm modifies the iterative solution such that it is forced to converge to a filtered version of the residual ΦTy, where y is the compressive measurement vector. We show that the filtered-based algorithm converges to better quality performance results than the unfiltered version. Simulation results highlight the relative performance gain over the existing iterative algorithms.

DNA-COMPACT: DNA COMpression Based on a Pattern-Aware Contextual Modeling Technique

PubMed Central

Li, Pinghao; Wang, Shuang; Kim, Jihoon; Xiong, Hongkai; Ohno-Machado, Lucila; Jiang, Xiaoqian

2013-01-01

Genome data are becoming increasingly important for modern medicine. As the rate of increase in DNA sequencing outstrips the rate of increase in disk storage capacity, the storage and data transferring of large genome data are becoming important concerns for biomedical researchers. We propose a two-pass lossless genome compression algorithm, which highlights the synthesis of complementary contextual models, to improve the compression performance. The proposed framework could handle genome compression with and without reference sequences, and demonstrated performance advantages over best existing algorithms. The method for reference-free compression led to bit rates of 1.720 and 1.838 bits per base for bacteria and yeast, which were approximately 3.7% and 2.6% better than the state-of-the-art algorithms. Regarding performance with reference, we tested on the first Korean personal genome sequence data set, and our proposed method demonstrated a 189-fold compression rate, reducing the raw file size from 2986.8 MB to 15.8 MB at a comparable decompression cost with existing algorithms. DNAcompact is freely available at https://sourceforge.net/projects/dnacompact/for research purpose. PMID:24282536

SCALCE: boosting sequence compression algorithms using locally consistent encoding

PubMed Central

Hach, Faraz; Numanagić, Ibrahim; Sahinalp, S Cenk

2012-01-01

Motivation: The high throughput sequencing (HTS) platforms generate unprecedented amounts of data that introduce challenges for the computational infrastructure. Data management, storage and analysis have become major logistical obstacles for those adopting the new platforms. The requirement for large investment for this purpose almost signalled the end of the Sequence Read Archive hosted at the National Center for Biotechnology Information (NCBI), which holds most of the sequence data generated world wide. Currently, most HTS data are compressed through general purpose algorithms such as gzip. These algorithms are not designed for compressing data generated by the HTS platforms; for example, they do not take advantage of the specific nature of genomic sequence data, that is, limited alphabet size and high similarity among reads. Fast and efficient compression algorithms designed specifically for HTS data should be able to address some of the issues in data management, storage and communication. Such algorithms would also help with analysis provided they offer additional capabilities such as random access to any read and indexing for efficient sequence similarity search. Here we present SCALCE, a ‘boosting’ scheme based on Locally Consistent Parsing technique, which reorganizes the reads in a way that results in a higher compression speed and compression rate, independent of the compression algorithm in use and without using a reference genome. Results: Our tests indicate that SCALCE can improve the compression rate achieved through gzip by a factor of 4.19—when the goal is to compress the reads alone. In fact, on SCALCE reordered reads, gzip running time can improve by a factor of 15.06 on a standard PC with a single core and 6 GB memory. Interestingly even the running time of SCALCE + gzip improves that of gzip alone by a factor of 2.09. When compared with the recently published BEETL, which aims to sort the (inverted) reads in lexicographic order for improving bzip2, SCALCE + gzip provides up to 2.01 times better compression while improving the running time by a factor of 5.17. SCALCE also provides the option to compress the quality scores as well as the read names, in addition to the reads themselves. This is achieved by compressing the quality scores through order-3 Arithmetic Coding (AC) and the read names through gzip through the reordering SCALCE provides on the reads. This way, in comparison with gzip compression of the unordered FASTQ files (including reads, read names and quality scores), SCALCE (together with gzip and arithmetic encoding) can provide up to 3.34 improvement in the compression rate and 1.26 improvement in running time. Availability: Our algorithm, SCALCE (Sequence Compression Algorithm using Locally Consistent Encoding), is implemented in C++ with both gzip and bzip2 compression options. It also supports multithreading when gzip option is selected, and the pigz binary is available. It is available at http://scalce.sourceforge.net. Contact: fhach@cs.sfu.ca or cenk@cs.sfu.ca Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23047557

Interdisciplinary Investigations in Support of Project DI-MOD

NASA Technical Reports Server (NTRS)

Starks, Scott A. (Principal Investigator)

1996-01-01

Various concepts from time series analysis are used as the basis for the development of algorithms to assist in the analysis and interpretation of remote sensed imagery. An approach to trend detection that is based upon the fractal analysis of power spectrum estimates is presented. Additionally, research was conducted toward the development of a software architecture to support processing tasks associated with databases housing a variety of data. An algorithmic approach which provides for the automation of the state monitoring process is presented.

A contourlet transform based algorithm for real-time video encoding

NASA Astrophysics Data System (ADS)

Katsigiannis, Stamos; Papaioannou, Georgios; Maroulis, Dimitris

2012-06-01

In recent years, real-time video communication over the internet has been widely utilized for applications like video conferencing. Streaming live video over heterogeneous IP networks, including wireless networks, requires video coding algorithms that can support various levels of quality in order to adapt to the network end-to-end bandwidth and transmitter/receiver resources. In this work, a scalable video coding and compression algorithm based on the Contourlet Transform is proposed. The algorithm allows for multiple levels of detail, without re-encoding the video frames, by just dropping the encoded information referring to higher resolution than needed. Compression is achieved by means of lossy and lossless methods, as well as variable bit rate encoding schemes. Furthermore, due to the transformation utilized, it does not suffer from blocking artifacts that occur with many widely adopted compression algorithms. Another highly advantageous characteristic of the algorithm is the suppression of noise induced by low-quality sensors usually encountered in web-cameras, due to the manipulation of the transform coefficients at the compression stage. The proposed algorithm is designed to introduce minimal coding delay, thus achieving real-time performance. Performance is enhanced by utilizing the vast computational capabilities of modern GPUs, providing satisfactory encoding and decoding times at relatively low cost. These characteristics make this method suitable for applications like video-conferencing that demand real-time performance, along with the highest visual quality possible for each user. Through the presented performance and quality evaluation of the algorithm, experimental results show that the proposed algorithm achieves better or comparable visual quality relative to other compression and encoding methods tested, while maintaining a satisfactory compression ratio. Especially at low bitrates, it provides more human-eye friendly images compared to algorithms utilizing block-based coding, like the MPEG family, as it introduces fuzziness and blurring instead of artificial block artifacts.

Filetype Identification Using Long, Summarized N-Grams

DTIC Science & Technology

2011-03-01

compressed or encrypted data . If the algorithm used to compress or encrypt the data can be determined, then it is frequently possible to uncom- press...fragments. His implementation utilized the bzip2 library to compress the file fragments. The bzip2 library is based off the Lempel - Ziv -Markov chain... algorithm that uses a dictionary compression scheme to remove repeating data patterns within a set of data . The removed patterns are listed within the

Novel Near-Lossless Compression Algorithm for Medical Sequence Images with Adaptive Block-Based Spatial Prediction.

PubMed

Song, Xiaoying; Huang, Qijun; Chang, Sheng; He, Jin; Wang, Hao

2016-12-01

To address the low compression efficiency of lossless compression and the low image quality of general near-lossless compression, a novel near-lossless compression algorithm based on adaptive spatial prediction is proposed for medical sequence images for possible diagnostic use in this paper. The proposed method employs adaptive block size-based spatial prediction to predict blocks directly in the spatial domain and Lossless Hadamard Transform before quantization to improve the quality of reconstructed images. The block-based prediction breaks the pixel neighborhood constraint and takes full advantage of the local spatial correlations found in medical images. The adaptive block size guarantees a more rational division of images and the improved use of the local structure. The results indicate that the proposed algorithm can efficiently compress medical images and produces a better peak signal-to-noise ratio (PSNR) under the same pre-defined distortion than other near-lossless methods.

Intelligent bandwidth compression

NASA Astrophysics Data System (ADS)

Tseng, D. Y.; Bullock, B. L.; Olin, K. E.; Kandt, R. K.; Olsen, J. D.

1980-02-01

The feasibility of a 1000:1 bandwidth compression ratio for image transmission has been demonstrated using image-analysis algorithms and a rule-based controller. Such a high compression ratio was achieved by first analyzing scene content using auto-cueing and feature-extraction algorithms, and then transmitting only the pertinent information consistent with mission requirements. A rule-based controller directs the flow of analysis and performs priority allocations on the extracted scene content. The reconstructed bandwidth-compressed image consists of an edge map of the scene background, with primary and secondary target windows embedded in the edge map. The bandwidth-compressed images are updated at a basic rate of 1 frame per second, with the high-priority target window updated at 7.5 frames per second. The scene-analysis algorithms used in this system together with the adaptive priority controller are described. Results of simulated 1000:1 bandwidth-compressed images are presented.

High-speed and high-ratio referential genome compression.

PubMed

Liu, Yuansheng; Peng, Hui; Wong, Limsoon; Li, Jinyan

2017-11-01

The rapidly increasing number of genomes generated by high-throughput sequencing platforms and assembly algorithms is accompanied by problems in data storage, compression and communication. Traditional compression algorithms are unable to meet the demand of high compression ratio due to the intrinsic challenging features of DNA sequences such as small alphabet size, frequent repeats and palindromes. Reference-based lossless compression, by which only the differences between two similar genomes are stored, is a promising approach with high compression ratio. We present a high-performance referential genome compression algorithm named HiRGC. It is based on a 2-bit encoding scheme and an advanced greedy-matching search on a hash table. We compare the performance of HiRGC with four state-of-the-art compression methods on a benchmark dataset of eight human genomes. HiRGC takes <30 min to compress about 21 gigabytes of each set of the seven target genomes into 96-260 megabytes, achieving compression ratios of 217 to 82 times. This performance is at least 1.9 times better than the best competing algorithm on its best case. Our compression speed is also at least 2.9 times faster. HiRGC is stable and robust to deal with different reference genomes. In contrast, the competing methods' performance varies widely on different reference genomes. More experiments on 100 human genomes from the 1000 Genome Project and on genomes of several other species again demonstrate that HiRGC's performance is consistently excellent. The C ++ and Java source codes of our algorithm are freely available for academic and non-commercial use. They can be downloaded from https://github.com/yuansliu/HiRGC. jinyan.li@uts.edu.au. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Fast and Adaptive Lossless Onboard Hyperspectral Data Compression System

NASA Technical Reports Server (NTRS)

Aranki, Nazeeh I.; Keymeulen, Didier; Kimesh, Matthew A.

2012-01-01

Modern hyperspectral imaging systems are able to acquire far more data than can be downlinked from a spacecraft. Onboard data compression helps to alleviate this problem, but requires a system capable of power efficiency and high throughput. Software solutions have limited throughput performance and are power-hungry. Dedicated hardware solutions can provide both high throughput and power efficiency, while taking the load off of the main processor. Thus a hardware compression system was developed. The implementation uses a field-programmable gate array (FPGA). The implementation is based on the fast lossless (FL) compression algorithm reported in Fast Lossless Compression of Multispectral-Image Data (NPO-42517), NASA Tech Briefs, Vol. 30, No. 8 (August 2006), page 26, which achieves excellent compression performance and has low complexity. This algorithm performs predictive compression using an adaptive filtering method, and uses adaptive Golomb coding. The implementation also packetizes the coded data. The FL algorithm is well suited for implementation in hardware. In the FPGA implementation, one sample is compressed every clock cycle, which makes for a fast and practical realtime solution for space applications. Benefits of this implementation are: 1) The underlying algorithm achieves a combination of low complexity and compression effectiveness that exceeds that of techniques currently in use. 2) The algorithm requires no training data or other specific information about the nature of the spectral bands for a fixed instrument dynamic range. 3) Hardware acceleration provides a throughput improvement of 10 to 100 times vs. the software implementation. A prototype of the compressor is available in software, but it runs at a speed that does not meet spacecraft requirements. The hardware implementation targets the Xilinx Virtex IV FPGAs, and makes the use of this compressor practical for Earth satellites as well as beyond-Earth missions with hyperspectral instruments.

Intelligent bandwith compression

NASA Astrophysics Data System (ADS)

Tseng, D. Y.; Bullock, B. L.; Olin, K. E.; Kandt, R. K.; Olsen, J. D.

1980-02-01

The feasibility of a 1000:1 bandwidth compression ratio for image transmission has been demonstrated using image-analysis algorithms and a rule-based controller. Such a high compression ratio was achieved by first analyzing scene content using auto-cueing and feature-extraction algorithms, and then transmitting only the pertinent information consistent with mission requirements. A rule-based controller directs the flow of analysis and performs priority allocations on the extracted scene content. The reconstructed bandwidth-compressed image consists of an edge map of the scene background, with primary and secondary target windows embedded in the edge map. The bandwidth-compressed images are updated at a basic rate of 1 frame per second, with the high-priority target window updated at 7.5 frames per second. The scene-analysis algorithms used in this system together with the adaptive priority controller are described. Results of simulated 1000:1 band width-compressed images are presented. A video tape simulation of the Intelligent Bandwidth Compression system has been produced using a sequence of video input from the data base.

Hierarchical layered and semantic-based image segmentation using ergodicity map

NASA Astrophysics Data System (ADS)

Yadegar, Jacob; Liu, Xiaoqing

2010-04-01

Image segmentation plays a foundational role in image understanding and computer vision. Although great strides have been made and progress achieved on automatic/semi-automatic image segmentation algorithms, designing a generic, robust, and efficient image segmentation algorithm is still challenging. Human vision is still far superior compared to computer vision, especially in interpreting semantic meanings/objects in images. We present a hierarchical/layered semantic image segmentation algorithm that can automatically and efficiently segment images into hierarchical layered/multi-scaled semantic regions/objects with contextual topological relationships. The proposed algorithm bridges the gap between high-level semantics and low-level visual features/cues (such as color, intensity, edge, etc.) through utilizing a layered/hierarchical ergodicity map, where ergodicity is computed based on a space filling fractal concept and used as a region dissimilarity measurement. The algorithm applies a highly scalable, efficient, and adaptive Peano- Cesaro triangulation/tiling technique to decompose the given image into a set of similar/homogenous regions based on low-level visual cues in a top-down manner. The layered/hierarchical ergodicity map is built through a bottom-up region dissimilarity analysis. The recursive fractal sweep associated with the Peano-Cesaro triangulation provides efficient local multi-resolution refinement to any level of detail. The generated binary decomposition tree also provides efficient neighbor retrieval mechanisms for contextual topological object/region relationship generation. Experiments have been conducted within the maritime image environment where the segmented layered semantic objects include the basic level objects (i.e. sky/land/water) and deeper level objects in the sky/land/water surfaces. Experimental results demonstrate the proposed algorithm has the capability to robustly and efficiently segment images into layered semantic objects/regions with contextual topological relationships.

Trajectory NG: portable, compressed, general molecular dynamics trajectories.

PubMed

Spångberg, Daniel; Larsson, Daniel S D; van der Spoel, David

2011-10-01

We present general algorithms for the compression of molecular dynamics trajectories. The standard ways to store MD trajectories as text or as raw binary floating point numbers result in very large files when efficient simulation programs are used on supercomputers. Our algorithms are based on the observation that differences in atomic coordinates/velocities, in either time or space, are generally smaller than the absolute values of the coordinates/velocities. Also, it is often possible to store values at a lower precision. We apply several compression schemes to compress the resulting differences further. The most efficient algorithms developed here use a block sorting algorithm in combination with Huffman coding. Depending on the frequency of storage of frames in the trajectory, either space, time, or combinations of space and time differences are usually the most efficient. We compare the efficiency of our algorithms with each other and with other algorithms present in the literature for various systems: liquid argon, water, a virus capsid solvated in 15 mM aqueous NaCl, and solid magnesium oxide. We perform tests to determine how much precision is necessary to obtain accurate structural and dynamic properties, as well as benchmark a parallelized implementation of the algorithms. We obtain compression ratios (compared to single precision floating point) of 1:3.3-1:35 depending on the frequency of storage of frames and the system studied.

HUGO: Hierarchical mUlti-reference Genome cOmpression for aligned reads

PubMed Central

Li, Pinghao; Jiang, Xiaoqian; Wang, Shuang; Kim, Jihoon; Xiong, Hongkai; Ohno-Machado, Lucila

2014-01-01

Background and objective Short-read sequencing is becoming the standard of practice for the study of structural variants associated with disease. However, with the growth of sequence data largely surpassing reasonable storage capability, the biomedical community is challenged with the management, transfer, archiving, and storage of sequence data. Methods We developed Hierarchical mUlti-reference Genome cOmpression (HUGO), a novel compression algorithm for aligned reads in the sorted Sequence Alignment/Map (SAM) format. We first aligned short reads against a reference genome and stored exactly mapped reads for compression. For the inexact mapped or unmapped reads, we realigned them against different reference genomes using an adaptive scheme by gradually shortening the read length. Regarding the base quality value, we offer lossy and lossless compression mechanisms. The lossy compression mechanism for the base quality values uses k-means clustering, where a user can adjust the balance between decompression quality and compression rate. The lossless compression can be produced by setting k (the number of clusters) to the number of different quality values. Results The proposed method produced a compression ratio in the range 0.5–0.65, which corresponds to 35–50% storage savings based on experimental datasets. The proposed approach achieved 15% more storage savings over CRAM and comparable compression ratio with Samcomp (CRAM and Samcomp are two of the state-of-the-art genome compression algorithms). The software is freely available at https://sourceforge.net/projects/hierachicaldnac/with a General Public License (GPL) license. Limitation Our method requires having different reference genomes and prolongs the execution time for additional alignments. Conclusions The proposed multi-reference-based compression algorithm for aligned reads outperforms existing single-reference based algorithms. PMID:24368726

Algorithm for Lossless Compression of Calibrated Hyperspectral Imagery

NASA Technical Reports Server (NTRS)

Kiely, Aaron B.; Klimesh, Matthew A.

2010-01-01

A two-stage predictive method was developed for lossless compression of calibrated hyperspectral imagery. The first prediction stage uses a conventional linear predictor intended to exploit spatial and/or spectral dependencies in the data. The compressor tabulates counts of the past values of the difference between this initial prediction and the actual sample value. To form the ultimate predicted value, in the second stage, these counts are combined with an adaptively updated weight function intended to capture information about data regularities introduced by the calibration process. Finally, prediction residuals are losslessly encoded using adaptive arithmetic coding. Algorithms of this type are commonly tested on a readily available collection of images from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral imager. On the standard calibrated AVIRIS hyperspectral images that are most widely used for compression benchmarking, the new compressor provides more than 0.5 bits/sample improvement over the previous best compression results. The algorithm has been implemented in Mathematica. The compression algorithm was demonstrated as beneficial on 12-bit calibrated AVIRIS images.

Wavelet-based audio embedding and audio/video compression

NASA Astrophysics Data System (ADS)

Mendenhall, Michael J.; Claypoole, Roger L., Jr.

2001-12-01

Watermarking, traditionally used for copyright protection, is used in a new and exciting way. An efficient wavelet-based watermarking technique embeds audio information into a video signal. Several effective compression techniques are applied to compress the resulting audio/video signal in an embedded fashion. This wavelet-based compression algorithm incorporates bit-plane coding, index coding, and Huffman coding. To demonstrate the potential of this audio embedding and audio/video compression algorithm, we embed an audio signal into a video signal and then compress. Results show that overall compression rates of 15:1 can be achieved. The video signal is reconstructed with a median PSNR of nearly 33 dB. Finally, the audio signal is extracted from the compressed audio/video signal without error.

Recce imagery compression options

NASA Astrophysics Data System (ADS)

Healy, Donald J.

1995-09-01

The errors introduced into reconstructed RECCE imagery by ATARS DPCM compression are compared to those introduced by the more modern DCT-based JPEG compression algorithm. For storage applications in which uncompressed sensor data is available JPEG provides better mean-square-error performance while also providing more flexibility in the selection of compressed data rates. When ATARS DPCM compression has already been performed, lossless encoding techniques may be applied to the DPCM deltas to achieve further compression without introducing additional errors. The abilities of several lossless compression algorithms including Huffman, Lempel-Ziv, Lempel-Ziv-Welch, and Rice encoding to provide this additional compression of ATARS DPCM deltas are compared. It is shown that the amount of noise in the original imagery significantly affects these comparisons.

Subjective evaluation of compressed image quality

NASA Astrophysics Data System (ADS)

Lee, Heesub; Rowberg, Alan H.; Frank, Mark S.; Choi, Hyung-Sik; Kim, Yongmin

1992-05-01

Lossy data compression generates distortion or error on the reconstructed image and the distortion becomes visible as the compression ratio increases. Even at the same compression ratio, the distortion appears differently depending on the compression method used. Because of the nonlinearity of the human visual system and lossy data compression methods, we have evaluated subjectively the quality of medical images compressed with two different methods, an intraframe and interframe coding algorithms. The evaluated raw data were analyzed statistically to measure interrater reliability and reliability of an individual reader. Also, the analysis of variance was used to identify which compression method is better statistically, and from what compression ratio the quality of a compressed image is evaluated as poorer than that of the original. Nine x-ray CT head images from three patients were used as test cases. Six radiologists participated in reading the 99 images (some were duplicates) compressed at four different compression ratios, original, 5:1, 10:1, and 15:1. The six readers agree more than by chance alone and their agreement was statistically significant, but there were large variations among readers as well as within a reader. The displacement estimated interframe coding algorithm is significantly better in quality than that of the 2-D block DCT at significance level 0.05. Also, 10:1 compressed images with the interframe coding algorithm do not show any significant differences from the original at level 0.05.

A modified approach combining FNEA and watershed algorithms for segmenting remotely-sensed optical images

NASA Astrophysics Data System (ADS)

Liu, Likun

2018-01-01

In the field of remote sensing image processing, remote sensing image segmentation is a preliminary step for later analysis of remote sensing image processing and semi-auto human interpretation, fully-automatic machine recognition and learning. Since 2000, a technique of object-oriented remote sensing image processing method and its basic thought prevails. The core of the approach is Fractal Net Evolution Approach (FNEA) multi-scale segmentation algorithm. The paper is intent on the research and improvement of the algorithm, which analyzes present segmentation algorithms and selects optimum watershed algorithm as an initialization. Meanwhile, the algorithm is modified by modifying an area parameter, and then combining area parameter with a heterogeneous parameter further. After that, several experiments is carried on to prove the modified FNEA algorithm, compared with traditional pixel-based method (FCM algorithm based on neighborhood information) and combination of FNEA and watershed, has a better segmentation result.

Fingerprint recognition of wavelet-based compressed images by neuro-fuzzy clustering

NASA Astrophysics Data System (ADS)

Liu, Ti C.; Mitra, Sunanda

1996-06-01

Image compression plays a crucial role in many important and diverse applications requiring efficient storage and transmission. This work mainly focuses on a wavelet transform (WT) based compression of fingerprint images and the subsequent classification of the reconstructed images. The algorithm developed involves multiresolution wavelet decomposition, uniform scalar quantization, entropy and run- length encoder/decoder and K-means clustering of the invariant moments as fingerprint features. The performance of the WT-based compression algorithm has been compared with JPEG current image compression standard. Simulation results show that WT outperforms JPEG in high compression ratio region and the reconstructed fingerprint image yields proper classification.

Lossless Astronomical Image Compression and the Effects of Random Noise

NASA Technical Reports Server (NTRS)

Pence, William

2009-01-01

In this paper we compare a variety of modern image compression methods on a large sample of astronomical images. We begin by demonstrating from first principles how the amount of noise in the image pixel values sets a theoretical upper limit on the lossless compression ratio of the image. We derive simple procedures for measuring the amount of noise in an image and for quantitatively predicting how much compression will be possible. We then compare the traditional technique of using the GZIP utility to externally compress the image, with a newer technique of dividing the image into tiles, and then compressing and storing each tile in a FITS binary table structure. This tiled-image compression technique offers a choice of other compression algorithms besides GZIP, some of which are much better suited to compressing astronomical images. Our tests on a large sample of images show that the Rice algorithm provides the best combination of speed and compression efficiency. In particular, Rice typically produces 1.5 times greater compression and provides much faster compression speed than GZIP. Floating point images generally contain too much noise to be effectively compressed with any lossless algorithm. We have developed a compression technique which discards some of the useless noise bits by quantizing the pixel values as scaled integers. The integer images can then be compressed by a factor of 4 or more. Our image compression and uncompression utilities (called fpack and funpack) that were used in this study are publicly available from the HEASARC web site.Users may run these stand-alone programs to compress and uncompress their own images.

Multi-pass encoding of hyperspectral imagery with spectral quality control

NASA Astrophysics Data System (ADS)

Wasson, Steven; Walker, William

2015-05-01

Multi-pass encoding is a technique employed in the field of video compression that maximizes the quality of an encoded video sequence within the constraints of a specified bit rate. This paper presents research where multi-pass encoding is extended to the field of hyperspectral image compression. Unlike video, which is primarily intended to be viewed by a human observer, hyperspectral imagery is processed by computational algorithms that generally attempt to classify the pixel spectra within the imagery. As such, these algorithms are more sensitive to distortion in the spectral dimension of the image than they are to perceptual distortion in the spatial dimension. The compression algorithm developed for this research, which uses the Karhunen-Loeve transform for spectral decorrelation followed by a modified H.264/Advanced Video Coding (AVC) encoder, maintains a user-specified spectral quality level while maximizing the compression ratio throughout the encoding process. The compression performance may be considered near-lossless in certain scenarios. For qualitative purposes, this paper presents the performance of the compression algorithm for several Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Hyperion datasets using spectral angle as the spectral quality assessment function. Specifically, the compression performance is illustrated in the form of rate-distortion curves that plot spectral angle versus bits per pixel per band (bpppb).

An improved Huffman coding with encryption for Radio Data System (RDS) for smart transportation

NASA Astrophysics Data System (ADS)

Wu, C. H.; Tseng, Kuo-Kun; Ng, C. K.; Ho, G. T. S.; Zeng, Fu-Fu; Tse, Y. K.

2018-02-01

As the development of Radio Data System (RDS) technology and its applications are getting more and more attention and promotion, people concern their personal privacy and communication efficiency, and therefore compression and encryption technologies are being more important for transferring RDS data. Unlike most of the current approaches which contain two stages, compression and encryption, we proposed a new algorithm called Swapped Huffman Table (SHT) based on Huffman algorithm to realise compression and encryption in a single process. In this paper, a good performance for both compression and encryption is obtained and a possible application of RDS with the proposed algorithm in smart transportation is illustrated.

Research on Optimization of Encoding Algorithm of PDF417 Barcodes

NASA Astrophysics Data System (ADS)

Sun, Ming; Fu, Longsheng; Han, Shuqing

The purpose of this research is to develop software to optimize the data compression of a PDF417 barcode using VC++6.0. According to the different compression mode and the particularities of Chinese, the relevant approaches which optimize the encoding algorithm of data compression such as spillage and the Chinese characters encoding are proposed, a simple approach to compute complex polynomial is introduced. After the whole data compression is finished, the number of the codeword is reduced and then the encoding algorithm is optimized. The developed encoding system of PDF 417 barcodes will be applied in the logistics management of fruits, therefore also will promote the fast development of the two-dimensional bar codes.

Adaptive intercolor error prediction coder for lossless color (rgb) picutre compression

NASA Astrophysics Data System (ADS)

Mann, Y.; Peretz, Y.; Mitchell, Harvey B.

2001-09-01

Most of the current lossless compression algorithms, including the new international baseline JPEG-LS algorithm, do not exploit the interspectral correlations that exist between the color planes in an input color picture. To improve the compression performance (i.e., lower the bit rate) it is necessary to exploit these correlations. A major concern is to find efficient methods for exploiting the correlations that, at the same time, are compatible with and can be incorporated into the JPEG-LS algorithm. One such algorithm is the method of intercolor error prediction (IEP), which when used with the JPEG-LS algorithm, results on average in a reduction of 8% in the overall bit rate. We show how the IEP algorithm can be simply modified and that it nearly doubles the size of the reduction in bit rate to 15%.

Compressed sensing for energy-efficient wireless telemonitoring of noninvasive fetal ECG via block sparse Bayesian learning.

PubMed

Zhang, Zhilin; Jung, Tzyy-Ping; Makeig, Scott; Rao, Bhaskar D

2013-02-01

Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. The design of a telemonitoring system via a wireless body area network with low energy consumption for ambulatory use is highly desirable. As an emerging technique, compressed sensing (CS) shows great promise in compressing/reconstructing data with low energy consumption. However, due to some specific characteristics of raw FECG recordings such as nonsparsity and strong noise contamination, current CS algorithms generally fail in this application. This paper proposes to use the block sparse Bayesian learning framework to compress/reconstruct nonsparse raw FECG recordings. Experimental results show that the framework can reconstruct the raw recordings with high quality. Especially, the reconstruction does not destroy the interdependence relation among the multichannel recordings. This ensures that the independent component analysis decomposition of the reconstructed recordings has high fidelity. Furthermore, the framework allows the use of a sparse binary sensing matrix with much fewer nonzero entries to compress recordings. Particularly, each column of the matrix can contain only two nonzero entries. This shows that the framework, compared to other algorithms such as current CS algorithms and wavelet algorithms, can greatly reduce code execution in CPU in the data compression stage.

Applications of wavelet-based compression to multidimensional Earth science data

NASA Technical Reports Server (NTRS)

Bradley, Jonathan N.; Brislawn, Christopher M.

1993-01-01

A data compression algorithm involving vector quantization (VQ) and the discrete wavelet transform (DWT) is applied to two different types of multidimensional digital earth-science data. The algorithms (WVQ) is optimized for each particular application through an optimization procedure that assigns VQ parameters to the wavelet transform subbands subject to constraints on compression ratio and encoding complexity. Preliminary results of compressing global ocean model data generated on a Thinking Machines CM-200 supercomputer are presented. The WVQ scheme is used in both a predictive and nonpredictive mode. Parameters generated by the optimization algorithm are reported, as are signal-to-noise (SNR) measurements of actual quantized data. The problem of extrapolating hydrodynamic variables across the continental landmasses in order to compute the DWT on a rectangular grid is discussed. Results are also presented for compressing Landsat TM 7-band data using the WVQ scheme. The formulation of the optimization problem is presented along with SNR measurements of actual quantized data. Postprocessing applications are considered in which the seven spectral bands are clustered into 256 clusters using a k-means algorithm and analyzed using the Los Alamos multispectral data analysis program, SPECTRUM, both before and after being compressed using the WVQ program.

A hybrid data compression approach for online backup service

NASA Astrophysics Data System (ADS)

Wang, Hua; Zhou, Ke; Qin, MingKang

2009-08-01

With the popularity of Saas (Software as a service), backup service has becoming a hot topic of storage application. Due to the numerous backup users, how to reduce the massive data load is a key problem for system designer. Data compression provides a good solution. Traditional data compression application used to adopt a single method, which has limitations in some respects. For example data stream compression can only realize intra-file compression, de-duplication is used to eliminate inter-file redundant data, compression efficiency cannot meet the need of backup service software. This paper proposes a novel hybrid compression approach, which includes two levels: global compression and block compression. The former can eliminate redundant inter-file copies across different users, the latter adopts data stream compression technology to realize intra-file de-duplication. Several compressing algorithms were adopted to measure the compression ratio and CPU time. Adaptability using different algorithm in certain situation is also analyzed. The performance analysis shows that great improvement is made through the hybrid compression policy.

A novel pulse compression algorithm for frequency modulated active thermography using band-pass filter

NASA Astrophysics Data System (ADS)

Chatterjee, Krishnendu; Roy, Deboshree; Tuli, Suneet

2017-05-01

This paper proposes a novel pulse compression algorithm, in the context of frequency modulated thermal wave imaging. The compression filter is derived from a predefined reference pixel in a recorded video, which contains direct measurement of the excitation signal alongside the thermal image of a test piece. The filter causes all the phases of the constituent frequencies to be adjusted to nearly zero value, so that on reconstruction a pulse is obtained. Further, due to band-limited nature of the excitation, signal-to-noise ratio is improved by suppressing out-of-band noise. The result is similar to that of a pulsed thermography experiment, although the peak power is drastically reduced. The algorithm is successfully demonstrated on mild steel and carbon fibre reference samples. Objective comparisons of the proposed pulse compression algorithm with the existing techniques are presented.

WE-E-17A-06: Assessing the Scale of Tumor Heterogeneity by Complete Hierarchical Segmentation On MRI

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gensheimer, M; Trister, A; Ermoian, R

2014-06-15

Purpose: In many cancers, intratumoral heterogeneity exists in vascular and genetic structure. We developed an algorithm which uses clinical imaging to interrogate different scales of heterogeneity. We hypothesize that heterogeneity of perfusion at large distance scales may correlate with propensity for disease recurrence. We applied the algorithm to initial diagnosis MRI of rhabdomyosarcoma patients to predict recurrence. Methods: The Spatial Heterogeneity Analysis by Recursive Partitioning (SHARP) algorithm recursively segments the tumor image. The tumor is repeatedly subdivided, with each dividing line chosen to maximize signal intensity difference between the two subregions. This process continues to the voxel level, producing segmentsmore » at multiple scales. Heterogeneity is measured by comparing signal intensity histograms between each segmented region and the adjacent region. We measured the scales of contrast enhancement heterogeneity of the primary tumor in 18 rhabdomyosarcoma patients. Using Cox proportional hazards regression, we explored the influence of heterogeneity parameters on relapse-free survival (RFS). To compare with existing methods, fractal and Haralick texture features were also calculated. Results: The complete segmentation produced by SHARP allows extraction of diverse features, including the amount of heterogeneity at various distance scales, the area of the tumor with the most heterogeneity at each scale, and for a given point in the tumor, the heterogeneity at different scales. 10/18 rhabdomyosarcoma patients suffered disease recurrence. On contrast-enhanced MRI, larger scale of maximum signal intensity heterogeneity, relative to tumor diameter, predicted for shorter RFS (p=0.05). Fractal dimension, fractal fit, and three Haralick features did not predict RFS (p=0.09-0.90). Conclusion: SHARP produces an automatic segmentation of tumor regions and reports the amount of heterogeneity at various distance scales. In rhabdomyosarcoma, RFS was shorter when the primary tumor exhibited larger scale of heterogeneity on contrast-enhanced MRI. If validated on a larger dataset, this imaging biomarker could be useful to help personalize treatment.« less

Simulating compressible-incompressible two-phase flows

NASA Astrophysics Data System (ADS)

Denner, Fabian; van Wachem, Berend

2017-11-01

Simulating compressible gas-liquid flows, e.g. air-water flows, presents considerable numerical issues and requires substantial computational resources, particularly because of the stiff equation of state for the liquid and the different Mach number regimes. Treating the liquid phase (low Mach number) as incompressible, yet concurrently considering the gas phase (high Mach number) as compressible, can improve the computational performance of such simulations significantly without sacrificing important physical mechanisms. A pressure-based algorithm for the simulation of two-phase flows is presented, in which a compressible and an incompressible fluid are separated by a sharp interface. The algorithm is based on a coupled finite-volume framework, discretised in conservative form, with a compressive VOF method to represent the interface. The bulk phases are coupled via a novel acoustically-conservative interface discretisation method that retains the acoustic properties of the compressible phase and does not require a Riemann solver. Representative test cases are presented to scrutinize the proposed algorithm, including the reflection of acoustic waves at the compressible-incompressible interface, shock-drop interaction and gas-liquid flows with surface tension. Financial support from the EPSRC (Grant EP/M021556/1) is gratefully acknowledged.

Toward a Better Compression for DNA Sequences Using Huffman Encoding

PubMed Central

Almarri, Badar; Al Yami, Sultan; Huang, Chun-Hsi

2017-01-01

Abstract Due to the significant amount of DNA data that are being generated by next-generation sequencing machines for genomes of lengths ranging from megabases to gigabases, there is an increasing need to compress such data to a less space and a faster transmission. Different implementations of Huffman encoding incorporating the characteristics of DNA sequences prove to better compress DNA data. These implementations center on the concepts of selecting frequent repeats so as to force a skewed Huffman tree, as well as the construction of multiple Huffman trees when encoding. The implementations demonstrate improvements on the compression ratios for five genomes with lengths ranging from 5 to 50 Mbp, compared with the standard Huffman tree algorithm. The research hence suggests an improvement on all such DNA sequence compression algorithms that use the conventional Huffman encoding. The research suggests an improvement on all DNA sequence compression algorithms that use the conventional Huffman encoding. Accompanying software is publicly available (AL-Okaily, 2016). PMID:27960065

Toward a Better Compression for DNA Sequences Using Huffman Encoding.

PubMed

Al-Okaily, Anas; Almarri, Badar; Al Yami, Sultan; Huang, Chun-Hsi

2017-04-01

Due to the significant amount of DNA data that are being generated by next-generation sequencing machines for genomes of lengths ranging from megabases to gigabases, there is an increasing need to compress such data to a less space and a faster transmission. Different implementations of Huffman encoding incorporating the characteristics of DNA sequences prove to better compress DNA data. These implementations center on the concepts of selecting frequent repeats so as to force a skewed Huffman tree, as well as the construction of multiple Huffman trees when encoding. The implementations demonstrate improvements on the compression ratios for five genomes with lengths ranging from 5 to 50 Mbp, compared with the standard Huffman tree algorithm. The research hence suggests an improvement on all such DNA sequence compression algorithms that use the conventional Huffman encoding. The research suggests an improvement on all DNA sequence compression algorithms that use the conventional Huffman encoding. Accompanying software is publicly available (AL-Okaily, 2016 ).

Use of zerotree coding in a high-speed pyramid image multiresolution decomposition

NASA Astrophysics Data System (ADS)

Vega-Pineda, Javier; Cabrera, Sergio D.; Lucero, Aldo

1995-03-01

A Zerotree (ZT) coding scheme is applied as a post-processing stage to avoid transmitting zero data in the High-Speed Pyramid (HSP) image compression algorithm. This algorithm has features that increase the capability of the ZT coding to give very high compression rates. In this paper the impact of the ZT coding scheme is analyzed and quantified. The HSP algorithm creates a discrete-time multiresolution analysis based on a hierarchical decomposition technique that is a subsampling pyramid. The filters used to create the image residues and expansions can be related to wavelet representations. According to the pixel coordinates and the level in the pyramid, N2 different wavelet basis functions of various sizes and rotations are linearly combined. The HSP algorithm is computationally efficient because of the simplicity of the required operations, and as a consequence, it can be very easily implemented with VLSI hardware. This is the HSP's principal advantage over other compression schemes. The ZT coding technique transforms the different quantized image residual levels created by the HSP algorithm into a bit stream. The use of ZT's compresses even further the already compressed image taking advantage of parent-child relationships (trees) between the pixels of the residue images at different levels of the pyramid. Zerotree coding uses the links between zeros along the hierarchical structure of the pyramid, to avoid transmission of those that form branches of all zeros. Compression performance and algorithm complexity of the combined HSP-ZT method are compared with those of the JPEG standard technique.

A new efficient method for color image compression based on visual attention mechanism

NASA Astrophysics Data System (ADS)

Shao, Xiaoguang; Gao, Kun; Lv, Lily; Ni, Guoqiang

2010-11-01

One of the key procedures in color image compression is to extract its region of interests (ROIs) and evaluate different compression ratios. A new non-uniform color image compression algorithm with high efficiency is proposed in this paper by using a biology-motivated selective attention model for the effective extraction of ROIs in natural images. When the ROIs have been extracted and labeled in the image, the subsequent work is to encode the ROIs and other regions with different compression ratios via popular JPEG algorithm. Furthermore, experiment results and quantitative and qualitative analysis in the paper show perfect performance when comparing with other traditional color image compression approaches.

Study on the Adsorption Phenomenon in Shale with the Combination of Molecular Dynamic Simulation and Fractal Analysis

NASA Astrophysics Data System (ADS)

Zhang, Liehui; Li, Jianchao; Jia, Du; Zhao, Yulong; Xie, Chunyu; Tao, Zhengwu

As one of the key status of gas in shale reservoir, adsorption gas accounts for considerable percentage of total gas amount. Due to the complexity and nanostructure of shale gas reservoir, it is very challenging to represent adsorption gas through traditional methods. However, the integration of the fractal theory and molecular dynamics (MD) simulation may provide a new perspective of understanding such nanostructure and the micro-phenomenon happening in it. The key purpose of this paper is to investigate the adsorption phenomenon in shale kerogen. By using MD simulation and grand canonical Monte Carlo (GCMC) algorithm, the adsorption of methane in 2, 5 and 10nm slit-like pores is simulated for different temperature and pressure status. According to the results, the average gas density in smaller pores is higher than that in bigger pores, and multilayer adsorption presents on some areas of pore surfaces. Then, the simulation results are analyzed using the multilayer fractal adsorption model. The analysis indicates that the number of adsorption layer increases with pressure increase: four-layer adsorption presents in 10nm pores while three-layer adsorption shows up in 2nm and 5nm pores due to pore volume limit. Fractal dimension of pore wall surface generated in this study is in the range of 2.31-2.63. Moreover, high temperature could decrease the adsorption behavior in reservoir condition.

Developing JSequitur to Study the Hierarchical Structure of Biological Sequences in a Grammatical Inference Framework of String Compression Algorithms.

PubMed

Galbadrakh, Bulgan; Lee, Kyung-Eun; Park, Hyun-Seok

2012-12-01

Grammatical inference methods are expected to find grammatical structures hidden in biological sequences. One hopes that studies of grammar serve as an appropriate tool for theory formation. Thus, we have developed JSequitur for automatically generating the grammatical structure of biological sequences in an inference framework of string compression algorithms. Our original motivation was to find any grammatical traits of several cancer genes that can be detected by string compression algorithms. Through this research, we could not find any meaningful unique traits of the cancer genes yet, but we could observe some interesting traits in regards to the relationship among gene length, similarity of sequences, the patterns of the generated grammar, and compression rate.

Authenticity examination of compressed audio recordings using detection of multiple compression and encoders' identification.

PubMed

Korycki, Rafal

2014-05-01

Since the appearance of digital audio recordings, audio authentication has been becoming increasingly difficult. The currently available technologies and free editing software allow a forger to cut or paste any single word without audible artifacts. Nowadays, the only method referring to digital audio files commonly approved by forensic experts is the ENF criterion. It consists in fluctuation analysis of the mains frequency induced in electronic circuits of recording devices. Therefore, its effectiveness is strictly dependent on the presence of mains signal in the recording, which is a rare occurrence. Recently, much attention has been paid to authenticity analysis of compressed multimedia files and several solutions were proposed for detection of double compression in both digital video and digital audio. This paper addresses the problem of tampering detection in compressed audio files and discusses new methods that can be used for authenticity analysis of digital recordings. Presented approaches consist in evaluation of statistical features extracted from the MDCT coefficients as well as other parameters that may be obtained from compressed audio files. Calculated feature vectors are used for training selected machine learning algorithms. The detection of multiple compression covers up tampering activities as well as identification of traces of montage in digital audio recordings. To enhance the methods' robustness an encoder identification algorithm was developed and applied based on analysis of inherent parameters of compression. The effectiveness of tampering detection algorithms is tested on a predefined large music database consisting of nearly one million of compressed audio files. The influence of compression algorithms' parameters on the classification performance is discussed, based on the results of the current study. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Multiple scaling power in liquid gallium under pressure conditions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Renfeng; Wang, Luhong; Li, Liangliang

Generally, a single scaling exponent, Df, can characterize the fractal structures of metallic glasses according to the scaling power law. However, when the scaling power law is applied to liquid gallium upon compression, the results show multiple scaling exponents and the values are beyond 3 within the first four coordination spheres in real space, indicating that the power law fails to describe the fractal feature in liquid gallium. The increase in the first coordination number with pressure leads to the fact that first coordination spheres at different pressures are not similar to each other in a geometrical sense. This multiplemore » scaling power behavior is confined within a correlation length of ξ ≈ 14–15 Å at applied pressure according to decay of G(r) in liquid gallium. Beyond this length the liquid gallium system could roughly be viewed as homogeneous, as indicated by the scaling exponent, Ds, which is close to 3 beyond the first four coordination spheres.« less

Multiscale Computer Simulation of Failure in Aerogels

NASA Technical Reports Server (NTRS)

Good, Brian S.

2008-01-01

Aerogels have been of interest to the aerospace community primarily for their thermal properties, notably their low thermal conductivities. While such gels are typically fragile, recent advances in the application of conformal polymer layers to these gels has made them potentially useful as lightweight structural materials as well. We have previously performed computer simulations of aerogel thermal conductivity and tensile and compressive failure, with results that are in qualitative, and sometimes quantitative, agreement with experiment. However, recent experiments in our laboratory suggest that gels having similar densities may exhibit substantially different properties. In this work, we extend our original diffusion limited cluster aggregation (DLCA) model for gel structure to incorporate additional variation in DLCA simulation parameters, with the aim of producing DLCA clusters of similar densities that nevertheless have different fractal dimension and secondary particle coordination. We perform particle statics simulations of gel strain on these clusters, and consider the effects of differing DLCA simulation conditions, and the resultant differences in fractal dimension and coordination, on gel strain properties.

Performance of target detection algorithm in compressive sensing miniature ultraspectral imaging compressed sensing system

NASA Astrophysics Data System (ADS)

Gedalin, Daniel; Oiknine, Yaniv; August, Isaac; Blumberg, Dan G.; Rotman, Stanley R.; Stern, Adrian

2017-04-01

Compressive sensing theory was proposed to deal with the high quantity of measurements demanded by traditional hyperspectral systems. Recently, a compressive spectral imaging technique dubbed compressive sensing miniature ultraspectral imaging (CS-MUSI) was presented. This system uses a voltage controlled liquid crystal device to create multiplexed hyperspectral cubes. We evaluate the utility of the data captured using the CS-MUSI system for the task of target detection. Specifically, we compare the performance of the matched filter target detection algorithm in traditional hyperspectral systems and in CS-MUSI multiplexed hyperspectral cubes. We found that the target detection algorithm performs similarly in both cases, despite the fact that the CS-MUSI data is up to an order of magnitude less than that in conventional hyperspectral cubes. Moreover, the target detection is approximately an order of magnitude faster in CS-MUSI data.

A block-based JPEG-LS compression technique with lossless region of interest

NASA Astrophysics Data System (ADS)

Deng, Lihua; Huang, Zhenghua; Yao, Shoukui

2018-03-01

JPEG-LS lossless compression algorithm is used in many specialized applications that emphasize on the attainment of high fidelity for its lower complexity and better compression ratios than the lossless JPEG standard. But it cannot prevent error diffusion because of the context dependence of the algorithm, and have low compression rate when compared to lossy compression. In this paper, we firstly divide the image into two parts: ROI regions and non-ROI regions. Then we adopt a block-based image compression technique to decrease the range of error diffusion. We provide JPEG-LS lossless compression for the image blocks which include the whole or part region of interest (ROI) and JPEG-LS near lossless compression for the image blocks which are included in the non-ROI (unimportant) regions. Finally, a set of experiments are designed to assess the effectiveness of the proposed compression method.

Audiovisual focus of attention and its application to Ultra High Definition video compression

NASA Astrophysics Data System (ADS)

Rerabek, Martin; Nemoto, Hiromi; Lee, Jong-Seok; Ebrahimi, Touradj

2014-02-01

Using Focus of Attention (FoA) as a perceptual process in image and video compression belongs to well-known approaches to increase coding efficiency. It has been shown that foveated coding, when compression quality varies across the image according to region of interest, is more efficient than the alternative coding, when all region are compressed in a similar way. However, widespread use of such foveated compression has been prevented due to two main conflicting causes, namely, the complexity and the efficiency of algorithms for FoA detection. One way around these is to use as much information as possible from the scene. Since most video sequences have an associated audio, and moreover, in many cases there is a correlation between the audio and the visual content, audiovisual FoA can improve efficiency of the detection algorithm while remaining of low complexity. This paper discusses a simple yet efficient audiovisual FoA algorithm based on correlation of dynamics between audio and video signal components. Results of audiovisual FoA detection algorithm are subsequently taken into account for foveated coding and compression. This approach is implemented into H.265/HEVC encoder producing a bitstream which is fully compliant to any H.265/HEVC decoder. The influence of audiovisual FoA in the perceived quality of high and ultra-high definition audiovisual sequences is explored and the amount of gain in compression efficiency is analyzed.

Design of a Lossless Image Compression System for Video Capsule Endoscopy and Its Performance in In-Vivo Trials

PubMed Central

Khan, Tareq H.; Wahid, Khan A.

2014-01-01

In this paper, a new low complexity and lossless image compression system for capsule endoscopy (CE) is presented. The compressor consists of a low-cost YEF color space converter and variable-length predictive with a combination of Golomb-Rice and unary encoding. All these components have been heavily optimized for low-power and low-cost and lossless in nature. As a result, the entire compression system does not incur any loss of image information. Unlike transform based algorithms, the compressor can be interfaced with commercial image sensors which send pixel data in raster-scan fashion that eliminates the need of having large buffer memory. The compression algorithm is capable to work with white light imaging (WLI) and narrow band imaging (NBI) with average compression ratio of 78% and 84% respectively. Finally, a complete capsule endoscopy system is developed on a single, low-power, 65-nm field programmable gate arrays (FPGA) chip. The prototype is developed using circular PCBs having a diameter of 16 mm. Several in-vivo and ex-vivo trials using pig's intestine have been conducted using the prototype to validate the performance of the proposed lossless compression algorithm. The results show that, compared with all other existing works, the proposed algorithm offers a solution to wireless capsule endoscopy with lossless and yet acceptable level of compression. PMID:25375753

VLSI Architectures and CAD

DTIC Science & Technology

1989-04-01

existing types of data compression methods amenable to our needs: Huffman, Arithmetic, BSTW, and Lempel - Ziv . The two algorithms with the most modest...APEX architecture. Recently we bega-, investigating various data compression algorithms with character- istics amenable to hardware implementation...This work has so far yielded a variant of the Lempel - Ziv algorithm that adapts continuously to its input and is appropriate to a hardware implementation

Neural network for image compression

NASA Astrophysics Data System (ADS)

Panchanathan, Sethuraman; Yeap, Tet H.; Pilache, B.

1992-09-01

In this paper, we propose a new scheme for image compression using neural networks. Image data compression deals with minimization of the amount of data required to represent an image while maintaining an acceptable quality. Several image compression techniques have been developed in recent years. We note that the coding performance of these techniques may be improved by employing adaptivity. Over the last few years neural network has emerged as an effective tool for solving a wide range of problems involving adaptivity and learning. A multilayer feed-forward neural network trained using the backward error propagation algorithm is used in many applications. However, this model is not suitable for image compression because of its poor coding performance. Recently, a self-organizing feature map (SOFM) algorithm has been proposed which yields a good coding performance. However, this algorithm requires a long training time because the network starts with random initial weights. In this paper we have used the backward error propagation algorithm (BEP) to quickly obtain the initial weights which are then used to speedup the training time required by the SOFM algorithm. The proposed approach (BEP-SOFM) combines the advantages of the two techniques and, hence, achieves a good coding performance in a shorter training time. Our simulation results demonstrate the potential gains using the proposed technique.

A Two-Stage Reconstruction Processor for Human Detection in Compressive Sensing CMOS Radar.

PubMed

Tsao, Kuei-Chi; Lee, Ling; Chu, Ta-Shun; Huang, Yuan-Hao

2018-04-05

Complementary metal-oxide-semiconductor (CMOS) radar has recently gained much research attraction because small and low-power CMOS devices are very suitable for deploying sensing nodes in a low-power wireless sensing system. This study focuses on the signal processing of a wireless CMOS impulse radar system that can detect humans and objects in the home-care internet-of-things sensing system. The challenges of low-power CMOS radar systems are the weakness of human signals and the high computational complexity of the target detection algorithm. The compressive sensing-based detection algorithm can relax the computational costs by avoiding the utilization of matched filters and reducing the analog-to-digital converter bandwidth requirement. The orthogonal matching pursuit (OMP) is one of the popular signal reconstruction algorithms for compressive sensing radar; however, the complexity is still very high because the high resolution of human respiration leads to high-dimension signal reconstruction. Thus, this paper proposes a two-stage reconstruction algorithm for compressive sensing radar. The proposed algorithm not only has lower complexity than the OMP algorithm by 75% but also achieves better positioning performance than the OMP algorithm especially in noisy environments. This study also designed and implemented the algorithm by using Vertex-7 FPGA chip (Xilinx, San Jose, CA, USA). The proposed reconstruction processor can support the 256 × 13 real-time radar image display with a throughput of 28.2 frames per second.

Applications of data compression techniques in modal analysis for on-orbit system identification

NASA Technical Reports Server (NTRS)

Carlin, Robert A.; Saggio, Frank; Garcia, Ephrahim

1992-01-01

Data compression techniques have been investigated for use with modal analysis applications. A redundancy-reduction algorithm was used to compress frequency response functions (FRFs) in order to reduce the amount of disk space necessary to store the data and/or save time in processing it. Tests were performed for both single- and multiple-degree-of-freedom (SDOF and MDOF, respectively) systems, with varying amounts of noise. Analysis was done on both the compressed and uncompressed FRFs using an SDOF Nyquist curve fit as well as the Eigensystem Realization Algorithm. Significant savings were realized with minimal errors incurred by the compression process.

Optimization of calcium carbonate content on synthesis of aluminum foam and its compressive strength characteristic

NASA Astrophysics Data System (ADS)

Sutarno, Nugraha, Bagja; Kusharjanto

2017-01-01

One of the most important characteristic of aluminum foam is compressive strength, which is reflected by its impact energy and Young's modulus. In the present research, optimization of calcium carbonate (CaCO3) content in the synthesized aluminum foam in order to obtain the highest compressive strength was carried out. The results of this study will be used to determine the CaCO3 content synthesis process parameter in pilot plant scale production of an aluminum foam. The experiment was performed by varying the concentration of calcium carbonate content, which was used as foaming agent, at constant alumina concentration (1.5 wt%), which was added as stabilizer, and temperature (725°C). It was found that 4 wt% CaCO3 gave the lowest relative density, which was 0.15, and the highest porosity, which was 85.29%, and compressive strength of as high as 0.26 Mpa. The pore morphology of the obtained aluminum foam at such condition was as follow: the average pore diameter was 4.42 mm, the wall thickness minimum of the pore was 83.24 µm, roundness of the pore was 0.91. Based on the fractal porosity, the compressive strength was inversely proportional to the porosity and huddled on a power law value of 2.91.

Application of Fractal Geometry in Evaluation of Effective Stimulated Reservoir Volume in Shale Gas Reservoirs

NASA Astrophysics Data System (ADS)

Sheng, Guanglong; Su, Yuliang; Wang, Wendong; Javadpour, Farzam; Tang, Meirong

According to hydraulic-fracturing practices conducted in shale reservoirs, effective stimulated reservoir volume (ESRV) significantly affects the production of hydraulic fractured well. Therefore, estimating ESRV is an important prerequisite for confirming the success of hydraulic fracturing and predicting the production of hydraulic fracturing wells in shale reservoirs. However, ESRV calculation remains a longstanding challenge in hydraulic-fracturing operation. In considering fractal characteristics of the fracture network in stimulated reservoir volume (SRV), this paper introduces a fractal random-fracture-network algorithm for converting the microseismic data into fractal geometry. Five key parameters, including bifurcation direction, generating length (d), deviation angle (α), iteration times (N) and generating rules, are proposed to quantitatively characterize fracture geometry. Furthermore, we introduce an orthogonal-fractures coupled dual-porosity-media representation elementary volume (REV) flow model to predict the volumetric flux of gas in shale reservoirs. On the basis of the migration of adsorbed gas in porous kerogen of REV with different fracture spaces, an ESRV criterion for shale reservoirs with SRV is proposed. Eventually, combining the ESRV criterion and fractal characteristic of a fracture network, we propose a new approach for evaluating ESRV in shale reservoirs. The approach has been used in the Eagle Ford shale gas reservoir, and results show that the fracture space has a measurable influence on migration of adsorbed gas. The fracture network can contribute to enhancement of the absorbed gas recovery ratio when the fracture space is less than 0.2 m. ESRV is evaluated in this paper, and results indicate that the ESRV accounts for 27.87% of the total SRV in shale gas reservoirs. This work is important and timely for evaluating fracturing effect and predicting production of hydraulic fracturing wells in shale reservoirs.

Locating Encrypted Data Hidden Among Non-Encrypted Data Using Statistical Tools

DTIC Science & Technology

2007-03-01

length of a compressed sequence). If a bit sequence can be significantly compressed , then it is not random. Lempel - Ziv Compression Test This test...communication, targeting, and a host other of tasks. This software will most assuredly contain classified data or algorithms requiring protection in...containing the classified data and algorithms . As the program is executed the solider would have access to the common unclassified tasks, however, to

Multiscale Computer Simulation of Tensile and Compressive Strain in Polymer- Coated Silica Aerogels

NASA Technical Reports Server (NTRS)

Good, Brian

2009-01-01

While the low thermal conductivities of silica aerogels have made them of interest to the aerospace community as lightweight thermal insulation, the application of conformal polymer coatings to these gels increases their strength significantly, making them potentially useful as structural materials as well. In this work we perform multiscale computer simulations to investigate the tensile and compressive strain behavior of silica and polymer-coated silica aerogels. Aerogels are made up of clusters of interconnected particles of amorphous silica of less than bulk density. We simulate gel nanostructure using a Diffusion Limited Cluster Aggregation (DLCA) procedure, which produces aggregates that exhibit fractal dimensions similar to those observed in real aerogels. We have previously found that model gels obtained via DLCA exhibited stress-strain curves characteristic of the experimentally observed brittle failure. However, the strain energetics near the expected point of failure were not consistent with such failure. This shortcoming may be due to the fact that the DLCA process produces model gels that are lacking in closed-loop substructures, compared with real gels. Our model gels therefore contain an excess of dangling strands, which tend to unravel under tensile strain, producing non-brittle failure. To address this problem, we have incorporated a modification to the DLCA algorithm that specifically produces closed loops in the model gels. We obtain the strain energetics of interparticle connections via atomistic molecular statics, and abstract the collective energy of the atomic bonds into a Morse potential scaled to describe gel particle interactions. Polymer coatings are similarly described. We apply repeated small uniaxial strains to DLCA clusters, and allow relaxation of the center eighty percent of the cluster between strains. The simulations produce energetics and stress-strain curves for looped and nonlooped clusters, for a variety of densities and interaction parameters.

Unsupervised segmentation of lung fields in chest radiographs using multiresolution fractal feature vector and deformable models.

PubMed

Lee, Wen-Li; Chang, Koyin; Hsieh, Kai-Sheng

2016-09-01

Segmenting lung fields in a chest radiograph is essential for automatically analyzing an image. We present an unsupervised method based on multiresolution fractal feature vector. The feature vector characterizes the lung field region effectively. A fuzzy c-means clustering algorithm is then applied to obtain a satisfactory initial contour. The final contour is obtained by deformable models. The results show the feasibility and high performance of the proposed method. Furthermore, based on the segmentation of lung fields, the cardiothoracic ratio (CTR) can be measured. The CTR is a simple index for evaluating cardiac hypertrophy. After identifying a suspicious symptom based on the estimated CTR, a physician can suggest that the patient undergoes additional extensive tests before a treatment plan is finalized.

A NEW LOG EVALUATION METHOD TO APPRAISE MESAVERDE RE-COMPLETION OPPORTUNITIES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Albert Greer

2003-09-11

Artificial intelligence tools, fuzzy logic and neural networks were used to evaluate the potential of the behind pipe Mesaverde formation in BMG's Mancos formation wells. A fractal geostatistical mapping algorithm was also used to predict Mesaverde production. Additionally, a conventional geological study was conducted. To date one Mesaverde completion has been performed. The Janet No.3 Mesaverde completion was non-economic. Both the AI method and the geostatistical methods predicted the failure of the Janet No.3. The Gavilan No.1 in the Mesaverde was completed during the course of the study and was an extremely good well. This well was not included inmore » the statistical dataset. The AI method predicted very good production while the fractal map predicted a poor producer.« less

Compressible Flow Toolbox

NASA Technical Reports Server (NTRS)

Melcher, Kevin J.

2006-01-01

The Compressible Flow Toolbox is primarily a MATLAB-language implementation of a set of algorithms that solve approximately 280 linear and nonlinear classical equations for compressible flow. The toolbox is useful for analysis of one-dimensional steady flow with either constant entropy, friction, heat transfer, or Mach number greater than 1. The toolbox also contains algorithms for comparing and validating the equation-solving algorithms against solutions previously published in open literature. The classical equations solved by the Compressible Flow Toolbox are as follows: The isentropic-flow equations, The Fanno flow equations (pertaining to flow of an ideal gas in a pipe with friction), The Rayleigh flow equations (pertaining to frictionless flow of an ideal gas, with heat transfer, in a pipe of constant cross section), The normal-shock equations, The oblique-shock equations, and The expansion equations.

Light-weight reference-based compression of FASTQ data.

PubMed

Zhang, Yongpeng; Li, Linsen; Yang, Yanli; Yang, Xiao; He, Shan; Zhu, Zexuan

2015-06-09

The exponential growth of next generation sequencing (NGS) data has posed big challenges to data storage, management and archive. Data compression is one of the effective solutions, where reference-based compression strategies can typically achieve superior compression ratios compared to the ones not relying on any reference. This paper presents a lossless light-weight reference-based compression algorithm namely LW-FQZip to compress FASTQ data. The three components of any given input, i.e., metadata, short reads and quality score strings, are first parsed into three data streams in which the redundancy information are identified and eliminated independently. Particularly, well-designed incremental and run-length-limited encoding schemes are utilized to compress the metadata and quality score streams, respectively. To handle the short reads, LW-FQZip uses a novel light-weight mapping model to fast map them against external reference sequence(s) and produce concise alignment results for storage. The three processed data streams are then packed together with some general purpose compression algorithms like LZMA. LW-FQZip was evaluated on eight real-world NGS data sets and achieved compression ratios in the range of 0.111-0.201. This is comparable or superior to other state-of-the-art lossless NGS data compression algorithms. LW-FQZip is a program that enables efficient lossless FASTQ data compression. It contributes to the state of art applications for NGS data storage and transmission. LW-FQZip is freely available online at: http://csse.szu.edu.cn/staff/zhuzx/LWFQZip.

Observation Uncertainty in Gaussian Sensor Networks

DTIC Science & Technology

2006-01-23

Ziv , J., and Lempel , A. A universal algorithm for sequential data compression . IEEE Transactions on Information Theory 23, 3 (1977), 337–343. 73 ...using the Lempel - Ziv algorithm [42], context-tree weighting [41], or the Burrows-Wheeler Trans- form [4], [15], for example. These source codes will...and Computation (Monticello, IL, September 2004). [4] Burrows, M., and Wheeler, D. A block sorting lossless data compression algorithm . Tech.

Comparative performance between compressed and uncompressed airborne imagery

NASA Astrophysics Data System (ADS)

Phan, Chung; Rupp, Ronald; Agarwal, Sanjeev; Trang, Anh; Nair, Sumesh

2008-04-01

The US Army's RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD), Countermine Division is evaluating the compressibility of airborne multi-spectral imagery for mine and minefield detection application. Of particular interest is to assess the highest image data compression rate that can be afforded without the loss of image quality for war fighters in the loop and performance of near real time mine detection algorithm. The JPEG-2000 compression standard is used to perform data compression. Both lossless and lossy compressions are considered. A multi-spectral anomaly detector such as RX (Reed & Xiaoli), which is widely used as a core algorithm baseline in airborne mine and minefield detection on different mine types, minefields, and terrains to identify potential individual targets, is used to compare the mine detection performance. This paper presents the compression scheme and compares detection performance results between compressed and uncompressed imagery for various level of compressions. The compression efficiency is evaluated and its dependence upon different backgrounds and other factors are documented and presented using multi-spectral data.

Algorithmic commonalities in the parallel environment

NASA Technical Reports Server (NTRS)

Mcanulty, Michael A.; Wainer, Michael S.

1987-01-01

The ultimate aim of this project was to analyze procedures from substantially different application areas to discover what is either common or peculiar in the process of conversion to the Massively Parallel Processor (MPP). Three areas were identified: molecular dynamic simulation, production systems (rule systems), and various graphics and vision algorithms. To date, only selected graphics procedures have been investigated. They are the most readily available, and produce the most visible results. These include simple polygon patch rendering, raycasting against a constructive solid geometric model, and stochastic or fractal based textured surface algorithms. Only the simplest of conversion strategies, mapping a major loop to the array, has been investigated so far. It is not entirely satisfactory.

Lossless compression of VLSI layout image data.

PubMed

Dai, Vito; Zakhor, Avideh

2006-09-01

We present a novel lossless compression algorithm called Context Copy Combinatorial Code (C4), which integrates the advantages of two very disparate compression techniques: context-based modeling and Lempel-Ziv (LZ) style copying. While the algorithm can be applied to many lossless compression applications, such as document image compression, our primary target application has been lossless compression of integrated circuit layout image data. These images contain a heterogeneous mix of data: dense repetitive data better suited to LZ-style coding, and less dense structured data, better suited to context-based encoding. As part of C4, we have developed a novel binary entropy coding technique called combinatorial coding which is simultaneously as efficient as arithmetic coding, and as fast as Huffman coding. Compression results show C4 outperforms JBIG, ZIP, BZIP2, and two-dimensional LZ, and achieves lossless compression ratios greater than 22 for binary layout image data, and greater than 14 for gray-pixel image data.

Bitshuffle: Filter for improving compression of typed binary data

NASA Astrophysics Data System (ADS)

Masui, Kiyoshi

2017-12-01

Bitshuffle rearranges typed, binary data for improving compression; the algorithm is implemented in a python/C package within the Numpy framework. The library can be used alongside HDF5 to compress and decompress datasets and is integrated through the dynamically loaded filters framework. Algorithmically, Bitshuffle is closely related to HDF5's Shuffle filter except it operates at the bit level instead of the byte level. Arranging a typed data array in to a matrix with the elements as the rows and the bits within the elements as the columns, Bitshuffle "transposes" the matrix, such that all the least-significant-bits are in a row, etc. This transposition is performed within blocks of data roughly 8kB long; this does not in itself compress data, but rearranges it for more efficient compression. A compression library is necessary to perform the actual compression. This scheme has been used for compression of radio data in high performance computing.

Wireless EEG System Achieving High Throughput and Reduced Energy Consumption Through Lossless and Near-Lossless Compression.

PubMed

Alvarez, Guillermo Dufort Y; Favaro, Federico; Lecumberry, Federico; Martin, Alvaro; Oliver, Juan P; Oreggioni, Julian; Ramirez, Ignacio; Seroussi, Gadiel; Steinfeld, Leonardo

2018-02-01

This work presents a wireless multichannel electroencephalogram (EEG) recording system featuring lossless and near-lossless compression of the digitized EEG signal. Two novel, low-complexity, efficient compression algorithms were developed and tested in a low-power platform. The algorithms were tested on six public EEG databases comparing favorably with the best compression rates reported up to date in the literature. In its lossless mode, the platform is capable of encoding and transmitting 59-channel EEG signals, sampled at 500 Hz and 16 bits per sample, at a current consumption of 337 A per channel; this comes with a guarantee that the decompressed signal is identical to the sampled one. The near-lossless mode allows for significant energy savings and/or higher throughputs in exchange for a small guaranteed maximum per-sample distortion in the recovered signal. Finally, we address the tradeoff between computation cost and transmission savings by evaluating three alternatives: sending raw data, or encoding with one of two compression algorithms that differ in complexity and compression performance. We observe that the higher the throughput (number of channels and sampling rate) the larger the benefits obtained from compression.

The Unsupervised Acquisition of a Lexicon from Continuous Speech.

DTIC Science & Technology

1995-11-01

Com- munication, 2(1):57{89, 1982. [42] J. Ziv and A. Lempel . Compression of individual sequences by variable rate coding. IEEE Trans- actions on...parameters of the compression algorithm , in a never-ending attempt to identify and eliminate the predictable. They lead us to a class of grammars in...the rst 10 sentences of the test set, previously unseen by the algorithm . Vertical bars indicate word boundaries. 7.1 Text Compression and Language

Detecting double compressed MPEG videos with the same quantization matrix and synchronized group of pictures structure

NASA Astrophysics Data System (ADS)

Aghamaleki, Javad Abbasi; Behrad, Alireza

2018-01-01

Double compression detection is a crucial stage in digital image and video forensics. However, the detection of double compressed videos is challenging when the video forger uses the same quantization matrix and synchronized group of pictures (GOP) structure during the recompression history to conceal tampering effects. A passive approach is proposed for detecting double compressed MPEG videos with the same quantization matrix and synchronized GOP structure. To devise the proposed algorithm, the effects of recompression on P frames are mathematically studied. Then, based on the obtained guidelines, a feature vector is proposed to detect double compressed frames on the GOP level. Subsequently, sparse representations of the feature vectors are used for dimensionality reduction and enrich the traces of recompression. Finally, a support vector machine classifier is employed to detect and localize double compression in temporal domain. The experimental results show that the proposed algorithm achieves the accuracy of more than 95%. In addition, the comparisons of the results of the proposed method with those of other methods reveal the efficiency of the proposed algorithm.

Lossless compression algorithm for multispectral imagers

NASA Astrophysics Data System (ADS)

Gladkova, Irina; Grossberg, Michael; Gottipati, Srikanth

2008-08-01

Multispectral imaging is becoming an increasingly important tool for monitoring the earth and its environment from space borne and airborne platforms. Multispectral imaging data consists of visible and IR measurements from a scene across space and spectrum. Growing data rates resulting from faster scanning and finer spatial and spectral resolution makes compression an increasingly critical tool to reduce data volume for transmission and archiving. Research for NOAA NESDIS has been directed to finding for the characteristics of satellite atmospheric Earth science Imager sensor data what level of Lossless compression ratio can be obtained as well as appropriate types of mathematics and approaches that can lead to approaching this data's entropy level. Conventional lossless do not achieve the theoretical limits for lossless compression on imager data as estimated from the Shannon entropy. In a previous paper, the authors introduce a lossless compression algorithm developed for MODIS as a proxy for future NOAA-NESDIS satellite based Earth science multispectral imagers such as GOES-R. The algorithm is based on capturing spectral correlations using spectral prediction, and spatial correlations with a linear transform encoder. In decompression, the algorithm uses a statistically computed look up table to iteratively predict each channel from a channel decompressed in the previous iteration. In this paper we present a new approach which fundamentally differs from our prior work. In this new approach, instead of having a single predictor for each pair of bands we introduce a piecewise spatially varying predictor which significantly improves the compression results. Our new algorithm also now optimizes the sequence of channels we use for prediction. Our results are evaluated by comparison with a state of the art wavelet based image compression scheme, Jpeg2000. We present results on the 14 channel subset of the MODIS imager, which serves as a proxy for the GOES-R imager. We will also show results of the algorithm for on NOAA AVHRR data and data from SEVIRI. The algorithm is designed to be adapted to the wide range of multispectral imagers and should facilitate distribution of data throughout globally. This compression research is managed by Roger Heymann, PE of OSD NOAA NESDIS Engineering, in collaboration with the NOAA NESDIS STAR Research Office through Mitch Goldberg, Tim Schmit, Walter Wolf.

Beyond multi-fractals: surrogate time series and fields

NASA Astrophysics Data System (ADS)

Venema, V.; Simmer, C.

2007-12-01

Most natural complex are characterised by variability on a large range of temporal and spatial scales. The two main methodologies to generate such structures are Fourier/FARIMA based algorithms and multifractal methods. The former is restricted to Gaussian data, whereas the latter requires the structure to be self-similar. This work will present so-called surrogate data as an alternative that works with any (empirical) distribution and power spectrum. The best-known surrogate algorithm is the iterative amplitude adjusted Fourier transform (IAAFT) algorithm. We have studied six different geophysical time series (two clouds, runoff of a small and a large river, temperature and rain) and their surrogates. The power spectra and consequently the 2nd order structure functions were replicated accurately. Even the fourth order structure function was more accurately reproduced by the surrogates as would be possible by a fractal method, because the measured structure deviated too strong from fractal scaling. Only in case of the daily rain sums a fractal method could have been more accurate. Just as Fourier and multifractal methods, the current surrogates are not able to model the asymmetric increment distributions observed for runoff, i.e., they cannot reproduce nonlinear dynamical processes that are asymmetric in time. Furthermore, we have found differences for the structure functions on small scales. Surrogate methods are especially valuable for empirical studies, because the time series and fields that are generated are able to mimic measured variables accurately. Our main application is radiative transfer through structured clouds. Like many geophysical fields, clouds can only be sampled sparsely, e.g. with in-situ airborne instruments. However, for radiative transfer calculations we need full 3-dimensional cloud fields. A first study relating the measured properties of the cloud droplets and the radiative properties of the cloud field by generating surrogate cloud fields yielded good results within the measurement error. A further test of the suitability of the surrogate clouds for radiative transfer is evaluated by comparing the radiative properties of model cloud fields of sparse cumulus and stratocumulus with their surrogate fields. The bias and root mean square error in various radiative properties is small and the deviations in the radiances and irradiances are not statistically significant, i.e. these deviations can be attributed to the Monte Carlo noise of the radiative transfer calculations. We compared these results with optical properties of synthetic clouds that have either the correct distribution (but no spatial correlations) or the correct power spectrum (but a Gaussian distribution). These clouds did show statistical significant deviations. For more information see: http://www.meteo.uni-bonn.de/venema/themes/surrogates/

Hybrid sparse blind deconvolution: an implementation of SOOT algorithm to real data

NASA Astrophysics Data System (ADS)

Pakmanesh, Parvaneh; Goudarzi, Alireza; Kourki, Meisam

2018-06-01

Getting information of seismic data depends on deconvolution as an important processing step; it provides the reflectivity series by signal compression. This compression can be obtained by removing the wavelet effects on the traces. The recently blind deconvolution has provided reliable performance for sparse signal recovery. In this study, two deconvolution methods have been implemented to the seismic data; the convolution of these methods provides a robust spiking deconvolution approach. This hybrid deconvolution is applied using the sparse deconvolution (MM algorithm) and the Smoothed-One-Over-Two algorithm (SOOT) in a chain. The MM algorithm is based on the minimization of the cost function defined by standards l1 and l2. After applying the two algorithms to the seismic data, the SOOT algorithm provided well-compressed data with a higher resolution than the MM algorithm. The SOOT algorithm requires initial values to be applied for real data, such as the wavelet coefficients and reflectivity series that can be achieved through the MM algorithm. The computational cost of the hybrid method is high, and it is necessary to be implemented on post-stack or pre-stack seismic data of complex structure regions.

Interband coding extension of the new lossless JPEG standard

NASA Astrophysics Data System (ADS)

Memon, Nasir D.; Wu, Xiaolin; Sippy, V.; Miller, G.

1997-01-01

Due to the perceived inadequacy of current standards for lossless image compression, the JPEG committee of the International Standards Organization (ISO) has been developing a new standard. A baseline algorithm, called JPEG-LS, has already been completed and is awaiting approval by national bodies. The JPEG-LS baseline algorithm despite being simple is surprisingly efficient, and provides compression performance that is within a few percent of the best and more sophisticated techniques reported in the literature. Extensive experimentations performed by the authors seem to indicate that an overall improvement by more than 10 percent in compression performance will be difficult to obtain even at the cost of great complexity; at least not with traditional approaches to lossless image compression. However, if we allow inter-band decorrelation and modeling in the baseline algorithm, nearly 30 percent improvement in compression gains for specific images in the test set become possible with a modest computational cost. In this paper we propose and investigate a few techniques for exploiting inter-band correlations in multi-band images. These techniques have been designed within the framework of the baseline algorithm, and require minimal changes to the basic architecture of the baseline, retaining its essential simplicity.

Fast and robust wavelet-based dynamic range compression and contrast enhancement model with color restoration

NASA Astrophysics Data System (ADS)

Unaldi, Numan; Asari, Vijayan K.; Rahman, Zia-ur

2009-05-01

Recently we proposed a wavelet-based dynamic range compression algorithm to improve the visual quality of digital images captured from high dynamic range scenes with non-uniform lighting conditions. The fast image enhancement algorithm that provides dynamic range compression, while preserving the local contrast and tonal rendition, is also a good candidate for real time video processing applications. Although the colors of the enhanced images produced by the proposed algorithm are consistent with the colors of the original image, the proposed algorithm fails to produce color constant results for some "pathological" scenes that have very strong spectral characteristics in a single band. The linear color restoration process is the main reason for this drawback. Hence, a different approach is required for the final color restoration process. In this paper the latest version of the proposed algorithm, which deals with this issue is presented. The results obtained by applying the algorithm to numerous natural images show strong robustness and high image quality.

High Performance Compression of Science Data

NASA Technical Reports Server (NTRS)

Storer, James A.; Carpentieri, Bruno; Cohn, Martin

1994-01-01

Two papers make up the body of this report. One presents a single-pass adaptive vector quantization algorithm that learns a codebook of variable size and shape entries; the authors present experiments on a set of test images showing that with no training or prior knowledge of the data, for a given fidelity, the compression achieved typically equals or exceeds that of the JPEG standard. The second paper addresses motion compensation, one of the most effective techniques used in interframe data compression. A parallel block-matching algorithm for estimating interframe displacement of blocks with minimum error is presented. The algorithm is designed for a simple parallel architecture to process video in real time.

Image compression evaluation for digital cinema: the case of Star Wars: Episode II

NASA Astrophysics Data System (ADS)

Schnuelle, David L.

2003-05-01

A program of evaluation of compression algorithms proposed for use in a digital cinema application is described and the results presented in general form. The work was intended to aid in the selection of a compression system to be used for the digital cinema release of Star Wars: Episode II, in May 2002. An additional goal was to provide feedback to the algorithm proponents on what parameters and performance levels the feature film industry is looking for in digital cinema compression. The primary conclusion of the test program is that any of the current digital cinema compression proponents will work for digital cinema distribution to today's theaters.

Generation new MP3 data set after compression

NASA Astrophysics Data System (ADS)

Atoum, Mohammed Salem; Almahameed, Mohammad

2016-02-01

The success of audio steganography techniques is to ensure imperceptibility of the embedded secret message in stego file and withstand any form of intentional or un-intentional degradation of secret message (robustness). Crucial to that using digital audio file such as MP3 file, which comes in different compression rate, however research studies have shown that performing steganography in MP3 format after compression is the most suitable one. Unfortunately until now the researchers can not test and implement their algorithm because no standard data set in MP3 file after compression is generated. So this paper focuses to generate standard data set with different compression ratio and different Genre to help researchers to implement their algorithms.

EVALUATION OF REGISTRATION, COMPRESSION AND CLASSIFICATION ALGORITHMS

NASA Technical Reports Server (NTRS)

Jayroe, R. R.

1994-01-01

Several types of algorithms are generally used to process digital imagery such as Landsat data. The most commonly used algorithms perform the task of registration, compression, and classification. Because there are different techniques available for performing registration, compression, and classification, imagery data users need a rationale for selecting a particular approach to meet their particular needs. This collection of registration, compression, and classification algorithms was developed so that different approaches could be evaluated and the best approach for a particular application determined. Routines are included for six registration algorithms, six compression algorithms, and two classification algorithms. The package also includes routines for evaluating the effects of processing on the image data. This collection of routines should be useful to anyone using or developing image processing software. Registration of image data involves the geometrical alteration of the imagery. Registration routines available in the evaluation package include image magnification, mapping functions, partitioning, map overlay, and data interpolation. The compression of image data involves reducing the volume of data needed for a given image. Compression routines available in the package include adaptive differential pulse code modulation, two-dimensional transforms, clustering, vector reduction, and picture segmentation. Classification of image data involves analyzing the uncompressed or compressed image data to produce inventories and maps of areas of similar spectral properties within a scene. The classification routines available include a sequential linear technique and a maximum likelihood technique. The choice of the appropriate evaluation criteria is quite important in evaluating the image processing functions. The user is therefore given a choice of evaluation criteria with which to investigate the available image processing functions. All of the available evaluation criteria basically compare the observed results with the expected results. For the image reconstruction processes of registration and compression, the expected results are usually the original data or some selected characteristics of the original data. For classification processes the expected result is the ground truth of the scene. Thus, the comparison process consists of determining what changes occur in processing, where the changes occur, how much change occurs, and the amplitude of the change. The package includes evaluation routines for performing such comparisons as average uncertainty, average information transfer, chi-square statistics, multidimensional histograms, and computation of contingency matrices. This collection of routines is written in FORTRAN IV for batch execution and has been implemented on an IBM 360 computer with a central memory requirement of approximately 662K of 8 bit bytes. This collection of image processing and evaluation routines was developed in 1979.

Quantized Overcomplete Expansions: Analysis, Synthesis and Algorithms

DTIC Science & Technology

1995-07-01

would be in the spirit of the Lempel - Ziv algorithm . The decoder would have to be aware of changes in the dictionary, but depending on the nature of the...37 3.4 A General Vector Compression Algorithm Based on Frames : : : : : : : : : : 40 ii 3.4.1 Design Considerations...x3.3. Along with exploring general properties of matching pursuit, we are interested in its application to compressing data vectors in RN. A general

A block-based algorithm for the solution of compressible flows in rotor-stator combinations

NASA Technical Reports Server (NTRS)

Akay, H. U.; Ecer, A.; Beskok, A.

1990-01-01

A block-based solution algorithm is developed for the solution of compressible flows in rotor-stator combinations. The method allows concurrent solution of multiple solution blocks in parallel machines. It also allows a time averaged interaction at the stator-rotor interfaces. Numerical results are presented to illustrate the performance of the algorithm. The effect of the interaction between the stator and rotor is evaluated.

Relation between the Hurst Exponent and the Efficiency of Self-organization of a Deformable System

NASA Astrophysics Data System (ADS)

Alfyorova, E. A.; Lychagin, D. V.

2018-04-01

We have established the degree of self-organization of a system under plastic deformation at different scale levels. Using fractal analysis, we have determined the Hurst exponent and correlation lengths in the region of formation of a corrugated (wrinkled) structure in [111] nickel single crystals under compression. This has made it possible to single out two (micro-and meso-) levels of self-organization in the deformable system. A qualitative relation between the values of the Hurst exponent and the stages of the stress-strain curve has been established.

A Two-Stage Reconstruction Processor for Human Detection in Compressive Sensing CMOS Radar

PubMed Central

Tsao, Kuei-Chi; Lee, Ling; Chu, Ta-Shun

2018-01-01

Complementary metal-oxide-semiconductor (CMOS) radar has recently gained much research attraction because small and low-power CMOS devices are very suitable for deploying sensing nodes in a low-power wireless sensing system. This study focuses on the signal processing of a wireless CMOS impulse radar system that can detect humans and objects in the home-care internet-of-things sensing system. The challenges of low-power CMOS radar systems are the weakness of human signals and the high computational complexity of the target detection algorithm. The compressive sensing-based detection algorithm can relax the computational costs by avoiding the utilization of matched filters and reducing the analog-to-digital converter bandwidth requirement. The orthogonal matching pursuit (OMP) is one of the popular signal reconstruction algorithms for compressive sensing radar; however, the complexity is still very high because the high resolution of human respiration leads to high-dimension signal reconstruction. Thus, this paper proposes a two-stage reconstruction algorithm for compressive sensing radar. The proposed algorithm not only has lower complexity than the OMP algorithm by 75% but also achieves better positioning performance than the OMP algorithm especially in noisy environments. This study also designed and implemented the algorithm by using Vertex-7 FPGA chip (Xilinx, San Jose, CA, USA). The proposed reconstruction processor can support the 256×13 real-time radar image display with a throughput of 28.2 frames per second. PMID:29621170

Learning Computer Programming: Implementing a Fractal in a Turing Machine

ERIC Educational Resources Information Center

Pereira, Hernane B. de B.; Zebende, Gilney F.; Moret, Marcelo A.

2010-01-01

It is common to start a course on computer programming logic by teaching the algorithm concept from the point of view of natural languages, but in a schematic way. In this sense we note that the students have difficulties in understanding and implementation of the problems proposed by the teacher. The main idea of this paper is to show that the…

Synthesis, Analysis, and Processing of Fractal Signals

DTIC Science & Technology

1991-10-01

coordinator in hockey, squash, volleyball, and softball, but also for reminding me periodically that 1/f noise can exist outside a computer. More...similar signals as Fourier-based representations are for stationary and periodic signals. Furthermore, because wave- let transformations can be...and periodic signals. Furthermore, just as the discovery of fast Fourier transform (FFT) algorithms dramatically increased the viability the Fourier

Compression embedding

DOEpatents

Sandford, M.T. II; Handel, T.G.; Bradley, J.N.

1998-07-07

A method and apparatus for embedding auxiliary information into the digital representation of host data created by a lossy compression technique and a method and apparatus for constructing auxiliary data from the correspondence between values in a digital key-pair table with integer index values existing in a representation of host data created by a lossy compression technique are disclosed. The methods apply to data compressed with algorithms based on series expansion, quantization to a finite number of symbols, and entropy coding. Lossy compression methods represent the original data as ordered sequences of blocks containing integer indices having redundancy and uncertainty of value by one unit, allowing indices which are adjacent in value to be manipulated to encode auxiliary data. Also included is a method to improve the efficiency of lossy compression algorithms by embedding white noise into the integer indices. Lossy compression methods use loss-less compression to reduce to the final size the intermediate representation as indices. The efficiency of the loss-less compression, known also as entropy coding compression, is increased by manipulating the indices at the intermediate stage. Manipulation of the intermediate representation improves lossy compression performance by 1 to 10%. 21 figs.

Compression embedding

DOEpatents

Sandford, II, Maxwell T.; Handel, Theodore G.; Bradley, Jonathan N.

1998-01-01

A method and apparatus for embedding auxiliary information into the digital representation of host data created by a lossy compression technique and a method and apparatus for constructing auxiliary data from the correspondence between values in a digital key-pair table with integer index values existing in a representation of host data created by a lossy compression technique. The methods apply to data compressed with algorithms based on series expansion, quantization to a finite number of symbols, and entropy coding. Lossy compression methods represent the original data as ordered sequences of blocks containing integer indices having redundancy and uncertainty of value by one unit, allowing indices which are adjacent in value to be manipulated to encode auxiliary data. Also included is a method to improve the efficiency of lossy compression algorithms by embedding white noise into the integer indices. Lossy compression methods use loss-less compression to reduce to the final size the intermediate representation as indices. The efficiency of the loss-less compression, known also as entropy coding compression, is increased by manipulating the indices at the intermediate stage. Manipulation of the intermediate representation improves lossy compression performance by 1 to 10%.

Domain-wall excitations in the two-dimensional Ising spin glass

NASA Astrophysics Data System (ADS)

Khoshbakht, Hamid; Weigel, Martin

2018-02-01

The Ising spin glass in two dimensions exhibits rich behavior with subtle differences in the scaling for different coupling distributions. We use recently developed mappings to graph-theoretic problems together with highly efficient implementations of combinatorial optimization algorithms to determine exact ground states for systems on square lattices with up to 10 000 ×10 000 spins. While these mappings only work for planar graphs, for example for systems with periodic boundary conditions in at most one direction, we suggest here an iterative windowing technique that allows one to determine ground states for fully periodic samples up to sizes similar to those for the open-periodic case. Based on these techniques, a large number of disorder samples are used together with a careful finite-size scaling analysis to determine the stiffness exponents and domain-wall fractal dimensions with unprecedented accuracy, our best estimates being θ =-0.2793 (3 ) and df=1.273 19 (9 ) for Gaussian couplings. For bimodal disorder, a new uniform sampling algorithm allows us to study the domain-wall fractal dimension, finding df=1.279 (2 ) . Additionally, we also investigate the distributions of ground-state energies, of domain-wall energies, and domain-wall lengths.

Real-time mental arithmetic task recognition from EEG signals.

PubMed

Wang, Qiang; Sourina, Olga

2013-03-01

Electroencephalography (EEG)-based monitoring the state of the user's brain functioning and giving her/him the visual/audio/tactile feedback is called neurofeedback technique, and it could allow the user to train the corresponding brain functions. It could provide an alternative way of treatment for some psychological disorders such as attention deficit hyperactivity disorder (ADHD), where concentration function deficit exists, autism spectrum disorder (ASD), or dyscalculia where the difficulty in learning and comprehending the arithmetic exists. In this paper, a novel method for multifractal analysis of EEG signals named generalized Higuchi fractal dimension spectrum (GHFDS) was proposed and applied in mental arithmetic task recognition from EEG signals. Other features such as power spectrum density (PSD), autoregressive model (AR), and statistical features were analyzed as well. The usage of the proposed fractal dimension spectrum of EEG signal in combination with other features improved the mental arithmetic task recognition accuracy in both multi-channel and one-channel subject-dependent algorithms up to 97.87% and 84.15% correspondingly. Based on the channel ranking, four channels were chosen which gave the accuracy up to 97.11%. Reliable real-time neurofeedback system could be implemented based on the algorithms proposed in this paper.

Compression of the Global Land 1-km AVHRR dataset

USGS Publications Warehouse

Kess, B. L.; Steinwand, D.R.; Reichenbach, S.E.

1996-01-01

Large datasets, such as the Global Land 1-km Advanced Very High Resolution Radiometer (AVHRR) Data Set (Eidenshink and Faundeen 1994), require compression methods that provide efficient storage and quick access to portions of the data. A method of lossless compression is described that provides multiresolution decompression within geographic subwindows of multi-spectral, global, 1-km, AVHRR images. The compression algorithm segments each image into blocks and compresses each block in a hierarchical format. Users can access the data by specifying either a geographic subwindow or the whole image and a resolution (1,2,4, 8, or 16 km). The Global Land 1-km AVHRR data are presented in the Interrupted Goode's Homolosine map projection. These images contain masked regions for non-land areas which comprise 80 per cent of the image. A quadtree algorithm is used to compress the masked regions. The compressed region data are stored separately from the compressed land data. Results show that the masked regions compress to 0·143 per cent of the bytes they occupy in the test image and the land areas are compressed to 33·2 per cent of their original size. The entire image is compressed hierarchically to 6·72 per cent of the original image size, reducing the data from 9·05 gigabytes to 623 megabytes. These results are compared to the first order entropy of the residual image produced with lossless Joint Photographic Experts Group predictors. Compression results are also given for Lempel-Ziv-Welch (LZW) and LZ77, the algorithms used by UNIX compress and GZIP respectively. In addition to providing multiresolution decompression of geographic subwindows of the data, the hierarchical approach and the use of quadtrees for storing the masked regions gives a marked improvement over these popular methods.

Toward an image compression algorithm for the high-resolution electronic still camera

NASA Technical Reports Server (NTRS)

Nerheim, Rosalee

1989-01-01

Taking pictures with a camera that uses a digital recording medium instead of film has the advantage of recording and transmitting images without the use of a darkroom or a courier. However, high-resolution images contain an enormous amount of information and strain data-storage systems. Image compression will allow multiple images to be stored in the High-Resolution Electronic Still Camera. The camera is under development at Johnson Space Center. Fidelity of the reproduced image and compression speed are of tantamount importance. Lossless compression algorithms are fast and faithfully reproduce the image, but their compression ratios will be unacceptably low due to noise in the front end of the camera. Future efforts will include exploring methods that will reduce the noise in the image and increase the compression ratio.

2D-RBUC for efficient parallel compression of residuals

NASA Astrophysics Data System (ADS)

Đurđević, Đorđe M.; Tartalja, Igor I.

2018-02-01

In this paper, we present a method for lossless compression of residuals with an efficient SIMD parallel decompression. The residuals originate from lossy or near lossless compression of height fields, which are commonly used to represent models of terrains. The algorithm is founded on the existing RBUC method for compression of non-uniform data sources. We have adapted the method to capture 2D spatial locality of height fields, and developed the data decompression algorithm for modern GPU architectures already present even in home computers. In combination with the point-level SIMD-parallel lossless/lossy high field compression method HFPaC, characterized by fast progressive decompression and seamlessly reconstructed surface, the newly proposed method trades off small efficiency degradation for a non negligible compression ratio (measured up to 91%) benefit.

ICER-3D Hyperspectral Image Compression Software

NASA Technical Reports Server (NTRS)

Xie, Hua; Kiely, Aaron; Klimesh, matthew; Aranki, Nazeeh

2010-01-01

Software has been developed to implement the ICER-3D algorithm. ICER-3D effects progressive, three-dimensional (3D), wavelet-based compression of hyperspectral images. If a compressed data stream is truncated, the progressive nature of the algorithm enables reconstruction of hyperspectral data at fidelity commensurate with the given data volume. The ICER-3D software is capable of providing either lossless or lossy compression, and incorporates an error-containment scheme to limit the effects of data loss during transmission. The compression algorithm, which was derived from the ICER image compression algorithm, includes wavelet-transform, context-modeling, and entropy coding subalgorithms. The 3D wavelet decomposition structure used by ICER-3D exploits correlations in all three dimensions of sets of hyperspectral image data, while facilitating elimination of spectral ringing artifacts, using a technique summarized in "Improving 3D Wavelet-Based Compression of Spectral Images" (NPO-41381), NASA Tech Briefs, Vol. 33, No. 3 (March 2009), page 7a. Correlation is further exploited by a context-modeling subalgorithm, which exploits spectral dependencies in the wavelet-transformed hyperspectral data, using an algorithm that is summarized in "Context Modeler for Wavelet Compression of Hyperspectral Images" (NPO-43239), which follows this article. An important feature of ICER-3D is a scheme for limiting the adverse effects of loss of data during transmission. In this scheme, as in the similar scheme used by ICER, the spatial-frequency domain is partitioned into rectangular error-containment regions. In ICER-3D, the partitions extend through all the wavelength bands. The data in each partition are compressed independently of those in the other partitions, so that loss or corruption of data from any partition does not affect the other partitions. Furthermore, because compression is progressive within each partition, when data are lost, any data from that partition received prior to the loss can be used to reconstruct that partition at lower fidelity. By virtue of the compression improvement it achieves relative to previous means of onboard data compression, this software enables (1) increased return of hyperspectral scientific data in the presence of limits on the rates of transmission of data from spacecraft to Earth via radio communication links and/or (2) reduction in spacecraft radio-communication power and/or cost through reduction in the amounts of data required to be downlinked and stored onboard prior to downlink. The software is also suitable for compressing hyperspectral images for ground storage or archival purposes.

Application of a Noise Adaptive Contrast Sensitivity Function to Image Data Compression

NASA Astrophysics Data System (ADS)

Daly, Scott J.

1989-08-01

The visual contrast sensitivity function (CSF) has found increasing use in image compression as new algorithms optimize the display-observer interface in order to reduce the bit rate and increase the perceived image quality. In most compression algorithms, increasing the quantization intervals reduces the bit rate at the expense of introducing more quantization error, a potential image quality degradation. The CSF can be used to distribute this error as a function of spatial frequency such that it is undetectable by the human observer. Thus, instead of being mathematically lossless, the compression algorithm can be designed to be visually lossless, with the advantage of a significantly reduced bit rate. However, the CSF is strongly affected by image noise, changing in both shape and peak sensitivity. This work describes a model of the CSF that includes these changes as a function of image noise level by using the concepts of internal visual noise, and tests this model in the context of image compression with an observer study.

CPAC: Energy-Efficient Data Collection through Adaptive Selection of Compression Algorithms for Sensor Networks

PubMed Central

Lee, HyungJune; Kim, HyunSeok; Chang, Ik Joon

2014-01-01

We propose a technique to optimize the energy efficiency of data collection in sensor networks by exploiting a selective data compression. To achieve such an aim, we need to make optimal decisions regarding two aspects: (1) which sensor nodes should execute compression; and (2) which compression algorithm should be used by the selected sensor nodes. We formulate this problem into binary integer programs, which provide an energy-optimal solution under the given latency constraint. Our simulation results show that the optimization algorithm significantly reduces the overall network-wide energy consumption for data collection. In the environment having a stationary sink from stationary sensor nodes, the optimized data collection shows 47% energy savings compared to the state-of-the-art collection protocol (CTP). More importantly, we demonstrate that our optimized data collection provides the best performance in an intermittent network under high interference. In such networks, we found that the selective compression for frequent packet retransmissions saves up to 55% energy compared to the best known protocol. PMID:24721763

User Guide for Compressible Flow Toolbox Version 2.1 for Use With MATLAB(Registered Trademark); Version 7

NASA Technical Reports Server (NTRS)

Melcher, Kevin J.

2006-01-01

This report provides a user guide for the Compressible Flow Toolbox, a collection of algorithms that solve almost 300 linear and nonlinear classical compressible flow relations. The algorithms, implemented in the popular MATLAB programming language, are useful for analysis of one-dimensional steady flow with constant entropy, friction, heat transfer, or shock discontinuities. The solutions do not include any gas dissociative effects. The toolbox also contains functions for comparing and validating the equation-solving algorithms against solutions previously published in the open literature. The classical equations solved by the Compressible Flow Toolbox are: isentropic-flow equations, Fanno flow equations (pertaining to flow of an ideal gas in a pipe with friction), Rayleigh flow equations (pertaining to frictionless flow of an ideal gas, with heat transfer, in a pipe of constant cross section.), normal-shock equations, oblique-shock equations, and Prandtl-Meyer expansion equations. At the time this report was published, the Compressible Flow Toolbox was available without cost from the NASA Software Repository.

GPU-accelerated algorithms for compressed signals recovery with application to astronomical imagery deblurring

NASA Astrophysics Data System (ADS)

Fiandrotti, Attilio; Fosson, Sophie M.; Ravazzi, Chiara; Magli, Enrico

2018-04-01

Compressive sensing promises to enable bandwidth-efficient on-board compression of astronomical data by lifting the encoding complexity from the source to the receiver. The signal is recovered off-line, exploiting GPUs parallel computation capabilities to speedup the reconstruction process. However, inherent GPU hardware constraints limit the size of the recoverable signal and the speedup practically achievable. In this work, we design parallel algorithms that exploit the properties of circulant matrices for efficient GPU-accelerated sparse signals recovery. Our approach reduces the memory requirements, allowing us to recover very large signals with limited memory. In addition, it achieves a tenfold signal recovery speedup thanks to ad-hoc parallelization of matrix-vector multiplications and matrix inversions. Finally, we practically demonstrate our algorithms in a typical application of circulant matrices: deblurring a sparse astronomical image in the compressed domain.

On system behaviour using complex networks of a compression algorithm

NASA Astrophysics Data System (ADS)

Walker, David M.; Correa, Debora C.; Small, Michael

2018-01-01

We construct complex networks of scalar time series using a data compression algorithm. The structure and statistics of the resulting networks can be used to help characterize complex systems, and one property, in particular, appears to be a useful discriminating statistic in surrogate data hypothesis tests. We demonstrate these ideas on systems with known dynamical behaviour and also show that our approach is capable of identifying behavioural transitions within electroencephalogram recordings as well as changes due to a bifurcation parameter of a chaotic system. The technique we propose is dependent on a coarse grained quantization of the original time series and therefore provides potential for a spatial scale-dependent characterization of the data. Finally the method is as computationally efficient as the underlying compression algorithm and provides a compression of the salient features of long time series.

Trabecular Bone Mechanical Properties and Fractal Dimension

NASA Technical Reports Server (NTRS)

Hogan, Harry A.

1996-01-01

Countermeasures for reducing bone loss and muscle atrophy due to extended exposure to the microgravity environment of space are continuing to be developed and improved. An important component of this effort is finite element modeling of the lower extremity and spinal column. These models will permit analysis and evaluation specific to each individual and thereby provide more efficient and effective exercise protocols. Inflight countermeasures and post-flight rehabilitation can then be customized and targeted on a case-by-case basis. Recent Summer Faculty Fellowship participants have focused upon finite element mesh generation, muscle force estimation, and fractal calculations of trabecular bone microstructure. Methods have been developed for generating the three-dimensional geometry of the femur from serial section magnetic resonance images (MRI). The use of MRI as an imaging modality avoids excessive exposure to radiation associated with X-ray based methods. These images can also detect trabecular bone microstructure and architecture. The goal of the current research is to determine the degree to which the fractal dimension of trabecular architecture can be used to predict the mechanical properties of trabecular bone tissue. The elastic modulus and the ultimate strength (or strain) can then be estimated from non-invasive, non-radiating imaging and incorporated into the finite element models to more accurately represent the bone tissue of each individual of interest. Trabecular bone specimens from the proximal tibia are being studied in this first phase of the work. Detailed protocols and procedures have been developed for carrying test specimens through all of the steps of a multi-faceted test program. The test program begins with MRI and X-ray imaging of the whole bones before excising a smaller workpiece from the proximal tibia region. High resolution MRI scans are then made and the piece further cut into slabs (roughly 1 cm thick). The slabs are X-rayed again and also scanned using dual-energy X-ray absorptiometry (DEXA). Cube specimens are then cut from the slabs and tested mechanically in compression. Correlations between mechanical properties and fractal dimension will then be examined to assess and quantify the predictive capability of the fractal calculations.

Multidimensional incremental parsing for universal source coding.

PubMed

Bae, Soo Hyun; Juang, Biing-Hwang

2008-10-01

A multidimensional incremental parsing algorithm (MDIP) for multidimensional discrete sources, as a generalization of the Lempel-Ziv coding algorithm, is investigated. It consists of three essential component schemes, maximum decimation matching, hierarchical structure of multidimensional source coding, and dictionary augmentation. As a counterpart of the longest match search in the Lempel-Ziv algorithm, two classes of maximum decimation matching are studied. Also, an underlying behavior of the dictionary augmentation scheme for estimating the source statistics is examined. For an m-dimensional source, m augmentative patches are appended into the dictionary at each coding epoch, thus requiring the transmission of a substantial amount of information to the decoder. The property of the hierarchical structure of the source coding algorithm resolves this issue by successively incorporating lower dimensional coding procedures in the scheme. In regard to universal lossy source coders, we propose two distortion functions, the local average distortion and the local minimax distortion with a set of threshold levels for each source symbol. For performance evaluation, we implemented three image compression algorithms based upon the MDIP; one is lossless and the others are lossy. The lossless image compression algorithm does not perform better than the Lempel-Ziv-Welch coding, but experimentally shows efficiency in capturing the source structure. The two lossy image compression algorithms are implemented using the two distortion functions, respectively. The algorithm based on the local average distortion is efficient at minimizing the signal distortion, but the images by the one with the local minimax distortion have a good perceptual fidelity among other compression algorithms. Our insights inspire future research on feature extraction of multidimensional discrete sources.

Modeling of frost crystal growth over a flat plate using artificial neural networks and fractal geometries

NASA Astrophysics Data System (ADS)

Tahavvor, Ali Reza

2017-03-01

In the present study artificial neural network and fractal geometry are used to predict frost thickness and density on a cold flat plate having constant surface temperature under forced convection for different ambient conditions. These methods are very applicable in this area because phase changes such as melting and solidification are simulated by conventional methods but frost formation is a most complicated phase change phenomenon consists of coupled heat and mass transfer. Therefore conventional mathematical techniques cannot capture the effects of all parameters on its growth and development because this process influenced by many factors and it is a time dependent process. Therefore, in this work soft computing method such as artificial neural network and fractal geometry are used to do this manner. The databases for modeling are generated from the experimental measurements. First, multilayer perceptron network is used and it is found that the back-propagation algorithm with Levenberg-Marquardt learning rule is the best choice to estimate frost growth properties due to accurate and faster training procedure. Second, fractal geometry based on the Von-Koch curve is used to model frost growth procedure especially in frost thickness and density. Comparison is performed between experimental measurements and soft computing methods. Results show that soft computing methods can be used more efficiently to determine frost properties over a flat plate. Based on the developed models, wide range of frost formation over flat plates can be determined for various conditions.

Some Practical Universal Noiseless Coding Techniques

NASA Technical Reports Server (NTRS)

Rice, Robert F.

1994-01-01

Report discusses noiseless data-compression-coding algorithms, performance characteristics and practical consideration in implementation of algorithms in coding modules composed of very-large-scale integrated circuits. Report also has value as tutorial document on data-compression-coding concepts. Coding techniques and concepts in question "universal" in sense that, in principle, applicable to streams of data from variety of sources. However, discussion oriented toward compression of high-rate data generated by spaceborne sensors for lower-rate transmission back to earth.

An Image Processing Technique for Achieving Lossy Compression of Data at Ratios in Excess of 100:1

DTIC Science & Technology

1992-11-01

5 Lempel , Ziv , Welch (LZW) Compression ............... 7 Lossless Compression Tests Results ................. 9 Exact...since IBM holds the patent for this technique. Lempel , Ziv , Welch (LZW) Compression The LZW compression is related to two compression techniques known as... compression , using the input stream as data . This step is possible because the compression algorithm always outputs the phrase and character components of a

Space-Filling Supercapacitor Carpets: Highly scalable fractal architecture for energy storage

NASA Astrophysics Data System (ADS)

Tiliakos, Athanasios; Trefilov, Alexandra M. I.; Tanasǎ, Eugenia; Balan, Adriana; Stamatin, Ioan

2018-04-01

Revamping ground-breaking ideas from fractal geometry, we propose an alternative micro-supercapacitor configuration realized by laser-induced graphene (LIG) foams produced via laser pyrolysis of inexpensive commercial polymers. The Space-Filling Supercapacitor Carpet (SFSC) architecture introduces the concept of nested electrodes based on the pre-fractal Peano space-filling curve, arranged in a symmetrical equilateral setup that incorporates multiple parallel capacitor cells sharing common electrodes for maximum efficiency and optimal length-to-area distribution. We elucidate on the theoretical foundations of the SFSC architecture, and we introduce innovations (high-resolution vector-mode printing) in the LIG method that allow for the realization of flexible and scalable devices based on low iterations of the Peano algorithm. SFSCs exhibit distributed capacitance properties, leading to capacitance, energy, and power ratings proportional to the number of nested electrodes (up to 4.3 mF, 0.4 μWh, and 0.2 mW for the largest tested model of low iteration using aqueous electrolytes), with competitively high energy and power densities. This can pave the road for full scalability in energy storage, reaching beyond the scale of micro-supercapacitors for incorporating into larger and more demanding applications.

Texture Classification by Texton: Statistical versus Binary

PubMed Central

Guo, Zhenhua; Zhang, Zhongcheng; Li, Xiu; Li, Qin; You, Jane

2014-01-01

Using statistical textons for texture classification has shown great success recently. The maximal response 8 (Statistical_MR8), image patch (Statistical_Joint) and locally invariant fractal (Statistical_Fractal) are typical statistical texton algorithms and state-of-the-art texture classification methods. However, there are two limitations when using these methods. First, it needs a training stage to build a texton library, thus the recognition accuracy will be highly depended on the training samples; second, during feature extraction, local feature is assigned to a texton by searching for the nearest texton in the whole library, which is time consuming when the library size is big and the dimension of feature is high. To address the above two issues, in this paper, three binary texton counterpart methods were proposed, Binary_MR8, Binary_Joint, and Binary_Fractal. These methods do not require any training step but encode local feature into binary representation directly. The experimental results on the CUReT, UIUC and KTH-TIPS databases show that binary texton could get sound results with fast feature extraction, especially when the image size is not big and the quality of image is not poor. PMID:24520346

Singularity analysis based on wavelet transform of fractal measures for identifying geochemical anomaly in mineral exploration

NASA Astrophysics Data System (ADS)

Chen, Guoxiong; Cheng, Qiuming

2016-02-01

Multi-resolution and scale-invariance have been increasingly recognized as two closely related intrinsic properties endowed in geofields such as geochemical and geophysical anomalies, and they are commonly investigated by using multiscale- and scaling-analysis methods. In this paper, the wavelet-based multiscale decomposition (WMD) method was proposed to investigate the multiscale natures of geochemical pattern from large scale to small scale. In the light of the wavelet transformation of fractal measures, we demonstrated that the wavelet approximation operator provides a generalization of box-counting method for scaling analysis of geochemical patterns. Specifically, the approximation coefficient acts as the generalized density-value in density-area fractal modeling of singular geochemical distributions. Accordingly, we presented a novel local singularity analysis (LSA) using the WMD algorithm which extends the conventional moving averaging to a kernel-based operator for implementing LSA. Finally, the novel LSA was validated using a case study dealing with geochemical data (Fe2O3) in stream sediments for mineral exploration in Inner Mongolia, China. In comparison with the LSA implemented using the moving averaging method the novel LSA using WMD identified improved weak geochemical anomalies associated with mineralization in covered area.

Age-Related Changes in Electroencephalographic Signal Complexity

PubMed Central

Zappasodi, Filippo; Marzetti, Laura; Olejarczyk, Elzbieta; Tecchio, Franca; Pizzella, Vittorio

2015-01-01

The study of active and healthy aging is a primary focus for social and neuroscientific communities. Here, we move a step forward in assessing electrophysiological neuronal activity changes in the brain with healthy aging. To this end, electroencephalographic (EEG) resting state activity was acquired in 40 healthy subjects (age 16–85). We evaluated Fractal Dimension (FD) according to the Higuchi algorithm, a measure which quantifies the presence of statistical similarity at different scales in temporal fluctuations of EEG signals. Our results showed that FD increases from age twenty to age fifty and then decreases. The curve that best fits the changes in FD values across age over the whole sample is a parabola, with the vertex located around age fifty. Moreover, FD changes are site specific, with interhemispheric FD asymmetry being pronounced in elderly individuals in the frontal and central regions. The present results indicate that fractal dimension well describes the modulations of brain activity with age. Since fractal dimension has been proposed to be related to the complexity of the signal dynamics, our data demonstrate that the complexity of neuronal electric activity changes across the life span of an individual, with a steady increase during young adulthood and a decrease in the elderly population. PMID:26536036

Converting Panax ginseng DNA and chemical fingerprints into two-dimensional barcode.

PubMed

Cai, Yong; Li, Peng; Li, Xi-Wen; Zhao, Jing; Chen, Hai; Yang, Qing; Hu, Hao

2017-07-01

In this study, we investigated how to convert the Panax ginseng DNA sequence code and chemical fingerprints into a two-dimensional code. In order to improve the compression efficiency, GATC2Bytes and digital merger compression algorithms are proposed. HPLC chemical fingerprint data of 10 groups of P. ginseng from Northeast China and the internal transcribed spacer 2 (ITS2) sequence code as the DNA sequence code were ready for conversion. In order to convert such data into a two-dimensional code, the following six steps were performed: First, the chemical fingerprint characteristic data sets were obtained through the inflection filtering algorithm. Second, precompression processing of such data sets is undertaken. Third, precompression processing was undertaken with the P. ginseng DNA (ITS2) sequence codes. Fourth, the precompressed chemical fingerprint data and the DNA (ITS2) sequence code were combined in accordance with the set data format. Such combined data can be compressed by Zlib, an open source data compression algorithm. Finally, the compressed data generated a two-dimensional code called a quick response code (QR code). Through the abovementioned converting process, it can be found that the number of bytes needed for storing P. ginseng chemical fingerprints and its DNA (ITS2) sequence code can be greatly reduced. After GTCA2Bytes algorithm processing, the ITS2 compression rate reaches 75% and the chemical fingerprint compression rate exceeds 99.65% via filtration and digital merger compression algorithm processing. Therefore, the overall compression ratio even exceeds 99.36%. The capacity of the formed QR code is around 0.5k, which can easily and successfully be read and identified by any smartphone. P. ginseng chemical fingerprints and its DNA (ITS2) sequence code can form a QR code after data processing, and therefore the QR code can be a perfect carrier of the authenticity and quality of P. ginseng information. This study provides a theoretical basis for the development of a quality traceability system of traditional Chinese medicine based on a two-dimensional code.

Robust QRS detection for HRV estimation from compressively sensed ECG measurements for remote health-monitoring systems.

PubMed

Pant, Jeevan K; Krishnan, Sridhar

2018-03-15

To present a new compressive sensing (CS)-based method for the acquisition of ECG signals and for robust estimation of heart-rate variability (HRV) parameters from compressively sensed measurements with high compression ratio. CS is used in the biosensor to compress the ECG signal. Estimation of the locations of QRS segments is carried out by applying two algorithms on the compressed measurements. The first algorithm reconstructs the ECG signal by enforcing a block-sparse structure on the first-order difference of the signal, so the transient QRS segments are significantly emphasized on the first-order difference of the signal. Multiple block-divisions of the signals are carried out with various block lengths, and multiple reconstructed signals are combined to enhance the robustness of the localization of the QRS segments. The second algorithm removes errors in the locations of QRS segments by applying low-pass filtering and morphological operations. The proposed CS-based method is found to be effective for the reconstruction of ECG signals by enforcing transient QRS structures on the first-order difference of the signal. It is demonstrated to be robust not only to high compression ratio but also to various artefacts present in ECG signals acquired by using on-body wireless sensors. HRV parameters computed by using the QRS locations estimated from the signals reconstructed with a compression ratio as high as 90% are comparable with that computed by using QRS locations estimated by using the Pan-Tompkins algorithm. The proposed method is useful for the realization of long-term HRV monitoring systems by using CS-based low-power wireless on-body biosensors.

Wavelet compression techniques for hyperspectral data

NASA Technical Reports Server (NTRS)

Evans, Bruce; Ringer, Brian; Yeates, Mathew

1994-01-01

Hyperspectral sensors are electro-optic sensors which typically operate in visible and near infrared bands. Their characteristic property is the ability to resolve a relatively large number (i.e., tens to hundreds) of contiguous spectral bands to produce a detailed profile of the electromagnetic spectrum. In contrast, multispectral sensors measure relatively few non-contiguous spectral bands. Like multispectral sensors, hyperspectral sensors are often also imaging sensors, measuring spectra over an array of spatial resolution cells. The data produced may thus be viewed as a three dimensional array of samples in which two dimensions correspond to spatial position and the third to wavelength. Because they multiply the already large storage/transmission bandwidth requirements of conventional digital images, hyperspectral sensors generate formidable torrents of data. Their fine spectral resolution typically results in high redundancy in the spectral dimension, so that hyperspectral data sets are excellent candidates for compression. Although there have been a number of studies of compression algorithms for multispectral data, we are not aware of any published results for hyperspectral data. Three algorithms for hyperspectral data compression are compared. They were selected as representatives of three major approaches for extending conventional lossy image compression techniques to hyperspectral data. The simplest approach treats the data as an ensemble of images and compresses each image independently, ignoring the correlation between spectral bands. The second approach transforms the data to decorrelate the spectral bands, and then compresses the transformed data as a set of independent images. The third approach directly generalizes two-dimensional transform coding by applying a three-dimensional transform as part of the usual transform-quantize-entropy code procedure. The algorithms studied all use the discrete wavelet transform. In the first two cases, a wavelet transform coder was used for the two-dimensional compression. The third case used a three dimensional extension of this same algorithm.

A new set of wavelet- and fractals-based features for Gleason grading of prostate cancer histopathology images

NASA Astrophysics Data System (ADS)

Mosquera Lopez, Clara; Agaian, Sos

2013-02-01

Prostate cancer detection and staging is an important step towards patient treatment selection. Advancements in digital pathology allow the application of new quantitative image analysis algorithms for computer-assisted diagnosis (CAD) on digitized histopathology images. In this paper, we introduce a new set of features to automatically grade pathological images using the well-known Gleason grading system. The goal of this study is to classify biopsy images belonging to Gleason patterns 3, 4, and 5 by using a combination of wavelet and fractal features. For image classification we use pairwise coupling Support Vector Machine (SVM) classifiers. The accuracy of the system, which is close to 97%, is estimated through three different cross-validation schemes. The proposed system offers the potential for automating classification of histological images and supporting prostate cancer diagnosis.

High performance compression of science data

NASA Technical Reports Server (NTRS)

Storer, James A.; Cohn, Martin

1994-01-01

Two papers make up the body of this report. One presents a single-pass adaptive vector quantization algorithm that learns a codebook of variable size and shape entries; the authors present experiments on a set of test images showing that with no training or prior knowledge of the data, for a given fidelity, the compression achieved typically equals or exceeds that of the JPEG standard. The second paper addresses motion compensation, one of the most effective techniques used in the interframe data compression. A parallel block-matching algorithm for estimating interframe displacement of blocks with minimum error is presented. The algorithm is designed for a simple parallel architecture to process video in real time.

A novel color image compression algorithm using the human visual contrast sensitivity characteristics

NASA Astrophysics Data System (ADS)

Yao, Juncai; Liu, Guizhong

2017-03-01

In order to achieve higher image compression ratio and improve visual perception of the decompressed image, a novel color image compression scheme based on the contrast sensitivity characteristics of the human visual system (HVS) is proposed. In the proposed scheme, firstly the image is converted into the YCrCb color space and divided into sub-blocks. Afterwards, the discrete cosine transform is carried out for each sub-block, and three quantization matrices are built to quantize the frequency spectrum coefficients of the images by combining the contrast sensitivity characteristics of HVS. The Huffman algorithm is used to encode the quantized data. The inverse process involves decompression and matching to reconstruct the decompressed color image. And simulations are carried out for two color images. The results show that the average structural similarity index measurement (SSIM) and peak signal to noise ratio (PSNR) under the approximate compression ratio could be increased by 2.78% and 5.48%, respectively, compared with the joint photographic experts group (JPEG) compression. The results indicate that the proposed compression algorithm in the text is feasible and effective to achieve higher compression ratio under ensuring the encoding and image quality, which can fully meet the needs of storage and transmission of color images in daily life.

A review of lossless audio compression standards and algorithms

NASA Astrophysics Data System (ADS)

Muin, Fathiah Abdul; Gunawan, Teddy Surya; Kartiwi, Mira; Elsheikh, Elsheikh M. A.

2017-09-01

Over the years, lossless audio compression has gained popularity as researchers and businesses has become more aware of the need for better quality and higher storage demand. This paper will analyse various lossless audio coding algorithm and standards that are used and available in the market focusing on Linear Predictive Coding (LPC) specifically due to its popularity and robustness in audio compression, nevertheless other prediction methods are compared to verify this. Advanced representation of LPC such as LSP decomposition techniques are also discussed within this paper.

Lossless Video Sequence Compression Using Adaptive Prediction

NASA Technical Reports Server (NTRS)

Li, Ying; Sayood, Khalid

2007-01-01

We present an adaptive lossless video compression algorithm based on predictive coding. The proposed algorithm exploits temporal, spatial, and spectral redundancies in a backward adaptive fashion with extremely low side information. The computational complexity is further reduced by using a caching strategy. We also study the relationship between the operational domain for the coder (wavelet or spatial) and the amount of temporal and spatial redundancy in the sequence being encoded. Experimental results show that the proposed scheme provides significant improvements in compression efficiencies.

Synthetic aperture radar signal data compression using block adaptive quantization

NASA Technical Reports Server (NTRS)

Kuduvalli, Gopinath; Dutkiewicz, Melanie; Cumming, Ian

1994-01-01

This paper describes the design and testing of an on-board SAR signal data compression algorithm for ESA's ENVISAT satellite. The Block Adaptive Quantization (BAQ) algorithm was selected, and optimized for the various operational modes of the ASAR instrument. A flexible BAQ scheme was developed which allows a selection of compression ratio/image quality trade-offs. Test results show the high quality of the SAR images processed from the reconstructed signal data, and the feasibility of on-board implementation using a single ASIC.

[A wavelet neural network algorithm of EEG signals data compression and spikes recognition].

PubMed

Zhang, Y; Liu, A; Yu, K

1999-06-01

A novel method of EEG signals compression representation and epileptiform spikes recognition based on wavelet neural network and its algorithm is presented. The wavelet network not only can compress data effectively but also can recover original signal. In addition, the characters of the spikes and the spike-slow rhythm are auto-detected from the time-frequency isoline of EEG signal. This method is well worth using in the field of the electrophysiological signal processing and time-frequency analyzing.

Compressed/reconstructed test images for CRAF/Cassini

NASA Technical Reports Server (NTRS)

Dolinar, S.; Cheung, K.-M.; Onyszchuk, I.; Pollara, F.; Arnold, S.

1991-01-01

A set of compressed, then reconstructed, test images submitted to the Comet Rendezvous Asteroid Flyby (CRAF)/Cassini project is presented as part of its evaluation of near lossless high compression algorithms for representing image data. A total of seven test image files were provided by the project. The seven test images were compressed, then reconstructed with high quality (root mean square error of approximately one or two gray levels on an 8 bit gray scale), using discrete cosine transforms or Hadamard transforms and efficient entropy coders. The resulting compression ratios varied from about 2:1 to about 10:1, depending on the activity or randomness in the source image. This was accomplished without any special effort to optimize the quantizer or to introduce special postprocessing to filter the reconstruction errors. A more complete set of measurements, showing the relative performance of the compression algorithms over a wide range of compression ratios and reconstruction errors, shows that additional compression is possible at a small sacrifice in fidelity.

Bilevel thresholding of sliced image of sludge floc.

PubMed

Chu, C P; Lee, D J

2004-02-15

This work examined the feasibility of employing various thresholding algorithms to determining the optimal bilevel thresholding value for estimating the geometric parameters of sludge flocs from the microtome sliced images and from the confocal laser scanning microscope images. Morphological information extracted from images depends on the bilevel thresholding value. According to the evaluation on the luminescence-inverted images and fractal curves (quadric Koch curve and Sierpinski carpet), Otsu's method yields more stable performance than other histogram-based algorithms and is chosen to obtain the porosity. The maximum convex perimeter method, however, can probe the shapes and spatial distribution of the pores among the biomass granules in real sludge flocs. A combined algorithm is recommended for probing the sludge floc structure.

Image quality enhancement in low-light-level ghost imaging using modified compressive sensing method

NASA Astrophysics Data System (ADS)

Shi, Xiaohui; Huang, Xianwei; Nan, Suqin; Li, Hengxing; Bai, Yanfeng; Fu, Xiquan

2018-04-01

Detector noise has a significantly negative impact on ghost imaging at low light levels, especially for existing recovery algorithm. Based on the characteristics of the additive detector noise, a method named modified compressive sensing ghost imaging is proposed to reduce the background imposed by the randomly distributed detector noise at signal path. Experimental results show that, with an appropriate choice of threshold value, modified compressive sensing ghost imaging algorithm can dramatically enhance the contrast-to-noise ratio of the object reconstruction significantly compared with traditional ghost imaging and compressive sensing ghost imaging methods. The relationship between the contrast-to-noise ratio of the reconstruction image and the intensity ratio (namely, the average signal intensity to average noise intensity ratio) for the three reconstruction algorithms are also discussed. This noise suppression imaging technique will have great applications in remote-sensing and security areas.

Modeling of video traffic in packet networks, low rate video compression, and the development of a lossy+lossless image compression algorithm

NASA Technical Reports Server (NTRS)

Sayood, K.; Chen, Y. C.; Wang, X.

1992-01-01

During this reporting period we have worked on three somewhat different problems. These are modeling of video traffic in packet networks, low rate video compression, and the development of a lossy + lossless image compression algorithm, which might have some application in browsing algorithms. The lossy + lossless scheme is an extension of work previously done under this grant. It provides a simple technique for incorporating browsing capability. The low rate coding scheme is also a simple variation on the standard discrete cosine transform (DCT) coding approach. In spite of its simplicity, the approach provides surprisingly high quality reconstructions. The modeling approach is borrowed from the speech recognition literature, and seems to be promising in that it provides a simple way of obtaining an idea about the second order behavior of a particular coding scheme. Details about these are presented.

A new interferential multispectral image compression algorithm based on adaptive classification and curve-fitting

NASA Astrophysics Data System (ADS)

Wang, Ke-Yan; Li, Yun-Song; Liu, Kai; Wu, Cheng-Ke

2008-08-01

A novel compression algorithm for interferential multispectral images based on adaptive classification and curve-fitting is proposed. The image is first partitioned adaptively into major-interference region and minor-interference region. Different approximating functions are then constructed for two kinds of regions respectively. For the major interference region, some typical interferential curves are selected to predict other curves. These typical curves are then processed by curve-fitting method. For the minor interference region, the data of each interferential curve are independently approximated. Finally the approximating errors of two regions are entropy coded. The experimental results show that, compared with JPEG2000, the proposed algorithm not only decreases the average output bit-rate by about 0.2 bit/pixel for lossless compression, but also improves the reconstructed images and reduces the spectral distortion greatly, especially at high bit-rate for lossy compression.

A Brief Historical Introduction to Fractals and Fractal Geometry

ERIC Educational Resources Information Center

Debnath, Lokenath

2006-01-01

This paper deals with a brief historical introduction to fractals, fractal dimension and fractal geometry. Many fractals including the Cantor fractal, the Koch fractal, the Minkowski fractal, the Mandelbrot and Given fractal are described to illustrate self-similar geometrical figures. This is followed by the discovery of dynamical systems and…

Comparison of various contact algorithms for poroelastic tissues.

PubMed

Galbusera, Fabio; Bashkuev, Maxim; Wilke, Hans-Joachim; Shirazi-Adl, Aboulfazl; Schmidt, Hendrik

2014-01-01

Capabilities of the commercial finite element package ABAQUS in simulating frictionless contact between two saturated porous structures were evaluated and compared with those of an open source code, FEBio. In ABAQUS, both the default contact implementation and another algorithm based on an iterative approach requiring script programming were considered. Test simulations included a patch test of two cylindrical slabs in a gapless contact and confined compression conditions; a confined compression test of a porous cylindrical slab with a spherical porous indenter; and finally two unconfined compression tests of soft tissues mimicking diarthrodial joints. The patch test showed almost identical results for all algorithms. On the contrary, the confined and unconfined compression tests demonstrated large differences related to distinct physical and boundary conditions considered in each of the three contact algorithms investigated in this study. In general, contact with non-uniform gaps between fluid-filled porous structures could be effectively simulated with either ABAQUS or FEBio. The user should be aware of the parameter definitions, assumptions and limitations in each case, and take into consideration the physics and boundary conditions of the problem of interest when searching for the most appropriate model.

Pan-sharpening via compressed superresolution reconstruction and multidictionary learning

NASA Astrophysics Data System (ADS)

Shi, Cheng; Liu, Fang; Li, Lingling; Jiao, Licheng; Hao, Hongxia; Shang, Ronghua; Li, Yangyang

2018-01-01

In recent compressed sensing (CS)-based pan-sharpening algorithms, pan-sharpening performance is affected by two key problems. One is that there are always errors between the high-resolution panchromatic (HRP) image and the linear weighted high-resolution multispectral (HRM) image, resulting in spatial and spectral information lost. The other is that the dictionary construction process depends on the nontruth training samples. These problems have limited applications to CS-based pan-sharpening algorithm. To solve these two problems, we propose a pan-sharpening algorithm via compressed superresolution reconstruction and multidictionary learning. Through a two-stage implementation, compressed superresolution reconstruction model reduces the error effectively between the HRP and the linear weighted HRM images. Meanwhile, the multidictionary with ridgelet and curvelet is learned for both the two stages in the superresolution reconstruction process. Since ridgelet and curvelet can better capture the structure and directional characteristics, a better reconstruction result can be obtained. Experiments are done on the QuickBird and IKONOS satellites images. The results indicate that the proposed algorithm is competitive compared with the recent CS-based pan-sharpening methods and other well-known methods.

An ECG signals compression method and its validation using NNs.

PubMed

Fira, Catalina Monica; Goras, Liviu

2008-04-01

This paper presents a new algorithm for electrocardiogram (ECG) signal compression based on local extreme extraction, adaptive hysteretic filtering and Lempel-Ziv-Welch (LZW) coding. The algorithm has been verified using eight of the most frequent normal and pathological types of cardiac beats and an multi-layer perceptron (MLP) neural network trained with original cardiac patterns and tested with reconstructed ones. Aspects regarding the possibility of using the principal component analysis (PCA) to cardiac pattern classification have been investigated as well. A new compression measure called "quality score," which takes into account both the reconstruction errors and the compression ratio, is proposed.

Fast computational scheme of image compression for 32-bit microprocessors

NASA Technical Reports Server (NTRS)

Kasperovich, Leonid

1994-01-01

This paper presents a new computational scheme of image compression based on the discrete cosine transform (DCT), underlying JPEG and MPEG International Standards. The algorithm for the 2-d DCT computation uses integer operations (register shifts and additions / subtractions only); its computational complexity is about 8 additions per image pixel. As a meaningful example of an on-board image compression application we consider the software implementation of the algorithm for the Mars Rover (Marsokhod, in Russian) imaging system being developed as a part of Mars-96 International Space Project. It's shown that fast software solution for 32-bit microprocessors may compete with the DCT-based image compression hardware.

Crunchiness Loss and Moisture Toughening in Puffed Cereals and Snacks.

PubMed

Peleg, Micha

2015-09-01

Upon moisture uptake, dry cellular cereals and snacks loose their brittleness and become soggy. This familiar phenomenon is manifested in smoothing their compressive force-displacement curves. These curves' degree of jaggedness, expressed by their apparent fractal dimension, can serve as an instrumental measure of the particles' crunchiness. The relationship between the apparent fractal dimension and moisture content or water activity has a characteristic sigmoid shape. The relationship between the sensorily perceived crunchiness and moisture also has a sigmoid shape whose inflection point lies at about the same location. The transition between the brittle and soggy states, however, appears sharper in the apparent fractal dimension compared with moisture plot. Less familiar is the observation that at moderate levels of moisture content, while the particles' crunchiness is being lost, their stiffness actually rises, a phenomenon that can be dubbed "moisture toughening." We show this phenomenon in commercial Peanut Butter Crunch® (sweet starch-based cereal), Cheese Balls (salty starch-based snack), and Pork Rind also known as "Chicharon" (salty deep-fried pork skin), 3 crunchy foods that have very different chemical composition. We also show that in the first 2 foods, moisture toughening was perceived sensorily as increased "hardness." We have concluded that the partial plasticization, which caused the brittleness loss, also inhibited failure propagation, which allowed the solid matrix to sustain higher stresses. This can explain other published reports of the phenomenon in different foods and model systems. © 2015 Institute of Food Technologists®

Advanced End-to-end Simulation for On-board Processing (AESOP)

NASA Technical Reports Server (NTRS)

Mazer, Alan S.

1994-01-01

Developers of data compression algorithms typically use their own software together with commercial packages to implement, evaluate and demonstrate their work. While convenient for an individual developer, this approach makes it difficult to build on or use another's work without intimate knowledge of each component. When several people or groups work on different parts of the same problem, the larger view can be lost. What's needed is a simple piece of software to stand in the gap and link together the efforts of different people, enabling them to build on each other's work, and providing a base for engineers and scientists to evaluate the parts as a cohesive whole and make design decisions. AESOP (Advanced End-to-end Simulation for On-board Processing) attempts to meet this need by providing a graphical interface to a developer-selected set of algorithms, interfacing with compiled code and standalone programs, as well as procedures written in the IDL and PV-Wave command languages. As a proof of concept, AESOP is outfitted with several data compression algorithms integrating previous work on different processors (AT&T DSP32C, TI TMS320C30, SPARC). The user can specify at run-time the processor on which individual parts of the compression should run. Compressed data is then fed through simulated transmission and uncompression to evaluate the effects of compression parameters, noise and error correction algorithms. The following sections describe AESOP in detail. Section 2 describes fundamental goals for usability. Section 3 describes the implementation. Sections 4 through 5 describe how to add new functionality to the system and present the existing data compression algorithms. Sections 6 and 7 discuss portability and future work.

Information extraction and transmission techniques for spaceborne synthetic aperture radar images

NASA Technical Reports Server (NTRS)

Frost, V. S.; Yurovsky, L.; Watson, E.; Townsend, K.; Gardner, S.; Boberg, D.; Watson, J.; Minden, G. J.; Shanmugan, K. S.

1984-01-01

Information extraction and transmission techniques for synthetic aperture radar (SAR) imagery were investigated. Four interrelated problems were addressed. An optimal tonal SAR image classification algorithm was developed and evaluated. A data compression technique was developed for SAR imagery which is simple and provides a 5:1 compression with acceptable image quality. An optimal textural edge detector was developed. Several SAR image enhancement algorithms have been proposed. The effectiveness of each algorithm was compared quantitatively.

Effects of image compression and degradation on an automatic diabetic retinopathy screening algorithm

NASA Astrophysics Data System (ADS)

Agurto, C.; Barriga, S.; Murray, V.; Pattichis, M.; Soliz, P.

2010-03-01

Diabetic retinopathy (DR) is one of the leading causes of blindness among adult Americans. Automatic methods for detection of the disease have been developed in recent years, most of them addressing the segmentation of bright and red lesions. In this paper we present an automatic DR screening system that does approach the problem through the segmentation of features. The algorithm determines non-diseased retinal images from those with pathology based on textural features obtained using multiscale Amplitude Modulation-Frequency Modulation (AM-FM) decompositions. The decomposition is represented as features that are the inputs to a classifier. The algorithm achieves 0.88 area under the ROC curve (AROC) for a set of 280 images from the MESSIDOR database. The algorithm is then used to analyze the effects of image compression and degradation, which will be present in most actual clinical or screening environments. Results show that the algorithm is insensitive to illumination variations, but high rates of compression and large blurring effects degrade its performance.

Deformation Failure Characteristics of Coal Body and Mining Induced Stress Evolution Law

PubMed Central

Wen, Zhijie; Wen, Jinhao; Shi, Yongkui; Jia, Chuanyang

2014-01-01

The results of the interaction between coal failure and mining pressure field evolution during mining are presented. Not only the mechanical model of stope and its relative structure division, but also the failure and behavior characteristic of coal body under different mining stages are built and demonstrated. Namely, the breaking arch and stress arch which influence the mining area are quantified calculated. A systematic method of stress field distribution is worked out. All this indicates that the pore distribution of coal body with different compressed volume has fractal character; it appears to be the linear relationship between propagation range of internal stress field and compressed volume of coal body and nonlinear relationship between the range of outburst coal mass and the number of pores which is influenced by mining pressure. The results provide theory reference for the research on the range of mining-induced stress and broken coal wall. PMID:24967438

Lossless, Multi-Spectral Data Compressor for Improved Compression for Pushbroom-Type Instruments

NASA Technical Reports Server (NTRS)

Klimesh, Matthew

2008-01-01

A low-complexity lossless algorithm for compression of multispectral data has been developed that takes into account pushbroom-type multispectral imagers properties in order to make the file compression more effective.

Imaging industry expectations for compressed sensing in MRI

NASA Astrophysics Data System (ADS)

King, Kevin F.; Kanwischer, Adriana; Peters, Rob

2015-09-01

Compressed sensing requires compressible data, incoherent acquisition and a nonlinear reconstruction algorithm to force creation of a compressible image consistent with the acquired data. MRI images are compressible using various transforms (commonly total variation or wavelets). Incoherent acquisition of MRI data by appropriate selection of pseudo-random or non-Cartesian locations in k-space is straightforward. Increasingly, commercial scanners are sold with enough computing power to enable iterative reconstruction in reasonable times. Therefore integration of compressed sensing into commercial MRI products and clinical practice is beginning. MRI frequently requires the tradeoff of spatial resolution, temporal resolution and volume of spatial coverage to obtain reasonable scan times. Compressed sensing improves scan efficiency and reduces the need for this tradeoff. Benefits to the user will include shorter scans, greater patient comfort, better image quality, more contrast types per patient slot, the enabling of previously impractical applications, and higher throughput. Challenges to vendors include deciding which applications to prioritize, guaranteeing diagnostic image quality, maintaining acceptable usability and workflow, and acquisition and reconstruction algorithm details. Application choice depends on which customer needs the vendor wants to address. The changing healthcare environment is putting cost and productivity pressure on healthcare providers. The improved scan efficiency of compressed sensing can help alleviate some of this pressure. Image quality is strongly influenced by image compressibility and acceleration factor, which must be appropriately limited. Usability and workflow concerns include reconstruction time and user interface friendliness and response. Reconstruction times are limited to about one minute for acceptable workflow. The user interface should be designed to optimize workflow and minimize additional customer training. Algorithm concerns include the decision of which algorithms to implement as well as the problem of optimal setting of adjustable parameters. It will take imaging vendors several years to work through these challenges and provide solutions for a wide range of applications.

Adaptive efficient compression of genomes

PubMed Central

2012-01-01

Modern high-throughput sequencing technologies are able to generate DNA sequences at an ever increasing rate. In parallel to the decreasing experimental time and cost necessary to produce DNA sequences, computational requirements for analysis and storage of the sequences are steeply increasing. Compression is a key technology to deal with this challenge. Recently, referential compression schemes, storing only the differences between a to-be-compressed input and a known reference sequence, gained a lot of interest in this field. However, memory requirements of the current algorithms are high and run times often are slow. In this paper, we propose an adaptive, parallel and highly efficient referential sequence compression method which allows fine-tuning of the trade-off between required memory and compression speed. When using 12 MB of memory, our method is for human genomes on-par with the best previous algorithms in terms of compression ratio (400:1) and compression speed. In contrast, it compresses a complete human genome in just 11 seconds when provided with 9 GB of main memory, which is almost three times faster than the best competitor while using less main memory. PMID:23146997

Novel 3D Compression Methods for Geometry, Connectivity and Texture

NASA Astrophysics Data System (ADS)

Siddeq, M. M.; Rodrigues, M. A.

2016-06-01

A large number of applications in medical visualization, games, engineering design, entertainment, heritage, e-commerce and so on require the transmission of 3D models over the Internet or over local networks. 3D data compression is an important requirement for fast data storage, access and transmission within bandwidth limitations. The Wavefront OBJ (object) file format is commonly used to share models due to its clear simple design. Normally each OBJ file contains a large amount of data (e.g. vertices and triangulated faces, normals, texture coordinates and other parameters) describing the mesh surface. In this paper we introduce a new method to compress geometry, connectivity and texture coordinates by a novel Geometry Minimization Algorithm (GM-Algorithm) in connection with arithmetic coding. First, each vertex ( x, y, z) coordinates are encoded to a single value by the GM-Algorithm. Second, triangle faces are encoded by computing the differences between two adjacent vertex locations, which are compressed by arithmetic coding together with texture coordinates. We demonstrate the method on large data sets achieving compression ratios between 87 and 99 % without reduction in the number of reconstructed vertices and triangle faces. The decompression step is based on a Parallel Fast Matching Search Algorithm (Parallel-FMS) to recover the structure of the 3D mesh. A comparative analysis of compression ratios is provided with a number of commonly used 3D file formats such as VRML, OpenCTM and STL highlighting the performance and effectiveness of the proposed method.

Impact of JPEG2000 compression on spatial-spectral endmember extraction from hyperspectral data

NASA Astrophysics Data System (ADS)

Martín, Gabriel; Ruiz, V. G.; Plaza, Antonio; Ortiz, Juan P.; García, Inmaculada

2009-08-01

Hyperspectral image compression has received considerable interest in recent years. However, an important issue that has not been investigated in the past is the impact of lossy compression on spectral mixture analysis applications, which characterize mixed pixels in terms of a suitable combination of spectrally pure spectral substances (called endmembers) weighted by their estimated fractional abundances. In this paper, we specifically investigate the impact of JPEG2000 compression of hyperspectral images on the quality of the endmembers extracted by algorithms that incorporate both the spectral and the spatial information (useful for incorporating contextual information in the spectral endmember search). The two considered algorithms are the automatic morphological endmember extraction (AMEE) and the spatial spectral endmember extraction (SSEE) techniques. Experimental results are conducted using a well-known data set collected by AVIRIS over the Cuprite mining district in Nevada and with detailed ground-truth information available from U. S. Geological Survey. Our experiments reveal some interesting findings that may be useful to specialists applying spatial-spectral endmember extraction algorithms to compressed hyperspectral imagery.

Lossless Compression of Classification-Map Data

NASA Technical Reports Server (NTRS)

Hua, Xie; Klimesh, Matthew

2009-01-01

A lossless image-data-compression algorithm intended specifically for application to classification-map data is based on prediction, context modeling, and entropy coding. The algorithm was formulated, in consideration of the differences between classification maps and ordinary images of natural scenes, so as to be capable of compressing classification- map data more effectively than do general-purpose image-data-compression algorithms. Classification maps are typically generated from remote-sensing images acquired by instruments aboard aircraft (see figure) and spacecraft. A classification map is a synthetic image that summarizes information derived from one or more original remote-sensing image(s) of a scene. The value assigned to each pixel in such a map is the index of a class that represents some type of content deduced from the original image data for example, a type of vegetation, a mineral, or a body of water at the corresponding location in the scene. When classification maps are generated onboard the aircraft or spacecraft, it is desirable to compress the classification-map data in order to reduce the volume of data that must be transmitted to a ground station.

Human Motion Capture Data Tailored Transform Coding.

PubMed

Junhui Hou; Lap-Pui Chau; Magnenat-Thalmann, Nadia; Ying He

2015-07-01

Human motion capture (mocap) is a widely used technique for digitalizing human movements. With growing usage, compressing mocap data has received increasing attention, since compact data size enables efficient storage and transmission. Our analysis shows that mocap data have some unique characteristics that distinguish themselves from images and videos. Therefore, directly borrowing image or video compression techniques, such as discrete cosine transform, does not work well. In this paper, we propose a novel mocap-tailored transform coding algorithm that takes advantage of these features. Our algorithm segments the input mocap sequences into clips, which are represented in 2D matrices. Then it computes a set of data-dependent orthogonal bases to transform the matrices to frequency domain, in which the transform coefficients have significantly less dependency. Finally, the compression is obtained by entropy coding of the quantized coefficients and the bases. Our method has low computational cost and can be easily extended to compress mocap databases. It also requires neither training nor complicated parameter setting. Experimental results demonstrate that the proposed scheme significantly outperforms state-of-the-art algorithms in terms of compression performance and speed.

Cluster compression algorithm: A joint clustering/data compression concept

NASA Technical Reports Server (NTRS)

Hilbert, E. E.

1977-01-01

The Cluster Compression Algorithm (CCA), which was developed to reduce costs associated with transmitting, storing, distributing, and interpreting LANDSAT multispectral image data is described. The CCA is a preprocessing algorithm that uses feature extraction and data compression to more efficiently represent the information in the image data. The format of the preprocessed data enables simply a look-up table decoding and direct use of the extracted features to reduce user computation for either image reconstruction, or computer interpretation of the image data. Basically, the CCA uses spatially local clustering to extract features from the image data to describe spectral characteristics of the data set. In addition, the features may be used to form a sequence of scalar numbers that define each picture element in terms of the cluster features. This sequence, called the feature map, is then efficiently represented by using source encoding concepts. Various forms of the CCA are defined and experimental results are presented to show trade-offs and characteristics of the various implementations. Examples are provided that demonstrate the application of the cluster compression concept to multi-spectral images from LANDSAT and other sources.

Digital watermarking algorithm research of color images based on quaternion Fourier transform

NASA Astrophysics Data System (ADS)

An, Mali; Wang, Weijiang; Zhao, Zhen

2013-10-01

A watermarking algorithm of color images based on the quaternion Fourier Transform (QFFT) and improved quantization index algorithm (QIM) is proposed in this paper. The original image is transformed by QFFT, the watermark image is processed by compression and quantization coding, and then the processed watermark image is embedded into the components of the transformed original image. It achieves embedding and blind extraction of the watermark image. The experimental results show that the watermarking algorithm based on the improved QIM algorithm with distortion compensation achieves a good tradeoff between invisibility and robustness, and better robustness for the attacks of Gaussian noises, salt and pepper noises, JPEG compression, cropping, filtering and image enhancement than the traditional QIM algorithm.

General purpose graphic processing unit implementation of adaptive pulse compression algorithms

NASA Astrophysics Data System (ADS)

Cai, Jingxiao; Zhang, Yan

2017-07-01

This study introduces a practical approach to implement real-time signal processing algorithms for general surveillance radar based on NVIDIA graphical processing units (GPUs). The pulse compression algorithms are implemented using compute unified device architecture (CUDA) libraries such as CUDA basic linear algebra subroutines and CUDA fast Fourier transform library, which are adopted from open source libraries and optimized for the NVIDIA GPUs. For more advanced, adaptive processing algorithms such as adaptive pulse compression, customized kernel optimization is needed and investigated. A statistical optimization approach is developed for this purpose without needing much knowledge of the physical configurations of the kernels. It was found that the kernel optimization approach can significantly improve the performance. Benchmark performance is compared with the CPU performance in terms of processing accelerations. The proposed implementation framework can be used in various radar systems including ground-based phased array radar, airborne sense and avoid radar, and aerospace surveillance radar.

A High Performance Image Data Compression Technique for Space Applications

NASA Technical Reports Server (NTRS)

Yeh, Pen-Shu; Venbrux, Jack

2003-01-01

A highly performing image data compression technique is currently being developed for space science applications under the requirement of high-speed and pushbroom scanning. The technique is also applicable to frame based imaging data. The algorithm combines a two-dimensional transform with a bitplane encoding; this results in an embedded bit string with exact desirable compression rate specified by the user. The compression scheme performs well on a suite of test images acquired from spacecraft instruments. It can also be applied to three-dimensional data cube resulting from hyper-spectral imaging instrument. Flight qualifiable hardware implementations are in development. The implementation is being designed to compress data in excess of 20 Msampledsec and support quantization from 2 to 16 bits. This paper presents the algorithm, its applications and status of development.

Exploring compression techniques for ROOT IO

NASA Astrophysics Data System (ADS)

Zhang, Z.; Bockelman, B.

2017-10-01

ROOT provides an flexible format used throughout the HEP community. The number of use cases - from an archival data format to end-stage analysis - has required a number of tradeoffs to be exposed to the user. For example, a high “compression level” in the traditional DEFLATE algorithm will result in a smaller file (saving disk space) at the cost of slower decompression (costing CPU time when read). At the scale of the LHC experiment, poor design choices can result in terabytes of wasted space or wasted CPU time. We explore and attempt to quantify some of these tradeoffs. Specifically, we explore: the use of alternate compressing algorithms to optimize for read performance; an alternate method of compressing individual events to allow efficient random access; and a new approach to whole-file compression. Quantitative results are given, as well as guidance on how to make compression decisions for different use cases.

A survey of the state-of-the-art and focused research in range systems, task 1

NASA Technical Reports Server (NTRS)

Omura, J. K.

1986-01-01

This final report presents the latest research activity in voice compression. We have designed a non-real time simulation system that is implemented around the IBM-PC where the IBM-PC is used as a speech work station for data acquisition and analysis of voice samples. A real-time implementation is also proposed. This real-time Voice Compression Board (VCB) is built around the Texas Instruments TMS-3220. The voice compression algorithm investigated here was described in an earlier report titled, Low Cost Voice Compression for Mobile Digital Radios, by the author. We will assume the reader is familiar with the voice compression algorithm discussed in this report. The VCB compresses speech waveforms at data rates ranging from 4.8 K bps to 16 K bps. This board interfaces to the IBM-PC 8-bit bus, and plugs into a single expansion slot on the mother board.

Image compression-encryption scheme based on hyper-chaotic system and 2D compressive sensing

NASA Astrophysics Data System (ADS)

Zhou, Nanrun; Pan, Shumin; Cheng, Shan; Zhou, Zhihong

2016-08-01

Most image encryption algorithms based on low-dimensional chaos systems bear security risks and suffer encryption data expansion when adopting nonlinear transformation directly. To overcome these weaknesses and reduce the possible transmission burden, an efficient image compression-encryption scheme based on hyper-chaotic system and 2D compressive sensing is proposed. The original image is measured by the measurement matrices in two directions to achieve compression and encryption simultaneously, and then the resulting image is re-encrypted by the cycle shift operation controlled by a hyper-chaotic system. Cycle shift operation can change the values of the pixels efficiently. The proposed cryptosystem decreases the volume of data to be transmitted and simplifies the keys distribution simultaneously as a nonlinear encryption system. Simulation results verify the validity and the reliability of the proposed algorithm with acceptable compression and security performance.

Search for New Highly Energetic Phases under Compression and Shear

DTIC Science & Technology

2015-05-01

bar barn British thermal unit (thermochemical) calorie (thermochemical) cal (thermochemical/cm ) curie degree (angle) degree Fahrenheit...corresponding finite element algorithms and subroutines are developed. (c) Problems on compression and shear of a sample in rotational diamond anvil...element algorithms and subroutines are developed. Model problems on martensitic microstructure evolution are solved. (f) Experimental approaches to study

Proposed data compression schemes for the Galileo S-band contingency mission

NASA Technical Reports Server (NTRS)

Cheung, Kar-Ming; Tong, Kevin

1993-01-01

The Galileo spacecraft is currently on its way to Jupiter and its moons. In April 1991, the high gain antenna (HGA) failed to deploy as commanded. In case the current efforts to deploy the HGA fails, communications during the Jupiter encounters will be through one of two low gain antenna (LGA) on an S-band (2.3 GHz) carrier. A lot of effort has been and will be conducted to attempt to open the HGA. Also various options for improving Galileo's telemetry downlink performance are being evaluated in the event that the HGA will not open at Jupiter arrival. Among all viable options the most promising and powerful one is to perform image and non-image data compression in software onboard the spacecraft. This involves in-flight re-programming of the existing flight software of Galileo's Command and Data Subsystem processors and Attitude and Articulation Control System (AACS) processor, which have very limited computational and memory resources. In this article we describe the proposed data compression algorithms and give their respective compression performance. The planned image compression algorithm is a 4 x 4 or an 8 x 8 multiplication-free integer cosine transform (ICT) scheme, which can be viewed as an integer approximation of the popular discrete cosine transform (DCT) scheme. The implementation complexity of the ICT schemes is much lower than the DCT-based schemes, yet the performances of the two algorithms are indistinguishable. The proposed non-image compression algorith is a Lempel-Ziv-Welch (LZW) variant, which is a lossless universal compression algorithm based on a dynamic dictionary lookup table. We developed a simple and efficient hashing function to perform the string search.

A novel fractal approach for predicting G-protein-coupled receptors and their subfamilies with support vector machines.

PubMed

Nie, Guoping; Li, Yong; Wang, Feichi; Wang, Siwen; Hu, Xuehai

2015-01-01

G-protein-coupled receptors (GPCRs) are seven membrane-spanning proteins and regulate many important physiological processes, such as vision, neurotransmission, immune response and so on. GPCRs-related pathways are the targets of a large number of marketed drugs. Therefore, the design of a reliable computational model for predicting GPCRs from amino acid sequence has long been a significant biomedical problem. Chaos game representation (CGR) reveals the fractal patterns hidden in protein sequences, and then fractal dimension (FD) is an important feature of these highly irregular geometries with concise mathematical expression. Here, in order to extract important features from GPCR protein sequences, CGR algorithm, fractal dimension and amino acid composition (AAC) are employed to formulate the numerical features of protein samples. Four groups of features are considered, and each group is evaluated by support vector machine (SVM) and 10-fold cross-validation test. To test the performance of the present method, a new non-redundant dataset was built based on latest GPCRDB database. Comparing the results of numerical experiments, the group of combined features with AAC and FD gets the best result, the accuracy is 99.22% and Matthew's correlation coefficient (MCC) is 0.9845 for identifying GPCRs from non-GPCRs. Moreover, if it is classified as a GPCR, it will be further put into the second level, which will classify a GPCR into one of the five main subfamilies. At this level, the group of combined features with AAC and FD also gets best accuracy 85.73%. Finally, the proposed predictor is also compared with existing methods and shows better performances.

Influence of chest compression artefact on capnogram-based ventilation detection during out-of-hospital cardiopulmonary resuscitation.

PubMed

Leturiondo, Mikel; Ruiz de Gauna, Sofía; Ruiz, Jesus M; Julio Gutiérrez, J; Leturiondo, Luis A; González-Otero, Digna M; Russell, James K; Zive, Dana; Daya, Mohamud

2018-03-01

Capnography has been proposed as a method for monitoring the ventilation rate during cardiopulmonary resuscitation (CPR). A high incidence (above 70%) of capnograms distorted by chest compression induced oscillations has been previously reported in out-of-hospital (OOH) CPR. The aim of the study was to better characterize the chest compression artefact and to evaluate its influence on the performance of a capnogram-based ventilation detector during OOH CPR. Data from the MRx monitor-defibrillator were extracted from OOH cardiac arrest episodes. For each episode, presence of chest compression artefact was annotated in the capnogram. Concurrent compression depth and transthoracic impedance signals were used to identify chest compressions and to annotate ventilations, respectively. We designed a capnogram-based ventilation detection algorithm and tested its performance with clean and distorted episodes. Data were collected from 232 episodes comprising 52 654 ventilations, with a mean (±SD) of 227 (±118) per episode. Overall, 42% of the capnograms were distorted. Presence of chest compression artefact degraded algorithm performance in terms of ventilation detection, estimation of ventilation rate, and the ability to detect hyperventilation. Capnogram-based ventilation detection during CPR using our algorithm was compromised by the presence of chest compression artefact. In particular, artefact spanning from the plateau to the baseline strongly degraded ventilation detection, and caused a high number of false hyperventilation alarms. Further research is needed to reduce the impact of chest compression artefact on capnographic ventilation monitoring. Copyright © 2017 Elsevier B.V. All rights reserved.

Describing soil surface microrelief by crossover length and fractal dimension

NASA Astrophysics Data System (ADS)

Vidal Vázquez, E.; Miranda, J. G. V.; Paz González, A.

2007-05-01

Accurate description of soil surface topography is essential because different tillage tools produce different soil surface roughness conditions, which in turn affects many processes across the soil surface boundary. Advantages of fractal analysis in soil microrelief assessment have been recognised but the use of fractal indices in practice remains challenging. There is also little information on how soil surface roughness decays under natural rainfall conditions. The objectives of this work were to investigate the decay of initial surface roughness induced by natural rainfall under different soil tillage systems and to compare the performances of a classical statistical index and fractal microrelief indices. Field experiments were performed on an Oxisol at Campinas, São Paulo State (Brazil). Six tillage treatments, namely, disc harrow, disc plow, chisel plow, disc harrow + disc level, disc plow + disc level and chisel plow + disc level were tested. Measurements were made four times, firstly just after tillage and subsequently with increasing amounts of natural rainfall. Duplicated measurements were taken per treatment and date, yielding a total of 48 experimental surfaces. The sampling scheme was a square grid with 25×25 mm point spacing and the plot size was 1350×1350 mm, so that each data set consisted of 3025 individual elevation points. Statistical and fractal indices were calculated both for oriented and random roughness conditions, i.e. after height reading have been corrected for slope and for slope and tillage tool marks. The main drawback of the standard statistical index random roughness, RR, lies in its no spatial nature. The fractal approach requires two indices, fractal dimension, D, which describes how roughness changes with scale, and crossover length, l, specifying the variance of surface microrelief at a reference scale. Fractal parameters D and l, were estimated by two independent self-affine models, semivariogram (SMV) and local root mean square (RMS). Both algorithms, SMV and RMS, gave equivalent results for D and l indices, irrespective of trend removal procedure, even if some bias was present which is in accordance with previous work. Treatments with two tillage operations had the greatest D values, irrespective of evolution stage under rainfall and trend removal procedure. Primary tillage had the greatest initial values of RR and l. Differences in D values between treatments with primary tillage and those with two successive tillage operations were significant for oriented but not for random conditions. The statistical index RR and the fractal indices l and D decreased with increasing cumulative rainfall following different patterns. The l and D decay from initial value was very sharp after the first 24.4 mm cumulative rainfall. For five out of six tillage treatments a significant relationship between D and l was found for the random microrelief conditions allowing a covariance analysis. It was concluded that using RR or l together with D best allow joint description of vertical and horizontal soil roughness variations.

Streamlined Genome Sequence Compression using Distributed Source Coding

PubMed Central

Wang, Shuang; Jiang, Xiaoqian; Chen, Feng; Cui, Lijuan; Cheng, Samuel

2014-01-01

We aim at developing a streamlined genome sequence compression algorithm to support alternative miniaturized sequencing devices, which have limited communication, storage, and computation power. Existing techniques that require heavy client (encoder side) cannot be applied. To tackle this challenge, we carefully examined distributed source coding theory and developed a customized reference-based genome compression protocol to meet the low-complexity need at the client side. Based on the variation between source and reference, our protocol will pick adaptively either syndrome coding or hash coding to compress subsequences of changing code length. Our experimental results showed promising performance of the proposed method when compared with the state-of-the-art algorithm (GRS). PMID:25520552

A Comparison of LBG and ADPCM Speech Compression Techniques

NASA Astrophysics Data System (ADS)

Bachu, Rajesh G.; Patel, Jignasa; Barkana, Buket D.

Speech compression is the technology of converting human speech into an efficiently encoded representation that can later be decoded to produce a close approximation of the original signal. In all speech there is a degree of predictability and speech coding techniques exploit this to reduce bit rates yet still maintain a suitable level of quality. This paper is a study and implementation of Linde-Buzo-Gray Algorithm (LBG) and Adaptive Differential Pulse Code Modulation (ADPCM) algorithms to compress speech signals. In here we implemented the methods using MATLAB 7.0. The methods we used in this study gave good results and performance in compressing the speech and listening tests showed that efficient and high quality coding is achieved.

Time irreversibility and intrinsics revealing of series with complex network approach

NASA Astrophysics Data System (ADS)

Xiong, Hui; Shang, Pengjian; Xia, Jianan; Wang, Jing

2018-06-01

In this work, we analyze time series on the basis of the visibility graph algorithm that maps the original series into a graph. By taking into account the all-round information carried by the signals, the time irreversibility and fractal behavior of series are evaluated from a complex network perspective, and considered signals are further classified from different aspects. The reliability of the proposed analysis is supported by numerical simulations on synthesized uncorrelated random noise, short-term correlated chaotic systems and long-term correlated fractal processes, and by the empirical analysis on daily closing prices of eleven worldwide stock indices. Obtained results suggest that finite size has a significant effect on the evaluation, and that there might be no direct relation between the time irreversibility and long-range correlation of series. Similarity and dissimilarity between stock indices are also indicated from respective regional and global perspectives, showing the existence of multiple features of underlying systems.

Modeling small cell lung cancer (SCLC) biology through deterministic and stochastic mathematical models.

PubMed

Salgia, Ravi; Mambetsariev, Isa; Hewelt, Blake; Achuthan, Srisairam; Li, Haiqing; Poroyko, Valeriy; Wang, Yingyu; Sattler, Martin

2018-05-25

Mathematical cancer models are immensely powerful tools that are based in part on the fractal nature of biological structures, such as the geometry of the lung. Cancers of the lung provide an opportune model to develop and apply algorithms that capture changes and disease phenotypes. We reviewed mathematical models that have been developed for biological sciences and applied them in the context of small cell lung cancer (SCLC) growth, mutational heterogeneity, and mechanisms of metastasis. The ultimate goal is to develop the stochastic and deterministic nature of this disease, to link this comprehensive set of tools back to its fractalness and to provide a platform for accurate biomarker development. These techniques may be particularly useful in the context of drug development research, such as combination with existing omics approaches. The integration of these tools will be important to further understand the biology of SCLC and ultimately develop novel therapeutics.

Performance comparison of the Prophecy (forecasting) Algorithm in FFT form for unseen feature and time-series prediction

NASA Astrophysics Data System (ADS)

Jaenisch, Holger; Handley, James

2013-06-01

We introduce a generalized numerical prediction and forecasting algorithm. We have previously published it for malware byte sequence feature prediction and generalized distribution modeling for disparate test article analysis. We show how non-trivial non-periodic extrapolation of a numerical sequence (forecast and backcast) from the starting data is possible. Our ancestor-progeny prediction can yield new options for evolutionary programming. Our equations enable analytical integrals and derivatives to any order. Interpolation is controllable from smooth continuous to fractal structure estimation. We show how our generalized trigonometric polynomial can be derived using a Fourier transform.

Fractal dimension and fuzzy logic systems for broken rotor bar detection in induction motors at start-up and steady-state regimes

NASA Astrophysics Data System (ADS)

Amezquita-Sanchez, Juan P.; Valtierra-Rodriguez, Martin; Perez-Ramirez, Carlos A.; Camarena-Martinez, David; Garcia-Perez, Arturo; Romero-Troncoso, Rene J.

2017-07-01

Squirrel-cage induction motors (SCIMs) are key machines in many industrial applications. In this regard, the monitoring of their operating condition aiming at avoiding damage and reducing economical losses has become a demanding task for industry. In the literature, several techniques and methodologies to detect faults that affect the integrity and performance of SCIMs have been proposed. However, they have only been focused on analyzing either the start-up transient or the steady-state operation regimes, two common operating scenarios in real practice. In this work, a novel methodology for broken rotor bar (BRB) detection in SCIMs during both start-up and steady-state operation regimes is proposed. It consists of two main steps. In the first one, the analysis of three-axis vibration signals using fractal dimension (FD) theory is carried out. Since different FD-based algorithms can give different results, three algorithms named Katz’ FD, Higuchi’s FD, and box dimension, are tested. In the second step, a fuzzy logic system for each regime is presented for automatic diagnosis. To validate the proposal, a motor with different damage levels has been tested: one with a partially BRB, a second with one fully BRB, and the third with two BRBs. The obtained results demonstrate the proposed effectiveness.

Impact of JPEG2000 compression on endmember extraction and unmixing of remotely sensed hyperspectral data

NASA Astrophysics Data System (ADS)

Martin, Gabriel; Gonzalez-Ruiz, Vicente; Plaza, Antonio; Ortiz, Juan P.; Garcia, Inmaculada

2010-07-01

Lossy hyperspectral image compression has received considerable interest in recent years due to the extremely high dimensionality of the data. However, the impact of lossy compression on spectral unmixing techniques has not been widely studied. These techniques characterize mixed pixels (resulting from insufficient spatial resolution) in terms of a suitable combination of spectrally pure substances (called endmembers) weighted by their estimated fractional abundances. This paper focuses on the impact of JPEG2000-based lossy compression of hyperspectral images on the quality of the endmembers extracted by different algorithms. The three considered algorithms are the orthogonal subspace projection (OSP), which uses only spatial information, and the automatic morphological endmember extraction (AMEE) and spatial spectral endmember extraction (SSEE), which integrate both spatial and spectral information in the search for endmembers. The impact of compression on the resulting abundance estimation based on the endmembers derived by different methods is also substantiated. Experimental results are conducted using a hyperspectral data set collected by NASA Jet Propulsion Laboratory over the Cuprite mining district in Nevada. The experimental results are quantitatively analyzed using reference information available from U.S. Geological Survey, resulting in recommendations to specialists interested in applying endmember extraction and unmixing algorithms to compressed hyperspectral data.

Low complexity lossless compression of underwater sound recordings.

PubMed

Johnson, Mark; Partan, Jim; Hurst, Tom

2013-03-01

Autonomous listening devices are increasingly used to study vocal aquatic animals, and there is a constant need to record longer or with greater bandwidth, requiring efficient use of memory and battery power. Real-time compression of sound has the potential to extend recording durations and bandwidths at the expense of increased processing operations and therefore power consumption. Whereas lossy methods such as MP3 introduce undesirable artifacts, lossless compression algorithms (e.g., flac) guarantee exact data recovery. But these algorithms are relatively complex due to the wide variety of signals they are designed to compress. A simpler lossless algorithm is shown here to provide compression factors of three or more for underwater sound recordings over a range of noise environments. The compressor was evaluated using samples from drifting and animal-borne sound recorders with sampling rates of 16-240 kHz. It achieves >87% of the compression of more-complex methods but requires about 1/10 of the processing operations resulting in less than 1 mW power consumption at a sampling rate of 192 kHz on a low-power microprocessor. The potential to triple recording duration with a minor increase in power consumption and no loss in sound quality may be especially valuable for battery-limited tags and robotic vehicles.

Improved Spectral Calculations for Discrete Schrődinger Operators

NASA Astrophysics Data System (ADS)

Puelz, Charles

This work details an O(n2) algorithm for computing spectra of discrete Schrődinger operators with periodic potentials. Spectra of these objects enhance our understanding of fundamental aperiodic physical systems and contain rich theoretical structure of interest to the mathematical community. Previous work on the Harper model led to an O(n2) algorithm relying on properties not satisfied by other aperiodic operators. Physicists working with the Fibonacci Hamiltonian, a popular quasicrystal model, have instead used a problematic dynamical map approach or a sluggish O(n3) procedure for their calculations. The algorithm presented in this work, a blend of well-established eigenvalue/vector algorithms, provides researchers with a more robust computational tool of general utility. Application to the Fibonacci Hamiltonian in the sparsely studied intermediate coupling regime reveals structure in canonical coverings of the spectrum that will prove useful in motivating conjectures regarding band combinatorics and fractal dimensions.

Sparse signals recovered by non-convex penalty in quasi-linear systems.

PubMed

Cui, Angang; Li, Haiyang; Wen, Meng; Peng, Jigen

2018-01-01

The goal of compressed sensing is to reconstruct a sparse signal under a few linear measurements far less than the dimension of the ambient space of the signal. However, many real-life applications in physics and biomedical sciences carry some strongly nonlinear structures, and the linear model is no longer suitable. Compared with the compressed sensing under the linear circumstance, this nonlinear compressed sensing is much more difficult, in fact also NP-hard, combinatorial problem, because of the discrete and discontinuous nature of the [Formula: see text]-norm and the nonlinearity. In order to get a convenience for sparse signal recovery, we set the nonlinear models have a smooth quasi-linear nature in this paper, and study a non-convex fraction function [Formula: see text] in this quasi-linear compressed sensing. We propose an iterative fraction thresholding algorithm to solve the regularization problem [Formula: see text] for all [Formula: see text]. With the change of parameter [Formula: see text], our algorithm could get a promising result, which is one of the advantages for our algorithm compared with some state-of-art algorithms. Numerical experiments show that our method performs much better than some state-of-the-art methods.

RMP: Reduced-set matching pursuit approach for efficient compressed sensing signal reconstruction.

PubMed

Abdel-Sayed, Michael M; Khattab, Ahmed; Abu-Elyazeed, Mohamed F

2016-11-01

Compressed sensing enables the acquisition of sparse signals at a rate that is much lower than the Nyquist rate. Compressed sensing initially adopted [Formula: see text] minimization for signal reconstruction which is computationally expensive. Several greedy recovery algorithms have been recently proposed for signal reconstruction at a lower computational complexity compared to the optimal [Formula: see text] minimization, while maintaining a good reconstruction accuracy. In this paper, the Reduced-set Matching Pursuit (RMP) greedy recovery algorithm is proposed for compressed sensing. Unlike existing approaches which either select too many or too few values per iteration, RMP aims at selecting the most sufficient number of correlation values per iteration, which improves both the reconstruction time and error. Furthermore, RMP prunes the estimated signal, and hence, excludes the incorrectly selected values. The RMP algorithm achieves a higher reconstruction accuracy at a significantly low computational complexity compared to existing greedy recovery algorithms. It is even superior to [Formula: see text] minimization in terms of the normalized time-error product, a new metric introduced to measure the trade-off between the reconstruction time and error. RMP superior performance is illustrated with both noiseless and noisy samples.

Binaural model-based dynamic-range compression.

PubMed

Ernst, Stephan M A; Kortlang, Steffen; Grimm, Giso; Bisitz, Thomas; Kollmeier, Birger; Ewert, Stephan D

2018-01-26

Binaural cues such as interaural level differences (ILDs) are used to organise auditory perception and to segregate sound sources in complex acoustical environments. In bilaterally fitted hearing aids, dynamic-range compression operating independently at each ear potentially alters these ILDs, thus distorting binaural perception and sound source segregation. A binaurally-linked model-based fast-acting dynamic compression algorithm designed to approximate the normal-hearing basilar membrane (BM) input-output function in hearing-impaired listeners is suggested. A multi-center evaluation in comparison with an alternative binaural and two bilateral fittings was performed to assess the effect of binaural synchronisation on (a) speech intelligibility and (b) perceived quality in realistic conditions. 30 and 12 hearing impaired (HI) listeners were aided individually with the algorithms for both experimental parts, respectively. A small preference towards the proposed model-based algorithm in the direct quality comparison was found. However, no benefit of binaural-synchronisation regarding speech intelligibility was found, suggesting a dominant role of the better ear in all experimental conditions. The suggested binaural synchronisation of compression algorithms showed a limited effect on the tested outcome measures, however, linking could be situationally beneficial to preserve a natural binaural perception of the acoustical environment.

Comparison of two SVD-based color image compression schemes.

PubMed

Li, Ying; Wei, Musheng; Zhang, Fengxia; Zhao, Jianli

2017-01-01

Color image compression is a commonly used process to represent image data as few bits as possible, which removes redundancy in the data while maintaining an appropriate level of quality for the user. Color image compression algorithms based on quaternion are very common in recent years. In this paper, we propose a color image compression scheme, based on the real SVD, named real compression scheme. First, we form a new real rectangular matrix C according to the red, green and blue components of the original color image and perform the real SVD for C. Then we select several largest singular values and the corresponding vectors in the left and right unitary matrices to compress the color image. We compare the real compression scheme with quaternion compression scheme by performing quaternion SVD using the real structure-preserving algorithm. We compare the two schemes in terms of operation amount, assignment number, operation speed, PSNR and CR. The experimental results show that with the same numbers of selected singular values, the real compression scheme offers higher CR, much less operation time, but a little bit smaller PSNR than the quaternion compression scheme. When these two schemes have the same CR, the real compression scheme shows more prominent advantages both on the operation time and PSNR.

Comparison of two SVD-based color image compression schemes

PubMed Central

Li, Ying; Wei, Musheng; Zhang, Fengxia; Zhao, Jianli

2017-01-01

Color image compression is a commonly used process to represent image data as few bits as possible, which removes redundancy in the data while maintaining an appropriate level of quality for the user. Color image compression algorithms based on quaternion are very common in recent years. In this paper, we propose a color image compression scheme, based on the real SVD, named real compression scheme. First, we form a new real rectangular matrix C according to the red, green and blue components of the original color image and perform the real SVD for C. Then we select several largest singular values and the corresponding vectors in the left and right unitary matrices to compress the color image. We compare the real compression scheme with quaternion compression scheme by performing quaternion SVD using the real structure-preserving algorithm. We compare the two schemes in terms of operation amount, assignment number, operation speed, PSNR and CR. The experimental results show that with the same numbers of selected singular values, the real compression scheme offers higher CR, much less operation time, but a little bit smaller PSNR than the quaternion compression scheme. When these two schemes have the same CR, the real compression scheme shows more prominent advantages both on the operation time and PSNR. PMID:28257451

Transform-Based Channel-Data Compression to Improve the Performance of a Real-Time GPU-Based Software Beamformer.

PubMed

Lok, U-Wai; Li, Pai-Chi

2016-03-01

Graphics processing unit (GPU)-based software beamforming has advantages over hardware-based beamforming of easier programmability and a faster design cycle, since complicated imaging algorithms can be efficiently programmed and modified. However, the need for a high data rate when transferring ultrasound radio-frequency (RF) data from the hardware front end to the software back end limits the real-time performance. Data compression methods can be applied to the hardware front end to mitigate the data transfer issue. Nevertheless, most decompression processes cannot be performed efficiently on a GPU, thus becoming another bottleneck of the real-time imaging. Moreover, lossless (or nearly lossless) compression is desirable to avoid image quality degradation. In a previous study, we proposed a real-time lossless compression-decompression algorithm and demonstrated that it can reduce the overall processing time because the reduction in data transfer time is greater than the computation time required for compression/decompression. This paper analyzes the lossless compression method in order to understand the factors limiting the compression efficiency. Based on the analytical results, a nearly lossless compression is proposed to further enhance the compression efficiency. The proposed method comprises a transformation coding method involving modified lossless compression that aims at suppressing amplitude data. The simulation results indicate that the compression ratio (CR) of the proposed approach can be enhanced from nearly 1.8 to 2.5, thus allowing a higher data acquisition rate at the front end. The spatial and contrast resolutions with and without compression were almost identical, and the process of decompressing the data of a single frame on a GPU took only several milliseconds. Moreover, the proposed method has been implemented in a 64-channel system that we built in-house to demonstrate the feasibility of the proposed algorithm in a real system. It was found that channel data from a 64-channel system can be transferred using the standard USB 3.0 interface in most practical imaging applications.

A Compressed Sensing-based Image Reconstruction Algorithm for Solar Flare X-Ray Observations

NASA Astrophysics Data System (ADS)

Felix, Simon; Bolzern, Roman; Battaglia, Marina

2017-11-01

One way of imaging X-ray emission from solar flares is to measure Fourier components of the spatial X-ray source distribution. We present a new compressed sensing-based algorithm named VIS_CS, which reconstructs the spatial distribution from such Fourier components. We demonstrate the application of the algorithm on synthetic and observed solar flare X-ray data from the Reuven Ramaty High Energy Solar Spectroscopic Imager satellite and compare its performance with existing algorithms. VIS_CS produces competitive results with accurate photometry and morphology, without requiring any algorithm- and X-ray-source-specific parameter tuning. Its robustness and performance make this algorithm ideally suited for the generation of quicklook images or large image cubes without user intervention, such as for imaging spectroscopy analysis.

A Compressed Sensing-based Image Reconstruction Algorithm for Solar Flare X-Ray Observations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Felix, Simon; Bolzern, Roman; Battaglia, Marina, E-mail: simon.felix@fhnw.ch, E-mail: roman.bolzern@fhnw.ch, E-mail: marina.battaglia@fhnw.ch

One way of imaging X-ray emission from solar flares is to measure Fourier components of the spatial X-ray source distribution. We present a new compressed sensing-based algorithm named VIS-CS, which reconstructs the spatial distribution from such Fourier components. We demonstrate the application of the algorithm on synthetic and observed solar flare X-ray data from the Reuven Ramaty High Energy Solar Spectroscopic Imager satellite and compare its performance with existing algorithms. VIS-CS produces competitive results with accurate photometry and morphology, without requiring any algorithm- and X-ray-source-specific parameter tuning. Its robustness and performance make this algorithm ideally suited for the generation ofmore » quicklook images or large image cubes without user intervention, such as for imaging spectroscopy analysis.« less

Efficient two-dimensional compressive sensing in MIMO radar

NASA Astrophysics Data System (ADS)

Shahbazi, Nafiseh; Abbasfar, Aliazam; Jabbarian-Jahromi, Mohammad

2017-12-01

Compressive sensing (CS) has been a way to lower sampling rate leading to data reduction for processing in multiple-input multiple-output (MIMO) radar systems. In this paper, we further reduce the computational complexity of a pulse-Doppler collocated MIMO radar by introducing a two-dimensional (2D) compressive sensing. To do so, we first introduce a new 2D formulation for the compressed received signals and then we propose a new measurement matrix design for our 2D compressive sensing model that is based on minimizing the coherence of sensing matrix using gradient descent algorithm. The simulation results show that our proposed 2D measurement matrix design using gradient decent algorithm (2D-MMDGD) has much lower computational complexity compared to one-dimensional (1D) methods while having better performance in comparison with conventional methods such as Gaussian random measurement matrix.

A simple and efficient algorithm operating with linear time for MCEEG data compression.

PubMed

Titus, Geevarghese; Sudhakar, M S

2017-09-01

Popularisation of electroencephalograph (EEG) signals in diversified fields have increased the need for devices capable of operating at lower power and storage requirements. This has led to a great deal of research in data compression, that can address (a) low latency in the coding of the signal, (b) reduced hardware and software dependencies, (c) quantify the system anomalies, and (d) effectively reconstruct the compressed signal. This paper proposes a computationally simple and novel coding scheme named spatial pseudo codec (SPC), to achieve lossy to near lossless compression of multichannel EEG (MCEEG). In the proposed system, MCEEG signals are initially normalized, followed by two parallel processes: one operating on integer part and the other, on fractional part of the normalized data. The redundancies in integer part are exploited using spatial domain encoder, and the fractional part is coded as pseudo integers. The proposed method has been tested on a wide range of databases having variable sampling rates and resolutions. Results indicate that the algorithm has a good recovery performance with an average percentage root mean square deviation (PRD) of 2.72 for an average compression ratio (CR) of 3.16. Furthermore, the algorithm has a complexity of only O(n) with an average encoding and decoding time per sample of 0.3 ms and 0.04 ms respectively. The performance of the algorithm is comparable with recent methods like fast discrete cosine transform (fDCT) and tensor decomposition methods. The results validated the feasibility of the proposed compression scheme for practical MCEEG recording, archiving and brain computer interfacing systems.

Evaluation of H.264 and H.265 full motion video encoding for small UAS platforms

NASA Astrophysics Data System (ADS)

McGuinness, Christopher D.; Walker, David; Taylor, Clark; Hill, Kerry; Hoffman, Marc

2016-05-01

Of all the steps in the image acquisition and formation pipeline, compression is the only process that degrades image quality. A selected compression algorithm succeeds or fails to provide sufficient quality at the requested compression rate depending on how well the algorithm is suited to the input data. Applying an algorithm designed for one type of data to a different type often results in poor compression performance. This is mostly the case when comparing the performance of H.264, designed for standard definition data, to HEVC (High Efficiency Video Coding), which the Joint Collaborative Team on Video Coding (JCT-VC) designed for high-definition data. This study focuses on evaluating how HEVC compares to H.264 when compressing data from small UAS platforms. To compare the standards directly, we assess two open-source traditional software solutions: x264 and x265. These software-only comparisons allow us to establish a baseline of how much improvement can generally be expected of HEVC over H.264. Then, specific solutions leveraging different types of hardware are selected to understand the limitations of commercial-off-the-shelf (COTS) options. Algorithmically, regardless of the implementation, HEVC is found to provide similar quality video as H.264 at 40% lower data rates for video resolutions greater than 1280x720, roughly 1 Megapixel (MPx). For resolutions less than 1MPx, H.264 is an adequate solution though a small (roughly 20%) compression boost is earned by employing HEVC. New low cost, size, weight, and power (CSWAP) HEVC implementations are being developed and will be ideal for small UAS systems.

A novel high-frequency encoding algorithm for image compression

NASA Astrophysics Data System (ADS)

Siddeq, Mohammed M.; Rodrigues, Marcos A.

2017-12-01

In this paper, a new method for image compression is proposed whose quality is demonstrated through accurate 3D reconstruction from 2D images. The method is based on the discrete cosine transform (DCT) together with a high-frequency minimization encoding algorithm at compression stage and a new concurrent binary search algorithm at decompression stage. The proposed compression method consists of five main steps: (1) divide the image into blocks and apply DCT to each block; (2) apply a high-frequency minimization method to the AC-coefficients reducing each block by 2/3 resulting in a minimized array; (3) build a look up table of probability data to enable the recovery of the original high frequencies at decompression stage; (4) apply a delta or differential operator to the list of DC-components; and (5) apply arithmetic encoding to the outputs of steps (2) and (4). At decompression stage, the look up table and the concurrent binary search algorithm are used to reconstruct all high-frequency AC-coefficients while the DC-components are decoded by reversing the arithmetic coding. Finally, the inverse DCT recovers the original image. We tested the technique by compressing and decompressing 2D images including images with structured light patterns for 3D reconstruction. The technique is compared with JPEG and JPEG2000 through 2D and 3D RMSE. Results demonstrate that the proposed compression method is perceptually superior to JPEG with equivalent quality to JPEG2000. Concerning 3D surface reconstruction from images, it is demonstrated that the proposed method is superior to both JPEG and JPEG2000.

Compressing climate model simulations: reducing storage burden while preserving information

NASA Astrophysics Data System (ADS)

Hammerling, Dorit; Baker, Allison; Xu, Haiying; Clyne, John; Li, Samuel

2017-04-01

Climate models, which are run at high spatial and temporal resolutions, generate massive quantities of data. As our computing capabilities continue to increase, storing all of the generated data is becoming a bottleneck, which negatively affects scientific progress. It is thus important to develop methods for representing the full datasets by smaller compressed versions, which still preserve all the critical information and, as an added benefit, allow for faster read and write operations during analysis work. Traditional lossy compression algorithms, as for example used for image files, are not necessarily ideally suited for climate data. While visual appearance is relevant, climate data has additional critical features such as the preservation of extreme values and spatial and temporal gradients. Developing alternative metrics to quantify information loss in a manner that is meaningful to climate scientists is an ongoing process still in its early stages. We will provide an overview of current efforts to develop such metrics to assess existing algorithms and to guide the development of tailored compression algorithms to address this pressing challenge.

Evaluating the effect of online data compression on the disk cache of a mass storage system

NASA Technical Reports Server (NTRS)

Pentakalos, Odysseas I.; Yesha, Yelena

1994-01-01

A trace driven simulation of the disk cache of a mass storage system was used to evaluate the effect of an online compression algorithm on various performance measures. Traces from the system at NASA's Center for Computational Sciences were used to run the simulation and disk cache hit ratios, number of files and bytes migrating to tertiary storage were measured. The measurements were performed for both an LRU and a size based migration algorithm. In addition to seeing the effect of online data compression on the disk cache performance measure, the simulation provided insight into the characteristics of the interactive references, suggesting that hint based prefetching algorithms are the only alternative for any future improvements to the disk cache hit ratio.

Lossless compression algorithm for REBL direct-write e-beam lithography system

NASA Astrophysics Data System (ADS)

Cramer, George; Liu, Hsin-I.; Zakhor, Avideh

2010-03-01

Future lithography systems must produce microchips with smaller feature sizes, while maintaining throughputs comparable to those of today's optical lithography systems. This places stringent constraints on the effective data throughput of any maskless lithography system. In recent years, we have developed a datapath architecture for direct-write lithography systems, and have shown that compression plays a key role in reducing throughput requirements of such systems. Our approach integrates a low complexity hardware-based decoder with the writers, in order to decompress a compressed data layer in real time on the fly. In doing so, we have developed a spectrum of lossless compression algorithms for integrated circuit layout data to provide a tradeoff between compression efficiency and hardware complexity, the latest of which is Block Golomb Context Copy Coding (Block GC3). In this paper, we present a modified version of Block GC3 called Block RGC3, specifically tailored to the REBL direct-write E-beam lithography system. Two characteristic features of the REBL system are a rotary stage resulting in arbitrarily-rotated layout imagery, and E-beam corrections prior to writing the data, both of which present significant challenges to lossless compression algorithms. Together, these effects reduce the effectiveness of both the copy and predict compression methods within Block GC3. Similar to Block GC3, our newly proposed technique Block RGC3, divides the image into a grid of two-dimensional "blocks" of pixels, each of which copies from a specified location in a history buffer of recently-decoded pixels. However, in Block RGC3 the number of possible copy locations is significantly increased, so as to allow repetition to be discovered along any angle of orientation, rather than horizontal or vertical. Also, by copying smaller groups of pixels at a time, repetition in layout patterns is easier to find and take advantage of. As a side effect, this increases the total number of copy locations to transmit; this is combated with an extra region-growing step, which enforces spatial coherence among neighboring copy locations, thereby improving compression efficiency. We characterize the performance of Block RGC3 in terms of compression efficiency and encoding complexity on a number of rotated Metal 1, Poly, and Via layouts at various angles, and show that Block RGC3 provides higher compression efficiency than existing lossless compression algorithms, including JPEG-LS, ZIP, BZIP2, and Block GC3.

Definition of fractal topography to essential understanding of scale-invariance

NASA Astrophysics Data System (ADS)

Jin, Yi; Wu, Ying; Li, Hui; Zhao, Mengyu; Pan, Jienan

2017-04-01

Fractal behavior is scale-invariant and widely characterized by fractal dimension. However, the cor-respondence between them is that fractal behavior uniquely determines a fractal dimension while a fractal dimension can be related to many possible fractal behaviors. Therefore, fractal behavior is independent of the fractal generator and its geometries, spatial pattern, and statistical properties in addition to scale. To mathematically describe fractal behavior, we propose a novel concept of fractal topography defined by two scale-invariant parameters, scaling lacunarity (P) and scaling coverage (F). The scaling lacunarity is defined as the scale ratio between two successive fractal generators, whereas the scaling coverage is defined as the number ratio between them. Consequently, a strictly scale-invariant definition for self-similar fractals can be derived as D = log F /log P. To reflect the direction-dependence of fractal behaviors, we introduce another parameter Hxy, a general Hurst exponent, which is analytically expressed by Hxy = log Px/log Py where Px and Py are the scaling lacunarities in the x and y directions, respectively. Thus, a unified definition of fractal dimension is proposed for arbitrary self-similar and self-affine fractals by averaging the fractal dimensions of all directions in a d-dimensional space, which . Our definitions provide a theoretical, mechanistic basis for understanding the essentials of the scale-invariant property that reduces the complexity of modeling fractals.

Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach

PubMed Central

Al-Ali, Abdulla; Mohamed, Amr; Ward, Rabab

2018-01-01

Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B/K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compression performance (CR=6 and PRD=1.88) and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity) over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring. PMID:29337892

Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach.

PubMed

Elgendi, Mohamed; Al-Ali, Abdulla; Mohamed, Amr; Ward, Rabab

2018-01-16

Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B / K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compression performance ( CR = 6 and PRD = 1.88 ) and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity) over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring.

Comparison of reversible methods for data compression

NASA Astrophysics Data System (ADS)

Heer, Volker K.; Reinfelder, Hans-Erich

1990-07-01

Widely differing methods for data compression described in the ACR-NEMA draft are used in medical imaging. In our contribution we will review various methods briefly and discuss the relevant advantages and disadvantages. In detail we evaluate 1st order DPCM pyramid transformation and S transformation. We compare as coding algorithms both fixed and adaptive Huffman coding and Lempel-Ziv coding. Our comparison is performed on typical medical images from CT MR DSA and DLR (Digital Luminescence Radiography). Apart from the achieved compression factors we take into account CPU time required and main memory requirement both for compression and for decompression. For a realistic comparison we have implemented the mentioned algorithms in the C program language on a MicroVAX II and a SPARC station 1. 2.

The FBI compression standard for digitized fingerprint images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brislawn, C.M.; Bradley, J.N.; Onyshczak, R.J.

1996-10-01

The FBI has formulated national standards for digitization and compression of gray-scale fingerprint images. The compression algorithm for the digitized images is based on adaptive uniform scalar quantization of a discrete wavelet transform subband decomposition, a technique referred to as the wavelet/scalar quantization method. The algorithm produces archival-quality images at compression ratios of around 15 to 1 and will allow the current database of paper fingerprint cards to be replaced by digital imagery. A compliance testing program is also being implemented to ensure high standards of image quality and interchangeability of data between different implementations. We will review the currentmore » status of the FBI standard, including the compliance testing process and the details of the first-generation encoder.« less

FBI compression standard for digitized fingerprint images

NASA Astrophysics Data System (ADS)

Brislawn, Christopher M.; Bradley, Jonathan N.; Onyshczak, Remigius J.; Hopper, Thomas

1996-11-01

The FBI has formulated national standards for digitization and compression of gray-scale fingerprint images. The compression algorithm for the digitized images is based on adaptive uniform scalar quantization of a discrete wavelet transform subband decomposition, a technique referred to as the wavelet/scalar quantization method. The algorithm produces archival-quality images at compression ratios of around 15 to 1 and will allow the current database of paper fingerprint cards to be replaced by digital imagery. A compliance testing program is also being implemented to ensure high standards of image quality and interchangeability of data between different implementations. We will review the current status of the FBI standard, including the compliance testing process and the details of the first-generation encoder.

A compression scheme for radio data in high performance computing

NASA Astrophysics Data System (ADS)

Masui, K.; Amiri, M.; Connor, L.; Deng, M.; Fandino, M.; Höfer, C.; Halpern, M.; Hanna, D.; Hincks, A. D.; Hinshaw, G.; Parra, J. M.; Newburgh, L. B.; Shaw, J. R.; Vanderlinde, K.

2015-09-01

We present a procedure for efficiently compressing astronomical radio data for high performance applications. Integrated, post-correlation data are first passed through a nearly lossless rounding step which compares the precision of the data to a generalized and calibration-independent form of the radiometer equation. This allows the precision of the data to be reduced in a way that has an insignificant impact on the data. The newly developed Bitshuffle lossless compression algorithm is subsequently applied. When the algorithm is used in conjunction with the HDF5 library and data format, data produced by the CHIME Pathfinder telescope is compressed to 28% of its original size and decompression throughputs in excess of 1 GB/s are obtained on a single core.

Application of content-based image compression to telepathology

NASA Astrophysics Data System (ADS)

Varga, Margaret J.; Ducksbury, Paul G.; Callagy, Grace

2002-05-01

Telepathology is a means of practicing pathology at a distance, viewing images on a computer display rather than directly through a microscope. Without compression, images take too long to transmit to a remote location and are very expensive to store for future examination. However, to date the use of compressed images in pathology remains controversial. This is because commercial image compression algorithms such as JPEG achieve data compression without knowledge of the diagnostic content. Often images are lossily compressed at the expense of corrupting informative content. None of the currently available lossy compression techniques are concerned with what information has been preserved and what data has been discarded. Their sole objective is to compress and transmit the images as fast as possible. By contrast, this paper presents a novel image compression technique, which exploits knowledge of the slide diagnostic content. This 'content based' approach combines visually lossless and lossy compression techniques, judiciously applying each in the appropriate context across an image so as to maintain 'diagnostic' information while still maximising the possible compression. Standard compression algorithms, e.g. wavelets, can still be used, but their use in a context sensitive manner can offer high compression ratios and preservation of diagnostically important information. When compared with lossless compression the novel content-based approach can potentially provide the same degree of information with a smaller amount of data. When compared with lossy compression it can provide more information for a given amount of compression. The precise gain in the compression performance depends on the application (e.g. database archive or second opinion consultation) and the diagnostic content of the images.

Observer detection of image degradation caused by irreversible data compression processes

NASA Astrophysics Data System (ADS)

Chen, Ji; Flynn, Michael J.; Gross, Barry; Spizarny, David

1991-05-01

Irreversible data compression methods have been proposed to reduce the data storage and communication requirements of digital imaging systems. In general, the error produced by compression increases as an algorithm''s compression ratio is increased. We have studied the relationship between compression ratios and the detection of induced error using radiologic observers. The nature of the errors was characterized by calculating the power spectrum of the difference image. In contrast with studies designed to test whether detected errors alter diagnostic decisions, this study was designed to test whether observers could detect the induced error. A paired-film observer study was designed to test whether induced errors were detected. The study was conducted with chest radiographs selected and ranked for subtle evidence of interstitial disease, pulmonary nodules, or pneumothoraces. Images were digitized at 86 microns (4K X 5K) and 2K X 2K regions were extracted. A full-frame discrete cosine transform method was used to compress images at ratios varying between 6:1 and 60:1. The decompressed images were reprinted next to the original images in a randomized order with a laser film printer. The use of a film digitizer and a film printer which can reproduce all of the contrast and detail in the original radiograph makes the results of this study insensitive to instrument performance and primarily dependent on radiographic image quality. The results of this study define conditions for which errors associated with irreversible compression cannot be detected by radiologic observers. The results indicate that an observer can detect the errors introduced by this compression algorithm for compression ratios of 10:1 (1.2 bits/pixel) or higher.

Integer cosine transform for image compression

NASA Technical Reports Server (NTRS)

Cheung, K.-M.; Pollara, F.; Shahshahani, M.

1991-01-01

This article describes a recently introduced transform algorithm called the integer cosine transform (ICT), which is used in transform-based data compression schemes. The ICT algorithm requires only integer operations on small integers and at the same time gives a rate-distortion performance comparable to that offered by the floating-point discrete cosine transform (DCT). The article addresses the issue of implementation complexity, which is of prime concern for source coding applications of interest in deep-space communications. Complexity reduction in the transform stage of the compression scheme is particularly relevant, since this stage accounts for most (typically over 80 percent) of the computational load.

The CCSDS Lossless Data Compression Algorithm for Space Applications

NASA Technical Reports Server (NTRS)

Yeh, Pen-Shu; Day, John H. (Technical Monitor)

2001-01-01

In the late 80's, when the author started working at the Goddard Space Flight Center (GSFC) for the National Aeronautics and Space Administration (NASA), several scientists there were in the process of formulating the next generation of Earth viewing science instruments, the Moderate Resolution Imaging Spectroradiometer (MODIS). The instrument would have over thirty spectral bands and would transmit enormous data through the communications channel. This was when the author was assigned the task of investigating lossless compression algorithms for space implementation to compress science data in order to reduce the requirement on bandwidth and storage.

Computing sparse derivatives and consecutive zeros problem

NASA Astrophysics Data System (ADS)

Chandra, B. V. Ravi; Hossain, Shahadat

2013-02-01

We describe a substitution based sparse Jacobian matrix determination method using algorithmic differentiation. Utilizing the a priori known sparsity pattern, a compression scheme is determined using graph coloring. The "compressed pattern" of the Jacobian matrix is then reordered into a form suitable for computation by substitution. We show that the column reordering of the compressed pattern matrix (so as to align the zero entries into consecutive locations in each row) can be viewed as a variant of traveling salesman problem. Preliminary computational results show that on the test problems the performance of nearest-neighbor type heuristic algorithms is highly encouraging.

SEMG signal compression based on two-dimensional techniques.

PubMed

de Melo, Wheidima Carneiro; de Lima Filho, Eddie Batista; da Silva Júnior, Waldir Sabino

2016-04-18

Recently, two-dimensional techniques have been successfully employed for compressing surface electromyographic (SEMG) records as images, through the use of image and video encoders. Such schemes usually provide specific compressors, which are tuned for SEMG data, or employ preprocessing techniques, before the two-dimensional encoding procedure, in order to provide a suitable data organization, whose correlations can be better exploited by off-the-shelf encoders. Besides preprocessing input matrices, one may also depart from those approaches and employ an adaptive framework, which is able to directly tackle SEMG signals reassembled as images. This paper proposes a new two-dimensional approach for SEMG signal compression, which is based on a recurrent pattern matching algorithm called multidimensional multiscale parser (MMP). The mentioned encoder was modified, in order to efficiently work with SEMG signals and exploit their inherent redundancies. Moreover, a new preprocessing technique, named as segmentation by similarity (SbS), which has the potential to enhance the exploitation of intra- and intersegment correlations, is introduced, the percentage difference sorting (PDS) algorithm is employed, with different image compressors, and results with the high efficiency video coding (HEVC), H.264/AVC, and JPEG2000 encoders are presented. Experiments were carried out with real isometric and dynamic records, acquired in laboratory. Dynamic signals compressed with H.264/AVC and HEVC, when combined with preprocessing techniques, resulted in good percent root-mean-square difference [Formula: see text] compression factor figures, for low and high compression factors, respectively. Besides, regarding isometric signals, the modified two-dimensional MMP algorithm outperformed state-of-the-art schemes, for low compression factors, the combination between SbS and HEVC proved to be competitive, for high compression factors, and JPEG2000, combined with PDS, provided good performance allied to low computational complexity, all in terms of percent root-mean-square difference [Formula: see text] compression factor. The proposed schemes are effective and, specifically, the modified MMP algorithm can be considered as an interesting alternative for isometric signals, regarding traditional SEMG encoders. Besides, the approach based on off-the-shelf image encoders has the potential of fast implementation and dissemination, given that many embedded systems may already have such encoders available, in the underlying hardware/software architecture.

Real-time demonstration hardware for enhanced DPCM video compression algorithm

NASA Technical Reports Server (NTRS)

Bizon, Thomas P.; Whyte, Wayne A., Jr.; Marcopoli, Vincent R.

1992-01-01

The lack of available wideband digital links as well as the complexity of implementation of bandwidth efficient digital video CODECs (encoder/decoder) has worked to keep the cost of digital television transmission too high to compete with analog methods. Terrestrial and satellite video service providers, however, are now recognizing the potential gains that digital video compression offers and are proposing to incorporate compression systems to increase the number of available program channels. NASA is similarly recognizing the benefits of and trend toward digital video compression techniques for transmission of high quality video from space and therefore, has developed a digital television bandwidth compression algorithm to process standard National Television Systems Committee (NTSC) composite color television signals. The algorithm is based on differential pulse code modulation (DPCM), but additionally utilizes a non-adaptive predictor, non-uniform quantizer and multilevel Huffman coder to reduce the data rate substantially below that achievable with straight DPCM. The non-adaptive predictor and multilevel Huffman coder combine to set this technique apart from other DPCM encoding algorithms. All processing is done on a intra-field basis to prevent motion degradation and minimize hardware complexity. Computer simulations have shown the algorithm will produce broadcast quality reconstructed video at an average transmission rate of 1.8 bits/pixel. Hardware implementation of the DPCM circuit, non-adaptive predictor and non-uniform quantizer has been completed, providing realtime demonstration of the image quality at full video rates. Video sampling/reconstruction circuits have also been constructed to accomplish the analog video processing necessary for the real-time demonstration. Performance results for the completed hardware compare favorably with simulation results. Hardware implementation of the multilevel Huffman encoder/decoder is currently under development along with implementation of a buffer control algorithm to accommodate the variable data rate output of the multilevel Huffman encoder. A video CODEC of this type could be used to compress NTSC color television signals where high quality reconstruction is desirable (e.g., Space Station video transmission, transmission direct-to-the-home via direct broadcast satellite systems or cable television distribution to system headends and direct-to-the-home).

Fusion of High Resolution Multispectral Imagery in Vulnerable Coastal and Land Ecosystems.

PubMed

Ibarrola-Ulzurrun, Edurne; Gonzalo-Martin, Consuelo; Marcello-Ruiz, Javier; Garcia-Pedrero, Angel; Rodriguez-Esparragon, Dionisio

2017-01-25

Ecosystems provide a wide variety of useful resources that enhance human welfare, but these resources are declining due to climate change and anthropogenic pressure. In this work, three vulnerable ecosystems, including shrublands, coastal areas with dunes systems and areas of shallow water, are studied. As far as these resources' reduction is concerned, remote sensing and image processing techniques could contribute to the management of these natural resources in a practical and cost-effective way, although some improvements are needed for obtaining a higher quality of the information available. An important quality improvement is the fusion at the pixel level. Hence, the objective of this work is to assess which pansharpening technique provides the best fused image for the different types of ecosystems. After a preliminary evaluation of twelve classic and novel fusion algorithms, a total of four pansharpening algorithms was analyzed using six quality indices. The quality assessment was implemented not only for the whole set of multispectral bands, but also for the subset of spectral bands covered by the wavelength range of the panchromatic image and outside of it. A better quality result is observed in the fused image using only the bands covered by the panchromatic band range. It is important to highlight the use of these techniques not only in land and urban areas, but a novel analysis in areas of shallow water ecosystems. Although the algorithms do not show a high difference in land and coastal areas, coastal ecosystems require simpler algorithms, such as fast intensity hue saturation, whereas more heterogeneous ecosystems need advanced algorithms, as weighted wavelet ' à trous ' through fractal dimension maps for shrublands and mixed ecosystems. Moreover, quality map analysis was carried out in order to study the fusion result in each band at the local level. Finally, to demonstrate the performance of these pansharpening techniques, advanced Object-Based (OBIA) support vector machine classification was applied, and a thematic map for the shrubland ecosystem was obtained, which corroborates wavelet ' à trous ' through fractal dimension maps as the best fusion algorithm for this ecosystem.

Fusion of High Resolution Multispectral Imagery in Vulnerable Coastal and Land Ecosystems

PubMed Central

Ibarrola-Ulzurrun, Edurne; Gonzalo-Martin, Consuelo; Marcello-Ruiz, Javier; Garcia-Pedrero, Angel; Rodriguez-Esparragon, Dionisio

2017-01-01

Ecosystems provide a wide variety of useful resources that enhance human welfare, but these resources are declining due to climate change and anthropogenic pressure. In this work, three vulnerable ecosystems, including shrublands, coastal areas with dunes systems and areas of shallow water, are studied. As far as these resources’ reduction is concerned, remote sensing and image processing techniques could contribute to the management of these natural resources in a practical and cost-effective way, although some improvements are needed for obtaining a higher quality of the information available. An important quality improvement is the fusion at the pixel level. Hence, the objective of this work is to assess which pansharpening technique provides the best fused image for the different types of ecosystems. After a preliminary evaluation of twelve classic and novel fusion algorithms, a total of four pansharpening algorithms was analyzed using six quality indices. The quality assessment was implemented not only for the whole set of multispectral bands, but also for the subset of spectral bands covered by the wavelength range of the panchromatic image and outside of it. A better quality result is observed in the fused image using only the bands covered by the panchromatic band range. It is important to highlight the use of these techniques not only in land and urban areas, but a novel analysis in areas of shallow water ecosystems. Although the algorithms do not show a high difference in land and coastal areas, coastal ecosystems require simpler algorithms, such as fast intensity hue saturation, whereas more heterogeneous ecosystems need advanced algorithms, as weighted wavelet ‘à trous’ through fractal dimension maps for shrublands and mixed ecosystems. Moreover, quality map analysis was carried out in order to study the fusion result in each band at the local level. Finally, to demonstrate the performance of these pansharpening techniques, advanced Object-Based (OBIA) support vector machine classification was applied, and a thematic map for the shrubland ecosystem was obtained, which corroborates wavelet ‘à trous’ through fractal dimension maps as the best fusion algorithm for this ecosystem. PMID:28125055

Fractals: To Know, to Do, to Simulate.

ERIC Educational Resources Information Center

Talanquer, Vicente; Irazoque, Glinda

1993-01-01

Discusses the development of fractal theory and suggests fractal aggregates as an attractive alternative for introducing fractal concepts. Describes methods for producing metallic fractals and a computer simulation for drawing fractals. (MVL)

The effects of video compression on acceptability of images for monitoring life sciences experiments

NASA Astrophysics Data System (ADS)

Haines, Richard F.; Chuang, Sherry L.

1992-07-01

Future manned space operations for Space Station Freedom will call for a variety of carefully planned multimedia digital communications, including full-frame-rate color video, to support remote operations of scientific experiments. This paper presents the results of an investigation to determine if video compression is a viable solution to transmission bandwidth constraints. It reports on the impact of different levels of compression and associated calculational parameters on image acceptability to investigators in life-sciences research at ARC. Three nonhuman life-sciences disciplines (plant, rodent, and primate biology) were selected for this study. A total of 33 subjects viewed experimental scenes in their own scientific disciplines. Ten plant scientists viewed still images of wheat stalks at various stages of growth. Each image was compressed to four different compression levels using the Joint Photographic Expert Group (JPEG) standard algorithm, and the images were presented in random order. Twelve and eleven staffmembers viewed 30-sec videotaped segments showing small rodents and a small primate, respectively. Each segment was repeated at four different compression levels in random order using an inverse cosine transform (ICT) algorithm. Each viewer made a series of subjective image-quality ratings. There was a significant difference in image ratings according to the type of scene viewed within disciplines; thus, ratings were scene dependent. Image (still and motion) acceptability does, in fact, vary according to compression level. The JPEG still-image-compression levels, even with the large range of 5:1 to 120:1 in this study, yielded equally high levels of acceptability. In contrast, the ICT algorithm for motion compression yielded a sharp decline in acceptability below 768 kb/sec. Therefore, if video compression is to be used as a solution for overcoming transmission bandwidth constraints, the effective management of the ratio and compression parameters according to scientific discipline and experiment type is critical to the success of remote experiments.

The effects of video compression on acceptability of images for monitoring life sciences experiments

NASA Technical Reports Server (NTRS)

Haines, Richard F.; Chuang, Sherry L.

1992-01-01

Future manned space operations for Space Station Freedom will call for a variety of carefully planned multimedia digital communications, including full-frame-rate color video, to support remote operations of scientific experiments. This paper presents the results of an investigation to determine if video compression is a viable solution to transmission bandwidth constraints. It reports on the impact of different levels of compression and associated calculational parameters on image acceptability to investigators in life-sciences research at ARC. Three nonhuman life-sciences disciplines (plant, rodent, and primate biology) were selected for this study. A total of 33 subjects viewed experimental scenes in their own scientific disciplines. Ten plant scientists viewed still images of wheat stalks at various stages of growth. Each image was compressed to four different compression levels using the Joint Photographic Expert Group (JPEG) standard algorithm, and the images were presented in random order. Twelve and eleven staffmembers viewed 30-sec videotaped segments showing small rodents and a small primate, respectively. Each segment was repeated at four different compression levels in random order using an inverse cosine transform (ICT) algorithm. Each viewer made a series of subjective image-quality ratings. There was a significant difference in image ratings according to the type of scene viewed within disciplines; thus, ratings were scene dependent. Image (still and motion) acceptability does, in fact, vary according to compression level. The JPEG still-image-compression levels, even with the large range of 5:1 to 120:1 in this study, yielded equally high levels of acceptability. In contrast, the ICT algorithm for motion compression yielded a sharp decline in acceptability below 768 kb/sec. Therefore, if video compression is to be used as a solution for overcoming transmission bandwidth constraints, the effective management of the ratio and compression parameters according to scientific discipline and experiment type is critical to the success of remote experiments.

A Streaming PCA VLSI Chip for Neural Data Compression.

PubMed

Wu, Tong; Zhao, Wenfeng; Guo, Hongsun; Lim, Hubert H; Yang, Zhi

2017-12-01

Neural recording system miniaturization and integration with low-power wireless technologies require compressing neural data before transmission. Feature extraction is a procedure to represent data in a low-dimensional space; its integration into a recording chip can be an efficient approach to compress neural data. In this paper, we propose a streaming principal component analysis algorithm and its microchip implementation to compress multichannel local field potential (LFP) and spike data. The circuits have been designed in a 65-nm CMOS technology and occupy a silicon area of 0.06 mm. Throughout the experiments, the chip compresses LFPs by 10 at the expense of as low as 1% reconstruction errors and 144-nW/channel power consumption; for spikes, the achieved compression ratio is 25 with 8% reconstruction errors and 3.05-W/channel power consumption. In addition, the algorithm and its hardware architecture can swiftly adapt to nonstationary spiking activities, which enables efficient hardware sharing among multiple channels to support a high-channel count recorder.

Optimizing management of the condensing heat and cooling of gases compression in oxy block using of a genetic algorithm

NASA Astrophysics Data System (ADS)

Brzęczek, Mateusz; Bartela, Łukasz

2013-12-01

This paper presents the parameters of the reference oxy combustion block operating with supercritical steam parameters, equipped with an air separation unit and a carbon dioxide capture and compression installation. The possibility to recover the heat in the analyzed power plant is discussed. The decision variables and the thermodynamic functions for the optimization algorithm were identified. The principles of operation of genetic algorithm and methodology of conducted calculations are presented. The sensitivity analysis was performed for the best solutions to determine the effects of the selected variables on the power and efficiency of the unit. Optimization of the heat recovery from the air separation unit, flue gas condition and CO2 capture and compression installation using genetic algorithm was designed to replace the low-pressure section of the regenerative water heaters of steam cycle in analyzed unit. The result was to increase the power and efficiency of the entire power plant.

Flood inundation extent mapping based on block compressed tracing

NASA Astrophysics Data System (ADS)

Shen, Dingtao; Rui, Yikang; Wang, Jiechen; Zhang, Yu; Cheng, Liang

2015-07-01

Flood inundation extent, depth, and duration are important factors affecting flood hazard evaluation. At present, flood inundation analysis is based mainly on a seeded region-growing algorithm, which is an inefficient process because it requires excessive recursive computations and it is incapable of processing massive datasets. To address this problem, we propose a block compressed tracing algorithm for mapping the flood inundation extent, which reads the DEM data in blocks before transferring them to raster compression storage. This allows a smaller computer memory to process a larger amount of data, which solves the problem of the regular seeded region-growing algorithm. In addition, the use of a raster boundary tracing technique allows the algorithm to avoid the time-consuming computations required by the seeded region-growing. Finally, we conduct a comparative evaluation in the Chin-sha River basin, results show that the proposed method solves the problem of flood inundation extent mapping based on massive DEM datasets with higher computational efficiency than the original method, which makes it suitable for practical applications.

Quantum autoencoders for efficient compression of quantum data

NASA Astrophysics Data System (ADS)

Romero, Jonathan; Olson, Jonathan P.; Aspuru-Guzik, Alan

2017-12-01

Classical autoencoders are neural networks that can learn efficient low-dimensional representations of data in higher-dimensional space. The task of an autoencoder is, given an input x, to map x to a lower dimensional point y such that x can likely be recovered from y. The structure of the underlying autoencoder network can be chosen to represent the data on a smaller dimension, effectively compressing the input. Inspired by this idea, we introduce the model of a quantum autoencoder to perform similar tasks on quantum data. The quantum autoencoder is trained to compress a particular data set of quantum states, where a classical compression algorithm cannot be employed. The parameters of the quantum autoencoder are trained using classical optimization algorithms. We show an example of a simple programmable circuit that can be trained as an efficient autoencoder. We apply our model in the context of quantum simulation to compress ground states of the Hubbard model and molecular Hamiltonians.

Temporal compressive imaging for video

NASA Astrophysics Data System (ADS)

Zhou, Qun; Zhang, Linxia; Ke, Jun

2018-01-01

In many situations, imagers are required to have higher imaging speed, such as gunpowder blasting analysis and observing high-speed biology phenomena. However, measuring high-speed video is a challenge to camera design, especially, in infrared spectrum. In this paper, we reconstruct a high-frame-rate video from compressive video measurements using temporal compressive imaging (TCI) with a temporal compression ratio T=8. This means that, 8 unique high-speed temporal frames will be obtained from a single compressive frame using a reconstruction algorithm. Equivalently, the video frame rates is increased by 8 times. Two methods, two-step iterative shrinkage/threshold (TwIST) algorithm and the Gaussian mixture model (GMM) method, are used for reconstruction. To reduce reconstruction time and memory usage, each frame of size 256×256 is divided into patches of size 8×8. The influence of different coded mask to reconstruction is discussed. The reconstruction qualities using TwIST and GMM are also compared.

Real-Time Aggressive Image Data Compression

DTIC Science & Technology

1990-03-31

implemented with higher degrees of modularity, concurrency, and higher levels of machine intelligence , thereby providing higher data -throughput rates...Project Summary Project Title: Real-Time Aggressive Image Data Compression Principal Investigators: Dr. Yih-Fang Huang and Dr. Ruey-wen Liu Institution...Summary The objective of the proposed research is to develop reliable algorithms !.hat can achieve aggressive image data compression (with a compression

A Distributed Compressive Sensing Scheme for Event Capture in Wireless Visual Sensor Networks

NASA Astrophysics Data System (ADS)

Hou, Meng; Xu, Sen; Wu, Weiling; Lin, Fei

2018-01-01

Image signals which acquired by wireless visual sensor network can be used for specific event capture. This event capture is realized by image processing at the sink node. A distributed compressive sensing scheme is used for the transmission of these image signals from the camera nodes to the sink node. A measurement and joint reconstruction algorithm for these image signals are proposed in this paper. Make advantage of spatial correlation between images within a sensing area, the cluster head node which as the image decoder can accurately co-reconstruct these image signals. The subjective visual quality and the reconstruction error rate are used for the evaluation of reconstructed image quality. Simulation results show that the joint reconstruction algorithm achieves higher image quality at the same image compressive rate than the independent reconstruction algorithm.

A Space-Saving Approximation Algorithm for Grammar-Based Compression

NASA Astrophysics Data System (ADS)

Sakamoto, Hiroshi; Maruyama, Shirou; Kida, Takuya; Shimozono, Shinichi

A space-efficient approximation algorithm for the grammar-based compression problem, which requests for a given string to find a smallest context-free grammar deriving the string, is presented. For the input length n and an optimum CFG size g, the algorithm consumes only O(g log g) space and O(n log*n) time to achieve O((log*n)log n) approximation ratio to the optimum compression, where log*n is the maximum number of logarithms satisfying log log…log n > 1. This ratio is thus regarded to almost O(log n), which is the currently best approximation ratio. While g depends on the string, it is known that g =Ω(log n) and g=\\\\Omega(\\\\log n) and g=O\\\\left(\\\\frac{n}{log_kn}\\\\right) for strings from k-letter alphabet[12].

A CAM-based LZ data compression IC

NASA Technical Reports Server (NTRS)

Winters, K.; Bode, R.; Schneider, E.

1993-01-01

A custom CMOS processor is introduced that implements the Data Compression Lempel-Ziv (DCLZ) standard, a variation of the LZ2 Algorithm. This component presently achieves a sustained compression and decompression rate of 10 megabytes/second by employing an on-chip content-addressable memory for string table storage.

Ultrasonic data compression via parameter estimation.

PubMed

Cardoso, Guilherme; Saniie, Jafar

2005-02-01

Ultrasonic imaging in medical and industrial applications often requires a large amount of data collection. Consequently, it is desirable to use data compression techniques to reduce data and to facilitate the analysis and remote access of ultrasonic information. The precise data representation is paramount to the accurate analysis of the shape, size, and orientation of ultrasonic reflectors, as well as to the determination of the properties of the propagation path. In this study, a successive parameter estimation algorithm based on a modified version of the continuous wavelet transform (CWT) to compress and denoise ultrasonic signals is presented. It has been shown analytically that the CWT (i.e., time x frequency representation) yields an exact solution for the time-of-arrival and a biased solution for the center frequency. Consequently, a modified CWT (MCWT) based on the Gabor-Helstrom transform is introduced as a means to exactly estimate both time-of-arrival and center frequency of ultrasonic echoes. Furthermore, the MCWT also has been used to generate a phase x bandwidth representation of the ultrasonic echo. This representation allows the exact estimation of the phase and the bandwidth. The performance of this algorithm for data compression and signal analysis is studied using simulated and experimental ultrasonic signals. The successive parameter estimation algorithm achieves a data compression ratio of (1-5N/J), where J is the number of samples and N is the number of echoes in the signal. For a signal with 10 echoes and 2048 samples, a compression ratio of 96% is achieved with a signal-to-noise ratio (SNR) improvement above 20 dB. Furthermore, this algorithm performs robustly, yields accurate echo estimation, and results in SNR enhancements ranging from 10 to 60 dB for composite signals having SNR as low as -10 dB.

Fractal analysis of INSAR and correlation with graph-cut based image registration for coastline deformation analysis: post seismic hazard assessment of the 2011 Tohoku earthquake region

NASA Astrophysics Data System (ADS)

Dutta, P. K.; Mishra, O. P.

2012-04-01

Satellite imagery for 2011 earthquake off the Pacific coast of Tohoku has provided an opportunity to conduct image transformation analyses by employing multi-temporal images retrieval techniques. In this study, we used a new image segmentation algorithm to image coastline deformation by adopting graph cut energy minimization framework. Comprehensive analysis of available INSAR images using coastline deformation analysis helped extract disaster information of the affected region of the 2011 Tohoku tsunamigenic earthquake source zone. We attempted to correlate fractal analysis of seismic clustering behavior with image processing analogies and our observations suggest that increase in fractal dimension distribution is associated with clustering of events that may determine the level of devastation of the region. The implementation of graph cut based image registration technique helps us to detect the devastation across the coastline of Tohoku through change of intensity of pixels that carries out regional segmentation for the change in coastal boundary after the tsunami. The study applies transformation parameters on remotely sensed images by manually segmenting the image to recovering translation parameter from two images that differ by rotation. Based on the satellite image analysis through image segmentation, it is found that the area of 0.997 sq km for the Honshu region was a maximum damage zone localized in the coastal belt of NE Japan forearc region. The analysis helps infer using matlab that the proposed graph cut algorithm is robust and more accurate than other image registration methods. The analysis shows that the method can give a realistic estimate for recovered deformation fields in pixels corresponding to coastline change which may help formulate the strategy for assessment during post disaster need assessment scenario for the coastal belts associated with damages due to strong shaking and tsunamis in the world under disaster risk mitigation programs.

Systems aspects of COBE science data compression

NASA Technical Reports Server (NTRS)

Freedman, I.; Boggess, E.; Seiler, E.

1993-01-01

A general approach to compression of diverse data from large scientific projects has been developed and this paper addresses the appropriate system and scientific constraints together with the algorithm development and test strategy. This framework has been implemented for the COsmic Background Explorer spacecraft (COBE) by retrofitting the existing VAS-based data management system with high-performance compression software permitting random access to the data. Algorithms which incorporate scientific knowledge and consume relatively few system resources are preferred over ad hoc methods. COBE exceeded its planned storage by a large and growing factor and the retrieval of data significantly affects the processing, delaying the availability of data for scientific usage and software test. Embedded compression software is planned to make the project tractable by reducing the data storage volume to an acceptable level during normal processing.

FRESCO: Referential compression of highly similar sequences.

PubMed

Wandelt, Sebastian; Leser, Ulf

2013-01-01

In many applications, sets of similar texts or sequences are of high importance. Prominent examples are revision histories of documents or genomic sequences. Modern high-throughput sequencing technologies are able to generate DNA sequences at an ever-increasing rate. In parallel to the decreasing experimental time and cost necessary to produce DNA sequences, computational requirements for analysis and storage of the sequences are steeply increasing. Compression is a key technology to deal with this challenge. Recently, referential compression schemes, storing only the differences between a to-be-compressed input and a known reference sequence, gained a lot of interest in this field. In this paper, we propose a general open-source framework to compress large amounts of biological sequence data called Framework for REferential Sequence COmpression (FRESCO). Our basic compression algorithm is shown to be one to two orders of magnitudes faster than comparable related work, while achieving similar compression ratios. We also propose several techniques to further increase compression ratios, while still retaining the advantage in speed: 1) selecting a good reference sequence; and 2) rewriting a reference sequence to allow for better compression. In addition,we propose a new way of further boosting the compression ratios by applying referential compression to already referentially compressed files (second-order compression). This technique allows for compression ratios way beyond state of the art, for instance,4,000:1 and higher for human genomes. We evaluate our algorithms on a large data set from three different species (more than 1,000 genomes, more than 3 TB) and on a collection of versions of Wikipedia pages. Our results show that real-time compression of highly similar sequences at high compression ratios is possible on modern hardware.

FIVQ algorithm for interference hyper-spectral image compression

NASA Astrophysics Data System (ADS)

Wen, Jia; Ma, Caiwen; Zhao, Junsuo

2014-07-01

Based on the improved vector quantization (IVQ) algorithm [1] which was proposed in 2012, this paper proposes a further improved vector quantization (FIVQ) algorithm for LASIS (Large Aperture Static Imaging Spectrometer) interference hyper-spectral image compression. To get better image quality, IVQ algorithm takes both the mean values and the VQ indices as the encoding rules. Although IVQ algorithm can improve both the bit rate and the image quality, it still can be further improved in order to get much lower bit rate for the LASIS interference pattern with the special optical characteristics based on the pushing and sweeping in LASIS imaging principle. In the proposed algorithm FIVQ, the neighborhood of the encoding blocks of the interference pattern image, which are using the mean value rules, will be checked whether they have the same mean value as the current processing block. Experiments show the proposed algorithm FIVQ can get lower bit rate compared to that of the IVQ algorithm for the LASIS interference hyper-spectral sequences.

Four-dimensional wavelet compression of arbitrarily sized echocardiographic data.

PubMed

Zeng, Li; Jansen, Christian P; Marsch, Stephan; Unser, Michael; Hunziker, Patrick R

2002-09-01

Wavelet-based methods have become most popular for the compression of two-dimensional medical images and sequences. The standard implementations consider data sizes that are powers of two. There is also a large body of literature treating issues such as the choice of the "optimal" wavelets and the performance comparison of competing algorithms. With the advent of telemedicine, there is a strong incentive to extend these techniques to higher dimensional data such as dynamic three-dimensional (3-D) echocardiography [four-dimensional (4-D) datasets]. One of the practical difficulties is that the size of this data is often not a multiple of a power of two, which can lead to increased computational complexity and impaired compression power. Our contribution in this paper is to present a genuine 4-D extension of the well-known zerotree algorithm for arbitrarily sized data. The key component of our method is a one-dimensional wavelet algorithm that can handle arbitrarily sized input signals. The method uses a pair of symmetric/antisymmetric wavelets (10/6) together with some appropriate midpoint symmetry boundary conditions that reduce border artifacts. The zerotree structure is also adapted so that it can accommodate noneven data splitting. We have applied our method to the compression of real 3-D dynamic sequences from clinical cardiac ultrasound examinations. Our new algorithm compares very favorably with other more ad hoc adaptations (image extension and tiling) of the standard powers-of-two methods, in terms of both compression performance and computational cost. It is vastly superior to slice-by-slice wavelet encoding. This was seen not only in numerical image quality parameters but also in expert ratings, where significant improvement using the new approach could be documented. Our validation experiments show that one can safely compress 4-D data sets at ratios of 128:1 without compromising the diagnostic value of the images. We also display some more extreme compression results at ratios of 2000:1 where some key diagnostically relevant key features are preserved.

Fractal Electronic Circuits Assembled From Nanoclusters

NASA Astrophysics Data System (ADS)

Fairbanks, M. S.; McCarthy, D.; Taylor, R. P.; Brown, S. A.

2009-07-01

Many patterns in nature can be described using fractal geometry. The effect of this fractal character is an array of properties that can include high internal connectivity, high dispersivity, and enhanced surface area to volume ratios. These properties are often desirable in applications and, consequently, fractal geometry is increasingly employed in technologies ranging from antenna to storm barriers. In this paper, we explore the application of fractal geometry to electrical circuits, inspired by the pervasive fractal structure of neurons in the brain. We show that, under appropriate growth conditions, nanoclusters of Sb form into islands on atomically flat substrates via a process close to diffusion-limited aggregation (DLA), establishing fractal islands that will form the basis of our fractal circuits. We perform fractal analysis of the islands to determine the spatial scaling properties (characterized by the fractal dimension, D) of the proposed circuits and demonstrate how varying growth conditions can affect D. We discuss fabrication approaches for establishing electrical contact to the fractal islands. Finally, we present fractal circuit simulations, which show that the fractal character of the circuit translates into novel, non-linear conduction properties determined by the circuit's D value.

Research on key technologies for data-interoperability-based metadata, data compression and encryption, and their application

NASA Astrophysics Data System (ADS)

Yu, Xu; Shao, Quanqin; Zhu, Yunhai; Deng, Yuejin; Yang, Haijun

2006-10-01

With the development of informationization and the separation between data management departments and application departments, spatial data sharing becomes one of the most important objectives for the spatial information infrastructure construction, and spatial metadata management system, data transmission security and data compression are the key technologies to realize spatial data sharing. This paper discusses the key technologies for metadata based on data interoperability, deeply researches the data compression algorithms such as adaptive Huffman algorithm, LZ77 and LZ78 algorithm, studies to apply digital signature technique to encrypt spatial data, which can not only identify the transmitter of spatial data, but also find timely whether the spatial data are sophisticated during the course of network transmission, and based on the analysis of symmetric encryption algorithms including 3DES,AES and asymmetric encryption algorithm - RAS, combining with HASH algorithm, presents a improved mix encryption method for spatial data. Digital signature technology and digital watermarking technology are also discussed. Then, a new solution of spatial data network distribution is put forward, which adopts three-layer architecture. Based on the framework, we give a spatial data network distribution system, which is efficient and safe, and also prove the feasibility and validity of the proposed solution.

New algorithms for processing time-series big EEG data within mobile health monitoring systems.

PubMed

Serhani, Mohamed Adel; Menshawy, Mohamed El; Benharref, Abdelghani; Harous, Saad; Navaz, Alramzana Nujum

2017-10-01

Recent advances in miniature biomedical sensors, mobile smartphones, wireless communications, and distributed computing technologies provide promising techniques for developing mobile health systems. Such systems are capable of monitoring epileptic seizures reliably, which are classified as chronic diseases. Three challenging issues raised in this context with regard to the transformation, compression, storage, and visualization of big data, which results from a continuous recording of epileptic seizures using mobile devices. In this paper, we address the above challenges by developing three new algorithms to process and analyze big electroencephalography data in a rigorous and efficient manner. The first algorithm is responsible for transforming the standard European Data Format (EDF) into the standard JavaScript Object Notation (JSON) and compressing the transformed JSON data to decrease the size and time through the transfer process and to increase the network transfer rate. The second algorithm focuses on collecting and storing the compressed files generated by the transformation and compression algorithm. The collection process is performed with respect to the on-the-fly technique after decompressing files. The third algorithm provides relevant real-time interaction with signal data by prospective users. It particularly features the following capabilities: visualization of single or multiple signal channels on a smartphone device and query data segments. We tested and evaluated the effectiveness of our approach through a software architecture model implementing a mobile health system to monitor epileptic seizures. The experimental findings from 45 experiments are promising and efficiently satisfy the approach's objectives in a price of linearity. Moreover, the size of compressed JSON files and transfer times are reduced by 10% and 20%, respectively, while the average total time is remarkably reduced by 67% through all performed experiments. Our approach successfully develops efficient algorithms in terms of processing time, memory usage, and energy consumption while maintaining a high scalability of the proposed solution. Our approach efficiently supports data partitioning and parallelism relying on the MapReduce platform, which can help in monitoring and automatic detection of epileptic seizures. Copyright © 2017 Elsevier B.V. All rights reserved.

A gradient based algorithm to solve inverse plane bimodular problems of identification

NASA Astrophysics Data System (ADS)

Ran, Chunjiang; Yang, Haitian; Zhang, Guoqing

2018-02-01

This paper presents a gradient based algorithm to solve inverse plane bimodular problems of identifying constitutive parameters, including tensile/compressive moduli and tensile/compressive Poisson's ratios. For the forward bimodular problem, a FE tangent stiffness matrix is derived facilitating the implementation of gradient based algorithms, for the inverse bimodular problem of identification, a two-level sensitivity analysis based strategy is proposed. Numerical verification in term of accuracy and efficiency is provided, and the impacts of initial guess, number of measurement points, regional inhomogeneity, and noisy data on the identification are taken into accounts.

Fast Compressive Tracking.

PubMed

Zhang, Kaihua; Zhang, Lei; Yang, Ming-Hsuan

2014-10-01

It is a challenging task to develop effective and efficient appearance models for robust object tracking due to factors such as pose variation, illumination change, occlusion, and motion blur. Existing online tracking algorithms often update models with samples from observations in recent frames. Despite much success has been demonstrated, numerous issues remain to be addressed. First, while these adaptive appearance models are data-dependent, there does not exist sufficient amount of data for online algorithms to learn at the outset. Second, online tracking algorithms often encounter the drift problems. As a result of self-taught learning, misaligned samples are likely to be added and degrade the appearance models. In this paper, we propose a simple yet effective and efficient tracking algorithm with an appearance model based on features extracted from a multiscale image feature space with data-independent basis. The proposed appearance model employs non-adaptive random projections that preserve the structure of the image feature space of objects. A very sparse measurement matrix is constructed to efficiently extract the features for the appearance model. We compress sample images of the foreground target and the background using the same sparse measurement matrix. The tracking task is formulated as a binary classification via a naive Bayes classifier with online update in the compressed domain. A coarse-to-fine search strategy is adopted to further reduce the computational complexity in the detection procedure. The proposed compressive tracking algorithm runs in real-time and performs favorably against state-of-the-art methods on challenging sequences in terms of efficiency, accuracy and robustness.

QualComp: a new lossy compressor for quality scores based on rate distortion theory

PubMed Central

2013-01-01

Background Next Generation Sequencing technologies have revolutionized many fields in biology by reducing the time and cost required for sequencing. As a result, large amounts of sequencing data are being generated. A typical sequencing data file may occupy tens or even hundreds of gigabytes of disk space, prohibitively large for many users. This data consists of both the nucleotide sequences and per-base quality scores that indicate the level of confidence in the readout of these sequences. Quality scores account for about half of the required disk space in the commonly used FASTQ format (before compression), and therefore the compression of the quality scores can significantly reduce storage requirements and speed up analysis and transmission of sequencing data. Results In this paper, we present a new scheme for the lossy compression of the quality scores, to address the problem of storage. Our framework allows the user to specify the rate (bits per quality score) prior to compression, independent of the data to be compressed. Our algorithm can work at any rate, unlike other lossy compression algorithms. We envisage our algorithm as being part of a more general compression scheme that works with the entire FASTQ file. Numerical experiments show that we can achieve a better mean squared error (MSE) for small rates (bits per quality score) than other lossy compression schemes. For the organism PhiX, whose assembled genome is known and assumed to be correct, we show that it is possible to achieve a significant reduction in size with little compromise in performance on downstream applications (e.g., alignment). Conclusions QualComp is an open source software package, written in C and freely available for download at https://sourceforge.net/projects/qualcomp. PMID:23758828

Music and fractals

NASA Astrophysics Data System (ADS)

Wuorinen, Charles

2015-03-01

Any of the arts may produce exemplars that have fractal characteristics. There may be fractal painting, fractal poetry, and the like. But these will always be specific instances, not necessarily displaying intrinsic properties of the art-medium itself. Only music, I believe, of all the arts possesses an intrinsically fractal character, so that its very nature is fractally determined. Thus, it is reasonable to assert that any instance of music is fractal...

Compressed sensing of ECG signal for wireless system with new fast iterative method.

PubMed

Tawfic, Israa; Kayhan, Sema

2015-12-01

Recent experiments in wireless body area network (WBAN) show that compressive sensing (CS) is a promising tool to compress the Electrocardiogram signal ECG signal. The performance of CS is based on algorithms use to reconstruct exactly or approximately the original signal. In this paper, we present two methods work with absence and presence of noise, these methods are Least Support Orthogonal Matching Pursuit (LS-OMP) and Least Support Denoising-Orthogonal Matching Pursuit (LSD-OMP). The algorithms achieve correct support recovery without requiring sparsity knowledge. We derive an improved restricted isometry property (RIP) based conditions over the best known results. The basic procedures are done by observational and analytical of a different Electrocardiogram signal downloaded them from PhysioBankATM. Experimental results show that significant performance in term of reconstruction quality and compression rate can be obtained by these two new proposed algorithms, and help the specialist gathering the necessary information from the patient in less time if we use Magnetic Resonance Imaging (MRI) application, or reconstructed the patient data after sending it through the network. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Automatic attention-based prioritization of unconstrained video for compression

NASA Astrophysics Data System (ADS)

Itti, Laurent

2004-06-01

We apply a biologically-motivated algorithm that selects visually-salient regions of interest in video streams to multiply-foveated video compression. Regions of high encoding priority are selected based on nonlinear integration of low-level visual cues, mimicking processing in primate occipital and posterior parietal cortex. A dynamic foveation filter then blurs (foveates) every frame, increasingly with distance from high-priority regions. Two variants of the model (one with continuously-variable blur proportional to saliency at every pixel, and the other with blur proportional to distance from three independent foveation centers) are validated against eye fixations from 4-6 human observers on 50 video clips (synthetic stimuli, video games, outdoors day and night home video, television newscast, sports, talk-shows, etc). Significant overlap is found between human and algorithmic foveations on every clip with one variant, and on 48 out of 50 clips with the other. Substantial compressed file size reductions by a factor 0.5 on average are obtained for foveated compared to unfoveated clips. These results suggest a general-purpose usefulness of the algorithm in improving compression ratios of unconstrained video.

Comparison and analysis of nonlinear algorithms for compressed sensing in MRI.

PubMed

Yu, Yeyang; Hong, Mingjian; Liu, Feng; Wang, Hua; Crozier, Stuart

2010-01-01

Compressed sensing (CS) theory has been recently applied in Magnetic Resonance Imaging (MRI) to accelerate the overall imaging process. In the CS implementation, various algorithms have been used to solve the nonlinear equation system for better image quality and reconstruction speed. However, there are no explicit criteria for an optimal CS algorithm selection in the practical MRI application. A systematic and comparative study of those commonly used algorithms is therefore essential for the implementation of CS in MRI. In this work, three typical algorithms, namely, the Gradient Projection For Sparse Reconstruction (GPSR) algorithm, Interior-point algorithm (l(1)_ls), and the Stagewise Orthogonal Matching Pursuit (StOMP) algorithm are compared and investigated in three different imaging scenarios, brain, angiogram and phantom imaging. The algorithms' performances are characterized in terms of image quality and reconstruction speed. The theoretical results show that the performance of the CS algorithms is case sensitive; overall, the StOMP algorithm offers the best solution in imaging quality, while the GPSR algorithm is the most efficient one among the three methods. In the next step, the algorithm performances and characteristics will be experimentally explored. It is hoped that this research will further support the applications of CS in MRI.

High performance MPEG-audio decoder IC

NASA Technical Reports Server (NTRS)

Thorn, M.; Benbassat, G.; Cyr, K.; Li, S.; Gill, M.; Kam, D.; Walker, K.; Look, P.; Eldridge, C.; Ng, P.

1993-01-01

The emerging digital audio and video compression technology brings both an opportunity and a new challenge to IC design. The pervasive application of compression technology to consumer electronics will require high volume, low cost IC's and fast time to market of the prototypes and production units. At the same time, the algorithms used in the compression technology result in complex VLSI IC's. The conflicting challenges of algorithm complexity, low cost, and fast time to market have an impact on device architecture and design methodology. The work presented in this paper is about the design of a dedicated, high precision, Motion Picture Expert Group (MPEG) audio decoder.

An Algorithm to Compress Line-transition Data for Radiative-transfer Calculations

NASA Astrophysics Data System (ADS)

Cubillos, Patricio E.

2017-11-01

Molecular line-transition lists are an essential ingredient for radiative-transfer calculations. With recent databases now surpassing the billion-line mark, handling them has become computationally prohibitive, due to both the required processing power and memory. Here I present a temperature-dependent algorithm to separate strong from weak line transitions, reformatting the large majority of the weaker lines into a cross-section data file, and retaining the detailed line-by-line information of the fewer strong lines. For any given molecule over the 0.3-30 μm range, this algorithm reduces the number of lines to a few million, enabling faster radiative-transfer computations without a significant loss of information. The final compression rate depends on how densely populated the spectrum is. I validate this algorithm by comparing Exomol’s HCN extinction-coefficient spectra between the complete (65 million line transitions) and compressed (7.7 million) line lists. Over the 0.6-33 μm range, the average difference between extinction-coefficient values is less than 1%. A Python/C implementation of this algorithm is open-source and available at https://github.com/pcubillos/repack. So far, this code handles the Exomol and HITRAN line-transition format.

Effective degrees of freedom of a random walk on a fractal

NASA Astrophysics Data System (ADS)

Balankin, Alexander S.

2015-12-01

We argue that a non-Markovian random walk on a fractal can be treated as a Markovian process in a fractional dimensional space with a suitable metric. This allows us to define the fractional dimensional space allied to the fractal as the ν -dimensional space Fν equipped with the metric induced by the fractal topology. The relation between the number of effective spatial degrees of freedom of walkers on the fractal (ν ) and fractal dimensionalities is deduced. The intrinsic time of random walk in Fν is inferred. The Laplacian operator in Fν is constructed. This allows us to map physical problems on fractals into the corresponding problems in Fν. In this way, essential features of physics on fractals are revealed. Particularly, subdiffusion on path-connected fractals is elucidated. The Coulomb potential of a point charge on a fractal embedded in the Euclidean space is derived. Intriguing attributes of some types of fractals are highlighted.

Vector Quantization Algorithm Based on Associative Memories

NASA Astrophysics Data System (ADS)

Guzmán, Enrique; Pogrebnyak, Oleksiy; Yáñez, Cornelio; Manrique, Pablo

This paper presents a vector quantization algorithm for image compression based on extended associative memories. The proposed algorithm is divided in two stages. First, an associative network is generated applying the learning phase of the extended associative memories between a codebook generated by the LBG algorithm and a training set. This associative network is named EAM-codebook and represents a new codebook which is used in the next stage. The EAM-codebook establishes a relation between training set and the LBG codebook. Second, the vector quantization process is performed by means of the recalling stage of EAM using as associative memory the EAM-codebook. This process generates a set of the class indices to which each input vector belongs. With respect to the LBG algorithm, the main advantages offered by the proposed algorithm is high processing speed and low demand of resources (system memory); results of image compression and quality are presented.

Multi-source feature extraction and target recognition in wireless sensor networks based on adaptive distributed wavelet compression algorithms

NASA Astrophysics Data System (ADS)

Hortos, William S.

2008-04-01

Proposed distributed wavelet-based algorithms are a means to compress sensor data received at the nodes forming a wireless sensor network (WSN) by exchanging information between neighboring sensor nodes. Local collaboration among nodes compacts the measurements, yielding a reduced fused set with equivalent information at far fewer nodes. Nodes may be equipped with multiple sensor types, each capable of sensing distinct phenomena: thermal, humidity, chemical, voltage, or image signals with low or no frequency content as well as audio, seismic or video signals within defined frequency ranges. Compression of the multi-source data through wavelet-based methods, distributed at active nodes, reduces downstream processing and storage requirements along the paths to sink nodes; it also enables noise suppression and more energy-efficient query routing within the WSN. Targets are first detected by the multiple sensors; then wavelet compression and data fusion are applied to the target returns, followed by feature extraction from the reduced data; feature data are input to target recognition/classification routines; targets are tracked during their sojourns through the area monitored by the WSN. Algorithms to perform these tasks are implemented in a distributed manner, based on a partition of the WSN into clusters of nodes. In this work, a scheme of collaborative processing is applied for hierarchical data aggregation and decorrelation, based on the sensor data itself and any redundant information, enabled by a distributed, in-cluster wavelet transform with lifting that allows multiple levels of resolution. The wavelet-based compression algorithm significantly decreases RF bandwidth and other resource use in target processing tasks. Following wavelet compression, features are extracted. The objective of feature extraction is to maximize the probabilities of correct target classification based on multi-source sensor measurements, while minimizing the resource expenditures at participating nodes. Therefore, the feature-extraction method based on the Haar DWT is presented that employs a maximum-entropy measure to determine significant wavelet coefficients. Features are formed by calculating the energy of coefficients grouped around the competing clusters. A DWT-based feature extraction algorithm used for vehicle classification in WSNs can be enhanced by an added rule for selecting the optimal number of resolution levels to improve the correct classification rate and reduce energy consumption expended in local algorithm computations. Published field trial data for vehicular ground targets, measured with multiple sensor types, are used to evaluate the wavelet-assisted algorithms. Extracted features are used in established target recognition routines, e.g., the Bayesian minimum-error-rate classifier, to compare the effects on the classification performance of the wavelet compression. Simulations of feature sets and recognition routines at different resolution levels in target scenarios indicate the impact on classification rates, while formulas are provided to estimate reduction in resource use due to distributed compression.

A biological compression model and its applications.

PubMed

Cao, Minh Duc; Dix, Trevor I; Allison, Lloyd

2011-01-01

A biological compression model, expert model, is presented which is superior to existing compression algorithms in both compression performance and speed. The model is able to compress whole eukaryotic genomes. Most importantly, the model provides a framework for knowledge discovery from biological data. It can be used for repeat element discovery, sequence alignment and phylogenetic analysis. We demonstrate that the model can handle statistically biased sequences and distantly related sequences where conventional knowledge discovery tools often fail.

[A quality controllable algorithm for ECG compression based on wavelet transform and ROI coding].

PubMed

Zhao, An; Wu, Baoming

2006-12-01

This paper presents an ECG compression algorithm based on wavelet transform and region of interest (ROI) coding. The algorithm has realized near-lossless coding in ROI and quality controllable lossy coding outside of ROI. After mean removal of the original signal, multi-layer orthogonal discrete wavelet transform is performed. Simultaneously,feature extraction is performed on the original signal to find the position of ROI. The coefficients related to the ROI are important coefficients and kept. Otherwise, the energy loss of the transform domain is calculated according to the goal PRDBE (Percentage Root-mean-square Difference with Baseline Eliminated), and then the threshold of the coefficients outside of ROI is determined according to the loss of energy. The important coefficients, which include the coefficients of ROI and the coefficients that are larger than the threshold outside of ROI, are put into a linear quantifier. The map, which records the positions of the important coefficients in the original wavelet coefficients vector, is compressed with a run-length encoder. Huffman coding has been applied to improve the compression ratio. ECG signals taken from the MIT/BIH arrhythmia database are tested, and satisfactory results in terms of clinical information preserving, quality and compress ratio are obtained.

An Approach to Study Elastic Vibrations of Fractal Cylinders

NASA Astrophysics Data System (ADS)

Steinberg, Lev; Zepeda, Mario

2016-11-01

This paper presents our study of dynamics of fractal solids. Concepts of fractal continuum and time had been used in definitions of a fractal body deformation and motion, formulation of conservation of mass, balance of momentum, and constitutive relationships. A linearized model, which was written in terms of fractal time and spatial derivatives, has been employed to study the elastic vibrations of fractal circular cylinders. Fractal differential equations of torsional, longitudinal and transverse fractal wave equations have been obtained and solution properties such as size and time dependence have been revealed.

Elasticity of fractal materials using the continuum model with non-integer dimensional space

NASA Astrophysics Data System (ADS)

Tarasov, Vasily E.

2015-01-01

Using a generalization of vector calculus for space with non-integer dimension, we consider elastic properties of fractal materials. Fractal materials are described by continuum models with non-integer dimensional space. A generalization of elasticity equations for non-integer dimensional space, and its solutions for the equilibrium case of fractal materials are suggested. Elasticity problems for fractal hollow ball and cylindrical fractal elastic pipe with inside and outside pressures, for rotating cylindrical fractal pipe, for gradient elasticity and thermoelasticity of fractal materials are solved.

Fractal vector optical fields.

PubMed

Pan, Yue; Gao, Xu-Zhen; Cai, Meng-Qiang; Zhang, Guan-Lin; Li, Yongnan; Tu, Chenghou; Wang, Hui-Tian

2016-07-15

We introduce the concept of a fractal, which provides an alternative approach for flexibly engineering the optical fields and their focal fields. We propose, design, and create a new family of optical fields-fractal vector optical fields, which build a bridge between the fractal and vector optical fields. The fractal vector optical fields have polarization states exhibiting fractal geometry, and may also involve the phase and/or amplitude simultaneously. The results reveal that the focal fields exhibit self-similarity, and the hierarchy of the fractal has the "weeding" role. The fractal can be used to engineer the focal field.

Easy-to-learn cardiopulmonary resuscitation training programme: a randomised controlled trial on laypeople’s resuscitation performance

PubMed Central

Ko, Rachel Jia Min; Lim, Swee Han; Wu, Vivien Xi; Leong, Tak Yam; Liaw, Sok Ying

2018-01-01

INTRODUCTION Simplifying the learning of cardiopulmonary resuscitation (CPR) is advocated to improve skill acquisition and retention. A simplified CPR training programme focusing on continuous chest compression, with a simple landmark tracing technique, was introduced to laypeople. The study aimed to examine the effectiveness of the simplified CPR training in improving lay rescuers’ CPR performance as compared to standard CPR. METHODS A total of 85 laypeople (aged 21–60 years) were recruited and randomly assigned to undertake either a two-hour simplified or standard CPR training session. They were tested two months after the training on a simulated cardiac arrest scenario. Participants’ performance on the sequence of CPR steps was observed and evaluated using a validated CPR algorithm checklist. The quality of chest compression and ventilation was assessed from the recording manikins. RESULTS The simplified CPR group performed significantly better on the CPR algorithm when compared to the standard CPR group (p < 0.01). No significant difference was found between the groups in time taken to initiate CPR. However, a significantly higher number of compressions and proportion of adequate compressions was demonstrated by the simplified group than the standard group (p < 0.01). Hands-off time was significantly shorter in the simplified CPR group than in the standard CPR group (p < 0.001). CONCLUSION Simplifying the learning of CPR by focusing on continuous chest compressions, with simple hand placement for chest compression, could lead to better acquisition and retention of CPR algorithms, and better quality of chest compressions than standard CPR. PMID:29167910

Easy-to-learn cardiopulmonary resuscitation training programme: a randomised controlled trial on laypeople's resuscitation performance.

PubMed

Ko, Rachel Jia Min; Lim, Swee Han; Wu, Vivien Xi; Leong, Tak Yam; Liaw, Sok Ying

2018-04-01

Simplifying the learning of cardiopulmonary resuscitation (CPR) is advocated to improve skill acquisition and retention. A simplified CPR training programme focusing on continuous chest compression, with a simple landmark tracing technique, was introduced to laypeople. The study aimed to examine the effectiveness of the simplified CPR training in improving lay rescuers' CPR performance as compared to standard CPR. A total of 85 laypeople (aged 21-60 years) were recruited and randomly assigned to undertake either a two-hour simplified or standard CPR training session. They were tested two months after the training on a simulated cardiac arrest scenario. Participants' performance on the sequence of CPR steps was observed and evaluated using a validated CPR algorithm checklist. The quality of chest compression and ventilation was assessed from the recording manikins. The simplified CPR group performed significantly better on the CPR algorithm when compared to the standard CPR group (p < 0.01). No significant difference was found between the groups in time taken to initiate CPR. However, a significantly higher number of compressions and proportion of adequate compressions was demonstrated by the simplified group than the standard group (p < 0.01). Hands-off time was significantly shorter in the simplified CPR group than in the standard CPR group (p < 0.001). Simplifying the learning of CPR by focusing on continuous chest compressions, with simple hand placement for chest compression, could lead to better acquisition and retention of CPR algorithms, and better quality of chest compressions than standard CPR. Copyright: © Singapore Medical Association.

Binary video codec for data reduction in wireless visual sensor networks

NASA Astrophysics Data System (ADS)

Khursheed, Khursheed; Ahmad, Naeem; Imran, Muhammad; O'Nils, Mattias

2013-02-01

Wireless Visual Sensor Networks (WVSN) is formed by deploying many Visual Sensor Nodes (VSNs) in the field. Typical applications of WVSN include environmental monitoring, health care, industrial process monitoring, stadium/airports monitoring for security reasons and many more. The energy budget in the outdoor applications of WVSN is limited to the batteries and the frequent replacement of batteries is usually not desirable. So the processing as well as the communication energy consumption of the VSN needs to be optimized in such a way that the network remains functional for longer duration. The images captured by VSN contain huge amount of data and require efficient computational resources for processing the images and wide communication bandwidth for the transmission of the results. Image processing algorithms must be designed and developed in such a way that they are computationally less complex and must provide high compression rate. For some applications of WVSN, the captured images can be segmented into bi-level images and hence bi-level image coding methods will efficiently reduce the information amount in these segmented images. But the compression rate of the bi-level image coding methods is limited by the underlined compression algorithm. Hence there is a need for designing other intelligent and efficient algorithms which are computationally less complex and provide better compression rate than that of bi-level image coding methods. Change coding is one such algorithm which is computationally less complex (require only exclusive OR operations) and provide better compression efficiency compared to image coding but it is effective for applications having slight changes between adjacent frames of the video. The detection and coding of the Region of Interest (ROIs) in the change frame efficiently reduce the information amount in the change frame. But, if the number of objects in the change frames is higher than a certain level then the compression efficiency of both the change coding and ROI coding becomes worse than that of image coding. This paper explores the compression efficiency of the Binary Video Codec (BVC) for the data reduction in WVSN. We proposed to implement all the three compression techniques i.e. image coding, change coding and ROI coding at the VSN and then select the smallest bit stream among the results of the three compression techniques. In this way the compression performance of the BVC will never become worse than that of image coding. We concluded that the compression efficiency of BVC is always better than that of change coding and is always better than or equal that of ROI coding and image coding.

Renormalization of the unitary evolution equation for coined quantum walks

NASA Astrophysics Data System (ADS)

Boettcher, Stefan; Li, Shanshan; Portugal, Renato

2017-03-01

We consider discrete-time evolution equations in which the stochastic operator of a classical random walk is replaced by a unitary operator. Such a problem has gained much attention as a framework for coined quantum walks that are essential for attaining the Grover limit for quantum search algorithms in physically realizable, low-dimensional geometries. In particular, we analyze the exact real-space renormalization group (RG) procedure recently introduced to study the scaling of quantum walks on fractal networks. While this procedure, when implemented numerically, was able to provide some deep insights into the relation between classical and quantum walks, its analytic basis has remained obscure. Our discussion here is laying the groundwork for a rigorous implementation of the RG for this important class of transport and algorithmic problems, although some instances remain unresolved. Specifically, we find that the RG fixed-point analysis of the classical walk, which typically focuses on the dominant Jacobian eigenvalue {λ1} , with walk dimension dw\\text{RW}={{log}2}{λ1} , needs to be extended to include the subdominant eigenvalue {λ2} , such that the dimension of the quantum walk obtains dw\\text{QW}={{log}2}\\sqrt{{λ1}{λ2}} . With that extension, we obtain analytically previously conjectured results for dw\\text{QW} of Grover walks on all but one of the fractal networks that have been considered.

Human physiological benefits of viewing nature: EEG responses to exact and statistical fractal patterns.

PubMed

Hagerhall, C M; Laike, T; Küller, M; Marcheschi, E; Boydston, C; Taylor, R P

2015-01-01

Psychological and physiological benefits of viewing nature have been extensively studied for some time. More recently it has been suggested that some of these positive effects can be explained by nature's fractal properties. Virtually all studies on human responses to fractals have used stimuli that represent the specific form of fractal geometry found in nature, i.e. statistical fractals, as opposed to fractal patterns which repeat exactly at different scales. This raises the question of whether human responses like preference and relaxation are being driven by fractal geometry in general or by the specific form of fractal geometry found in nature. In this study we consider both types of fractals (statistical and exact) and morph one type into the other. Based on the Koch curve, nine visual stimuli were produced in which curves of three different fractal dimensions evolve gradually from an exact to a statistical fractal. The patterns were shown for one minute each to thirty-five subjects while qEEG was continuously recorded. The results showed that the responses to statistical and exact fractals differ, and that the natural form of the fractal is important for inducing alpha responses, an indicator of a wakefully relaxed state and internalized attention.

Secure Oblivious Hiding, Authentication, Tamper Proofing, and Verification Techniques

DTIC Science & Technology

2002-08-01

compressing the bit- planes. The algorithm always starts with inspecting the 5th LSB plane. For color images , all three color-channels are compressed...use classical encryption engines, such as IDEA or DES . These algorithms have a fixed encryption block size, and, depending on the image dimensions, we...information can be stored either in a separate file, in the image header, or embedded in the image itself utilizing the modern concepts of steganography

Physics-Based Computational Algorithm for the Multi-Fluid Plasma Model

DTIC Science & Technology

2014-06-30

applying it to study laser - 20 Physics-Based Multi-Fluid Plasma Algorithm Shumlak Figure 6: Blended finite element method applied to the species...separation problem in capsule implosions. Number densities and electric field are shown after the laser drive has compressed the multi-fluid plasma and...6 after the laser drive has started the compression. A separation clearly develops. The solution is found using an explicit advance (CFL=1) for the

A General Classification Rule for Probability Measures

DTIC Science & Technology

1993-08-12

1989) proposed an estimator based on relative entropy, related it to the Lempel - Ziv compression algorithm , and proved its asymptotic optimality in...327, 1991. 19 [12] Merhav, N., Gutman, M. and Ziv , J. (1989). On the determination of the order of a Markov chain and universal data compression ...over some compact Polish space E, we want to decide whether or not the unknown distribution belongs to A or its complement. We propose an algorithm which

Method and algorithm for efficient calibration of compressive hyperspectral imaging system based on a liquid crystal retarder

NASA Astrophysics Data System (ADS)

Shecter, Liat; Oiknine, Yaniv; August, Isaac; Stern, Adrian

2017-09-01

Recently we presented a Compressive Sensing Miniature Ultra-spectral Imaging System (CS-MUSI)1 . This system consists of a single Liquid Crystal (LC) phase retarder as a spectral modulator and a gray scale sensor array to capture a multiplexed signal of the imaged scene. By designing the LC spectral modulator in compliance with the Compressive Sensing (CS) guidelines and applying appropriate algorithms we demonstrated reconstruction of spectral (hyper/ ultra) datacubes from an order of magnitude fewer samples than taken by conventional sensors. The LC modulator is designed to have an effective width of a few tens of micrometers, therefore it is prone to imperfections and spatial nonuniformity. In this work, we present the study of this nonuniformity and present a mathematical algorithm that allows the inference of the spectral transmission over the entire cell area from only a few calibration measurements.

An Online Dictionary Learning-Based Compressive Data Gathering Algorithm in Wireless Sensor Networks

PubMed Central

Wang, Donghao; Wan, Jiangwen; Chen, Junying; Zhang, Qiang

2016-01-01

To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG) algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure. It’s theoretically demonstrated that the sensing matrix satisfies the restricted isometry property (RIP) with high probability. In addition, the lower bound of necessary number of measurements for compressive sensing (CS) reconstruction is given. Simulation results show that the proposed ODL-CDG algorithm can enhance the recovery accuracy in the presence of noise, and reduce the energy consumption in comparison with other dictionary based data gathering methods. PMID:27669250

An Online Dictionary Learning-Based Compressive Data Gathering Algorithm in Wireless Sensor Networks.

PubMed

Wang, Donghao; Wan, Jiangwen; Chen, Junying; Zhang, Qiang

2016-09-22

To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG) algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure. It's theoretically demonstrated that the sensing matrix satisfies the restricted isometry property (RIP) with high probability. In addition, the lower bound of necessary number of measurements for compressive sensing (CS) reconstruction is given. Simulation results show that the proposed ODL-CDG algorithm can enhance the recovery accuracy in the presence of noise, and reduce the energy consumption in comparison with other dictionary based data gathering methods.

On the radiative properties of soot aggregates part 1: Necking and overlapping

NASA Astrophysics Data System (ADS)

Yon, J.; Bescond, A.; Liu, F.

2015-09-01

There is a strong interest in accurately modelling the radiative properties of soot aggregates (also known as black carbon particles) emitted from combustion systems and fires to gain improved understanding of the role of black carbon to global warming. This study conducted a systematic investigation of the effects of overlapping and necking between neighbouring primary particles on the radiative properties of soot aggregates using the discrete dipole approximation. The degrees of overlapping and necking are quantified by the overlapping and necking parameters. Realistic soot aggregates were generated numerically by constructing overlapping and necking to fractal aggregates formed by point-touch primary particles simulated using a diffusion-limited cluster aggregation algorithm. Radiative properties (differential scattering, absorption, total scattering, specific extinction, asymmetry factor and single scattering albedo) were calculated using the experimentally measured soot refractive index over the spectral range of 266-1064 nm for 9 combinations of the overlapping and necking parameters. Overlapping and necking affect significantly the absorption and scattering properties of soot aggregates, especially in the near UV spectrum due to the enhanced multiple scattering effects within an aggregate. By using correctly modified aggregate properties (fractal dimension, prefactor, primary particle radius, and the number of primary particle) and by accounting for the effects of multiple scattering, the simple Rayleigh-Debye-Gans theory for fractal aggregates can reproduce reasonably accurate radiative properties of realistic soot aggregates.

Application to recognition of ferrography image with fractal neural network

NASA Astrophysics Data System (ADS)

Tian, Xianzhong; Hu, Tongsen; Zhang, Jian

2005-10-01

Because wear particles have fractal characteristics, it is necessary that adding fractal parameters to studying wear particles and diagnosing machine troubles. This paper discusses fractal parameters of wear particles, presents arithmetic calculating fractal dimension, and constructs a fractal neural network which can recognize wear particles image. It is proved by experiments that this fractal neural network can recognize some characteristics of wear particles image, and can also classify wear types.

Video bandwidth compression system

NASA Astrophysics Data System (ADS)

Ludington, D.

1980-08-01

The objective of this program was the development of a Video Bandwidth Compression brassboard model for use by the Air Force Avionics Laboratory, Wright-Patterson Air Force Base, in evaluation of bandwidth compression techniques for use in tactical weapons and to aid in the selection of particular operational modes to be implemented in an advanced flyable model. The bandwidth compression system is partitioned into two major divisions: the encoder, which processes the input video with a compression algorithm and transmits the most significant information; and the decoder where the compressed data is reconstructed into a video image for display.

Lossless compression of otoneurological eye movement signals.

PubMed

Tossavainen, Timo; Juhola, Martti

2002-12-01

We studied the performance of several lossless compression algorithms on eye movement signals recorded in otoneurological balance and other physiological laboratories. Despite the wide use of these signals their compression has not been studied prior to our research. The compression methods were based on the common model of using a predictor to decorrelate the input and using an entropy coder to encode the residual. We found that these eye movement signals recorded at 400 Hz and with 13 bit amplitude resolution could losslessly be compressed with a compression ratio of about 2.7.

Compressed sensing based missing nodes prediction in temporal communication network

NASA Astrophysics Data System (ADS)

Cheng, Guangquan; Ma, Yang; Liu, Zhong; Xie, Fuli

2018-02-01

The reconstruction of complex network topology is of great theoretical and practical significance. Most research so far focuses on the prediction of missing links. There are many mature algorithms for link prediction which have achieved good results, but research on the prediction of missing nodes has just begun. In this paper, we propose an algorithm for missing node prediction in complex networks. We detect the position of missing nodes based on their neighbor nodes under the theory of compressed sensing, and extend the algorithm to the case of multiple missing nodes using spectral clustering. Experiments on real public network datasets and simulated datasets show that our algorithm can detect the locations of hidden nodes effectively with high precision.

A split finite element algorithm for the compressible Navier-Stokes equations

NASA Technical Reports Server (NTRS)

Baker, A. J.

1979-01-01

An accurate and efficient numerical solution algorithm is established for solution of the high Reynolds number limit of the Navier-Stokes equations governing the multidimensional flow of a compressible essentially inviscid fluid. Finite element interpolation theory is used within a dissipative formulation established using Galerkin criteria within the Method of Weighted Residuals. An implicit iterative solution algorithm is developed, employing tensor product bases within a fractional steps integration procedure, that significantly enhances solution economy concurrent with sharply reduced computer hardware demands. The algorithm is evaluated for resolution of steep field gradients and coarse grid accuracy using both linear and quadratic tensor product interpolation bases. Numerical solutions for linear and nonlinear, one, two and three dimensional examples confirm and extend the linearized theoretical analyses, and results are compared to competitive finite difference derived algorithms.

An effective fractal-tree closure model for simulating blood flow in large arterial networks.

PubMed

Perdikaris, Paris; Grinberg, Leopold; Karniadakis, George Em

2015-06-01

The aim of the present work is to address the closure problem for hemodynamic simulations by developing a flexible and effective model that accurately distributes flow in the downstream vasculature and can stably provide a physiological pressure outflow boundary condition. To achieve this goal, we model blood flow in the sub-pixel vasculature by using a non-linear 1D model in self-similar networks of compliant arteries that mimic the structure and hierarchy of vessels in the meso-vascular regime (radii [Formula: see text]). We introduce a variable vessel length-to-radius ratio for small arteries and arterioles, while also addressing non-Newtonian blood rheology and arterial wall viscoelasticity effects in small arteries and arterioles. This methodology aims to overcome substantial cut-off radius sensitivities, typically arising in structured tree and linearized impedance models. The proposed model is not sensitive to outflow boundary conditions applied at the end points of the fractal network, and thus does not require calibration of resistance/capacitance parameters typically required for outflow conditions. The proposed model convergences to a periodic state in two cardiac cycles even when started from zero-flow initial conditions. The resulting fractal-trees typically consist of thousands to millions of arteries, posing the need for efficient parallel algorithms. To this end, we have scaled up a Discontinuous Galerkin solver that utilizes the MPI/OpenMP hybrid programming paradigm to thousands of computer cores, and can simulate blood flow in networks of millions of arterial segments at the rate of one cycle per 5 min. The proposed model has been extensively tested on a large and complex cranial network with 50 parent, patient-specific arteries and 21 outlets to which fractal trees where attached, resulting to a network of up to 4,392,484 vessels in total, and a detailed network of the arm with 276 parent arteries and 103 outlets (a total of 702,188 vessels after attaching the fractal trees), returning physiological flow and pressure wave predictions without requiring any parameter estimation or calibration procedures. We present a novel methodology to overcome substantial cut-off radius sensitivities.

An efective fractal-tree closure model for simulating blood flow in large arterial networks

PubMed Central

Perdikaris, Paris; Grinberg, Leopold; Karniadakis, George Em.

2014-01-01

The aim of the present work is to address the closure problem for hemodynamic simulations by developing a exible and effective model that accurately distributes flow in the downstream vasculature and can stably provide a physiological pressure out flow boundary condition. To achieve this goal, we model blood flow in the sub-pixel vasculature by using a non-linear 1D model in self-similar networks of compliant arteries that mimic the structure and hierarchy of vessels in the meso-vascular regime (radii 500 μm – 10 μm). We introduce a variable vessel length-to-radius ratio for small arteries and arterioles, while also addressing non-Newtonian blood rheology and arterial wall viscoelasticity effects in small arteries and arterioles. This methodology aims to overcome substantial cut-off radius sensitivities, typically arising in structured tree and linearized impedance models. The proposed model is not sensitive to out flow boundary conditions applied at the end points of the fractal network, and thus does not require calibration of resistance/capacitance parameters typically required for out flow conditions. The proposed model convergences to a periodic state in two cardiac cycles even when started from zero-flow initial conditions. The resulting fractal-trees typically consist of thousands to millions of arteries, posing the need for efficient parallel algorithms. To this end, we have scaled up a Discontinuous Galerkin solver that utilizes the MPI/OpenMP hybrid programming paradigm to thousands of computer cores, and can simulate blood flow in networks of millions of arterial segments at the rate of one cycle per 5 minutes. The proposed model has been extensively tested on a large and complex cranial network with 50 parent, patient-specific arteries and 21 outlets to which fractal trees where attached, resulting to a network of up to 4,392,484 vessels in total, and a detailed network of the arm with 276 parent arteries and 103 outlets (a total of 702,188 vessels after attaching the fractal trees), returning physiological flow and pressure wave predictions without requiring any parameter estimation or calibration procedures. We present a novel methodology to overcome substantial cut-off radius sensitivities PMID:25510364

New Algorithms and Lower Bounds for Sequential-Access Data Compression

NASA Astrophysics Data System (ADS)

Gagie, Travis

2009-02-01

This thesis concerns sequential-access data compression, i.e., by algorithms that read the input one or more times from beginning to end. In one chapter we consider adaptive prefix coding, for which we must read the input character by character, outputting each character's self-delimiting codeword before reading the next one. We show how to encode and decode each character in constant worst-case time while producing an encoding whose length is worst-case optimal. In another chapter we consider one-pass compression with memory bounded in terms of the alphabet size and context length, and prove a nearly tight tradeoff between the amount of memory we can use and the quality of the compression we can achieve. In a third chapter we consider compression in the read/write streams model, which allows us passes and memory both polylogarithmic in the size of the input. We first show how to achieve universal compression using only one pass over one stream. We then show that one stream is not sufficient for achieving good grammar-based compression. Finally, we show that two streams are necessary and sufficient for achieving entropy-only bounds.

Effective degrees of freedom of a random walk on a fractal.

PubMed

Balankin, Alexander S

2015-12-01

We argue that a non-Markovian random walk on a fractal can be treated as a Markovian process in a fractional dimensional space with a suitable metric. This allows us to define the fractional dimensional space allied to the fractal as the ν-dimensional space F(ν) equipped with the metric induced by the fractal topology. The relation between the number of effective spatial degrees of freedom of walkers on the fractal (ν) and fractal dimensionalities is deduced. The intrinsic time of random walk in F(ν) is inferred. The Laplacian operator in F(ν) is constructed. This allows us to map physical problems on fractals into the corresponding problems in F(ν). In this way, essential features of physics on fractals are revealed. Particularly, subdiffusion on path-connected fractals is elucidated. The Coulomb potential of a point charge on a fractal embedded in the Euclidean space is derived. Intriguing attributes of some types of fractals are highlighted.

Compressive Sensing Based Bio-Inspired Shape Feature Detection CMOS Imager

NASA Technical Reports Server (NTRS)

Duong, Tuan A. (Inventor)

2015-01-01

A CMOS imager integrated circuit using compressive sensing and bio-inspired detection is presented which integrates novel functions and algorithms within a novel hardware architecture enabling efficient on-chip implementation.

Efficient Decoding of Compressed Data.

ERIC Educational Resources Information Center

Bassiouni, Mostafa A.; Mukherjee, Amar

1995-01-01

Discusses the problem of enhancing the speed of Huffman decoding of compressed data. Topics addressed include the Huffman decoding tree; multibit decoding; binary string mapping problems; and algorithms for solving mapping problems. (22 references) (LRW)

Compression of next-generation sequencing reads aided by highly efficient de novo assembly

PubMed Central

Jones, Daniel C.; Ruzzo, Walter L.; Peng, Xinxia

2012-01-01

We present Quip, a lossless compression algorithm for next-generation sequencing data in the FASTQ and SAM/BAM formats. In addition to implementing reference-based compression, we have developed, to our knowledge, the first assembly-based compressor, using a novel de novo assembly algorithm. A probabilistic data structure is used to dramatically reduce the memory required by traditional de Bruijn graph assemblers, allowing millions of reads to be assembled very efficiently. Read sequences are then stored as positions within the assembled contigs. This is combined with statistical compression of read identifiers, quality scores, alignment information and sequences, effectively collapsing very large data sets to <15% of their original size with no loss of information. Availability: Quip is freely available under the 3-clause BSD license from http://cs.washington.edu/homes/dcjones/quip. PMID:22904078

Fast and Adaptive Lossless On-Board Hyperspectral Data Compression System for Space Applications

NASA Technical Reports Server (NTRS)

Aranki, Nazeeh; Bakhshi, Alireza; Keymeulen, Didier; Klimesh, Matthew

2009-01-01

Efficient on-board lossless hyperspectral data compression reduces the data volume necessary to meet NASA and DoD limited downlink capabilities. The techniques also improves signature extraction, object recognition and feature classification capabilities by providing exact reconstructed data on constrained downlink resources. At JPL a novel, adaptive and predictive technique for lossless compression of hyperspectral data was recently developed. This technique uses an adaptive filtering method and achieves a combination of low complexity and compression effectiveness that far exceeds state-of-the-art techniques currently in use. The JPL-developed 'Fast Lossless' algorithm requires no training data or other specific information about the nature of the spectral bands for a fixed instrument dynamic range. It is of low computational complexity and thus well-suited for implementation in hardware, which makes it practical for flight implementations of pushbroom instruments. A prototype of the compressor (and decompressor) of the algorithm is available in software, but this implementation may not meet speed and real-time requirements of some space applications. Hardware acceleration provides performance improvements of 10x-100x vs. the software implementation (about 1M samples/sec on a Pentium IV machine). This paper describes a hardware implementation of the JPL-developed 'Fast Lossless' compression algorithm on a Field Programmable Gate Array (FPGA). The FPGA implementation targets the current state of the art FPGAs (Xilinx Virtex IV and V families) and compresses one sample every clock cycle to provide a fast and practical real-time solution for Space applications.

Hardware Implementation of Lossless Adaptive and Scalable Hyperspectral Data Compression for Space

NASA Technical Reports Server (NTRS)

Aranki, Nazeeh; Keymeulen, Didier; Bakhshi, Alireza; Klimesh, Matthew

2009-01-01

On-board lossless hyperspectral data compression reduces data volume in order to meet NASA and DoD limited downlink capabilities. The technique also improves signature extraction, object recognition and feature classification capabilities by providing exact reconstructed data on constrained downlink resources. At JPL a novel, adaptive and predictive technique for lossless compression of hyperspectral data was recently developed. This technique uses an adaptive filtering method and achieves a combination of low complexity and compression effectiveness that far exceeds state-of-the-art techniques currently in use. The JPL-developed 'Fast Lossless' algorithm requires no training data or other specific information about the nature of the spectral bands for a fixed instrument dynamic range. It is of low computational complexity and thus well-suited for implementation in hardware. A modified form of the algorithm that is better suited for data from pushbroom instruments is generally appropriate for flight implementation. A scalable field programmable gate array (FPGA) hardware implementation was developed. The FPGA implementation achieves a throughput performance of 58 Msamples/sec, which can be increased to over 100 Msamples/sec in a parallel implementation that uses twice the hardware resources This paper describes the hardware implementation of the 'Modified Fast Lossless' compression algorithm on an FPGA. The FPGA implementation targets the current state-of-the-art FPGAs (Xilinx Virtex IV and V families) and compresses one sample every clock cycle to provide a fast and practical real-time solution for space applications.

Bohman-Frieze-Wormald model on the lattice, yielding a discontinuous percolation transition

NASA Astrophysics Data System (ADS)

Schrenk, K. J.; Felder, A.; Deflorin, S.; Araújo, N. A. M.; D'Souza, R. M.; Herrmann, H. J.

2012-03-01

The BFW model introduced by Bohman, Frieze, and Wormald [Random Struct. Algorithms1042-983210.1002/rsa.20038, 25, 432 (2004)], and recently investigated in the framework of discontinuous percolation by Chen and D'Souza [Phys. Rev. Lett.PRLTAO0031-900710.1103/PhysRevLett.106.115701 106, 115701 (2011)], is studied on the square and simple-cubic lattices. In two and three dimensions, we find numerical evidence for a strongly discontinuous transition. In two dimensions, the clusters at the threshold are compact with a fractal surface of fractal dimension df=1.49±0.02. On the simple-cubic lattice, distinct jumps in the size of the largest cluster are observed. We proceed to analyze the tree-like version of the model, where only merging bonds are sampled, for dimension two to seven. The transition is again discontinuous in any considered dimension. Finally, the dependence of the cluster-size distribution at the threshold on the spatial dimension is also investigated.

Detection of neovascularization based on fractal and texture analysis with interaction effects in diabetic retinopathy.

PubMed

Lee, Jack; Zee, Benny Chung Ying; Li, Qing

2013-01-01

Diabetic retinopathy is a major cause of blindness. Proliferative diabetic retinopathy is a result of severe vascular complication and is visible as neovascularization of the retina. Automatic detection of such new vessels would be useful for the severity grading of diabetic retinopathy, and it is an important part of screening process to identify those who may require immediate treatment for their diabetic retinopathy. We proposed a novel new vessels detection method including statistical texture analysis (STA), high order spectrum analysis (HOS), fractal analysis (FA), and most importantly we have shown that by incorporating their associated interactions the accuracy of new vessels detection can be greatly improved. To assess its performance, the sensitivity, specificity and accuracy (AUC) are obtained. They are 96.3%, 99.1% and 98.5% (99.3%), respectively. It is found that the proposed method can improve the accuracy of new vessels detection significantly over previous methods. The algorithm can be automated and is valuable to detect relatively severe cases of diabetic retinopathy among diabetes patients.

Performance comparison between total variation (TV)-based compressed sensing and statistical iterative reconstruction algorithms.

PubMed

Tang, Jie; Nett, Brian E; Chen, Guang-Hong

2009-10-07

Of all available reconstruction methods, statistical iterative reconstruction algorithms appear particularly promising since they enable accurate physical noise modeling. The newly developed compressive sampling/compressed sensing (CS) algorithm has shown the potential to accurately reconstruct images from highly undersampled data. The CS algorithm can be implemented in the statistical reconstruction framework as well. In this study, we compared the performance of two standard statistical reconstruction algorithms (penalized weighted least squares and q-GGMRF) to the CS algorithm. In assessing the image quality using these iterative reconstructions, it is critical to utilize realistic background anatomy as the reconstruction results are object dependent. A cadaver head was scanned on a Varian Trilogy system at different dose levels. Several figures of merit including the relative root mean square error and a quality factor which accounts for the noise performance and the spatial resolution were introduced to objectively evaluate reconstruction performance. A comparison is presented between the three algorithms for a constant undersampling factor comparing different algorithms at several dose levels. To facilitate this comparison, the original CS method was formulated in the framework of the statistical image reconstruction algorithms. Important conclusions of the measurements from our studies are that (1) for realistic neuro-anatomy, over 100 projections are required to avoid streak artifacts in the reconstructed images even with CS reconstruction, (2) regardless of the algorithm employed, it is beneficial to distribute the total dose to more views as long as each view remains quantum noise limited and (3) the total variation-based CS method is not appropriate for very low dose levels because while it can mitigate streaking artifacts, the images exhibit patchy behavior, which is potentially harmful for medical diagnosis.

Sequential neural text compression.

PubMed

Schmidhuber, J; Heil, S

1996-01-01

The purpose of this paper is to show that neural networks may be promising tools for data compression without loss of information. We combine predictive neural nets and statistical coding techniques to compress text files. We apply our methods to certain short newspaper articles and obtain compression ratios exceeding those of the widely used Lempel-Ziv algorithms (which build the basis of the UNIX functions "compress" and "gzip"). The main disadvantage of our methods is that they are about three orders of magnitude slower than standard methods.

The effect of lossy image compression on image classification

NASA Technical Reports Server (NTRS)

Paola, Justin D.; Schowengerdt, Robert A.

1995-01-01

We have classified four different images, under various levels of JPEG compression, using the following classification algorithms: minimum-distance, maximum-likelihood, and neural network. The training site accuracy and percent difference from the original classification were tabulated for each image compression level, with maximum-likelihood showing the poorest results. In general, as compression ratio increased, the classification retained its overall appearance, but much of the pixel-to-pixel detail was eliminated. We also examined the effect of compression on spatial pattern detection using a neural network.

Data Compression With Application to Geo-Location

DTIC Science & Technology

2010-08-01

wireless sensor network requires the estimation of time-difference-of-arrival (TDOA) parameters using data collected by a set of spatially separated sensors. Compressing the data that is shared among the sensors can provide tremendous savings in terms of the energy and transmission latency. Traditional MSE and perceptual based data compression schemes fail to accurately capture the effects of compression on the TDOA estimation task; therefore, it is necessary to investigate compression algorithms suitable for TDOA parameter estimation. This thesis explores the

Classification Techniques for Digital Map Compression

DTIC Science & Technology

1989-03-01

classification improved the performance of the K-means classification algorithm resulting in a compression of 8.06:1 with Lempel - Ziv coding. Run-length coding... compression performance are run-length coding [2], [8] and Lempel - Ziv coding 110], [11]. These techniques are chosen because they are most efficient when...investigated. After the classification, some standard file compression methods, such as Lempel - Ziv and run-length encoding were applied to the

Heterogeneous Compression of Large Collections of Evolutionary Trees.

PubMed

Matthews, Suzanne J

2015-01-01

Compressing heterogeneous collections of trees is an open problem in computational phylogenetics. In a heterogeneous tree collection, each tree can contain a unique set of taxa. An ideal compression method would allow for the efficient archival of large tree collections and enable scientists to identify common evolutionary relationships over disparate analyses. In this paper, we extend TreeZip to compress heterogeneous collections of trees. TreeZip is the most efficient algorithm for compressing homogeneous tree collections. To the best of our knowledge, no other domain-based compression algorithm exists for large heterogeneous tree collections or enable their rapid analysis. Our experimental results indicate that TreeZip averages 89.03 percent (72.69 percent) space savings on unweighted (weighted) collections of trees when the level of heterogeneity in a collection is moderate. The organization of the TRZ file allows for efficient computations over heterogeneous data. For example, consensus trees can be computed in mere seconds. Lastly, combining the TreeZip compressed (TRZ) file with general-purpose compression yields average space savings of 97.34 percent (81.43 percent) on unweighted (weighted) collections of trees. Our results lead us to believe that TreeZip will prove invaluable in the efficient archival of tree collections, and enables scientists to develop novel methods for relating heterogeneous collections of trees.

Optimizing Complexity Measures for fMRI Data: Algorithm, Artifact, and Sensitivity

PubMed Central

Rubin, Denis; Fekete, Tomer; Mujica-Parodi, Lilianne R.

2013-01-01

Introduction Complexity in the brain has been well-documented at both neuronal and hemodynamic scales, with increasing evidence supporting its use in sensitively differentiating between mental states and disorders. However, application of complexity measures to fMRI time-series, which are short, sparse, and have low signal/noise, requires careful modality-specific optimization. Methods Here we use both simulated and real data to address two fundamental issues: choice of algorithm and degree/type of signal processing. Methods were evaluated with regard to resilience to acquisition artifacts common to fMRI as well as detection sensitivity. Detection sensitivity was quantified in terms of grey-white matter contrast and overlap with activation. We additionally investigated the variation of complexity with activation and emotional content, optimal task length, and the degree to which results scaled with scanner using the same paradigm with two 3T magnets made by different manufacturers. Methods for evaluating complexity were: power spectrum, structure function, wavelet decomposition, second derivative, rescaled range, Higuchi’s estimate of fractal dimension, aggregated variance, and detrended fluctuation analysis. To permit direct comparison across methods, all results were normalized to Hurst exponents. Results Power-spectrum, Higuchi’s fractal dimension, and generalized Hurst exponent based estimates were most successful by all criteria; the poorest-performing measures were wavelet, detrended fluctuation analysis, aggregated variance, and rescaled range. Conclusions Functional MRI data have artifacts that interact with complexity calculations in nontrivially distinct ways compared to other physiological data (such as EKG, EEG) for which these measures are typically used. Our results clearly demonstrate that decisions regarding choice of algorithm, signal processing, time-series length, and scanner have a significant impact on the reliability and sensitivity of complexity estimates. PMID:23700424

Aesthetic Responses to Exact Fractals Driven by Physical Complexity

PubMed Central

Bies, Alexander J.; Blanc-Goldhammer, Daryn R.; Boydston, Cooper R.; Taylor, Richard P.; Sereno, Margaret E.

2016-01-01

Fractals are physically complex due to their repetition of patterns at multiple size scales. Whereas the statistical characteristics of the patterns repeat for fractals found in natural objects, computers can generate patterns that repeat exactly. Are these exact fractals processed differently, visually and aesthetically, than their statistical counterparts? We investigated the human aesthetic response to the complexity of exact fractals by manipulating fractal dimensionality, symmetry, recursion, and the number of segments in the generator. Across two studies, a variety of fractal patterns were visually presented to human participants to determine the typical response to exact fractals. In the first study, we found that preference ratings for exact midpoint displacement fractals can be described by a linear trend with preference increasing as fractal dimension increases. For the majority of individuals, preference increased with dimension. We replicated these results for other exact fractal patterns in a second study. In the second study, we also tested the effects of symmetry and recursion by presenting asymmetric dragon fractals, symmetric dragon fractals, and Sierpinski carpets and Koch snowflakes, which have radial and mirror symmetry. We found a strong interaction among recursion, symmetry and fractal dimension. Specifically, at low levels of recursion, the presence of symmetry was enough to drive high preference ratings for patterns with moderate to high levels of fractal dimension. Most individuals required a much higher level of recursion to recover this level of preference in a pattern that lacked mirror or radial symmetry, while others were less discriminating. This suggests that exact fractals are processed differently than their statistical counterparts. We propose a set of four factors that influence complexity and preference judgments in fractals that may extend to other patterns: fractal dimension, recursion, symmetry and the number of segments in a pattern. Conceptualizations such as Berlyne’s and Redies’ theories of aesthetics also provide a suitable framework for interpretation of our data with respect to the individual differences that we detect. Future studies that incorporate physiological methods to measure the human aesthetic response to exact fractal patterns would further elucidate our responses to such timeless patterns. PMID:27242475

Paradigms of Complexity: Fractals and Structures in the Sciences

NASA Astrophysics Data System (ADS)

Novak, Miroslav M.

The Table of Contents for the book is as follows: * Preface * The Origin of Complexity (invited talk) * On the Existence of Spatially Uniform Scaling Laws in the Climate System * Multispectral Backscattering: A Fractal-Structure Probe * Small-Angle Multiple Scattering on a Fractal System of Point Scatterers * Symmetric Fractals Generated by Cellular Automata * Bispectra and Phase Correlations for Chaotic Dynamical Systems * Self-Organized Criticality Models of Neural Development * Altered Fractal and Irregular Heart Rate Behavior in Sick Fetuses * Extract Multiple Scaling in Long-Term Heart Rate Variability * A Semi-Continous Box Counting Method for Fractal Dimension Measurement of Short Single Dimension Temporal Signals - Preliminary Study * A Fractional Brownian Motion Model of Cracking * Self-Affine Scaling Studies on Fractography * Coarsening of Fractal Interfaces * A Fractal Model of Ocean Surface Superdiffusion * Stochastic Subsurface Flow and Transport in Fractal Fractal Conductivity Fields * Rendering Through Iterated Function Systems * The σ-Hull - The Hull Where Fractals Live - Calculating a Hull Bounded by Log Spirals to Solve the Inverse IFS-Problem by the Detected Orbits * On the Multifractal Properties of Passively Convected Scalar Fields * New Statistical Textural Transforms for Non-Stationary Signals: Application to Generalized Mutlifractal Analysis * Laplacian Growth of Parallel Needles: Their Mullins-Sekerka Instability * Entropy Dynamics Associated with Self-Organization * Fractal Properties in Economics (invited talk) * Fractal Approach to the Regional Seismic Event Discrimination Problem * Fractal and Topological Complexity of Radioactive Contamination * Pattern Selection: Nonsingular Saffman-Taylor Finger and Its Dynamic Evolution with Zero Surface Tension * A Family of Complex Wavelets for the Characterization of Singularities * Stabilization of Chaotic Amplitude Fluctuations in Multimode, Intracavity-Doubled Solid-State Lasers * Chaotic Dynamics of Elastic-Plastic Beams * The Riemann Non-Differentiable Function and Identities for the Gaussian Sums * Revealing the Multifractal Nature of Failure Sequence * The Fractal Nature of wood Revealed by Drying * Squaring the Circle: Diffusion Volume and Acoustic Behaviour of a Fractal Structure * Relationship Between Acupuncture Holographic Units and Fetus Development; Fractal Features of Two Acupuncture Holographic Unit Systems * The Fractal Properties of the Large-Scale Magnetic Fields on the Sun * Fractal Analysis of Tide Gauge Data * Author Index

Chest compression rate measurement from smartphone video.

PubMed

Engan, Kjersti; Hinna, Thomas; Ryen, Tom; Birkenes, Tonje S; Myklebust, Helge

2016-08-11

Out-of-hospital cardiac arrest is a life threatening situation where the first person performing cardiopulmonary resuscitation (CPR) most often is a bystander without medical training. Some existing smartphone apps can call the emergency number and provide for example global positioning system (GPS) location like Hjelp 113-GPS App by the Norwegian air ambulance. We propose to extend functionality of such apps by using the built in camera in a smartphone to capture video of the CPR performed, primarily to estimate the duration and rate of the chest compression executed, if any. All calculations are done in real time, and both the caller and the dispatcher will receive the compression rate feedback when detected. The proposed algorithm is based on finding a dynamic region of interest in the video frames, and thereafter evaluating the power spectral density by computing the fast fourier transform over sliding windows. The power of the dominating frequencies is compared to the power of the frequency area of interest. The system is tested on different persons, male and female, in different scenarios addressing target compression rates, background disturbances, compression with mouth-to-mouth ventilation, various background illuminations and phone placements. All tests were done on a recording Laerdal manikin, providing true compression rates for comparison. Overall, the algorithm is seen to be promising, and it manages a number of disturbances and light situations. For target rates at 110 cpm, as recommended during CPR, the mean error in compression rate (Standard dev. over tests in parentheses) is 3.6 (0.8) for short hair bystanders, and 8.7 (6.0) including medium and long haired bystanders. The presented method shows that it is feasible to detect the compression rate of chest compressions performed by a bystander by placing the smartphone close to the patient, and using the built-in camera combined with a video processing algorithm performed real-time on the device.