Concept Of Data Compression Research Articles

Detailed Review on Cloud Computing and Cloud Security

In the era of Information, we have analyzed that many people are facing the problem of data handling, data storage, and data security. Another problem we have analyzed is that people want an integrated platform with greater accuracy and better security. To solve this problem, we have come up with a solution of integrated cloud storage and browsing application where the user will be allocated some space on the server where one can store their files in a separated and well-mannered form, which will help them to manage their content in an organized manner. We will also provide a marketplace for the user so they can get some required media files from there, which may give integrity to users. For the backend, we will use the concept of data compression using a backpropagation algorithm (using neural networks) which will help to user store more files in a compressed manner on the server. The data will be encrypted using cryptography algorithms, which will increase the security of data by multiple times.

Data compression can discriminate broilers by selection line, detect haplotypes, and estimate genetic potential for complex phenotypes

Accurately establishing the relationships among individuals lays the foundation for genetic analyses such as genome-wide association studies and identification of selection signatures. Of particular interest to the poultry industry are estimates of genetic merit based on molecular data. These estimates can be commercially exploited in marker-assisted breeding programs to accelerate genetic improvement. Here, we test the utility of a new method we have recently developed to estimate animal relatedness and applied it to genetic parameter estimation in commercial broilers. Our approach is based on the concept of data compression from information theory. Using the real-world compressor gzip to estimate normalized compression distance (NCD) we have built compression-based relationship matrices (CRM) for 988 chickens from 4 commercial broiler lines—2 male and 2 female lines. For all pairs of individuals, we found a strong negative relationship between the commonly used genomic relationship matrix (GRM) and NCD. This reflects the fact that “similarity” is the inverse of “distance.” The CRM explained more genetic variation than the corresponding GRM in 2 of 3 phenotypes, with corresponding improvements in accuracy of genomic-enabled predictions of breeding value. A sliding-window version of the analysis highlighted haplotype regions of the genome apparently under selection in a line-specific manner. In the male lines, we retrieved high population-specific scores for IGF-1 and a cognate receptor, INSR. For the female lines, we detected an extreme score for a region containing a reproductive hormone receptor (GNRHR). We conclude that our compression-based method is a valid approach to established relationships and identify regions under selective pressure in commercial lines of broiler chickens.

Irreversible data compression concepts with polynomial fitting in time-order of particle trajectory for visualization of huge particle system

We propose in this paper a data compression scheme for large-scale particle simulations, which has favorable prospects for scientific visualization of particle systems. Our data compression concepts deal with the data of particle orbits obtained by simulation directly and have the following features: (i) Through control over the compression scheme, the difference between the simulation variables and the reconstructed values for the visualization from the compressed data becomes smaller than a given constant. (ii) The particles in the simulation are regarded as independent particles and the time-series data for each particle is compressed with an independent time-step for the particle. (iii) A particle trajectory is approximated by a polynomial function based on the characteristic motion of the particle. It is reconstructed as a continuous curve through interpolation from the values of the function for intermediate values of the sample data. We name this concept "TOKI (Time-Order Kinetic Irreversible compression)". In this paper, we present an example of an implementation of a data-compression scheme with the above features. Several application results are shown for plasma and galaxy formation simulation data.

Data Security Using Private Key Encryption System Based on Arithmetic Coding

Problem faced by today’s communicators is not only security but also the speed of communication and size of content.In the present paper, a scheme has been proposed which uses the concept of compression and data encryption. In first phase the focus has been made on data compression and cryptography. In the next phase we have emphasized on compression cryptosystem. Finally, proposed technique has been discussed which used the concept of data compression and encryption. In this first data is compressed to reduce the size of the data and increase the data transfer rate. Thereafter compress data is encrypted to provide security. Hence our proposed technique is effective that can reduce data size, increase data transfer rate and provide the security during communication.

Data Compression Concepts and Algorithms and Their Applications to Bioinformatics

Data compression at its base is concerned with how information is organized in data. Understanding this organization can lead to efficient ways of representing the information and hence data compression. In this paper we review the ways in which ideas and approaches fundamental to the theory and practice of data compression have been used in the area of bioinformatics. We look at how basic theoretical ideas from data compression, such as the notions of entropy, mutual information, and complexity have been used for analyzing biological sequences in order to discover hidden patterns, infer phylogenetic relationships between organisms and study viral populations. Finally, we look at how inferred grammars for biological sequences have been used to uncover structure in biological sequences.

Updating the principal angle decomposition

A class of fast Householder-based sequential algorithms for updating the Principal Angle Decomposition is introduced. The updated Principal Angle Decomposition is of key importance in the adaptive implementation of several fundamental operations on correlated processes, such as adaptive Wiener filtering, rank-adaptive system identification, and rank and data compression concepts using canonical coordinates. An instructive example of rank-adaptive system identification is examined experimentally.

On-board Data Processing to Lower Bandwidth Requirements on an Infrared Astronomy Satellite: Case of Herschel-PACS Camera

This paper presents a new data compression concept, on-board processing, for infrared astronomy, where space observatories have limited processing resources. The proposed approach has been developed and tested for the PACS camera from the European Space Agency (ESA) mission, Herschel. Using lossy and lossless compression, the presented method offers high compression ratio with a minimal loss of potentially useful scientific data. It also provides higher signal-to-noise ratio than that for standard compression techniques. Furthermore, the proposed approach presents low algorithmic complexity such that it is implementable on the resource-limited hardware. The various modules of the data compression concept are discussed in detail.

A context-free data compression approach to measuring information transmission by action potentials

Action potentials allow nervous systems to transmit information rapidly and efficiently over considerable distances, but what is the information they carry and how much can be carried by one neuron? Often, qualitative and vague descriptions are used, such as the firing rate representing intensity. Recent attempts to quantify information transmission by action potentials have concentrated on treating neurons as communication channels, whose information capacity can be estimated from their signal-to-noise ratios. However, this only indicates how much information could theoretically be carried, not the actual amount at any given time, and the ratio itself depends on assumptions about information coding. Here we introduce a different approach based on the concept of data compression, which has become familiar with the widespread use of digital computers and networks. Compression takes advantage of redundancy in a sequence of numbers to reduce its size, but allows it to be reconstructed later without error. We show that data compression by a context-free grammar can quantitatively estimate the real information content of action potential signals without any prior assumptions about coding, or knowledge of neural inputs. Measurements of information coding by mechanosensory neurons are used as examples, but a major advantage of this approach is its generality. It can estimate information transmission by any neuron whose output can be measured, regardless of neuronal type, connectivity or function.

A study on wavelet data compression of a real-time monitoring system for large hydraulic machines

The general concept of data compression consists in removing the redundancy existing in data to find a more compact representation. This paper is concerned with a new method of compression using thesecond generation wavelets based on thelifting scheme, which is a simple but powerful wavelet construction method. It has been proved by its successful application to a real-time monitoring system of large hydraulic machines that it is a promising compression method.

On a least squares predictive-transform modeling methodology

A deterministic least squares (LS) predictive-transform (PT) multichannel modeling framework is presented. In a manner analogous to the development of minimum mean square error (MSE) PT, the LS PT signal model is obtained as an inherent byproduct of an optimized predictive-transform signal source "encoder", thereby preserving the direct integration of specific data compression concepts into the basic modeling procedure that have proven very useful in the application of minimum MSE PT to coding, detection, estimation, and control. Fundamental properties of the LS PT signal model are presented, and a recursive least squares (RLS) PT modeling procedure is developed. In addition to subsuming conventional RLS signal modeling as a special case and the presence of an integrated transformation mechanism, RLS PT offers greater flexibility when independent "fading memory" weighting of both the first- and second-order sample moments is desired. Sufficient conditions for the convergence of the RLS PT parameters to their minimum MSE PT counterparts are developed. In addition, the zero mean constraint (either deterministic or stochastic) imposed on the LS PT signal model's innovation sequence is shown to provide a mechanism for mitigating the deleterious effects of a singular or near-singular data correlation matrix.

New Techniques for Teach-In, Acceleration and Learning in Sensor-Controlled Robots

The paper tries to outline a unified approach to sensory feedback in robotics . By a few generalizing definitions relating positions/orientations and (pseudo-) forces/torques the problem of teaching a robot a certain task including continuous sensory Information, is Simplified. Already in the teach phase the paths are generated via and together with sensory pattern motion commands and sensor data are stored together. The latter ones are then availab1e as reference values for the repetition mode in a possibly changing environment. Self-adaptions and teacher-induced corrections may be superposed in an associative memory. A New data compression concept that allow to store long paths with only a few parameters and that Compensate robot dynamics in high speed operations are outlined.