Data Compression Applications Research Articles

IMPLEMENTASI KOMPRESI CITRA DIGITAL DENGAN MENGATUR KUALITAS CITRA DIGITAL

Image is one of the most popular media used in today’s information exchange. Increased needs for the use of images must also be supported by available storage media. The imagery generated from each high-resolution camera device has a relatively large size. Image compression is a data compression application performed on digital image in order to reduce the redundancy of the data contained in the image so that it can be stored or transmitted efficiently.Image compression techniques can be grouped into two namely lossless compression and lossy compression. Lossy compression is a method to compress the image where compressed image decompression results are not the same as the original image because there is missing information, but still can be tolerated by the eye. Lossless compression is an image compression where the decompression of compressed images is the same as the original image, no information is lost. For the following image compression by reducing image quality that can still be tolerated by the eye.

Real Time Object Tracking and Protection: Hybrid Logistics Architecture in Ubiquitous Environments (HLAiUE)

Realtime object security and vehicle tracking have not been successfully implemented in commercial vehicles due to limitations in processing large amount of data and hardware capabilities. This paper aimed to improve the object security, processing power and data storage in a distributed environment. The proposed system (HLAiUE) consists of a three-layer architecture, first layer is used to collect data, second layer distributes the data processing and the last layer stores the processed data. The designed architecture improves the performance and security by applying data compression algorithms in a real time and removes the hardware dependency by processing the data in a distributed environment using Hadoop and Spark framework. The results demonstrate that the proposed architecture improves processing time by 80% compared to other algorithms and provides more reliability, security and flexibility due to ubiquity and the absence of hardware dependency in comparison with other existing architectures which are hardware dependent. Also, using an NOSQL database server in a distributed environment optimizes data storage by up to 80% and is flexible because all the infrastructure is in a distributed environment. The proposed architecture improves the object tracking by implementing compression technique in a distributed architecture. Thus, this system improves the accuracy up to 80% without depending on hardware resources.

Stream parallelism with ordered data constraints on multi-core systems

It is often a challenge to keep input/output tasks/results in order for parallel computations over data streams, particularly when stateless task operators are replicated to increase parallelism when there are irregular tasks. Maintaining input/output order requires additional coding effort and may significantly impact the application’s actual throughput. Thus, we propose a new implementation technique designed to be easily integrated with any of the existing C++ parallel programming frameworks that support stream parallelism. In this paper, it is first implemented and studied using SPar, our high-level domain-specific language for stream parallelism. We discuss the results of a set of experiments with real-world applications revealing how significant performance improvements may be achieved when our proposed solution is integrated within SPar, especially for data compression applications. Also, we show the results of experiments performed after integrating our solution within FastFlow and TBB, revealing no significant overheads.

Efficient blind adaptive Karhunen–Loéve transform via parallel search

This article introduces a new algorithm that constructs an efficient search strategy, called parallel search, for blind adaptive Karhunen–Loéve transform. Unlike anterior Karhunen–Loéve transform, the proposed algorithm converges quickly by searching for solutions in different directions simultaneously. Moreover, the process is “blind,” which means that minimal information about the original data is used. The new algorithm also avoids repeating the Karhunen–Loéve transform basis learning step in data compression applications. Numerical simulation results verify the validity of the theory and illustrate the capability of the proposed algorithm.

Multi-step ahead time series forecasting via sparse coding and dictionary based techniques

Sparse coding is based on the concept of having a large dictionary of candidate basis vectors. Any given vector is expressed as a sparse linear combination of the dictionary vectors. It has been developed in the signal processing field, and has many applications in data compression and image processing. In this paper we propose applying sparse coding to the time series forecasting field. Specifically, the paper investigates different dictionary based local learning techniques for building predictive models for the time series forecasting problem. The proposed methodology is based on a local learning framework whereby the query point is embedded and coded in terms of a sparse combination of the training dictionary atoms (vectors). Then this embedding is used for estimating the target value of the query point, by applying the same embedding to the target vectors of the dictionary training atoms.We present an experimental study of several sparse coding algorithms. Experiments are performed on the large monthly time series benchmark from the M3 competition, and these experiments showed that the sparse methods Lasso and Elastic-Net presented the best results among the sparse coding algorithms. Moreover, they outperformed the K-nearest neighbor (KNN) regression and most of the compared machine learning and statistical forecasting techniques, especially for higher horizons.

Chemical reaction optimization for solving longest common subsequence problem for multiple string

Longest common subsequence (LCS) is a well-known NP-hard optimization problem that finds out the longest subsequence of each member of a given set of strings. In computational biology, sequence alignment is a fundamental technique to measure the similarity of biological sequences, such as DNA and genome sequences. A high sequence similarity often applied to molecular structural as well as functional similarities and can be used to determine whether (and how) sequences are related. Finding the longest common subsequence (LCS) is one way to measure the similarity of sequences. It has also applications in data compression, FPGA circuit minimization, and bioinformatics, etc. Exact algorithms are impractical since they fail to solve this problem for multiple instances of long lengths in polynomial time. There are some approximations, heuristic, and metaheuristic methods proposed to solve the problem. Chemical reaction optimization (CRO) is a new metaheuristic method that mimics the nature of chemical reaction into optimization problems. In this paper, we have proposed chemical reaction optimization technique to solve the longest common subsequence problem for multiple instances. Here, we have redesigned four elementary operators of CRO for LCS problem. Operators of CRO algorithm are used to explore the search space both locally and globally. A novel correction method has been designed to correct the solution. Correction method works after each search operator to ensure the validity of the changes made by operators. Both solution quality and execution time are considered while designing the operators and the correction method. Thus proposed system brings robustness, efficiency, and effectiveness while solving MLCS problem. Our approach is compared with hyper-heuristic, ant colony optimization, beam ant colony optimization, and memory-bound anytime algorithms. The experimental results in lengths of the returned common sequences show that our proposed algorithm gives either same or better results than all other algorithms in less execution time.

RETRACTED ARTICLE: Augmented Recurrence Hopping Based Run-Length Coding for Test Data Compression Applications

The advancement in technologies has been increasing with increase in integrating scales which allows fabricating millions of transistors on a chip. This demands the efficient testing circuit to evaluate the fault present, where the large volume of data volume needs to be tested during manufacturing and fabrication. Therefore, the challenging fact arises in methodologies to achieve the test data compression. Even though various techniques in the present scenario reduce the testing time, improvement in data volume reduction is still a demanding factor. The proposed scheme aims to obtain a Large Compression Ratio. By using Augmented Recurrence Hopping based Run-Length Coding (ARHRLC) Test Data volume can be reduced and data can be compressed. The proposed ARHRLC coding technique which compares the group code based test vector and it’s duplicate. Data sequence can be decreased in terms volume and area overhead. From the analysis of ISCAS 89 benchmark circuit’s performance shows the proposed coding scheme outperforms the conventional test data compression methods. The Augmented Recurrence Hopping based Run-Length Coding procedure skips the repeated sequence in the test vector group and estimate conversely perfect example of a separate test information section or various test information fragment. The test result demonstrates that the compression proportion and calculation time is lessened.

New Results on Competitive Analysis of Move To Middle(MTM) List Update Algorithm using Doubly Linked List

On-line algorithm is an emerging area of research since last five decades with immense theoretical and practical significance. Here the sequence of inputs is received and processed by the algorithm one by one in order. At any instant of time, the algorithm has the knowledge of past and present inputs without knowledge of future inputs. Competitive analysis is a standard performance measure for on-line algorithms in which the performance of an on-line algorithm is compared with that of an optimum offline algorithm. List update problem is a well studied research problem in the area of on-line algorithms, which finds applications in data-compression, dictionary maintenance, collision resolution in hash table, symbol table organization in compiler and computing convex hull in computational geometry. From the literature it is known that Move To Front(MTF) is the best performing deterministic list update algorithm using singly linked list in all practical applications. In this paper, we study and analyse the performance of a deterministic on-line list update algorithm known as Move To Middle(MTM) using doubly linked list. We make a first attempt to find the competitive ratio of MTM algorithm, which was only experimentally studied in the literature. In our study by considering doubly linked list as the data structure and a new variant of standard full cost model, we obtain new competitive analysis result and prove that MTM is 2-competitive. From our competitive analysis result, we show that MTM outperforms MTF.

Group divisible covering designs with block size four

AbstractGroup divisible covering designs (GDCDs) were introduced by Heinrich and Yin as a natural generalization of both covering designs and group divisible designs. They have applications in software testing and universal data compression. The minimum number of blocks in a k‐GDCD of type is a covering number denoted by . When , the values of have been determined completely for all possible pairs . When , Francetić et al. constructed many families of optimal GDCDs, but the determination remained far from complete. In this paper, two specific 4‐IGDDs are constructed, thereby completing the existence problem for 4‐IGDDs of type . Then, additional families of optimal 4‐GDCDs are constructed. Consequently the cases for whose status remains undetermined arise when and , when and , and in several small families for which one of g and u is fixed.

Relationship between superstring and compression measures: New insights on the greedy conjecture

A superstring of a set of words is a string that contains each input word as a substring. Given such a set, the Shortest Superstring Problem (SSP) asks for a superstring of minimum length. SSP is an important theoretical problem related to the Asymmetric Travelling Salesman Problem, and also has practical applications in data compression and in bioinformatics. Indeed, it models the question of assembling a genome from a set of sequencing reads. Unfortunately, SSP is known to be NP-hard even on a binary alphabet and also hard to approximate with respect to the superstring length or to the compression achieved by the superstring. Even the variant in which all words share the same length r, called r-SSP, is NP-hard whenever r>2. Numerous involved approximation algorithms achieve approximation ratio above 2 for the superstring, but remain difficult to implement in practice. In contrast the greedy conjecture asked in 1988 whether a simple greedy algorithm achieves ratio of 2 for SSP. Here, we present a novel approach to bound the superstring approximation ratio with the compression ratio, which, when applied to the greedy algorithm, shows a 2 approximation ratio for 3-SSP, and also that greedy achieves ratios smaller than 2. This leads to a new version of the greedy conjecture.

Faster Average Case Low Memory Semi-external Construction of the Burrows\u2013Wheeler Transform

The Burrows–Wheeler transform has applications in data compression as well as full text indexing. Despite its important applications and various existing algorithmic approaches the construction of the transform for large data sets is still challenging. In this paper we present a new semi external memory algorithm for constructing the Burrows–Wheeler transform. It is capable of constructing the transform for an input text of length n over a finite alphabet in time O(nlog ^2log n) on average, if sufficient internal memory is available to hold a fixed fraction of the input text. In the worst case the run-time is O(nlog n log log n). The amount of space used by the algorithm in external memory is O(n) bits. Based on the serial version we also present a shared memory parallel algorithm running in time O(frac{n}{p}max {log ^2log n+log p}) on average when p processors are available.

Low-power DCT-based compressor for wireless capsule endoscopy

Discrete Cosine Transform (DCT) is known as one of the most significant, fundamental, and prevailing transforms in data compression, watermarking, and medical digital image processing applications for secure storing, sending, and transmitting in cyberspace. This paper addresses a low power, area efficient, and low complexity structure of COordinate Rotation Digital Computer (CORDIC)-based DCT for applying to the Wireless Capsule Endoscopy (WCE) application. In this work, a novel structure is developed called low-power Lookahead (LPLA) CORDIC to overcome the problems of power supply restriction and small dimensions while maintaining the image quality in the WCE. The key idea in the proposed structure is that the successive computations of CORDIC algorithm are performed by utilizing the pipeline property of Lookahead (LA) CORDIC. This leads to employ a single common hardware for the two CORDIC outputs. Thus, the number of the adder and shifter blocks required for CORDIC computation decreases in the proposed structure. Moreover, we utilize pipelining, clock gating and data gating techniques which result in further reduction of power consumption. The simulation results by TSMC 0.13 μm technology at 1.2v power supply demonstrate that the proposed LPLA CORDIC-based DCT structure averagely consumes 64% of the power, 43% of the area, and 8% of the power-delay-product (PDP) of the most efficient structures reported in the literature. Furthermore, by applying the proposed method to JPEG compression standard, it is shown that the reconstructed images of the proposed structure have favorable quality and compression ratio, in addition to achieving lower power consumption and area overhead. According to the aforementioned features, the proposed LPLA CORDIC-based DCT structure is suitable for utilizing in the compressor part of wireless endoscopy capsule and other low-power and high-quality systems which are battery-based.

On The Characterization and Uniqueness of Centroidal Voronoi Tessellations

Vector quantization is a classical signal-processing technique with significant applications in data compression, pattern recognition, clustering, and data stream mining. It is well known that for critical points of the quantization energy, the tessellation of the domain is a centroidal Voronoi tessellation. However, for dimensions greater than one, rigorously verifying a given centroidal Voronoi tessellation is a local minimum can prove difficult. Using variational techniques, we give a full characterization of the second variation of a centroidal Voronoi tessellation and give sufficient conditions for a centroidal Voronoi tessellation to be a local minimum. In addition, the conditions under which a centroidal Voronoi tessellation for a given density and domain is unique have been elusive for dimensions greater than one. We prove that there does not exist a unique two generator centroidal Voronoi tessellation for dimensions greater than one.

Controllable Curve Fitting Based Swing Door Trending Algorithm and its Application in Process Data Compression

Controllable Curve Fitting Based Swing Door Trending Algorithm and its Application in Process Data Compression

Chemical reaction optimization for solving shortest common supersequence problem

Shortest common supersequence (SCS) is a classical NP-hard problem, where a string to be constructed that is the supersequence of a given string set. The SCS problem has an enormous application of data compression, query optimization in the database and different bioinformatics activities. Due to NP-hardness, the exact algorithms fail to compute SCS for larger instances. Many heuristics and meta-heuristics approaches were proposed to solve this problem. In this paper, we propose a meta-heuristics approach based on chemical reaction optimization, CRO_SCS that is designed inspired by the nature of the chemical reactions. For different optimization problems like 0-1 knapsack, quadratic assignment, global numeric optimization problems CRO algorithm shows very good performance. We have redesigned the reaction operators and a new reform function to solve the SCS problem. The outcomes of the proposed CRO_SCS algorithm are compared with those of the enhanced beam search (IBS_SCS), deposition and reduction (DR), ant colony optimization (ACO) and artificial bee colony (ABC) algorithms. The length of supersequence, execution time and standard deviation of all related algorithms show that CRO_SCS gives better results on the average than all other algorithms.

Energy-efficient adaptive data compression in wireless sensor networks

In wireless sensor networks WSNs, a large number of tiny, inexpensive and computable sensor nodes are usually deployed randomly to monitor one or more physical phenomena. The sensor nodes collect and process the sensed data and send the data to the sink wirelessly. Energy consumption is however a serious problem affecting WSNs lifetime. Radio communication is often the major cause of energy consumption in wireless sensor nodes. Thus, applying data compression before transmission can significantly help in reducing the total power consumption of a sensor node. In this paper, we propose an efficient and robust adaptive data compression scheme ADCS. The proposed scheme independently compresses each block of source data losslessly or lossily on local nodes based on the given application. Simulation results show the merits of the proposed compression scheme in comparison with other recently proposed compression algorithms for WSNs including S-LZW, LEC, MPDC, Two-modal GPC and LTC.

Dynamic Virtual Chunks: On Supporting Efficient Accesses to Compressed Scientific Data

Data compression could ameliorate the I/O pressure of data-intensive scientific applications. Unfortunately, the conventional wisdom of naively applying data compression to the file or block brings the dilemma between efficient random accesses and high compression ratios. File-level compression barely supports efficient random accesses to the compressed data: any retrieval request need trigger the decompression from the beginning of the compressed file. Block-level compression provides flexible random accesses to the compressed blocks, but introduces extra overhead when applying the compressor to each and every block that results in a degraded overall compression ratio. This paper extends our prior work that introduces virtual chunks offering efficient random accesses to the compressed scientific data without sacrificing the compression ratio. Virtual chunks are logical blocks pointed at by appended references without breaking the physical continuity of the file content. These references allow the decompression to start from an arbitrary position (efficient random accesses), while no per-block overhead is introduced because the file's physical entirety is retained (high compression ratio). One limitation of virtual chunk is it only supports static references. This paper presents the algorithms, analysis, and evaluations of dynamic virtual chunks to deal with the cases where the references are updated dynamically.

The MAVEN Magnetic Field Investigation

The MAVEN magnetic field investigation is part of a comprehensive particles and fields subsystem that will measure the magnetic and electric fields and plasma environment of Mars and its interaction with the solar wind. The magnetic field instrumentation consists of two independent tri-axial fluxgate magnetometer sensors, remotely mounted at the outer extremity of the two solar arrays on small extensions (boomlets). The sensors are controlled by independent and functionally identical electronics assemblies that are integrated within the particles and fields subsystem and draw their power from redundant power supplies within that system. Each magnetometer measures the ambient vector magnetic field over a wide dynamic range (to 65,536 nT per axis) with a quantization uncertainty of 0.008 nT in the most sensitive dynamic range and an accuracy of better than 0.05%. Both magnetometers sample the ambient magnetic field at an intrinsic sample rate of 32 vector samples per second. Telemetry is transferred from each magnetometer to the particles and fields package once per second and subsequently passed to the spacecraft after some reformatting. The magnetic field data volume may be reduced by averaging and decimation, when necessary to meet telemetry allocations, and application of data compression, utilizing a lossless 8-bit differencing scheme. The MAVEN magnetic field experiment may be reconfigured in flight to meet unanticipated needs and is fully hardware redundant. A spacecraft magnetic control program was implemented to provide a magnetically clean environment for the magnetic sensors and the MAVEN mission plan provides for occasional spacecraft maneuvers - multiple rotations about the spacecraft x and z axes - to characterize spacecraft fields and/or instrument offsets in flight.

APLIKASI KOMPRESI DATA MENGGUNAKAN METODE HUFFMAN STATIK PADA PERANGKAT MOBILE BERBASIS ANDROID

Efficient use of bandwidth and memory into the main focus that must be considered in the process of data exchange. More and more data is stored, the greater the size of the data. One alternative that can be implemented is to reduce the size of the (compressed) data. In addition to the process of data transmission, compression is also useful in the data storage medium in the secondary. Compression aims to reduce the amount of data used to represent the contents of a text file, image, audio, and video without reducing the quality of the original data. Compression is done by reducing the number of bits needed to store or transmit the digital media. For data compression, one algorithm has a good performance is the type of the Huffman algorithm.In this study, the Static Huffman algorithm which is one technique that is lossless data compression. Static Huffman algorithm uses frequency/probability of occurrences of a symbol in a string as input and produce output in the form of a prefix code that encodes a string using the little bits of all possible binary prefix code that may be. Static Huffman algorithm implementation is aimed to compress the data so that the compressed data size is smaller than the size of the original data. So that the compressed data can be retrieved, then the decompression process. This application was made and in the making based on Android using Eclipse and the methodology GRAPPLE (Guidlines for Rappid Application Engineering). GRAPPLE is a process modeling in software development which emphasizes the actions carried out in a number of stages , each stage will result in products that work with object-oriented form.With applications of data compression with Huffman Static method in Android -based mobile devices, the data size becomes smaller so that it can speed up the process of delivery and storage. Compression using Huffman code would be optimal if the range of the character of the information is not too much despite the high frequency of occurrence and the size of the compression results obtained that the compression of each file depending on the characteristics of the file to be compressed. Keywords : Compression, Decompression, Static Huffman Algorithm, Lossless, Android

Application of a Real-Time Data Compression and Adapted Protocol Technique for WAMS

This paper proposes a real-time data compression and adapted protocol technique for wide-area measurement systems (WAMS). The compression algorithm combines exception compression (EC) with swing door trending (SDT) compression. The compression logic is designed to perform this algorithm in real time. Selection of compression parameters and data reconstruction are presented. An adapted protocol is introduced by improving the format of data frames defined by IEEE standard C37.118 for compressed data packets. The proposed compression technique and protocol were applied to the phasor measurement units (PMUs) of a hydropower plant in Guizhou Power Grid in Southwest China. A low-frequency oscillation incident was recorded by this technique. The raw, compressed and reconstructed data were analyzed to verify the compression and determine the accuracy of the proposed technique. Also, the wavelet-based data compression, the standalone EC and SDT are compared with the proposed compression technique. Our results demonstrated that this compression can reach the compression ratios in the range of 6 to 11. Also, this compression and adapted protocol technique can reduce the size of data packets by approximately 75% with high accuracy in both dynamic and steady states.