Two-Dimensional Source Coding by Means of Subblock Enumeration

A technique of lossless compression via substring enumeration (CSE) attains compression ratios as well as popular lossless compressors for one-dimensional (1D) sources. The CSE utilizes a probabilistic model built from the circular string of an input source for encoding the source.The CSE is applicable to two-dimensional (2D) sources such as images by dealing with a line of pixels of 2D source as a symbol of an extended alphabet. At the initial step of the CSE encoding process, we need to output the number of occurrences of all symbols of the extended alphabet, so that the time complexity increase exponentially when the size of source becomes large. To reduce the time complexity, we propose a new CSE which can encode a 2D source in block-by-block instead of line-by-line. The proposed CSE utilizes the flat torus of an input 2D source as a probabilistic model for encoding the source instead of the circular string of the source. Moreover, we analyze the limit of the average codeword length of the proposed CSE for general sources.Comment: 5 pages, Submitted to ISIT201

A Universal Two-Dimensional Source Coding by Means of Subblock Enumeration

The technique of lossless compression via substring enumeration (CSE) is a kind of enumerative code and uses a probabilistic model built from the circular string of an input source for encoding a one-dimensional (1D) source. CSE is applicable to two-dimensional (2D) sources, such as images, by dealing with a line of pixels of a 2D source as a symbol of an extended alphabet. At the initial step of CSE encoding process, we need to output the number of occurrences of all symbols of the extended alphabet, so that the time complexity increases exponentially when the size of source becomes large. To reduce computational time, we can rearrange pixels of a 2D source into a 1D source string along a space-filling curve like a Hilbert curve. However, information on adjacent cells in a 2D source may be lost in the conversion. To reduce the time complexity and compress a 2D source without converting to a 1D source, we propose a new CSE which can encode a 2D source in a block-by-block fashion instead of in a line-by-line fashion. The proposed algorithm uses the flat torus of an input 2D source as a probabilistic model instead of the circular string of the source. Moreover, we prove the asymptotic optimality of the proposed algorithm for 2D general sources

Technology Mapping for Circuit Optimization Using Content-Addressable Memory

The growing complexity of Field Programmable Gate Arrays (FPGA's) is leading to architectures with high input cardinality look-up tables (LUT's). This thesis describes a methodology for area-minimizing technology mapping for combinational logic, specifically designed for such FPGA architectures. This methodology, called LURU, leverages the parallel search capabilities of Content-Addressable Memories (CAM's) to outperform traditional mapping algorithms in both execution time and quality of results. The LURU algorithm is fundamentally different from other techniques for technology mapping in that LURU uses textual string representations of circuit topology in order to efficiently store and search for circuit patterns in a CAM. A circuit is mapped to the target LUT technology using both exact and inexact string matching techniques. Common subcircuit expressions (CSE's) are also identified and used for architectural optimization---a small set of CSE's is shown to effectively cover an average of 96% of the test circuits. LURU was tested with the ISCAS'85 suite of combinational benchmark circuits and compared with the mapping algorithms FlowMap and CutMap. The area reduction shown by LURU is, on average, 20% better compared to FlowMap and CutMap. The asymptotic runtime complexity of LURU is shown to be better than that of both FlowMap and CutMap

Compression by Substring Enumeration Using Sorted Contingency Tables

This paper proposes two variants of improved Compression by Substring Enumeration (CSE) with a finite alphabet. In previous studies on CSE, an encoder utilizes inequalities which evaluate the number of occurrences of a substring or a minimal forbidden word (MFW) to be encoded. The inequalities are derived from a contingency table including the number of occurrences of a substring or an MFW. Moreover, codeword length of a substring and an MFW grows with the difference between the upper and lower bounds deduced from the inequalities, however the lower bound is not tight. Therefore, we derive a new tight lower bound based on the contingency table and consequently propose a new CSE algorithm using the new inequality. We also propose a new encoding order of substrings and MFWs based on a sorted contingency table such that both its row and column marginal total are sorted in descending order instead of a lexicographical order used in previous studies. We then propose a new CSE algorithm which is the first proposed CSE algorithm using the new encoding order. Experimental results show that compression ratios of all files of the Calgary corpus in the proposed algorithms are better than those of a previous study on CSE with a finite alphabet. Moreover, compression ratios under the second proposed CSE get better than or equal to that under a well-known compressor for 11 files amongst 14 files in the corpus

Tilatiivis toteutus tiedon tiivistämiseen osamerkkijonoja luettelemalla

Häviöttömässä tiedon tiivistämisessä annetusta datasta luodaan tiiviste, joka vie mahdollisimman vähän tilaa suhteessa alkuperäiseen dataan. Tiivisteestä on voitava palauttaa identtinen kopio alkuperäisestä datasta. Tutkielmassa käsitellään häviötöntä tiivistysmenetelmää, joka tutkii tiivistettävää dataa, eli merkkijonoa tai tekstiä, kokonaisuutena, eikä esimerkiksi pieni osa kerrallaan. Menetelmä välittää tiivisteen purkajalle osamerkkijonojen esiintymismääriä tekstissä. Osamerkkijonot käsitellään ennalta tunnetussa järjestyksessä lyhyimmästä pisimpään, jolloin kumpikin osapuoli osaa liittää esiintymismäärän oikeaan osamerkkijonoon. Jotkut esiintymismäärät voivat olla nollia kertomassa, ettei osamerkkijono esiinny tekstissä. Tiivistyvyys saavutetaan huomaamalla, että aiemmin välitetyt osamerkkijonot rajaavat millaisia pidemmät merkkijonot voivat olla. Tällöin osa esiintymismääristä voidaan jättää välittämättä, tai välittämiseen käyttää vähemmän tilaa. Osamerkkijonoja, joiden esiintymismäärä täytyy välittää, karakterisoidaan maksimaalisuuden käsitteen avulla. Maksimaalisten osamerkkijonojen etsiminen ja osamerkkijonojen esiintymismäärien laskeminen paljaasta tekstistä on hidasta. Siksi teksti täytyy tallettaa tietorakenteeseen, joka tukee tarvittuja operaatioita tehden niistä nopeita. Tällaiset tietorakenteet vievät enemmän tilaa kuin paljas teksti. Koska tutkittavassa tiivistysmenetelmässä koko tiivistettävä teksti käsitellään kokonaisuutena, muistinkäytön tehokkuus korostuu. Tutkielmassa toteutetaan tiivistysmenetelmä käyttäen tilatiiviistä tietorakennetta nimeltä kaksisuuntainen BWT-indeksi. Tilatiiviit tietorakenteet vievät vain vähän enemmän tilaa, kuin niihin talletettu data. Tästä huolimatta ne toteuttavat talletettua dataa käsitteleviä operaatioita tehokkaasti. Toteutukselle suoritetut kokeet osoittavat muistinkäytön pysyvän kohtuullisena, jolloin suurempienkin tietomäärien tiivistys on mahdollista

A Survey on Semantic Communications for Intelligent Wireless Networks

With deployment of 6G technology, it is envisioned that competitive edge of wireless networks will be sustained and next decade's communication requirements will be stratified. Also 6G will aim to aid development of a human society which is ubiquitous and mobile, simultaneously providing solutions to key challenges such as, coverage, capacity, etc. In addition, 6G will focus on providing intelligent use-cases and applications using higher data-rates over mill-meter waves and Tera-Hertz frequency. However, at higher frequencies multiple non-desired phenomena such as atmospheric absorption, blocking, etc., occur which create a bottleneck owing to resource (spectrum and energy) scarcity. Hence, following same trend of making efforts towards reproducing at receiver, exact information which was sent by transmitter, will result in a never ending need for higher bandwidth. A possible solution to such a challenge lies in semantic communications which focuses on meaning (context) of received data as opposed to only reproducing correct transmitted data. This in turn will require less bandwidth, and will reduce bottleneck due to various undesired phenomenon. In this respect, current article presents a detailed survey on recent technological trends in regard to semantic communications for intelligent wireless networks. We focus on semantic communications architecture including model, and source and channel coding. Next, we detail cross-layer interaction, and various goal-oriented communication applications. We also present overall semantic communications trends in detail, and identify challenges which need timely solutions before practical implementation of semantic communications within 6G wireless technology. Our survey article is an attempt to significantly contribute towards initiating future research directions in area of semantic communications for intelligent 6G wireless networks

Topics in genomic image processing

The image processing methodologies that have been actively studied and developed now play a very significant role in the flourishing biotechnology research. This work studies, develops and implements several image processing techniques for M-FISH and cDNA microarray images. In particular, we focus on three important areas: M-FISH image compression, microarray image processing and expression-based classification. Two schemes, embedded M-FISH image coding (EMIC) and Microarray BASICA: Background Adjustment, Segmentation, Image Compression and Analysis, have been introduced for M-FISH image compression and microarray image processing, respectively. In the expression-based classification area, we investigate the relationship between optimal number of features and sample size, either analytically or through simulation, for various classifiers

The Fifth NASA Symposium on VLSI Design

The fifth annual NASA Symposium on VLSI Design had 13 sessions including Radiation Effects, Architectures, Mixed Signal, Design Techniques, Fault Testing, Synthesis, Signal Processing, and other Featured Presentations. The symposium provides insights into developments in VLSI and digital systems which can be used to increase data systems performance. The presentations share insights into next generation advances that will serve as a basis for future VLSI design

Lossless Differential Compression for Synchronizing Arbitrary Single-Dimensional Strings

Differential compression allows expressing a modified document as differences relative to another version of the document. A compressed string requires space relative to amount of changes, irrespective of original document sizes. The purpose of this study was to answer what algorithms are suitable for universal lossless differential compression for synchronizing two arbitrary documents either locally or remotely. Two main problems in differential compression are finding the differences (differencing), and compactly communicating the differences (encoding). We discussed local differencing algorithms based on subsequence searching, hashtable lookups, suffix searching, and projection. We also discussed probabilistic remote algorithms based on both recursive comparison and characteristic polynomial interpolation of hashes computed from variable-length content-defined substrings. We described various heuristics for approximating optimal algorithms as arbitrary long strings and memory limitations force discarding information. Discussion also included compact delta encoding and in-place reconstruction. We presented results from empirical testing using discussed algorithms. The conclusions were that multiple algorithms need to be integrated into a hybrid implementation, which heuristically chooses algorithms based on evaluation of the input data. Algorithms based on hashtable lookups are faster on average and require less memory, but algorithms based on suffix searching find least differences. Interpolating characteristic polynomials was found to be too slow for general use. With remote hash comparison, content-defined chunks and recursive comparison can reduce protocol overhead. A differential compressor should be merged with a state-of-art non-differential compressor to enable more compact delta encoding. Input should be processed multiple times to allow constant a space bound without significant reduction in compression efficiency. Compression efficiently of current popular synchronizers could be improved, as our empiral testing showed that a non-differential compressor produced smaller files without having access to one of the two strings