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Relevant bibliographies by topics / Parsing (computer grammar)

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Academic literature on the topic 'Parsing (computer grammar)'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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Journal articles on the topic "Parsing (computer grammar)"

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Handzhiyski, Nikolay, and Elena Somova. "Tunnel Parsing with Ambiguous Grammars." Cybernetics and Information Technologies 23, no. 2 (June 1, 2023): 34–53. http://dx.doi.org/10.2478/cait-2023-0012.

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Abstract The article proposes an addition to the tunnel parsing algorithm that enables it to parse grammars having countable repetitions and configurations of grammar elements generating empty words without refactoring the grammar. The equivalency of trees built by the use of ambiguous grammar is discussed. The class of the ε-ambiguous grammars is defined as a subclass of the ambiguous grammars relative to these trees. The ε-deterministic grammars are then defined as a subclass of the ε-ambiguous grammars. A technique for linearly parsing on the basis of non-left recursive ε-deterministic grammars with the tunnel parsing algorithm is shown.

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Zhang, Songmao. "Story Parsing Grammar." Journal of Computer Science and Technology 9, no. 3 (July 1994): 215–28. http://dx.doi.org/10.1007/bf02939503.

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Kuhlmann, Marco, Giorgio Satta, and Peter Jonsson. "On the Complexity of CCG Parsing." Computational Linguistics 44, no. 3 (September 2018): 447–82. http://dx.doi.org/10.1162/coli_a_00324.

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We study the parsing complexity of Combinatory Categorial Grammar (CCG) in the formalism of Vijay-Shanker and Weir ( 1994 ). As our main result, we prove that any parsing algorithm for this formalism will take in the worst case exponential time when the size of the grammar, and not only the length of the input sentence, is included in the analysis. This sets the formalism of Vijay-Shanker and Weir ( 1994 ) apart from weakly equivalent formalisms such as Tree Adjoining Grammar, for which parsing can be performed in time polynomial in the combined size of grammar and input sentence. Our results contribute to a refined understanding of the class of mildly context-sensitive grammars, and inform the search for new, mildly context-sensitive versions of CCG.

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Shi, Zhi Yuan, Yu Qiang Sun, Yu Wan Gu, Fu Quan Ji, and Jing Fen Du. "The Study about Parsing of RGG Grammar." Advanced Materials Research 204-210 (February 2011): 255–58. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.255.

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Visualization is the main form of human-computer interaction. The grammar formal description of visual language opens and explores the s application cope and theoretical research field of grammar. At present, graph grammar describing visual language is one of the best the formal methods. In the paper, formal parsing method of RGG grammar is studied deeply, a parsing algorithm about RGG grammar is described, and its application on Petri net is discussed.

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BORDIM, JACIR L., OSCAR H. IBARRA, YASUAKI ITO, and KOJI NAKANO. "INSTANCE-SPECIFIC SOLUTIONS FOR ACCELERATING THE CKY PARSING OF LARGE CONTEXT-FREE GRAMMARS." International Journal of Foundations of Computer Science 15, no. 02 (April 2004): 403–15. http://dx.doi.org/10.1142/s0129054104002492.

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The main contribution of this paper is an FPGA-based implementation of an instance-specific hardware which accelerates the CKY (Cocke-Kasami- Younger) parsing of context-free grammars. Given a context-free grammar G and a string x, the CKY parsing determines whether G derives x. We developed a hardware generator that creates a Verilog HDL source to perform the CKY parsing for any fixed context-free grammar G. The generated source is embedded in an FPGA using the design software provided by the FPGA vendor. The results show that our instance-specific hardware solution attains an astonishing speed-up factor of up to 3,700 over traditional software solutions.

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Kallmeyer, L., W. Maier, Y. Parmentier, and J. Dellert. "TuLiPA - Parsing extensions of TAG with range concatenation grammars." Bulletin of the Polish Academy of Sciences: Technical Sciences 58, no. 3 (September 1, 2010): 377–91. http://dx.doi.org/10.2478/v10175-010-0036-0.

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TuLiPA - Parsing extensions of TAG with range concatenation grammarsIn this paper we present a parsing framework for extensions of Tree Adjoining Grammar (TAG) called TuLiPA (Tübingen Linguistic Parsing Architecture). In particular, besides TAG, the parser can process Tree-Tuple MCTAG with Shared Nodes (TT-MCTAG), a TAG-extension which has been proposed to deal with scrambling in free word order languages such as German. The central strategy of the parser is such that the incoming TT-MCTAG (or TAG) is transformed into an equivalent Range Concatenation Grammar (RCG) which, in turn, is then used for parsing. The RCG parser is an incremental Earley-style chart parser. In addition to the syntactic anlysis, TuLiPA computes also an underspecified semantic analysis for grammars that are equipped with semantic representations.

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Underwood, William. "Grammar-Based Specification and Parsing of Binary File Formats." International Journal of Digital Curation 7, no. 1 (March 9, 2012): 95–106. http://dx.doi.org/10.2218/ijdc.v7i1.217.

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The capability to validate and view or play binary file formats, as well as to convert binary file formats to standard or current file formats, is critically important to the preservation of digital data and records. This paper describes the extension of context-free grammars from strings to binary files. Binary files are arrays of data types, such as long and short integers, floating-point numbers and pointers, as well as characters. The concept of an attribute grammar is extended to these context-free array grammars. This attribute grammar has been used to define a number of chunk-based and directory-based binary file formats. A parser generator has been used with some of these grammars to generate syntax checkers (recognizers) for validating binary file formats. Among the potential benefits of an attribute grammar-based approach to specification and parsing of binary file formats is that attribute grammars not only support format validation, but support generation of error messages during validation of format, validation of semantic constraints, attribute value extraction (characterization), generation of viewers or players for file formats, and conversion to current or standard file formats. The significance of these results is that with these extensions to core computer science concepts, traditional parser/compiler technologies can potentially be used as a part of a general, cost effective curation strategy for binary file formats.

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Handzhiyski, Nikolay, and Elena Somova. "Tunnel Parsing with the Token’s Lexeme." Cybernetics and Information Technologies 22, no. 2 (June 1, 2022): 125–44. http://dx.doi.org/10.2478/cait-2022-0021.

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Abstract The article describes a string recognition approach, engraved in the parsers generated by Tunnel Grammar Studio that use the tunnel parsing algorithm, of how a lexer and a parser can operate on the input during its recognition. Proposed is an addition of the augmented Backus-Naur form syntax that enables the formal language to be expressed with a parser grammar and optionally with an additional lexer grammar. The tokens outputted from the lexer are matched to the phrases in the parser grammar by their name and optionally by their lexeme, case sensitively or insensitively.

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Zhang, Songmao. "Weak precedence story parsing grammar." Journal of Computer Science and Technology 10, no. 1 (January 1995): 53–64. http://dx.doi.org/10.1007/bf02939522.

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Kulkarni, Amba. "Sanskrit Parsing Following Indian Theories of Verbal Cognition." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 2 (April 8, 2021): 1–38. http://dx.doi.org/10.1145/3418061.

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Pāṇini’s grammar is an important milestone in the Indian grammatical tradition. Unlike grammars of other languages, it is almost exhaustive and together with the theories of śābdabodha (verbal cognition), this grammar provides a system for language analysis as well as generation. The theories of śābdabodha describe three conditions necessary for verbal cognition. They are ākāṅkṣā (expectancy), yogyatā (meaning congruity), and sannidhi (proximity). We examine them from a computational viewpoint and provide appropriate computational models for their representation. Next, we describe the design of a parser following the theories of śābdabodha and present three algorithms for solving the constraints imposed by the theories of śābdabodha . The first algorithm is modeled as a constraint satisfaction problem, the second one as a vertex-centric graph traversal, and the third one as an edge-centric binary join, each one being an improvement over the previous one.

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Dissertations / Theses on the topic "Parsing (computer grammar)"

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Ghosh, Debajit 1974. "Automatic grammar induction from semantic parsing." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/50435.

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Teboul, Olivier. "Shape Grammar Parsing : application to Image-based Modeling." Phd thesis, Ecole Centrale Paris, 2011. http://tel.archives-ouvertes.fr/tel-00628906.

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The purpose of this thesis was to perform facade image parsing with shape grammars in order to tackle single-view image-based 3D building modeling. The scope of the thesis was lying at the border of Computer Graphics and Computer Vision, both in terms of methods and applications.Two different and complementary approaches have been proposed: a bottom-up parsing algorithm that aimed at grouping similar regions of a facade image so as to retrieve the underlying layout, and a top-down parsing algorithm based on a very powerful framework: Reinforcement Learning. This novel parsing algorithm uses pixel-wise image supports based on supervised learning in a global optimization of a Markov Decision Process.Both methods were evaluated quantitatively and qualitatively. The second one was proved to support various architectures, several shape grammars and image supports, and showed robustness to challenging viewing conditions; illumination and large occlusions. The second method outperformed the state-of-the-art both in terms of segmentation and speed performances. It also provides a much more flexible framework, in which many extensions may be envisioned.The conclusion of this work was that the problem of single-view image-based 3D building modeling could be solved elegantly by using shape grammar as a Rosetta stone to decipher the language of Architecture through a well-suited Reinforcement Learning formulation. This solution was a potential answer to large-scale reconstruction of urban environments from images, but also suggested the possibility of introducing Reinforcement Learning in other vision tasks such as generic image parsing, where it have been barely explored so far.

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Aycock, John Daniel. "Practical Earley parsing and the SPARK toolkit." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/NQ58556.pdf.

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Pan, Yinfei. "Parallel XML parsing." Diss., Online access via UMI:, 2009.

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Yang, Yongsheng. "A maximum entropy approach to Chinese language parsing /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?COMP%202002%20YANG.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2002.
Includes bibliographical references (leaves 54-55). Also available in electronic version. Access restricted to campus users.

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Bhalerao, Rohit Dinesh. "Parallel XML parsing." Diss., Online access via UMI:, 2007.

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Van, Delden Sebastian Alexander. "Larger-first partial parsing." Doctoral diss., University of Central Florida, 2003. http://digital.library.ucf.edu/cdm/ref/collection/RTD/id/2038.

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University of Central Florida College of Engineering Thesis
Larger-first partial parsing is a primarily top-down approach to partial parsing that is opposite to current easy-fzrst, or primarily bottom-up, strategies. A rich partial tree structure is captured by an algorithm that assigns a hierarchy of structural tags to each of the input tokens in a sentence. Part-of-speech tags are first assigned to the words in a sentence by a part-of-speech tagger. A cascade of Deterministic Finite State Automata then uses this part-of-speech information to identify syntactic relations primarily in a descending order of their size. The cascade is divided into four specialized sections: (1) a Comma Network, which identifies syntactic relations associated with commas; (2) a Conjunction Network, which partially disambiguates phrasal conjunctions and llly disambiguates clausal conjunctions; (3) a Clause Network, which identifies non-comma-delimited clauses; and (4) a Phrase Network, which identifies the remaining base phrases in the sentence. Each automaton is capable of adding one or more levels of structural tags to the tokens in a sentence. The larger-first approach is compared against a well-known easy-first approach. The results indicate that this larger-first approach is capable of (1) producing a more detailed partial parse than an easy first approach; (2) providing better containment of attachment ambiguity; (3) handling overlapping syntactic relations; and (4) achieving a higher accuracy than the easy-first approach. The automata of each network were developed by an empirical analysis of several sources and are presented here in detail.
Ph.D.
Doctorate;
Department of Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering and Computer Science
215 p.
xiv, 212 leaves, bound : ill. ; 28 cm.

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Walenski, Matthew S. "Relating parsers and grammars : on the structure and real-time comprehension of English infinitival complements /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2002. http://wwwlib.umi.com/cr/ucsd/fullcit?p3044770.

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Lakeland, Corrin, and n/a. "Lexical approaches to backoff in statistical parsing." University of Otago. Department of Computer Science, 2006. http://adt.otago.ac.nz./public/adt-NZDU20060913.134736.

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This thesis develops a new method for predicting probabilities in a statistical parser so that more sophisticated probabilistic grammars can be used. A statistical parser uses a probabilistic grammar derived from a training corpus of hand-parsed sentences. The grammar is represented as a set of constructions - in a simple case these might be context-free rules. The probability of each construction in the grammar is then estimated by counting its relative frequency in the corpus. A crucial problem when building a probabilistic grammar is to select an appropriate level of granularity for describing the constructions being learned. The more constructions we include in our grammar, the more sophisticated a model of the language we produce. However, if too many different constructions are included, then our corpus is unlikely to contain reliable information about the relative frequency of many constructions. In existing statistical parsers two main approaches have been taken to choosing an appropriate granularity. In a non-lexicalised parser constructions are specified as structures involving particular parts-of-speech, thereby abstracting over individual words. Thus, in the training corpus two syntactic structures involving the same parts-of-speech but different words would be treated as two instances of the same event. In a lexicalised grammar the assumption is that the individual words in a sentence carry information about its syntactic analysis over and above what is carried by its part-of-speech tags. Lexicalised grammars have the potential to provide extremely detailed syntactic analyses; however, Zipf�s law makes it hard for such grammars to be learned. In this thesis, we propose a method for optimising the trade-off between informative and learnable constructions in statistical parsing. We implement a grammar which works at a level of granularity in between single words and parts-of-speech, by grouping words together using unsupervised clustering based on bigram statistics. We begin by implementing a statistical parser to serve as the basis for our experiments. The parser, based on that of Michael Collins (1999), contains a number of new features of general interest. We then implement a model of word clustering, which we believe is the first to deliver vector-based word representations for an arbitrarily large lexicon. Finally, we describe a series of experiments in which the statistical parser is trained using categories based on these word representations.

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Moss, William B. "Evaluating inherited attributes using Haskell and lazy evaluation." Diss., Connect to the thesis, 2005. http://hdl.handle.net/10066/1486.

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Books on the topic "Parsing (computer grammar)"

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Kübler, Sandra. Dependency parsing. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool Publishers, 2009.

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Bunt, Harry C. Trends in Parsing Technology: Dependency Parsing, Domain Adaptation, and Deep Parsing. Dordrecht: Springer Science+Business Media B.V., 2011.

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Sippu, Seppo. Parsing Theory: Volume I Languages and Parsing. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988.

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name, No. Data-oriented parsing. Stanford, CA: CSLI Publications, 2003.

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Volokh, Alexander. Performance-oriented dependency parsing. Saarbrücken: German Research Center for Artificial Intelligence, 2013.

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Sippu, Seppo. Parsing Theory: Volume II LR(k) and LL(k) Parsing. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990.

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Gerry, Altmann, ed. Parsing and interpretation. Hove: Erlbaum, 1989.

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Rodríguez, Carlos Gómez. Parsing schemata for practical text analysis. London: Imperial College Press, 2010.

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Merlo, Paola. Parsing with principles and classes of information. Dordrecht, The Netherlands: Kluwer Academic Publishers, 1996.

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Kindermann, Jörg. Experten-Parsing: Parsing und Wissensrepräsentation im texttheoretischen Rahmen. Hamburg: H. Buske, 1987.

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Book chapters on the topic "Parsing (computer grammar)"

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Adebara, Ife, Veronica Dahl, and Sergio Tessaris. "Parsing with Partially Known Grammar." In Lecture Notes in Computer Science, 334–46. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27947-3_18.

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Sikkel, Klaas. "Topics in unification grammar parsing." In Texts in Theoretical Computer Science An EATCS Series, 173–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60541-3_9.

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Zhang, Xiaomei, Yingying Chen, Bingke Zhu, Jinqiao Wang, and Ming Tang. "Blended Grammar Network for Human Parsing." In Computer Vision – ECCV 2020, 189–205. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58586-0_12.

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Gatward, Richard. "Implementation Efficiency Considerations in Parsing Functional Grammar." In Functional Grammar and the Computer, edited by Simon C. Dik and John Connolly, 77–92. Berlin, Boston: De Gruyter, 1989. http://dx.doi.org/10.1515/9783110871685-006.

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Dignum, Frank. "Parsing an English Text Using Functional Grammar." In Functional Grammar and the Computer, edited by Simon C. Dik and John Connolly, 109–34. Berlin, Boston: De Gruyter, 1989. http://dx.doi.org/10.1515/9783110871685-008.

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Janssen, Theo M. V. "Towards a Universal Parsing Algorithm for Functional Grammar." In Functional Grammar and the Computer, edited by Simon C. Dik and John Connolly, 65–76. Berlin, Boston: De Gruyter, 1989. http://dx.doi.org/10.1515/9783110871685-005.

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Pingali, Keshav, and Gianfranco Bilardi. "A Graphical Model for Context-Free Grammar Parsing." In Lecture Notes in Computer Science, 3–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46663-6_1.

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Nirmal, Yumnam, and Utpal Sharma. "A Grammar-Driven Approach for Parsing Manipuri Language." In Lecture Notes in Computer Science, 267–74. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34872-4_30.

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Verdú-Mas, Jose L., Mikel L. Forcada, Rafael C. Carrasco, and Jorge Calera-Rubio. "Tree k-Grammar Models for Natural Language Modelling and Parsing." In Lecture Notes in Computer Science, 56–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-70659-3_5.

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Sobernig, Stefan. "Object Parsing Expressions for Unplanned, Unmodified, and Incremental Grammar Reuse." In Communications in Computer and Information Science, 24–40. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11513-4_2.

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Conference papers on the topic "Parsing (computer grammar)"

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Teboul, Olivier, Iasonas Kokkinos, Loic Simon, Panagiotis Koutsourakis, and Nikos Paragios. "Shape grammar parsing via Reinforcement Learning." In 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2011. http://dx.doi.org/10.1109/cvpr.2011.5995319.

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Goswami, Manish M., M. M. Raghuwanshi, and Latesh Malik. "Optimization of * operator in Parsing Expression Grammar." In 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, 2015. http://dx.doi.org/10.1109/icecct.2015.7226088.

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Kostanyan, Armen, and Anna Gevorgyan. "String Parsing Using a Fuzzy Context-Free Pattern." In Computer Science and Information Technologies 2023. Institute for Informatics and Automation Problems, 2023. http://dx.doi.org/10.51408/csit2023_02.

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This paper is devoted to determining the degree of compliance of a given string with a pattern represented as a context-free grammar, the terminal symbols of which are fuzzy properties of the characters of the base alphabet. Parsing is performed by converting the fuzzy context-free pattern into a fuzzy context-free grammar over the base alphabet, after which the fuzzy version of the Cocke-Younger-Kasami (CYK) algorithm is applied to determine the measure by which the given string can be parsed according to the given pattern. The proposed approach makes it possible to classify strings in the basic alphabet based on a fuzzy context-free pattern. This work may find application in bioinformatics to classify DNA sequences using fuzzy prototypes described in one way or another. Another application concerns the parsing of natural languages, where fuzzy methods have been used for a long time and give good results.

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Riemenschneider, H., U. Krispel, W. Thaller, M. Donoser, S. Havemann, D. Fellner, and H. Bischof. "Irregular lattices for complex shape grammar facade parsing." In 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2012. http://dx.doi.org/10.1109/cvpr.2012.6247857.

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Liu, Xiaobai, Yibiao Zhao, and Song-chun Zhu. "Single-View 3D Scene Parsing by Attributed Grammar." In 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2014. http://dx.doi.org/10.1109/cvpr.2014.93.

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Whitmore, Stacey. "Procedure Parsing: A Method for Parsing Handwritten Documents into Computer-Based Procedures." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002518.

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The nuclear industry is heavily procedure driven, where almost everything has a step-by-step instruction that is expected to be followed in detail. Historically, these procedures were printed on paper copies. Recently, the industry transitioned towards electronic copies (i.e., PDFs on tablets). One major drive for this transition is the introduction of human error and loss of situation awareness when using paper copies. However, electronic copies of documents inherently have the same error traps as their paper cousins. Therefore, there is an increased interest in a way to utilize the information in the step-by-step guidance, but to present it in a dynamic manner that guides the user and adapts to any encountered conditions. Researchers at Idaho National Laboratory propose a flexible, automated method based on document parsing and augmented by natural language processing (NLP) techniques, to address these shortcomings and capitalize on these recent advancements in machine learning. The proposed method provides a cost-effective solution for computer-assisted procedure parsing of hand-written control room procedures, originally authored in Word or PDF formats, into instructions that can be displayed as computer-based procedures (CBP) in a modern graphical user interface. The researchers devised, implemented and demonstrated the Operating Procedure Extender for Novel Systems (OPENS) method in 2020. The key to OPENS is to map the original procedure text into a context-free grammar, tying content to equipment, locations, and other steps, actions, etc. This formal grammar is then used to isolate and define keywords and actions verbs, such as “measure” or “evaluate” and tie them to specific equipment referenced within that step or located in other steps, substeps, actions, subactions and tables throughout the procedure. OPENS generates an abstract syntax tree from the document which it uses to store a copy of this information in the open-standard, machine-readable and human-readable file formats XML and JSON. The XML is useful to preserve the relational aspects of the procedure for referencing tables and branching information so the user can be directed to the next appropriate active step based on the values entered for that step and previous steps. The JSON is useful for storing and exchanging data objects used to track responses to previous steps and state changes in simulated environments. In future iterations, these formats can also be used for storing more detailed information about input during plant operation or simulation. The techniques the researcher developed could further be improved by integration of recent advancements in machine learning. NLP methods could standardize documents, correct for grammatical error, and provide automated semantic validation. The researcher expects that self-supervised techniques applied to collections of natural language instructions could strengthen the model with broader context. All these methods together give us a practical way to automatically extract protocols from documents and user interactions, empowering researchers, procedure writers and nuclear operators while moving the industry forward.

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Feng Han and Song-Chun Zhu. "Bottom-up/top-down image parsing by attribute graph grammar." In Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1. IEEE, 2005. http://dx.doi.org/10.1109/iccv.2005.50.

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Yao, B., Xiong Yang, and Tianfu Wu. "Image parsing with stochastic grammar: The Lotus Hill dataset and inference scheme." In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR Workshops). IEEE, 2009. http://dx.doi.org/10.1109/cvpr.2009.5204331.

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Vo, Nam N., and Aaron F. Bobick. "From Stochastic Grammar to Bayes Network: Probabilistic Parsing of Complex Activity." In 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2014. http://dx.doi.org/10.1109/cvpr.2014.338.

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Maroof, Mohammad Kamrul Huq, Lamia Alam, and Mohammed Moshiul Hoque. "Transformational generative grammar (TGG): An efficient way of parsing Bangla sentences." In 2016 2nd International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE). IEEE, 2016. http://dx.doi.org/10.1109/icecte.2016.7879583.

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