All Publications | Pranta Sarker

2024

IJICT
A Huffman based short message service compression technique using adjacent distance array

Pranta Sarker, and Mir Lutfur Rahman

International Journal of Information and Communication Technology, Aug 2024

Indexing: Scopus-indexed publication. DOI: 10.1504/IJICT.2024.140309

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The short message service (SMS) is a wireless medium of transmission that allows you to send brief text messages. Cell phone devices have an uttermost SMS capacity of 1,120 bits in the traditional system. Moreover, the conventional SMS employs seven bits for each character, allowing the highest 160 characters for an SMS text message to be transmitted. This research demonstrated that an SMS message could contain more than 200 characters by representing around five bits each, introducing a data structure, namely, adjacent distance array (ADA) using the Huffman principle. Allowing the concept of lossless data compression technique, the proposed method of the research generates character’s codeword utilising the standard Huffman. However, the ADA encodes the message by putting the ASCII value distances of all characters, and decoding performs by avoiding the whole Huffman tree traverse, which is the pivotal contribution of the research to develop an effective SMS compression technique for personal digital assistants (PDAs). The encoding and decoding processes have been discussed and contrasted with the conventional SMS text message system, where our proposed ADA technique performs outstandingly better from every aspect discovered after evaluating all outcomes.
@article{Sarker2024SMSComp, title = {{A Huffman based short message service compression technique using adjacent distance array}}, year = {2024}, month = aug, journal = {International Journal of Information and Communication Technology}, author = {Sarker, Pranta and Rahman, Mir Lutfur}, number = {2}, pages = {118-136}, volume = {25}, publisher = {International Journal of Information and Communication Technology,}, doi = {10.1504/IJICT.2024.140309}, note = {Indexing: Scopus-indexed publication. DOI: 10.1504/IJICT.2024.140309}, dimensions = {true}, }

2022

IJCDS
Method of Adjacent Distance Array Outperforms Conventional Huffman Codes to Decode Bengali Transliterated Text Swiftly

Pranta Sarker, and Mir Lutfur Rahman

International Journal of Computing and Digital Systems, Jan 2022

Indexing: Scopus-indexed publication. DOI: 10.12785/ijcds/110148

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This research works on high symbolic Bengali text and transforms it into corresponding less symbolic English complying with the transliteration method. The Huffman-based approaches serve to compress retaining the original quality of the data. On the other hand, faster encoding and decoding is the most sophisticated sphere in data compression. We propose an adjacent distance array, a novel data structure based on the Huffman principle for encoding and decoding the character of transliterated text. The encoding and decoding algorithms have been explained for the introduced modus operandi and juxtaposed with conventional Huffman-based algorithms. Our research is outdoing than any regular Huffman-based algorithms, concentrating on the speed of the encoding and decoding manner discovered after estimating all decisions.
@article{Sarker2022MethodSwiftly, year = {2022}, month = jan, journal = {International Journal of Computing and Digital Systems}, author = {Sarker, Pranta and Rahman, Mir Lutfur}, number = {1}, pages = {595--608}, volume = {11}, publisher = {International Journal of Computing and Digital Systems,}, doi = {10.12785/ijcds/110148}, note = {Indexing: Scopus-indexed publication. DOI: 10.12785/ijcds/110148}, dimensions = {true}, }

2021

AISC
Introduction to Adjacent Distance Array with Huffman Principle: A New Encoding and Decoding Technique for Transliteration Based Bengali Text Compression

Pranta Sarker, and Mir Lutfur Rahman

In Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1299. Springer, Singapore., Apr 2021

Indexing: Scopus-indexed publication. DOI: 10.1007/978-981-33-4299-6_45

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Constructing a binary tree, the Huffman algorithm introduced the method of text compression that helps to reduce the size keeping the original message of the file. Nowadays, Huffman-based algorithm assessment can be measured in two ways; one in terms of space, another is decoding speed. The requirement of memory for a text file is going to be reasonable while the time effectiveness of Huffman decoding is being more significant. Meanwhile, this research is introducing the adjacent distance array with Huffman principle as a new data structure for encoding, and decoding the Bengali text compression using transliterated English text. Since the transliterated English text accommodates to reduce the unit of symbols accordingly, we transliterated the Bengali text into English and then applied the Huffman principle with adjacent distance array. By calculating the ASCII values, adjacent distance array is used to save the distances for each adjacent symbols. Apart from the regular Huffman algorithm, a codeword has produced by traversing the whole Huffman tree for a character in case, respectively adopting the threshold value and adjacent distance array can skip the lengthy codeword and perform the decoding manner to decode estimating the distances for all adjacent symbols except traversing the whole tree. Our findings have acquired 27.54% and 20.94% compression ratios for some specimen transliterated Bengali texts, as well as accomplished a significant ratio on different corpora.
@incollection{10.1007/978-981-33-4299-6_45, title = {{Introduction to Adjacent Distance Array with Huffman Principle: A New Encoding and Decoding Technique for Transliteration Based Bengali Text Compression}}, year = {2021}, month = apr, booktitle = {Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1299. Springer, Singapore.}, author = {Sarker, Pranta and Rahman, Mir Lutfur}, editor = {Panigrahi, Chhabi Rani and Pati, Bibudhendu and Pattanayak, Binod Kumar and Amic, Seeven and Li, Kuan-Ching}, volume = {1299}, pages = {543--555}, publisher = {Springer Nature,}, doi = {10.1007/978-981-33-4299-6_45}, address = {Singapore}, isbn = {978-981-33-4299-6}, note = {Indexing: Scopus-indexed publication. DOI: 10.1007/978-981-33-4299-6_45}, dimensions = {true}, }

2020

AIS
A Faster Decoding Technique for Huffman Codes Using Adjacent Distance Array

Mir Lutfur Rahman, Pranta Sarker, and Ahsan Habib

In Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore., May 2020

Indexing: Springer proceedings/book chapter. DOI: 10.1007/978-981-15-3607-6_25

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The evaluation of Huffman-based algorithm can be measured in two ways: one in terms of space and another is decoding speed. As memory requirement is going to be reasonable, the time effectiveness of Huffman decoding is more significant. So, this research concentrated on making the decoding speed faster for Huffman codes as well as maintaining the compression ratio for text data. For this, a new technique based on an adjacent distance array is introduced here. Adjacent distance array can save the distances for each adjacent symbol by calculating their ASCII values. Also, we have used a threshold value so that we can skip large codeword for all the adjacent symbols that would be produced by traversing whole Huffman tree. During the decoding process, the adjacent distance array is used to decode adjacent symbols by computing the distances instead of traversing the whole Huffman tree. We have described the encoding and decoding processes for the proposed technique. Besides, we have tested the proposed method on a few renowned corpuses and compared the performance with existing Huffman-based methods. Therefore, we have found that the proposed method performs more effectively in terms of compression–decompression time.
@incollection{10.1007/978-981-15-3607-6_25, title = {{A Faster Decoding Technique for Huffman Codes Using Adjacent Distance Array}}, year = {2020}, booktitle = {Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore.}, author = {Rahman, Mir Lutfur and Sarker, Pranta and Habib, Ahsan}, editor = {Uddin, Mohammad Shorif and Bansal, Jagdish Chand}, pages = {309--316}, publisher = {Springer Nature,}, month = may, doi = {10.1007/978-981-15-3607-6_25}, address = {Singapore}, isbn = {978-981-15-3607-6}, note = {Indexing: Springer proceedings/book chapter. DOI: 10.1007/978-981-15-3607-6_25}, dimensions = {true}, }