Research Background

My undergraduate thesis, A Faster Decoding Technique for Huffman Codes Using Adjacent Distance Array, relied on data compression and Huffman coding. The research introduced an adjacent distance array data structure that significantly mitigates the encoding-decoding timing for English corpora .

The research was further updated with a transliteration-based approach by publishing Introduction to Adjacent Distance Array with Huffman Principle: A New Encoding and Decoding Technique for Transliteration Based Bengali Text Compression. Nevertheless, at that time, the method could compress the Bengali text by introducing the data structure, allowing the transliteration method. The transliteration approach is a mechanism that represents a Bengali character to one or more corresponding English alphabets or ASCII symbols, such as Avro Phonetic Layout.

In addition, proof has been found that the research's compression methodology outperforms traditional any Huffman-based approaches in terms of compression-decompression time for the Bengali transliterated text. The study's outcome is entitled Method of Adjacent Distance Array Outperforms Conventional Huffman Codes to Decode Bengali Transliterated Text Swiftly published in a Scopus-indexed journal.

However, we, the authors, have recently applied the modus operandi to compress short text messages, which achieved a significant compression ratio for the SMS messages by evaluating a prestigious corpus containing various short texts of people. Eventually, the study's ultimate findings have been accepted and yet to be published as an article titled A Huffman Based Short Message Service (SMS) Compression Technique Using Adjacent Distance Array in another Scopus-indexed journal, the International Journal of Information and Communication Technology.