CAM-based VLSI architectures for dynamic Huffman coding

1994 ◽  
Vol 40 (3) ◽  
pp. 282-289 ◽  
Author(s):  
Liang-Ying Liu ◽  
Jhing-Fa Wang ◽  
Ruey-Jen Wang ◽  
Jau-Yien Lee
Keyword(s):  
Huffman Coding ◽  
Vlsi Architectures

Cam-Based VLSI Architectures for Dynamic Huffman Coding

1994 ◽  
Author(s):  
Liang-Ying Liu ◽  
Jhing-Fa Wang ◽  
Ruey-Jen Wang ◽  
Jau-Yien Lee
Keyword(s):  
Huffman Coding ◽  
Vlsi Architectures

Text Encryption based on Huffman Coding and ElGamal Cryptosystem

2020 ◽  
Vol 14 ◽  
Author(s):  
Khoirom Motilal Singh ◽  
Laiphrakpam Dolendro Singh ◽  
Themrichon Tuithung
Keyword(s):  
Data Transfer ◽  
Huffman Coding ◽  
Large Integer ◽  
Text Data ◽  
Storage Devices ◽  
Scientific World ◽  
Elgamal Cryptosystem ◽  
Encryption Schemes ◽  
Transfer Operation ◽  
And Performance

Background: Data which are in the form of text, audio, image and video are used everywhere in our modern scientific world. These data are stored in physical storage, cloud storage and other storage devices. Some of it are very sensitive and requires efficient security while storing as well as in transmitting from the sender to the receiver. Objective: With the increase in data transfer operation, enough space is also required to store these data. Many researchers have been working to develop different encryption schemes, yet there exist many limitations in their works. There is always a need for encryption schemes with smaller cipher data, faster execution time and low computation cost. Methods: A text encryption based on Huffman coding and ElGamal cryptosystem is proposed. Initially, the text data is converted to its corresponding binary bits using Huffman coding. Next, the binary bits are grouped and again converted into large integer values which will be used as the input for the ElGamal cryptosystem. Results: Encryption and Decryption are successfully performed where the data size is reduced using Huffman coding and advance security with the smaller key size is provided by the ElGamal cryptosystem. Conclusion: Simulation results and performance analysis specifies that our encryption algorithm is better than the existing algorithms under consideration.

scholarly journals Huffman Coding Based Encoding Techniques for Fast Distributed Deep Learning

2020 ◽  
Author(s):  
Rishikesh R. Gajjala ◽  
Shashwat Banchhor ◽  
Ahmed M. Abdelmoniem ◽  
Aritra Dutta ◽  
Marco Canini ◽  
...  
Keyword(s):  
Deep Learning ◽  
Huffman Coding
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