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Article
Publication date: 22 June 2021

Anil Kumar Budati, Ganesh Snv, Kumar Cherukupalli, Anil Kumar P. and Venkata Krishna Moorthy T.

The privacy of the information is a major challenge in the communication process. In the present modern generation, the cryptography plays a vital role in providing security for…

180

Abstract

Purpose

The privacy of the information is a major challenge in the communication process. In the present modern generation, the cryptography plays a vital role in providing security for data, such as text, images and video while transmitting from source to destination through internet or intranet. The Rivest-Shamir-Adleman (RSA) is an asymmetric key cryptographic system, where the security of the method works on the strength of the key.

Design/methodology/approach

In an asymmetric key crypto system, a pair of keys is generated one public key for encryption and one private key for decryption. The major challenge of implementing the RSA is the power function which becomes tedious and time consuming as the exponential value increases. The Chinese remainder theorem proves to be the best for data encryption when it comes to execution time of the algorithm. The proposed novel RSA algorithm with lookup table (LUT) is an extension to the Chinese remainder algorithm, which works better for image and video in terms of time complexity.

Findings

This paper presents a LUT approach for implementing the RSA with a minimal processing time. The proposed algorithm was compared with the standard algorithms like, Chinese remainder theorem, binary approach and squared multiplication approach. As the size of the exponent value increases, the proposed method shows better performance compared to other standard methods.

Originality/value

This paper presents a LUT approach for implementing the RSA with a minimal processing time. The proposed algorithm was compared with the standard algorithms like, Chinese remainder theorem, binary approach and squared multiplication approach. As the size of the exponent value increases, the proposed method shows better performance compared to other standard methods.

Details

Circuit World, vol. 47 no. 3
Type: Research Article
ISSN: 0305-6120

Keywords

Available. Content available

Abstract

Details

International Journal of Intelligent Unmanned Systems, vol. 11 no. 1
Type: Research Article
ISSN: 2049-6427

Available. Content available
Article
Publication date: 21 July 2021

Budati Anil Kumar, Peter Ho Chiung Ching, Pachara Venkateswara Rao and Shuichi Torii

260

Abstract

Details

International Journal of Pervasive Computing and Communications, vol. 17 no. 3
Type: Research Article
ISSN: 1742-7371

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Article
Publication date: 26 March 2021

Hima Bindu Valiveti, Anil Kumar B., Lakshmi Chaitanya Duggineni, Swetha Namburu and Swaraja Kuraparthi

Road accidents, an inadvertent mishap can be detected automatically and alerts sent instantly with the collaboration of image processing techniques and on-road video surveillance…

132

Abstract

Purpose

Road accidents, an inadvertent mishap can be detected automatically and alerts sent instantly with the collaboration of image processing techniques and on-road video surveillance systems. However, to rely exclusively on visual information especially under adverse conditions like night times, dark areas and unfavourable weather conditions such as snowfall, rain, and fog which result in faint visibility lead to incertitude. The main goal of the proposed work is certainty of accident occurrence.

Design/methodology/approach

The authors of this work propose a method for detecting road accidents by analyzing audio signals to identify hazardous situations such as tire skidding and car crashes. The motive of this project is to build a simple and complete audio event detection system using signal feature extraction methods to improve its detection accuracy. The experimental analysis is carried out on a publicly available real time data-set consisting of audio samples like car crashes and tire skidding. The Temporal features of the recorded audio signal like Energy Volume Zero Crossing Rate 28ZCR2529 and the Spectral features like Spectral Centroid Spectral Spread Spectral Roll of factor Spectral Flux the Psychoacoustic features Energy Sub Bands ratio and Gammatonegram are computed. The extracted features are pre-processed and trained and tested using Support Vector Machine (SVM) and K-nearest neighborhood (KNN) classification algorithms for exact prediction of the accident occurrence for various SNR ranges. The combination of Gammatonegram with Temporal and Spectral features of the validates to be superior compared to the existing detection techniques.

Findings

Temporal, Spectral, Psychoacoustic features, gammetonegram of the recorded audio signal are extracted. A High level vector is generated based on centroid and the extracted features are classified with the help of machine learning algorithms like SVM, KNN and DT. The audio samples collected have varied SNR ranges and the accuracy of the classification algorithms is thoroughly tested.

Practical implications

Denoising of the audio samples for perfect feature extraction was a tedious chore.

Originality/value

The existing literature cites extraction of Temporal and Spectral features and then the application of classification algorithms. For perfect classification, the authors have chosen to construct a high level vector from all the four extracted Temporal, Spectral, Psycho acoustic and Gammetonegram features. The classification algorithms are employed on samples collected at varied SNR ranges.

Details

International Journal of Pervasive Computing and Communications, vol. 17 no. 3
Type: Research Article
ISSN: 1742-7371

Keywords

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