Efficient modular arithmetic units for low power cryptographic applications

The demand for high security in energy constrained devices such as mobiles and PDAs is growing rapidly. This leads to the need for efficient design of cryptographic algorithms which offer data integrity, authentication, non-repudiation and confidentiality of the encrypted data and communication channels. The public key cryptography is an ideal choice for data integrity, authentication and non-repudiation whereas the private key cryptography ensures the confidentiality of the data transmitted. The latter has an extremely high encryption speed but it has certain limitations which make it unsuitable for use in certain applications. Numerous public key cryptographic algorithms are available in the literature which comprise modular arithmetic modules such as modular addition, multiplication, inversion and exponentiation. Recently, numerous cryptographic algorithms have been proposed based on modular arithmetic which are scalable, do word based operations and efficient in various aspects. The modular arithmetic modules play a crucial role in the overall performance of the cryptographic processor. Hence, better results can be obtained by designing efficient arithmetic modules such as modular addition, multiplication, exponentiation and squaring. This thesis is organized into three papers, describes the efficient implementation of modular arithmetic units, application of these modules in International Data Encryption Algorithm (IDEA). Second paper describes the IDEA algorithm implementation using the existing techniques and using the proposed efficient modular units. The third paper describes the fault tolerant design of a modular unit which has online self-checking capability --Abstract, page iv

A comparison and a combination of SST and AGM algorithms for counting points of elliptic curves in characteristic 2

International audienceSince the first use of a p-adic method for counting points of elliptic curves, by Satoh in 1999, several variants of his algorithm have been proposed. In the current state, the AGM algorithm, proposed by Mestre is thought to be the fastest in practice, and the algorithm by Satoh­-Skjernaa­-Taguchi has the best asymptotic complexity but requires precomputations. We present an amelioration of the SST algorithm, borrowing ideas from the AGM. We make a precise comparison between this modified SST algorithm and the AGM, thus demonstrating that the former is faster by a significant factor, even for small cryptographic sizes

Space programs summary no. 37-49, volume 3 for the period December 1, 1967 to January 30, 1968. Supporting research and advanced development

Space program research projects on systems analysis and engineering, telecommunications, guidance and control, propulsion, and data system

Design and performance analysis of quadratic-form receivers for fading channels

Ph.DDOCTOR OF PHILOSOPH

A computer simulation model suite for the analysis of all optical networks

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (p. 203).by Gregory S. Campbell.M.Eng

Proceedings of the Twentieth Conference of the Association of Christians in the Mathematical Sciences

The proceedings of the twentieth conference of the Associate of Christians in the Mathematical Sciences held at Redeemer University College from May 27-30, 2015

Progress Report No. 7

Progress report of the Biomedical Computer Laboratory, covering period 1 July 1970 to 30 June 1971

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

Proceedings of the Third International Mobile Satellite Conference (IMSC 1993)

Satellite-based mobile communications systems provide voice and data communications to users over a vast geographic area. The users may communicate via mobile or hand-held terminals, which may also provide access to terrestrial cellular communications services. While the first and second International Mobile Satellite Conferences (IMSC) mostly concentrated on technical advances, this Third IMSC also focuses on the increasing worldwide commercial activities in Mobile Satellite Services. Because of the large service areas provided by such systems, it is important to consider political and regulatory issues in addition to technical and user requirements issues. Topics covered include: the direct broadcast of audio programming from satellites; spacecraft technology; regulatory and policy considerations; advanced system concepts and analysis; propagation; and user requirements and applications