In the present paper the authors have introduced a new symmetric key cryptographic method where they have clubbed both bit level and byte level generalized modified vernam cipher method with feedback. Nath et al already developed methods bit level encryption standard(BLES) Ver-I and Ver-II where they have used extensive bit level permutation, bit exchange, bit xor and bit shift encryption metho...

This letter demonstrates a CMOS impulse radio ultrawideband (IR-UWB) on–off keying (OOK) generator with multipulses per bit generation. The proposed IR-UWB OOK generator is implemented in 0.11 lm CMOS process where 1.33 mm proto-type accomplishes pulse train of 250 Mbps for one bit duration while consuming 10 mW from 1 V supply voltage. This pulse generator complies with the FCC spectral mask f...

This paper demonstrates the vulnerability of Kirchhoff-Law-Johnson-Noise (KLJN) secure key exchanger to compromised random number generator(s) even if these numbers are used solely generate noises emulating Johnson noise Alice's and Bob's resistors. The attacks shown deterministic in sense that Eve's knowledge and/or is basically deterministic. Moreover, no statistical evaluation needed, except...

Pseudorandomness has played a central role in modern cryptography, finding theoretical and practical applications to various fields of computer science. A function that generates pseudorandom strings from shorter but truly random seeds is known as a pseudorandom generator. Our generators are designed to fool languages (or equivalently, Boolean-valued functions). In particular, our generator foo...

Since Rijndael algorithm was selected as the Advanced Encryption Standard (AES) by NIST, optimization research for AES has been actively conducted on various IoT-based processors. In an 8-bit AVR environment, LIGHT version of Fast CTR-mode (FACE-LIGHT) proposed at ICISC'2019 conference. However, in a Wireless Sensor Network where sessions are frequently changed, FACE-LIGHT seems not efficient t...

Monte Carlo techniques are often the only practical way to evaluate difficult integrals or to sample random variables governed by complicated probability density functions. Here we describe an assortment of methods for sampling some commonly occurring probability density functions. Most Monte Carlo sampling or integration techniques assume a " random number generator, " which generates uniform ...

Monte Carlo techniques are often the only practical way to evaluate difficult integrals or to sample random variables governed by complicated probability density functions. Here we describe an assortment of methods for sampling some commonly occurring probability density functions. Most Monte Carlo sampling or integration techniques assume a " random number generator, " which generates uniform ...

Monte Carlo techniques are often the only practical way to evaluate difficult integrals or to sample random variables governed by complicated probability density functions. Here we describe an assortment of methods for sampling some commonly occurring probability density functions. Most Monte Carlo sampling or integration techniques assume a " random number generator, " which generates uniform ...

Monte Carlo techniques are often the only practical way to evaluate difficult integrals or to sample random variables governed by complicated probability density functions. Here we describe an assortment of methods for sampling some commonly occurring probability density functions. Most Monte Carlo sampling or integration techniques assume a " random number generator, " which generates uniform ...

Monte Carlo techniques are often the only practical way to evaluate difficult integrals or to sample random variables governed by complicated probability density functions. Here we describe an assortment of methods for sampling some commonly occurring probability density functions. Most Monte Carlo sampling or integration techniques assume a " random number generator, " which generates uniform ...