The Experts below are selected from a list of 17394 Experts worldwide ranked by ideXlab platform

Israel A Wagner - One of the best experts on this subject based on the ideXlab platform.

  • a robust Random Number generator based on a differential current mode chaos
    IEEE Transactions on Very Large Scale Integration Systems, 2008
    Co-Authors: O Katz, Dan Ramon, Israel A Wagner
    Abstract:

    This paper demonstrates a differential current-mode chaos-based circuit used to Generate Random Number sequences, which was implemented on 90-nm CMOS-SOI technology. The proposed design is more suitable for circuit implementation of a chaotic map, and diminishes non-idealities such as asymmetry, offset and low slope values. The differential design also exhibits superior robustness to supply voltage, temperature, and process variations. Behavioral and SPICE simulations are used to show the advantages of the differential chaos circuit in comparison to a single ended version. Furthermore, to validate that the circuit can serve as a white noise generator, a statistical Random Number generator test, as suggested by the Federal Information Processing Standard (FIPS), was conducted on the simulation results and verified on the hardware. The results of the test demonstrated that the circuit functions with very high robustness.

  • a robust Random Number generator based on a differential current mode chaos
    IEEE International Conference on Microwaves Communications Antennas and Electronic Systems, 2008
    Co-Authors: O Katz, Dan Ramon, Israel A Wagner
    Abstract:

    This paper demonstrates a differential current-mode chaos-based circuit used to Generate Random Number sequences, which was implemented on 90 nm CMOS-SOI technology. The proposed design implements an improved and robust chaotic map, and diminishes non-idealities such as asymmetry, offset. Randomness tests were conducted and show the advantages of the differential chaos circuit.

O Katz - One of the best experts on this subject based on the ideXlab platform.

  • a robust Random Number generator based on a differential current mode chaos
    IEEE Transactions on Very Large Scale Integration Systems, 2008
    Co-Authors: O Katz, Dan Ramon, Israel A Wagner
    Abstract:

    This paper demonstrates a differential current-mode chaos-based circuit used to Generate Random Number sequences, which was implemented on 90-nm CMOS-SOI technology. The proposed design is more suitable for circuit implementation of a chaotic map, and diminishes non-idealities such as asymmetry, offset and low slope values. The differential design also exhibits superior robustness to supply voltage, temperature, and process variations. Behavioral and SPICE simulations are used to show the advantages of the differential chaos circuit in comparison to a single ended version. Furthermore, to validate that the circuit can serve as a white noise generator, a statistical Random Number generator test, as suggested by the Federal Information Processing Standard (FIPS), was conducted on the simulation results and verified on the hardware. The results of the test demonstrated that the circuit functions with very high robustness.

  • a robust Random Number generator based on a differential current mode chaos
    IEEE International Conference on Microwaves Communications Antennas and Electronic Systems, 2008
    Co-Authors: O Katz, Dan Ramon, Israel A Wagner
    Abstract:

    This paper demonstrates a differential current-mode chaos-based circuit used to Generate Random Number sequences, which was implemented on 90 nm CMOS-SOI technology. The proposed design implements an improved and robust chaotic map, and diminishes non-idealities such as asymmetry, offset. Randomness tests were conducted and show the advantages of the differential chaos circuit.

Dan Ramon - One of the best experts on this subject based on the ideXlab platform.

  • a robust Random Number generator based on a differential current mode chaos
    IEEE Transactions on Very Large Scale Integration Systems, 2008
    Co-Authors: O Katz, Dan Ramon, Israel A Wagner
    Abstract:

    This paper demonstrates a differential current-mode chaos-based circuit used to Generate Random Number sequences, which was implemented on 90-nm CMOS-SOI technology. The proposed design is more suitable for circuit implementation of a chaotic map, and diminishes non-idealities such as asymmetry, offset and low slope values. The differential design also exhibits superior robustness to supply voltage, temperature, and process variations. Behavioral and SPICE simulations are used to show the advantages of the differential chaos circuit in comparison to a single ended version. Furthermore, to validate that the circuit can serve as a white noise generator, a statistical Random Number generator test, as suggested by the Federal Information Processing Standard (FIPS), was conducted on the simulation results and verified on the hardware. The results of the test demonstrated that the circuit functions with very high robustness.

  • a robust Random Number generator based on a differential current mode chaos
    IEEE International Conference on Microwaves Communications Antennas and Electronic Systems, 2008
    Co-Authors: O Katz, Dan Ramon, Israel A Wagner
    Abstract:

    This paper demonstrates a differential current-mode chaos-based circuit used to Generate Random Number sequences, which was implemented on 90 nm CMOS-SOI technology. The proposed design implements an improved and robust chaotic map, and diminishes non-idealities such as asymmetry, offset. Randomness tests were conducted and show the advantages of the differential chaos circuit.

Yongting Wang - One of the best experts on this subject based on the ideXlab platform.

  • a method to Generate Random Number for cryptographic application
    Intelligent Information Hiding and Multimedia Signal Processing, 2014
    Co-Authors: Xiamu Niu, Yongting Wang
    Abstract:

    Random Number is widely used in cryptographic applications, which is mainly used as key. Because the security of key totally depends on the amount and Randomness of itself, it's very important to produce Random Numbers for cryptographic applications. This paper presents a method to Generate Random Numbers for cryptographic applications. NIST Statistical Test Suite which provides 15 statistical methods is used to test the Randomness of the Random Number Generated by this method. Because the tests focus on a variety of different types of non-Randomness, not all tests are needed. The chosen statistical tests are Frequency (Monobit) Test, Frequency Test within a Block, The Cumulative Sums (Cusums) Test, The Runs Test, Test for the Longest Run of Ones in a Block, Discrete Fourier Transform (Specral) Test, Approximate Entropy Test and Serial Test. The result of tests shows that the Random Number Generated by the Random Number generator is Random. Therefore the conclusion is the Random Number Generated is Random enough for cryptographic applications.

Xiamu Niu - One of the best experts on this subject based on the ideXlab platform.

  • a method to Generate Random Number for cryptographic application
    Intelligent Information Hiding and Multimedia Signal Processing, 2014
    Co-Authors: Xiamu Niu, Yongting Wang
    Abstract:

    Random Number is widely used in cryptographic applications, which is mainly used as key. Because the security of key totally depends on the amount and Randomness of itself, it's very important to produce Random Numbers for cryptographic applications. This paper presents a method to Generate Random Numbers for cryptographic applications. NIST Statistical Test Suite which provides 15 statistical methods is used to test the Randomness of the Random Number Generated by this method. Because the tests focus on a variety of different types of non-Randomness, not all tests are needed. The chosen statistical tests are Frequency (Monobit) Test, Frequency Test within a Block, The Cumulative Sums (Cusums) Test, The Runs Test, Test for the Longest Run of Ones in a Block, Discrete Fourier Transform (Specral) Test, Approximate Entropy Test and Serial Test. The result of tests shows that the Random Number Generated by the Random Number generator is Random. Therefore the conclusion is the Random Number Generated is Random enough for cryptographic applications.