Design and Analysis of Sobol Sequence Generator using gray code parallelization

2021 ◽  
Author(s):  
Akriti Dikshit ◽  
Gayathri Prasad ◽  
Maran Ponnambalam
Keyword(s):  
Gray Code ◽  
Sequence Generator ◽  
Code Parallelization

Gray-Code Ranking and Unranking on Left-Weight Sequences of Binary Trees

2016 ◽  
Vol E99.A (6) ◽  
pp. 1067-1074 ◽  
Author(s):  
Ro-Yu WU ◽  
Jou-Ming CHANG ◽  
Sheng-Lung PENG ◽  
Chun-Liang LIU
Keyword(s):  
Gray Code ◽  
Binary Trees

Ranking and Unranking of Non-regular Trees in Gray-Code Order

2013 ◽  
Vol E96.A (6) ◽  
pp. 1059-1065 ◽  
Author(s):  
Ro-Yu WU ◽  
Jou-Ming CHANG ◽  
An-Hang CHEN ◽  
Ming-Tat KO
Keyword(s):  
Gray Code

scholarly journals The Dynamic Analysis of a Novel Reconfigurable Cubic Chaotic Map and Its Application in Finite Field

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1420
Author(s):  
Chuanfu Wang ◽  
Yi Di ◽  
Jianyu Tang ◽  
Jing Shuai ◽  
Yuchen Zhang ◽  
...  
Keyword(s):  
Secure Communication ◽  
Chaotic Map ◽  
Digital Circuit ◽  
Pseudorandom Sequence ◽  
Periodic Behavior ◽  
Output Sequence ◽  
Digital Devices ◽  
Theoretical And Numerical Analysis ◽  
Dynamic Degradation ◽  
Sequence Generator

Dynamic degradation occurs when chaotic systems are implemented on digital devices, which seriously threatens the security of chaos-based pseudorandom sequence generators. The chaotic degradation shows complex periodic behavior, which is often ignored by designers and seldom analyzed in theory. Not knowing the exact period of the output sequence is the key problem that affects the application of chaos-based pseudorandom sequence generators. In this paper, two cubic chaotic maps are combined, which have symmetry and reconfigurable form in the digital circuit. The dynamic behavior of the cubic chaotic map and the corresponding digital cubic chaotic map are analyzed respectively, and the reasons for the complex period and weak randomness of output sequences are studied. On this basis, the digital cubic chaotic map is optimized, and the complex periodic behavior is improved. In addition, a reconfigurable pseudorandom sequence generator based on the digital cubic chaotic map is constructed from the point of saving consumption of logical resources. Through theoretical and numerical analysis, the pseudorandom sequence generator solves the complex period and weak randomness of the cubic chaotic map after digitization and makes the output sequence have better performance and less resource consumption, which lays the foundation for applying it to the field of secure communication.

A 73dB-A Audio VCO-ADC based on a Maximum Length Sequence Generator in 130nm CMOS

2021 ◽  
pp. 1-1
Author(s):  
Carlos Perez ◽  
Andres Quintero ◽  
Pedro Amaral ◽  
Andreas Wiesbauer ◽  
L. Hernandez
Keyword(s):  
Maximum Length ◽  
Sequence Generator

A Note on Gray Code and Odd-Even Merge

1980 ◽  
Vol 9 (1) ◽  
pp. 142-158 ◽  
Author(s):  
P. Flajolet ◽  
Lyle Ramshaw
Keyword(s):  
Gray Code

scholarly journals Coexisting Attractors and Multistability in a Simple Memristive Wien-Bridge Chaotic Circuit

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 678 ◽  
Author(s):  
Yixuan Song ◽  
Fang Yuan ◽  
Yuxia Li
Keyword(s):  
Chaotic System ◽  
Analog Circuit ◽  
Random Sequence ◽  
Mathematical Expression ◽  
Digital Signal ◽  
Approximate Entropy ◽  
Chaotic Circuit ◽  
Coexisting Attractors ◽  
Key Space ◽  
Sequence Generator

In this paper, a new voltage-controlled memristor is presented. The mathematical expression of this memristor has an absolute value term, so it is called an absolute voltage-controlled memristor. The proposed memristor is locally active, which is proved by its DC V–I (Voltage–Current) plot. A simple three-order Wien-bridge chaotic circuit without inductor is constructed on the basis of the presented memristor. The dynamical behaviors of the simple chaotic system are analyzed in this paper. The main properties of this system are coexisting attractors and multistability. Furthermore, an analog circuit of this chaotic system is realized by the Multisim software. The multistability of the proposed system can enlarge the key space in encryption, which makes the encryption effect better. Therefore, the proposed chaotic system can be used as a pseudo-random sequence generator to provide key sequences for digital encryption systems. Thus, the chaotic system is discretized and implemented by Digital Signal Processing (DSP) technology. The National Institute of Standards and Technology (NIST) test and Approximate Entropy analysis of the proposed chaotic system are conducted in this paper.

Betalogger: Smartphone Sensor-based Side-channel Attack Detection and Text Inference Using Language Modeling and Dense MultiLayer Neural Network

2021 ◽  
Vol 20 (5) ◽  
pp. 1-17
Author(s):  
Abdul Rehman Javed ◽  
Saif Ur Rehman ◽  
Mohib Ullah Khan ◽  
Mamoun Alazab ◽  
Habib Ullah Khan
Keyword(s):  
Neural Network ◽  
Language Modeling ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Second Phase ◽  
Sensitive Data ◽  
Phase Sequence ◽  
Smartphone Technology ◽  
Conventional Machine ◽  
Sequence Generator

With the recent advancement of smartphone technology in the past few years, smartphone usage has increased on a tremendous scale due to its portability and ability to perform many daily life tasks. As a result, smartphones have become one of the most valuable targets for hackers to perform cyberattacks, since the smartphone can contain individuals’ sensitive data. Smartphones are embedded with highly accurate sensors. This article proposes BetaLogger , an Android-based application that highlights the issue of leaking smartphone users’ privacy using smartphone hardware sensors (accelerometer, magnetometer, and gyroscope). BetaLogger efficiently infers the typed text (long or short) on a smartphone keyboard using Language Modeling and a Dense Multi-layer Neural Network (DMNN). BetaLogger is composed of two major phases: In the first phase, Text Inference Vector is given as input to the DMNN model to predict the target labels comprising the alphabet, and in the second phase, sequence generator module generate the output sequence in the shape of a continuous sentence. The outcomes demonstrate that BetaLogger generates highly accurate short and long sentences, and it effectively enhances the inference rate in comparison with conventional machine learning algorithms and state-of-the-art studies.

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