Pseudorandom Number Generator Research Articles

A Triple-Memristor Hopfield Neural Network With Space Multistructure Attractors and Space Initial-Offset Behaviors

Memristors have recently demonstrated great promise in constructing memristive neural networks with complex dynamics. This paper proposes a memristive Hopfield neural network with three memristive coupling synaptic weights. The complex dynamical behaviors of the triple-memristor Hopfield neural network (TM-HNN), which have never been observed in previous Hopfield-type neural networks, include space multi-structure chaotic attractors and space initial-offset coexisting behaviors. Bifurcation diagrams, Lyapunov exponents, phase portraits, Poincaré maps, and basins of attraction are used to reveal and examine the specific dynamics. Theoretical analysis and numerical simulation show that the number of space multi-structure attractors can be adjusted by changing the control parameters of the memristors, and the position of space coexisting attractors can be changed by switching the initial states of the memristors. Extreme multistability emerges as a result of the TM-HNN’s unique dynamical behaviors, making it more suitable for applications based on chaos. Moreover, a digital hardware platform is developed and the space multi-structure attractors as well as the space coexisting attractors are experimentally demonstrated. Finally, we design a pseudo-random number generator to explore the potential application of the proposed TM-HNN.

Analysis of the influence of a pseudo-random number generator type on the kinetics of the cellular automata recrystallization model

Models based on discrete simulation methods, i.e., cellular automata (CA) or Monte Carlo (MC), often contain probabilistic elements in the code to capture the stochastic character of material behaviour. They can be related to various physical aspects reflected in the model, e.g., heterogeneous energy distribution or random crystallographic orientation assignment to subsequent grains. Therefore, the paper's main aim is to analyze the influence of such random elements in the CA recrystallization model used to predict the material microstructure morphology after thermo-mechanical treatment. The first part of the work presents the available tools for pseudo-random number generation and the main elements of the investigated CA recrystallization model. Different variants of input data preparation prior to the simulation, including deformation energy distribution, are shown. The role of the investigated pseudo-random number generators (rand, lcg, mt, ranlux) and their distribution (uniform, normal, lognormal) is highlighted in this part. Finally, the input data are used to perform simulations of recrystallization progress, and the effect of pseudo-random number generation parameters on transformation kinetics and growth of recrystallized grains is evaluated. With this work, we have demonstrated that the choice of the generator type, particularly the method of its distribution, influences the simulation results in the form of final material morphology.

Lossless Image Encryption using Robust Chaos-based Dynamic DNA Coding, XORing and Complementing

In this paper, we present a lossless image encryption algorithm utilizing robust chaos based dynamic DNA coding and DNA operations (DNA XOR and DNA Complement). The entire process of encryption is controlled by the pseudo-random number sequences generated through a 1D robust chaos map that exhibits chaotic behaviour in a very large region of parameter space with no apparent periodic window and therefore possesses a fairly large key space. Due to peculiar feed-forward and feedback mechanisms, which modify the synthetic image (created to initiate the encryption process) at the encryption of each pixel, the proposed algorithm possesses extreme sensitivity to the plain image, cipher image and secret key. The performance analysis proves that the proposed algorithm exhibits excellent features (as expected from ideal image encryption algorithms) and is robust against various statistical and cryptanalytic attacks.

Simulation of background neuronal activity and noise in human intracranial microwire recordings

BackgroundHuman intracranial microwire recordings allow measurement of neuronal activity in human subjects at a fine temporal and spatial scale. The recorded extracellular potentials represent a mixture of action potentials from nearby neurons, local field potentials, and other noise sources. Signal processing of these recordings is used to separate the activity of putative single neurons from other background and noise. To better understand the separation of single neuron activity, one approach is to simulate the signals produced by neurons firing action potentials combined with background activity and noise. New methodThis paper characterizes the background activity and noise in human intracranial microwire recordings and presents an accurate and efficient method of simulation using an infinite impulse response filter to color white noise. Results and comparisonThis method reproduces the power spectral density of the background activity and noise over a frequency range of 1–5000 Hz and is over 200 times faster than previously used methods. It thus facilitates large scale studies of variation of noise sources, field potentials, and processing parameters. It performs equivalently in terms of spike sorting to simulation using white noise. Another advantage is that the simulated signals are known to arise from a pseudorandom number generator and cannot be the result of detecting simulated background spiking activity. ConclusionsThis approach provides a rapid and accurate method of simulating background noise and neural activity in human intracranial microwire recordings. It is suitable for use in large scale simulations to study spike sorting in this type of recording.

Cryptographically Secured Pseudo-Random Number Generators: Analysis and Testing with NIST Statistical Test Suite

There are several areas of knowledge in which (pseudo-)random numbers are necessary, for example, in statistical–mathematical simulation or in cryptography and system security, among others. Depending on the area of application, it will be necessary that the sequences used meet certain requirements. In general, randomness and uniformity conditions are required in the generated sequences, which are checked with statistical tests, and conditions on sequence unpredictability if the application is in security. In the present work, a literature review on cryptographically secure pseudo-random number generators (CSPRNGs) is carried out, they are implemented, and a critical analysis of their statistical quality and computational efficiency is performed. For this purpose, different programming languages will be used, and the sequences obtained will be checked by means of the NIST Statistical Test Suite (NIST STS). In addition, a user’s guide will be provided to allow the selection of one generator over another according to its statistical properties and computational implementation characteristics.

Josephson junction oscillator embedded in the microcontroller: Pseudo-random number generation and combination synchronization

Dynamical scrutiny of the resistive capacitive inductive shunted Josephson junction (JJ) oscillator (RCLSJJO), microcontroller realization, pseudo-random number generation (PRNG) and combination synchronization are achieved in this paper. Numerical probing led to the establishment that the RCLSJJO is characterized by regular behaviors, bistable periodic-2-oscillations, periodic bursting characteristics and various shapes of chaotic dynamics. Thereafter, the vast dynamical characteristics obtained theoretically are realized by the microcontroller realization with qualitative agreements. Moreover, a chaos-based PRNG is designed by using chaotic RCLSJJO and linear feedback shift register (LFSR) as post-processing unit. Satisfactory results are obtained from the NIST 800–22 test suite and the randomness of binary data generated from the proposed RCLSJJO-based PRNG is confirmed for chaos-based digital applications. Lastly, the combination chaos synchronization of two drive and one response RCLSJJO is proven thanks to the theoretical analysis.

The Probabilistic Image Encryption Algorithm Based on Galois Field GF(257)

Image encryption technology is an essential measure to protect image information from illegal access. A new probabilistic image encryption algorithm with 480-bit external secret key was proposed in this paper. A chaotic pseudo-random generator is employed to produce the equivalent keys. Then the plain image is covered by the equivalent keys under exclusive or operation to obtain a pending sequence. The pending sequence is transformed into a cipher sequence by arithmetic operations based on GF(257). Finally, the cipher sequence is also covered by the equivalent keys to obtain the cipher image. In the transformation, a 1024-bit pseudo-random number sequence is introduced as the base numbers for the power operation to assist the encryption. The proposed image encryption algorithm has the characteristic of probabilistic encryption. Encrypting the same plain image with the same secret key will result in completely different cipher images. Experiments show that the proposed algorithm has the low time complexity (9.67pi/bit) and space complexity (13.07bit/bit), and high sensitivities on the auxiliary sequence (49.94%), secret key (BACI 26.7698% typically), and plain image (BACI 26.7845% typically), and is a preferred image encryption scheme for commercial and personal image information security.

Random Switching Period SVPWM Technique with Variable Sampling Frequency for Three‐Phase Power Converters

To improve the pulse width modulation (PWM) harmonic suppression effect of the three‐phase VSI‐fed PMSM system, this paper proposed a novel random switching period (RSP)‐Space Vector Pulse Width Modulation (SVPWM) technique for the three‐phase power converter. First, the proposed technique replaces the 10‐bit linear feedback shift register pseudorandom number generator (LFSR‐PRNG) with a Tiny Mersenne Twister (MT)‐PRNG to quickly generate high‐quality pseudorandom number sequences. Then, to reduce the sampling error caused by the current ripple, this paper analyzes the sampling timing under different modulation techniques. Based on the interrupted update mechanism of the Infineon AURIX‐TC264, the variable frequency sampling of the RSP‐SVPWM technique is implemented to ensure the best sampling of the motor phase current. Finally, the simulation and experimental results show that the proposed technique can effectively disperse PWM harmonic energy. Meanwhile, the RSP‐SVPWM technique based on variable sampling frequency effectively improves the phase current quality of the PMSM system. The proposed technique shows great potential for suppressing PWM harmonics in integrated PMSM systems. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

A novel pseudo-random number generator based on multivariable optimization for image-cryptographic applications

Pseudo-random number generators (PRNGs) play an important role to ensure the security and confidentiality of image cryptographic algorithms. Their primary function is to generate a sequence of numbers that possesses unpredictability and randomness, which is crucial for the algorithms to work effectively and provide the desired level of security. However, traditional PRNGs frequently encounter limitations like insufficient randomness, predictability, and vulnerability to cryptanalysis attacks. To overcome these limitations, we propose a novel method namely an elliptic curve genetic algorithm (ECGA) for the construction of an image-dependent pseudo-random number generator (IDPRNG) that merges elliptic curves (ECs) and a genetic algorithm (GA). The ECGA consists of two primary stages. First, we generate an EC-based initial sequence of random numbers using pixels of a plain-image and parameters of an EC that depart from traditional methods of population initialization. In our proposed approach, the image itself serves as the seed for the initial population in the genetic algorithm optimization, taking into account the image-dependent nature of cryptographic applications. This allows the PRNG to adapt its behavior to the unique characteristics of the input image, leading to enhanced security and improved resistance against differential attacks. Furthermore, the use of a good initial population reduces the number of generations required by a genetic algorithm which results in decreased computational cost. In the second stage, we use well-known operations of a genetic algorithm to optimize the generated sequence by maximizing a multi-variable fitness function that is based on both the information entropy and the period of the PRNG. By combining elliptic curves and genetic algorithms, we enhance the randomness and security of the ECGA. To evaluate the effectiveness and security of our generator, we conducted comprehensive experiments using various benchmark images and applied several standard tests, including the National Institute of Standards and Technology (NIST) test suite. We then compared the results with the state-of-the-art PRNGs. The experimental results demonstrate that the ECGA outperforms the state-of-the-art PRNGs in terms of uniformity, randomness, and cryptographic strength.

Improved artificial gorilla troops optimizer with chaotic adaptive parameters - application to the parameter estimation problem of mixed additive and multiplicative random error models

For mixed additive and multiplicative random error models (MAM models), due to the complex correlation between the parameters and the model power array, derivative operations will be inevitable in the actual calculation. When the observation equation is in nonlinear form, the operations will be more complicated. The swarm intelligence optimization algorithm (SIO) can effectively solve the derivative problem when estimating the nonlinear model parameters using conventional iterative algorithms. However, for different problems, the conventional SIO cannot effectively balance the ability of global and local behavior, resulting in the algorithm falling into prematureness and failing to output effective parameter information. To address the above problems, the improved artificial gorilla troops optimizer (CAGTO) algorithm with chaotic adaptive behavior is proposed. To address the problem that the population generated by the algorithm using pseudo-random numbers in the initialization population phase has poor traversability in the feasible domain, the chaotic sequence is applied to initialize the population instead of pseudo-random number generation to ensure that the population can traverse the feasible domain as much as possible and improve the global search capability of the algorithm. Adaptive parameters that vary linearly and nonlinearly with the algorithm process are constructed to balance the global search and local search ability, while accelerating the convergence speed. Two CAGTO algorithms with different parameter settings are constructed for different problems, and the experimental results show that both CAGTO algorithms can effectively solve the parameter estimation problem of MAM models with different nonlinear forms of observation equations compared with several other comparative algorithms.

High prevalence of burnout syndrome in Czech general practitioners: A cross-sectional survey

ObjectiveA wide range in prevalence rates of burnout among general practitioners (GPs) has been reported in various regions, with an increasing trend. This nationwide cross-sectional study aimed to estimate the prevalence and associated determinants of burnout in Czech GPs. Methods1000 randomly selected physicians from the Czech Society of General Practitioners (through a pseudorandom number generator) were emailed an online survey based on the Maslach Burnout Inventory – Human Services Survey. Data collection was performed between January and February 2023. Results331 questionnaires were obtained (227 females and 104 males, mean age – 49.9 years, the mean number of registered patients – 1951). 21.8 % of GPs scored a high level of burnout in all three of its dimensions and 23.9 % in no dimension at all. The most prevalent dimension was reduced personal accomplishment (PA, 56.2 %) followed by emotional exhaustion (EE, 50.2 %) and depersonalization (DP, 40.5 %). Reaching burnout in all three dimensions was significantly more frequent in males and in GPs registering a number of patients above the median. Increasing age and years of practice were protective factors for DP but risk factors for reduced PA. Employed GPs had lower EE scores than GP practice owners. The respondents' basic characteristics reflected their presence among Czech GPs, which testifies against selection bias. ConclusionsThe high rate of burnout (∼22 %) should be addressed by promoting personal resources along with the perception of the importance of GPs in society. A sufficiently dense network of GPs should allow them to register a lower number of patients.

A review of Monte Carlo and quasi‐Monte Carlo sampling techniques

AbstractThis article presents a comprehensive review and comparison of the Monte Carlo and quasi‐Monte Carlo sampling techniques, which are widely used in numerical integration, simulation, and optimization. Monte Carlo sampling involves the generation of pseudorandom numbers or vectors to estimate unknown quantities of interest. In contrast, quasi‐Monte Carlo sampling is specialized for situations where uniformity and reduced variance are important. It generates a deterministic low‐discrepancy sequence that spans the entire sampling space. This review aims to analyze the strengths and distinctions of these two sampling methodologies, offering valuable insights to researchers in search of sampling techniques aligned with their specific research objectives and needs. Furthermore, it seeks to equip practitioners with efficient algorithms for practical implementations.This article is categorized under: Statistical and Graphical Methods of Data Analysis > Monte Carlo Methods Algorithms and Computational Methods > Numerical Methods Statistical and Graphical Methods of Data Analysis > Sampling

Secure blind watermarking using Fractional-Order Lorenz system in the frequency domain

This paper investigates two different blind watermarking systems in the frequency domain with the development of a Pseudo Random Number Generator (PRNG), based on a fractional-order chaotic system, for watermark encryption. The methodology is based on converting the cover image to the YCbCr color domain and applying two different techniques of frequency transforms, Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), to the Y channel. Then, the encrypted watermark is embedded in the middle-frequency band and HH band coefficients for the DCT and DWT, respectively. For more security and long encryption key size, the fractional-order Lorenz system is used to double the encryption key size and make it secure against brute-force attacks. The proposed algorithms successfully detect the hidden watermark by using the statistical properties of the embedding media, where the PRNG is examined using statistical tests and the watermarking systems are evaluated using standard imperceptibility and robustness measures. Common attacks such as noise-adding attacks, image enhancement attacks and geometric transformation attacks are discussed. Results of the PRNG demonstrate sensitivity to the system parameters, and results of the watermarking systems show good imperceptibility while keeping the robustness measures in a good range.

Generation of random sequences using DNA cryptography for OTP encryption

DNA cryptography is considered as a promising new field for improving cybersecurity and privacy using parallelism and storage capacity of DNA molecules. In this work, novel technique is proposed for generating random and unpredictable keys for symmetric One Time Pad cryptosystems. To achieve this, publicly available DNA sequences stored in genetic databases are used as a source of inherently random, diverse, and variable-length sequences. The proposed approach is based on two steps. The first step uses a DNA self-assembly structure, mathematical operations and DNA techniques. In the second step, an entropy source and chaotic function are incorporated as a random number generator and pseudorandom number generator, respectively, to further enhance the randomness and unpredictability of the generated OTP keys. Additionally, a secure key exchange approach is also proposed including minimization and optimization in both secure and public channels used for key exchange.

Оценка точности цифровой обработки сильно зашумленных сигналов с помощью эвристических алгоритмов

Heuristic algorithms are often used as an alternative when solving problems of high computational complexity or lacking an exact solution, allowing to quickly obtain the desired result. Usually, they do not have a strict mathematical justification, but their application is justified in terms of practicality. Formally, algorithms that use approximate methods can be classified as heuristic. However, when applying them, the problem of determinism lack is often arises, which does not always allow one to evaluate the solution obtained accuracy. The paper considers a methodical approach to assessing the accuracy of heuristic algorithms designed to determine the useful signal shape and parameters on the strong noise component background. It is based on the method of analogy and consists in modeling an artificial signal with given parameters and a background noise interference similar in its characteristics to additive white Gaussian noise. In this case, the noise component is formed by software using a pseudo-random number sequence generator. Such generators are included in the packages of almost all high-level programming languages built-in functions. A comparative analysis of the real and artificial noise characteristics is presented, that shown the problem solving by numerical modeling possibility. The results of accuracy estimation in determining the artificial signal parameters, that is separated from the noise component using piecewise linear approximation and averaging heuristic algorithms, are obtained. The problem of empirical data smoothing with the discrete signal equivalent replacement by a quadratic functions whose parameters provide a piecewise parabolic approximation its shape is also considered. This procedure eliminates the residual signal bounce that inevitably occurs as a result of linearization and allows further recording at any sampling rate. Thus, the proposed approach allows us to quantify the accuracy of heuristic algorithms used in determining the expected signal parameters.

A physical memristor-based chaotic system and its application in colour image encryption scheme

This work proposes a physical memristor (TaOx) based new 4D chaotic system with 3D multi-scroll, no equilibrium point, spiking behaviour, coexistence bursting oscillation and multistability. Using this physical memristor-based chaotic system, a novel and efficient colour image encryption algorithm has been developed using a unique box scrambling method and bit-wise XOR operations. Many interesting and new dynamics of a material-based memristive chaotic system are reported here, like 3D multi-scroll chaotic attractors, bursting characteristics, multistability, a neuronal system like spiking behaviours etc using Lyapunov spectrum and bifurcation plots. It is observed that the number of scrolls is changed with the total simulation time. This novel memristive chaotic system has limit cycles with controllable spikes and bursting oscillation. In addition, the system shows chaotic bursting oscillation under a different set of parameters and initial conditions. The coexistence of the bursting phenomena is studied here. The bursting and spiking characteristic is important for material-based memristors in neuromorphic applications. 3D Chaotic multi-scroll and multistability properties make the image encryption method more efficient and secure. Such characteristics are rare in physical memristor-based chaotic systems and using this, the image encryption algorithm is also rare in recent findings. Therefore, a new secure image encryption algorithm for colour images is proposed here, based on the unique box scrambling method, bitwise XOR operation and pseudo-random number generation using the proposed memristive chaotic system. Various tests like NPCR, UACI, histogram analysis, correlation study, information entropy analysis, robustness against external noise, etc have been performed to check the algorithm’s robustness and efficiency and test the capability to resist statistical and differential attacks.

Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function, a brand-new tristable locally active memristor model is first proposed in this paper. Here, a novel four-dimensional fractional-order memristive cellular neural network (FO-MCNN) model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its performance. Then, its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation platforms. Subsequently, it is used toward secure communication application scenarios. Taking it as the pseudo-random number generator (PRNG), a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing (ASR-CS) model. Eventually, the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

Cellular Automaton-Based Emulation of the Mersenne Twiste

The Mersenne Twister (MT) (MT19937), developed 30 years ago, is the de facto pseudorandom number generator (PRNG) used in many computer programs. This paper proposes a candidate that offers a randomness quality that is better than MT19937 and its sisters SFMT19937 and TinyMT. A special three-neighborhood, two-state cellular automaton (CA), called CA (150′) is the underlying model of this PRNG. The same working style of MT19937 is used, while avoiding the problems of the MT, like a large state space and the zero-access initial state problem. Nonlinearity is added in the base simple linear CA such that the properties of the base CA are not violated. Finally, a PRNG is developed using this CA that beats MT19937 as well as its advanced versions over the standard empirical platforms Dieharder, TestU01 and NIST.

Cryptographic Grade Chaotic Random Number Generator Based on Tent-Map

In recent years, there has been an increasing interest in employing chaotic-based random number generators for cryptographic purposes. However, many of these generators produce sequences that lack the necessary strength for cryptographic systems, such as Tent-Map. However, these generators still suffer from common issues when generating random numbers, including issues related to speed, randomness, lack of statistical properties, and lack of uniformity. Therefore, this paper introduces an efficient pseudo-random number generator, called State-Based Tent-Map (SBTM), based on a modified Tent-Map, which addresses this and other limitations by providing highly robust sequences suitable for cryptographic applications. The proposed generator is specifically designed to generate sequences with exceptional statistical properties and a high degree of security. It utilizes a modified 1D chaotic Tent-Map with enhanced attributes to produce the chaotic sequences. Rigorous randomness testing using the Dieharder test suite confirmed the promising results of the generated keystream bits. The comprehensive evaluation demonstrated that approximately 97.4% of the tests passed successfully, providing further evidence of the SBTM’s capability to produce sequences with sufficient randomness and statistical properties.

SNPU: An Energy-Efficient Spike Domain Deep-Neural-Network Processor With Two-Step Spike Encoding and Shift-and-Accumulation Unit

In this article, an energy-efficient spike domain deep-neural-network processor (SNPU) is proposed. Recently, many sparsity-aware processors were proposed to increase energy efficiency. However, because the activation of the real-world network was not high compared to the ideal condition, they were unable to completely employ integrated zero skipping logic. In addition, they employed weight sparsity by pruning, but their zero skipping logic was designed to perform best only under specific sparsity condition. We adopt two-step encoding (neural and computing encoding) and an omni-sparsity handling unit to solve these issues. The input neural encoding eliminates pseudo-random number generators (PRNGs) for the previous encoding, and the output neural encoding reduces redundant memory access. As a result, weight can be reused by a delayed factor, while input spike encoding consumes 99.4% less power compared to the Poisson encoder. Also, SNPU uses computing encoding with multi-level encoding and spiketrain decomposition. The multi-level encoding reduces the time-window size by merging several spikes, and SNPU uses a single shift-and-accumulation (SAC) rather than several accumulations, reducing 43.5% of operations compared to single-level encoding. In addition, the spike-train decomposition is proposed to decrease the number of SACs by computation reuse, resulting in a reduction of 71.8% operations compared to previous spiking neural-network (SNN) processing, which cannot reuse computation result. As a result, the computing encoding reduces the operations by 83.3% compared to SNPU without multi-level encoding and spike-train decomposition. Moreover, SAC units in SNN PE process multi-level spikes while consuming 80.3% less power than accumulator arrays that do not use the two-step encoding. Also, the neuron link and spike-train allocator handle variable time-window sizes, and they can increase PE utilization of SNPU by 23.1%. To sum it up, SNPU shows the state-of-the-art energy efficiency of 13.7 TOPS/W for object detection and 31.5 TOPS/W for ImageNet classification. Moreover, SNPU shows the state-of-the-art power consumption of 63.2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu$</tex-math> </inline-formula> W for face recognition.