Multi-operation-based Constrained Random Verification for On-Chip Memory

Deterministic test pattern generation for combinational circuits is a time consuming process. So conducting a random test generation method at the initial stage and then using a deterministic test generation technique in order to generate test vectors for hard to detect faults is a common method. Generating random test patterns and then pruning them using a suitable heuristic is a viable solution that we are using in our paper. Our proposed method evaluates merit of a randomly generated test vector and decides to accept or reject it based on expected fault coverage. This expected value is dynamically updated as test vectors are chosen. The proposed method can be used in the initial phase of a test generation process instead of conventional pure random test generation method in order to reduce the number of test vectors in the final test set.

Gaussian (normal) distribution is a basic continuous probability distribution in statistics, it plays a substantial role in scientific and engineering problems that related to stochastic phenomena. This paper aims to review state-of-the-art of Gaussian random field generation methods, their applications in scientific and engineering issues of interest, and open-source software/packages for Gaussian random field generation. To this end, first, we briefly introduce basic mathematical concepts and theories in the Gaussian random field, then seven commonly used Gaussian random field generation methods are systematically presented. The basic idea, mathematical framework of each generation method are introduced in detail and comparisons of these methods are summarized. Then, representative applications of the Gaussian random field in various areas, especially of engineering interest in recent two decades, are reviewed. For readers’ convenience, four representative example codes are provided, and several relevant up-to-date open-source software and packages that freely available from the Internet are introduced.

In this paper, we discuss building an information dissemination model based on individual behavior. We analyze the individual behavior related to information dissemination and the factors that affect the sharing behavior of individuals, and we define and quantify these factors. We consider these factors as characteristic attributes and use a Bayesian classifier to classify individuals. Considering the forwarding delay characteristics of information dissemination, we present a random time generation method that simulates the delay of information dissemination. Given time and other constraints, a user might not look at all the information that his/her friends published. Therefore, this paper proposes an algorithm to predict information visibility, i.e., it estimates the probability that an individual will see the information. Based on the classification of individual behavior and combined with our random time generation and information visibility prediction method, we propose an information dissemination model based on individual behavior. The model can be used to predict the scale and speed of information propagation. We use data sets from Sina Weibo to validate and analyze the prediction methods of the individual behavior and information dissemination model based on individual behavior. A previously proposed information dissemination model provides the foundation for a subsequent study on the evolution of the network and social network analysis. Predicting the scale and speed of information dissemination can also be used for public opinion monitoring.

A new procedure for computer generation of random stable, linear, time invariant systems in state space form is described. Controllability and observability gramians are randomly generated according to a desired distribution. Then, a system which has the generated gramians is found. This new random system generation method is compared to several other methods using empirically observed distributions on Hankel singular values, eigenvalue magnitudes, and controllability/observability gramian condition numbers.

Maritime Autonomous Surface Ship (MASS) is promoted as the future of intelligent shipping. While autonomy technologies offer a solution for MASS, they have also resulted in new challenges for performance validation. To address this, a scenario-based validation method to test the autonomous collision avoidance system is proposed in this paper, including mining ship encounter scenarios from massive historical AIS data and randomly generated virtual test scenarios according to the parameter probability distributions from the collected real scenarios, as well as the final experiments: a total of 2900 generated scenarios including single ship and multi-ship encounter situations are created and applied to conduct testing experiments on the further assessment of our collision avoidance algorithm. The results indicate that the proposed method has the ability to quickly create appropriate testing scenarios according to AIS records, which are helpful to catch potential defects in a collision avoidance algorithm of MASS and to further analyze its navigating features. As a result, the research forms a systematic set of validation procedures from data gathering to practical experiments conduction, incorporating both the real statistics and the random generation method.

Parallel VLSI generation of random analog vectors with controlled statistics from deterministic chaos is the key to applications such as analog encryption and secure communications, analog built-in self-test, stochastic neural networks, and simulated annealing optimization as well as perturbation learning in neural hardware. We present a class of analog additive cellular automata which generate parallel streams of statistically independent, uniformly distributed random analog values. The underlying noise-shaping mechanism is essentially that of a MASH cascade of delta-sigma modulators. We present theory on the noise-shaping properties, scalable parallel VLSI architectures, and include experimental results from an analog VLSI prototype with 65 channels. The cell for each channel implements a switched-capacitor delta-sigma modulator, and measures 100 /spl mu/m/spl times/120 /spl mu/m in 2 /spl mu/m CMOS technology. The 65 cells are connected in a chain on a 2-D grid, and can be rearranged for use in various VLSI applications that require a parallel supply of random analog vectors.

Copulas are used in finance and insurance for modeling stochastic dependency. They comprehend the entire dependence structure, not only the correlations. Here they are estimated from measured samples of random vectors. The copula and the marginal distributions of the vector elements define a multivariate distribution of the sample which can be used to generate random vectors with this distribution. This can be applied as well to time series. A programmed algorithm is proposed. It is fast and allows for random vectors with high dimension, for example 100.

A multiaverage random switching frequency space vector pulsewidth modulation (MARSF-SVPWM) strategy is proposed in this article, in order to further disperse the high-order harmonics concentrated at the switching frequency and its integer multiplies in the output voltage of the inverter. The proposed strategy divides the fundamental period equally, with different stages corresponding to different average switching frequencies and spread-spectrum ranges, in contrast with the conventional random switching frequency space vector pulsewidth modulation (RSF-SVPWM), under the premise of maintaining the same overall average switching frequency and spread-spectrum range. The simulation and experimental results demonstrate that, even in the case of a narrow spread-spectrum range, the proposed MARSF-SVPWM strategy can obtain a better effect of high-order harmonic dispersion.

Monte Carlo (MC) method is widely used in resolving mathematical problems that are too complicated to solve analytically. The method involves with sampling process of the random numbers and probability to estimate the result. As MC method depending on a large number of good quality random numbers to produce a high accuracy result, developing a good random number generator (RNG) is vital. Most of random number generators are developed in software based, with the improvement of Field Programmable Gate Arrays (FPGA) density and speed in recent days, implementing random number generator (RNG) directly into hardware is feasible. Random Walk on the Boundary (WOB) is one of MC methods that applied to calculate the unit cube capacitance. The unique requirement of this method is the random numbers produced by RNG must fall within the Gaussian distribution and the maximum decimal values in the range of [0, 1]. Thus, in this paper we presented a novel hardware RNG for Random WOB method to calculate unit cube capacitance in FPGA. The RNG is implemented in floating-point base, using the combination of Cellular Automata Shift Register (CASR) and Linear Feedback Shift Register (LFSR) then channeled through Box-Muller transformation. There is linear approximation in computing logarithmic function applied in Box-Muller transformation. Based on statistical tests, the random numbers generated is nearly 97.5% resembling the standard normal distribution.

On generation of elliptical distributions with Gaussian form covariance matrix

A side channel analysis is major threat in secure communication device. Here our object is very clear to protect the sensitive information by adding DNA cryptography with chaotic countermeasure method. To overcome the side channel issues. Due to the side-channel attack can retrieve easily sensitive information from crypto embedded devices. So that the reason we need to protect the sensitive information (encryption key) in crypto embedded devices by applying the random number generation method. Mostly the random number generation used in medical data transmission, information security, and all sensitive areas. So in the research work, we apply a random generation by using the chaotic circuit. And also the paper concentrate to realize the chaotic circuit in hardware using FPGA with partially reconfigurable. And also based on Lorenz’s chaotic circuit used to protect sensitive information. Simulation and Synthesis verification done by using the VIVADO tool. Security analysis is done by hierarchical UACI, NPCR, and correlation.KeywordsSide channel attackFPGAChaotic

The excavation damage of deep tunnels is one of the most important factors contributing to the failure of tunnel structures. In order to investigate the influence of tunnel shapes and fissure geometries, the kernel function in the traditional SPH method has been improved, which can realize the brittle fracture characteristics of particles and can be called the Improved Kernel of Smoothed Particle Hydrodynamics (IKSPH-2D). Meanwhile, the random fissure generation method in IKSPH has been put forward. Different tunnel shapes, fissure geometries, and locations are considered during the simulation of tunnel excavation, and results show that (1) the typical “V”-shaped shear damage zones appear after the tunnel excavation, which is consistent with engineering practice. Meanwhile, tunnel excavation also has an “activating” effect on the preexisting fissures. (2) The stability of circular-shaped tunnel is the best, while horseshoe shaped tunnel is worse, and the “U”-shaped tunnel is the worst. (3) Fissures with small and large dip angles have the greatest influence on the stability of tunnel excavation. With the increase of fissure numbers and lengths, the tunnel tends to be instable. (4) The IKSPH method gets free from traditional grids in FEM, which can dynamically reflect the fracture processes of tunnel excavation. Meanwhile, developing 3D IKSPH parallel program will be the future directions.

A new method of noise generation based on software implementation of a 7-bit LFSR based on a common polynomial PRBS7 using microcontrollers equipped with internal ADCs and DACs and a microcontroller noise generator structure are proposed in the paper.Two software applications implementing the method: written in ANSI C and based on the LUT technique and written in AVR Assembler are also proposed.In the method the ADC results are used to reseed the LFSR after its each full work cycle, what improves randomness of generated data, which results in a greater similarity of the generated random signal to white noise, what was confirmed by the results of experimental research.The noise generator uses only the internal devices of the microcontroller, hence the proposed solution does not introduce hardware redundancy to the system.

In this paper the random test generation method for large logic circuits is analyzed. Formulas for the detection probability and the number of random input patterns required to complete the test generation with a high probability are obtained for an irredundant fan-out-free combinational network tree consisting of identical n-input NAND gates. The quantitative estimates for the number of random input patterns required for test generation appear to depend upon the number of levels in the circuit and the fan-ins of the gates. Experimental results for actual computer logic circuits are given and show the validity of the approach.

The key to improving the performance of graph convolutional networks (GCN) is to fully explore the correlation between neighboring and distant information. Aiming at the over-smoothing problem of GCN, in order to make full use of the relationship among features, graphs and labels, a graph residual generation network based on multi-information aggregation (MIA-GRGN) is proposed. Firstly, aiming at the defects of GCN, we design a deep initial residual graph convolution network (DIRGCN), which connects the initial input through residuals, so that each layer node retains part of the information of the initial features, ensuring the localization of the graph structure and effectively alleviating the problem of over-smoothing. Secondly, we propose a random graph generation method (RGGM) by utilizing graph edge sampling and negative edge sampling, and optimize the supervision loss function of DIRGCN in the form of generation framework. Finally, applying RGGM and DIRGCN as inference modules for modeling hypotheses and obtaining approximate posterior distributions of unknown labels, an optimized loss function is obtained, we construct a multi-information aggregation MIA-GRGN that combines graph structure, node characteristics and label joint distribution. Experiments on benchmark graph classification datasets show that MIA-GRGN achieves better classification results compared with the benchmark models and mainstream models, especially for datasets with less dense edge relationships between nodes.