AlphaSeq is a new paradigm to design sequencess with desired properties based on deep reinforcement learning (DRL). In this work, we propose a new metric function and a new reward function, to design an improved version of AlphaSeq. We show analytically and also through numerical simulations that the proposed algorithm can discover sequence sets with preferable properties faster than that of the previous algorithm.

A neural network that outputs reconstructed images based on projection data containing scattered X-rays is presented, and the proposed scheme exhibits better accuracy than conventional computed tomography (CT), in which the scatter information is removed. In medical X-ray CT, it is a common practice to remove scattered X-rays using a collimator placed in front of the detector. In this study, the scattered X-rays were assumed to have useful information, and a method was devised to utilize this information effectively using a neural network. Therefore, we generated 70,000 projection data by Monte Carlo simulations using a cube comprising 216 (6 × 6 × 6) smaller cubes having random density parameters as the target object. For each projection simulation, the densities of the smaller cubes were reset to different values, and detectors were deployed around the target object to capture the scattered X-rays from all directions. Then, a neural network was trained using these projection data to output the densities of the smaller cubes. We confirmed through numerical evaluations that the neural-network approach that utilized scattered X-rays reconstructed images with higher accuracy than did the conventional method, in which the scattered X-rays were removed. The results of this study suggest that utilizing the scattered X-ray information can help significantly reduce patient dosing during imaging.

Digital phase-locked loops (DPLLs) have been designed in a number of ways to correctly generate pulse signals in various systems. However, the existing DPLLs have poor acquisition performance or are prone to the divergence phenomenon when modeling and/or round-off errors exist and the noise statistics are incorrect. In this paper, we propose a novel DPLL whose phase estimator is designed in hybrid form that utilizes the advantages of Monte Carlo estimation, which is robust to nonlinear effects such as measurement quantization, and a finite memory estimator, which is robust against incorrect noise information and system model mismatch. The robustness of the proposed hybrid Monte Carlo/finite memory DPLL is demonstrated by comparing its phase estimation performance via a numerical example.

Sparse code multiple access (SCMA) based on the message passing algorithm (MPA) for multiuser detection is a competitive non-orthogonal multiple access technique for fifth-generation wireless communication networks Among the existing multiuser detection schemes for uplink (UP) SCMA systems, the serial MPA (S-MPA) scheme, where messages are updated sequentially, generally converges faster than the conventional MPA (C-MPA) scheme, where all messages are updated in a parallel manner. In this paper, the optimization of message scheduling in the S-MPA scheme is proposed. Firstly, some statistical results for the probability density function (PDF) of the received signal are obtained at various signal-to-noise ratios (SNR) by using the Monte Carlo method. Then, based on the non-orthogonal property of SCMA, the data mapping relationship between resource nodes and user nodes is comprehensively analyzed. A partial codeword transmission of S-MPA (PCTS-MPA) with threshold decision scheme of PDF is proposed and verified. Simulations show that the proposed PCTS-MPA not only reduces the complexity of MPA without changing the bit error ratio (BER), but also has a faster convergence than S-MPA, especially at high SNR values.

In X-ray computed tomography, scattered X-rays are generally removed by using a post-patient collimator located in front of the detector. In this paper, we show that the scattered X-rays have the potential to improve the estimation accuracy of the attenuation coefficient in computed tomography. In order to clarify the problem, we simplified the geometry of the computed tomography into a thin cylinder composed of a homogeneous material so that only one attenuation coefficient needs to be estimated. We then conducted a Monte Carlo numerical experiment on improving the estimation accuracy of attenuation coefficient by measuring the scattered X-rays with several dedicated toroidal detectors around the cylinder in addition to the primary X-rays. We further present a theoretical analysis to explain the experimental results. We employed a model that uses a T-junction (i.e., T-junction model) to divide the photon transport into primary and scattered components. This division is processed with respect to the attenuation coefficient. Using several T-junction models connected in series, we modeled the case of several scatter detectors. The estimation accuracy was evaluated according to the variance of the efficient estimator, i.e., the Cramer-Rao lower bound. We confirmed that the variance decreases as the number of scatter detectors increases, which implies that using scattered X-rays can reduce the irradiation dose for patients.

This paper focuses mainly on issues related to the pricing of American options under a fuzzy environment by taking into account the clustering of the underlying asset price volatility, leverage effect and stochastic jumps. By treating the volatility as a parabolic fuzzy number, we constructed a Levy-GJR-GARCH model based on an infinite pure jump process and combined the model with fuzzy simulation technology to perform numerical simulations based on the least squares Monte Carlo approach and the fuzzy binomial tree method. An empirical study was performed using American put option data from the Standard & Poor's 100 index. The findings are as follows: under a fuzzy environment, the result of the option valuation is more precise than the result under a clear environment, pricing simulations of short-term options have higher precision than those of medium- and long-term options, the least squares Monte Carlo approach yields more accurate valuation than the fuzzy binomial tree method, and the simulation effects of different Levy processes indicate that the NIG and CGMY models are superior to the VG model. Moreover, the option price increases as the time to expiration of options is extended and the exercise price increases, the membership function curve is asymmetric with an inclined left tendency, and the fuzzy interval narrows as the level set α and the exponent of membership function n increase. In addition, the results demonstrate that the quasi-random number and Brownian Bridge approaches can improve the convergence speed of the least squares Monte Carlo approach.

A circuit-aging simulation that efficiently calculates temporal change of rare circuit-failure probability is proposed. While conventional methods required a long computational time due to the necessity of conducting separate calculations of failure probability at each device age, the proposed Monte Carlo based method requires to run only a single set of simulation. By applying the augmented reliability and subset simulation framework, the change of failure probability along the lifetime of the device can be evaluated through the analysis of the Monte Carlo samples. Combined with the two-step sample generation technique, the proposed method reduces the computational time to about 1/6 of that of the conventional method while maintaining a sufficient estimation accuracy.

In this paper, we investigate the channel characteristics of underwater optical wireless communications (UOWC) based on Monte Carlo simulation method. The impulse response and channel time dispersion of the link are discussed. Also we consider the channel parameters comprehensively like the water type, attenuation length, divergence angle, beam width, field-of-view (FOV), receiver aperture and position. Simulation results suggest that in clear water, the channel can effectively be considered as non inter-symbol interference (ISI) when working over distance of up to 40m. Therefore, in practice the receiver does not need to perform computationally complex signal processing operations. However, in harbor water, the channel time dispersion will enlarge with larger FOV or divergence angle, and reduce the data transmission efficiency. When the attenuation length is smaller than diffused length, larger receivers offer lower intensity than smaller ones. In contrast, the intensity enhances with larger receiver at the small FOV, however, they trend to similar regardless of the apertures at large FOV. Furthermore, we study the effect of misalignment of the transmitter and receiver on the received intensity. The results give us some insight in terms of what constitutes an accurate UOWC channel.

A simple but efficient method for evaluating the channel capacity of 2×2 multiple-input multiple-output (MIMO) antenna systems is proposed. First, the channel capacity of a half-wavelength dipole array antenna is calculated using the Monte Carlo method by changing the incident-wave signal-to-noise power ratio, the power difference between two elements, and the correlation coefficient. Using the calculated results, a polynomial function is derived by multivariate regression analysis to estimate the channel capacity. The validity of the developed function is confirmed by comparing the channel capacity estimated by the developed function with that calculated by the Monte Carlo method using a MIMO array antenna operated under various scenarios, including antenna-human body electromagnetic interactions and radio-wave propagation environments, for future MIMO systems. The function is also validated by means of two experimental approaches: the use of radiation patterns measured in an anechoic chamber and the use of a spatial fading emulator that can create a two-dimensional fading environment.

The cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter has emerged as a promising tool for tracking a time-varying number of targets. However, the standard CBMeMBer filter may perform poorly when measurements are coupled with sensor biases. This paper extends the CBMeMBer filter for simultaneous target tracking and sensor biases estimation by introducing the sensor translational biases into the multi-Bernoulli distribution. In the extended CBMeMBer filter, the biases are modeled as the first order Gauss-Markov process and assumed to be uncorrelated with target states. Furthermore, the sequential Monte Carlo (SMC) method is adopted to handle the non-linearity and the non-Gaussian conditions. Simulations are carried out to examine the performance of the proposed filter.

In recent data centers, large-scale storage systems storing big data comprise thousands of large-capacity drives. Our goal is to establish a method for building highly reliable storage systems using more than a thousand low-cost large-capacity drives. Some large-scale storage systems protect data by erasure coding to prevent data loss. As the redundancy level of erasure coding is increased, the probability of data loss will decrease, but the increase in normal data write operation and additional storage for coding will be incurred. We therefore need to achieve high reliability at the lowest possible redundancy level. There are two concerns regarding reliability in large-scale storage systems: (i) as the number of drives increases, systems are more subject to multiple drive failures and (ii) distributing stripes among many drives can speed up the rebuild time but increase the risk of data loss due to multiple drive failures. If data loss occurs by multiple drive failure, it affects many users using a storage system. These concerns were not addressed in prior quantitative reliability studies based on realistic settings. In this work, we analyze the reliability of large-scale storage systems with distributed stripes, focusing on an effective rebuild method which we call Dynamic Refuging. Dynamic Refuging rebuilds failed blocks from those with the lowest redundancy and strategically selects blocks to read for repairing lost data. We modeled the dynamic change of amount of storage at each redundancy level caused by multiple drive failures, and performed reliability analysis with Monte Carlo simulation using realistic drive failure characteristics. We showed a failure impact model and a method for localizing the failure. When stripes with redundancy level 3 were sufficiently distributed and rebuilt by Dynamic Refuging, the proposed technique turned out to scale well, and the probability of data loss decreased by two orders of magnitude for systems with a thousand drives compared to normal RAID. The appropriate setting of a stripe distribution level could localize the failure.

Majority logic is quite important for various applications such as fault tolerant systems, threshold logic, spectrum spread coding, and artificial neural networks. The circuit implementation of majority logic is difficult when the number of inputs becomes large because the number of transistors becomes huge and serious delay would occur. In this paper, we propose a new majority circuit with large fan-in. The circuit is composed of ordinary CMOS transistors and the total number of transistors is approximately only 4N, where N is the total number of inputs. We confirmed a correct operation by using HSPICE simulation. The yield of the proposed circuit was evaluated with respect to N under the variations of device parameters by using Monte Carlo simulation.

An efficient Monte Carlo (MC) method for the calculation of failure probability degradation of an SRAM cell due to negative bias temperature instability (NBTI) is proposed. In the proposed method, a particle filter is utilized to incrementally track temporal performance changes in an SRAM cell. The number of simulations required to obtain stable particle distribution is greatly reduced, by reusing the final distribution of the particles in the last time step as the initial distribution. Combining with the use of a binary classifier, with which an MC sample is quickly judged whether it causes a malfunction of the cell or not, the total number of simulations to capture the temporal change of failure probability is significantly reduced. The proposed method achieves 13.4× speed-up over the state-of-the-art method.

A simplified circuit has been utilized for fast computation of the current flowing in the cross-point memory array. However, the circuit has a constraint in that the selected cell is located farthest from current drivers so as to estimate the current degraded by metal wire resistance. This is because the length of the current path along the metal wire varies with the selected address in the cross-point memory array. In this paper, a new simplified circuit is proposed for calculating the current at every address in order to take account of the metal wire resistance. By employing the Monte Carlo simulation to solve the proposed simplified circuit, the current distribution across the array is obtained, so that failure rates of read disturbance and write error are estimated precisely. By comparing the conventional and the proposed simplified circuits, it was found that the conventional simplified circuit estimated optimistic failure rates for read disturbance and for write error when the wire resistance was prominent enough as a parasitic resistance.

A novel approach for the multiple-model multi-sensor Bernoulli filter (MM-MSBF) based on the theory of finite set statistics (FISST) is proposed for a single maneuvering target tracking in the presence of detection uncertainty and clutter. First, the FISST is used to derive the multi-sensor likelihood function of MSBF, and then combining the MSBF filter with the interacting multiple models (IMM) algorithm to track the maneuvering target. Moreover, the sequential Monte Carlo (SMC) method is used to implement the MM-MSBF algorithm. Eventually, the simulation results are provided to demonstrate the effectiveness of the proposed filter.

In this paper, we study on an availability analysis for a multibase system with lateral resupply of spare items between bases. We construct a basic model that a spare item of a base is transported for operation to another base without spare upon occurrence of failure, and simultaneously, the base that supplies the spare item receives the failed item of the other base for repair. We propose an approximation method to obtain the availability of the system and show the accuracy of the solution through numerical experiments. Also, two modified models are constructed to show the efficiency of the basic model. The two models modify the assumption on the lateral resupply of spare items between bases in the basic model. We numerically illustrate that the basic model can increase the availability of the system compared with the two modified models through Monte Carlo simulation.

The composite binary offset carrier (CBOC) modulated signal contains multi-peaks in its auto-correlation function, which brings ambiguity to the signal acquisition process of a GNSS receiver. Currently, most traditional ambiguity-removing schemes for CBOC signal acquisition approximate CBOC signal as a BOC signal, which may incur performance degradation. Based on Galileo E1 CBOC signal, this paper proposes a novel adaptive ambiguity-removing acquisition scheme which doesn't adopt the approximation used in traditional schemes. According to the energy ratio of each sub-code of CBOC signal, the proposed scheme can self-adjust its local reference code to achieve unambiguous and precise signal synchronization. Monte Carlo simulation is conducted in this paper to analyze the performance of the proposed scheme and three traditional schemes. Simulation results show that the proposed scheme has higher detection probability and less mean acquisition time than the other three schemes, which verify the superiority of the proposed scheme.

Four calculation techniques for the Q-factor determination of resonant structures are compared on the basis of the influence of the VNA measurement uncertainty. The influence is evaluated using Monte Carlo calculations. On the basis of the deviation, the dispersion, and the effect of nearby resonances, the circle fitting method is the most appropriate technique. Although the 3dB method is the most popular technique, the Q-factors calculated by this method exhibit deviations, and the sign and amount of the deviation depend on the measurement setup. Comparisons using measurement data demonstrate that the uncertainty of the dielectric loss tangent calculated by the circle fitting method is less than a third of those calculated by the other three techniques.

This paper proposes a novel and an efficient method termed hypersphere sampling to estimate the circuit yield of low-failure probability with a large number of variable sources. Importance sampling using a mean-shift Gaussian mixture distribution as an alternative distribution is used for yield estimation. Further, the proposed method is used to determine the shift locations of the Gaussian distributions. This method involves the bisection of cones whose bases are part of the hyperspheres, in order to locate probabilistically important regions of failure; the determination of these regions accelerates the convergence speed of importance sampling. Clustering of the failure samples determines the required number of Gaussian distributions. Successful static random access memory (SRAM) yield estimations of 6- to 24-dimensional problems are presented. The number of Monte Carlo trials has been reduced by 2-5 orders of magnitude as compared to conventional Monte Carlo simulation methods.

We consider Monte Carlo tree search problem, a variant of Min-Max tree search problem where the score of each leaf is the expectation of some Bernoulli variables and not explicitly given but can be estimated through (random) playouts. The goal of this problem is, given a game tree and an oracle that returns an outcome of a playout, to find a child node of the root which attains an approximate min-max score. This problem arises in two player games such as computer Go. We propose a simple and efficient algorithm for Monte Carlo tree search problem.