We propose a sampling method that incorporates a normally distributed sampling series for EEG measurements using compressed sensing. We confirmed that the ADC sampling count and amount of wirelessly transmitted data can be reduced by 11% while maintaining a reconstruction accuracy similar to that of the conventional method.

Contamination of water resources with pathogenic microorganisms excreted in human feces is a worldwide public health concern. Surveillance of fecal contamination is commonly performed by routine monitoring for a single type or a few types of microorganism(s). To design a feasible routine for periodic monitoring and to control risks of exposure to pathogens, reliable statistical algorithms for inferring correlations between concentrations of microorganisms in water need to be established. Moreover, because pathogens are often present in low concentrations, some contaminations are likely to be under a detection limit. This yields a pairwise left-censored dataset and complicates computation of correlation coefficients. Errors of correlation estimation can be smaller if undetected values are imputed better. To obtain better imputations, we utilize side information and develop a new technique, the asymmetric Tobit model which is an extension of the Tobit model so that domain knowledge can be exploited effectively when fitting the model to a censored dataset. The empirical results demonstrate that imputation with domain knowledge is effective for this task.

This paper proposes to pre-compute approximate normal distribution functions and store them in textures such that real-time applications can process complex specular surfaces simply by sampling the textures. The proposed method is compatible with the GPU pipeline-based algorithms, and rendering is completed at real time. The experimental results show that the features of complex specular surfaces, such as the glinty appearance of leather and metallic flakes, are successfully reproduced.

This paper proposes and theoretically analyzes the performance of amplify-and-forward (AF) relaying free-space optical (FSO) systems using avalanche photodiode (APD) over atmospheric turbulence channels. APD is used at each relay node and at the destination for optical signal conversion and amplification. Both serial and parallel relaying configurations are considered and the subcarrier binary phase-shift keying (SC-BPSK) signaling is employed. Closed-form expressions for the outage probability and the bit-error rate (BER) of the proposed system are analytically derived, taking into account the accumulating amplification noise as well as the receiver noise at the relay nodes and at the destination. Monte-Carlo simulations are used to validate the theoretical analysis, and an excellent agreement between the analytical and simulation results is confirmed.

In Recent years, a paradigm of optimization algorithms referred to as “meta-heuristics” have been gaining attention as a means of obtaining approximate solutions to optimization problems quickly without any special prior knowledge of the problems. Meta-heuristics are characterized by flexibility in implementation. In practical applications, we can make use of not only existing algorithms but also revised algorithms that reflect the prior knowledge of the problems. Most meta-heuristic algorithms lack mathematical grounds, however, and therefore generally require a process of trial and error for the algorithm design and its parameter adjustment. For one of the resolution of the problem, we propose an approach to design algorithms with mathematical grounds. The approach consists of first constructing a “framework” of which dynamic characteristics can be derived theoretically and then designing concrete algorithms within the framework. In this paper, we propose such a framework that employs two following basic strategies commonly used in existing meta-heuristic algorithms, namely, (1) multipoint searching, and (2) stochastic searching with pseudo-random numbers. In the framework, the update-formula of search point positions is given by a linear combination of normally distributed random numbers and a fixed input term. We also present a stability theory of the search point distribution for the proposed framework, using the variance of the search point positions as the index of stability. This theory can be applied to any algorithm that is designed within the proposed framework, and the results can be used to obtain a control rule for the search point distribution of each algorithm. We also verify the stability theory and the optimization capability of an algorithm based on the proposed framework by numerical simulation.

Detecting a variety of anomalies caused by attacks or accidents in computer networks has been one of the real challenges for both researchers and network operators. An effective technique that could quickly and accurately detect a wide range of anomalies would be able to prevent serious consequences for system security or reliability. In this article, we characterize detection techniques on the basis of learning models and propose an unsupervised learning model for real-time anomaly detection in computer networks. We also conducted a series of experiments to examine capabilities of the proposed model by employing three well-known machine learning algorithms, namely multivariate normal distribution, k-nearest neighbor, and one-class support vector machine. The results of these experiments on real network traffic suggest that the proposed model is a promising solution and has a number of flexible capabilities to detect several types of anomalies in real time.

Distribution of current onset voltage (COV) as well as threshold voltage (VTH) and drain induced barrier lowering (DIBL) in MOSFETs fabricated by 65 nm technology is statistically analyzed. Although VTH distribution follows the normal distribution, COV and DIBL deviate from the normal distribution. It is newly found that COV follows the Gumbel distribution, which is known as one of the extreme value distributions. This result of statistical COV analysis supports our model that COV is mainly determined by the deepest potential valley between source and drain.

GaAs MMICs (Monolithic Microwave Integrated Circuits) reliability is a critical part of the overall reliability of the thermal solution in semiconductor devices. With MMICs reliability improved, GaAs MMICs failure rates will reach levels which are impractical to measure with conventional methods in the near future. This letter proposes a methodology to predict the GaAs MMICs reliability by combining empirical and statistical methods based on zero-failure GaAs MMICs life testing data. Besides, we investigate the effect of accelerated factors on MMICs degradation and make a comparison between the Weibull and lognormal distributions. The method has been used in the reliability evaluation of GaAs MMICs successfully.

This paper develops a complete architecture for constant false alarm rate (CFAR) detection based on a goodness-of-fit (GOF) test. This architecture begins with a logarithmic amplifier, which transforms the background distribution, whether Weibull or lognormal into a location-scale (LS) one, some relevant properties of which are exploited to ensure CFAR. A GOF test is adopted at last to decide whether the samples under test belong to the background or are abnormal given the background and so should be declared to be a target of interest. The performance of this new CFAR scheme is investigated both in homogeneous and multiple interfering targets environment.

In order for the interconnect effects due to process-induced variations to be applied to the designs in 0.13 µm and below, it is necessary to determine and characterize the realistic interconnect worstcase models with high accuracy and speed. This paper proposes new statistically-based approaches to the characterization of realistic interconnect worstcase models which take into account process-induced variations. The Effective Common Geometry (ECG) and Accumulated Maximum Probability (AMP) algorithms have been developed and implemented into the new statistical interconnect worstcase design environment. To verify this statistical interconnect worstcase design environment, the 31-stage ring oscillators are fabricated and measured with UMC 0.13 µm Logic process. The 15-stage ring oscillators are fabricated and measured with 0.18 µm standard CMOS process for investigating its flexibility in other technologies. The results show that the relative errors of the new method are less than 1.00%, which is two times more accurate than the conventional worstcase method. Furthermore, the new interconnect worstcase design environment improves optimization speed by 29.61-32.01% compared to that of the conventional worstcase optimization. The new statistical interconnect worstcase design environment accurately predicts the worstcase and bestcase corners of non-normal distribution where conventional methods cannot do well.

Formulae required for accurate approximate calculation of transition probabilities of embedded Markov chain for single-server queues of the GI/ M/1,GI/M/1/K,M/G/1,M/G/1/K type with heavy-tail lognormal distribution of inter-arrival or service time are given.

We propose a stochastic model for signals generated through the electron multiplying effect of detectors in charged particle beam equipments. This model is based on a stochastic variable characterized by a log-normal type distribution. The model is simple and can be used to represent a wide dynamic range of signals from pulse-like signals when the primary beam current is small to continuous signals when the primary beam current is large. For the model base reference a normalization of actual signal detectors is presented. This base reference yields the unique stochastic parameter used in our model. The proposed model better approximates the actual signals in the power spectrum distribution as compared to the filtered Poisson method presented elsewhere.

In this paper, we present a new algorithm for statistical static timing analysis of a CMOS combinatorial circuit, which takes correlations into account to improve accuracy of the distribution of the maximum delay of the circuit. The correlations treated in the algorithm are not only the one between distributions of arrival times of input signals to a logic gate but also correlation between switching delays of a logic gate and correlation between interconnect delays of a net. We model each delay by a normal distribution, and use a normal distribution of two stochastic variables with a coefficient of correlation for computing the maximum of two delays. Since the algorithm takes the correlation into account, the time complexity is O(m2) in the worst-case, where m is the number of edges of the graph representing a given circuit. But, for real combinatorial circuits, the complexity is expected to be less than this.

A rapid growth of the Internet and proliferation of new multimedia applications lead to demands of high speed and broadband network technologies. Routers are also necessary to follow up the growth of link bandwidths. From this reason, there have been many researches on high speed routers having switching capabilities. To have an expected effect, however, a control parameters set based on traffic characteristics are necessary. In this paper, we analyze the network traffic using the network traffic monitor and investigate the Internet traffic characteristics through a statistical analysis. We next show the application of our analytical results to parameter settings of high speed switching routers. Simulation results show that our approach makes highly utilized VC space and high performance in packet processing delay. We also show the effect of flow aggregation on MPLS. From our results, the flow aggregation has a great impact on the performance of MPLS.

Hierarchical macrocell/microcell architectures have been proposed for future cellular mobile communication. The performance analysis for the hierarchical cellular system becomes an important issue. In this paper, extending the analytical methods from[1][2][8], assuming that the imperfect power control follows log-normal statistics, and employing different attenuation models for macrocells and microcells, the capacity plane and outage probability of the system are examined and quantified with and without perfect sectorization. From the numerical results of parameters of IS-95 protocol, the high user capacity and lower outage probability may be expected in the case of relatively tight power control and narrower overlap between sectors. These results are compared with the previously published CDMA nonhierarchical cellular system estimation. When we employ the hierarchical cellular system, we can increase the user capacity 2.3 times with the same bandwidth 1.25 MHz than the one of the nonhierarchical cellular system.

Sea clutter was measured using an X-band radar at very high grazing angles between 8.2 and 17.5. The sea state was 7 with the wave height of 6 to 9m. The wind velocity was 25m/s. It is shown that sea clutter amplitudes obey the log-normal and K distributions using the Akaike Information Criterion (AIC) , which is more rigorous fit to the distribution to the data than the least squares method.