Fast method for spreading sequence estimation of DSSS signal based on maximum likelihood function

To estimate the spreading sequence of the direct sequence spread spectrum (DSSS) signal, a fast algorithm based on maximum likelihood function is proposed, and the theoretical derivation of the algorithm is provided. By simplifying the objective function of maximum likelihood estimation, the algorithm can realize sequence synchronization and sequence estimation via adaptive iteration and sliding window. Since it avoids the correlation matrix computation, the algorithm significantly reduces the storage requirement and the computation complexity. Simulations show that it is a fast convergent algorithm, and can perform well in low signal to noise ratio (SNR).

Parameter Estimation for Constantinides-Ingersoll Model from Discrete Observations

The parameter estimation problem for an economic model called Constantinides-Ingersoll model is investigated based on discrete observations. Euler-Maruyama scheme and iterative method are applied to getting the joint conditional probability density function. The maximum likelihood technique is employed for obtaining the parameter estimators and the explicit expressions of the estimation error are given. The strong consistency properties of the estimators are proved by using the law of large numbers for martingales and the strong law of large numbers. The asymptotic normality of the estimation error for the diffusion parameter is obtained with the help of the strong law of large numbers and central-limit theorem. The simulation for the absolute error between estimators and true values is given and the hypothesis testing is made to verify the effectiveness of the estimators.

MAXIMUM LIKELIHOOD ESTIMATION FOR INSAR IMAGE CO-REGISTRATION

This paper presents a closed-form robust phase correlation based algorithm for performing image registration to subpixel accuracy.The subpixel translational shift information is directly obtained from the phase of the normalized cross power spectrum by using Maximum Likelihood Estimation(MLE).The proposed algorithm also has slighter time complexity.Experimental results show that the proposed algorithm yields superior registration precision on the Cramér-Rao Bound(CRB) in the presence of aliasing and noise.

Immune Clone Maximum Likelihood Estimation of Improved Non-homogeneous Poisson Process Model Parameters

Aiming at the solving problem of improved nonhomogeneous Poisson process( NHPP) model in engineering application,the immune clone maximum likelihood estimation( MLE)method for solving model parameters was proposed. The minimum negative log-likelihood function was used as the objective function to optimize instead of using iterative method to solve complex system of equations,and the problem of parameter estimation of improved NHPP model was solved by immune clone algorithm. And the interval estimation of reliability indices was given by using fisher information matrix method and delta method. An example of failure truncated data from multiple numerical control( NC) machine tools was taken to prove the method. and the results show that the algorithm has a higher convergence rate and computational accuracy, which demonstrates the feasibility of the method.

Estimation of Ordered Means of Two Normal Distributions with Ordered Variances

The authors consider the problem of estimating the ordered means of two normal distributions with unknown ordered variances. The authors discuss the estimation of two ordered means, individually, in terms of stochastic domination and MSE （mean squared error）. The authors show that in estimating the mean with larger variance, the usual estimator under order restriction on means can be improved upon. However, in estimating the mean with smaller variance, the usual estimator can＇t be improved upon even under MSE. The authors also discuss simultaneous estimation problem of two ordered means when unknown variances are ordered.

Vector tracking loops in GNSS receivers for dynamic weak signals

Vehicle positioning with the global navigation satellite system （GNSS） in urban environments faces two problems which are attenuation and dynamic. For traditional GNSS receivers hardly able to track dynamic weak signals, the coupling between all visible satellite signals is ignored in the absence of navigation state feedback, and thermal noise error and dynamic stress threshold are contradictory due to non-coherent discriminators. The vector delay/frequency locked loop （VDFLL） with navigation state feedback and the joint vector tracking loop （JVTL） with coherent discriminator which is a synchronization parameter tracking loop based on maximum likelihood estimation （MLE） are proposed to improve the tracking sensitivity of GNSS receiver in dynamic weak signal environments. A joint vector position tracking loop （JVPTL） directly tracking user position and velocity is proposed to further improve tracking sensitivity. The coherent navigation parameter discriminator of JVPTL, being able to ease the contradiction between thermal noise error and dynamic stress threshold, is based on MLE according to the navigation parameter based linear model of received baseband signals. Simulation results show that JVPTL, which combines the advantages of both VDFLL and JVTL, performs better than both VDFLL and JVTL in dynamic weak signal environments.

An improved wind retrieval algorithm for the HY-2A scatterometer

Since January 2012,the National Satellite Ocean Application Service has released operational wind products from the HY-2A scatterometer(HY2-SCAT),using the maximum-likelihood estimation(MLE) method with a median filter. However,the quality of the winds retrieved from HY2-SCAT depends on the sub-satellite cross-track location,and poor azimuth separation in the nadir region causes particularly low-quality wind products in this region. However,an improved scheme,i.e.,a multiple solution scheme(MSS) with a two-dimensional variational analysis method(2DVAR),has been proposed by the Royal Netherlands Meteorological Institute to overcome such problems. The present study used the MSS in combination with a 2DVAR technique to retrieve wind data from HY2-SCAT observations. The parameter of the empirical probability function,used to indicate the probability of each ambiguous solution being the "true" wind,was estimated based on HY2-SCAT data,and the 2DVAR method used to remove ambiguity in the wind direction. A comparison between MSS and ECMWF winds showed larger deviations at both low wind speeds(below 4 m/s) and high wind speeds(above 17 m/s),whereas the wind direction exhibited lower bias and good stability,even at high wind speeds greater than 24 m/s. The two HY2-SCAT wind data sets,retrieved by the standard MLE and the MSS procedures were compared with buoy observations. The RMS error of wind speed and direction were 1.3 m/s and 17.4°,and 1.3 m/s and 24.0° for the MSS and MLE wind data,respectively,indicating that MSS wind data had better agreement with the buoy data. Furthermore,the distributions of wind fields for a case study of typhoon Soulik were compared,which showed that MSS winds were spatially more consistent and meteorologically better balanced than MLE winds.

Improving Energy and Power Efficiency Using NComputing and Approaches for Predicting Reliability of Complex Computing Systems

Opting to follow the computing-design philosophy that the best way to reduce power consumption and increase energy efficiency is to reduce waste, we propose an architecture with a very simple ready-implementation by using an NComputing device that can allow multi-users but only one computer is needed. This intuitively can save energy, space as well as cost. In this paper, we propose a simple and realistic NComputing architecture to study the energy and power-efficient consumption of desktop computer systems by using the NComputing device. We also propose new approaches to estimate the reliability of k-out-of-n systems based on the delta method. The k-out-of-n system consisting of n subsystems works if and only if at least k-of-the-n subsystems work. More specificly, we develop approaches to obtain the reliability estimation for the k-out-of-n systems which is composed of n independent and identically distributed subsystems where each subsystem （or energy-efficient usage application） can be assumed to follow a two-parameter exponential lifetime distribution function. The detailed derivations of reliability estimation of k-out-of-n systems based on the biased-corrected estimator, known as delta method, the uniformly minimum variance unbiased estimate （UMVUE） and maximum likelihood estimate （MLE） are discussed. An energy-management NComputing application is discussed to illustrate the reliability results in terms of the energy consumption usages of a computer system with qua（t-core, 8 GB of RAM, and a GeForce 9800GX-2 graphics card to perform various complex applications. The estimated reliability values of systems based on the UMVUE and the delta method differ only slightly. Often the UMVUE of reliability for a complex system is a lot more difficult to obtain, if not impossible. The delta method seems to be a simple and better approach to obtain the reliability estimation of complex systems. The results of this study also show that, in practice, the NComputing architecture improves both energy cost saving and energy efficient living spaces.

Inference for constant-stress accelerated life test with Type-I progressively hybrid censored data from Burr-XII distribution

This paper proposes a simple constant-stress accel- erated life test （ALT） model from Burr type XII distribution when the data are Type-I progressively hybrid censored. The maximum likelihood estimation （MLE） of the parameters is obtained through the numerical method for solving the likelihood equations. Approxi- mate confidence interval （CI）, based on normal approximation to the asymptotic distribution of MLE and percentile bootstrap Cl is derived. Finally, a numerical example is introduced and then a Monte Carlo simulation study is carried out to illustrate the pro- posed method.

Odorant discrimination using functional near-infrared spectroscopy of the main olfactory bulb in rats

We characterize the hemodynamic response changes near-infrared spectroscopy （NIRS） during the presentation of in the main olfactory bulb （MOB） of anesthetized rats with three different odorants： （i） plain air as a reference （Blank）, （ii） 2-heptanone （HEP）, and （iii） isopropylbenzene （Ib）. Odorants generate different changes in the concentrations of oxy- hemoglobin. Our results suggest that NIRS technology might be useful in discriminating various odorants in a non-invasive manner using animals with a superb olfactory system.

Asymptotic Efficiency of the Maximum Likelihood Estimator for the Box-Cox Transformation Model with Heteroscedastic Disturbances

This paper considers the asymptotic efficiency of the maximum likelihood estimator (MLE) for the Box-Cox transformation model with heteroscedastic disturbances. The MLE under the normality assumption (BC MLE) is a consistent and asymptotically efficient estimator if the “small ” condition is satisfied and the number of parameters is finite. However, the BC MLE cannot be asymptotically efficient and its rate of convergence is slower than ordinal order when the number of parameters goes to infinity. Anew consistent estimator of order is proposed. One important implication of this study is that estimation methods should be carefully chosen when the model contains many parameters in actual empirical studies.

Movement and behavior analysis using neural spike signals in CA1 of rat hippocampus

The hippocampus which lies in the temporal lobe plays an important role in spatial navigation,learning and memory.Several studies have been made on the place cell activity,spatial memory,prediction of future locations and various learning paradigms.However,there are no attempts which have focused on finding whether neurons which contribute largely to both spatial memory and learning about the reward exist.This paper proposes that there are neurons that can simultaneously engage in forming place memory and reward learning in a rat hippocampus' s CA1 area.With a trained rat,a reward experiment was conducted in a modified 8-shaped maze with five stages,and utterance information was obtained from a CA1 neuron.The firing rate which is the count of spikes per unit time was calculated.The decoding was conducted with log-maximum likelihood estimation(Log-MLE) using Gaussian distribution model.Our outcomes provide evidence of neurons which play a part in spatial memory and learning regarding reward.

Integrating petrophysical data into efficient iterative cluster analysis for electrofacies identification in clastic reservoirs

Efficient iterative unsupervised machine learning involving probabilistic clustering analysis with the expectation-maximization(EM)clustering algorithm is applied to categorize reservoir facies by exploiting latent and observable well-log variables from a clastic reservoir in the Majnoon oilfield,southern Iraq.The observable well-log variables consist of conventional open-hole,well-log data and the computer-processed interpretation of gamma rays,bulk density,neutron porosity,compressional sonic,deep resistivity,shale volume,total porosity,and water saturation,from three wells located in the Nahr Umr reservoir.The latent variables include shale volume and water saturation.The EM algorithm efficiently characterizes electrofacies through iterative machine learning to identify the local maximum likelihood estimates(MLE)of the observable and latent variables in the studied dataset.The optimized EM model developed successfully predicts the core-derived facies classification in two of the studied wells.The EM model clusters the data into three distinctive reservoir electrofacies(F1,F2,and F3).F1 represents a gas-bearing electrofacies with low shale volume(Vsh)and water saturation(Sw)and high porosity and permeability values identifying it as an attractive reservoir target.The results of the EM model are validated using nuclear magnetic resonance(NMR)data from the third studied well for which no cores were recovered.The NMR results confirm the effectiveness and accuracy of the EM model in predicting electrofacies.The utilization of the EM algorithm for electrofacies classification/cluster analysis is innovative.Specifically,the clusters it establishes are less rigidly constrained than those derived from the more commonly used K-means clustering method.The EM methodology developed generates dependable electrofacies estimates in the studied reservoir intervals where core samples are not available.Therefore,once calibrated with core data in some wells,the model is suitable for application to other wells that lack core data.

PARAMETER ESTIMATION OF EXPONENTIAL DISTRIBUTION

Because of the importance of grouped data, many scholars have been devoted to the study of this kind of data. But, few documents have been concerned with the thresh-old parameter. In this paper, we assume that the threshold parameter is smaller than the first observing point. Then, on the basis of the two-parameter exponential distribution, the maximum likelihood estimations of both parameters are given, the sufficient and necessary conditions for their existence and uniqueness are argued, and the asymptotic properties of the estimations are also presented, according to which approximate confidence intervals of the parameters are derived. At the same time, the estimation of the parameters is generalized, and some methods are introduced to get explicit expressions of these generalized estimations. Also, a special case where the first failure time of the units is observed is considered.

A Genetic Algorithm for Estimations of Parameters under Partial Order Restrictions

The parameters of probability distributions under partial order restrictions are usually estimated by maximum likelihood estimates(MLE) and formulated as a maximization problem with a multimodal objective function subject to partial order restrictions. In order to obtain the global optimal estimation of parameters, this paper presents a genetic algorithm and illustrates its effectiveness by some numerical examples.

Species Abundance in a Forest Community in South China：A Case of Poisson Lognormal Distribution

: Case studies on Poisson lognormal distribution of species abundance have been rare, especially in forest communities. We propose a numerical method to fit the Poisson lognormal to the species abundance data at an evergreen mixed forest in the Dinghushan Biosphere Reserve, South China. Plants in the tree, shrub and herb layers in 25 quadrats of 20 m× 20 m, 5 m× 5 m, and 1 m× 1 m were surveyed. Results indicated that: (i) for each layer, the observed species abundance with a similarly small median, mode, and a variance larger than the mean was reverse J-shaped and followed well the zero-truncated Poisson lognormal; (ii) the coefficient of variation, skewness and kurtosis of abundance, and two Poisson lognormal parameters (& and μ) for shrub layer were closer to those for the herb layer than those for the tree layer; and (iii) from the tree to the shrub to the herb layer, the α and the coefficient of variation decreased, whereas diversity increased. We suggest that: (i) the species abundance distributions in the three layers reflects the overall community characteristics; (ii) the Poisson lognormal can describe the species abundance distribution in diverse communities with a few abundant species but many rare species; and (iii) 1/α should be an alternative measure of diversity.

Time and phase synchronization using clutter observations in airborne distributed coherent aperture radars

Airborne Distributed Coherent Aperture Radar(ADCAR)is one of the most promising next-generation radars to significantly improve target detection and discrimination abilities.However,time and phase synchronization among unit radars should be done before an ADCAR is intended to cohere on a potential target.To address this problem,a time and phase synchronization technique using clutter observations is proposed in this paper.Clutter returns from different azimuths and elevations on the surface of the earth are employed to calibrate system uncertainties.Two stages are mainly considered:a scene registration among range-Doppler units from different transmit/receive pairs is performed to enhance the clutter coherence in the first stage,followed by a joint estimation of those synchronization errors in the second stage.To relieve the computational burden,a novel Separable and Sequential Estimation(SSE)method is provided to separate the unknowns at the sacrifice of a range-Doppler unit.Moreover,performance analyses including the clutter coherence ability,estimation lower bound,and signal coherence loss are also performed.Finally,simulation results indicate that ADCAR time and phase synchronization is realized by using our methods.

Stochastic Approximation for Expensive One-Bit Feedback Systems

One-bit feedback systems generate binary data as their output and the system performance is usually measured by the success rate with a fixed parameter combination. Traditional methods need many executions for parameter optimization. Hence, it is impractical to utilize these methods in Expensive One-Bit Feedback Systems （EOBFSs）, where a single system execution is costly in terms of time or money. In this paper, we propose a novel algorithm, named Iterative Regression and Optimization （IRO）, for parameter optimization and its corresponding scheme based on the Maximum Likelihood Estimation （MLE） method and Particle Swarm Optimization （PSO） method, named MLEPSO-IRO, for parameter optimization in EOBFSs. The IRO algorithm is an iterative algorithm, with each iteration comprising two parts： regression and optimization. Considering the structure of IRO and the Bernoulli distribution property of the output of EOBFSs, MLE and a modified PSO are selected to implement the regression and optimization sections, respectively, in MLEPSO-IRO. We also provide a theoretical analysis for the convergence of MLEPSO-IRO and provide numerical experiments on hypothesized EOBFSs and one real EOBFS in comparison to traditional methods. The results indicate that MLEPSO-IRO can provide a much better result with only a small amount of system executions.

A novel energy-efficient ICI cancellation technique for bandwidth improvements through cyclic prefix reuse in an OFDM system

Orthogonal frequency division multiplexing （OFDM）, a very promising technique that is leading the evolution in wireless mobile communication to sideline the bandwidth scarcity issue in spectrum allocation, is severely affected by the undesirable effects of the frequency offset error, which generates inter cartier interference （ICI） due to the Doppler shift and local oscillator frequency synchronization errors. There are many ICI cancellation techniques available in the literature, such as self-cancellation （SC）, maximum likelihood estimation （MLE）, and windowing, but they present a tradeoff between bandwidth redundancy and system complexity. In this study, a new energy-efficient, bandwidth-effective technique is proposed to mitigate ICI through cyclic prefix （CP） reuse at the receiver end. Unlike SC and MLE where the whole OFDM symbol data is transmitted in duplicate to create redundancy at the transmitter end, the proposed technique uses the CP data （which is only 20% of the total symbol bandwidth） to estimate the channel, and it produces similar results with a huge bandwidth saving. The simulation results show that the proposed technique has a significant improvement in error performance, and a comparative analysis demonstrates the substantial improvement in energy efficiency with high bandwidth gain. Therefore, it outperforms the legacy IC1 cancellation schemes under consideration.

Biclustering of ARMA time series

Biclustering is a method of grouping objects and attributes simultaneously in order to find multiple hidden patterns.When dealing with a long time series,there is a low possibility of finding meaningful clusters of whole time sequence.However,we may find more significant clusters containing partial time sequence by applying a biclustering method.This paper proposed a new biclustering algorithm for time series data following an autoregressive moving average (ARMA) model.We assumed the plaid model but modified the algorithm to incorporate the sequential nature of time series data.The maximum likelihood estimation (MLE) method was used to estimate coefficients of ARMA in each bicluster.We applied the proposed method to several synthetic data which were generated from different ARMA orders.Results from the experiments showed that the proposed method compares favorably with other biclustering methods for time series data.