Approximate MLEs for the location and scale parameters of theextreme value distribution with censoring

For the extreme value distribution, the maximum likelihood method does not provide explicit estimators for the location and scale parameters. A method of deriving explicit estimators by approximating the likelihood function is provided. The authors derive the asymptotic variances, covariance, and conditional bias of these estimators, and show that they are almost as efficient as the maximum likelihood estimators and just as efficient as the best linear unbiased and the best linear invariant estimators. Two examples illustrate this method of estimation     

Approximate MLEs for the location and scale parameters of thehalf-logistic distribution with type-II right-censoring

For the half-logistic distribution the maximum likelihood method does not provide an explicit estimator for the scale parameter based on either complete or right-censored samples. The authors provide a simple method of deriving an explicit estimator by approximating the likelihood function. The bias and variance of this estimator are studied, and it is shown that this estimator is as efficient as the best linear unbiased estimator. An example to illustrate the method is presented     

BLUEs of location and scale parameters of laplace distribution based on type-II censored samples and associated inference

In this paper, we first present the Best Linear Unbiased Estimators (BLUEs) of the location and scale parameters, μ and σ, of the Laplace distribution based on Type-II censored samples. We next present some simulated percentage points for three pivotal quantities which will enable one to construct confidence intervals or carry out tests of hypotheses for the parameters μ and σ. Finally, we present an example to illustrate the methods of inference developed in this paper.     

Approximate formulas for crossed-field potential-minimum parameters

A parabolic potential distribution is assumed in order to study the static properties of the crossed-field potential minimum. The half-Maxwellian normal velocity distribution function and the full-Maxwellian transverse velocity distribution function are integrated over the initial velocity plane with the proper boundary conditions to find an expression for the ratio of beam current to emission current. For conditions satisfying most practical cases, this current-sorting expression is reduced to a very simple form. Using the current-sorting equation together with the expression which relates the potential minimum position to the minimum potential for zero magnetic field, an approximate method of determining the potential-minimum parameters is devised. The values obtained in this manner agree closely with the results obtained by other more exact (and more complicated) numerical methods.     

Modified KS, AD, and C-vM tests for the Pareto distribution withunknown location and scale parameters

Standard goodness-of-fit tests based on the empirical CdF (Edf) require continuous underlying distributions with all parameters specified. Three modified Edf-type tests, the Kolmogorov-Smirnov (K-S), Anderson-Darling (A-D), and Cramer-von Mises (C-vM), are developed for the Pareto distribution with unknown parameters of location and scale and known shape parameter. The unknown parameters are estimated using best linear unbiased estimators. For each test, Monte Carlo techniques are used to generate critical values for sample sizes 5(5)30 and Pareto shape parameters 0.5(0.5)4.0. The powers of the modified tests are investigated under eight alterative distributions. In most cases, the powers of the modified K-S, A-D, C-vM tests are considerably higher than the chi-square test. Finally, a functional relationship is identified between the modified K-S and C-vM test statistics and the Pareto shape parameter. Powerful goodness-of-fit tests that supplement the best linear unbiased estimates are provided     

A nonstationary traffic train model for fine scale inference from coarse scale counts

The self-similarity of network traffic has been convincingly established based on detailed packet traces. This fundamental result promises the possibility of solving on-line and off-line traffic engineering problems using easily collectible coarse time-scale data, such as simple network management protocol measurements. This paper proposes a statistical model that supports predicting fine time-scale behavior of network traffic from coarse time-scale aggregate measurements. The model generalizes the commonly used fractional Gaussian noise process in two important ways: (1) it accommodates the recurring daily load patterns commonly observed on backbone links and (2) features of long range dependence and self-similarity are modeled only at fine time scales and are progressively damped as the time period increases. Using the data we collected on the Chinese Education and Research Network, we demonstrate that the proposed model fits 5-min data and generates 10-s aggregates that are similar to actual 10-s data.     

Coupled-mode formulations for parallel-laser resonators withapplication to vertical-cavity semiconductor-laser arrays

Laser resonators, with laterally patterned end mirrors, are considered in the framework of coupled-mode theory. Two distinct coupled-mode formulations are presented. The first is applicable to cavities having high-reflectivity mirrors surrounded by a significantly lower reflectivity background. The second formulation applies to high-reflectivity mirrors with a slightly lower reflectivity background. Unlike coupling between parallel waveguides, the interaction between neighboring elements of the parallel cavities is achieved via diffraction, and the perturbation is of the boundary conditions rather than of the dielectric constant. These formulations are directly applicable to the case of arrays of coupled vertical-cavity semiconductor lasers     

Approximate formulas for frequency dependence of microstrip parameters

Some commonly used formulas for the frequency dependence of microstrip parameters have a number of shortcomings such as limited range of validity and faulty asymptotic behaviour. Therefore it is recommended to use the planar waveguide model with a recently published dispersion formula and a modified formula for the effective width.     

Application of Bayesian inference to fMRI data analysis

The methods of Bayesian statistics are applied to the analysis of fMRI data. Three specific models are examined. The first is the familiar linear model with white Gaussian noise. In this section, the Jeffreys' Rule for noninformative prior distributions is stated and it is shown how the posterior distribution may be used to infer activation in individual pixels. Next, linear time-invariant (LTI) systems are introduced as an example of statistical models with nonlinear parameters. It is shown that the Bayesian approach can lead to quite complex bimodal distributions of the parameters when the specific case of a delta function response with a spatially varying delay is analyzed. Finally, a linear model with auto-regressive noise is discussed as an alternative to that with uncorrelated white Gaussian noise. The analysis isolates those pixels that have significant temporal correlation under the model. It is shown that the number of pixels that have a significantly large auto-regression parameter is dependent on the terms used to account for confounding effects.     

A polynomial approach to optimal and adaptive filtering withapplication to speech

Explicit polynomial solutions to the Wiener filtering problem are given. They rely on the identification of innovations models for the disturbance and for the noisy signal. The Wiener filter is found from the solution of a diophantine equation. Results illustrating the attenuation of background interference in a speech signal are presented. The explicit approach presented does not rely on minimizing a prediction error over a performance surface and can be applied where two input techniques are impracticable or impossible     

A general packet replication scheme for multicasting withapplication to shuffle-exchange networks

Multicasting in broadband packet switches and metropolitan networks can be achieved by first replicating the packets and then routing them to their destinations. This paper studies a simple but general replication scheme that can be applied to arbitrary interconnection-network topologies. The replication process of a packet adapts itself according to the network topology and the traffic condition. Hot spots of replication activities are diffused by this scheme which automatically migrates the replication efforts to less active network regions. The scheme can potentially be used in networks (e.g., the Manhattan-street network) in which multicasting was thought to be inherently difficult. This paper, however, focuses on the shuffle-exchange copy network for a detailed study of the replication algorithm and its implementation at the logic-diagram level. It is found that the performance of the algorithm improves with the increase in network dimensions. Cascading the copy network with a point-to-point switch makes a multicast switch. A novel strategy for reducing the memory size of its routing tables is proposed     

Convolution backprojection formulas for 3-D vector tomography withapplication to MRI

Vector tomography is the reconstruction of vector fields from measurements of their projections. In previous work, it has been shown that the reconstruction of a general three-dimensional (3-D) vector field is possible from the so-called inner product measurements. It has also been shown how the reconstruction of either the irrotational or solenoidal component of a vector field can be accomplished with fewer measurements than that required for the full field. The present paper makes three contributions. First, in analogy to the two-dimensional (2-D) approach of Norton (1988), several 3-D projection theorems are developed. These lead directly to new vector field reconstruction formulas that are convolution backprojection formulas. It is shown how the local reconstruction property of these 3-D reconstruction formulas permits reconstruction of point flow or of regional flow from a limited data set. Second, simulations demonstrating 3-D reconstructions, both local and nonlocal, are presented. Using the formulas derived herein and those derived in previous work, these results demonstrate the reconstruction of the irrotational and solenoidal components, their potential functions, and the field itself from simulated inner product measurement data. Finally, it is shown how 3-D inner product measurements can be acquired using a magnetic resonance scanner     

Probabilistic inference of multijoint movements, skeletal parameters and marker attachments from diverse motion capture data

This paper describes a comprehensive solution to the problem of reconstructing the multijoint movement trajectories of the human body from diverse motion capture data. The problem is formulated in a probabilistic framework so as to handle multiple and unavoidable sources of uncertainty: sensor noise, soft tissue deformation and marker slip, inaccurate marker placement and limb measurement, and missing data due to occlusions. All unknown quantities are treated as state variables even though some of them are constant. In this way, state estimation and system identification can be performed simultaneously, obtaining not only the most likely values but also the confidence intervals of the joint angles, skeletal parameters, and marker positions and orientations relative to the limb segments. The inference method is a Gauss-Newton generalization of the extended Kalman filter. It is adapted to the kinematic domain by expressing spatial rotations via quaternions and computing the sensor residuals and their Jacobians analytically. The ultimate goal of this project is to provide a reliable data analysis tool used in practice. The software implementation is available online.     

Approximate MLE's of the parameters of a discrete Weibull distribution with type I censored data

Approximate Maximum Likelihood Estimators of the parameters of a discrete Weibull distribution under censoring is discussed. These approximations are obtained by approximating the likelihood functions at a set of estimators which are easy to obtain. Simulations are carried cut to illustrate the performance of the suggested procedure.     

Loss performance analysis for heterogeneous on-off sources withapplication to connection admission control

The bufferless fluid flow model (BFFM) is often used in the literature for loss performance analysis. We propose an efficient and effective means of investigating cell loss using the BFFM. We define the cell loss rate function (CLRF) and use it to characterize the loss performance of traffic sources in the BFFM. Stochastic ordering theory is used to study the CLRF. The introduction of the stochastic ordering theory not only simplifies the theoretical analysis but also makes it possible to extend the scope of applications and theoretical analysis presented. A cell loss upper bound for heterogeneous on-off sources is proposed. The proposed cell loss upper bound is tighter than those previously proposed in the literature. A connection admission control (CAC) scheme using online measurements is designed based on the cell loss upper bound. Extensive simulation is carried out to study the performance of the CAC scheme. Particular attention is paid to the impact of inaccuracies in user-declared traffic parameters on the performance of the CAC scheme. Simulation results indicate that the proposed CAC scheme can ensure QoS guarantee, is robust to inaccuracies in declared traffic parameters, and is capable of achieving high link utilization     

Spectrum resource allocation for wireless packet access withapplication to advanced cellular Internet service

The advanced cellular Internet service (ACIS) is targeted for applications such as Web browsing with a peak downlink transmission rate on the order of 1-2 Mbits/s using a wide-area cellular infrastructure. In order to provide bandwidth on demand using scarce radio spectrum, the medium-access control (MAC) protocol must: 1) handle dynamic and diverse traffic with high throughput, and 2) efficiently reuse limited spectrum with high peak rates and good quality. Most of the existing approaches do not sufficiently address the second aspect. This paper proposes a dynamic packet assignment (DPA) scheme which, without coordinating base stations, allocates spectrum on demand with no collisions and low interference to provide high downlink throughput. Interference sensing and priority ordering are employed to reduce interference probability. A staggered frame assignment schedule is also proposed to prevent adjacent base stations from allocating the same channel to multiple mobiles at the same time. Simulation results based on a packet data traffic model derived from wide-area network traffic statistics, which exhibit a “self-similar” property when aggregating multiple sources, confirm that this method is able to reuse spectrum efficiently in a large cellular system having many users with short active periods. Distributed iterative power control further enhances spectrum efficiency such that the same channel can be simultaneously reused in every base station     

Current-mode techniques for high-speed VLSI circuits withapplication to current sense amplifier for CMOS SRAM's

The speed of VLSI chips is increasingly limited by signal delay in long interconnect lines. A simple analysis shows that major speed improvements are possible when using current-mode rather than conventional voltage-mode signal transporting techniques. The key to this approach is the use of low-resistance current-signal circuits to drastically reduce the impedance level and the voltage swings on long interconnect lines. As an example, a simple four-transistor current-sense amplifier for fast CMOS SRAMs is proposed. The circuit presents a virtual short circuit to the bit lines, thus reducing the sensing delay, which is rendered practically insensitive to the bit-line capacitance. In addition, the virtual short circuit ensures equal bit-line voltages, thus eliminating the need for bit-line equalization during a read access     

Multiscale modeling and estimation of Poisson processes withapplication to photon-limited imaging

Many important problems in engineering and science are well-modeled by Poisson processes. In many applications it is of great interest to accurately estimate the intensities underlying observed Poisson data. In particular, this work is motivated by photon-limited imaging problems. This paper studies a new Bayesian approach to Poisson intensity estimation based on the Haar wavelet transform. It is shown that the Haar transform provides a very natural and powerful framework for this problem. Using this framework, a novel multiscale Bayesian prior to model intensity functions is devised. The new prior leads to a simple Bayesian intensity estimation procedure. Furthermore, we characterize the correlation behavior of the new prior and show that it has 1/f spectral characteristics. The new framework is applied to photon-limited image estimation, and its potential to improve nuclear medicine imaging is examined     

Low-cost approach for steerable flat-plate antenna design withapplication to reception of broadcasting from satellite

A novel design approach for circularly polarised flat-plate antennas has been developed. The proposed antenna is of multilayer construction, rotatable about its normal axis, and comprises a polarisation convertor, an array of elements and a beamforming network. In one potentially large application, the reception of satellite broadcast signals in the 12 GHz band, it can be mounted flat on the most suitable wall of an arbitrarily orientated house and be readily aligned