On the nonstationary Erlang loss model

Nonstationary loss queueing system (Erlang model) is considered. We study weak ergodicity, bounds on the rate of convergence, approximations, bounds for limit characteristics.     

On sinusoid estimation in nonstationary noise

This note presents a sinusoid estimation algorithm, which will converge to the spectrum of the sinusoid process in nonstationary additive noise. The algorithm is in the framework of the tangential Nevanlinna-Pick setting. The procedure is a generalization of Capon's maximum likelihood estimate and some classical limit theorem for orthogonal polynomials. The method is used in an experimental example to find the natural frequencies of a lightly damped structure.     

Coordination of nonstationary systems

In this paper on-line optimization of a complex system subject to disturbances is considered. A hierarchical control structure is described, which uses feedback from the controlled system along with simplified mathematical models. Operation of the control structure is analyzed for the case of time-varying disturbances. The effect of model-reality differences and delays caused by computation and communication is investigated. Theoretical considerations are illustrated by a simulation study.     

Target seeking in the presence of nonstationary Gaussian fluctuations

The problem of estimating the position, scale, and orientation of a localized target or extended scene in the presence of additive, spatially nonstationary Gaussian noise is investigated. The nonstationary part of the noise is represented by a finite sum of basis functions with Gaussian random coefficients. In the case of a localized target the nonstationary part of the noise can be regarded as a random target. The exact decision-theoretic solution is given for two cases: (1) where the input image is fully known within a specified domain, and (2) where a scanning process gives only partial knowledge of the image.     

Optimal sensor location in the presence of nonstationary noise

Observability conditions for distributed parameter systems with nonstationary state excitation noise are derived. Because these conditions are violated in most cases of interest in process control an optimal projection approach and a sensor location criterion are developed to minimize the error caused by the lack of observability. The value of these concepts was demonstrated in real time experiments on a system with two spatial dimensions, the heating of a cylindrical ingot. It was found that through the nonstationary noise model and the optimal sensor location significantly improved state estimates can be obtained when persistent or slowly varying unmeasured disturbances affect the system. The real-time requirements for the filter were very modest and an application in the process industries where these disturbances always present a serious problem, seems entirely feasible.     

Bayesian nonstationary source separation

A Bayesian nonstationary source separation algorithm is proposed in this paper to recover nonstationary sources from noisy mixtures. In order to exploit the temporal structure of the data, we use a time-varying autoregressive (TVAR) process to model each source signal. Then variational Bayesian (VB) learning is adopted to integrate the source model with blind source separation (BSS) in probabilistic form. Our separation algorithm makes full use of temporally correlated prior information and avoids overfitting in separation process. Experimental results demonstrate that our vbICA-TVAR algorithm learns the temporal structure of sources and acquires cleaner source reconstruction.     

A nonstationary generalization of the Kerr congruence

Making use of the Kerr theorem for shear-free null congruences and of Newman’s representation for a virtual charge “moving” in complex space-time, we obtain an axisymmetric time-dependent generalization of the Kerr congruence, with a singular ring uniformly contracting to a point and expanding then to infinity. Electromagnetic and complex eikonal field distributions are naturally associated with the obtained congruence, with electric charge being necessarily unit (“elementary”).     

Information processing in assembly tasks--a case study

In a surface wiring task the terminals of electrical components are connected by wires according to a wiring list. This series of experiments examined one such task in a batch assembly environment where production runs are short. The findings show that information theory is a good predictor of performance, that different sources of information within the task are processed in different ways, and that considerable symmetric transfer can be expected between different batches.     

Motion detection with nonstationary background

Abstract. This paper proposes a new background subtraction method for detecting moving foreground objects from a nonstationary background. While background subtraction has traditionally worked well for a stationary background, the same cannot be implied for a nonstationary viewing sensor. To a limited extent, motion compensation for the nonstationary background can be applied. However, in practice, it is difficult to realize the motion compensation to sufficient pixel accuracy, and the traditional background subtraction algorithm will fail for a moving scene. The problem is further complicated when the moving target to be detected/tracked is small, since the pixel error in motion that is compensating the background will subsume the small target. A spatial distribution of Gaussians (SDG) model is proposed to deal with moving object detection having motion compensation that is only approximately extracted. The distribution of each background pixel is temporally and spatially modeled. Based on this statistical model, a pixel in the current frame is then classified as belonging to the foreground or background. For this system to perform under lighting and environmental changes over an extended period of time, the background distribution must be updated with each incoming frame. A new background restoration and adaptation algorithm is developed for the nonstationary background. Test cases involving the detection of small moving objects within a highly textured background and with a pan-tilt tracking system are demonstrated successfully. Received: 30 July 2001 / Accepted: 20 April 2002 Correspondence to: Chin-Seng Chau     

Control of nonstationary LPV systems

This paper considers control of nonstationary linear parameter-varying systems, and is motivated by interest in the control of nonlinear systems along prespecified trajectories. In the paper, synthesis conditions are derived for such systems using an operator theoretical framework with the ?₂ induced norm as the performance measure. These conditions are given in terms of structured operator inequalities. In general, evaluating the validity of these conditions is an infinite dimensional convex optimization problem; however, if the initial system is eventually periodic, they reduce to a finite dimensional semi-definite programming problem. The paper concludes with an in-depth example on the control of a two-thruster hovercraft along an eventually periodic trajectory.     

Information systems investment decisions in business practice: the Spanish case

Previous research has reviewed the foundations of the most significant methods for the evaluation of investments in information systems (IS). However, in our view, it is also necessary to provide an updated view on the evaluation methods actually used in practice by organizations. This paper offers a view on the investment in IS in business practice in Spain, analysing: (i) the evaluation methods currently used by Spanish firms; (ii) how companies make decisions concerning investments in IS; (iii) the nature and quality of the decisions; and (iv) the consequences derived from them.     

Detection of hidden structures in nonstationary spike trains

We propose an algorithm for simultaneously estimating state transitions among neural states and nonstationary firing rates using a switching state-space model (SSSM). This algorithm enables us to detect state transitions on the basis of not only discontinuous changes in mean firing rates but also discontinuous changes in the temporal profiles of firing rates (e.g., temporal correlation). We construct estimation and learning algorithms for a nongaussian SSSM, whose nongaussian property is caused by binary spike events. Local variational methods can transform the binary observation process into a quadratic form. The transformed observation process enables us to construct a variational Bayes algorithm that can determine the number of neural states based on automatic relevance determination. Additionally, our algorithm can estimate model parameters from single-trial data using a priori knowledge about state transitions and firing rates. Synthetic data analysis reveals that our algorithm has higher performance for estimating nonstationary firing rates than previous methods. The analysis also confirms that our algorithm can detect state transitions on the basis of discontinuous changes in temporal correlation, which are transitions that previous hidden Markov models could not detect. We also analyze neural data recorded from the medial temporal area. The statistically detected neural states probably coincide with transient and sustained states that have been detected heuristically. Estimated parameters suggest that our algorithm detects the state transitions on the basis of discontinuous changes in the temporal correlation of firing rates. These results suggest that our algorithm is advantageous in real-data analysis.     

Kalman-Mesarovic nonstationary realization in terms of Rayleigh-Ritz operator

The paper outlines the methodology underlying the Kalman-Mesarovic realization for dynamic systems with equations of state in the class of linear nonstationary ordinary differential equations. In this sense, the key approaches to solving problems of realization theory in terms of the Rayleigh-Ritz operator are defined. The study was sponsored by the Russian Fund for Basic Research (Grant No. 05-01-00623), Program of Basic Research No. 22 of the Presidium of the Russian Academy of Science, the Grant of the President of the Russian Federation on the State Support of Scientific Schools of the Russian Federation (Grant No. NSh-9508.2006.1). __________ Translated from Kibernetika i Sistemnyi Analiz, No. 1, pp. 82–90, January–February 2007.     

Information technology and the accountant: a case study

The factors leading to the success or failure of IT implementations by accountants are investigated in a health service situation. Tbe case study adopts the participant observation methodology and the factors observed are classified as motivators and demotivators. Using a project dependent definition of success, a catastrophe theory framework is used to describe the outcome of the combined influence of these factors. Four particular IT projects are discussed and the factors observed include: the influence of higher levels in the organizational hierarchy, some systems characteristics, the seduction of technology, external pressures, the 'rate of change', skills shortage and reliability. By comparing the examples described with features mentioned by other authors, it is noted that some factors appear to be generally applicable, whereas others may be context dependent.     

On the spectral factorization of nonstationary vector random processes

Conditions which depend on the covariance of a vector random process, sufficient to ensure the process can be generated by a linear, invertible system of finite order driven by white noise are derived, and equations which determine the parameters of the system are found. Some structural properties of lumped covariances are given; these stress the close relation between the structure of linear systems and that of lumped covariances and provide a means of establishing the minimal order of generating systems.     

Truncation error for band-limited nonstationary processes

Let x(t) be a real-valued random process band-limited to the interval [$\text{?1}\text{2T}\text{,}\text{1}\text{2T}$] for some T > 0. In this note we find an upper bound on the mean square of the truncation error involved when x(t) is approximated in the interval $\text{|t| ?}\text{T}\text{2}$ by the finite selection

$\text{∑}\text{n=?N}{}_1\text{N}{}_2\text{x(nt)}\text{sin}\text{π(t?nT)}\text{T}\text{π(t?nT)}\text{T}$

of terms from its sampling expansion representation.