Performance aspects of data broadcast in wireless networks with user retrials

The user retrial phenomenon and its significant impact on network performance in unicast wireless systems are known and relatively well studied in the literature. However, there have been no previous studies on the impact of the user retrial phenomenon on other types of wireless networks. The objective of this paper is to extend the analysis of the user retrial phenomenon to wireless systems which, in addition to unicast service, also support a data broadcast service. This objective is realized by defining several performance measures appropriate for the analysis of hybrid unicast-broadcast systems in the presence of users' retrials. Subsequently, we derive the exact mathematical expression for each of the measures. Based on these expressions, we prove the existence of a single broadcast scheduling scheme, which ensures optimal system performance, with respect to the given set of proposed measures, the system's throughput, and the grade and quality of service. We also take a closer look at the class of hybrid unicast-broadcast systems with autonomous estimation of data item popularities, and we elaborate on the major challenges associated with such systems. Finally, we evaluate our theoretical expressions through simulation, and we discuss their robustness with respect to moderate deviations in the underlying model.     

The Covariance Least-Squares Algorithm for Spectral Estimation of Processes of Short Data Length

A new method for generating the autoregressive (AR) process parameters for spectral estimation is introduced. The method fits AR models to the data optimally in the sense of minimizing the sum of squares of the error covariance function within the model prediction region, and is thus designated as the Covariance Least-Squares (CLS) algorithm. This minimization is shown to be identical with minimizing the weighted average one-step, linear prediction errors with adaptive weights corresponding to the energy of the data within the prediction region. The CLS algorithm is compared to the Least-Squares (LS) algorithm [1], [2] by simulation and asymptotic properties. It is shown that the CLS method combines all the desirable properties of the comparison algorithm with improved robustness in the presence of nonstationarity, namely, additive transients and envelope modulation. It is also shown that the CLS algorithm provides asymptotically unbiased AR parameters, a property also shared by the comparison LS algorithm.     

Control of Continuous-Time Linear Gaussian Systems Over Additive Gaussian Wireless Fading Channels: A Separation Principle

This note is concerned with the control of continuous-time linear Gaussian systems over additive white noise wireless fading channels subject to capacity constraints. Necessary and sufficient conditions are derived, for bounded asymptotic and asymptotic observability and stabilizability in the mean square sense, for controlling such systems. For the case of a noiseless time-invariant system controlled over a continuous-time additive white Gaussian noise channel, the sufficient condition for stabilizability and observability states that the capacity of the channel C must satisfy C > Sigma_{{i;Re(lambdai(A))ges0}} Re(lambda_i(A)), where A is the system matrix and lambda_i(A) denotes the eigenvalues of A. The necessary condition states that the channel capacity must satisfy C ges Sigma _{{i;Re(lambdai(A))ges0}} Re(lambda_i(A)). Further, it is shown that a separation principle holds between the design of the communication and the control subsystems, implying that the controller that would be optimal in the absence of the communication channel is also optimal for the problem of controlling the system over the communication channel.     

Maximum likelihood parameter estimation from incomplete data viathe sensitivity equations: the continuous-time case

This paper deals with maximum likelihood (ML) parameter estimation of continuous-time nonlinear partially observed stochastic systems, via the expectation maximization (EM) algorithm. It is shown that the EM algorithm can be executed efficiently, provided the unnormalized conditional density of nonlinear filtering is either explicitly solvable or numerically implemented. The methodology exploits the relationships between incomplete and complete data, log-likelihood and its gradient     

Two iterative image restoration algorithms with applications to nuclear medicine

Two methods for recovering an image that has been degraded while being processed are presented. The restoration problem is formulated as a constrained optimization problem in which a measure of smoothness based on the second derivatives of the restored image is maximized subject to the constraint that noise energy is equal to the energy in the difference between the distorted and blurred images. The approach is based on the Lagrange multiplier method. The first algorithm reduces the problem to the computation of few discrete Fourier transforms and allows control of the degree of sharpness and smoothness of the restored image. The second algorithm with weight matrices included allows the handling of edges and flat regions in the image in a pleasing manner for the human visual system. In this case the iterative conjugate gradient method is used in conjunction with the discrete Fourier transform to minimize the Lagrangian function. The application of these algorithms to nuclear medicine images is presented.     

Sensitivity studies on power transformer ferroresonance of a 400 kV double circuit

The ability to predict ferroresonance significantly relies on the accuracy of the transformer model and the power system's parameters. The accomplishment of a suitable simulation model allows the sensitivity studies to be performed to determine the degree of influence of various components and parameters of the ferroresonance phenomenon such as line length, point-on-wave switching and transformer core loss. The modelling work carried out in ATP (commercially available software) on a 1000 MVA 400/275/13 kV power transformer model is described and the simulation with field ferroresonance test recordings is verified. The maps that define the boundaries between safe and ferroresonant (fundamental, subharmonic, chaotic) regions as a function of system parameters are created through the sensitivity studies performed.     

Robust control of uncertain context-sensitive probabilistic Boolean networks

Uncertainty is an intrinsic phenomenon in control of gene regulatory networks (GRNs). The presence of uncertainty is related to impreciseness of GRN models due to: (1) Errors caused by imperfection of measurement devices and (2) Models' inability to fully capture a complex structure of the GRN. Consequently, there is a discrepancy between actual behaviour of the GRN and what is predicted by its mathematical model. This can result in false control signals, which can drive a cell to an undesirable state. To address the problem of control under uncertainties, a risk-sensitive control paradigm is proposed. Robustness is accomplished by minimisation of the mean exponential cost as opposed to, for instance, minimisation of the mean square cost by risk-neutral controllers. The authors derive an optimal risk-sensitive controller when a GRN is modelled by a context-sensitive probabilistic Boolean network (CSPBN). By using a relation between the relative entropy and free-energy, a relative stability of the cost achieved by the risk-sensitive controller is demonstrated when the distribution of the CSPBN attractors is perturbed, as opposed to the cost of the risk-neutral controller that exhibits increase. The use of the relation between the relative entropy and free-energy to analyse the influence of a particular attractor on the robustness of the controller is studied. The efficiency of the risk-sensitive controller is tested for the CSPBN obtained from the study of malignant melanoma.     

A Simulation Tool to Predict the Impact of Soil Topologies on Coupling Between a Light Rail System and Buried Third-Party Infrastructure

The production of stray currents by DC light rail systems leads to the corrosion of the supporting and third-party infrastructure in close proximity to the rail system. This paper simulates two parallel tracks that are occupied by two trains: one on each track. This type of modeling constitutes a case study that is utilized to investigate the effect of soil topologies on the corrosion performance of a floating DC light rail system focusing on the supporting and third-party infrastructure. The modeling technique used involves the accurate computation of the shunt and series parameters for use in a resistive-type model using a commercially available software package. The results demonstrate the importance that soil resistivity has on the corrosion risk to traction system and third-party infrastructure. Such information could ultimately be used to vary the level of stray current protection across a light rail system to ensure a consistent lifetime across the whole system.