An asymptotically efficient ARMA estimator based on sample covariances

An asymptotically efficient autoregressive moving-average (ARMA) spectral estimator is presented, based on the sample covariances of observed time series. The estimate of the autoregressive (AR) part is shown to be identical to the optimal instrumental variable (IV) estimator in [7] although derived here using a different approach. The moving-average (MA) spectral parameter estimate is new.     

MODE-type algorithm for estimating damped, undamped, or explosive modes

We propose a new algorithm for estimating the parameters of damped, undamped, or explosive sinusoidal signals. The algorithm resembles the MODE algorithm, which is commonly used for direction of arrival estimation in the array signal processing field. It is derived as a natural approximation to an asymptotically (high-SNR) optimal parameter estimator and has excellent statistical accuracy. Nevertheless, it is computationally simple and easy to implement. Numerical examples are included to illustrate the performance of the proposed method.This work has been supported by the Swedish Research Council for Engineering Sciences (TFR).     

Hidden-Action in Network Routing

In communication networks, such as the Internet or mobile ad-hoc networks, the actions taken by intermediate nodes or links are typically hidden from the communicating endpoints; all the endpoints can observe is whether or not the end-to-end transmission was successful. Therefore, in the absence of incentives to the contrary, rational (i.e., selfish) intermediaries may choose to forward messages at a low priority or simply not forward messages at all. Using a principal-agent model, we show how the hidden-action problem can be overcome through appropriate design of contracts in both the direct (the endpoints contract with each individual router directly) and the recursive (each router contracts with the next downstream router) cases. We further show that, depending on the network topology, per-hop or per-path monitoring may not necessarily improve the utility of the principal or the social welfare of the system.     

Complex amplitude estimation in the known steering matrix and generalized waveform case

We consider a special growth-curve (SGC) model with a known steering matrix and generalized waveform in the presence of unknown interference and noise. Several estimators of the complex amplitude based on this model are derived, including the methods of approximate maximum likelihood (AML), minimum variance distortionless response (MVDR), and amplitude and phase estimation (APES). We analyze the statistical properties of these estimators and show that in the presence of temporally white but spatially correlated noise and interference, AML is asymptotically statistically efficient for a large snapshot number while MVDR and APES are asymptotically equivalent but not statistically efficient. Via several numerical examples, we also show that when the noise and interference are both spatially and temporally correlated, the APES estimator can achieve better estimation accuracy and exhibit greater robustness than the other methods.     

Orthogonal space-time block codes: maximum likelihood detection for unknown channels and unstructured interferences

Space-time coding (STC) schemes for communication systems employing multiple transmit and receive antennas have been attracting increased attention. The so-called orthogonal space-time block codes (OSTBCs) have been of particular interest due to their good performance and low decoding complexity. In this paper, we take a systematic maximum-likelihood (ML) approach to the decoding of OSTBC for unknown propagation channels and unknown noise and interference conditions. We derive a low-complexity ML decoding algorithm based on cyclic minimization and assisted by a minimum amount of training data. Furthermore, we discuss the design of optimal training sequences and optimal information transfer to an outer decoder. Numerical examples demonstrate the performance of our algorithm.     

On LP-MUSIC

Studies the consistency properties of a method recently proposed for temporal or spatial frequency estimation from noisy data. The method in question is a MUSIC technique that makes use of a linear prediction algorithm to determine the signal subspace. It is shown that the signal subspace determined by the subject linear prediction-MUSIC (LP-MUSIC) algorithm can collapse in certain scenarios and. Hence, that the LP-MUSIC frequency estimates are not always consistent. The difficulties LP-MUSIC may encounter in some cases are illustrated by means of numerical examples     

Robust‐Adaptive Control Strategies for a Time Delay Bioelectrochemical Process Using Interval Observers

The paper addresses the design and the analysis of adaptive and robust‐adaptive control strategies for a complex recycled wastewater treatment bioprocess. The design procedures are developed under the realistic assumptions that the bacterial growth rates are unknown and the influent flow rates are time‐varying and uncertain, but some lower and upper bounds of these uncertainties are known. The proposed control structures are achieved by combining a linearizing control law with an appropriately (asymptotic or interval based) state observer and with a parameter estimator used for on‐line estimation of unknown kinetics. These approaches are applied to a complex time delay bioprocess resulting from the association of a recycling bioreactor with an electrochemical reactor. Numerical simulations are performed in order to validate the proposed algorithms.     

On Forward–Backward MODE for Array Signal Processing

We apply the MODE (method of direction estimation) principle to the forward–backward (FB) covariance of the output vector of a sensor array to obtain what we call the FB-MODE procedure. The derivation of FB-MODE is an interesting exercise in matrix analysis, the outcome of which was somewhat unexpected: FB-MODE simply consists of applying the standard MODE approach to the eigenelements of the FB sample covariance matrix. By using an asymptotic expansion technique we also establish the surprising result that FB-MODE is outperformed, from a statistical standpoint, by the standard MODE applied to the forward-only sample covariance (F-MODE). We believe this to be an important result that shows that the FB approach, which proved quite useful for improving the performance of many suboptimal array processing methods, shouldnotbe used with a statistically optimal method such as F-MODE.     

A multi-institutional, multinational study of programming concepts using card sort data

Abstract: This paper presents a case study of the use of a repeated single‐criterion card sort with an unusually large, diverse participant group. The study, whose goal was to elicit novice programmers' knowledge of programming concepts, involved over 20 researchers from four continents and 276 participants drawn from 20 different institutions. In this paper we present the design of the study and the unexpected result that there were few discernible systematic differences in the population. The study was one of the activities of the National Science Foundation funded Bootstrapping Research in Computer Science Education project (2003).