An LFT approach to parameter estimation

In this paper we consider a unified framework for parameter estimation problems. Under this framework, the unknown parameters appear in a linear fractional transformation (LFT). A key advantage of the LFT problem formulation is that it allows us to efficiently compute gradients, Hessians, and Gauss–Newton directions for general parameter estimation problems without resorting to inefficient finite-difference approximations. The generality of this approach also allows us to consider issues such as identifiability, persistence of excitation, and convergence for a large class of model structures under a single unified framework.     

Robust control of linear time-invariant plants using periodic compensation

This paper considers the use and design of linear periodic time-varying controllers for the feedback control of linear time-invariant discrete-time plants. We will show that for a large class of robustness problems, periodic compensators are superior to time-invariant ones. We will give explicit design techniques which can be easily implemented. In the context of periodic controllers, we also consider the strong and simultaneous stabilization problems. Finally, we show that for the problem of weighted sensitivity minimization for linear time-invariant plants, time-varying controllers offer no advantage over the time-invariant ones.     

The effect of ethical leadership on employee social innovation tendency in social enterprises: Mediating role of perceived social capital

Although social innovation has gained prominence in policy, academic, and practitioner debates, there remains a lack of understanding of how leader aspects such as behavior and ethics contribute to fostering social innovation among employees in the social enterprise, owing primarily to a dearth of empirical research on this subject. The goal of this study is to empirically examine the effect of ethical leadership on the social innovation tendency among employees in social enterprises; also, employees' perceived social capital is hypothesized as a mediator through which the ethical leader is likely to influence their social innovation tendency. Using data collected from 189 employees associated with a variety of social enterprises, the results of structural equation modeling indicate that ethical leadership has both a direct and indirect effect on the social innovation tendency among employees, where the indirect effect of ethical leadership involves influencing the employees' perceived social capital, which subsequently influences their tendency for social innovation. Accordingly, the study offers key implications for social enterprise leaders aspiring to boost innovation in their endeavors aimed at achieving the enterprise's objective of creating positive social impact.     

InAs/GaAs nanostructures grown on patterned Si(001) by molecular beam epitaxy

The potential benefit from the combination of the optoelectronic and electronic functionality of III-V semiconductors with silicon technology is one of the most desired outcomes to date. Here we have systematically investigated the optical properties of InAs quantum structure embedded in GaAs grown on patterned sub-micron and nanosize holes on Si(001). III-V material tends to accumulate in the patterned sub-micron holes and a material depletion region is observed around holes when GaAs/InAs/GaAs is deposited directly on patterned Si(001). By use of a 60?nm SiO(2) layer and patterning sub-micron and nanosize holes through the oxide layer to the substrate, we demonstrate that high optical quality InAs nanostructures, both quantum dots and quantum wells, formed by a two-monolayer InAs layer embedded in GaAs can be epitaxially grown on Si(001). We also report the power-dependent and temperature-dependent photoluminescence spectra of these structures. The results show that hole diameter (sub-micron versus nanosize) has a strong effect on the structural and optical properties of GaAs/InAs/GaAs nanostructures.     

Autonomous on-wafer sensors for process modeling, diagnosis, andcontrol

This paper explores the feasibility of constructing an autonomous sensor array on a standard silicon wafer. Such a sensor-wafer would include integrated electronics, power, and communications, and would be capable of being placed into a standard production process step, or short sequence of steps. During the processing of the sensor-wafer, various process parameters would be measured and recorded. There are several uses for such a sensor wafer, including equipment characterization and design, process calibration, and equipment qualification and diagnosis. In this paper, various sensor architectures, power supplies, communications methods, and isolation techniques are discussed, and particular choices are made. Several proof-of-concept designs that measure film-thickness and temperature are discussed, and test results are reviewed for each design     

Kalman filtering with intermittent observations

Motivated by navigation and tracking applications within sensor networks, we consider the problem of performing Kalman filtering with intermittent observations. When data travel along unreliable communication channels in a large, wireless, multihop sensor network, the effect of communication delays and loss of information in the control loop cannot be neglected. We address this problem starting from the discrete Kalman filtering formulation, and modeling the arrival of the observation as a random process. We study the statistical convergence properties of the estimation error covariance, showing the existence of a critical value for the arrival rate of the observations, beyond which a transition to an unbounded state error covariance occurs. We also give upper and lower bounds on this expected state error covariance.     

Threshold modeling of autonomic control of heart rate variability

Even in the absence of external perturbation to the human cardiovascular system, measures of cardiac function, such as heart rate, vary with time in normal physiology. The primary source of the variation is constant regulation by a complex control system which modulates cardiac function through the autonomic nervous system. Here, we present methods of characterizing the statistical properties of the underlying processes that result in variations in ECG R-wave event times within the framework of an integrate-and-fire model. We first present techniques for characterizing the noise processes that result in heart rate variability even in the absence of autonomic input. A relationship is derived that relates the spectrum of R-R intervals to the spectrum of the underlying noise process. We then develop a technique for the characterization of the dynamic nature of autonomically related variability resulting from exogenous inputs, such as respiratory-related modulation. A method is presented for the estimation of the transfer function that relates the respiratory-related input to the variations in R-wave event times. The result is a very direct analysis of autonomic control of heart rate variability through noninvasive measures, which provides a method for assessing autonomic function in normal and pathological states.     

Fractional representations for systems over a P.I.D.: A constructive technique

In this note we present a constructive technique for obtaining polynomial matrix fraction representations for reachable linear systems over a principal ideal domain. An example is given to illustrate the algorithm.     

Time-domain validation for sample-data uncertainty models

We consider time-domain model validation for sampled-data systems with H_∞-compatible uncertainty models. These uncertainty models consist of a nominal continuous-time plant model together with given bounds for system uncertainty and signal disturbances. The validation problem is to determine whether or not a given discrete sampled input-output data record is consistent with a postulated uncertainty model. Based on continuous-time interpolation theorems, we provide validation algorithms for unstructured, additive uncertainty models that reduce to convex programming. We treat both linear time-invariant and linear time-varying modeling uncertainty sets