Dual-mode structure digital repetitive control

A flexible repetitive control (RC) scheme named “dual-mode structure repetitive control” (DMRC) is presented in this article. A robust stability criterion for DMRC systems is derived in terms of two parameters: odd-harmonic RC gain and even-harmonic RC gain. Several useful corollaries for the stability are addressed to reveal the compatibility of DMRC. The general framework of DMRC offers the flexibility in the development of various RC controllers. Without additional complexity and loss of tracking accuracy, DMRC can achieve faster error convergence rate than conventional RCs. DMRC requires the same data memory size as that of conventional RC one. An application example of DMRC controlled PWM inverter illustrates the validity of our proposed DMRC scheme. Comparisons of DMRC, conventional RC and odd-harmonic RC highlight the advantages of the presented DMRC approach.     

Correlation-based tuning of decoupling multivariable controllers

The iterative method labelled correlation-based tuning (CbT) is considered in this paper for tuning linear time-invariant multivariable controllers. The approach allows one to tune some elements of the controller transfer function matrix to satisfy the desired closed-loop performance, while the other elements are tuned to mutually decouple the closed-loop outputs. Decoupling is achieved by decorrelating a given reference with the non-corresponding outputs. The controller parameters are calculated either by solving a correlation equation (decorrelation procedure) or by minimizing a cross-correlation function (correlation reduction). In addition, the preferred way of exciting a 2×2 system for CbT is investigated via the accuracy of the estimated controller parameters. It is shown that simultaneous excitation of both reference signals does not improve the accuracy of the estimated controller parameters compared to the case of sequential excitation. In fact, one must choose between low experimental cost (simultaneous excitation) and better accuracy of the estimated parameters (sequential excitation). The theoretical results are illustrated via three simulation studies.     

A survey of skin-color modeling and detection methods

Skin detection plays an important role in a wide range of image processing applications ranging from face detection, face tracking, gesture analysis, content-based image retrieval systems and to various human computer interaction domains. Recently, skin detection methodologies based on skin-color information as a cue has gained much attention as skin-color provides computationally effective yet, robust information against rotations, scaling and partial occlusions. Skin detection using color information can be a challenging task as the skin appearance in images is affected by various factors such as illumination, background, camera characteristics, and ethnicity. Numerous techniques are presented in literature for skin detection using color. In this paper, we provide a critical up-to-date review of the various skin modeling and classification strategies based on color information in the visual spectrum. The review is divided into three different categories: first, we present the various color spaces used for skin modeling and detection. Second, we present different skin modeling and classification approaches. However, many of these works are limited in performance due to real-world conditions such as illumination and viewing conditions. To cope up with the rapidly changing illumination conditions, illumination adaptation techniques are applied along with skin-color detection. Third, we present various approaches that use skin-color constancy and dynamic adaptation techniques to improve the skin detection performance in dynamically changing illumination and environmental conditions. Wherever available, we also indicate the various factors under which the skin detection techniques perform well.     

“Wanted: Some Black Long Distance [Writers]”: Blackboard Flava-Flavin and other AfroDigital experiences in the classroom

This essay examines how students of African descent at a predominantly black college on the East Coast digitally perform their ethnic identities and rhetorics in a freshman composition course. The essay begins by showing how multiple uses of signifying frame students’ Blackboard discussions where they use a type of trickster motif to enact their agreements, disagreements, challenges, and questions, very much akin to Flava Flav's initial cultural role as part of the Rap/activist group, Public Enemy. Students’ online writing groups are then examined by focusing on one particular group, the “Black Long Distance Writers,” whose title signifies and signals the work of the African American writer and activist, John Oliver Killens, most notably, his seminal 1973 essay, “Wanted: Some Black Long Distance Runners.” The understandings of these “Black Long Distance Writers” bear the most powerful definition of literacy and computer-based writing instruction because their framework is not contingent upon making digitally divided minorities more technologically advanced and better at one type of English, its culture of power, or its academic discourses. Instead, these students experience rhetoric and writing as a way to alter the ways that knowledge is constructed for them and about them, “revocabularizing” the academy and its technologies. Such freshman writers are re-envisioned in this kind of cyberspace as constructors of and co-participants in black intellectual and rhetorical traditions … now AfroDigitized.     

Constraints propagation techniques on intervals for a guaranteed localization using redundant data

In order to estimate continuously the dynamic location of a car, dead reckoning and absolute sensors are usually merged. The models used for this fusion are non-linear and, therefore, classical tools (such as Bayesian estimation) cannot provide a guaranteed estimation. In some applications, integrity is essential and the ability to guaranty the result is a crucial point. There are bounded-error approaches that are insensitive to non-linearity. In this context, the random errors are only modeled by their maximum bounds. This paper presents a new technique to merge the data of redundant sensors with a guaranteed result based on constraints propagation techniques on real intervals. We have thus developed an approach for the fusion of the two ABS wheel encoders of the rear wheels of a car, a fiber optic gyro and a differential GPS receiver in order to estimate the absolute location of a car. Experimental results show that the precision that one can obtain is acceptable, with a guaranteed result, in comparison with an extended Kalman filter. Moreover, constraints propagation techniques are well adapted to a real-time context.     

A spectral approach to learning structural variations in graphs

This paper shows how to construct a linear deformable model for graph structure by performing principal components analysis (PCA) on the vectorised adjacency matrix. We commence by using correspondence information to place the nodes of each of a set of graphs in a standard reference order. Using the correspondences order, we convert the adjacency matrices to long-vectors and compute the long-vector covariance matrix. By projecting the vectorised adjacency matrices onto the leading eigenvectors of the covariance matrix, we embed the graphs in a pattern-space. We illustrate the utility of the resulting method for shape-analysis.     

Complexity, class dynamics, and distance learning

Classroom participants learn early on that each classroom has its own dynamic comprised of personalities, motivation levels, skills, and other variables. This paper explores features of complexity theory—nonlinearity and emergent self-organization—relevant to dynamics in physical or virtual classrooms. These central notions of complexity theory and their importance in composition classrooms help explain why students in virtual classrooms are often less successful than their physical classroom counterparts in negotiating the eddies of virtual interactions. The paper closes with a brief consideration of how teachers can interrogate all the elements of teaching and classroom context (whether physical or virtual) to influence the emergent dynamic of our classrooms.     

Generalized adaptive notch filters with frequency debiasing for tracking of polynomial phase systems

Generalized adaptive notch filters are used for identification/tracking of quasi-periodically varying dynamic systems and can be considered an extension, to the system case, of classical adaptive notch filters. For general patterns of frequency variation the generalized adaptive notch filtering algorithms yield biased frequency estimates. We show that when system frequencies change slowly in a smooth way, the estimation bias can be substantially reduced by means of post-filtering of the frequency estimates. The modified (debiased) algorithm has better tracking capabilities than the original algorithm.     

Global adaptive output regulation of a class of nonlinear systems with nonlinear exosystems

This paper deals with global output regulation with nonlinear exosystems for a class of uncertain nonlinear output feedback systems. The circle criterion is exploited for the internal model design to accommodate the nonlinearities in the exosystems, and the explicit conditions are given for the exosystems such that the proposed internal model design can be applied. The uncertainties of the output feedback systems are in the form of unknown constant parameters, and adaptive control techniques are used to ensure the global stability of the proposed control design for output regulation.