Descent algorithms for optimal periodic output feedback control

Periodic output feedback is investigated in the context of linear-quadratic regulation for finite-dimensional time-invariant linear systems. Discrete output samples are multiplied by a periodic gain function to generate a continuous feedback control. The optimal solution is obtained in two steps by separating the continuous-time from the discrete-time structure. First, the optimal pole placement problem under periodic output feedback is solved explicitly under the assumption that the behavior at the sample times has been specified in terms of a gain matrix G. Then the minimum value, which depends on G, is substituted into the overall objective. This results in a finite-dimensional nonlinear programming problem over all admissible gain matrices G. The solution defines the optimal periodic output feedback control via the formulas of the optimal pole placement problem. A steepest descent and a direct iterative method for solving this problem are formulated and compared. Numerical examples show that the performance using periodic output feedback is almost equivalent to that using optimal continuous-state feedback     

Quadratic cost output feedback control for bilinear systems

This paper considers output feedback control problems for bilinear systems with a quadratic cost function and develops a robust control theory approach. A key point of the approach is to regard once the bilinear term as a term with unknown parameters and then use some robust control synthesis methods. This approach derives two types of nonlinear output feedback controller that guarantee an upper bound for the cost function. Efficiencies of the proposed approach are also demonstrated through a numerical example.     

The use of configurable computing for computational kernels in scientific simulations

In many scientific simulation codes, the bulk of the floating-point arithmetic required is done by a small number of compact computational kernels. In this paper, we explore the potential use of configurable computers to instantiate the hardware required for such kernels and, thus, improve their performance. We present algorithms and analysis for two such kernels: fast, problem-specific multipliers and the efficient evaluation of Taylor series. A novel aspect of the algorithm for Taylor series evaluation is that it takes advantage of the variable precision arithmetic available to a configurable computer. Experimental results obtained on a Xilinx field-programmable gate array (FPGA) are presented for the proposed algorithms.     

Pole placement for linear discrete-time systems by periodic output feedbacks

Necessary and sufficient conditions for the existence of a solution to the pole placement problem by periodic output feedbacks for single-input single-output linear discrete-time systems are established. An algorithm for finding a sequence of output-feedback gains is presented and illustrated by a simple numerical example.     

A high performance scientific cloud computing environment for materials simulations

We describe the development of a scientific cloud computing (SCC) platform that offers high performance computation capability. The platform consists of a scientific virtual machine prototype containing a UNIX operating system and several materials science codes, together with essential interface tools (an SCC toolset) that offers functionality comparable to local compute clusters. In particular, our SCC toolset provides automatic creation of virtual clusters for parallel computing, including tools for execution and monitoring performance, as well as efficient I/O utilities that enable seamless connections to and from the cloud. Our SCC platform is optimized for the Amazon Elastic Compute Cloud (EC2). We present benchmarks for prototypical scientific applications and demonstrate performance comparable to local compute clusters. To facilitate code execution and provide user-friendly access, we have also integrated cloud computing capability in a JAVA-based GUI. Our SCC platform may be an alternative to traditional HPC resources for materials science or quantum chemistry applications.     

Pole assignment for linear time-invariant systems by periodic memoryless output feedback

It is well known that the poles of a linear time-invariant controllable and observable system can be assigned arbitrarily by state feedback. When only the output is available, pole assignment is still possible by means of dynamic output feedback. In this paper the potential of time-varying memoryless output feedback is considered. It is shown that, up to some technical conditions, it is indeed possible to allocate the poles of a linear time-invariant discrete-time system by memoryless output feedback with periodic gains. The period of the gains is (n + 1) with n the order of the system. The power of the design technique is proved to be comparable to what can be achieved by the classical dynamic feedback approach.     

Receding horizon output feedback control for constrained uncertain systems using periodic invariance

In this article, we consider a receding horizon output feedback control (RHOC) method for linear discrete-time systems with polytopic model uncertainties and input constraints. First, we derive a set of estimator gains and then we obtain, on the basis of the periodic invariance, a series of state feedback gains stabilising the augmented output feedback system with these estimator gains. These procedures are formulated as linear matrix inequalities. An RHOC strategy is proposed based on these state feedback and state estimator gains in conjunction with their corresponding periodically invariant sets. The proposed RHOC strategy enhances the performance in comparison with the case in which static periodic gains are used, and increases the size of the stabilisable region by introducing a degree of freedom to steer the augmented state into periodically invariant sets.     

Reversible data hiding in JPEG images based on zero coefficients and distortion cost function

Multimedia Tools and Applications - Recently, reversible data hiding (RDH) in joint photographic experts group (JPEG) images has received a great deal of attention since the JPEG image is one of...     

On a general optimal algorithm for multirate output feedbackcontrollers for linear stochastic periodic systems

A modified optimal algorithm for multirate output feedback controllers of linear stochastic periodic systems is developed. By combining the discrete-time linear quadratic regulation (LQR) control problem and the discrete-time stochastic linear quadratic regulation (SLQR) control problem to obtain an extended linear quadratic regulation (ELQR) control problem, one derives a general optimal algorithm to balance the advantages of the optimal transient response of the LQR control problem and the optimal steady-state regulation of the SLQR control problem. In general, the solution of this algorithm is obtained by solving a set of coupled matrix equations. Special cases for which the coupled matrix equations can be reduced to a discrete-time algebraic Riccati equation are discussed. A reducable case is the optimal algorithm derived by H.M. Al-Rahmani and G.F. Franklin (1990), where the system has complete state information and the discrete-time quadratic performance index is transformed from a continuous-time one     

On-demand minimum cost benchmarking for intermediate dataset storage in scientific cloud workflow systems

Many scientific workflows are data intensive: large volumes of intermediate datasets are generated during their execution. Some valuable intermediate datasets need to be stored for sharing or reuse. Traditionally, they are selectively stored according to the system storage capacity, determined manually. As doing science on clouds has become popular nowadays, more intermediate datasets in scientific cloud workflows can be stored by different storage strategies based on a pay-as-you-go model. In this paper, we build an intermediate data dependency graph (IDG) from the data provenances in scientific workflows. With the IDG, deleted intermediate datasets can be regenerated, and as such we develop a novel algorithm that can find a minimum cost storage strategy for the intermediate datasets in scientific cloud workflow systems. The strategy achieves the best trade-off of computation cost and storage cost by automatically storing the most appropriate intermediate datasets in the cloud storage. This strategy can be utilised on demand as a minimum cost benchmark for all other intermediate dataset storage strategies in the cloud. We utilise Amazon clouds’ cost model and apply the algorithm to general random as well as specific astrophysics pulsar searching scientific workflows for evaluation. The results show that benchmarking effectively demonstrates the cost effectiveness over other representative storage strategies.     

Minimization of the life cycle cost for a multistate system under periodic maintenance

Although preventive maintenance policies have received extensive interest, limited attention has been paid to implementing multiple maintenance actions for a multistate system over a finite horizon in continuous time. Therefore, this study closely examines such maintenance actions by viewing a coherent multicomponent system as a multistate system and assuming that the necessary maintenance action and cost which move the current state to an extremely better state depends on the current state. The above problem is also formulated as a periodic maintenance model. In addition, the model's characteristics are elucidated to obtain the optimal cycle time of maintenance actions, thereby minimizing the life cycle cost over a specific finite horizon.     

Robust stabilization and guaranteed cost control for discrete-time linear systems by static output feedback

This paper proposes a systematic approach for the static output feedback control design for discrete-time uncertain linear systems. It is shown that if the open-loop system satisfies some particular structural conditions and the uncertainty has a specific structure, a static output feedback gain can be calculated easily, using a formula only involving the original system matrices. Among the conditions the system has to satisfy, the strongest one relies on a minimum phase argument. Square and nonsquare systems are considered. The performance problem through a quadratic criterion is also discussed (guaranteed cost control).     

Easy Java Simulations: a software tool to create scientific simulations in Java

We introduce Easy Java Simulations, Ejs, a tool created by science teachers to help teach and learn science. Ejs allows users to create simulations using their knowledge of the scientific model. The author needs to supply a small amount of code for the model; the tool provides a graphical drag-and-drop interface to build the program. The resulting Ejs generated program is an independent, high quality Java application or applet ready to be published on a Web server. Ejs can serve as an effective teaching and learning tool if used in an appropriate pedagogical setting; for instance, to help students create their own simulations in order to express their conceptions on how a given scientific process works.The Ejs program and documentation can be freely downloaded from the site http://fem.um.es/Ejs.     

Data hiding in binary image for authentication and annotation

This paper proposes a new method to embed data in binary images, including scanned text, figures, and signatures. The method manipulates "flippable" pixels to enforce specific block-based relationship in order to embed a significant amount of data without causing noticeable artifacts. Shuffling is applied before embedding to equalize the uneven embedding capacity from region to region. The hidden data can be extracted without using the original image, and can also be accurately extracted after high quality printing and scanning with the help of a few registration marks. The proposed data embedding method can be used to detect unauthorized use of a digitized signature, and annotate or authenticate binary documents. The paper also presents analysis and discussions on robustness and security issues.     

Input–output feedback linearization for nonminimum phase nonlinear systems through periodic use of synthetic outputs

Input–output feedback linearization provides a convenient means of extending linear control strategies such as output zeroing or pole placement to the case of nonlinear affine in the input systems, but such extensions cannot be applied in the presence of nonminimum phase characteristics. This paper overcomes this difficulty through the periodic use of a finite number of synthetic outputs which are so constructed as to define embedded dynamics with stable zero dynamics. The efficacy of the method is demonstrated by means of a numerical example.     

Asymptotic rejection of finite frequency modes of general periodic disturbances in output feedback nonlinear systems

This paper deals with asymptotic rejection of individual frequency modes in nonlinear systems under general periodic disturbances. An iterative observer design is proposed for disturbance estimation, and it is shown that the proposed observer design successfully extracts individual frequency modes from general periodic disturbances. The proposed iterative observer design is then applied to asymptotic rejection of nonlinear systems under general periodic disturbances.     

Recursive code construction for reversible data hiding in DCT domain

Multimedia Tools and Applications - Reversible data hiding has extensive applications in fields like data authentication, medical data management and error concealment. In this paper, we formulate...     

Genetic algorithms for optimality of data hiding in digital images

This paper investigates the scope of usage of Genetic Algorithms (GA) for data hiding in digital images. The tool has been explored in this topic of research to achieve an optimal solution in multidimensional nonlinear problem of conflicting nature that exists among imperceptibility, robustness, security and payload capacity. Two spatial domain data hiding methods are proposed where GA is used separately for (i) improvement in detection and (ii) optimal imperceptibility of hidden data in digital images respectively. In the first method, GA is used to achieve a set of parameter values (used as Key) to represent optimally the derived watermark in the form of approximate difference signal used for embedding. In the second method, GA is used for finding out values of parameters, namely reference amplitude (A) and modulation index (μ) both with linear and non linear transformation functions, for achieving the optimal data imperceptibility. Results on robustness for both the methods against linear, non linear filtering, noise addition, and lossy compression as well as statistical invisibility of the hidden data are reported here for some benchmark images.