Novel Parameter Identification by Using a High-Gain Observer With Application to a Gas Turbine Engine
In this study, a novel identification technique, that is high-gain observer-based identification approach, is proposed for systems with bounded process and measurement noises. For system parameters with abnormal changes, an adaptive change detection and parameter identification algorithm is next presented. The presented technique and algorithm are finally applied to the parameter identification of the gas turbine engine by using the recorded input data from the engine test-bed. The identified parameters and the response curves are desirable. The simulations have proved the effectiveness of the proposed procedure compared with the previous identification approach.

title: Advances in stochastic distribution control
abstract: Stochastic distribution control systems aims at the controller design so as to realize a shape control of the distributions of certain random variables in the process. Once the probability density functions (PDFs) of these variables are used to describe their distributions, the control task is to obtain control signals so that the output PFDs of the system are made to follow their target PDFs. In this paper, a survey of the recent developments on the research of stochastic distribution control systems will be made.

title: Distribution function tracking filter design using hybrid characteristic functions
abstract: A new tracking filtering algorithm for a class of multivariate dynamic stochastic systems is presented. The system is expressed by a set of time-varying discrete systems with non-Gaussian stochastic input and nonlinear output. A new concept, such as hybrid characteristic function, is introduced to describe the stochastic nature of the dynamic conditional estimation errors, where the key idea is to ensure the distribution of the conditional estimation error to follow a target distribution. For this purpose, the relationships between the hybrid characteristic functions of the multivariate stochastic input and the outputs, and the properties of the hybrid characteristic function, are established. A new performance index of the tracking filter is then constructed based on the form of the hybrid characteristic function of the conditional estimation error. An analytical solution, which guarantees the filter gain matrix to be an optimal one, is then obtained. A simulation case study is included to show the effectiveness of the proposed algorithm, and encouraging results have been obtained.

title: Reliable Observer-Based Control Against Sensor Failures for Systems With Time Delays in Both State and Input
abstract: For systems with both state and input time delays, a novel state and sensor fault observer is proposed in this paper to estimate system states and sensor faults simultaneously. In this design, a descriptor system approach and a linear matrix inequality technique are adopted, where the considered sensor fault may be in any form, even unbounded. Unbounded sensor faults will make the system fail unavoidably; it is indispensable to derive a reliable control scheme against sensor failures. Using the estimated state and sensor fault, a reliable observer-based controller is proposed, which makes the system work well no matter whether sensor faults occur or not. The present approaches are next extended to the case for systems with multiple time delays. Finally, a simulation example of the network of three cascaded reactors is used to illustrate the design procedure and demonstrate the efficiency of the present techniques.

title: Robust minimum entropy tracking control with guaranteed stability for nonlinear stochastic systems under modeling errors
abstract: In this paper, robust minimum entropy tracking control problem is considered for nonlinear stochastic systems. The controlled systems are described by nonlinear non-Gaussian difference equations with the un-modeled uncertainty and modeling error, as well as time delays. Entropy is adopted to characterize the uncertainty of the tracking error. The nonlinear multi-step-ahead predictive cost function is used and the relationship between the probability density functions of the input and the tracking error via the uncertain mapping is established. With these formulations, the cost function can be bounded as a nonlinear functional of the control input and the known bounds of the errors. Explicit design algorithms are presented for the robust suboptimal controller and further for the stabilization controllers. The Renyi's entropy has also been used to simplify the cost function. Simulations are given to demonstrate the effectiveness of the proposed control algorithm.

title: Descriptor observer approaches for multivariable systems with measurement noises and application in fault detection and diagnosis
abstract: In this paper, new descriptor observer design approaches are presented for multivariable systems with measurement noises. If the system matrix is stable, the proposed modified proportional and derivative observer technique allows us not only to decouple the measurement noise in any forms completely, but also obtain accurate estimations of both system states and measurement noises. If the system is detectable and the noise is bounded, it has been shown that two design parameters can provide more degrees of freedom. In this context, the derivative gain parameter is chosen to reduce the amplification of the noise, and the proportional gain is then selected to guarantee the stability of the error dynamics. For systems with both measurement output noises and input disturbances, a modified proportional, derivative and integral observer is constructed to asymptotically estimate states, input disturbances, and output noises at the same time. The applications of the proposed observers for systems with actuator and sensor faults are also discussed. Numerical examples are given to illustrate the design procedures, and the simulation results show the satisfactory tracking performance.

title: Real-time pose estimation for outdoor mobile robots using range data
abstract: Pose estimation is a key issue in mobile robots and important to moving object tracking. In this paper, we present a tangent based hybrid pose estimation algorithm with real-time performance using range data from laser radar, which consists of Iterative Tangent weighted Closest Point (ITCP) and Hough transform based Tangent Angle Histogram (HTAH) algorithms to overcome problems with past methods, such as local minimum, aperture-like, and high computation problems. The algorithm has been tested on both synthetic and real range data in outdoor environments. Experimental results demonstrate its high accuracy, low computation, wide applicability and high robustness to aperture-like problems, occlusion and noise.

title: Best of both latency and throughput
abstract: This paper describes the tradeoff between latency performance and throughput performance in a power-constrained environment. We show that the key to achieving both excellent latency performance as well as excellent throughput performance is to dynamically vary the amount of energy expended to process instructions according to the amount of parallelism available in the software. We survey four techniques for achieving variable energy per instruction: voltage/frequency scaling, asymmetric cores, variable-size cores, and speculation control. We estimate the potential range of energies obtainable by each technique and conclude that a combination of asymmetric cores and voltage/frequency scaling offers the most promising approach to design a chip-level multiprocessor that can achieve both excellent latency performance and excellent throughput performance.

title: A Performance Study of Secure Data Mining on the Cell Processor
abstract: This paper examines the potential of the Cell processor as a platform for secure data mining on the future volunteer computing systems. Volunteer computing platforms have the potential to provide massive computing power. However, privacy and security concerns prevent using volunteer computing for data mining of sensitive data. The Cell processor comes with a hardware security feature. The secure volunteer data mining can be achieved by using this hardware security feature. In this paper, we present a general security scheme for the volunteer computing, and a secure parallelized K-Means clustering algorithm for the Cell processor. We also evaluate the performance of the algorithm on the Cell secure system simulator. Evaluation results indicate that the proposed secure data clustering outperforms a non-secure clustering algorithm on the general purpose CPU, but incurs a huge performance overhead introduced by the decryption process of the Cell security features.

title: Quantitative evaluation of the register stack engine and optimizations for future Itanium processors
abstract: This paper examines the efficiency of the register stack engine (RSE) in the canonical Itanium architecture, and introduces novel optimization techniques to enhance the RSE performance. To minimize spills and fills of the physical register file, optimizations are applied to reduce internal fragmentation in statically allocated register stack frames. Through the use of dynamic register usage (DRU) and dead register value information (DVI), the processor can dynamically guide allocation and deallocation of register frames. Consequently, a speculatively allocated register frame with a dynamically determined frame size can be much smaller than the statically determined frame size, thus achieving minimum spills and fills. Using the register stack engine (RSE) in the canonical Itanium architecture as the baseline reference, we thoroughly study and gauge the tradeoffs of the RSE and the proposed optimizations using a set of SPEC CPU2000 benchmarks built with different compiler optimizations. A combination of frame allocation policies using the most frequent frame size and deallocation policies using dead register information proves to be highly effective. On average, a 71% reduction in aggregate spills and fills can be achieved over the baseline reference.

title: A Scalable Pipelined Associative SIMD Array with Reconfigurable PE Interconnection Network for Embedded Applications
abstract: This paper describes the FPGA implementation of a specialized SIMD processor array for embedded applications. An alternative to traditional SoC or MPSoC architectures, this array combines the massive parallelism inherent in SIMD architectures with the search capabilities of associative computing, producing a SIMD Processor Array System on a Chip (PASoC) well suited for applications such as data mining and bioinformatics. This paper first describes the architecture of the system, and then the pipelining of the processing elements and the addition of a reconfigurable interconnection network. Finally, the paper concludes with a brief description of a string-matching algorithm that can be used for the applications cited above.