Reliable Observer-Based Control Against Sensor Failures for Systems With Time Delays in Both State and Input
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: 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: 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: 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: MIMO Probability Density Function Control Using Simple LOG-MLP Neural Networks
abstract: This paper presents a new model for the control of multivariable output probability density function (PDF). Firstly, a Multi-Layer Perceptron (MLP) neural network is adopted to approximate the static output PDF of the MIMO systems. Nonlinear principal component analysis (NLPCA) is then used to reduce the order of the obtained static neural network model and the dynamics of the system is considered based on the lower-order model. After this, an integrated solution is provided to set up the system with lower-order dynamics for the purpose of stochastic distribution control. The controller design is then presented in detail. Finally, a simulation example is given to demonstrate the effectiveness of the method and encouraging results have been obtained.

title: Minimum entropy control of nonlinear ARMA systems over a communication network
abstract: In this paper, the entropy concept has been utilized to characterize the uncertainty of the tracking error for nonlinear ARMA stochastic systems over a communication network, where time delays in the communication channels are of random nature. A recursive optimization solution has been developed. In addition, an alternative algorithm is also proposed based on the probability density function of the tracking error, which is estimated by a neural network. Finally, a simulation example is given to illustrate the efficiency and feasibility of the proposed approach.

title: EXOCHI: architecture and programming environment for a heterogeneous multi-core multithreaded system
abstract: Future mainstream microprocessors will likely integrate specialized accelerators, such as GPUs, onto a single die to achieve better performance and power efficiency. However, it remains a keen challenge to program such a heterogeneous multicore platform, since these specialized accelerators feature ISAs and functionality that are significantly different from the general purpose CPU cores. In this paper, we present EXOCHI: (1) Exoskeleton Sequencer(EXO), an architecture to represent heterogeneous acceleratorsas ISA-based MIMD architecture resources, and a shared virtual memory heterogeneous multithreaded program execution model that tightly couples specialized accelerator cores with generalpurpose CPU cores, and (2) C for Heterogeneous Integration(CHI), an integrated C/C++ programming environment that supports accelerator-specific inline assembly and domain-specific languages. The CHI compiler extends the OpenMP pragma for heterogeneous multithreading programming, and produces a single fat binary with code sections corresponding to different instruction sets. The runtime can judiciously spread parallel computation across the heterogeneous cores to optimize performance and power.
 We have prototyped the EXO architecture on a physical heterogeneous platform consisting of an Intel® Core™ 2 Duo processor and an 8-core 32-thread Intel® Graphics Media Accelerator X3000. In addition, we have implemented the CHI integrated programming environment with the Intel® C++ Compiler, runtime toolset, and debugger. On the EXO prototype system, we have enhanced a suite of production-quality media kernels for video and image processing to utilize the accelerator through the CHI programming interface, achieving significant speedup (1.41X to10.97X) over execution on the IA32 CPU alone.

title: Color-based road detection in urban traffic scenes
abstract: Road detection is a key issue for autonomous driving in urban traffic. In this paper, after a brief overview of existing methods, we present a road-area detection algorithm based on color images. This algorithm is composed of two modules: boundaries are first estimated based on the intensity image and road areas are subsequently detected based on the full color image. In the first module, an edge image of the scene is analyzed to obtain the candidates for the left and right road borders and to delimit the area that will subsequently be used to compute the mean and variance of the Gaussian distribution, assumed to be obeyed by the color components of road surfaces. The second module effectively extracts the road area and reinforces boundaries that most appropriately fit the road-extraction result. The combination of these modules can overcome basic problems due to inaccuracies in edge detection based on the intensity image alone and due to the computational complexity of segmentation algorithms based on color images. Experimental results on real road scenes have substantiated the effectiveness of the proposed method.

title: Hardware Support for Prescient Instruction Prefetch
abstract: This paper proposes and evaluates hardware mechanisms for supporting prescient instruction prefetch — an approach to improving single-threaded application performance by using helper threads to perform instruction prefetch. We demonstrate the need for enabling store-to-load communication and selective instruction execution when directly pre-executing future regions of an application that suffer I-cache misses. Two novel hardware mechanisms, safe-store and YAT-bits, are introduced that help satisfy these requirements. This paper also proposes and evaluates .nite state machine recall, a technique for limiting pre-execution to branches that are hard to predict by leveraging a counted I-prefetch mechanism. On a research Itanium®SMT processor with next line and streaming I-prefetch mechanisms that incurs latencies representative of next generation processors, prescient instruction prefetch can improve performance by an average of 10.0% to 22% on a set of SPEC 2000 benchmarks that suffer significant I-cache misses. Prescient instruction prefetch is found to be competitive against even the most aggressive research hardware instruction prefetch technique: fetch directed instruction prefetch.

title: Mitosis: A Speculative Multithreaded Processor Based on Precomputation Slices
abstract: This paper presents the Mitosis framework, which is a combined hardware-software approach to speculative multithreading, even in the presence of frequent dependences among threads. Speculative multithreading increases single-threaded application performance by exploiting thread-level parallelism speculatively, that is, executing code in parallel, even when the compiler or runtime system cannot guarantee that the parallelism exists. The proposed approach is based on predicting/computing thread input values via software through a piece of code that is added at the beginning of each thread (the precomputation slice). A precomputation slice is expected to compute the correct thread input values most of the time but not necessarily always. This allows aggressive optimization techniques to be applied to the slice to make it very short. This paper focuses on the microarchitecture that supports this execution model. The primary novelty of the microarchitecture is the hardware support for the execution and validation of precomputation slices. Additionally, this paper presents new architectures for the register file and the cache memory in order to support multiple versions of each variable and allow for efficient rollback in case of misspeculation. We show that the proposed microarchitecture, together with the compiler support, achieves an average speedup of 2.2 for applications that conventional nonspeculative approaches are not able to parallelize at all.

title: Partition of a directed bipartite graph into two directed cycles
abstract: Let D = (V1, V2; A) be a directed bipartite graph with |V1| = |V2| = n ⩾ 2. Suppose that dD(x) + dD(y) ⩾ 3n + 1 for all x ϵ V1 and y ϵ V2. Then D contains two vertex-disjoint directed cycles of lengths 2n1 and 2n2, respectively, for any positive integer partition n = n1 + n2. Moreover, the condition is sharp for even n and nearly sharp for odd n.