Active fault tolerant control for nonlinear systems with simultaneous actuator and sensor faults

The goal of this paper is to describe a novel fault tolerant tracking control (FTTC) strategy based on robust fault estimation and compensation of simultaneous actuator and sensor faults. Within the framework of fault tolerant control (FTC) the challenge is to develop an FTTC design strategy for nonlinear systems to tolerate simultaneous actuator and sensor faults that have bounded first time derivatives. The main contribution of this paper is the proposal of a new architecture based on a combination of actuator and sensor Takagi-Sugeno (T-S) proportional state estimators augmented with proportional and integral feedback (PPI) fault estimators together with a T-S dynamic output feedback control (TSDOFC) capable of time-varying reference tracking. Within this architecture the design freedom for each of the T-S estimators and the control system are available separately with an important consequence on robust L ₂ norm fault estimation and robust L ₂ norm closed-loop tracking performance. The FTTC strategy is illustrated using a nonlinear inverted pendulum example with time-varying tracking of a moving linear position reference.     

Analysis of Two-Dimensional Non-Rigid Shapes

Analysis of deformable two-dimensional shapes is an important problem, encountered in numerous pattern recognition, computer vision and computer graphics applications. In this paper, we address three major problems in the analysis of non-rigid shapes: similarity, partial similarity, and correspondence. We present an axiomatic construction of similarity criteria for deformation-invariant shape comparison, based on intrinsic geometric properties of the shapes, and show that such criteria are related to the Gromov-Hausdorff distance. Next, we extend the problem of similarity computation to shapes which have similar parts but are dissimilar when considered as a whole, and present a construction of set-valued distances, based on the notion of Pareto optimality. Finally, we show that the correspondence between non-rigid shapes can be obtained as a byproduct of the non-rigid similarity problem. As a numerical framework, we use the generalized multidimensional scaling (GMDS) method, which is the numerical core of the three problems addressed in this paper.     

Over-Parameterized Variational Optical Flow

A novel optical flow estimation process based on a spatio-temporal model with varying coefficients multiplying a set of basis functions at each pixel is introduced. Previous optical flow estimation methodologies did not use such an over parameterized representation of the flow field as the problem is ill-posed even without introducing any additional parameters: Neighborhood based methods of the Lucas–Kanade type determine the flow at each pixel by constraining the flow to be described by a few parameters in small neighborhoods. Modern variational methods represent the optic flow directly via the flow field components at each pixel. The benefit of over-parametrization becomes evident in the smoothness term, which instead of directly penalizing for changes in the optic flow, accumulates a cost of deviating from the assumed optic flow model. Our proposed method is very general and the classical variational optical flow techniques are special cases of it, when used in conjunction with constant basis functions. Experimental results with the novel flow estimation process yield significant improvements with respect to the best results published so far.     

An Architectural Framework for Accurate Characterization of Network Traffic

In networks carrying large volume of traffic, accurate traffic characterization is necessary for understanding the dynamics and patterns of network resource usage. Previous approaches to flow characterization are based on random sampling of the packets (e.g., Cisco's NetFlow) or inferring characteristics solely based on long lived flows (LLFs) or on lossy data structures (e.g., bloom filters, hash tables). However, none of these approaches takes into account the heavy-tailed nature of the Internet traffic and separates the estimation algorithm from the flow measurement architecture.In this paper, we propose an alternate approach to traffic characterization by closely linking the flow measurement architecture with the estimation algorithm. Our measurement framework stores complete information related to short lived flows (SLFs) while collecting partial information related to LLFs. For real-time separation of LLFs and SLFs, we propose a novel algorithm based on typical sequences from Information theory. The distribution (pdf) and sample space of the underlying traffic is estimated using the non-parametric Parzen window technique and likelihood function defined over the Coupon collector problem. We validate the accuracy and performance of our estimation technique using traffic traces from the internal LAN in our laboratory and from National Library for Applied Network Research (NLANR).     

NodeWiz: Fault-tolerant grid information service

Large scale grid computing systems may provide multitudinous services, from different providers, whose quality of service will vary. Moreover, services are deployed and undeployed in the grid with no central coordination. Thus, to find out the most suitable service to fulfill their needs, or to find the most suitable set of resources on which to deploy their services, grid users must resort to a Grid Information Service (GIS). This service allows users to submit rich queries that are normally composed of multiple attributes and range operations. The ability to efficiently execute complex searches in a scalable and reliable way is a key challenge for current GIS designs. Scalability issues are normally dealt with by using peer-to-peer technologies. However, the more reliable peer-to-peer approaches do not cater for rich queries in a natural way. On the other hand, approaches that can easily support these rich queries are less robust in the presence of failures. In this paper we present the design of NodeWiz, a GIS that allows multi-attribute range queries to be performed efficiently in a distributed manner, while maintaining load balance and resilience to failures.     

Development of a new radioanalytical technique for the determination of manganese(II) using potassium periodate

A radiometric method based on redox substoichiometry was applied to accurate and precise determination of manganese. The proposed technique is based on the substoichiometric oxidation of bivalent manganese to permanganate ions with potassium periodate, followed by isolation of the oxidized species by the extraction with tri-n-octylamine in chloroform. The method was successfully applied to the estimation of manganese content of various certified ores and alloys.     

Hysteresis‐Free 1D Network Mixed Halide‐Perovskite Semitransparent Solar Cells

The morphology of hybrid organic–inorganic perovskite films is known to strongly affect the performance of perovskite‐based solar cells. CH₃NH₃PbI_3‐xCl_x (MAPbI_3‐xCl_x) films have been previously fabricated with 100% surface coverage in glove boxes. In ambient air, fabrication generally relies on solvent engineering to obtain compact films. In contrast, this work explores the potential of altering the perovskites microstructure for solar cell engineering. This work starts with CH₃NH₃PbI_3‐xCl_x, films with grain morphology carefully controlled by varying the deposition speed during the spin‐coating process to fabricate efficient and partially transparent solar cells. Devices produced with a CH₃NH₃PbI_3‐xCl_x film and a compact thick top gold electrode reach a maximum efficiency of 10.2% but display a large photocurrent hysteresis. As it is demonstrated, the introduction of different concentrations of bromide in the precursor solution addresses the hysteresis issues and turns the film morphology into a partially transparent interconnected network of 1D microstructures. This approach leads to semitransparent solar cells with negligible hysteresis and efficiencies up to 7.2%, while allowing average transmission of 17% across the visible spectrum. This work demonstrates that the optimization of the perovskites composition can mitigate the hysteresis effects commonly attributed to the charge trapping within the perovskite film.     

Single Nanoparticle Magnetic Spin Memristor

There is an increasing demand for the development of a simple Si‐based universal memory device at the nanoscale that operates at high frequencies. Spin‐electronics (spintronics) can, in principle, increase the efficiency of devices and allow them to operate at high frequencies. A primary challenge for reducing the dimensions of spintronic devices is the requirement for high spin currents. To overcome this problem, a new approach is presented that uses helical chiral molecules exhibiting spin‐selective electron transport, which is called the chiral‐induced spin selectivity (CISS) effect. Using the CISS effect, the active memory device is miniaturized for the first time from the micrometer scale to 30 nm in size, and this device presents memristor‐like nonlinear logic operation at low voltages under ambient conditions and room temperature. A single nanoparticle, along with Au contacts and chiral molecules, is sufficient to function as a memory device. A single ferromagnetic nanoplatelet is used as a fixed hard magnet combined with Au contacts in which the gold contacts act as soft magnets due to the adsorbed chiral molecules.     

Common Knowledge and Update in Finite Environments

Logics of knowledge have been shown to provide a useful approach to the high level specification and analysis of distributed systems. It has been proposed that such systems can be developed using knowledge- based protocols, in which agents' actions have preconditions that test their state of knowledge. Both computer-assisted analysis of the knowledge properties of systems and automated compilation of knowledge-based protocols require the development of algorithms for the computation of states of knowledge. This paper studies one of the computational problems of interest, the model checking problem for knowledge formulae in the S5_nKripke structures generated by finite state environments in which states determine an observation for each agent. Agents are assumed to have perfect recall and may operate synchronously or asynchronously. It is shown that, in this setting, model checking of common knowledge formulae is intractable, but efficient incremental algorithms are developed for formulae containing only knowledge operators. Connections to knowledge updates and compilation of knowledge-based protocols are discussed.     

The first measurement of an absolute surface concentration of reaction intermediates in ethylene hydrogenation

The first measurement of a turnover rate with respect to surface intermediate concentration in a high pressure heterogeneous catalytic reaction is reported. By using infrared-visible sum frequency generation to study the hydrogenation of ethylene on Pt(111), it was found that the surface concentration of -bonded ethylene, the key reaction intermediate, represented approximately 4% of a monolayer. Thus the absolute turnover rate per surface adsorbed ethylene molecule is 25 times faster than the rate measured per platinum atom. To explain these results, we propose a model of weakly adsorbed ethylene intermediates reacting on atop sites.