A Deterministic Analysis of an Online Convex Mixture of Expert Algorithms

Cataloged from PDF version of article.We analyze an online learning algorithm that adaptively combines outputs of two constituent algorithms (or the experts) running in parallel to model an unknown desired signal. This online learning algorithm is shown to achieve (and in some cases outperform) the mean-square error (MSE) performance of the best constituent algorithm in the mixture in the steady-state. However, the MSE analysis of this algorithm in the literature uses approximations and relies on statistical models on the underlying signals and systems. Hence, such an analysis may not be useful or valid for signals generated by various real life systems that show high degrees of nonstationarity, limit cycles and, in many cases, that are even chaotic. In this paper, we produce results in an individual sequence manner. In particular, we relate the time-accumulated squared estimation error of this online algorithm at any time over any interval to the time-accumulated squared estimation error of the optimal convex mixture of the constituent algorithms directly tuned to the underlying signal in a deterministic sense without any statistical assumptions. In this sense, our analysis provides the transient, steady-state and tracking behavior of this algorithm in a strong sense without any approximations in the derivations or statistical assumptions on the underlying signals such that our results are guaranteed to hold. We illustrate the introduced results through examples. © 2012 IEEE

Qualitative Theory of Functional Differential and Integral Equations

Functional differential equations arise in many areas of science and technology: whenever a deterministic relationship involving some varying quantities and their rates of change in space and/or time (expressed as derivatives or differences) is known or postulated. This is illustrated in classical mechanics, where the motion of a body is described by its position and velocity as the time varies. In some cases, this differential equation (called an equation of motion) may be solved explicitly. In fact, differential equations play an important role in modelling virtually every physical, technical, biological, ecological, and epidemiological process, from celestial motion, to bridge design, to interactions between neurons, to interaction between species, to spread of diseases with a population, and so forth. Also many fundamental laws of chemistry can be formulated as differential equations and in economy differential equations are used to model the behavior of complex systems. However, the mathematical models can also take different forms depending on the time scale and space structure of the problem; it can be modeled by delay differential equations, difference equations, partial delay differential equations, partial delay difference equations, or the combination of these equations

Web-based Integrated Development Environment for Event-Driven Applications

Event-driven programming is a popular methodology for the development of resource-constrained embedded systems. While it is a natural abstraction for applications that interface with the physical world, the disadvantage is that the control flow of a program is hidden in the maze of event handlers and call-back functions. TinyOS is a representative event-driven operating system, designed for wireless sensor networks, featuring a component-based architecture that promotes code reuse. In this paper, we present a web-based model-driven graphical design environment for TinyOS that visualizes the component hierarchy of an application, and captures its eventbased scheduling mechanism. In contrast with existing visual environments, our representation explicitly captures the control flow of the application through events and commands, which makes it easier to understand the program logic than studying the source code. The design environment supports two-way code generation: mapping the visual representation to TinyOS source code, as well as building visual models from existing sources

Systematic Design of edical Capsule Robots

Medical capsule robots that navigate inside the body as diagnostic and interventional tools are an emerging and challenging research area within medical CPSs. These robots must provide locomotion, sensing, actuation, and communication within severe size, power, and computational constraints. This paper presents the first effort for an open architecture, platform design, software infrastructure, and a supporting modular design environment for medical capsule robots to further this research area

On Hermite-Hadamard type inequalities for F-convex function

WOS: 000471249600014In this study, we firstly give some properties the family F and F-convex function which are defined by B. Samet. Then, we obtain some midpoint inequalities for differentiable function. Moreover, we establish some midpoint and trapezoid type inequalities for function whose second derivatives in absolute value are F-convex

Particle swarm optimization for SAGE maximization step in channel parameter estimation

This paper presents an application of particle swarm optimization (PSO) in space alternating generalized expectation maximization (SAGE) algorithm. SAGE algorithm is a powerful tool for estimating channel parameters like delay, angles (azimuth and elevation) of arrival and departure, Doppler frequency and polarization. To demonstrate the improvement in processing time by utilizing PSO in SAGE algorithm, the channel parameters are estimated from a synthetic data and the computational expense of SAGE algorithm with PSO is discussed. (4 pages)

A Testpart for Interdisciplinary Analyses in Micro Production Engineering

AbstractIn 2011, a round robin test was initiated within the group of CIRP Research Affiliates. The aim was to establish a platform for linking interdisciplinary research in order to share the expertise and experiences of participants all over the world. This paper introduces a testpart which has been designed to allow an analysis of different manufacturing technologies, simulation methods, machinery and metrology as well as process and production planning aspects. Current investigations are presented focusing on the machining and additive processes to produce the geometry, simulation approaches, machine analysis, and a comparison of measuring technologies. Challenges and limitations regarding the manufacturing and evaluation of the testpart features by the applied methods are discussed.Video abstrac

Label-free nanometer-resolution imaging of biological architectures through surface enhanced raman scattering

Label free imaging of the chemical environment of biological specimens would readily bridge the supramolecular and the cellular scales, if a chemical fingerprint technique such as Raman scattering can be coupled with super resolution imaging. We demonstrate the possibility of label-free super-resolution Raman imaging, by applying stochastic reconstruction to temporal fluctuations of the surface enhanced Raman scattering (SERS) signal which originate from biomolecular layers on large-area plasmonic surfaces with a high and uniform hot-spot density (>1011/cm2, 20 to 35 nm spacing). A resolution of 20 nm is demonstrated in reconstructed images of self-assembled peptide network and fibrilated lamellipodia of cardiomyocytes. Blink rate density is observed to be proportional to the excitation intensity and at high excitation densities (>10 kW/cm2) blinking is accompanied by molecular breakdown. However, at low powers, simultaneous Raman measurements show that SERS can provide sufficient blink rates required for image reconstruction without completely damaging the chemical structure

Label-Free Nanometer-Resolution Imaging of Biological Architectures through Surface Enhanced Raman Scattering

Label free imaging of the chemical environment of biological specimens would readily bridge the supramolecular and the cellular scales, if a chemical fingerprint technique such as Raman scattering can be coupled with super resolution imaging. We demonst

Optical response of Ag-Au bimetallic nanoparticles to electron storage in aqueous medium

Composition and structure dependence of the shift in the position of the surface plasmon resonance band upon introduction of NaBH 4 to aqueous solutions of gold and silver nanoparticles are presented. Silver and gold nanoalloys in different compositions were prepared by co-reduction of the corresponding salt mixtures using sodium citrate as the reducing agent. After addition of NaBH 4 to the resultant nanoalloys, the maximum of their surface plasmon resonance band, ranging between that of pure silver (ca. 400 nm) and of pure gold (ca. 530 nm), is blue-shifted as a result of electron storage on the particles. The extent of this blue shift increases non-linearly with the mole fraction of silver in the nanoparticle, parallel to the trends reported previously for both the frequency and the extinction coefficient of the plasmon band shifts. Gold(core)@silver(shell) nanoparticles were prepared by sequential reduction of gold and silver, where addition of NaBH 4 results in relatively large spectral shift in the plasmon resonance band when compared with the nanoalloys having a similar overall composition. The origin of the large plasmon band shift in the core-shell is related with a higher silver surface concentration on these particles. Hence, the chemical nature of the nanoparticle emerges as the dominating factor contributing to the extent of the spectral shift as a result of electron storage in bimetallic systems. Copyright © 2008 American Scientific Publishers All rights reserved