New chiral ether derivatives of 2-arylidene-p-menthane-3-ones as components of induced ferroelectric systems

New 2-arylidene-p-menthane-3-ones containing the ether bridging group in the arylidene fragment have been synthesized and studied as chiral dopants in ferroelectric liquid crystal mixtures. The ferroelectric properties of these compositions were compared with those for compositions including chiral dopants that do not contain any bridging group. The influence of bridging group and terminal alkyl substituent length in the dopant molecule on the ferroelectric parameters of systems studied is discussed. © 2001 OPA (Overseas Publishers Association) N.V. Published by license under the Gordon and Breach Science Publishers imprint, a member of the Taylor & Francis Group,.

New (1R,4R)-2-arylidene-p-menthan-3-ones with a bridging ester group in the arylidene fragment. Synthesis and behavior in liquid-crystalline systems

(1R,4R)-2-(4-Hydroxybenzylidene)- and (1R,4R)-2-(4′-hydroxybiphenyl- 4-yl)methylene-p-menthan-3-ones were synthesized by condensation of (-)-menthone with O-tetrahydropyran-2-yl derivatives of 4-hydroxybenzaldehyde and 4′-hydroxy-4-formylbiphenyl, respectively, in a DMSO - base medium followed by the removal of the protective group. The reactions of these hydroxy derivatives with 4-alkylbenzoic, 4-alkyloxybenzoic, trans-4-alkylcyclohexane-4- carboxylic, and 4′-alkylbiphenyl-4-carboxylic acids afforded three series of new chiral esters. Compounds containing the arylidene moiety with three benzene rings were found to exhibit liquid-crystalline properties. The characteristic features of these compounds are discussed based on the results of studies by polarizing microscopy, differential scanning calorimetry, and small-angle X-ray scattering. It was found that the mesomorphic compounds under study can form a smectic A mesophase, twist grain boundary mesophases (TGBA), and blue phases in a wide temperature range. Upon dissolution of certain of chiral compounds in 4′-cyano-4-pentylbiphenyl, a rather high twisting power and the thermal stabilizing effect on mesophases were observed.

New chiral mesogens 3(R)-methylcyclohexanone derivatives

New chiral compounds 3R-methylcyclohexanone derivatives were synthesized. These compounds were revealed to exhibit the mesomorphic behavior within rather wide temperature ranges. Types of formed mesophases and phase transition temperatures were determined by polarizing microscopy, differential scanning calorimetry and small angle scattering of X-ray. Mesomorphic properties of the new chiral compounds were compared with those for the chiral 2-arylidene derivatives of 3R,6R-3-methyl-6-isopropylcyclohexanone (d-isomenthone) studied earlier. Distinctions between these two types of compounds in an ability to form mesophases and also in twisting properties as chiral dopants in induced cholesteric mesophases are considered.

Global stabilization of feedforward systems with exponentially unstable Jacobian linearization

The global stabilization of a class of feedforward systems having an exponentially unstable Jacobian linearization is achieved by a high-gain feedback saturated at a low level. The control law forces the derivatives of the state variables to small values along the closed-loop trajectories. This "slow control" design is illustrated with a benchmark example and its limitations are emphasized. © 1999 Elsevier Science B.V. All rights reserved.

Distributed maximum likelihood for self-localization in sensor networks

We show that the sensor localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we develop fully decentralized versions of the Recursive Maximum Likelihood and the Expectation-Maximization algorithms to localize the network. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a message passing algorithm to propagate the derivatives of the likelihood. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we show that the developed algorithms are able to learn the localization parameters well.

Distributed maximum likelihood for simultaneous self-localization and tracking in sensor networks

We show that the sensor self-localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we implement fully decentralized versions of the Recursive Maximum Likelihood and on-line Expectation-Maximization algorithms to localize the sensor network simultaneously with target tracking. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a novel message passing algorithm. The latter allows each node to compute the local derivatives of the likelihood or the sufficient statistics needed for Expectation-Maximization. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we demonstrate that the developed algorithms are able to learn the localization parameters. © 2012 IEEE.

Efficient decoding with generative score-spaces using the expectation semiring

State-of-the-art speech recognisers are usually based on hidden Markov models (HMMs). They model a hidden symbol sequence with a Markov process, with the observations independent given that sequence. These assumptions yield efficient algorithms, but limit the power of the model. An alternative model that allows a wide range of features, including word- and phone-level features, is a log-linear model. To handle, for example, word-level variable-length features, the original feature vectors must be segmented into words. Thus, decoding must find the optimal combination of segmentation of the utterance into words and word sequence. Features must therefore be extracted for each possible segment of audio. For many types of features, this becomes slow. In this paper, long-span features are derived from the likelihoods of word HMMs. Derivatives of the log-likelihoods, which break the Markov assumption, are appended. Previously, decoding with this model took cubic time in the length of the sequence, and longer for higher-order derivatives. This paper shows how to decode in quadratic time. © 2013 IEEE.