A printable optical time-temperature integrator based on shape memory in a chiral nematic polymer network

An optical and irreversible temperature sensor (e.g., a time-temperature integrator) is reported based on a mechanically embossed chiral-nematic polymer network. The polymer consists of a chemical and a physical (hydrogen-bonded) network and has a reflection band in the visible wavelength range. The sensors are produced by mechanical embossing at elevated temperatures. A relative large compressive deformation (up to 10%) is obtained inducing a shift to shorter wavelength of the reflection band (>30 nm). After embossing, a temperature sensor is obtained that exhibits an irreversible optical response. A permanent color shift to longer wavelengths (red) is observed upon heating of the polymer material to temperatures above the glass transition temperature. It is illustrated that the observed permanent color shift is related to shape memory in the polymer material. The films can be printed on a foil, thus showing that these sensors are potentially interesting as time-temperature integrators for applications in food and pharmaceutical products. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Novel nonvolatile memory with multibit storage based on a ZnO nanowire transistor.

We demonstrate a room temperature processed ferroelectric (FE) nonvolatile memory based on a ZnO nanowire (NW) FET where the NW channel is coated with FE nanoparticles. A single device exhibits excellent memory characteristics with the large modulation in channel conductance between ON and OFF states exceeding 10(4), a long retention time of over 4 × 10(4) s, and multibit memory storage ability. Our findings provide a viable way to create new functional high-density nonvolatile memory devices compatible with simple processing techniques at low temperature for flexible devices made on plastic substrates.

Modified Kanerva model. Results for real time word recognition

This paper describes results obtained using the modified Kanerva model to perform word recognition in continuous speech after being trained on the multi-speaker Alvey 'Hotel' speech corpus. Theoretical discoveries have recently enabled us to increase the speed of execution of part of the model by two orders of magnitude over that previously reported by Prager & Fallside. The memory required for the operation of the model has been similarly reduced. The recognition accuracy reaches 95% without syntactic constraints when tested on different data from seven trained speakers. Real time simulation of a model with 9,734 active units is now possible in both training and recognition modes using the Alvey PARSIFAL transputer array. The modified Kanerva model is a static network consisting of a fixed nonlinear mapping (location matching) followed by a single layer of conventional adaptive links. A section of preprocessed speech is transformed by the non-linear mapping to a high dimensional representation. From this intermediate representation a simple linear mapping is able to perform complex pattern discrimination to form the output, indicating the nature of the speech features present in the input window.

New space-time trellis codes for two-antenna quasi-static channels

New space-time trellis codes with four- and eight-level phase-shift keying (PSK) and 16-phase quadrature amplitude modulation (QAM) for two transmit antennas in slow-fading channels are presented in this paper. Unlike most of the codes that are reported in the literature, the proposed codes are specifically designed to minimize the frame error probability from a union-bound perspective. The performance of the proposed codes with various memory orders and receive antennas is evaluated by simulation. It is shown that the proposed codes outperform previously known codes in all studied cases.

Decentralised minimal-time dynamic consensus

This paper considers a group of agents that aim to reach an agreement on individually received time-varying signals by local communication. In contrast to static network averaging problem, the consensus considered in this paper is reached in a dynamic sense. A discrete-time dynamic average consensus protocol can be designed to allow all the agents tracking the average of their reference inputs asymptotically. We propose a minimal-time dynamic consensus algorithm, which only utilises a minimal number of local observations of a randomly picked node in a network to compute the final consensus signal. Our results illustrate that with memory and computational ability, the running time of distributed averaging algorithms can be indeed improved dramatically as suggested by Olshevsky and Tsitsiklis. © 2012 AACC American Automatic Control Council).