Channels that heat up

This paper considers an additive noise channel where the time-κ noise variance is a weighted sum of the squared magnitudes of the previous channel inputs plus a constant. This channel model accounts for the dependence of the intrinsic thermal noise on the data due to the heat dissipation associated with the transmission of data in electronic circuits: the data determine the transmitted signal, which in turn heats up the circuit and thus influences the power of the thermal noise. The capacity of this channel (both with and without feedback) is studied at low transmit powers and at high transmit powers. At low transmit powers, the slope of the capacity-versus-power curve at zero is computed and it is shown that the heating-up effect is beneficial. At high transmit powers, conditions are determined under which the capacity is bounded, i.e., under which the capacity does not grow to infinity as the allowed average power tends to infinity. © 2009 IEEE.

A hot channel

This paper studies on-chip communication with non-ideal heat sinks. A channel model is proposed where the variance of the additive noise depends on the weighted sum of the past channel input powers. It is shown that, depending on the weights, the capacity can be either bounded or unbounded in the input power. A necessary condition and a sufficient condition for the capacity to be bounded are presented. © 2007 IEEE.

Stability enhancement by casing grooves: The importance of stall inception mechanism and solidity

This paper concerns the optimisation of casing grooves and the important influence of stall inception mechanism on groove performance. Installing casing grooves is a well known technique for improving the stable operating range of a compressor, but the wide-spread use of grooves is restricted by the loss of efficiency and flow capacity. In this paper, laboratory tests are used to examine the conditions under which casing treatment can be used to greatest effect. The use of a single casing groove was investigated in a recently published companion paper. The current work extends this to multiple-groove treatments and considers their performance in relation to stall inception mechanisms. Here it is shown that the stall margin gain from multiple grooves is less than the sum of the gains if the grooves were used individually. By contrast, the loss of efficiency is additive as the number of grooves increases. It is then shown that casing grooves give the greatest stall margin improvement when used in a compressor which exhibits spike-type stall inception, while modal activity before stall can dramatically reduce the effectiveness of the grooves. This finding highlights the importance of being able to predict the stall inception mechanism which might occur in a given compressor before and after grooves are added. Some published prediction techniques are therefore examined, but found wanting. Lastly, it is shown that casing grooves can, in some cases, be used to remove rotor blades and produce a more efficient, stable and light-weight rotor. © 2010 by ASME.

Improving reverberant VTS for hands-free robust speech recognition

Model-based approaches to handling additive background noise and channel distortion, such as Vector Taylor Series (VTS), have been intensively studied and extended in a number of ways. In previous work, VTS has been extended to handle both reverberant and background noise, yielding the Reverberant VTS (RVTS) scheme. In this work, rather than assuming the observation vector is generated by the reverberation of a sequence of background noise corrupted speech vectors, as in RVTS, the observation vector is modelled as a superposition of the background noise and the reverberation of clean speech. This yields a new compensation scheme RVTS Joint (RVTSJ), which allows an easy formulation for joint estimation of both additive and reverberation noise parameters. These two compensation schemes were evaluated and compared on a simulated reverberant noise corrupted AURORA4 task. Both yielded large gains over VTS baseline system, with RVTSJ outperforming the previous RVTS scheme. © 2011 IEEE.

Blue-phase templated fabrication of three-dimensional nanostructures for photonic applications.

A promising approach to the fabrication of materials with nanoscale features is the transfer of liquid-crystalline structure to polymers. However, this has not been achieved in systems with full three-dimensional periodicity. Here we demonstrate the fabrication of self-assembled three-dimensional nanostructures by polymer templating blue phase I, a chiral liquid crystal with cubic symmetry. Blue phase I was photopolymerized and the remaining liquid crystal removed to create a porous free-standing cast, which retains the chiral three-dimensional structure of the blue phase, yet contains no chiral additive molecules. The cast may in turn be used as a hard template for the fabrication of new materials. By refilling the cast with an achiral nematic liquid crystal, we created templated blue phases that have unprecedented thermal stability in the range -125 to 125 °C, and that act as both mirrorless lasers and switchable electro-optic devices. Blue-phase templated materials will facilitate advances in device architectures for photonics applications in particular.

Blue-phase templated fabrication of three-dimensional nanostructures for photonic applications

A promising approach to the fabrication of materials with nanoscale features is the transfer of liquid-crystalline structure to polymers. However, this has not been achieved in systems with full three-dimensional periodicity. Here we demonstrate the fabrication of self-assembled three-dimensional nanostructures by polymer templating blue phase I, a chiral liquid crystal with cubic symmetry. Blue phase I was photopolymerized and the remaining liquid crystal removed to create a porous free-standing cast, which retains the chiral three-dimensional structure of the blue phase, yet contains no chiral additive molecules. The cast may in turn be used as a hard template for the fabrication of new materials. By refilling the cast with an achiral nematic liquid crystal, we created templated blue phases that have unprecedented thermal stability in the range-125 to 125°C, and that act as both mirrorless lasers and switchable electro-optic devices. Blue-phase templated materials will facilitate advances in device architectures for photonics applications in particular. © 2012 Macmillan Publishers Limited. All rights reserved.

Impact of heterogeneous link qualities and network connectivity on binary consensus

In this paper we consider a network that is trying to reach consensus over the occurrence of an event while communicating over Additive White Gaussian Noise (AWGN) channels. We characterize the impact of different link qualities and network connectivity on consensus performance by analyzing both the asymptotic and transient behaviors. More specifically, we derive a tight approximation for the second largest eigenvalue of the probability transition matrix. We furthermore characterize the dynamics of each individual node. © 2009 AACC.

Synthesis of 10 nm Ag nanoparticle polymer composite pastes for low temperature production of high conductivity films

The conversion of silver nanoparticle (NP) paste films into highly conductive films at low sintering temperature is an important requirement for the developing areas of additive fabrication and printed electronics. Ag NPs with a diameter of ∼10 nm were prepared via an improved chemical process to produce viscous paste with a high wt%. The paste consisted of as-prepared Ag NP and an organic vehicle of ethylcellulose that was deposited on glass and Si substrates using a contact lithographic technique. The morphology and conductivity of the imprinted paste film were measured as a function of sintering temperature, sintering time and the percentage ratio of Ag NP and ethylcellulose. The morphology and conductivity were examined using scanning electron microscopy (SEM) and a two-point probe electrical conductivity measurement. The results show that the imprinted films were efficiently converted into conducting states when exposed to sintering temperature in the range of 200-240 °C, this temperature is lower than the previously reported values for Ag paste. © 2010 Elsevier B.V. All rights reserved.

Comparative analysis of carbonization drivers in China's megacities

This study investigates the key drivers affecting emission increases in terms of population growth, economic growth, industrial transformation, and energy use in six Chinese megacities: Beijing, Shanghai, Tianjin, Chongqing, Guangzhou, and Hong Kong. The six cities represent the most-developed regions in China and they have similar per capita carbon dioxide (CO 2) emissions as many developed countries. There is an urgent need to quantify the magnitude of each factor in driving the emissions changes in those cities so that a potential bottom-up climate mitigation policy design at the city and sectoral levels can be initiated. We adopt index decomposition analysis and present the results in both additive and multiplicative approaches to reveal the absolute and relative levels of each factor in driving emission changes during 1985-2007. Among all cities, economic effect and energy intensity effect have always been the two dominant factors contributing to the changes in carbon emissions. This study reveals that there are large variations in the ways driving forces contribute to emission levels in different cities and industrial sectors. © 2012 by Yale University.

Modeling natural sounds with modulation cascade processes

Natural sounds are structured on many time-scales. A typical segment of speech, for example, contains features that span four orders of magnitude: Sentences ($\sim1$s); phonemes ($\sim10$−$1$ s); glottal pulses ($\sim 10$−$2$s); and formants ($\sim 10$−$3$s). The auditory system uses information from each of these time-scales to solve complicated tasks such as auditory scene analysis [1]. One route toward understanding how auditory processing accomplishes this analysis is to build neuroscience-inspired algorithms which solve similar tasks and to compare the properties of these algorithms with properties of auditory processing. There is however a discord: Current machine-audition algorithms largely concentrate on the shorter time-scale structures in sounds, and the longer structures are ignored. The reason for this is two-fold. Firstly, it is a difficult technical problem to construct an algorithm that utilises both sorts of information. Secondly, it is computationally demanding to simultaneously process data both at high resolution (to extract short temporal information) and for long duration (to extract long temporal information). The contribution of this work is to develop a new statistical model for natural sounds that captures structure across a wide range of time-scales, and to provide efficient learning and inference algorithms. We demonstrate the success of this approach on a missing data task.

Probabilistic Amplitude Demodulation

Auditory scene analysis is extremely challenging. One approach, perhaps that adopted by the brain, is to shape useful representations of sounds on prior knowledge about their statistical structure. For example, sounds with harmonic sections are common and so time-frequency representations are efficient. Most current representations concentrate on the shorter components. Here, we propose representations for structures on longer time-scales, like the phonemes and sentences of speech. We decompose a sound into a product of processes, each with its own characteristic time-scale. This demodulation cascade relates to classical amplitude demodulation, but traditional algorithms fail to realise the representation fully. A new approach, probabilistic amplitude demodulation, is shown to out-perform the established methods, and to easily extend to representation of a full demodulation cascade.

State estimation schemes for independent component coupled hidden Markov models

Conventional Hidden Markov models generally consist of a Markov chain observed through a linear map corrupted by additive noise. This general class of model has enjoyed a huge and diverse range of applications, for example, speech processing, biomedical signal processing and more recently quantitative finance. However, a lesser known extension of this general class of model is the so-called Factorial Hidden Markov Model (FHMM). FHMMs also have diverse applications, notably in machine learning, artificial intelligence and speech recognition [13, 17]. FHMMs extend the usual class of HMMs, by supposing the partially observed state process is a finite collection of distinct Markov chains, either statistically independent or dependent. There is also considerable current activity in applying collections of partially observed Markov chains to complex action recognition problems, see, for example, [6]. In this article we consider the Maximum Likelihood (ML) parameter estimation problem for FHMMs. Much of the extant literature concerning this problem presents parameter estimation schemes based on full data log-likelihood EM algorithms. This approach can be slow to converge and often imposes heavy demands on computer memory. The latter point is particularly relevant for the class of FHMMs where state space dimensions are relatively large. The contribution in this article is to develop new recursive formulae for a filter-based EM algorithm that can be implemented online. Our new formulae are equivalent ML estimators, however, these formulae are purely recursive and so, significantly reduce numerical complexity and memory requirements. A computer simulation is included to demonstrate the performance of our results. © Taylor & Francis Group, LLC.

Influence of specific HSP70 domains on fibril formation of the yeast prion protein Ure2.

Ure2p is the protein determinant of the Saccharomyces cerevisiae prion state [URE3]. Constitutive overexpression of the HSP70 family member SSA1 cures cells of [URE3]. Here, we show that Ssa1p increases the lag time of Ure2p fibril formation in vitro in the presence or absence of nucleotide. The presence of the HSP40 co-chaperone Ydj1p has an additive effect on the inhibition of Ure2p fibril formation, whereas the Ydj1p H34Q mutant shows reduced inhibition alone and in combination with Ssa1p. In order to investigate the structural basis of these effects, we constructed and tested an Ssa1p mutant lacking the ATPase domain, as well as a series of C-terminal truncation mutants. The results indicate that Ssa1p can bind to Ure2p and delay fibril formation even in the absence of the ATPase domain, but interaction of Ure2p with the substrate-binding domain is strongly influenced by the C-terminal lid region. Dynamic light scattering, quartz crystal microbalance assays, pull-down assays and kinetic analysis indicate that Ssa1p interacts with both native Ure2p and fibril seeds, and reduces the rate of Ure2p fibril elongation in a concentration-dependent manner. These results provide new insights into the structural and mechanistic basis for inhibition of Ure2p fibril formation by Ssa1p and Ydj1p.

An example of the use of neural computing techniques in materials science - The modelling of fatigue thresholds in Ni-base superalloys

Two adaptive numerical modelling techniques have been applied to prediction of fatigue thresholds in Ni-base superalloys. A Bayesian neural network and a neurofuzzy network have been compared, both of which have the ability to automatically adjust the network's complexity to the current dataset. In both cases, despite inevitable data restrictions, threshold values have been modelled with some degree of success. However, it is argued in this paper that the neurofuzzy modelling approach offers real benefits over the use of a classical neural network as the mathematical complexity of the relationships can be restricted to allow for the paucity of data, and the linguistic fuzzy rules produced allow assessment of the model without extensive interrogation and examination using a hypothetical dataset. The additive neurofuzzy network structure means that redundant inputs can be excluded from the model and simple sub-networks produced which represent global output trends. Both of these aspects are important for final verification and validation of the information extracted from the numerical data. In some situations neurofuzzy networks may require less data to produce a stable solution, and may be easier to verify in the light of existing physical understanding because of the production of transparent linguistic rules. © 1999 Elsevier Science S.A.

Approximate Bayesian Computation for Smoothing

We consider a method for approximate inference in hidden Markov models (HMMs). The method circumvents the need to evaluate conditional densities of observations given the hidden states. It may be considered an instance of Approximate Bayesian Computation (ABC) and it involves the introduction of auxiliary variables valued in the same space as the observations. The quality of the approximation may be controlled to arbitrary precision through a parameter ε > 0. We provide theoretical results which quantify, in terms of ε, the ABC error in approximation of expectations of additive functionals with respect to the smoothing distributions. Under regularity assumptions, this error is, where n is the number of time steps over which smoothing is performed. For numerical implementation, we adopt the forward-only sequential Monte Carlo (SMC) scheme of [14] and quantify the combined error from the ABC and SMC approximations. This forms some of the first quantitative results for ABC methods which jointly treat the ABC and simulation errors, with a finite number of data and simulated samples. © Taylor & Francis Group, LLC.