The manufacture of high-value carbon from coal-tar pitch

World-wide, 17 million ta^?1 of coal-tar are obtained as a by product in the chamber coking process for the production of metallurgical coke. Refining of this aromatic raw material yields coal-tar pitch which is the traditional coal-derived starting material for the manufacture of carbon precursors and carbon artefacts. Considerable progress has been made in the elucidation of the physical and chemical nature of this material by means of chromatography, n.m.r. spectroscopy, thermal analysis and chemical reactions schemes. The dominant fields of application of pitch are the manufacture of pitch coke and electrode binders. Delayed coking and horizontal chamber coking are the technologies currently used for the production of cokes with low sulphur and metal content, for anodes for the aluminium-refining industry and the electric steel process. Coal-tar pitch, low in quinoline-insolubles (QI), is an excellent raw material for the manufacture of needle-cokes with a low coefficient of thermal expension (CTE). The separation of inherent QI can be performed via gravity settling in aliphatic hydrocarbon mixtures, by centrifugation in a disc separator or by filtration. The possible co-carbonization with aromatic petroleum-derived residues yields premium coke suitable for the manufacture of UHP-electrodes. New developments in the production of coke from coal-tar pitch aim to improve coke yields and increase anisotropy (i.e. low CTE and high electrical conductivity values). Further technological progress has been made in the manufacture of hard pitch which can be used as a starting material for the production of pitch coke in the chamber coking process and for the production of electrode binders by means of a continuous flash process with optimized thermal and pressure treatment of pitch, thus facilitating the ‘tailored’ manufacture of binder pitches of different qualities.     

Modelling non-stationary noise with spectral factorisation in automatic speech recognition

Speech recognition systems intended for everyday use must be able to cope with a large variety of noise types and levels, including highly non-stationary multi-source mixtures. This study applies spectral factorisation algorithms and long temporal context for separating speech and noise from mixed signals. To adapt the system to varying environments, noise models are acquired from the context, or learnt from the mixture itself without prior information. We also propose methods for reducing the size of the bases used for speech and noise modelling by 20–40 times for better practical applicability. We evaluate the performance of the methods both as a standalone classifier and as a signal-enhancing front-end for external recognisers. For the CHiME noisy speech corpus containing non-stationary multi-source household noises at signal-to-noise ratios ranging from +9 to ?6 dB, we report average keyword recognition rates up to 87.8% using a single-stream sparse classification algorithm.     

Mask estimation and imputation methods for missing data speech recognition in a multisource reverberant environment

We present an automatic speech recognition system that uses a missing data approach to compensate for challenging environmental noise containing both additive and convolutive components. The unreliable and noise-corrupted (“missing”) components are identified using a Gaussian mixture model (GMM) classifier based on a diverse range of acoustic features. To perform speech recognition using the partially observed data, the missing components are substituted with clean speech estimates computed using both sparse imputation and cluster-based GMM imputation. Compared to two reference mask estimation techniques based on interaural level and time difference-pairs, the proposed missing data approach significantly improved the keyword accuracy rates in all signal-to-noise ratio conditions when evaluated on the CHiME reverberant multisource environment corpus. Of the imputation methods, cluster-based imputation was found to outperform sparse imputation. The highest keyword accuracy was achieved when the system was trained on imputed data, which made it more robust to possible imputation errors.     

Implementation of scalable power and area efficient high-throughputViterbi decoders

Today's data reconstruction in digital communication systems requires designs of highest throughput rate at low power. The Viterbi algorithm is a key element in such digital signal processing applications. The nonlinear and recursive nature of the Viterbi decoder makes its high-speed implementation challenging. Several promising approaches to achieve either high throughput or low power have been proposed in the past. A combination of these is developed in this paper. Additional new concepts allow building a signal-flow graph suitable for the design of high-speed Viterbi decoders with low power. Using a flexible datapath generator facilitates the essential quantitative optimization from architectural down to physical level to fully exploit the low-power and high-speed potential of a given technology. With parameterizable design entry, this datapath generator establishes the basis of a scalable platform-based design library. Altogether, this allows coverage of the range of today's industrial interest in high throughput rates, from 150 Msymbols/s up to 1.2 Gsymbols/s using conventional CMOS logic. The features of two exemplary Viterbi decoder implementations prove the benefit of this physically oriented design methodology in terms of speed and low power, when compared to other leading edge implementations     

Design optimization of low-power high-performance DSP building blocks

In recent years, power dissipation along with silicon area has become the key figure in chip design. The increasing demands on system performance require high-performance digital signal processing (DSP) systems to include dedicated number-crunching units as individually optimized building blocks. The various design methodologies in use stress one of the following figures: power dissipation, throughput, or silicon area. This paper presents a design methodology reducing any combination of cost drivers subject to a specified throughput. As a basic principle, the underlying optimization regards the existing interactions within the design space of a building block. Crucial in such optimization is the proper dimensioning of device sizes in contrast to the common use of minimal dimensions in low-power implementations. Taking the design space of an FIR filter as an example, the different steps of the design process are highlighted resulting in a low-power high-throughput filter implementation. It is part of an industrial read-write channel chip for hard disks with a worst case throughput of 1.6 GSamples/s at 23 mW in a 0.13-/spl mu/m CMOS technology. This filter requires less silicon area than other state-of-the-art filter implementations, and it disrupts the average trend of power dissipation by a factor of 6.     

Sparse imputation for large vocabulary noise robust ASR

An effective way to increase noise robustness in automatic speech recognition is to label the noisy speech features as either reliable or unreliable (‘missing’), and replace (‘impute’) the missing ones by clean speech estimates. Conventional imputation techniques employ parametric models and impute the missing features on a frame-by-frame basis. At low SNRs, frame-based imputation techniques fail because many time frames contain few, if any, reliable features. In previous work, we introduced an exemplar-based method, dubbed sparse imputation, which can impute missing features using reliable features from neighbouring frames. We achieved substantial gains in performance at low SNRs for a connected digit recognition task. In this work, we investigate whether the exemplar-based approach can be generalised to a large vocabulary task.Experiments on artificially corrupted speech show that sparse imputation substantially outperforms a conventional imputation technique when the ideal ‘oracle’ reliability of features is used. With error-prone estimates of feature reliability, sparse imputation performance is comparable to our baseline imputation technique in the cleanest conditions, and substantially better at lower SNRs. With noisy speech recorded in realistic noise conditions, sparse imputation performs slightly worse than our baseline imputation technique in the cleanest conditions, but substantially better in the noisier conditions.