Distributed flow algorithms for scalable similarity visualization

We describe simple yet scalable and distributed algorithms for solving the maximum flow problem and its minimum cost flow variant, motivated by problems of interest in objects similarity visualization. We formulate the fundamental problem as a convex-concave saddle point problem. We then show that this problem can be efficiently solved by a first order method or by exploiting faster quasi-Newton steps. Our proposed approach costs at most O(|ε|) per iteration for a graph with |ε| edges. Further, the number of required iterations can be shown to be independent of number of edges for the first order approximation method. We present experimental results in two applications: mosaic generation and color similarity based image layouting. © 2010 IEEE.

Minimum maneuver time calculation using convex optimization

The problem of calculating the minimum lap or maneuver time of a nonlinear vehicle, which is linearized at each time step, is formulated as a convex optimization problem. The formulation provides an alternative to previously used quasi-steady-state analysis or nonlinear optimization. Key steps are: the use of model predictive control; expressing the minimum time problem as one of maximizing distance traveled along the track centerline; and linearizing the track and vehicle trajectories by expressing them as small displacements from a fixed reference. A consequence of linearizing the vehicle dynamics is that nonoptimal steering control action can be generated, but attention to the constraints and the cost function minimizes the effect. Optimal control actions and vehicle responses for a 90 deg bend are presented and compared to the nonconvex nonlinear programming solution. Copyright © 2013 by ASME.

Cost-effectiveness of alternative powertrains for reduced energy use and CO2 emissions in passenger vehicles

This work analysed the cost-effectiveness of avoiding carbon dioxide (CO2) emissions using advanced internal combustion engines, hybrids, plug-in hybrids, fuel cell vehicles and electric vehicles across the nine UK passenger vehicles segments. Across all vehicle types and powertrain groups, minimum installed motive power was dependent most on the time to accelerate from zero to 96.6km/h (60mph). Hybridising the powertrain reduced the difference in energy use between vehicles with slow (t z - 60 > 8 s) and fast acceleration (t z - 60 < 8 s) times. The cost premium associated with advanced powertrains was dependent most on the powertrain chosen, rather than the performance required. Improving non-powertrain components reduced vehicle road load and allowed total motive capacity to decrease by 17%, energy use by 11%, manufacturing cost premiums by 13% and CO2 emissions abatement costs by 15%. All vehicles with advanced internal combustion engines, most hybrid and plug-in hybrid powertrains reduced net CO2 emissions and had lower lifetime operating costs than the respective segment reference vehicle. Most powertrains using fuel cells and all electric vehicles had positive CO2 emissions abatement costs. However, only vehicles using advanced internal combustion engines and parallel hybrid vehicles may be attractive to consumers by the fuel savings offsetting increases in vehicle cost within two years. This work demonstrates that fuel savings are possible relative to today's fleet, but indicates that the most cost-effective way of reducing fuel consumption and CO2 emissions is by advanced combustion technologies and hybridisation with a parallel topology. © 2014 Elsevier Ltd.

Nanometer scale thermal drift of 4Gbit/s uncooled laser for tight-channel-spacing low-cost WDM

A 4Gbit/s directly modulated DBR laser is demonstrated with nanometre scale thermal tuning over an extended 20-70°C temperature range. >40dB side mode suppression over the entire temperature range is achieved. © 2005 Optical Society of America.

Uncooled DBR laser directly modulated at 3.125 Gb/s as athermal transmitter for low-cost WDM systems

An uncooled three-section tunable distributed Bragg reflector laser is demonstrated as an athermal transmitter for low-cost uncooled wavelength-division-multiplexing (WDM) systems with tight channel spacing. A ±0.02-nm thermal wavelength drift is achieved under continuous-wave operation up to 70 °C. Dynamic sidemode suppression ratio of greater than 35 dB is consistently obtained under 3.125-Gb/s direct modulation over a 20 °C-70 °C temperature range, with wavelength variation of as low as ±0.2 nm. This indicates that more than an order of magnitude reduction in coarse WDM channel spacing is possible using this source. © 2005 IEEE.

Relay node placement for wireless sensor networks deployed in tunnels

Node placement plays a significant role in the effective and successful deployment of Wireless Sensor Networks (WSNs), i.e., meeting design goals such as cost effectiveness, coverage, connectivity, lifetime and data latency. In this paper, we propose a new strategy to assist in the placement of Relay Nodes (RNs) for a WSN monitoring underground tunnel infrastructure. By applying for the first time an accurate empirical mean path loss propagation model along with a well fitted fading distribution model specifically defined for the tunnel environment, we address the RN placement problem with guaranteed levels of radio link performance. The simulation results show that the choice of appropriate path loss model and fading distribution model for a typical environment is vital in the determination of the number and the positions of RNs. Furthermore, we adapt a two-tier clustering multi-hop framework in which the first tier of the RN placement is modelled as the minimum set cover problem, and the second tier placement is solved using the search-and-find algorithm. The implementation of the proposed scheme is evaluated by simulation, and it lays the foundations for further work in WSN planning for underground tunnel applications. © 2010 IEEE.

Discriminative language model adaptation for Mandarin broadcast speech transcription and translation

This paper investigates unsupervised test-time adaptation of language models (LM) using discriminative methods for a Mandarin broadcast speech transcription and translation task. A standard approach to adapt interpolated language models to is to optimize the component weights by minimizing the perplexity on supervision data. This is a widely made approximation for language modeling in automatic speech recognition (ASR) systems. For speech translation tasks, it is unclear whether a strong correlation still exists between perplexity and various forms of error cost functions in recognition and translation stages. The proposed minimum Bayes risk (MBR) based approach provides a flexible framework for unsupervised LM adaptation. It generalizes to a variety of forms of recognition and translation error metrics. LM adaptation is performed at the audio document level using either the character error rate (CER), or translation edit rate (TER) as the cost function. An efficient parameter estimation scheme using the extended Baum-Welch (EBW) algorithm is proposed. Experimental results on a state-of-the-art speech recognition and translation system are presented. The MBR adapted language models gave the best recognition and translation performance and reduced the TER score by up to 0.54% absolute. © 2007 IEEE.