Ecological condition of coastal ocean waters along the U.S. Mid-Atlantic Bight: 2006

In May 2006, the NOAA National Ocean Service (NOS), in conjunction with the EPA National Health and Environmental Effects Laboratory (NHEERL), conducted an assessment of the status of ecological condition of soft-bottom habitat and overlying waters throughout the mid-Atlantic Bight (MAB) portion of the eastern U.S. continental shelf. The study area encompassed the region from Cape Cod, MA and Nantucket Shoals in the northeast to Cape Hatteras in the south, and was defined using a one nautical mile buffer of the shoreline extended seaward to the shelf break (~100-m depth contour). A total of 50 stations were targeted for sampling using standard methods and indicators applied in prior NOAA coastal studies and EPA’s Environmental Monitoring and Assessment Program (EMAP) and National Coastal Assessment (NCA). A key feature adopted from these studies was the incorporation of a random probabilistic sampling design. Such a design provides a basis for making unbiased statistical estimates of the spatial extent of ecological condition relative to various measured indicators and corresponding thresholds of concern. Indicators included multiple measures of water quality, sediment quality, and biological condition (benthic fauna). Through coordination with the NOAA Fisheries Service/Northeast Fisheries Science Center (NFS/NEFSC), samples of summer flounder (Paralichthys dentatus) also were obtained from 30 winter 2007 bottom-trawl survey stations in overlapping portions of the study area and used for analysis of chemical-contaminant body burdens.

Characterisation of technology roadmaps: Purpose and format

A report on characterisation of technology roadmaps, its purpose and formats are presented. A fast-start process is developed to support the initiation of technology roadmapping in firms to address the industrial needs. The purpose of each roadmap is related to a number of planning aims: product, capability, integration, strategic, long-range, programme and process planning. The second set of categories are related to the format of the roadmap, based on observed structure: multiple or single layers, bars, tables, graphs, pictorial forms, flow diagrams and text. It is concluded that technology roadmaps processes a great potential for supporting the development and implementation of business product and technology strategy.

Starting-up roadmapping fast

Technology roadmapping is a powerful technique for supporting technology management and planning in the firm. The roadmap enables the evolution of markets, products and technologies to be explored, together with the linkages between the various perspectives. A process called T-Plan, which has been developed to support the rapid initiation of roadmapping and thus address these challenges is described.

Viewing the future

As many industrial organizations have learned to apply roadmapping successfully, they have also learned that it is "roadmapping" rather than "the roadmap" that generates value. This two-part special report has focused primarily on product and technology roadmapping in industry. The first part (RTM, March-April 2003, pp. 26-59) examined the workings of the process at Lucent Technologies, Rockwell Automation, the pharmaceutical/biotechnology industry, and United Kingdom-based Domino Printing Sciences. This second part examines roadmapping in the UK, Motorola, General Motors, the services sector, and in cases that demand major investment decisions under conditions of volatility.

Reasons for roadmapping: A study of the Singaporean SME manufacturing sector

To encourage Singaporean small and medium sized enterprises to move up the value chain, Operation and Technology Roadmapping (OTR) has been developed and introduced to the sector. This technique is based upon the T-Plan process developed at Cambridge University. To-date close to thirty companies have used the process to create a 'first-cut' roadmap. This paper initially reviews the application of roadmapping in small companies, and then highlights the various areas where the companies have applied the roadmapping technique. © 2004 IEEE.

Spark ignition of turbulent premixed and non-premixed opposed jet flames

The probabilistic nature of ignition of premixed and non-premixecl turbulent opposed-jet flames has been examined and the flame structures following ignition have been visualized directly and with OH-PLIF. It has been found that high bulk velocities decrease the ignition probability in all locations and for all flames. Ignition is sometimes possible even in locations where there is negligible probability of finding flammable mixture and is sometimes impossible in locations with high probability of flammable fluid. The edge flame propagation speed is also estimated.

Cluster analysis of heterogeneous rank data

Cluster analysis of ranking data, which occurs in consumer questionnaires, voting forms or other inquiries of preferences, attempts to identify typical groups of rank choices. Empirically measured rankings are often incomplete, i.e. different numbers of filled rank positions cause heterogeneity in the data. We propose a mixture approach for clustering of heterogeneous rank data. Rankings of different lengths can be described and compared by means of a single probabilistic model. A maximum entropy approach avoids hidden assumptions about missing rank positions. Parameter estimators and an efficient EM algorithm for unsupervised inference are derived for the ranking mixture model. Experiments on both synthetic data and real-world data demonstrate significantly improved parameter estimates on heterogeneous data when the incomplete rankings are included in the inference process.

Video tracking using dual-treewavelet polar matching and particle filtering

In this paper, we describe a video tracking application using the dual-tree polar matching algorithm. The models are specified in a probabilistic setting, and a particle ilter is used to perform the sequential inference. Computer simulations demonstrate the ability of the algorithm to track a simulated video moving target in an urban environment with complete and partial occlusions. © The Institution of Engineering and Technology.

Roadmapping in industrial companies: Experiences

From its origins in the US electronics sector in the 1970s, technology roadmapping has been adapted (and adopted) widely, for many different innovation, strategy and policy applications. Communication is commonly cited as one of the key benefi ts of roadmapping, particularly in terms of the process that brings different organizational perspectives together, with the roadmap providing a common visual 'language'. There is signifi cant demand for methods that are agile, in the sense of being rapid, flexible and effective to apply, focused on strategic decisions and actions. 'Fast-start' roadmapping workshop techniques enable key stakeholders to address strategic issues efficiently using the visual structure of roadmaps to capture, discuss, prioritize, explore and communicate. This paper presents the learning from a set of five diverse applications of the fast-start approach in the Basque Country, which demonstrate the agility of the technique.

Learning shape priors for single view reconstruction

In this paper, we aim to reconstruct free-from 3D models from a single view by learning the prior knowledge of a specific class of objects. Instead of heuristically proposing specific regularities and defining parametric models as previous research, our shape prior is learned directly from existing 3D models under a framework based on the Gaussian Process Latent Variable Model (GPLVM). The major contributions of the paper include: 1) a probabilistic framework for prior-based reconstruction we propose, which requires no heuristic of the object, and can be easily generalized to handle various categories of 3D objects, and 2) an attempt at automatic reconstruction of more complex 3D shapes, like human bodies, from 2D silhouettes only. Qualitative and quantitative experimental results on both synthetic and real data demonstrate the efficacy of our new approach. ©2009 IEEE.

Handling uncertainty in housing stock models

Housing stock models can be useful tools in helping to assess the environmental and socio-economic impacts of retrofits to residential buildings; however, existing housing stock models are not able to quantify the uncertainties that arise in the modelling process from various sources, thus limiting the role that they can play in helping decision makers. This paper examines the different sources of uncertainty involved in housing stock models and proposes a framework for handling these uncertainties. This framework involves integrating probabilistic sensitivity analysis with a Bayesian calibration process in order to quantify uncertain parameters more accurately. The proposed framework is tested on a case study building, and suggestions are made on how to expand the framework for retrofit analysis at an urban-scale. © 2011 Elsevier Ltd.

Predicting the performance of an office under climate change: A study of metrics, sensitivity and zonal resolution

Climate change is expected to have significant impact on the future thermal performance of buildings. Building simulation and sensitivity analysis can be employed to predict these impacts, guiding interventions to adapt buildings to future conditions. This article explores the use of simulation to study the impact of climate change on a theoretical office building in the UK, employing a probabilistic approach. The work studies (1) appropriate performance metrics and underlying modelling assumptions, (2) sensitivity of computational results to identify key design parameters and (3) the impact of zonal resolution. The conclusions highlight the importance of assumptions in the field of electricity conversion factors, proper management of internal heat gains, and the need to use an appropriately detailed zonal resolution. © 2010 Elsevier B.V. All rights reserved.

Bayesian classification of acoustical waveforms under environmental variability

We present in this paper a new multivariate probabilistic approach to Acoustic Pulse Recognition (APR) for tangible interface applications. This model uses Principle Component Analysis (PCA) in a probabilistic framework to classify tapping pulses with a high degree of variability. It was found that this model, achieves a higher robustness to pulse variability than simpler template matching methods, specifically when allowed to train on data containing high variability. © 2011 IEEE.

Frontier experiences from industry-academia consortia

Technology roadmapping has been applied successfully in many industrial organizations. Designed to facilitate and communicate technology strategy and planning, roadmaps (or, as in Europe, route maps) can take a variety of specific forms, depending on the type (opportunities, capabilities, products, technologies, etc.) and particular company context. While roadmaps are generally manifest in a number of "program elements or levels" superimposed upon a timeline, experienced mappers often claim that it is "roadmapping" rather than "the roadmap" that generates the value. This special report focuses primarily on product and technology roadmaps. Following an introduction to the evolution, purpose and applications of corporate/industry roadmapping, four industry-developed articles examine roadmapping in Lucent Technologies, Rockwell Automation, the pharmaceutical/biotechnology industry, and UK-based Domino Printing Sciences.

Bayesian Active Learning for Classification and Preference Learning

Information theoretic active learning has been widely studied for probabilistic models. For simple regression an optimal myopic policy is easily tractable. However, for other tasks and with more complex models, such as classification with nonparametric models, the optimal solution is harder to compute. Current approaches make approximations to achieve tractability. We propose an approach that expresses information gain in terms of predictive entropies, and apply this method to the Gaussian Process Classifier (GPC). Our approach makes minimal approximations to the full information theoretic objective. Our experimental performance compares favourably to many popular active learning algorithms, and has equal or lower computational complexity. We compare well to decision theoretic approaches also, which are privy to more information and require much more computational time. Secondly, by developing further a reformulation of binary preference learning to a classification problem, we extend our algorithm to Gaussian Process preference learning.