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.

Environmental priorities in strategic product development

This paper reports some results from a major research project on the integration of environmental issues into design in the electrical and electronic industry. Product development practice and ecodesign methods were examined along with qualitative data from 19 manufacturers, using interviews and an action research methodology. A four-stage framework for ecodesign practice resulted: first, to make an environmental assessment from a life cycle viewpoint; then to communicate the analysis and collect feedback; thirdly to prioritize the environmental issues and finally to complete the design using relevant tools and methods. This 'ARPI' framework (analyse, report, prioritize, improve) applies to both strategic and operational levels. Initial testing of the framework in collaborating companies highlighted difficulties with communication with design teams; the role of 'ecodesign champions' is explained. Other challenges are the development of customized tools and the training of designers. The study concludes that the prioritization step is critical, as it simplifies and clarifies the tasks in design that take place after formulation of the specification. Copyright (C) 2000 John Wiley and Sons, Ltd. and ERP Environment.

Civil and Environmental Engineering Challenges for Data Sensing and Analysis

The objective of this study was to identify challenges in civil and environmental engineering that can potentially be solved using data sensing and analysis research. The challenges were recognized through extensive literature review in all disciplines of civil and environmental engineering. The literature review included journal articles, reports, expert interviews, and magazine articles. The challenges were ranked by comparing their impact on cost, time, quality, environment and safety. The result of this literature review includes challenges such as improving construction safety and productivity, improving roof safety, reducing building energy consumption, solving traffic congestion, managing groundwater, mapping and monitoring the underground, estimating sea conditions, and solving soil erosion problems. These challenges suggest areas where researchers can apply data sensing and analysis research.

Fluid Dynamics in Physics, Engineering and Environmental Applications

The book contains invited lectures and selected contributions presented at the Enzo Levi and XVII Annual Meeting of the Fluid Dynamic Division of the Mexican Physical Society in 2011.

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.

Extreme sensitivity of graphene photoconductivity to environmental gases.

Graphene is a single layer of covalently bonded carbon atoms, which was discovered only 8 years ago and yet has already attracted intense research and commercial interest. Initial research focused on its remarkable electronic properties, such as the observation of massless Dirac fermions and the half-integer quantum Hall effect. Now graphene is finding application in touch-screen displays, as channels in high-frequency transistors and in graphene-based integrated circuits. The potential for using the unique properties of graphene in terahertz-frequency electronics is particularly exciting; however, initial experiments probing the terahertz-frequency response of graphene are only just emerging. Here we show that the photoconductivity of graphene at terahertz frequencies is dramatically altered by the adsorption of atmospheric gases, such as nitrogen and oxygen. Furthermore, we observe the signature of terahertz stimulated emission from gas-adsorbed graphene. Our findings highlight the importance of environmental conditions on the design and fabrication of high-speed, graphene-based devices.