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.

Polymer film packaging for food: An environmental assessment

Plastics packaging is ubiquitous in the food industry, fulfilling a range of functions including a significant role in reducing food waste. The public perception of packaging, however, is dominated by end-of-life aspects, when the packaging becomes waste often found littering urban, rural and marine environments. A balanced analysis of the role of packaging demands that the whole lifecycle is examined, looking not only at the packaging itself but also at the product being packaged. This paper focuses on packaging in the meat and cheese industry, analysing the impact of films and bags. The functions of packaging are defined and the environmental impact of delivering these functions is assessed. The influence of packaging on levels of waste and energy consumption elsewhere in the system is examined, including the contentious issue of end-of-life for packaging. Strategies for minimizing the environmental impact of the packaging itself involve reduction in the amount of material used (thinner packaging), rather than emphasizing end-of-life issues. Currently, with polymer recycling not at a high level, evidence suggests that this strategy is justifiable. Biodegradable polymers may have some potential for improving environmental performance, but are still problematic. The conclusion is that although current packaging is in some ways wasteful and inefficient, the alternatives are even less desirable. © 2013 Elsevier B.V. All rights reserved.

Single camera pose estimation using Bayesian filtering and Kinect motion priors

Traditional approaches to upper body pose estimation using monocular vision rely on complex body models and a large variety of geometric constraints. We argue that this is not ideal and somewhat inelegant as it results in large processing burdens, and instead attempt to incorporate these constraints through priors obtained directly from training data. A prior distribution covering the probability of a human pose occurring is used to incorporate likely human poses. This distribution is obtained offline, by fitting a Gaussian mixture model to a large dataset of recorded human body poses, tracked using a Kinect sensor. We combine this prior information with a random walk transition model to obtain an upper body model, suitable for use within a recursive Bayesian filtering framework. Our model can be viewed as a mixture of discrete Ornstein-Uhlenbeck processes, in that states behave as random walks, but drift towards a set of typically observed poses. This model is combined with measurements of the human head and hand positions, using recursive Bayesian estimation to incorporate temporal information. Measurements are obtained using face detection and a simple skin colour hand detector, trained using the detected face. The suggested model is designed with analytical tractability in mind and we show that the pose tracking can be Rao-Blackwellised using the mixture Kalman filter, allowing for computational efficiency while still incorporating bio-mechanical properties of the upper body. In addition, the use of the proposed upper body model allows reliable three-dimensional pose estimates to be obtained indirectly for a number of joints that are often difficult to detect using traditional object recognition strategies. Comparisons with Kinect sensor results and the state of the art in 2D pose estimation highlight the efficacy of the proposed approach.