Temporally adaptive estimation of logistic classifiers on data streams

Modern technology has allowed real-time data collection in a variety of domains, ranging from environmental monitoring to healthcare. Consequently, there is a growing need for algorithms capable of performing inferential tasks in an online manner, continuously revising their estimates to reflect the current status of the underlying process. In particular, we are interested in constructing online and temporally adaptive classifiers capable of handling the possibly drifting decision boundaries arising in streaming environments. We first make a quadratic approximation to the log-likelihood that yields a recursive algorithm for fitting logistic regression online. We then suggest a novel way of equipping this framework with self-tuning forgetting factors. The resulting scheme is capable of tracking changes in the underlying probability distribution, adapting the decision boundary appropriately and hence maintaining high classification accuracy in dynamic or unstable environments. We demonstrate the scheme's effectiveness in both real and simulated streaming environments. © Springer-Verlag 2009.

Streaming covariance selection with applications to adaptive querying in sensor networks

Sensor networks can be naturally represented as graphical models, where the edge set encodes the presence of sparsity in the correlation structure between sensors. Such graphical representations can be valuable for information mining purposes as well as for optimizing bandwidth and battery usage with minimal loss of estimation accuracy. We use a computationally efficient technique for estimating sparse graphical models which fits a sparse linear regression locally at each node of the graph via the Lasso estimator. Using a recently suggested online, temporally adaptive implementation of the Lasso, we propose an algorithm for streaming graphical model selection over sensor networks. With battery consumption minimization applications in mind, we use this algorithm as the basis of an adaptive querying scheme. We discuss implementation issues in the context of environmental monitoring using sensor networks, where the objective is short-term forecasting of local wind direction. The algorithm is tested against real UK weather data and conclusions are drawn about certain tradeoffs inherent in decentralized sensor networks data analysis. © 2010 The Author. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.

Flexible electronics: The next ubiquitous platform

Thin-film electronics in its myriad forms has underpinned much of the technological innovation in the fields of displays, sensors, and energy conversion over the past four decades. This technology also forms the basis of flexible electronics. Here we review the current status of flexible electronics and attempt to predict the future promise of these pervading technologies in healthcare, environmental monitoring, displays and human-machine interactivity, energy conversion, management and storage, and communication and wireless networks. © 2012 IEEE.

Pulsed laser writing of holographic nanosensors

Tuneable optical sensors have been developed to sense chemical stimuli for a range of applications from bioprocess and environmental monitoring to medical diagnostics. Here, we present a porphyrin-functionalised optical sensor based on a holographic grating. The holographic sensor fulfils two key sensing functions simultaneously: it responds to external stimuli and serves as an optical transducer in the visible region of the spectrum. The sensor was fabricated via a 6 nanosecond-pulsed laser (350 mJ, λ = 532 nm) photochemical patterning process that enabled a facile fabrication. A novel porphyrin derivative was synthesised to function as the crosslinker of a polymer matrix, the light-absorbing material, the component of a diffraction grating, as well as the cation chelating agent in the sensor. The use of this multifunctional porphyrin permitted two-step fabrication of a narrow-band light diffracting photonic sensing structure. The resulting structure can be tuned finely to diffract narrow-band light based on the changes in the fringe spacing within the polymer and the system's overall index of refraction. We show the utility of the sensor by demonstrating its reversible colorimetric tuneability in response to variation in concentrations of organic solvents and metal cations (Cu 2+ and Fe2+) in the visible region of the spectrum (λmax ≈ 520-680 nm) with a response time within 50 s. Porphyrin-functionalised optical sensors offer great promise in fields varying from environmental monitoring to biochemical sensing to printable optical devices. This journal is © the Partner Organisations 2014.