Experimental demonstration of colourless real-time end-to-end optical OFDM transmission at 7.5Gb/s over 25km SSMF using a 1GHz RSOA

7.5Gb/s real-time end-to-end optical OFDM (OOFDM) transceivers incorporating variable power loading on each individual subcarrier are demonstrated experimentally, for the first time, using a live-optimized RSOA intensity modulator having a modulation bandwidth as narrow as 1GHz. Colourless real-time 16-QAM-encoded OOFDM signal transmission at 7.5Gb/s over 25km SSMF is achieved across the C-band in simple IMDD systems without in-line optical amplification and dispersion compensation. Copyright © 2010 The authors.

First experimental demonstration of low-cost VCSEL-intensity modulated end-to-end real-time optical OFDM signal transmission at 11.25Gb/s over 25km SSMFs

Low-cost, narrow modulation bandwidth, un-cooled VCSELs can be utilized to directly modulate 64-QAM-encoded 11.25Gb/s signals for end-to-end real-time optical OFDM transmission over 25km SSMF IMDD systems with excellent performance robustness. © 2011 Optical Society of America.

Experimental demonstration of real-time optical OFDM Transmission at 7.5 Gb/s over 25-km SSMF using a 1-GHz RSOA

The 7.5-Gb/s real-time end-to-end optical orthogonal frequency-division- multiplexing (OOFDM) transceivers incorporating variable power loading on each individual subcarrier are demonstrated experimentally using a live-optimized reflective semiconductor optical amplifier intensity modulator having a modulation bandwidth as narrow as 1 GHz. Real-time OOFDM signal transmission at 7.5 Gb/s over 25-km standard single-mode fiber is achieved across the $C$-band in simple intensity modulation and direct detection systems without in-line optical amplification and dispersion compensation. © 2006 IEEE.

Building more sustainable solutions in production-consumption systems: the case of food for people with reduced access

In new product development, the ability to integrate different dimensions of sustainability at a value chain level is still a complex, problematic goal. As product-service approaches are increasingly enabling the introduction of more sustainable paths, this paper describes the authors' experience thus far when building insights into conditions for the implementation of integrated solutions in a process of co-development and testing in real life conditions, which are driven by a social need focusing on food for people with reduced access. Throughout this process, which brought together producers, consumers and other stakeholders to design and test industrialised, sustainable solutions, empirical evidence demonstrates feasibility and usefulness of the approach and insight into the conditions for implementing interactive, comprehensive multi-stakeholder processes in real life situations. In addition, results show that the delivery of innovative solutions enabled to offer social added value, economic profits and environmental improvements under specific experimental conditions. © 2006 Elsevier Ltd. All rights reserved.

Reinforcement learning for parameter estimation in statistical spoken dialogue systems

Reinforcement techniques have been successfully used to maximise the expected cumulative reward of statistical dialogue systems. Typically, reinforcement learning is used to estimate the parameters of a dialogue policy which selects the system's responses based on the inferred dialogue state. However, the inference of the dialogue state itself depends on a dialogue model which describes the expected behaviour of a user when interacting with the system. Ideally the parameters of this dialogue model should be also optimised to maximise the expected cumulative reward. This article presents two novel reinforcement algorithms for learning the parameters of a dialogue model. First, the Natural Belief Critic algorithm is designed to optimise the model parameters while the policy is kept fixed. This algorithm is suitable, for example, in systems using a handcrafted policy, perhaps prescribed by other design considerations. Second, the Natural Actor and Belief Critic algorithm jointly optimises both the model and the policy parameters. The algorithms are evaluated on a statistical dialogue system modelled as a Partially Observable Markov Decision Process in a tourist information domain. The evaluation is performed with a user simulator and with real users. The experiments indicate that model parameters estimated to maximise the expected reward function provide improved performance compared to the baseline handcrafted parameters. © 2011 Elsevier Ltd. All rights reserved.

Towards real-time profiling of sprints using wearable pressure sensors

On-body sensor systems for sport are challenging since the sensors must be lightweight and small to avoid discomfort, and yet robust and highly accurate to withstand and capture the fast movements associated with sport. In this work, we detail our experience of building such an on-body system for track athletes. The paper describes the design, implementation and deployment of an on-body sensor system for sprint training sessions. We autonomously profile sprints to derive quantitative metrics to improve training sessions. Inexpensive Force Sensitive Resistors (FSRs) are used to capture foot events that are subsequently analysed and presented back to the coach. We show how to identify periods of sprinting from the FSR data and how to compute metrics such as ground contact time. We evaluate our system using force plates and show that millisecond-level accuracy is achievable when estimating contact times. © 2012 Elsevier B.V. All rights reserved.

On-line policy optimisation of spoken dialogue systems via live interaction with human subjects

Statistical dialogue models have required a large number of dialogues to optimise the dialogue policy, relying on the use of a simulated user. This results in a mismatch between training and live conditions, and significant development costs for the simulator thereby mitigating many of the claimed benefits of such models. Recent work on Gaussian process reinforcement learning, has shown that learning can be substantially accelerated. This paper reports on an experiment to learn a policy for a real-world task directly from human interaction using rewards provided by users. It shows that a usable policy can be learnt in just a few hundred dialogues without needing a user simulator and, using a learning strategy that reduces the risk of taking bad actions. The paper also investigates adaptation behaviour when the system continues learning for several thousand dialogues and highlights the need for robustness to noisy rewards. © 2011 IEEE.

Compression techniques applied to multiple speech recognition systems

Speech recognition systems typically contain many Gaussian distributions, and hence a large number of parameters. This makes them both slow to decode speech, and large to store. Techniques have been proposed to decrease the number of parameters. One approach is to share parameters between multiple Gaussians, thus reducing the total number of parameters and allowing for shared likelihood calculation. Gaussian tying and subspace clustering are two related techniques which take this approach to system compression. These techniques can decrease the number of parameters with no noticeable drop in performance for single systems. However, multiple acoustic models are often used in real speech recognition systems. This paper considers the application of Gaussian tying and subspace compression to multiple systems. Results show that two speech recognition systems can be modelled using the same number of Gaussians as just one system, with little effect on individual system performance. Copyright © 2009 ISCA.

Adaptive sequential Monte Carlo approach for real-time applications

This paper presents an adaptive Sequential Monte Carlo approach for real-time applications. Sequential Monte Carlo method is employed to estimate the states of dynamic systems using weighted particles. The proposed approach reduces the run-time computation complexity by adapting the size of the particle set. Multiple processing elements on FPGAs are dynamically allocated for improved energy efficiency without violating real-time constraints. A robot localisation application is developed based on the proposed approach. Compared to a non-adaptive implementation, the dynamic energy consumption is reduced by up to 70% without affecting the quality of solutions. © 2012 IEEE.

A Novel Passive UHF RFID Real Time Location Sensing System

Ubiquitous in-building Real Time Location Systems (RTLS) today are limited by costly active radio frequency identification (RFID) tags and short range portal readers of low cost passive RFID tags. We, however, present a novel technology locates RFID tags using a new approach based on (a) minimising RFID fading using antenna diversity, frequency dithering, phase dithering and narrow beam-width antennas, (b) measuring a combination of RSSI and phase shift in the coherent received tag backscatter signals and (c) being selective of use of information from the system by, applying weighting techniques to minimise error. These techniques make it possible to locate tags to an accuracy of less than one metre. This breakthrough will enable, for the first time, the low-cost tagging of items and the possibility of locating them at relatively high precision.

Real-time marker prediction and CoR estimation in optical motion capture

Optical motion capture systems suffer from marker occlusions resulting in loss of useful information. This paper addresses the problem of real-time joint localisation of legged skeletons in the presence of such missing data. The data is assumed to be labelled 3d marker positions from a motion capture system. An integrated framework is presented which predicts the occluded marker positions using a Variable Turn Model within an Unscented Kalman filter. Inferred information from neighbouring markers is used as observation states; these constraints are efficient, simple, and real-time implementable. This work also takes advantage of the common case that missing markers are still visible to a single camera, by combining predictions with under-determined positions, resulting in more accurate predictions. An Inverse Kinematics technique is then applied ensuring that the bone lengths remain constant over time; the system can thereby maintain a continuous data-flow. The marker and Centre of Rotation (CoR) positions can be calculated with high accuracy even in cases where markers are occluded for a long period of time. Our methodology is tested against some of the most popular methods for marker prediction and the results confirm that our approach outperforms these methods in estimating both marker and CoR positions. © 2012 Springer-Verlag.

Simultaneous reduction of particulate matter and NOx emissions using 4-way catalyzed filtration systems

The next generation of diesel emission control devices includes 4-way catalyzed filtration systems (4WCFS) consisting of both NOx and diesel particulate matter (DPM) control. A methodology was developed to simultaneously evaluate the NOx and DPM control performance of miniature 4WCFS made from acicular mullite, an advanced ceramic material (ACM), that were challenged with diesel exhaust. The impact of catalyst loading and substrate porosity on catalytic performance of the NOx trap was evaluated. Simultaneously with NOx measurements, the real-time solid particle filtration performance of catalyst-coated standard and high porosity filters was determined for steady-state and regenerative conditions. The use of high porosity ACM 4-way catalyzed filtration systems reduced NOx by 99% and solid and total particulate matter by 95% when averaged over 10 regeneration cycles. A "regeneration cycle" refers to an oxidizing ("lean") exhaust condition followed by a reducing ("rich") exhaust condition resulting in NOx storage and NOx reduction (i.e., trap "regeneration"), respectively. Standard porosity ACM 4-way catalyzed filtration systems reduced NOx by 60-75% and exhibited 99.9% filtration efficiency. The rich/lean cycling used to regenerate the filter had almost no impact on solid particle filtration efficiency but impacted NOx control. Cycling resulted in the formation of very low concentrations of semivolatile nucleation mode particles for some 4WCFS formulations. Overall, 4WCFS show promise for significantly reducing diesel emissions into the atmosphere in a single control device. © 2013 American Chemical Society.

Evaluation of the functionality of biodegradable polymeric platforms for drug delivery systems

We present the development of a drug-loaded triple-layer platform consisting of thin film biodegradable polymers, in a properly designed form for the desired gradual degradation. Poly(dl-lactide-co-glycolide) (PLGA (65:35), PLGA (75:25)) and polycaprolactone (PCL) were grown by spin coating technique, to synthesize the platforms with the order PCL/PLGA (75:25)/PLGA (65:35) that determine their degradation rates. The outer PLGA (65:35) layer was loaded with dipyridamole, an antiplatelet drug. Spectroscopic ellipsometry (SE) in the Vis-far UV range was used to determine the nanostructure, as well as the content of the incorporated drug in the as-grown platforms. In situ and real-time SE measurements were carried out using a liquid cell for the dynamic evaluation of the fibrinogen and albumin protein adsorption processes. Atomic force microscopy studies justified the SE results concerning the nanopores formation in the polymeric platforms, and the dominant adsorption mechanisms of the proteins, which were defined by the drug incorporation in the platforms. © 2013 Elsevier B.V. All rights reserved.