ROSA: A High-cadence, Synchronized Multi-camera Solar Imaging System

The Rapid Oscillations in the Solar Atmosphere (ROSA) instrument is a synchronized, six-camera high-cadence solar imaging instrument developed by Queen's University Belfast. The system is available on the Dunn Solar Telescope at the National Solar Observatory in Sunspot, New Mexico, USA, as a common-user instrument. Consisting of six 1k x 1k Peltier-cooled frame-transfer CCD cameras with very low noise (0.02 -aEuro parts per thousand 15 e s(-1) pixel(-1)), each ROSA camera is capable of full-chip readout speeds in excess of 30 Hz, or 200 Hz when the CCD is windowed. Combining multiple cameras and fast readout rates, ROSA will accumulate approximately 12 TB of data per 8 hours observing. Following successful commissioning during August 2008, ROSA will allow for multi-wavelength studies of the solar atmosphere at a high temporal resolution.

Food intake, milk production, and tissue changes of Holstein-Friesian and Jersey x Holstein-Friesian dairy cows within a medium-input grazing system and a high-input total confinement system

Although interest in crossbreeding within dairy systems has increased, the role of Jersey crossbred cows within high concentrate input systems has received little attention. This experiment was designed to examine the performance of Holstein-Friesian (HF) and Jersey x Holstein-Friesian (J x HF) cows within a high concentrate input total confinement system (CON) and a medium concentrate input grazing system (GRZ). Eighty spring-calving dairy cows were used in a 2 (cow genotype) x 2 (milk production system) factorial design experiment. The experiment commenced when cows calved and encompassed a full lactation. With GRZ, cows were offered diets containing grass silage and concentrates [70:30 dry matter (DM) ratio] until turnout, grazed grass plus 1.0 kg of concentrate/day during a 199-d grazing period, and grass silage and concentrates (75:25 DM ratio) following rehousing and until drying-off. With CON, cows were confined throughout the lactation and offered diets containing grass silage and concentrates (DM ratio; 40:60, 50:50, 40:40, and 75:25 during d 1 to 100, 101 to 200, 201 to 250, and 251 until drying-off, respectively). Full-lactation concentrate DM intakes were 791 and 2,905 kg/cow for systems GRZ and CON, respectively. Although HF cows had a higher lactation milk yield than J x HF cows, the latter produced milk with a higher fat and protein content, so that solids-corrected milk yield (SCM) was unaffected by genotype. Somatic cell score was higher with the J x HF cows. Throughout lactation, HF cows were on average 37 kg heavier than J x HF cows, whereas the J x HF cows had a higher body condition score. Within each system, food intake did not differ between genotypes, whereas full-lactation yields of milk, fat plus protein, and SCM were higher with CON than with GRZ. A significant genotype x environment interaction was observed for milk yield, and a trend was found for an interaction with SCM. Crossbred cows on CON gained more body condition than HF cows, and overall pregnancy rate was unaffected by either genotype or management system. In summary, milk and SCM yields were higher with CON than with GRZ, whereas genotype had no effect on SCM. However, HF cows exhibited a greater milk yield response and a trend toward a greater SCM yield response with increasing concentrate levels compared with the crossbred cows.

ROSA: A High-cadence, Synchronized Multi-camera Solar Imaging System

The Rapid Oscillations in the Solar Atmosphere (ROSA) instrument is a synchronized, six-camera high-cadence solar imaging instrument developed by Queen's University Belfast and recently commissioned at the Dunn Solar Telescope at the National Solar Observatory in Sunspot, New Mexico, USA, as a common-user instrument. Consisting of six 1k x 1k Peltier-cooled frame-transfer CCD cameras with very low noise (0.02 - 15 e/pixel/s), each ROSA camera is capable of full-chip readout speeds in excess of 30 Hz, and up to 200 Hz when the CCD is windowed. ROSA will allow for multi-wavelength studies of the solar atmosphere at a high temporal resolution. We will present the current instrument set-up and parameters, observing modes, and future plans, including a new high QE camera allowing 15 Hz for Halpha. Interested parties should see https://habu.pst.qub.ac.uk/groups/arcresearch/wiki/de502/ROSA.html

Feedforward Control of Shape Memory Alloy Actuators Using Fuzzy-Based Inverse Preisach Model

This brief investigates a possible application of the inverse Preisach model in combination with the feedforward and feedback control strategies to control shape memory alloy actuators. In the feedforward control design, a fuzzy-based inverse Preisach model is used to compensate for the hysteresis nonlinearity effect. An extrema input history and a fuzzy inference is utilized to replace the inverse classical Preisach model. This work allows for a reduction in the number of experimental parameters and computation time for the inversion of the classical Preisach model. A proportional-integral-derivative (PID) controller is used as a feedback controller to regulate the error between the desired output and the system output. To demonstrate the effectiveness of the proposed controller, real-time control experiment results are presented.

An Efficient LS-SVM-Based Method for Fuzzy System Construction

This paper proposes an efficient learning mechanism to build fuzzy rule-based systems through the construction of sparse least-squares support vector machines (LS-SVMs). In addition to the significantly reduced computational complexity in model training, the resultant LS-SVM-based fuzzy system is sparser while offers satisfactory generalization capability over unseen data. It is well known that the LS-SVMs have their computational advantage over conventional SVMs in the model training process; however, the model sparseness is lost, which is the main drawback of LS-SVMs. This is an open problem for the LS-SVMs. To tackle the nonsparseness issue, a new regression alternative to the Lagrangian solution for the LS-SVM is first presented. A novel efficient learning mechanism is then proposed in this paper to extract a sparse set of support vectors for generating fuzzy IF-THEN rules. This novel mechanism works in a stepwise subset selection manner, including a forward expansion phase and a backward exclusion phase in each selection step. The implementation of the algorithm is computationally very efficient due to the introduction of a few key techniques to avoid the matrix inverse operations to accelerate the training process. The computational efficiency is also confirmed by detailed computational complexity analysis. As a result, the proposed approach is not only able to achieve the sparseness of the resultant LS-SVM-based fuzzy systems but significantly reduces the amount of computational effort in model training as well. Three experimental examples are presented to demonstrate the effectiveness and efficiency of the proposed learning mechanism and the sparseness of the obtained LS-SVM-based fuzzy systems, in comparison with other SVM-based learning techniques.

Position control of shape memory alloy actuators using self tuning fuzzy PID controller

Shape Memory Alloy (SMA) actuators, which have the ability to return to a predetermined shape when heated, have many potential applications such as aeronautics, surgical tools, robotics and so on. Although the conventional PID controller can be used with slow response systems, there has been limited success in precise motion control of SMA actuators, since the systems are disturbed by unknown factors beside their inherent nonlinear hysteresis and changes in the surrounding environment of the systems. This paper presents a new development of a SMA position control system by using a self-tuning fuzzy PID controller. This control algorithm is used by tuning the parameters of the PID controller thereby integrating fuzzy inference and producing a fuzzy adaptive PID controller, which can then be used to improve the control performance of nonlinear systems. The experimental results of position control of SMA actuators using conventional and self-tuning fuzzy PID controllers are both included in this paper.

Internal model control for shape memory alloy actuators using fuzzy based Preisach model

This paper investigates a possible application of Preisach model to control shape memory alloy (SMA) actuators using an internal model control strategy. The developed strategy consists in including the Preisach hysteresis model of SMA actuator and the inverse Preisach model within the control structure. In this work, an extrema input hystory and a fuzzy inference is utilized to replace the classical Preisach model. This allows to reduce a large amount of experimental parameters and computation time of the classical Preisach model. To demonstrate the effectiveness of the proposed controller in improving control performance and hysteresis compensation of SMA actuators, experimental results from real time control are presented.

Evidential event inference in transport video surveillance

This paper presents a new framework for multi-subject event inference in surveillance video, where measurements produced by low-level vision analytics usually are noisy, incomplete or incorrect. Our goal is to infer the composite events undertaken by each subject from noise observations. To achieve this, we consider the temporal characteristics of event relations and propose a method to correctly associate the detected events with individual subjects. The Dempster–Shafer (DS) theory of belief functions is used to infer events of interest from the results of our vision analytics and to measure conflicts occurring during the event association. Our system is evaluated against a number of videos that present passenger behaviours on a public transport platform namely buses at different levels of complexity. The experimental results demonstrate that by reasoning with spatio-temporal correlations, the proposed method achieves a satisfying performance when associating atomic events and recognising composite events involving multiple subjects in dynamic environments.

Purification capacity of a highly loaded laboratory scale tidal flow reed bed system with effluent recirculation

The purification capacity of a laboratory scale tidal flow reed bed system with final effluent recirculation at a ratio of 1:1 was investigated in this study. In particular, this four-stage reed bed system was highly loaded with strong agricultural wastewater. Under the hydraulic and organic loading rates as high as 0.43 m3/m2d and 1055 gCOD/m2d, respectively, the average removal efficiencies of COD, BOD5, SS, NH4-N and P were 77%, 78%, 66%, 62% and 38%. Even with the high loading rates, approximately 30% of NH4-N was converted into NO2-N and NO3-N from the mid-stage of the system where nitrification took place. The results suggest that the multi-stage reed bed system could be employed to treat strong wastewater under high loading, especially for the substantive mass removal of solids, organic matter and ammoniacal-nitrogen. Tidal flow combined with effluent recirculation is a favourable operation strategy to achieve this objective.

Threshold-based substream selection for closed-loop spatial multiplexing

We propose a low-complexity closed-loop spatial multiplexing method with limited feedback over multi-input-multi-output (MIMO) fading channels. The transmit adaptation is simply performed by selecting transmit antennas (or substreams) by comparing their signal-to-noise ratios to a given threshold with a fixed nonadaptive constellation and fixed transmit power per substream. We analyze the performance of the proposed system by deriving closed-form expressions for spectral efficiency, average transmit power, and bit error rate (BER). Depending on practical system design constraints, the threshold is chosen to maximize the spectral efficiency (or minimize the average BER) subject to average transmit power and average BER (or spectral efficiency) constraints, respectively. We present numerical and Monte Carlo simulation results that validate our analysis. Compared to open-loop spatial multiplexing and other approaches that select the best antenna subset in spatial multiplexing, the numerical results illustrate that the proposed technique obtains significant power gains for the same BER and spectral efficiency. We also provide numerical results that show improvement over rate-adaptive orthogonal space-time block coding, which requires highly complex constellation adaptation. We analyze the impact of feedback delay using analytical and Monte Carlo approaches. The proposed approach is arguably the simplest possible adaptive spatial multiplexing system from an implementation point of view. However, our approach and analysis can be extended to other systems using multiple constellations and power levels.

Quantum homogenization for continuous variables: Realization with linear optical elements

Recently Ziman et al. [Phys. Rev. A 65, 042105 (2002)] have introduced a concept of a universal quantum homogenizer which is a quantum machine that takes as input a given (system) qubit initially in an arbitrary state rho and a set of N reservoir qubits initially prepared in the state xi. The homogenizer realizes, in the limit sense, the transformation such that at the output each qubit is in an arbitrarily small neighborhood of the state xi irrespective of the initial states of the system and the reservoir qubits. In this paper we generalize the concept of quantum homogenization for qudits, that is, for d-dimensional quantum systems. We prove that the partial-swap operation induces a contractive map with the fixed point which is the original state of the reservoir. We propose an optical realization of the quantum homogenization for Gaussian states. We prove that an incoming state of a photon field is homogenized in an array of beam splitters. Using Simon's criterion, we study entanglement between outgoing beams from beam splitters. We derive an inseparability condition for a pair of output beams as a function of the degree of squeezing in input beams.

A Ligand predication tool based on modeling and reasoning with imprecise probabilistic knowledge

Ligand prediction has been driven by a fundamental desire to understand more about how biomolecules recognize their ligands and by the commercial imperative to develop new drugs. Most of the current available software systems are very complex and time-consuming to use. Therefore, developing simple and efficient tools to perform initial screening of interesting compounds is an appealing idea. In this paper, we introduce our tool for very rapid screening for likely ligands (either substrates or inhibitors) based on reasoning with imprecise probabilistic knowledge elicited from past experiments. Probabilistic knowledge is input to the system via a user-friendly interface showing a base compound structure. A prediction of whether a particular compound is a substrate is queried against the acquired probabilistic knowledge base and a probability is returned as an indication of the prediction. This tool will be particularly useful in situations where a number of similar compounds have been screened experimentally, but information is not available for all possible members of that group of compounds. We use two case studies to demonstrate how to use the tool.

Maintaining design intent for aircraft manufacture

Design and manufacture of aircraft requires deep multi-disciplinary understanding of system behaviour. The intention of the designer can get lost due to the many changes occurring to the product and the inability of the methods and tools used to capture it. Systems engineering and optimisation tools underpin industrial approaches to design, but are not without issue. The challenge is to find a route from concept to manufacture which enables designers to maintain their original intent. The novelty in this work is that the parameterisation used to build the CAD model reflects the manufacturing capability, ensuring design intent is maintained from concept to manufacture.