Current methods for characterising mixing and flow in microchannels

This article reviews existing methods for the characterisation of mixing and flow in microchannels, micromixers and microreactors. In particular, it analyses the current experimental techniques and methods available for characterising mixing and the associated phenomena in single and multiphase flow. The review shows that the majority of the experimental techniques used for characterising mixing and two-phase flow in microchannels employ optical methods, which require optical access to the flow, or off-line measurements. Indeed visual measurements are very important for the fundamental understanding of the physics of these flows and the rapid advances in optical measurement techniques, like confocal scanning laser microscopy and high resolution stereo micro particle image velocimetry, are now making full field data retrieval possible. However, integration of microchannel devices in industrial processes will require on-line measurements for process control that do not necessarily rely on optical techniques. Developments are being made in the areas of non-intrusive sensors, magnetic resonance techniques, ultrasonic spectroscopy and on-line flow through measurement cells. The advances made in these areas will certainly be of increasing interest in the future as microchannels are more frequently employed in continuous flow equipment for industrial applications

Automated identification of river hydromorphological features using UAV high resolution aerial imagery

European legislation is driving the development of methods for river ecosystem protection in light of concerns over water quality and ecology. Key to their success is the accurate and rapid characterisation of physical features (i.e., hydromorphology) along the river. Image pattern recognition techniques have been successfully used for this purpose. The reliability of the methodology depends on both the quality of the aerial imagery and the pattern recognition technique used. Recent studies have proved the potential of Unmanned Aerial Vehicles (UAVs) to increase the quality of the imagery by capturing high resolution photography. Similarly, Artificial Neural Networks (ANN) have been shown to be a high precision tool for automated recognition of environmental patterns. This paper presents a UAV based framework for the identification of hydromorphological features from high resolution RGB aerial imagery using a novel classification technique based on ANNs. The framework is developed for a 1.4 km river reach along the river Dee in Wales, United Kingdom. For this purpose, a Falcon 8 octocopter was used to gather 2.5 cm resolution imagery. The results show that the accuracy of the framework is above 81%, performing particularly well at recognising vegetation. These results leverage the use of UAVs for environmental policy implementation and demonstrate the potential of ANNs and RGB imagery for high precision river monitoring and river management

The Photodetector Array Camera and Spectrometer (PACS) on the Herschel Space Observatory

The Photodetector Array Camera and Spectrometer (PACS) is one of the three science instruments on ESA's far infrared and submillimetre observatory. It employs two Ge:Ga photoconductor arrays (stressed and unstressed) with 16x25 pixels, each, and two filled silicon bolometer arrays with 16x32 and 32x64 pixels, respectively, to perform integral-field spectroscopy and imaging photometry in the 60-210\mu\ m wavelength regime. In photometry mode, it simultaneously images two bands, 60-85\mu\ m or 85-125\mu\m and 125-210\mu\ m, over a field of view of ~1.75'x3.5', with close to Nyquist beam sampling in each band. In spectroscopy mode, it images a field of 47"x47", resolved into 5x5 pixels, with an instantaneous spectral coverage of ~1500km/s and a spectral resolution of ~175km/s. We summarise the design of the instrument, describe observing modes, calibration, and data analysis methods, and present our current assessment of the in-orbit performance of the instrument based on the Performance Verification tests. PACS is fully operational, and the achieved performance is close to or better than the pre-launch predictions

Real-time sweat pH monitoring based on a wearable chemical barcode micro-fluidic platform incorporating ionic liquids

This work presents the fabrication, characterisation and the performance of a wearable, robust, flexible and disposable chemical barcode device based on a micro-fluidic platform that incorporates ionic liquid polymer gels (ionogels). The device has been applied to the monitoring of the pH of sweat in real time during an exercise period. The device is an ideal wearable sensor for measuring the pH of sweat since it does not contents any electronic part for fluidic handle or pH detection and because it can be directly incorporated into clothing, head- or wristbands, which are in continuous contact with the skin. In addition, due to the micro-fluidic structure, fresh sweat is continuously passing through the sensing area providing the capability to perform continuous real time analysis. The approach presented here ensures immediate feedback regarding sweat composition. Sweat analysis is attractive for monitoring purposes as it can provide physiological information directly relevant to the health and performance of the wearer without the need for an invasive sampling approac

Development and characterisation of an easily configurable range imaging system

Range imaging is becoming a popular tool for many applications, with several commercial variants now available. These systems find numerous real world applications such as interactive gaming and the automotive industry. This paper describes the development of a range imaging system employing the PMD-19 k sensor from PMD technologies. One specific advantage of our system is that it is extremely customisable in terms of modulation patterns to act as a platform for further research into time-of-flight range imaging. Experimental results are presented giving an indication of the precision and accuracy of the system, and how modifying certain operating parameters can improve system performance

Evaluation of changes in image appearance with changes in displayed image size

This research focused on the quantification of changes in image appearance when images are displayed at different image sizes on LCD devices. The final results provided in calibrated Just Noticeable Differences (JNDs) on relevant perceptual scales, allowing the prediction of sharpness and contrast appearance with changes in the displayed image size. A series of psychophysical experiments were conducted to enable appearance predictions. Firstly, a rank order experiment was carried out to identify the image attributes that were most affected by changes in displayed image size. Two digital cameras, exhibiting very different reproduction qualities, were employed to capture the same scenes, for the investigation of the effect of the original image quality on image appearance changes. A wide range of scenes with different scene properties was used as a test-set for the investigation of image appearance changes with scene type. The outcomes indicated that sharpness and contrast were the most important attributes for the majority of scene types and original image qualities. Appearance matching experiments were further conducted to quantify changes in perceived sharpness and contrast with respect to changes in the displayed image size. For the creation of sharpness matching stimuli, a set of frequency domain filters were designed to provide equal intervals in image quality, by taking into account the system’s Spatial Frequency Response (SFR) and the observation distance. For the creation of contrast matching stimuli, a series of spatial domain S-shaped filters were designed to provide equal intervals in image contrast, by gamma adjustments. Five displayed image sizes were investigated. Observers were always asked to match the appearance of the smaller version of each stimulus to its larger reference. Lastly, rating experiments were conducted to validate the derived JNDs in perceptual quality for both sharpness and contrast stimuli. Data obtained by these experiments finally converted into JND scales for each individual image attribute. Linear functions were fitted to the final data, which allowed the prediction of image appearance of images viewed at larger sizes than these investigated in this research

High resolution agriculture land cover using aerial digital photography and GIS : a case study for small island states

Chapter 7With the advent of site-specific crop management, sustainability and profitability, land farming now requires information and technology-based management system to identify, analyse and manage spatial and temporal resource variability. Th is approach is being made increasingly possible by recent innovation in information technologies such as mobile devices, geographic information systems, positioning technologies (such as Geographical Position system), and Earth Observations. Such innovation now off ers a holistic approach to micro-manage agricultural resources. (Robert et al., 1994). Basic mapping and farm-level record keeping is one of the first precision agriculture practices that must be implemented in a typical productive agriculture operation (Stombaugh et al., 2001). Typical tasks include mapping of variations that occur in largescale field features such as vegetation stress, crop rotation, inventorying, irrigation, soil drainage and erosion, pest control, etc. Th e search for a low cost methodology that takes into account the growth of information technology in data capture and surveying, data processing, database creation and geographic information systems becomes mandatory in order to respond to such needs. Th e study constitutes, for the first time in Malta, the collection of high precision farming statistics that makes use of an inexpensive system for aerial mapping that requires minimal ground truthing. Th e effectiveness of such a method for small areas was later demonstrated by Galdies and Borg (2006) related to coastal and beach management in the Maltese islands. In the current case, digital aerial remote sensing enabled the accurate mapping of agricultural variables, and coupled with ground survey data, resulted in the production of precise, high resolution agricultural crop-cover maps. Additional information can be further derived from this data that can be used for the optimisation of micro agriculture practices.peer-reviewe

Detection of irrigation inhomogeneities in an olive grove using the NDRE vegetation index obtained from UAV images

We have developed a simple photogrammetric method to identify heterogeneous areas of irrigated olive groves and vineyard crops using a commercial multispectral camera mounted on an unmanned aerial vehicle (UAV). By comparing NDVI, GNDVI, SAVI, and NDRE vegetation indices, we find that the latter shows irrigation irregularities in an olive grove not discernible with the other indices. This may render the NDRE as particularly useful to identify growth inhomogeneities in crops. Given the fact that few satellite detectors are sensible in the red-edge (RE) band and none with the spatial resolution offered by UAVs, this finding has the potential of turning UAVs into a local farmer’s favourite aid tool.Peer ReviewedPostprint (published version