Oil-accumulating microalgae have the potential to enable large-scale biodiesel production without competing for arable land or biodiverse natural landscapes. High lipid productivity of dominant, fast-growing algae is a major prerequisite for commercial production of microalgal oil-derived biodiesel. However, under optimal growth conditions, large amounts of algal biomass are produced, but with relatively low lipid contents, while species with high lipid contents are typically slow growing. Major advances in this area can be made through the induction of lipid biosynthesis, e.g., by environmental stresses. Lipids, in the form of triacylglycerides typically provide a storage function in the cell that enables microalgae to endure adverse environmental conditions. Essentially algal biomass and triacylglycerides compete for photosynthetic assimilate and a reprogramming of physiological pathways is required to stimulate lipid biosynthesis. There has been a wide range of studies carried out to identify and develop efficient lipid induction techniques in microalgae such as nutrients stress (e.g., nitrogen and/or phosphorus starvation), osmotic stress, radiation, pH, temperature, heavy metals and other chemicals. In addition, several genetic strategies for increased triacylglycerides production and inducibility are currently being developed. In this review, we discuss the potential of lipid induction techniques in microalgae and also their application at commercial scale for the production of biodiesel.
Consumer-grade range cameras such as the Kinect sensor have the potential to be used in mapping applications where accuracy requirements are less strict. To realize this potential insight into the geometric quality of the data acquired by the sensor is essential. In this paper we discuss the calibration of the Kinect sensor, and provide an analysis of the accuracy and resolution of its depth data. Based on a mathematical model of depth measurement from disparity a theoretical error analysis is presented, which provides an insight into the factors influencing the accuracy of the data. Experimental results show that the random error of depth measurement increases with increasing distance to the sensor, and ranges from a few millimeters up to about 4 cm at the maximum range of the sensor. The quality of the data is also found to be influenced by the low resolution of the depth measurements.
Unmanned Aerial Vehicles (UAVs) are an exciting new remote sensing tool capable of acquiring high resolution spatial data. Remote sensing with UAVs has the potential to provide imagery at an unprecedented spatial and temporal resolution. The small footprint of UAV imagery, however, makes it necessary to develop automated techniques to geometrically rectify and mosaic the imagery such that larger areas can be monitored. In this paper, we present a technique for geometric correction and mosaicking of UAV photography using feature matching and Structure from Motion (SfM) photogrammetric techniques. Images are processed to create three dimensional point clouds, initially in an arbitrary model space. The point clouds are transformed into a real-world coordinate system using either a direct georeferencing technique that uses estimated camera positions or via a Ground Control Point (GCP) technique that uses automatically identified GCPs within the point cloud. The point cloud is then used to generate a Digital Terrain Model (DTM) required for rectification of the images. Subsequent georeferenced images are then joined together to form a mosaic of the study area. The absolute spatial accuracy of the direct technique was found to be 65-120 cm whilst the GCP technique achieves an accuracy of approximately 10-15 cm.
Sensor miniaturisation, improved battery technology and the availability of low-cost yet advanced Unmanned Aerial Vehicles (UAV) have provided new opportunities for environmental remote sensing. The UAV provides a platform for close-range aerial photography. Detailed imagery captured from micro-UAV can produce dense point clouds using multi-view stereopsis (MVS) techniques combining photogrammetry and computer vision. This study applies MVS techniques to imagery acquired from a multi-rotor micro-UAV of a natural coastal site in southeastern Tasmania, Australia. A very dense point cloud (<1-3 cm point spacing) is produced in an arbitrary coordinate system using full resolution imagery, whereas other studies usually downsample the original imagery. The point cloud is sparse in areas of complex vegetation and where surfaces have a homogeneous texture. Ground control points collected with Differential Global Positioning System (DGPS) are identified and used for georeferencing via a Helmert transformation. This study compared georeferenced point clouds to a Total Station survey in order to assess and quantify their geometric accuracy. The results indicate that a georeferenced point cloud accurate to 25-40 mm can be obtained from imagery acquired from similar to 50 m. UAV-based image capture provides the spatial and temporal resolution required to map and monitor natural landscapes. This paper assesses the accuracy of the generated point clouds based on field survey points. Based on our key findings we conclude that sub-decimetre terrain change (in this case coastal erosion) can be monitored.
Tree species diversity is a key parameter to describe forest ecosystems. It is, for example, important for issues such as wildlife habitat modeling and close-to-nature forest management. We examined the suitability of 8-band WorldView-2 satellite data for the identification of 10 tree species in a temperate forest in Austria. We performed a Random Forest (RF) classification (object-based and pixel-based) using spectra of manually delineated sunlit regions of tree crowns. The overall accuracy for classifying 10 tree species was around 82% (8 bands, object-based). The class-specific producer's accuracies ranged between 33% (European hornbeam) and 94% (European beech) and the user's accuracies between 57% (European hornbeam) and 92% (Lawson's cypress). The object-based approach outperformed the pixel-based approach. We could show that the 4 new WorldView-2 bands (Coastal, Yellow, Red Edge, and Near Infrared 2) have only limited impact on classification accuracy if only the 4 main tree species (Norway spruce, Scots pine, European beech, and English oak) are to be separated. However, classification accuracy increased significantly using the full spectral resolution if further tree species were included. Beside the impact on overall classification accuracy, the importance of the spectral bands was evaluated with two measures provided by RF. An in-depth analysis of the RF output was carried out to evaluate the impact of reference data quality and the resulting reliability of final class assignments. Finally, an extensive literature review on tree species classification comprising about 20 studies is presented.
This article presents an environmental remote sensing application using a UAV that is specifically aimed at reducing the data gap between field scale and satellite scale in soil erosion monitoring in Morocco. A fixed-wing aircraft type Sirius I (MAVinci, Germany) equipped with a digital system camera (Panasonic) is employed. UAV surveys are conducted over different study sites with varying extents and flying heights in order to provide both very high resolution site-specific data and lower-resolution overviews, thus fully exploiting the large potential of the chosen UAV for multi-scale mapping purposes. Depending on the scale and area coverage, two different approaches for georeferencing are used, based on high-precision GCPs or the UAV's log file with exterior orientation values respectively. The photogrammetric image processing enables the creation of Digital Terrain Models (DTMs) and ortho-image mosaics with very high resolution on a sub-decimetre level. The created data products were used for quantifying gully and badland erosion in 2D and 3D as well as for the analysis of the surrounding areas and landscape development for larger extents.
Appliance Load Monitoring (ALM) is essential for energy management solutions, allowing them to obtain appliance-specific energy consumption statistics that can further be used to devise load scheduling strategies for optimal energy utilization. Fine-grained energy monitoring can be achieved by deploying smart power outlets on every device of interest; however it incurs extra hardware cost and installation complexity. Non-Intrusive Load Monitoring (NILM) is an attractive method for energy disaggregation, as it can discern devices from the aggregated data acquired from a single point of measurement. This paper provides a comprehensive overview of NILM system and its associated methods and techniques used for disaggregated energy sensing. We review the state-of-the art load signatures and disaggregation algorithms used for appliance recognition and highlight challenges and future research directions.
Bluetooth Low Energy (BLE) is an emerging low-power wireless technology developed for short-range control and monitoring applications that is expected to be incorporated into billions of devices in the next few years. This paper describes the main features of BLE, explores its potential applications, and investigates the impact of various critical parameters on its performance. BLE represents a trade-off between energy consumption, latency, piconet size, and throughput that mainly depends on parameters such as connInterval and connSlaveLatency. According to theoretical results, the lifetime of a BLE device powered by a coin cell battery ranges between 2.0 days and 14.1 years. The number of simultaneous slaves per master ranges between 2 and 5,917. The minimum latency for a master to obtain a sensor reading is 676 mu s, although simulation results show that, under high bit error rate, average latency increases by up to three orders of magnitude. The paper provides experimental results that complement the theoretical and simulation findings, and indicates implementation constraints that may reduce BLE performance.
Iron(II) spin crossover molecular materials are made of coordination centres switchable between two states by temperature, pressure or a visible light irradiation. The relevant macroscopic parameter which monitors the magnetic state of a given solid is the high-spin (HS) fraction denoted nHS, i.e., the relative population of HS molecules. Each spin crossover material is distinguished by a transition temperature T-1/2 where 50% of active molecules have switched to the low-spin (LS) state. In strongly interacting systems, the thermal spin switching occurs abruptly at T-1/2. Applying pressure induces a shift from HS to LS states, which is the direct consequence of the lower volume for the LS molecule. Each material has thus a well defined pressure value P-1/2. In both cases the spin state change is easily detectable by optical means thanks to a thermo/piezochromic effect that is often encountered in these materials. In this contribution, we discuss potential use of spin crossover molecular materials as temperature and pressure sensors with optical detection. The ones presenting smooth transitions behaviour, which have not been seriously considered for any application, are spotlighted as potential sensors which should stimulate a large interest on this well investigated class of materials.
The biosynthesis of nanoparticles has received increasing attention due to the growing need to develop safe, cost-effective and environmentally friendly technologies for nano-materials synthesis. In this report, silver nanoparticles (AgNPs) were synthesized using a reduction of aqueous Ag+ ion with the culture supernatants of Aspergillus terreus. The reaction occurred at ambient temperature and in a few hours. The bioreduction of AgNPs was monitored by ultraviolet-visible spectroscopy, and the AgNPs obtained were characterized by transmission electron microscopy and X-ray diffraction. The synthesized AgNPs were polydispersed spherical particles ranging in size from 1 to 20 nm and stabilized in the solution. Reduced nicotinamide adenine dinucleotide (NADH) was found to be an important reducing agent for the biosynthesis, and the formation of AgNPs might be an enzyme-mediated extracellular reaction process. Furthermore, the antimicrobial potential of AgNPs was systematically evaluated. The synthesized AgNPs could efficiently inhibit various pathogenic organisms, including bacteria and fungi. The current research opens a new avenue for the green synthesis of nano-materials.