Seasonal dynamics and operational monitoring of hedgerow olive tree transpiration in response to applied water

We used 2012 sap flow measurements to assess the seasonal dynamics of daily plant transpiration (ETc) in a high-density olive orchard (Olea europaea L. cv. ‘Arbequina’) with a well-watered (HI) control treatment A to supply 100 % of the crop water needs, and a moderately (MI) watered treatment B that replaced 70% of crop needs. To assure that treatment A was well-watered, we compared field daily ETc values against ETc obtained with the Penman-Monteith (PM) combination equation incorporating the Orgaz et al. (2007) bulk daily canopy conductance (gc) model, validated for our non-limiting conditions. We then tested the hypothesis of indirectly monitoring olive ETc from readily available vegetation index (VI) and ground-based plant water stress indicator. In the process we used the FAO56 dual crop coefficient (Kc) approach. For the HI olive trees we defined Kcb as the basal transpiration coefficient, and we related Kcb to remotely sensed Soil Adjusted Vegetation Index (SAVI) through a Kcb-SAVI functional relationship. For the MI treatment, we defined the actual transpiration ETc as the product of Kcb and the stress reduction coefficient Ks obtained as the ratio of actual to crop ETc, and we correlated Ks with MI midday stem water potential (ψst) values through a Ks-ψ functional relationship. Operational monitoring of ETc was then implemented with the ETc = Kcb(SAVI)Ks(ψ)ETo relationship stemmed from the FAO56 approach and validated taking as inputs collected SAVI and ψst data reporting to year 2011. Low validation error (6%) and high goodness-of-fit of prediction were observed (R2 = 0.94, RSME = 0.2 mm day-1, P = 0.0015), allowing to consider that under field conditions it is possible to predict ETc values for our hedgerow olive orchards if SAVI and water potential (ψst) values are known.

Cloud parameter retrievals from Meteosat and their effects on the shortwave radiation at the surface

A method based on Spinning Enhanced Visible and Infrared Imager (SEVIRI) measured reflectance at 0.6 and 3.9 µm is used to retrieve the cloud optical thickness (COT) and cloud effective radius (re) over the Iberian Peninsula. A sensitivity analysis of simulated retrievals to the input parameters demonstrates that the cloud top height is an important factor in satellite retrievals of COT and re with uncertainties around 10% for small values of COT and re; for water clouds these uncertainties can be greater than 10% for small values of re. The uncertainties found related with geometries are around 3%. The COT and re are assessed using well-known satellite cloud products, showing that the method used characterize the cloud field with more than 80% (82%) of the absolute differences between COT (re) mean values of all clouds (water plus ice clouds) centred in the range from ±10 (±10 µm), with absolute bias lower than 2 (2 μm) for COT (re) and root mean square error values lower than 10 (8 μm) for COT (re). The cloud water path (CWP), derived from satellite retrievals, and the shortwave cloud radiative effect at the surface (CRESW) are related for high fractional sky covers (Fsc >0.8), showing that water clouds produce more negative CRESW than ice clouds. The COT retrieved was also related to the cloud modification factor, which exhibits reductions and enhancements of the surface SW radiation of the order of 80% and 30%, respectively, for COT values lower than 10. A selected case study shows, using a ground-based sky camera that some situations classified by the satellite with high Fsc values correspond to situations of broken clouds where the enhancements actually occur. For this case study, a closure between the liquid water path (LWP) obtained from the satellite retrievals and the same cloud quantity obtained from ground-based microwave measurements was performed showing a good agreement between both LWP data set values.

Discrete element analysis for the assessment of the accuracy of load cell-based dynamic weighing systems in grape harvesters under different ground conditions

Dynamic weighing systems based on load cells are commonly used to estimate crop yields in the field. There is lack of data, however, regarding the accuracy of such weighing systems mounted on harvesting machinery, especially on that used to collect high value crops such as fruits and vegetables. Certainly, dynamic weighing systems mounted on the bins of grape harvesters are affected by the displacement of the load inside the bin when moving over terrain of changing topography. In this work, the load that would be registered in a grape harvester bin by a dynamic weighing system based on the use of a load cell was inferred by using the discrete element method (DEM). DEM is a numerical technique capable of accurately describing the behaviour of granular materials under dynamic situations and it has been proven to provide successful predictions in many different scenarios. In this work, different DEM models of a grape harvester bin were developed contemplating different influencing factors. Results obtained from these models were used to infer the output given by the load cell of a real bin. The mass detected by the load cell when the bin was inclined depended strongly on the distribution of the load within the bin, but was underestimated in all scenarios. The distribution of the load was found to be dependent on the inclination of the bin caused by the topography of the terrain, but also by the history of inclination (inclination rate, presence of static periods, etc.) since the effect of the inertia of the particles (i.e., representing the grapes) was not negligible. Some recommendations are given to try to improve the accuracy of crop load measurement in the field.

Measurement-based teleportation along quantum spin chains

We examine the teleportation of an unknown spin-1/2 quantum state along a quantum spin chain with an even number of sites. Our protocol, using a sequence of Bell measurements, may be viewed as an iterated version of the 2-qubit protocol of C. H. Bennett et al. [Phys. Rev. Lett. 70, 1895 (1993)]. A decomposition of the Hilbert space of the spin chain into 4 vector spaces, called Bell subspaces, is given. It is established that any state from a Bell subspace may be used as a channel to perform unit fidelity teleportation. The space of all spin-0 many-body states, which includes the ground states of many known antiferromagnetic systems, belongs to a common Bell subspace. A channel-dependent teleportation parameter O is introduced, and a bound on the teleportation fidelity is given in terms of O.

Rough-terrain groundspeed measurement:a radar-based commercial solution

Off-highway motive plant equipment is costly in capital outlay and maintenance. To reduce these overheads and increase site safety and workrate, a technique of assessing and limiting the velocity of such equipment is required. Due to the extreme environmental conditions met on such sites, conventional velocity measurement techniques are inappropriate. Ogden Electronics Limited were formed specifically to manufacture a motive plant safety system incorporating a speed sensor and sanction unit; to date, the only such commercial unit available. However, problems plague the reliability, accuracy and mass production of this unit. This project assesses the company's exisiting product, and in conjunction with an appreciation of the company history and structure, concludes that this unit is unsuited to its intended application. Means of improving the measurement accuracy and longevity of this unit, commensurate with the company's limited resources and experience, are proposed, both for immediate retrofit and for longer term use. This information is presented in the form of a number of internal reports for the company. The off-highway environment is examined; and in conjunction with an evaluation of means of obtaining a returned signal, comparisons of processing techniques, and on-site gathering of previously unavailable data, preliminary designs for an alternative product are drafted. Theoretical aspects are covered by a literature review of ground-pointing radar, vehicular radar, and velocity measuring systems. This review establishes and collates the body of knowledge in areas previously considered unrelated. Based upon this work, a new design is proposed which is suitable for incorporation into the existing company product range. Following production engineering of the design, five units were constructed, tested and evaluated on-site. After extended field trials, this design has shown itself to possess greater accuracy, reliability and versatility than the existing sensor, at a lower unit cost.

Investigation into submicrometer particle and gaseous emissions from airport ground running procedures

Emissions from airport operations are of significant concern because of their potential impact on local air quality and human health. The currently limited scientific knowledge of aircraft emissions is an important issue worldwide, when considering air pollution associated with airport operation, and this is especially so for ultrafine particles. This limited knowledge is due to scientific complexities associated with measuring aircraft emissions during normal operations on the ground. In particular this type of research has required the development of novel sampling techniques which must take into account aircraft plume dispersion and dilution as well as the various particle dynamics that can affect the measurements of the aircraft engine plume from an operational aircraft. In order to address this scientific problem, a novel mobile emission measurement method called the Plume Capture and Analysis System (PCAS), was developed and tested. The PCAS permits the capture and analysis of aircraft exhaust during ground level operations including landing, taxiing, takeoff and idle. The PCAS uses a sampling bag to temporarily store a sample, providing sufficient time to utilize sensitive but slow instrumental techniques to be employed to measure gas and particle emissions simultaneously and to record detailed particle size distributions. The challenges in relation to the development of the technique include complexities associated with the assessment of the various particle loss and deposition mechanisms which are active during storage in the PCAS. Laboratory based assessment of the method showed that the bag sampling technique can be used to accurately measure particle emissions (e.g. particle number, mass and size distribution) from a moving aircraft or vehicle. Further assessment of the sensitivity of PCAS results to distance from the source and plume concentration was conducted in the airfield with taxiing aircraft. The results showed that the PCAS is a robust method capable of capturing the plume in only 10 seconds. The PCAS is able to account for aircraft plume dispersion and dilution at distances of 60 to 180 meters downwind of moving a aircraft along with particle deposition loss mechanisms during the measurements. Characterization of the plume in terms of particle number, mass (PM2.5), gaseous emissions and particle size distribution takes only 5 minutes allowing large numbers of tests to be completed in a short time. The results were broadly consistent and compared well with the available data. Comprehensive measurements and analyses of the aircraft plumes during various modes of the landing and takeoff (LTO) cycle (e.g. idle, taxi, landing and takeoff) were conducted at Brisbane Airport (BNE). Gaseous (NOx, CO2) emission factors, particle number and mass (PM2.5) emission factors and size distributions were determined for a range of Boeing and Airbus aircraft, as a function of aircraft type and engine thrust level. The scientific complexities including the analysis of the often multimodal particle size distributions to describe the contributions of different particle source processes during the various stages of aircraft operation were addressed through comprehensive data analysis and interpretation. The measurement results were used to develop an inventory of aircraft emissions at BNE, including all modes of the aircraft LTO cycle and ground running procedures (GRP). Measurements of the actual duration of aircraft activity in each mode of operation (time-in-mode) and compiling a comprehensive matrix of gas and particle emission rates as a function of aircraft type and engine thrust level for real world situations was crucial for developing the inventory. The significance of the resulting matrix of emission rates in this study lies in the estimate it provides of the annual particle emissions due to aircraft operations, especially in terms of particle number. In summary, this PhD thesis presents for the first time a comprehensive study of the particle and NOx emission factors and rates along with the particle size distributions from aircraft operations and provides a basis for estimating such emissions at other airports. This is a significant addition to the scientific knowledge in terms of particle emissions from aircraft operations, since the standard particle number emissions rates are not currently available for aircraft activities.

Measurement of affect during simulation-based training

Training designed to support and strengthen higher-order mental abilities now often involves immersion in Virtual Reality where dangerous real world scenarios can be safely replicated. However despite the growing popularity of advanced training simulations, methods for evaluating their use rely heavily on subjective measures or analysis of final outcomes. Without dynamic, objective performance measures the outcome of training in terms of impact on cognitive skills and ability to transfer newly acquired skills to the real world is unknown. The relationship between affective intensity and cognitive learning provides a potential new approach to ensure the processing of cognitions which occur prior to final outcomes, such as problem-solving and decision-making, are adequately evaluated. This paper describes the technical aspects of pilot work recently undertaken to develop a new measurement tool designed to objectively track individual affect levels during simulation-based training.

Triangular Linguistic Fuzzy based performance measurement model for supply chain to assess leanness attributes

Increasing global competitiveness worldwide has forced manufacturing organizations to produce high-quality products more quickly and at a competitive cost. In order to reach these goals, they need good quality components from suppliers at optimum price and lead time. This actually forced all the companies to adapt different improvement practices such as lean manufacturing, Just in Time (JIT) and effective supply chain management. Applying new improvement techniques and tools cause higher establishment costs and more Information Delay (ID). On the contrary, these new techniques may reduce the risk of stock outs and affect supply chain flexibility to give a better overall performance. But industry people are unable to measure the overall affects of those improvement techniques with a standard evaluation model .So an effective overall supply chain performance evaluation model is essential for suppliers as well as manufacturers to assess their companies under different supply chain strategies. However, literature on lean supply chain performance evaluation is comparatively limited. Moreover, most of the models assumed random values for performance variables. The purpose of this paper is to propose an effective supply chain performance evaluation model using triangular linguistic fuzzy numbers and to recommend optimum ranges for performance variables for lean implementation. The model initially considers all the supply chain performance criteria (input, output and flexibility), converts the values to triangular linguistic fuzzy numbers and evaluates overall supply chain performance under different situations. Results show that with the proposed performance measurement model, improvement area for each variable can be accurately identified.

Integrated vision-based attitude estimation

For many years, computer vision has lured researchers with promises of a low-cost, passive, lightweight and information-rich sensor suitable for navigation purposes. The prime difficulty in vision-based navigation is that the navigation solution will continually drift with time unless external information is available, whether it be cues from the appearance of the scene, a map of features (whether built online or known a priori), or from an externally-referenced sensor. It is not merely position that is of interest in the navigation problem. Attitude (i.e. the angular orientation of a body with respect to a reference frame) is integral to a visionbased navigation solution and is often of interest in its own right (e.g. flight control). This thesis examines vision-based attitude estimation in an aerospace environment, and two methods are proposed for constraining drift in the attitude solution; one through a novel integration of optical flow and the detection of the sky horizon, and the other through a loosely-coupled integration of Visual Odometry and GPS position measurements. In the first method, roll angle, pitch angle and the three aircraft body rates are recovered though a novel method of tracking the horizon over time and integrating the horizonderived attitude information with optical flow. An image processing front-end is used to select several candidate lines in a image that may or may not correspond to the true horizon, and the optical flow is calculated for each candidate line. Using an Extended Kalman Filter (EKF), the previously estimated aircraft state is propagated using a motion model and a candidate horizon line is associated using a statistical test based on the optical flow measurements and location of the horizon in the image. Once associated, the selected horizon line, along with the associated optical flow, is used as a measurement to the EKF. To evaluate the accuracy of the algorithm, two flights were conducted, one using a highly dynamic Uninhabited Airborne Vehicle (UAV) in clear flight conditions and the other in a human-piloted Cessna 172 in conditions where the horizon was partially obscured by terrain, haze and smoke. The UAV flight resulted in pitch and roll error standard deviations of 0.42° and 0.71° respectively when compared with a truth attitude source. The Cessna 172 flight resulted in pitch and roll error standard deviations of 1.79° and 1.75° respectively. In the second method for estimating attitude, a novel integrated GPS/Visual Odometry (GPS/VO) navigation filter is proposed, using a structure similar to a classic looselycoupled GPS/INS error-state navigation filter. Under such an arrangement, the error dynamics of the system are derived and a Kalman Filter is developed for estimating the errors in position and attitude. Through similar analysis to the GPS/INS problem, it is shown that the proposed filter is capable of recovering the complete attitude (i.e. pitch, roll and yaw) of the platform when subjected to acceleration not parallel to velocity for both the monocular and stereo variants of the filter. Furthermore, it is shown that under general straight line motion (e.g. constant velocity), only the component of attitude in the direction of motion is unobservable. Numerical simulations are performed to demonstrate the observability properties of the GPS/VO filter in both the monocular and stereo camera configurations. Furthermore, the proposed filter is tested on imagery collected using a Cessna 172 to demonstrate the observability properties on real-world data. The proposed GPS/VO filter does not require additional restrictions or assumptions such as platform-specific dynamics, map-matching, feature-tracking, visual loop-closing, gravity vector or additional sensors such as an IMU or magnetic compass. Since no platformspecific dynamics are required, the proposed filter is not limited to the aerospace domain and has the potential to be deployed in other platforms such as ground robots or mobile phones.

Airborne collision scenario flight tests : impact of angle measurement errors on reactive vision-based avoidance control

The future emergence of many types of airborne vehicles and unpiloted aircraft in the national airspace means collision avoidance is of primary concern in an uncooperative airspace environment. The ability to replicate a pilot’s see and avoid capability using cameras coupled with vision based avoidance control is an important part of an overall collision avoidance strategy. But unfortunately without range collision avoidance has no direct way to guarantee a level of safety. Collision scenario flight tests with two aircraft and a monocular camera threat detection and tracking system were used to study the accuracy of image-derived angle measurements. The effect of image-derived angle errors on reactive vision-based avoidance performance was then studied by simulation. The results show that whilst large angle measurement errors can significantly affect minimum ranging characteristics across a variety of initial conditions and closing speeds, the minimum range is always bounded and a collision never occurs.

Monocular vision based autonomous navigation for a cost-effective MAV in GPS-denied environments

In this paper, we present a monocular vision based autonomous navigation system for Micro Aerial Vehicles (MAVs) in GPS-denied environments. The major drawback of monocular systems is that the depth scale of the scene can not be determined without prior knowledge or other sensors. To address this problem, we minimize a cost function consisting of a drift-free altitude measurement and up-to-scale position estimate obtained using the visual sensor. We evaluate the scale estimator, state estimator and controller performance by comparing with ground truth data acquired using a motion capture system. All resources including source code, tutorial documentation and system models are available online.

A fuzzy TOPSIS based multi criteria performance measurement model for lean supply chain

Increasing global competition, rapid technological changes, advances in manufacturing and information technology and discerning customers are forcing supply chains to adopt improvement practices that enable them to deliver high quality products at a lower cost and in a shorter period of time. A lean initiative is one of the most effective approaches toward achieving this goal. In the lean improvement process, it is critical to measure current and desired performance level in order to clearly evaluate the lean implementation efforts. Many attempts have tried to measure supply chain performance incorporating both quantitative and qualitative measures but failed to provide an effective method of measuring improvements in performances for dynamic lean supply chain situations. Therefore, the necessity of appropriate measurement of lean supply chain performance has become imperative. There are many lean tools available for supply chains; however, effectiveness of a lean tool depends on the type of the product and supply chain. One tool may be highly effective for a supply chain involved in high volume products but may not be effective for low volume products. There is currently no systematic methodology available for selecting appropriate lean strategies based on the type of supply chain and market strategy This thesis develops an effective method to measure the performance of supply chain consisting of both quantitative and qualitative metrics and investigates the effects of product types and lean tool selection on the supply chain performance Supply chain performance matrices and the effects of various lean tools over performance metrics mentioned in the SCOR framework have been investigated. A lean supply chain model based on the SCOR metric framework is then developed where non- lean and lean as well as quantitative and qualitative metrics are incorporated in appropriate metrics. The values of appropriate metrics are converted into triangular fuzzy numbers using similarity rules and heuristic methods. Data have been collected from an apparel manufacturing company for multiple supply chain products and then a fuzzy based method is applied to measure the performance improvements in supply chains. Using the fuzzy TOPSIS method, which chooses an optimum alternative to maximise similarities with positive ideal solutions and to minimise similarities with negative ideal solutions, the performances of lean and non- lean supply chain situations for three different apparel products have been evaluated. To address the research questions related to effective performance evaluation method and the effects of lean tools over different types of supply chains; a conceptual framework and two hypotheses are investigated. Empirical results show that implementation of lean tools have significant effects over performance improvements in terms of time, quality and flexibility. Fuzzy TOPSIS based method developed is able to integrate multiple supply chain matrices onto a single performance measure while lean supply chain model incorporates qualitative and quantitative metrics. It can therefore effectively measure the improvements for supply chain after implementing lean tools. It is demonstrated that product types involved in the supply chain and ability to select right lean tools have significant effect on lean supply chain performance. Future study can conduct multiple case studies in different contexts.