HDC poinsettia tracker: flexible graphical tracking software

Growing pot poinsettia and similar crops involves careful crop monitoring and management to ensure that height specifications are met. Graphical tracking represents a target driven approach to decision support with simple interpretation. HDC (Horticultural Development Council) Poinsettia Tracker implements a graphical track based on the Generalised Logistic Curve, similar to that of other tracking packages. Any set of curve parameters can be used to track crop progress. However, graphical tracks must be expected to be site and cultivar specific. By providing a simple Curve fitting function, growers can easily develop their own site and variety specific ideal tracks based on past records with increasing quality as more seasons' data are added. (C) 2009 Elsevier B.V. All rights reserved.

Graphical tracking systems revisited: a practical approach to computer scheduling in horticulture

Graphical tracking is a technique for crop scheduling where the actual plant state is plotted against an ideal target curve which encapsulates all crop and environmental characteristics. Management decisions are made on the basis of the position of the actual crop against the ideal position. Due to the simplicity of the approach it is possible for graphical tracks to be developed on site without the requirement for controlled experimentation. Growth models and graphical tracks are discussed, and an implementation of the Richards curve for graphical tracking described. In many cases, the more intuitively desirable growth models perform sub-optimally due to problems with the specification of starting conditions, environmental factors outside the scope of the original model and the introduction of new cultivars. Accurate specification for a biological model requires detailed and usually costly study, and as such is not adaptable to a changing cultivar range and changing cultivation techniques. Fitting of a new graphical track for a new cultivar can be conducted on site and improved over subsequent seasons. Graphical tracking emphasises the current position relative to the objective, and as such does not require the time consuming or system specific input of an environmental history, although it does require detailed crop measurement. The approach is flexible and could be applied to a variety of specification metrics, with digital imaging providing a route for added value. For decision making regarding crop manipulation from the observed current state, there is a role for simple predictive modelling over the short term to indicate the short term consequences of crop manipulation.

Modelling habitat selection and distribution of the critically endangered Jerdon's courser Rhinoptilus bitorquatus in scrub jungle: an application of a new tracking method

1. Jerdon's courser Rhinoptilus bitorquatus is a nocturnally active cursorial bird that is only known to occur in a small area of scrub jungle in Andhra Pradesh, India, and is listed as critically endangered by the IUCN. Information on its habitat requirements is needed urgently to underpin conservation measures. We quantified the habitat features that correlated with the use of different areas of scrub jungle by Jerdon's coursers, and developed a model to map potentially suitable habitat over large areas from satellite imagery and facilitate the design of surveys of Jerdon's courser distribution. 2. We used 11 arrays of 5-m long tracking strips consisting of smoothed fine soil to detect the footprints of Jerdon's coursers, and measured tracking rates (tracking events per strip night). We counted the number of bushes and trees, and described other attributes of vegetation and substrate in a 10-m square plot centred on each strip. We obtained reflectance data from Landsat 7 satellite imagery for the pixel within which each strip lay. 3. We used logistic regression models to describe the relationship between tracking rate by Jerdon's coursers and characteristics of the habitat around the strips, using ground-based survey data and satellite imagery. 4. Jerdon's coursers were most likely to occur where the density of large (>2 m tall) bushes was in the range 300-700 ha(-1) and where the density of smaller bushes was less than 1000 ha(-1). This habitat was detectable using satellite imagery. 5. Synthesis and applications. The occurrence of Jerdon's courser is strongly correlated with the density of bushes and trees, and is in turn affected by grazing with domestic livestock, woodcutting and mechanical clearance of bushes to create pasture, orchards and farmland. It is likely that there is an optimal level of grazing and woodcutting that would maintain or create suitable conditions for the species. Knowledge of the species' distribution is incomplete and there is considerable pressure from human use of apparently suitable habitats. Hence, distribution mapping is a high conservation priority. A two-step procedure is proposed, involving the use of ground surveys of bush density to calibrate satellite image-based mapping of potential habitat. These maps could then be used to select priority areas for Jerdon's courser surveys. The use of tracking strips to study habitat selection and distribution has potential in studies of other scarce and secretive species.

Improved fluorescent proteins for single-molecule research in molecular tracking and co-localization

Three promising variants of autofluorescent proteins have been analyzed photophysically for their proposed use in single-molecule microscopy studies in living cells to compare their superiority to other fluorescent proteins previously reported regarding the number of photons emitted. The first variant under investigation the F46L mutant of eYFP has a 10% greater photon emission rate and > 50% slower photobleaching rate on average than the standard eYFP fluorophore. The monomeric red fluorescent protein (mRFP) has a fivefold lower photon emission rate, likely due to the monomeric content, and also a tenfold faster photobleaching rate than the DsRed fluorescent protein. In contrast, the previously reported eqfp611 has a 50% lower emission rate yet photobleaches more than a factor 2 slowly. We conclude that the F46L YFP and the eqfp611 are superior new options for single molecule imaging and tracking studies in living cells. Studies were also performed on the effects of forced quenching of multiple fluorescent proteins in sub-micrometer regions that would show the effects of dimerization at low concentration levels of fluorescent proteins and also indicate corrections to stoichiometry patterns with fluorescent proteins previously in print. We also introduce properties at the single molecule level of new FRET pairs with combinations of fluorescent proteins and artificial fluorophores.

Quantitative analysis of accuracy of an inertial/acoustic 6DOF tracking system in motion

An increasing number of neuroscience experiments are using virtual reality to provide a more immersive and less artificial experimental environment. This is particularly useful to navigation and three-dimensional scene perception experiments. Such experiments require accurate real-time tracking of the observer's head in order to render the virtual scene. Here, we present data on the accuracy of a commonly used six degrees of freedom tracker (Intersense IS900) when it is moved in ways typical of virtual reality applications. We compared the reported location of the tracker with its location computed by an optical tracking method. When the tracker was stationary, the root mean square error in spatial accuracy was 0.64 mm. However, we found that errors increased over ten-fold (up to 17 mm) when the tracker moved at speeds common in virtual reality applications. We demonstrate that the errors we report here are predominantly due to inaccuracies of the IS900 system rather than the optical tracking against which it was compared. (c) 2006 Elsevier B.V. All rights reserved.

A fuzzy multi-criteria decision approach for enhanced auto-tracking of seismic events

The main activity carried out by the geophysicist when interpreting seismic data, in terms of both importance and time spent is tracking (or picking) seismic events. in practice, this activity turns out to be rather challenging, particularly when the targeted event is interrupted by discontinuities such as geological faults or exhibits lateral changes in seismic character. In recent years, several automated schemes, known as auto-trackers, have been developed to assist the interpreter in this tedious and time-consuming task. The automatic tracking tool available in modem interpretation software packages often employs artificial neural networks (ANN's) to identify seismic picks belonging to target events through a pattern recognition process. The ability of ANNs to track horizons across discontinuities largely depends on how reliably data patterns characterise these horizons. While seismic attributes are commonly used to characterise amplitude peaks forming a seismic horizon, some researchers in the field claim that inherent seismic information is lost in the attribute extraction process and advocate instead the use of raw data (amplitude samples). This paper investigates the performance of ANNs using either characterisation methods, and demonstrates how the complementarity of both seismic attributes and raw data can be exploited in conjunction with other geological information in a fuzzy inference system (FIS) to achieve an enhanced auto-tracking performance.

Two-pass hexagonal algorithm with parallel implementation for video coding

This paper presents a paralleled Two-Pass Hexagonal (TPA) algorithm constituted by Linear Hashtable Motion Estimation Algorithm (LHMEA) and Hexagonal Search (HEXBS) for motion estimation. In the TPA, Motion Vectors (MV) are generated from the first-pass LHMEA and are used as predictors for second-pass HEXBS motion estimation, which only searches a small number of Macroblocks (MBs). We introduced hashtable into video processing and completed parallel implementation. We propose and evaluate parallel implementations of the LHMEA of TPA on clusters of workstations for real time video compression. It discusses how parallel video coding on load balanced multiprocessor systems can help, especially on motion estimation. The effect of load balancing for improved performance is discussed. The performance of the algorithm is evaluated by using standard video sequences and the results are compared to current algorithms.

Linear hashtable motion estimation algorithm for distributed video processing

This paper presents a parallel Linear Hashtable Motion Estimation Algorithm (LHMEA). Most parallel video compression algorithms focus on Group of Picture (GOP). Based on LHMEA we proposed earlier [1][2], we developed a parallel motion estimation algorithm focus inside of frame. We divide each reference frames into equally sized regions. These regions are going to be processed in parallel to increase the encoding speed significantly. The theory and practice speed up of parallel LHMEA according to the number of PCs in the cluster are compared and discussed. Motion Vectors (MV) are generated from the first-pass LHMEA and used as predictors for second-pass Hexagonal Search (HEXBS) motion estimation, which only searches a small number of Macroblocks (MBs). We evaluated distributed parallel implementation of LHMEA of TPA for real time video compression.

Improved two-pass hexagonal algorithm with parallel implementation for video coding - art. no. 60630J

This paper presents an improved parallel Two-Pass Hexagonal (TPA) algorithm constituted by Linear Hashtable Motion Estimation Algorithm (LHMEA) and Hexagonal Search (HEXBS) for motion estimation. Motion Vectors (MV) are generated from the first-pass LHMEA and used as predictors for second-pass HEXBS motion estimation, which only searches a small number of Macroblocks (MBs). We used bashtable into video processing and completed parallel implementation. The hashtable structure of LHMEA is improved compared to the original TPA and LHMEA. We propose and evaluate parallel implementations of the LHMEA of TPA on clusters of workstations for real time video compression. The implementation contains spatial and temporal approaches. The performance of the algorithm is evaluated by using standard video sequences and the results are compared to current algorithms.

A swarm intelligence method for feature tracking in AMV derivation

Feature tracking is a key step in the derivation of Atmospheric Motion Vectors (AMV). Most operational derivation processes use some template matching technique, such as Euclidean distance or cross-correlation, for the tracking step. As this step is very expensive computationally, often shortrange forecasts generated by Numerical Weather Prediction (NWP) systems are used to reduce the search area. Alternatives, such as optical flow methods, have been explored, with the aim of improving the number and quality of the vectors generated and the computational efficiency of the process. This paper will present the research carried out to apply Stochastic Diffusion Search, a generic search technique in the Swarm Intelligence family, to feature tracking in the context of AMV derivation. The method will be described, and we will present initial results, with Euclidean distance as reference.

An FLIR video surveillance system to avoid bridge-ship collision

In this paper, a forward-looking infrared (FLIR) video surveillance system is presented for collision avoidance of moving ships to bridge piers. An image pre-processing algorithm is proposed to reduce clutter noises by multi-scale fractal analysis, in which the blanket method is used for fractal feature computation. Then, the moving ship detection algorithm is developed from image differentials of the fractal feature in the region of surveillance between regularly interval frames. Experimental results have shown that the approach is feasible and effective. It has achieved real-time and reliable alert to avoid collisions of moving ships to bridge piers