Using accelerometer, high sample rate GPS and magnetometer data to develop a cattle movement and behaviour model

The study described in this paper developed a model of animal movement, which explicitly recognised each individual as the central unit of measure. The model was developed by learning from a real dataset that measured and calculated, for individual cows in a herd, their linear and angular positions and directional and angular speeds. Two learning algorithms were implemented: a Hidden Markov model (HMM) and a long-term prediction algorithm. It is shown that a HMM can be used to describe the animal's movement and state transition behaviour within several “stay” areas where cows remained for long periods. Model parameters were estimated for hidden behaviour states such as relocating, foraging and bedding. For cows’ movement between the “stay” areas a long-term prediction algorithm was implemented. By combining these two algorithms it was possible to develop a successful model, which achieved similar results to the animal behaviour data collected. This modelling methodology could easily be applied to interactions of other animal species.

Visual sea-floor mapping from low overlap imagery using bi-objective bundle adjustment and constrained motion

In most visual mapping applications suited to Autonomous Underwater Vehicles (AUVs), stereo visual odometry (VO) is rarely utilised as a pose estimator as imagery is typically of very low framerate due to energy conservation and data storage requirements. This adversely affects the robustness of a vision-based pose estimator and its ability to generate a smooth trajectory. This paper presents a novel VO pipeline for low-overlap imagery from an AUV that utilises constrained motion and integrates magnetometer data in a bi-objective bundle adjustment stage to achieve low-drift pose estimates over large trajectories. We analyse the performance of a standard stereo VO algorithm and compare the results to the modified vo algorithm. Results are demonstrated in a virtual environment in addition to low-overlap imagery gathered from an AUV. The modified VO algorithm shows significantly improved pose accuracy and performance over trajectories of more than 300m. In addition, dense 3D meshes generated from the visual odometry pipeline are presented as a qualitative output of the solution.

Iron(II) spin transition complexes with dendritic ligands, Part I

The ligands G1- and G2-oligo (benzyl ether) (PBE) dendrons and their iron(II) complexes [Fe(Gn-PBE)3]A2·xH2O (with n = 1, 2 and A = triflate, tosylate) were prepared. The magnetic properties of the complexes were investigated by a SQUID magnetometer. All complexes exhibit gradual spin transition below room temperature. At very low temperatures the magnetic behaviour reflects zero-field splitting (ZFS) effects. 57Fe-Mössbauer spectroscopy was performed to distinguish between ZFS of high spin species and spin state conversion into the low spin state. Further characterisation was carried out by thermogravimetric analysis (TGA) and FT-IR spectroscopy. Structural features have been determined by powder XRD measurements.

Polymer-mediated synthesis of γ-Fe2O3 nano-particles

Nano-particles of γ-Fe2O3 were synthesized by reacting polyethylene oxide–FeCl3 complex with NH4OH. These were characterized by X-ray diffraction (XRD), scanning electron miscroscopy (SEM), selected area electron diffraction (SAED) and transmision electron microscopy (TEM). The average particle size was found to be 10 nm, as determined from the line broadening of the main XRD peak. The crystalline phase was a spinel-type tetragonal structure, which was confirmed from the electron diffraction pattern. The zero field cooled magnetization of samples with varying γ-Fe2O3 content as a function of temperature was measured using a vibrating sample magnetometer. The magnetization curves show a peak at low temperature (15 K) corresponding to the blocking temperature TB. The value of TB was found to decrease with decreasing particle size. The magnetization measurements with respect to field at 5 and 170 K confirmed the transition from superparamagnetic to spin-glass state at TB, as evidenced from the remanence and hysteresis. These results can be explained on the basis of Néel's theory of superparamagnetism as applied to nano-particles.