Layered titanate nanofibers as efficient adsorbents for removal of toxic radioactive and heavy metal ions from water

Titanate nanofibers with two formulas, Na2Ti3O7 and Na1.5H0.5Ti3O7, respectively, exhibit ideal properties for removal of radioactive and heavy metal ions in wastewater, such as Sr2+ , Ba2+ (as substitute of 226Ra2+), and Pb2+ ions. These nanofibers can be fabricated readily by a reaction between titania and caustic soda and have structures in which TiO6 octahedra join each other to form layers with negative charges; the sodium cations exist within the interlayer regions and are exchangeable. They can selectively adsorb the bivalent radioactive ions and heavy metal ions from water through ion exchange process. More importantly, such sorption finally induces considerable deformation of the layer structure, resulting in permanent entrapment of the toxic bivalent cations in the fibers so that the toxic ions can be safely deposited. This study highlights that nanoparticles of inorganic ion exchangers with layered structure are potential materials for efficient removal of the toxic ions from contaminated water.

Thermally activated seawater neutralised red mud used for the removal of arsenate, vanadate and molybdate from aqueous solutions.

The effectiveness of using thermally activated hydrotalcite materials has been investigated for the removal of arsenate, vanadate, and molybdate in individual and mixed solutions. Results show that increasing the Mg,Al ratio to 4:1 causes an increase in the percentage of anions removed from solution. The order of affinity of the three anions analysed in this investigation is arsenate, vanadate, and molybdate. By comparisons with several synthetic hydrotalcite materials, the hydrotalcite structure in the seawater neutralised red mud (SWN-RM) has been determined to consist of magnesium and aluminium with a ratio between 3.5:1 and 4:1. Thermally activated seawater neutralised red mud removes at least twice the concentration of anionic species than thermally activated red mud alone, due to the formation of 40 to 60 % Bayer hydrotalcite during the neutralisation process.

Improved simultaneous computation of motion detection and optical flow for object tracking

Object tracking systems require accurate segmentation of the objects from the background for effective tracking. Motion segmentation or optical flow can be used to segment incoming images. Whilst optical flow allows multiple moving targets to be separated based on their individual velocities, optical flow techniques are prone to errors caused by changing lighting and occlusions, both common in a surveillance environment. Motion segmentation techniques are more robust to fluctuating lighting and occlusions, but don't provide information on the direction of the motion. In this paper we propose a combined motion segmentation/optical flow algorithm for use in object tracking. The proposed algorithm uses the motion segmentation results to inform the optical flow calculations and ensure that optical flow is only calculated in regions of motion, and improve the performance of the optical flow around the edge of moving objects. Optical flow is calculated at pixel resolution and tracking of flow vectors is employed to improve performance and detect discontinuities, which can indicate the location of overlaps between objects. The algorithm is evaluated by attempting to extract a moving target within the flow images, given expected horizontal and vertical movement (i.e. the algorithms intended use for object tracking). Results show that the proposed algorithm outperforms other widely used optical flow techniques for this surveillance application.

Hippocampal models for simultaneous localisation and mapping on an autonomous robot

To navigate successfully in a novel environment a robot needs to be able to Simultaneously Localize And Map (SLAM) its surroundings. The most successful solutions to this problem so far have involved probabilistic algorithms, but there has been much promising work involving systems based on the workings of part of the rodent brain known as the hippocampus. In this paper we present a biologically plausible system called RatSLAM that uses competitive attractor networks to carry out SLAM in a probabilistic manner. The system can effectively perform parameter self-calibration and SLAM in one dimension. Tests in two dimensional environments revealed the inability of the RatSLAM system to maintain multiple pose hypotheses in the face of ambiguous visual input. These results support recent rat experimentation that suggest current competitive attractor models are not a complete solution to the hippocampal modelling problem.

A modified particle filter for simultaneous robot localization and landmark tracking in an indoor environment

This paper presents the implementation of a modified particle filter for vision-based simultaneous localization and mapping of an autonomous robot in a structured indoor environment. Through this method, artificial landmarks such as multi-coloured cylinders can be tracked with a camera mounted on the robot, and the position of the robot can be estimated at the same time. Experimental results in simulation and in real environments show that this approach has advantages over the extended Kalman filter with ambiguous data association and various levels of odometric noise.

Simultaneous localisation and mapping from natural landmarks using RatSLAM

This paper describes the current state of RatSLAM, a Simultaneous Localisation and Mapping (SLAM) system based on models of the rodent hippocampus. RatSLAM uses a competitive attractor network to fuse visual and odometry information. Energy packets in the network represent pose hypotheses, which are updated by odometry and can be enhanced or inhibited by visual input. This paper shows the effectiveness of the system in real robot tests in unmodified indoor environments using a learning vision system. Results are shown for two test environments; a large corridor loop and the complete floor of an office building.

Biologically inspired visual landmark processing for simultaneous localization and mapping

This paper illustrates a method for finding useful visual landmarks for performing simultaneous localization and mapping (SLAM). The method is based loosely on biological principles, using layers of filtering and pooling to create learned templates that correspond to different views of the environment. Rather than using a set of landmarks and reporting range and bearing to the landmark, this system maps views to poses. The challenge is to produce a system that produces the same view for small changes in robot pose, but provides different views for larger changes in pose. The method has been developed to interface with the RatSLAM system, a biologically inspired method of SLAM. The paper describes the method of learning and recalling visual landmarks in detail, and shows the performance of the visual system in real robot tests.

Use of hydrotalcites for the removal of toxic anions from aqueous solutions

The removal of toxic anions has been achieved using hydrotalcite via two methods: (1) coprecipitation and (2) thermal activation. Hydrotalcite formed via the coprecipitation method, using solutions containing arsenate and vanadate up to pH 10, are able to remove more than 95% of the toxic anions (0.2 M) from solution. The removal of toxic anions in solutions with a pH of >10 reduces the removal uptake percentage to 75%. Raman spectroscopy observed multiple A1 stretching modes of V−O and As−O at 930 and 810 cm−1, assigned to vanadate and arsenate, respectively. Analysis of the intensity and position of the A1 stretching modes helped to identify the vanadate and arsenate specie intercalated into the hydrotalcite structure. It has been determined that 3:1 hydrotalcite structure predominantly intercalate anions into the interlayer region, while the 2:1 and 4:1 hydrotalcite structures shows a large portion of anions being removed from solution by adsorption processes. Treatment of carbonate solutions (0.2 M) containing arsenate and vanadate (0.2 M) three times with thermally activated hydrotalcite has been shown to remove 76% and 81% of the toxic anions, respectively. Thermally activated hydrotalcite with a Mg:Al ratio of 2:1, 3:1, and 4:1 have all been shown to remove 95% of arsenate and vanadate (25 ppm). At increased concentrations of arsenate and vanadate, the removal uptake percentage decreased significantly, except for the 4:1 thermally activated hydrotalcite. Thermally activated Bayer hydrotalcite has also been shown to be highly effective in the removal of arsenate and vanadate. The thermal activation of the solid residue component (red mud) removes 30% of anions from solution (100 ppm of both anions), while seawater-neutralized red mud removes 70%. The formation of hydrotalcite during the seawater neutralization process removes anions via two mechanisms, rather than one observed for thermally activated red mud.

RatSLAM : a hippocampal model for simultaneous localization and mapping

The work presents a new approach to the problem of simultaneous localization and mapping - SLAM - inspired by computational models of the hippocampus of rodents. The rodent hippocampus has been extensively studied with respect to navigation tasks, and displays many of the properties of a desirable SLAM solution. RatSLAM is an implementation of a hippocampal model that can perform SLAM in real time on a real robot. It uses a competitive attractor network to integrate odometric information with landmark sensing to form a consistent representation of the environment. Experimental results show that RatSLAM can operate with ambiguous landmark information and recover from both minor and major path integration errors.

Probabilistic visual recognition of artificial landmarks for simultaneous localization and mapping

Probabilistic robotics, most often applied to the problem of simultaneous localisation and mapping (SLAM), requires measures of uncertainly to accompany observations of the environment. This paper describes how uncertainly can be characterised for a vision system that locates coloured landmark in a typical laboratory environment. The paper describes a model of the uncertainly in segmentation, the internal camera model and the mounting of the camera on the robot. It =plains the implementation of the system on a laboratory robot, and provides experimental results that show the coherence of the uncertainly model,

Sodium removal from Maramarua CSG waters using Ngakuru zeolites

Coal seam gas (CSG) waters are a by-product of natural gas extraction from un derground coal seams. The main issue with these waters is their elevated sodium content, which in conjunction with their low calcium and magnesium concentrations can generate soil infiltration problems in the long run , as well as short term toxicity effects in plants due to the sodium ion itself. Zeolites are minerals having a porous structure, crystalline characteristics, and an alumino-silicate configuration resulting in an overall negative charge which is balanced by loosely held cations. In New Zealand, Ngakuru zeolites have been mined for commercial use in wastewater treatment applications, cosmetics, and pet litter. This research focuses on assessing the capacity of Ngakuru zeolites to reduce sodium concentrations of CSG waters from Maramarua. Batch and column test (flow through) experiments revealed that Ngakuru zeolites are capable of sorbing sodium cations from concentrated solutions of sodium. In b atch tests, the sodium adsorption capacity ranged from 5.0 to 34.3meq/100g depending on the solution concentration and on the number of times the zeolite had been regenerated. Regeneration with CaCl2 was foun d to be effective. The calculated sodium adsorption capacity of Ngakuru zeolites under flow-through conditions ranged from 11 to 42meq/100g depending on the strength of the solution being treated and on w hether the zeolites had been previously regenerated. The slow kinetics and low cost of the zeolities, coupled with potentially remote sites for gas extraction, could make semi-batch operational processes without regeneration more favourable than in more industrial ion exchange situations.

A simultaneous equations model of crash frequency by collision type for rural intersections

Safety at roadway intersections is of significant interest to transportation professionals due to the large number of intersections in transportation networks, the complexity of traffic movements at these locations that leads to large numbers of conflicts, and the wide variety of geometric and operational features that define them. A variety of collision types including head-on, sideswipe, rear-end, and angle crashes occur at intersections. While intersection crash totals may not reveal a site deficiency, over exposure of a specific crash type may reveal otherwise undetected deficiencies. Thus, there is a need to be able to model the expected frequency of crashes by collision type at intersections to enable the detection of problems and the implementation of effective design strategies and countermeasures. Statistically, it is important to consider modeling collision type frequencies simultaneously to account for the possibility of common unobserved factors affecting crash frequencies across crash types. In this paper, a simultaneous equations model of crash frequencies by collision type is developed and presented using crash data for rural intersections in Georgia. The model estimation results support the notion of the presence of significant common unobserved factors across crash types, although the impact of these factors on parameter estimates is found to be rather modest.