OpenRatSLAM : an open source brain-based SLAM system

RatSLAM is a navigation system based on the neural processes underlying navigation in the rodent brain, capable of operating with low resolution monocular image data. Seminal experiments using RatSLAM include mapping an entire suburb with a web camera and a long term robot delivery trial. This paper describes OpenRatSLAM, an open-source version of RatSLAM with bindings to the Robot Operating System framework to leverage advantages such as robot and sensor abstraction, networking, data playback, and visualization. OpenRatSLAM comprises connected ROS nodes to represent RatSLAM’s pose cells, experience map, and local view cells, as well as a fourth node that provides visual odometry estimates. The nodes are described with reference to the RatSLAM model and salient details of the ROS implementation such as topics, messages, parameters, class diagrams, sequence diagrams, and parameter tuning strategies. The performance of the system is demonstrated on three publicly available open-source datasets.

Fast exact nearest neighbour matching in high dimensions using d-D Sort

Data structures such as k-D trees and hierarchical k-means trees perform very well in approximate k nearest neighbour matching, but are only marginally more effective than linear search when performing exact matching in high-dimensional image descriptor data. This paper presents several improvements to linear search that allows it to outperform existing methods and recommends two approaches to exact matching. The first method reduces the number of operations by evaluating the distance measure in order of significance of the query dimensions and terminating when the partial distance exceeds the search threshold. This method does not require preprocessing and significantly outperforms existing methods. The second method improves query speed further by presorting the data using a data structure called d-D sort. The order information is used as a priority queue to reduce the time taken to find the exact match and to restrict the range of data searched. Construction of the d-D sort structure is very simple to implement, does not require any parameter tuning, and requires significantly less time than the best-performing tree structure, and data can be added to the structure relatively efficiently.

General theory of stigmergy : modelling stigma semantics

Stigmergy is a biological term used when discussing a sub-set of insect swarm-behaviour describing the apparent organisation seen during their activities. Stigmergy describes a communication mechanism based on environment-mediated signals which trigger responses among the insects. This phenomenon is demonstrated in the behavior of ants and their food gathering process when following pheromone trails, where the pheromones are a form of environment-mediated communication. What is interesting with this phenomenon is that highly organized societies are achieved without an apparent management structure. Stigmergy is also observed in human environments, both natural and engineered. It is implicit in the Web where sites provide a virtual environment supporting coordinative contributions. Researchers in varying disciplines appreciate the power of this phenomenon and have studied how to exploit it. As stigmergy becomes more widely researched we see its definition mutate as papers citing original work become referenced themselves. Each paper interprets these works in ways very specific to the research being conducted. Our own research aims to better understand what improves the collaborative function of a Web site when exploiting the phenomenon. However when researching stigmergy to develop our understanding we discover a lack of a standardized and abstract model for the phenomenon. Papers frequently cited the same generic descriptions before becoming intimately focused on formal specifications of an algorithm, or esoteric discussions regarding sub-facets of the topic. None provide a holistic and macro-level view to model and standardize the nomenclature. This paper provides a content analysis of influential literature documenting the numerous theoretical and experimental papers that have focused on stigmergy. We establish that stigmergy is a phenomenon that transcends the insect world and is more than just a metaphor when applied to the human world. We present from our own research our general theory and abstract model of semantics of stigma in stigmergy. We hope our model will clarify the nuances of the phenomenon into a useful road-map, and standardise vocabulary that we witness becoming confused and divergent. Furthermore, this paper documents the analysis on which we base our next paper: Special Theory of Stigmergy: A Design Pattern for Web 2.0 Collaboration.

Has portfolio theory got any principles?

Recently, Portfolio Theory (PT) has been proposed for Information Retrieval. However, under non-trivial conditions PT violates the original Probability Ranking Principle (PRP). In this poster, we shall explore whether PT upholds a different ranking principle based on Quantum Theory, i.e. the Quantum Probability Ranking Principle (QPRP), and examine the relationship between this new model and the new ranking principle. We make a significant contribution to the theoretical development of PT and show that under certain circumstances PT upholds the QPRP, and thus guarantees an optimal ranking according to the QPRP. A practical implication of this finding is that the parameters of PT can be automatically estimated via the QPRP, instead of resorting to extensive parameter tuning.

Collaboration in Web N.0: Stigmergy and virtual pheromones

The aim of this project was to develop a general theory of stigmergy and a software design pattern to build collaborative websites. Stigmergy is a biological term used when describing some insect swarm-behaviour where 'food gathering' and 'nest building' activities demonstrate the emergence of self-organised societies achieved without an apparent management structure. The results of the project are an abstract model of stigmergy and a software design pattern for building Web 2.0 components exploiting this self-organizing phenomenon. A proof-of-concept implementation was also created demonstrating potential commercial viability for future website projects.

Some novel techniques of parameter estimation for the dynamical models in biological systems

Inverse problems based on using experimental data to estimate unknown parameters of a system often arise in biological and chaotic systems. In this paper, we consider parameter estimation in systems biology involving linear and non-linear complex dynamical models, including the Michaelis–Menten enzyme kinetic system, a dynamical model of competence induction in Bacillus subtilis bacteria and a model of feedback bypass in B. subtilis bacteria. We propose some novel techniques for inverse problems. Firstly, we establish an approximation of a non-linear differential algebraic equation that corresponds to the given biological systems. Secondly, we use the Picard contraction mapping, collage methods and numerical integration techniques to convert the parameter estimation into a minimization problem of the parameters. We propose two optimization techniques: a grid approximation method and a modified hybrid Nelder–Mead simplex search and particle swarm optimization (MH-NMSS-PSO) for non-linear parameter estimation. The two techniques are used for parameter estimation in a model of competence induction in B. subtilis bacteria with noisy data. The MH-NMSS-PSO scheme is applied to a dynamical model of competence induction in B. subtilis bacteria based on experimental data and the model for feedback bypass. Numerical results demonstrate the effectiveness of our approach.

Parameter estimation for a phenomenological model of the cardiac action potential

The action potential (ap) of a cardiac cell is made up of a complex balance of ionic currents which flow across the cell membrane in response to electrical excitation of the cell. Biophysically detailed mathematical models of the ap have grown larger in terms of the variables and parameters required to model new findings in subcellular ionic mechanisms. The fitting of parameters to such models has seen a large degree of parameter and module re-use from earlier models. An alternative method for modelling electrically exciteable cardiac tissue is a phenomenological model, which reconstructs tissue level ap wave behaviour without subcellular details. A new parameter estimation technique to fit the morphology of the ap in a four variable phenomenological model is presented. An approximation of a nonlinear ordinary differential equation model is established that corresponds to the given phenomenological model of the cardiac ap. The parameter estimation problem is converted into a minimisation problem for the unknown parameters. A modified hybrid Nelder–Mead simplex search and particle swarm optimization is then used to solve the minimisation problem for the unknown parameters. The successful fitting of data generated from a well known biophysically detailed model is demonstrated. A successful fit to an experimental ap recording that contains both noise and experimental artefacts is also produced. The parameter estimation method’s ability to fit a complex morphology to a model with substantially more parameters than previously used is established.

Optimal power system stabilizer tuning in multi-machine system via an improved differential evolution

Power system stabilizer (PSS) is one of the most important controllers in modern power systems for damping low frequency oscillations. Many efforts have been dedicated to design the tuning methodologies and allocation techniques to obtain optimal damping behaviors of the system. Traditionally, it is tuned mostly for local damping performance, however, in order to obtain a globally optimal performance, the tuning of PSS needs to be done considering more variables. Furthermore, with the enhancement of system interconnection and the increase of system complexity, new tools are required to achieve global tuning and coordination of PSS to achieve optimal solution in a global meaning. Differential evolution (DE) is a recognized as a simple and powerful global optimum technique, which can gain fast convergence speed as well as high computational efficiency. However, as many other evolutionary algorithms (EA), the premature of population restricts optimization capacity of DE. In this paper, a modified DE is proposed and applied for optimal PSS tuning of 39-Bus New-England system. New operators are introduced to reduce the probability of getting premature. To investigate the impact of system conditions on PSS tuning, multiple operating points will be studied. Simulation result is compared with standard DE and particle swarm optimization (PSO).

Fine-tuning of unmanned aerial surveillance for ecological systems

Much of our understanding and management of ecological processes requires knowledge of the distribution and abundance of species. Reliable abundance or density estimates are essential for managing both threatened and invasive populations, yet are often challenging to obtain. Recent and emerging technological advances, particularly in unmanned aerial vehicles (UAVs), provide exciting opportunities to overcome these challenges in ecological surveillance. UAVs can provide automated, cost-effective surveillance and offer repeat surveys for pest incursions at an invasion front. They can capitalise on manoeuvrability and advanced imagery options to detect species that are cryptic due to behaviour, life-history or inaccessible habitat. UAVs may also cause less disturbance, in magnitude and duration, for sensitive fauna than other survey methods such as transect counting by humans or sniffer dogs. The surveillance approach depends upon the particular ecological context and the objective. For example, animal, plant and microbial target species differ in their movement, spread and observability. Lag-times may exist between a pest species presence at a site and its detectability, prompting a need for repeat surveys. Operationally, however, the frequency and coverage of UAV surveys may be limited by financial and other constraints, leading to errors in estimating species occurrence or density. We use simulation modelling to investigate how movement ecology should influence fine-scale decisions regarding ecological surveillance using UAVs. Movement and dispersal parameter choices allow contrasts between locally mobile but slow-dispersing populations, and species that are locally more static but invasive at the landscape scale. We find that low and slow UAV flights may offer the best monitoring strategy to predict local population densities in transects, but that the consequent reduction in overall area sampled may sacrifice the ability to reliably predict regional population density. Alternative flight plans may perform better, but this is also dependent on movement ecology and the magnitude of relative detection errors for different flight choices. Simulated investigations such as this will become increasingly useful to reveal how spatio-temporal extent and resolution of UAV monitoring should be adjusted to reduce observation errors and thus provide better population estimates, maximising the efficacy and efficiency of unmanned aerial surveys.

A closed-form optimal tuning of mass dampers for one degree-of-freedom systems under rotating unbalance forcing

This paper is focused on the study of a vibrating system forced by a rotating unbalance and coupled to a tuned mass damper (TMD). The analysis of the dynamic response of the entire system is used to define the parameters of such device in order to achieve optimal damping properties. The inertial forcing due to the rotating unbalance depends quadratically on the forcing frequency and it leads to optimal tuning parameters that differ from classical values obtained for pure harmonic forcing. Analytical results demonstrate that frequency and damping ratios, as a function of the mass parameter, should be higher than classical optimal parameters. The analytical study is carried out for the undamped primary system, and numerically investigated for the damped primary system. We show that, for practical applications, proper TMD tuning allows to achieve a reduction in the steady-state response of about 20% with respect to the response achieved with a classically tuned damper. Copyright © 2015 by ASME.

Tuning the valence of the cerium center in (Na)phthalocyaninato and porphyrinato cerium double-deckers by changing the nature of the tetrapyrrole ligands

A series of 7 cerium double-decker complexes with various tetrapyrrole ligands including porphyrinates, phthalocyaninates, and 2,3-naphthalocyaninates have been prepared by previously described methodologies and characterized with elemental analysis and a range of spectroscopic methods. The molecular structures of two heteroleptic \[(na)phthalocyaninato](porphyrinato) complexes have also been determined by X-ray diffraction analysis which exhibit a slightly distorted square antiprismatic geometry with two domed ligands. Having a range of tetrapyrrole ligands with very different electronic properties, these compounds have been systematically investigated for the effects of ligands on the valence of the cerium center. On the basis of the spectroscopic (UV−vis, near-IR, IR, and Raman), electrochemical, and structural data of these compounds and compared with those of the other rare earth(III) counterparts reported earlier, it has been found that the cerium center adopts an intermediate valence in these complexes. It assumes a virtually trivalent state in cerium bis(tetra-tert-butylnaphthalocyaninate) as a result of the two electron rich naphthalocyaninato ligands, which facilitate the delocalization of electron from the ligands to the metal center. For the rest of the cerium double-deckers, the cerium center is predominantly tetravalent. The valences (3.59−3.68) have been quantified according to their LIII-edge X-ray absorption near-edge structure (XANES) profiles.