Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks

With the fast development of wireless communications, ZigBee and semiconductor devices, home automation networks have recently become very popular. Since typical consumer products deployed in home automation networks are often powered by tiny and limited batteries, one of the most challenging research issues is concerning energy reduction and the balancing of energy consumption across the network in order to prolong the home network lifetime for consumer devices. The introduction of clustering and sink mobility techniques into home automation networks have been shown to be an efficient way to improve the network performance and have received significant research attention. Taking inspiration from nature, this paper proposes an Ant Colony Optimization (ACO) based clustering algorithm specifically with mobile sink support for home automation networks. In this work, the network is divided into several clusters and cluster heads are selected within each cluster. Then, a mobile sink communicates with each cluster head to collect data directly through short range communications. The ACO algorithm has been utilized in this work in order to find the optimal mobility trajectory for the mobile sink. Extensive simulation results from this research show that the proposed algorithm significantly improves home network performance when using mobile sinks in terms of energy consumption and network lifetime as compared to other routing algorithms currently deployed for home automation networks.

Estimation of visual motion parameters used for bio-inspired navigation

Visual motion cues play an important role in animal and humans locomotion without the need to extract actual ego-motion information. This paper demonstrates a method for estimating the visual motion parameters, namely the Time-To-Contact (TTC), Focus of Expansion (FOE), and image angular velocities, from a sparse optical flow estimation registered from a downward looking camera. The presented method is capable of estimating the visual motion parameters in a complicated 6 degrees of freedom motion and in real time with suitable accuracy for mobile robots visual navigation.

Bio-inspired autonomous visual vertical control of a quadrotor UAV

Near ground maneuvers, such as hover, approach and landing, are key elements of autonomy in unmanned aerial vehicles. Such maneuvers have been tackled conventionally by measuring or estimating the velocity and the height above the ground often using ultrasonic or laser range finders. Near ground maneuvers are naturally mastered by flying birds and insects as objects below may be of interest for food or shelter. These animals perform such maneuvers efficiently using only the available vision and vestibular sensory information. In this paper, the time-to-contact (Tau) theory, which conceptualizes the visual strategy with which many species are believed to approach objects, is presented as a solution for Unmanned Aerial Vehicles (UAV) relative ground distance control. The paper shows how such an approach can be visually guided without knowledge of height and velocity relative to the ground. A control scheme that implements the Tau strategy is developed employing only visual information from a monocular camera and an inertial measurement unit. To achieve reliable visual information at a high rate, a novel filtering system is proposed to complement the control system. The proposed system is implemented on-board an experimental quadrotor UAV and shown not only to successfully land and approach ground, but also to enable the user to choose the dynamic characteristics of the approach. The methods presented in this paper are applicable to both aerial and space autonomous vehicles.

Nonlinear system identification using particle swarm optimisation tuned radial basis function models

A novel particle swarm optimisation (PSO) tuned radial basis function (RBF) network model is proposed for identification of non-linear systems. At each stage of orthogonal forward regression (OFR) model construction process, PSO is adopted to tune one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is often more efficient in model construction. The effectiveness of the proposed PSO aided OFR algorithm for constructing tunable node RBF models is demonstrated using three real data sets.

Expansion quality of epidemic protocols

Epidemic protocols are a bio-inspired communication and computation paradigm for large and extreme-scale networked systems. This work investigates the expansion property of the network overlay topologies induced by epidemic protocols. An expansion quality index for overlay topologies is proposed and adopted for the design of epidemic membership protocols. A novel protocol is proposed, which explicitly aims at improving the expansion quality of the overlay topologies. The proposed protocol is tested with a global aggregation task and compared to other membership protocols. The analysis by means of simulations indicates that the expansion quality directly relates to the speed of dissemination and convergence of epidemic protocols and can be effectively used to design better protocols.

Body-centric modelling, identification, and acceleration tracking control of a quadrotor UAV

This paper presents the mathematical development of a body-centric nonlinear dynamic model of a quadrotor UAV that is suitable for the development of biologically inspired navigation strategies. Analytical approximations are used to find an initial guess of the parameters of the nonlinear model, then parameter estimation methods are used to refine the model parameters using the data obtained from onboard sensors during flight. Due to the unstable nature of the quadrotor model, the identification process is performed with the system in closed-loop control of attitude angles. The obtained model parameters are validated using real unseen experimental data. Based on the identified model, a Linear-Quadratic (LQ) optimal tracker is designed to stabilize the quadrotor and facilitate its translational control by tracking body accelerations. The LQ tracker is tested on an experimental quadrotor UAV and the obtained results are a further means to validate the quality of the estimated model. The unique formulation of the control problem in the body frame makes the controller better suited for bio-inspired navigation and guidance strategies than conventional attitude or position based control systems that can be found in the existing literature.

Lipopeptides: from self-assembly to bioactivity

This Feature Article discusses several classes of lipopeptide with important biomedical applications as antimicrobial and antifungal agents, in immune therapies and in personal care applications among others. Two main classes of lipopeptide are considered: (i) bacterially-expressed lipopeptides with a cyclic peptide headgroup and (ii) linear lipopeptides (with one or more lipid chains) based on bio-derived and bio-inspired amino acid sequences with current clinical applications. The applications are briefly summarized, and the biophysical characterization of the molecules is reviewed, with a particular focus on self-assembly. For several of these types of biomolecule, the formation of micelles above a critical micelle concentration has been observed while others form bilayer structures, depending on conditions of pH and temperature. As yet, there are few studies on the possible relationship between self-assembly into structures such as micelles and bioactivity of this class of molecule although this is likely to attract further attention.

Connectivity recovery in epidemic membership protocols

Epidemic protocols are a bio-inspired communication and computation paradigm for extreme-scale network system based on randomized communication. The protocols rely on a membership service to build decentralized and random overlay topologies. In a weakly connected overlay topology, a naive mechanism of membership protocols can break the connectivity, thus impairing the accuracy of the application. This work investigates the factors in membership protocols that cause the loss of global connectivity and introduces the first topology connectivity recovery mechanism. The mechanism is integrated into the Expander Membership Protocol, which is then evaluated against other membership protocols. The analysis shows that the proposed connectivity recovery mechanism is effective in preserving topology connectivity and also helps to improve the application performance in terms of convergence speed.

Synthesis of poly(aspartimide)-based bio-glycoconjugates

The purpose of this programme was to synthesize and analyze new bioconjugates of interest for the potential inhibition of the influenza virus, using poly(aspartimide) as a polymer support. The macromolecular targets were obtained by attaching various sialic acid-linker-amine compounds to poly(aspartimide). 1H and 13C NMR studies were then performed to analyze the degree of incorporation of the sialic acid-linker-amine compounds within the poly(aspartimide). These studies illustrated that the incorporation was dependent on the nature of the spacer between the sugar and the amine functionality. Thus aliphatic spacers favoured the inclusion of sialic acid onto the polymer support whereas compounds having only an aromatic moiety between the sialic acid and the amine could not be easily incorporated.

Synapsing variable length crossover: Biologically inspired crossover for variable length genomes

Biological Crossover occurs during the early stages of meiosis. During this process the chromosomes undergoing crossover are synapsed together at a number of homogenous sequence sections, it is within such synapsed sections that crossover occurs. The SVLC (Synapsing Variable Length Crossover) Algorithm recurrently synapses homogenous genetic sequences together in order of length. The genomes are considered to be flexible with crossover only being permitted within the synapsed sections. Consequently, common sequences are automatically preserved with only the genetic differences being exchanged, independent of the length of such differences. In addition to providing a rationale for variable length crossover it also provides a genotypic similarity metric for variable length genomes enabling standard niche formation techniques to be utilised. In a simple variable length test problem the SVLC algorithm outperforms current variable length crossover techniques.