A comparison of sound localisation techniques using cross-correlation and spiking neural networks for mobile robotics

This paper outlines the development of a crosscorrelation algorithm and a spiking neural network (SNN) for sound localisation based on real sound recorded in a noisy and dynamic environment by a mobile robot. The SNN architecture aims to simulate the sound localisation ability of the mammalian auditory pathways by exploiting the binaural cue of interaural time difference (ITD). The medial superior olive was the inspiration for the SNN architecture which required the integration of an encoding layer which produced biologically realistic spike trains, a model of the bushy cells found in the cochlear nucleus and a supervised learning algorithm. The experimental results demonstrate that biologically inspired sound localisation achieved using a SNN can compare favourably to the more classical technique of cross-correlation.

Transient scaling and resurgence of chimera states in networks of Boolean phase oscillators.

We study networks of nonlocally coupled electronic oscillators that can be described approximately by a Kuramoto-like model. The experimental networks show long complex transients from random initial conditions on the route to network synchronization. The transients display complex behaviors, including resurgence of chimera states, which are network dynamics where order and disorder coexists. The spatial domain of the chimera state moves around the network and alternates with desynchronized dynamics. The fast time scale of our oscillators (on the order of 100ns) allows us to study the scaling of the transient time of large networks of more than a hundred nodes, which has not yet been confirmed previously in an experiment and could potentially be important in many natural networks. We find that the average transient time increases exponentially with the network size and can be modeled as a Poisson process in experiment and simulation. This exponential scaling is a result of a synchronization rate that follows a power law of the phase-space volume.

Data muling over underwater wireless sensor networks using an autonomous underwater vehicle

We present algorithms, systems, and experimental results for underwater data muling. In data muling a mobile agent interacts with static agents to upload, download, or transport data to a different physical location. We consider a system comprising an Autonomous Underwater Vehicle (AUV) and many static Underwater Sensor Nodes (USN) networked together optically and acoustically. The AUV can locate the static nodes using vision and hover above the static nodes for data upload. We describe the hardware and software architecture of this underwater system, as well as experimental data. © 2006 IEEE.

secFleck: A Public Key Technology Platform for Wireless Sensor Networks

We describe the design and implementation of a public-key platform, secFleck, based on a commodity Trusted Platform Module (TPM) chip that extends the capability of a standard node. Unlike previous software public-key implementations this approach provides E- Commerce grade security; is computationally fast, energy efficient; and has low financial cost — all essential attributes for secure large-scale sen- sor networks. We describe the secFleck message security services such as confidentiality, authenticity and integrity, and present performance re- sults including computation time, energy consumption and cost. This is followed by examples, built on secFleck, of symmetric key management, secure RPC and secure software update.

An empirical study of data collection protocols for wireless sensor networks

In the past few years, numerous data collection protocols have been developed for wireless sensor networks (WSNs). However, there has been no comparison of their relative performance in realistic environments. Here we report the results of an empirical study using a Fleck3 sensor network testbed for four different data collection protocols: One phase pull Directed Diffusion (DD), Expected Number of Transmissions (ETX), ETX with explicit acknowledgment (ETX-eAck), and ETX with implicit acknowledgment (ETX-iAck). Our empirical study provides useful insights for future sensor network deployments. When the required application end-to-end reliability is not strict (e.g., 70%) and link quality is good, DD and ETX are the best options because of their simplicity and low routing overhead. Both ETX-eAck and ETX-iAck achieve more than 90% end-to-end reliability when the link quality is reasonable (less than 25% packet loss). When the link quality is good, ETX-iAck introduces significantly less routing overhead (up to 50%) than ETX-eAck. However, if the radio transceiver supports variable packet length, ETX-eAck can outperform ETX-iAck when the link quality is poor. The important message from this paper is that choice of data collection protocol should come after the operating environment is understood. This understanding must include the characteristics of the radio transceiver, and link loss statistics from a long-term (across seasons and weather variation) radio survey of the site.

Coordinating aerial robots and sensor networks for localization and navigation

We consider multi-robot systems that include sensor nodes and aerial or ground robots networked together. We describe two cooperative algorithms that allow robots and sensors to enhance each other's performance. In the first algorithm, an aerial robot assists the localization of the sensors. In the second algorithm, a localized sensor network controls the navigation of an aerial robot. We present physical experiments with an flying robot and a large Mica Mote sensor network.

Design and implementation of sensor networks for the observation and research of harmful algal blooms in southern California coastal waters

Harmful Algal Blooms (HABs) have become an important environmental concern along the western coast of the United States. Toxic and noxious blooms adversely impact the economies of coastal communities in the region, pose risks to human health, and cause mortality events that have resulted in the deaths of thousands of fish, marine mammals and seabirds. One goal of field-based research efforts on this topic is the development of predictive models of HABs that would enable rapid response, mitigation and ultimately prevention of these events. In turn, these objectives are predicated on understanding the environmental conditions that stimulate these transient phenomena. An embedded sensor network (Fig. 1), under development in the San Pedro Shelf region off the Southern California coast, is providing tools for acquiring chemical, physical and biological data at high temporal and spatial resolution to help document the emergence and persistence of HAB events, supporting the design and testing of predictive models, and providing contextual information for experimental studies designed to reveal the environmental conditions promoting HABs. The sensor platforms contained within this network include pier-based sensor arrays, ocean moorings, HF radar stations, along with mobile sensor nodes in the form of surface and subsurface autonomous vehicles. FreewaveTM radio modems facilitate network communication and form a minimally-intrusive, wireless communication infrastructure throughout the Southern California coastal region, allowing rapid and cost-effective data transfer. An emerging focus of this project is the incorporation of a predictive ocean model that assimilates near-real time, in situ data from deployed Autonomous Underwater Vehicles (AUVs). The model then assimilates the data to increase the skill of both nowcasts and forecasts, thus providing insight into bloom initiation as well as the movement of blooms or other oceanic features of interest (e.g., thermoclines, fronts, river discharge, etc.). From these predictions, deployed mobile sensors can be tasked to track a designated feature. This focus has led to the creation of a technology chain in which algorithms are being implemented for the innovative trajectory design for AUVs. Such intelligent mission planning is required to maneuver a vehicle to precise depths and locations that are the sites of active blooms, or physical/chemical features that might be sources of bloom initiation or persistence. The embedded network yields high-resolution, temporal and spatial measurements of pertinent environmental parameters and resulting biology (see Fig. 1). Supplementing this with ocean current information and remotely sensed imagery and meteorological data, we obtain a comprehensive foundation for developing a fundamental understanding of HAB events. This then directs labor- intensive and costly sampling efforts and analyses. Additionally, we provide coastal municipalities, managers and state agencies with detailed information to aid their efforts in providing responsible environmental stewardship of their coastal waters.

Grounding action in visuo-haptic space using experience networks

Traditional approaches to the use of machine learning algorithms do not provide a method to learn multiple tasks in one-shot on an embodied robot. It is proposed that grounding actions within the sensory space leads to the development of action-state relationships which can be re-used despite a change in task. A novel approach called an Experience Network is developed and assessed on a real-world robot required to perform three separate tasks. After grounded representations were developed in the initial task, only minimal further learning was required to perform the second and third task.

Emergent task-specific object semantics through distributed experience networks

Autonomous development of sensorimotor coordination enables a robot to adapt and change its action choices to interact with the world throughout its lifetime. The Experience Network is a structure that rapidly learns coordination between visual and haptic inputs and motor action. This paper presents methods which handle the high dimensionality of the network state-space which occurs due to the simultaneous detection of multiple sensory features. The methods provide no significant increase in the complexity of the underlying representations and also allow emergent, task-specific, semantic information to inform action selection. Experimental results show rapid learning in a real robot, beginning with no sensorimotor mappings, to a mobile robot capable of wall avoidance and target acquisition.

Social insect colony as a biological regulatory system: modelling information flow in dominance networks

Social insects provide an excellent platform to investigate flow of information in regulatory systems since their successful social organization is essentially achieved by effective information transfer through complex connectivity patterns among the colony members. Network representation of such behavioural interactions offers a powerful tool for structural as well as dynamical analysis of the underlying regulatory systems. In this paper, we focus on the dominance interaction networks in the tropical social wasp Ropalidia marginata-a species where behavioural observations indicate that such interactions are principally responsible for the transfer of information between individuals about their colony needs, resulting in a regulation of their own activities. Our research reveals that the dominance networks of R. marginata are structurally similar to a class of naturally evolved information processing networks, a fact confirmed also by the predominance of a specific substructure-the `feed-forward loop'-a key functional component in many other information transfer networks. The dynamical analysis through Boolean modelling confirms that the networks are sufficiently stable under small fluctuations and yet capable of more efficient information transfer compared to their randomized counterparts. Our results suggest the involvement of a common structural design principle in different biological regulatory systems and a possible similarity with respect to the effect of selection on the organization levels of such systems. The findings are also consistent with the hypothesis that dominance behaviour has been shaped by natural selection to co-opt the information transfer process in such social insect species, in addition to its primal function of mediation of reproductive competition in the colony.

Robotics and neuroscience: A rhythmic interaction

At the crossing between motor control neuroscience and robotics system theory, the paper presents a rhythmic experiment that is amenable both to handy laboratory implementation and simple mathematical modeling. The experiment is based on an impact juggling task, requiring the coordination of two upper-limb effectors and some phase-locking with the trajectories of one or several juggled objects. We describe the experiment, its implementation and the mathematical model used for the analysis. Our underlying research focuses on the role of sensory feedback in rhythmic tasks. In a robotic implementation of our experiment, we study the minimum feedback that is required to achieve robust control. A limited source of feedback, measuring only the impact times, is shown to give promising results. A second field of investigation concerns the human behavior in the same impact juggling task. We study how a variation of the tempo induces a transition between two distinct control strategies with different sensory feedback requirements. Analogies and differences between the robotic and human behaviors are obviously of high relevance in such a flexible setup. © 2008 Elsevier Ltd. All rights reserved.

On equations for regular languages, finite automata, and sequential networks

We consider systems of equations of the form where A is the underlying alphabet, the Xi are variables, the Pi,a are boolean functions in the variables Xi, and each δi is either the empty word or the empty set. The symbols υ and denote concatenation and union of languages over A. We show that any such system has a unique solution which, moreover, is regular. These equations correspond to a type of automation, called boolean automation, which is a generalization of a nondeterministic automation. The equations are then used to determine the language accepted by a sequential network; they are obtainable directly from the network.

Data analysis with artificial immune systems, cluster analysis and kohonen networks: some comparisons.

Neal M J Timmis J and Hunt J. Data analysis with artificial immune systems, cluster analysis and kohonen networks: some comparisons. In Proceedings of IEEE international conference of systems, man and cybernetics, pages 922-927, Tokyo, 1999. IEEE.

Automated cross-modal mapping in robotic eye/hand systems using plastic radial basis function networks

Q. Meng and M. H Lee, Automated cross-modal mapping in robotic eye/hand systems using plastic radial basis function networks, Connection Science, 19(1), pp 25-52, 2007.