An efficient scheme for the evaluation of coding performance of high speed links incorporating joint error behaviour based on Monte Carlo Method

While channel coding is a standard method of improving a system’s energy efficiency in digital communications, its practice does not extend to high-speed links. Increasing demands in network speeds are placing a large burden on the energy efficiency of high-speed links and render the benefit of channel coding for these systems a timely subject. The low error rates of interest and the presence of residual intersymbol interference (ISI) caused by hardware constraints impede the analysis and simulation of coded high-speed links. Focusing on the residual ISI and combined noise as the dominant error mechanisms, this paper analyses error correlation through concepts of error region, channel signature, and correlation distance. This framework provides a deeper insight into joint error behaviours in high-speed links, extends the range of statistical simulation for coded high-speed links, and provides a case against the use of biased Monte Carlo methods in this setting

Efficient learning of reactive robot behaviors with a Neural-Q_learning approach

The purpose of this paper is to propose a Neural-Q_learning approach designed for online learning of simple and reactive robot behaviors. In this approach, the Q_function is generalized by a multi-layer neural network allowing the use of continuous states and actions. The algorithm uses a database of the most recent learning samples to accelerate and guarantee the convergence. Each Neural-Q_learning function represents an independent, reactive and adaptive behavior which maps sensorial states to robot control actions. A group of these behaviors constitutes a reactive control scheme designed to fulfill simple missions. The paper centers on the description of the Neural-Q_learning based behaviors showing their performance with an underwater robot in a target following task. Real experiments demonstrate the convergence and stability of the learning system, pointing out its suitability for online robot learning. Advantages and limitations are discussed

Architecture for Living Neuronal Network Control of a mobile robot

The intelligent controlling mechanism of a typical mobile robot is usually a computer system. Research is however now ongoing in which biological neural networks are being cultured and trained to act as the brain of an interactive real world robot – thereby either completely replacing or operating in a cooperative fashion with a computer system. Studying such neural systems can give a distinct insight into biological neural structures and therefore such research has immediate medical implications. The principal aims of the present research are to assess the computational and learning capacity of dissociated cultured neuronal networks with a view to advancing network level processing of artificial neural networks. This will be approached by the creation of an artificial hybrid system (animat) involving closed loop control of a mobile robot by a dissociated culture of rat neurons. This paper details the components of the overall animat closed loop system architecture and reports on the evaluation of the results from preliminary real-life and simulated robot experiments.

Distributed acceleration of mobile radio network optimisation algorithms

The past decade has witnessed explosive growth of mobile subscribers and services. With the purpose of providing better-swifter-cheaper services, radio network optimisation plays a crucial role but faces enormous challenges. The concept of Dynamic Network Optimisation (DNO), therefore, has been introduced to optimally and continuously adjust network configurations, in response to changes in network conditions and traffic. However, the realization of DNO has been seriously hindered by the bottleneck of optimisation speed performance. An advanced distributed parallel solution is presented in this paper, as to bridge the gap by accelerating the sophisticated proprietary network optimisation algorithm, while maintaining the optimisation quality and numerical consistency. The ariesoACP product from Arieso Ltd serves as the main platform for acceleration. This solution has been prototyped, implemented and tested. Real-project based results exhibit a high scalability and substantial acceleration at an average speed-up of 2.5, 4.9 and 6.1 on a distributed 5-core, 9-core and 16-core system, respectively. This significantly outperforms other parallel solutions such as multi-threading. Furthermore, augmented optimisation outcome, alongside high correctness and self-consistency, have also been fulfilled. Overall, this is a breakthrough towards the realization of DNO.

Neural network control of a simple mobile robot

In recent years researchers in the Department of Cybernetics have been developing simple mobile robots capable of exploring their environment on the basis of the information obtained from a few simple sensors. These robots are used as the test bed for exploring various behaviours of single and multiple organisms: the work is inspired by considerations of natural systems. In this paper we concentrate on that part of the work which involves neural networks and related techniques. These neural networks are used both to process the sensor information and to develop the strategy used to control the robot. Here the robots, their sensors, and the neural networks used and all described. 1.

A cross layer protocol for energy aware and critical data delivered applications using Wireless Sensor Networks

Environment monitoring applications using Wireless Sensor Networks (WSNs) have had a lot of attention in recent years. In much of this research tasks like sensor data processing, environment states and events decision making and emergency message sending are done by a remote server. A proposed cross layer protocol for two different applications where, reliability for delivered data, delay and life time of the network need to be considered, has been simulated and the results are presented in this paper. A WSN designed for the proposed applications needs efficient MAC and routing protocols to provide a guarantee for the reliability of the data delivered from source nodes to the sink. A cross layer based on the design given in [1] has been extended and simulated for the proposed applications, with new features, such as routes discovery algorithms added. Simulation results show that the proposed cross layer based protocol can conserve energy for nodes and provide the required performance such as life time of the network, delay and reliability.

The impact of location and type on the performance of Low-Voltage network connected Battery Energy Storage Systems

This paper assesses the impact of the location and configuration of Battery Energy Storage Systems (BESS) on Low-Voltage (LV) feeders. BESS are now being deployed on LV networks by Distribution Network Operators (DNOs) as an alternative to conventional reinforcement (e.g. upgrading cables and transformers) in response to increased electricity demand from new technologies such as electric vehicles. By storing energy during periods of low demand and then releasing that energy at times of high demand, the peak demand of a given LV substation on the grid can be reduced therefore mitigating or at least delaying the need for replacement and upgrade. However, existing research into this application of BESS tends to evaluate the aggregated impact of such systems at the substation level and does not systematically consider the impact of the location and configuration of BESS on the voltage profiles, losses and utilisation within a given feeder. In this paper, four configurations of BESS are considered: single-phase, unlinked three-phase, linked three-phase without storage for phase-balancing only, and linked three-phase with storage. These four configurations are then assessed based on models of two real LV networks. In each case, the impact of the BESS is systematically evaluated at every node in the LV network using Matlab linked with OpenDSS. The location and configuration of a BESS is shown to be critical when seeking the best overall network impact or when considering specific impacts on voltage, losses, or utilisation separately. Furthermore, the paper also demonstrates that phase-balancing without energy storage can provide much of the gains on unbalanced networks compared to systems with energy storage.

Mobile sink based fault diagnosis scheme for wireless sensor networks

Network diagnosis in Wireless Sensor Networks (WSNs) is a difficult task due to their improvisational nature, invisibility of internal running status, and particularly since the network structure can frequently change due to link failure. To solve this problem, we propose a Mobile Sink (MS) based distributed fault diagnosis algorithm for WSNs. An MS, or mobile fault detector is usually a mobile robot or vehicle equipped with a wireless transceiver that performs the task of a mobile base station while also diagnosing the hardware and software status of deployed network sensors. Our MS mobile fault detector moves through the network area polling each static sensor node to diagnose the hardware and software status of nearby sensor nodes using only single hop communication. Therefore, the fault detection accuracy and functionality of the network is significantly increased. In order to maintain an excellent Quality of Service (QoS), we employ an optimal fault diagnosis tour planning algorithm. In addition to saving energy and time, the tour planning algorithm excludes faulty sensor nodes from the next diagnosis tour. We demonstrate the effectiveness of the proposed algorithms through simulation and real life experimental results.

A Floating-point Extended Kalman Filter Implementation for Autonomous Mobile Robots

Localization and Mapping are two of the most important capabilities for autonomous mobile robots and have been receiving considerable attention from the scientific computing community over the last 10 years. One of the most efficient methods to address these problems is based on the use of the Extended Kalman Filter (EKF). The EKF simultaneously estimates a model of the environment (map) and the position of the robot based on odometric and exteroceptive sensor information. As this algorithm demands a considerable amount of computation, it is usually executed on high end PCs coupled to the robot. In this work we present an FPGA-based architecture for the EKF algorithm that is capable of processing two-dimensional maps containing up to 1.8 k features at real time (14 Hz), a three-fold improvement over a Pentium M 1.6 GHz, and a 13-fold improvement over an ARM920T 200 MHz. The proposed architecture also consumes only 1.3% of the Pentium and 12.3% of the ARM energy per feature.

Luminescence enhancement of the Tb(III) ion with the thenoyltrifluoroacetonate ligand acting as an efficient sensitizer

The synthesis, structural investigation, and photophysical properties of the complex [Tb(TTA)(2)(NO(3)) (TPPO)(2)] are reported. Unlike the analog tris-diketonate complex [Tb(TTA)(3)(TPPO)(2)], the new complex presents abnormally high luminescence intensity centered on the terbium ion. Our results clearly suggest a higher energy transfer efficiency from the TEA antenna ligand to the Tb(III) ion in the bis-diketonate complex compared with that in the tris-diketonate complex. A mechanism involving the increasing of triplet state energy when one TTA ligand is replaced by the NO(3)(-) group in the first coordination sphere is suggested and experimentally investigated to explain the anomalous luminescence properties of the new complex [Tb(TTA)(2)(NO(3))(TPPO)(2)]. (C) 2010 Elsevier B.V. All rights reserved.

Efficient and low cost devices for solar energy conversion: Efficiency and stability of some natural-dye-sensitized solar cells

Dye-sensitized solar cells, named by us Dye-Cells, are one of the most promising devices for solar energy conversion due to their reduced production cost and low environmental impact, especially those sensitized by natural dyes. The efficiency and stability of devices based on natural sensitizers such as mulberry (Morus alba Lam), blueberry (Vaccinium myrtillus Lam), and jaboticaba`s skin (Mirtus cauliflora Mart) were investigated. Dye-Cells prepared with aqueous mulberry extract presented the highest P(max) value (1.6 mW cm(-2)) with J(sc) = 6.14 mA cm(-2) and V(oc) = 0.49 V, Photoelectrochemical parameters of 16 cm(2) active area devices sensitized by mulberry dye were constant for 14 weeks of continuous evaluation. Moreover, the cell remained stable even after 36 weeks with a fairly good efficiency. Therefore, mulberry dye opens up a perspective of commercial feasibility for inexpensive and environmentally friendly Dye-Cells. (C) 2009 Elsevier B.V. All rights reserved.