Wide-band spectral power fluctuations characterize the response of simulated cortical networks to increasing stimulus intensity

The hippocampus is an anatomically distinct region of the medial temporal lobe that plays a critical role in the formation of declarative memories. Here we show that a computer simulation of simple compartmental cells organized with basic hippocampal connectivity is capable of producing stimulus intensity sensitive wide-band fluctuations of spectral power similar to that seen in real EEG. While previous computational models have been designed to assess the viability of the putative mechanisms of memory storage and retrieval, they have generally been too abstract to allow comparison with empirical data. Furthermore, while the anatomical connectivity and organization of the hippocampus is well defined, many questions regarding the mechanisms that mediate large-scale synaptic integration remain unanswered. For this reason we focus less on the specifics of changing synaptic weights and more on the population dynamics. Spectral power in four distinct frequency bands were derived from simulated field potentials of the computational model and found to depend on the intensity of a random input. The majority of power occurred in the lowest frequency band (3-6 Hz) and was greatest to the lowest intensity stimulus condition (1% maximal stimulus). In contrast, higher frequency bands ranging from 7-45 Hz show an increase in power directly related with an increase in stimulus intensity. This trend continues up to a stimulus level of 15% to 20% of the maximal input, above which power falls dramatically. These results suggest that the relative power of intrinsic network oscillations are dependent upon the level of activation and that above threshold levels all frequencies are damped, perhaps due to over activation of inhibitory interneurons.

Coordinated Operation of PV and Battery Energy Storage Systems

Despite tough economic times, the uptake of photovoltaic (PV) technology has seen tremendous growth over the past decade. More than 21 GW of rooftop PV systems were installed globally in the year 2012 alone. This is fueled by various incentives offered by policy makers around the world with a goal of enhancing renewable energy integration and reducing the dependence on fossil fuels. For instance, the goal of achieving 20% energy consumption from renewable resources by 2020 has been unanimously accepted by numerous countries in Europe, North America, and Australia. Uptake of PVs by residential and small businesses has been augmented by generous rebates offered by government on installations and on the amount of energy injected into the grid. Furthermore, the global market outlook report published by EPIA predicts that the rooftop PV installations will continue to grow for the foreseeable future.

Storing energy in plastics : a review on conducting polymers & their role in electrochemical energy storage

Conducting polymers have become the focus of research due to their interesting properties, such as a wide range of conductivity, facile production, mechanical stability, light weight and low cost and due to the ease with which conducting polymers can be nanostructured to meet the specific application. They have become valuable materials for many applications, such as energy storage and generation. Recently, conducting polymers have been studied to be used in supercapacitors, battery electrode and fuel cells. This article is to briefly discuss the background & theory behind their conductivity as well as to highlight the recent contributions of conducting polymers to the field of energy and their significance. Furthermore, the methods of production of the conducting polymers in addition to the different ways utilised to nano-engineer special morphologies are discussed.

Fabrication of Nb2O5 nanosheets for high-rate lithium ion storage applications

Nb2O5 nanosheets are successfully synthesized through a facile hydrothermal reaction and followed heating treatment in air. The structural characterization reveals that the thickness of these sheets is around 50 nm and the length of sheets is 500~800 nm. Such a unique two dimensional structure enables the nanosheet electrode with superior performance during the charge-discharge process, such as high specific capacity (~184 mAh.g-1) and rate capability. Even at a current density of 1 A.g-1, the nanosheet electrode still exhibits a specific capacity of ~90 mAh.g-1. These results suggest the Nb2O5 nanosheet is a promising candidate for high-rate lithium ion storage applications.

Transition metal layered double hydroxides for energy storage

Various types of layered double hydroxides, a type of clay, were synthesised. They were then electrochemically tested to determine whether the samples would be suitable to store energy as supercapacitors. A manganese aluminium layered double hydroxide was electrochemically tested for the first time and found to have a large capacitance.

Fuzzy-logic based frequency controller for wind farms augmented with energy storage systems

Displacement of conventional synchronous generators by non-inertial units such as wind or solar generators will result in reduced-system inertia affecting under-frequency response. Frequency control is important to avoid equipment damage, load shedding, and possible blackouts. Wind generators along with energy storage systems can be used to improve the frequency response of low-inertia power system. This paper proposes a fuzzy-logic based frequency controller (FFC) for wind farms augmented with energy storage systems (wind-storage system) to improve the primary frequency response in future low-inertia hybrid power system. The proposed controller provides bidirectional real power injection using system frequency deviations and rate of change of frequency (RoCoF). Moreover, FFC ensures optimal use of energy from wind farms and storage units by eliminating the inflexible de-loading of wind energy and minimizing the required storage capacity. The efficacy of the proposed FFC is verified on the low-inertia hybrid power system.

Synthesis and applications of carbon nanomaterials for energy generation and storage

The world is facing an energy crisis due to exponential population growth and limited availability of fossil fuels. Carbon, one of the most abundant materials found on earth, and its allotrope forms have been proposed in this project for novel energy generation and storage devices. This studied investigated the synthesis and properties of these carbon nanomaterials for applications in organic solar cells and supercapacitors.

Evaluation of object detection proposals under condition variations

Object detection is a fundamental task in many computer vision applications, therefore the importance of evaluating the quality of object detection is well acknowledged in this domain. This process gives insight into the capabilities of methods in handling environmental changes. In this paper, a new method for object detection is introduced that combines the Selective Search and EdgeBoxes. We tested these three methods under environmental variations. Our experiments demonstrate the outperformance of the combination method under illumination and view point variations.

Place recognition with ConvNet landmarks: Viewpoint-robust, condition-robust, training-free

Place recognition has long been an incompletely solved problem in that all approaches involve significant compromises. Current methods address many but never all of the critical challenges of place recognition – viewpoint-invariance, condition-invariance and minimizing training requirements. Here we present an approach that adapts state-of-the-art object proposal techniques to identify potential landmarks within an image for place recognition. We use the astonishing power of convolutional neural network features to identify matching landmark proposals between images to perform place recognition over extreme appearance and viewpoint variations. Our system does not require any form of training, all components are generic enough to be used off-the-shelf. We present a range of challenging experiments in varied viewpoint and environmental conditions. We demonstrate superior performance to current state-of-the- art techniques. Furthermore, by building on existing and widely used recognition frameworks, this approach provides a highly compatible place recognition system with the potential for easy integration of other techniques such as object detection and semantic scene interpretation.

Minimising the failure of rail joints through managing the localised condition of track

To effectively address the high rate of failure of Insulated Rail Joints (IRJs) in the heavy haul lines, a research plan was designed and implemented with particular attention to understand their mechanical behaviour and deterioration process. In this paper, part of this ongoing research is described. During the past decades many studies have tried to improve the service life of IRJs by introducing a new structural design or material for IRJ components. This paper looks into this problem from a different perspective highlighting the significance of localised condition of track to the loads and responses of the IRJs. Results from a series of field measurements conducted in a rail track within the Australian Rail Track Corporation (ARTC) network are discussed. The interactive effects of IRJ responses and localised track condition are further investigated using the results obtained from numerical simulations. The field measurements and the simulation results provide valuable insight on the influence of track condition to the behaviour of IRJs.