A Binary Neural Shape Matcher using Johnson Counters and Chain Codes

In this paper, we introduce a neural network-based shape matching algorithm that uses Johnson Counter codes coupled with chain codes. Shape matching is a fundamental requirement in content-based image retrieval systems. Chain codes describe shapes using sequences of numbers. They are simple and flexible. We couple this power with the efficiency and flexibility of a binary associative-memory neural network. We focus on the implementation details of the algorithm when it is constructed using the neural network. We demonstrate how the binary associative-memory neural network can index and match chain codes where the chain code elements are represented by Johnson codes

An investigation into the species distribution of woodlice (Isopoda, Oniscidea) and millipedes (Diplopoda) within a disused Magnesian limestone quarry.

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Performance and the Stratigraphy of Place: Everything You Need to Build a Town is Here

Referencing the public art project project Everything You Need to Build a Town is Here, Wrights & Sites invite the reader to treat the chapter as a site rather than as a treatise. We have structured the text in four layers. Some materials drop through from one layer to another. Elements are introduced at one level and imitated or digested at another. The layers are variously unfinished and indiscrete, subject to influences and interferences, partly reconciled and partly not. While each of the layers has been written towards something, they were all attenuated at approximately the same time. There are coincidences. The writing attempts to address the editor's brief to: consider how archaeological methodologies and techniques might be used to reflect more directly on the contemporary world itself; how we might undertake archaeologies of, as well as in the present

Wireless Sensor Networks for Condition Monitoring in the Railway Industry : a Survey

In recent years, the range of sensing technologies has expanded rapidly, whereas sensor devices have become cheaper. This has led to a rapid expansion in condition monitoring of systems, structures, vehicles, and machinery using sensors. Key factors are the recent advances in networking technologies such as wireless communication and mobile adhoc networking coupled with the technology to integrate devices. Wireless sensor networks (WSNs) can be used for monitoring the railway infrastructure such as bridges, rail tracks, track beds, and track equipment along with vehicle health monitoring such as chassis, bogies, wheels, and wagons. Condition monitoring reduces human inspection requirements through automated monitoring, reduces maintenance through detecting faults before they escalate, and improves safety and reliability. This is vital for the development, upgrading, and expansion of railway networks. This paper surveys these wireless sensors network technology for monitoring in the railway industry for analyzing systems, structures, vehicles, and machinery. This paper focuses on practical engineering solutions, principally,which sensor devices are used and what they are used for; and the identification of sensor configurations and network topologies. It identifies their respective motivations and distinguishes their advantages and disadvantages in a comparative review

Hadoop neural network for parallel and distributed feature selection

In this paper, we introduce a theoretical basis for a Hadoop-based neural network for parallel and distributed feature selection in Big Data sets. It is underpinned by an associative memory (binary) neural network which is highly amenable to parallel and distributed processing and fits with the Hadoop paradigm. There are many feature selectors described in the literature which all have various strengths and weaknesses. We present the implementation details of five feature selection algorithms constructed using our artificial neural network framework embedded in Hadoop YARN. Hadoop allows parallel and distributed processing. Each feature selector can be divided into subtasks and the subtasks can then be processed in parallel. Multiple feature selectors can also be processed simultaneously (in parallel) allowing multiple feature selectors to be compared. We identify commonalities among the five features selectors. All can be processed in the framework using a single representation and the overall processing can also be greatly reduced by only processing the common aspects of the feature selectors once and propagating these aspects across all five feature selectors as necessary. This allows the best feature selector and the actual features to select to be identified for large and high dimensional data sets through exploiting the efficiency and flexibility of embedding the binary associative-memory neural network in Hadoop

Fishing for nutrients in heterogeneous landscapes : modelling plant growth trade-offs in monocultures and mixed communities

The problem of how best to find and exploit essential resources, the quality and locations of which are unknown, is common throughout biology. For plants, the need to grow an efficient root system so as to acquire patchily distributed soil nutrients is typically complicated by competition between plants, and by the costs of maintaining the root system. Simple mechanistic models for root growth can help elucidate these complications, and here we argue that these models can be usefully informed by models initially developed for foraging fish larvae. Both plant and fish need to efficiently search a spatio-temporally variable environment using simple algorithms involving only local information, and both must perform this task against a backdrop of intra- and inter-specific competition and background mortality. Here we develop these parallels by using simple stochastic models describing the growth and efficiency of four contrasting idealized root growth strategies. We show that plants which grow identically in isolation in homogeneous substrates will typically perform very differently when grown in monocultures, in heterogeneous nutrient landscapes and in mixed-species competition. In particular, our simulations show a consistent result that plants which trade-off rapid growth in favour of a more efficient and durable root system perform better, both on average and in terms of the best performing individuals, than more rapidly growing ephemeral root systems. Moreover, when such slower growing but more efficient plants are grown in competition, the overall community productivity can exceed that of the constituent monocultures. These findings help to disentangle many of the context-dependent behaviours seen in the experimental literature, and may form a basis for future studies at the level of complex population dynamics and life history evolution

Core-Shell Electrospun Polycrystalline ZnO Nanofibers for Ultra-Sensitive NO2 Gas Sensing.

This Research Article discusses the growth of polycrystalline, self-supporting ZnO nanofibers, which can detect nitrogen dioxide (NO2) gas down to 1 part per billion (ppb), one of the smallest detection limits reported for NO2 using ZnO. A new and innovative method has been developed for growing polycrystalline ZnO nanofibers. These nanofibers have been created using core-shell electrospinning of inorganic metal precursor zinc neodecanoate, where growth occurs at the core of the nanofibers. This process produces contamination-free, self-supporting, polycrystalline ZnO nanofibers of an average diameter and grain size 50 and 8 nm, respectively, which are ideal for gas sensing applications. This process opens up an exciting opportunity for creating nanofibers from a variety of metal oxides, facilitating many new applications especially in the areas of sensors and wearable technologies.Llodys Register foundatio

Hierarchical Control of Headroom Reserve in Solar Power Plants For Frequency Response Capability

Renewable energy technologies including solar and wind inevitably play a leading role in meeting the growing demand for a decarbonized and clean power grid. However, these technologies are highly dependent of meteorological conditions of power plant site and the challenge remains on how to cope with their short-term and momentarily variability. This paper presents a hierarchical control system to provide ancillary services from a solar PV power plant to the grid without the need for additional non-solar resources. With coordinated management of each inverter in the system, the control system commands the power plant to proactively curtail a fraction of its instantaneous maximum power potential, which gives the plant enough headroom to ramp up or down power production from the overall power plant, for a service such as regulation reserve, even under changing cloud cover conditions. A case study from a site in Hawaii with one-second resolution solar irradiance data is used to verify the efficacy of the proposed control system. The algorithm is subsequently compared with an alternative control technology from the literature, the grouping control algorithm; the results show that the proposed hierarchical control system is over 10 times more effective in reducing generator mileage to support power fluctuations from solar PV power plants. &nbsp;</p