Michael Weeks

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 ad hoc 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.

A data repository and analysis framework for spontaneous neural activity recordings in developing retina

BackgroundDuring early development, neural circuits fire spontaneously, generating activity episodes with complex spatiotemporal patterns. Recordings of spontaneous activity have been made in many parts of the nervous system over the last 25 years, reporting developmental changes in activity patterns and the effects of various genetic perturbations.ResultsWe present a curated repository of multielectrode array recordings of spontaneous activity in developing mouse and ferret retina. The data have been annotated with minimal metadata and converted into HDF5. This paper describes the structure of the data, along with examples of reproducible research using these data files. We also demonstrate how these data can be analysed in the CARMEN workflow system. This article is written as a literate programming document; all programs and data described here are freely available.Conclusions1. We hope this repository will lead to novel analysis of spontaneous activity recorded in different laboratories. 2. We encourage published data to be added to the repository. 3. This repository serves as an example of how multielectrode array recordings can be stored for long-term reuse.

Improved AURA k-Nearest Neighbour Approach

The k-Nearest Neighbour (kNN) approach is a widely-used technique for pattern classification. Ranked distance measurements to a known sample set determine the classification of unknown samples. Though effective, kNN, like most classification methods does not scale well with increased sample size. This is due to their being a relationship between the unknown query and every other sample in the data space. In order to make this operation scalable, we apply AURA to the kNN problem. AURA is a highly-scalable associative-memory based binary neural-network intended for high-speed approximate search and match operations on large unstructured datasets. Previous work has seen AURA methods applied to this problem as a scalable, but approximate kNN classifier. This paper continues this work by using AURA in conjunction with kernel-based input vectors, in order to create a fast scalable kNN classifier, whilst improving recall accuracy to levels similar to standard kNN implementations.

The CARMEN software as a service infrastructure

The CARMEN platform allows neuroscientists to share data, metadata, services and workflows, and to execute these services and workflows remotely via a Web portal. This paper describes how we implemented a service-based infrastructure into the CARMEN Virtual Laboratory. A Software as a Service framework was developed to allow generic new and legacy code to be deployed as services on a heterogeneous execution framework. Users can submit analysis code typically written in Matlab, Python, C/C++ and R as non-interactive standalone command-line applications and wrap them as services in a form suitable for deployment on the platform. The CARMEN Service Builder tool enables neuroscientists to quickly wrap their analysis software for deployment to the CARMEN platform, as a service without knowledge of the service framework or the CARMEN system. A metadata schema describes each service in terms of both system and user requirements. The search functionality allows services to be quickly discovered from the many services available. Within the platform, services may be combined into more complicated analyses using the workflow tool. CARMEN and the service infrastructure are targeted towards the neuroscience community; however, it is a generic platform, and can be targeted towards any discipline.

Designing an SLA Protocol with Renegotiation to Maximize Revenues for the CMAC Platform

The emerging transformation from a product oriented economy to a service oriented economy based on Cloud environments envisions new scenarios where actual QoS mechanisms need to be redesigned. In such scenarios new models to negotiate and manage Service Level Agreements (SLAs) are necessary. An SLA is a formal contract which defines acceptable service levels to be provided by the Service Provider to its customers in measurable terms. This is meant to guarantee that consumers’ service quality expectation can be achieved. In fact, the level of customer satisfaction is crucial in Cloud environments, making SLAs one of the most important and active research topics. The aim of this paper is to explore the possibility of integrating an SLA approach for Cloud services based on the CMAC (Condition Monitoring on A Cloud) platform which offers condition monitoring services in cloud computing environments to detect events on assets as well as data storage services.

A hardware-accelerated novel IR system

AURA (Advanced Uncertain Reasoning Architecture) is a generic family of techniques and implementations intended for high-speed approximate search and match operations on large unstructured datasets. This paper continues the AURA II (Advanced Uncertain Reasoning Architecture) projects research into distributed binary Correlation Matrix Memory (CMM) based upon the PRESENCE (PaRallEl Structured Neural Computing Engine) hardware architecture [14]. Previous work has described how CMMs can be seamlessly implemented onto multiple hardware PRESENCE cards to accelerate core CMM operations. To demonstrate the system, this paper describes how a novel CMM-based information retrieval (IR) system, called MinerTaur, was implemented using multiple PRESENCE cards distributed across a cluster.

The ALife Zoo: cross-browser, platform-agnostic hosting of Artificial Life simulations

We describe a new approach to sharing software simulations that is of great potential benefit to Artificial Life researchers. youShare is an online collaborative facility that allows users to upload data, and software in the form of services. An attached execution environment allows services to be run over a heterogeneous cluster of compute nodes, where the service infrastructure guarantees that the service will be executed in the correct environment, and provide consistent results. It allows software to be made available as a service regardless of the operating system they run upon. This allows software to be maintained more easily, and to be available to all researchers with internet access. We demonstrate this by making three Artificial Life simulations available over the web: Tierra, Avida and Stringmol. These services form the foundation of an ALife “Zoo”, in which visitors can interact with ALife simulations for research and education. In addition, youShare offers a workflow facility whereby multiple services can be connected to create more complex tasks. We demonstrate the utility of this system in Artificial Life research via a workflow which calculates evolutionary activity for runs of Tierra and Stringmol.

Scalability of a distributed neural information retrieval system

Summary form only given. AURA (Advanced Uncertain Reasoning Architecture) is a generic family of techniques and implementations intended for high-speed approximate search and match operations on large unstructured datasets. AURA technology is fast, economical, and offers unique advantages for finding near-matches not available with other methods. AURA is based upon a high-performance binary neural network called a correlation matrix memory (CMM). Typically, several CMM elements are used in combination to solve soft or fuzzy pattern-matching problems. AURA takes large volumes of data and constructs a special type of compressed index. AURA finds exact and near-matches between indexed records and a given query, where the query itself may have omissions and errors. The degree of nearness required during matching can be varied through thresholding techniques. The PCI-based PRESENCE (Parallel Structured Neural Computing Engine) card is a hardware-accelerator architecture for the core CMM computations needed in AURA-based applications. The card is designed for use in low-cost workstations and incorporates 128 MByte of low-cost DRAM for CMM storage. To investigate the scalability of the distributed AURA system, we implement a word-to-document index of an AURA-based information retrieval system, called MinerTaur, over a distributed PRESENCE CMM.

A WS-Agreement Based SLA Implementation for the CMAC Platform

The emerging transformation from a product oriented economy to a service oriented economy based on Cloud environments envisions new scenarios where actual QoS (Quality of Service) mechanisms need to be redesigned. In such scenarios new models to negotiate and manage Service Level Agreements (SLAs) are necessary. An SLA is a formal contract which defines acceptable service levels to be provided by the Service Provider to its customers in measurable terms. SLAs are an essential component in building Cloud systems where commitments and assurances are specified, implemented, monitored and possibly negotiable. This is meant to guarantee that consumers’ service quality expectations can be achieved. In fact, the level of customer satisfaction is crucial in Cloud environments, making SLAs one of the most important and active research topics. This paper presents an SLA implementation for negotiation, monitoring and renegotiation of agreements for Cloud services based on the CMAC (Condition Monitoring on A Cloud) platform. CMAC offers condition monitoring services in cloud computing environments to detect events on assets as well as data storage services.

Mapping Correlation Matrix Memory Applications onto a Beowulf Cluster

The aim of the research reported in this paper was to assess the scalability of a binary correlation Matrix Memory (CMM) based on the PRESENCE (PaRallEl StructurEd Neural Computing Engine) architecture. A single PRESENCE card has a finite memory capacity, and this paper describes howm ultiple PCI-based PRESENCE cards are utilised in order to scale up memory capacity and performance. A Beowulf class cluster, called Cortex-1, provides the scalable I/O capacity needed for multiple cards, and techniques for mapping applications onto the system are described. The main aims of the work are to prove the scalability of the AURA architecture, and to demonstrate the capabilities of the architecture for commercial pattern matching problems.